WorldWideScience

Sample records for modeling reluctance-assisted pm

  1. Modelling a linear PM motor including magnetic saturation

    NARCIS (Netherlands)

    Polinder, H.; Slootweg, J.G.; Compter, J.C.; Hoeijmakers, M.J.

    2002-01-01

    The use of linear permanent-magnet (PM) actuators increases in a wide variety of applications because of the high force density, robustness and accuracy. The paper describes the modelling of a linear PM motor applied in, for example, wafer steppers, including magnetic saturation. This is important

  2. Indoor air quality modeling for PM 10, PM 2.5, and PM 1.0 in naturally ventilated classrooms of an urban Indian school building.

    Science.gov (United States)

    Goyal, Radha; Khare, Mukesh

    2011-05-01

    Assessment of indoor air quality (IAQ) in classrooms of school buildings is of prime concern due to its potential effects on student's health and performance as they spend a substantial amount of their time (6-7 h per day) in schools. A number of airborne contaminants may be present in urban school environment. However, respirable suspended particulate matter (RSPM) is of great significance as they may significantly affect occupants' health. The objectives of the present study are twofold, one, to measure the concentrations of PM(10) (building located near a heavy-traffic roadway (9,755 and 4,296 vehicles/hour during weekdays and weekends, respectively); and second, to develop single compartment mass balance-based IAQ models for PM(10) (NVIAQM(pm10)), PM(2.5) (NVIAQM(pm2.5)), and PM(1.0) (NVIAQM(pm1.0)) for predicting their indoor concentrations. Outdoor RSPM levels and classroom characteristics, such as size, occupancy level, temperature, relative humidity, and CO(2) concentrations have also been monitored during school hours. Predicted indoor PM(10) concentrations show poor correlations with observed indoor PM(10) concentrations (R (2) = 0.028 for weekdays, and 0.47 for weekends). However, a fair degree of agreement (d) has been found between observed and predicted concentrations, i.e., 0.42 for weekdays and 0.59 for weekends. Furthermore, NVIAQM(pm2.5) and NVIAQM(pm1.0) results show good correlations with observed concentrations of PM(2.5) (R(2) = 0.87 for weekdays and 0.9 for weekends) and PM(1.0) (R(2) = 0.86 for weekdays and 0.87 for weekends). NVIAQM(pm10) shows the tendency to underpredict indoor PM(10) concentrations during weekdays as it does not take into account the occupant's activities and its effects on the indoor concentrations during the class hours. Intense occupant's activities cause resuspension or delayed deposition of PM(10). The model results further suggests conductance of experimental and physical simulation studies on dispersion of

  3. Model independent measurement of the leptonic kaon decay $K^\\pm \\to \\mu^\\pm \

    CERN Document Server

    Bizzeti, Andrea

    2018-01-01

    Two recent results on rare kaon decays are presented, based on $\\sim 2 \\times 10^{11} K^{\\pm}$ decays recorded by the NA48/2 experiment at CERN SPS in 2003 and 2004. The branching ratio of the rare leptonic decay $K^{\\pm} \\to \\mu^{\\pm} \

  4. Model independent measurement of the leptonic kaon decay $K^\\pm \\to \\mu^\\pm \

    CERN Document Server

    Bizzeti, Andrea

    2017-01-01

    Two recent results on rare kaon decays are presented, based on $\\sim 2 \\times 10^{11} K^{\\pm}$ decays recorded by the NA48/2 experiment at CERN SPS in 2003 and 2004. The branching ratio of the rare leptonic decay $K^{\\pm} \\to \\mu^{\\pm} \

  5. Network modeling of PM10 concentration in Malaysia

    Science.gov (United States)

    Supian, Muhammad Nazirul Aiman Abu; Bakar, Sakhinah Abu; Razak, Fatimah Abdul

    2017-08-01

    Air pollution is not a new phenomenon in Malaysia. The Department of Environment (DOE) monitors the country's ambient air quality through a network of 51 stations. The air quality is measured using the Air Pollution Index (API) which is mainly recorded based on the concentration of particulate matter, PM10 readings. The Continuous Air Quality Monitoring (CAQM) stations are located in various places across the country. In this study, a network model of air quality based on PM10 concen tration for selected CAQM stations in Malaysia has been developed. The model is built using a graph formulation, G = (V, E) where vertex, V is a set of CAQM stations and edges, E is a set of correlation values for each pair of vertices. The network measurements such as degree distributions, closeness centrality, and betweenness centrality are computed to analyse the behaviour of the network. As a result, a rank of CAQM stations has been produced based on their centrality characteristics.

  6. To what extent can aerosol water explain the discrepancy between model calculated and gravimetric PM10 and PM2.5?

    Directory of Open Access Journals (Sweden)

    S. G. Tsyro

    2005-01-01

    Full Text Available Inter-comparisons of European air quality models show that regional transport models, including the EMEP (Co-operative Programme for monitoring and evaluation of the long-range transmission of air pollutants in Europe aerosol model, tend to underestimate the observed concentrations of PM10 and PM2.5. Obviously, an accurate representation of the individual aerosol constituents is a prerequisite for adequate calculation of PM concentrations. On the other hand, available measurements on the chemical characterization of ambient particles reveal that full chemical PM mass closure is rarely achieved. The fraction unaccounted for by chemical analysis can comprise as much as 30-40% of gravimetric PM10 or PM2.5 mass. The unaccounted PM mass can partly be due to non-C atoms in organic aerosols and/or due to sampling and measurement artefacts. Moreover, a part of the unaccounted PM mass is likely to consist of water associated with particles. Thus, the gravimetrically measured particle mass does not necessarily represent dry PM10 and PM2.5 mass. This is thought to be one of the reasons for models under-prediction of observed PM, if calculated dry PM10 and PM2.5 concentrations are compared with measurements. The EMEP aerosol model has been used to study to what extent particle-bound water can explain the chemically unidentified PM mass in filter-based particle samples. Water content of PM2.5 and PM10 has been estimated with the model for temperature 20°C and relative humidity 50%, which are conditions required for equilibration of dust-loaded filters according to the Reference method recommended by the European Committee for Standardization (CEN. Model calculations for Europe show that, depending on particle composition, particle-bound water constitutes 20-35% of the annual mean PM10 and PM2.5 concentrations, which is consistent with existing experimental estimates. At two Austrian sites, in Vienna and Streithofen, where daily measurements of PM2.5 mass

  7. Sparkle/PM7 Lanthanide Parameters for the Modeling of Complexes and Materials

    OpenAIRE

    Dutra, José Diogo L.; Filho, Manoel A. M.; Rocha, Gerd B.; Freire, Ricardo O.; Simas, Alfredo M.; Stewart, James J. P.

    2013-01-01

    The recently published Parametric Method number 7, PM7, is the first semiempirical method to be successfully tested by modeling crystal structures and heats of formation of solids. PM7 is thus also capable of producing results of useful accuracy for materials science, and constitutes a great improvement over its predecessor, PM6. In this article, we present Sparkle Model parameters to be used with PM7 that allow the prediction of geometries of metal complexes and materials which contain lanth...

  8. Generating CT-TH-PM surfaces using EPT-based aggregate modelling

    NARCIS (Netherlands)

    Veeger, C.P.L.; Etman, L.F.P.; Herk, van J.; Rooda, J.E.

    2010-01-01

    Cycle Time-Throughput-Product mix (CT-TH-PM) surfaces give the mean cycle time as a function of throughput and product mix for manufacturing workstations. To generate the CT-TH-PM surface, detailed simulation models may be used. However, detailed models require much development time, and it may not

  9. Analysis of Influence Factors of PM2.5 in Chengdu Based on VAR Model

    Science.gov (United States)

    Mingzhi, Luo

    2017-05-01

    Air pollution and smog are the serious harms to public health and has attracted public attention. Based on the vector auto-regressive (VAR) model, we analysed the influence factors of PM2.5 in Chengdu, investigated the effect of other kinds of air pollutants and meteorological factors onthe PM2.5 by using the methods of generalized impulse response function, variance decomposition analysis, Granger causality test and therelated daily data from December 1, 2013 to November 14, 2016 in Chengdu city to the empirical study. The resultsshow that the influence factors of PM2.5 were stable;the increase of nitrogen dioxide, ozone,precipitation and temperature difference led to the increase of PM2.5 concentration while the increase ofthe wind speed, PM10, sulphur dioxide and carbon monoxide resulted in the decrease of PM2.5 concentration.Climate conditions,nitrogen dioxide and ozone are Granger causes for PM2.5.It is suggestedthat the key for the control of PM2.5 must be based on the cause and formation rules of PM2.5.A further study on nitrogen dioxide and ozone may play an important role in finding out the real source and formation reasons of PM2.5.

  10. Improvement of PM10 prediction in East Asia using inverse modeling

    Science.gov (United States)

    Koo, Youn-Seo; Choi, Dae-Ryun; Kwon, Hi-Yong; Jang, Young-Kee; Han, Jin-Seok

    2015-04-01

    Aerosols from anthropogenic emissions in industrialized region in China as well as dust emissions from southern Mongolia and northern China that transport along prevailing northwestern wind have a large influence on the air quality in Korea. The emission inventory in the East Asia region is an important factor in chemical transport modeling (CTM) for PM10 (particulate matters less than 10 ㎛ in aerodynamic diameter) forecasts and air quality management in Korea. Most previous studies showed that predictions of PM10 mass concentration by the CTM were underestimated when comparing with observational data. In order to fill the gap in discrepancies between observations and CTM predictions, the inverse Bayesian approach with Comprehensive Air-quality Model with extension (CAMx) forward model was applied to obtain optimized a posteriori PM10 emissions in East Asia. The predicted PM10 concentrations with a priori emission were first compared with observations at monitoring sites in China and Korea for January and August 2008. The comparison showed that PM10 concentrations with a priori PM10 emissions for anthropogenic and dust sources were generally under-predicted. The result from the inverse modeling indicated that anthropogenic PM10 emissions in the industrialized and urbanized areas in China were underestimated while dust emissions from desert and barren soil in southern Mongolia and northern China were overestimated. A priori PM10 emissions from northeastern China regions including Shenyang, Changchun, and Harbin were underestimated by about 300% (i.e., the ratio of a posteriori to a priori PM10 emission was a factor of about 3). The predictions of PM10 concentrations with a posteriori emission showed better agreement with the observations, implying that the inverse modeling minimized the discrepancies in the model predictions by improving PM10 emissions in East Asia.

  11. Modelling daily PM2.5 concentrations at high spatio-temporal resolution across Switzerland.

    Science.gov (United States)

    de Hoogh, Kees; Héritier, Harris; Stafoggia, Massimo; Künzli, Nino; Kloog, Itai

    2018-02-01

    Spatiotemporal resolved models were developed predicting daily fine particulate matter (PM 2.5 ) concentrations across Switzerland from 2003 to 2013. Relatively sparse PM 2.5 monitoring data was supplemented by imputing PM 2.5 concentrations at PM 10 sites, using PM 2.5 /PM 10 ratios at co-located sites. Daily PM 2.5 concentrations were first estimated at a 1 × 1km resolution across Switzerland, using Multiangle Implementation of Atmospheric Correction (MAIAC) spectral aerosol optical depth (AOD) data in combination with spatiotemporal predictor data in a four stage approach. Mixed effect models (1) were used to predict PM 2.5 in cells with AOD but without PM 2.5 measurements (2). A generalized additive mixed model with spatial smoothing was applied to generate grid cell predictions for those grid cells where AOD was missing (3). Finally, local PM 2.5 predictions were estimated at each monitoring site by regressing the residuals from the 1 × 1km estimate against local spatial and temporal variables using machine learning techniques (4) and adding them to the stage 3 global estimates. The global (1 km) and local (100 m) models explained on average 73% of the total,71% of the spatial and 75% of the temporal variation (all cross validated) globally and on average 89% (total) 95% (spatial) and 88% (temporal) of the variation locally in measured PM 2.5 concentrations. Copyright © 2017 Elsevier Ltd. All rights reserved.

  12. Modeled PM2.5 removal by trees in ten US cities and associated health effects

    Science.gov (United States)

    David J. Nowak; Satoshi Hirabayashi; Allison Bodine; Robert. Hoehn

    2013-01-01

    Urban particulate air pollution is a serious health issue. Trees within cities can remove fine particles from the atmosphere and consequently improve air quality and human health. Tree effects on PM2.5 concentrations and human health are modeled for 10 U.S. cities. The total amount of PM2.5 removed annually by...

  13. Optimal interpolation schemes to constrain pmPM2.5 in regional modeling over the United States

    Science.gov (United States)

    Sousan, Sinan Dhia Jameel

    This thesis presents the use of data assimilation with optimal interpolation (OI) to develop atmospheric aerosol concentration estimates for the United States at high spatial and temporal resolutions. Concentration estimates are highly desirable for a wide range of applications, including visibility, climate, and human health. OI is a viable data assimilation method that can be used to improve Community Multiscale Air Quality (CMAQ) model fine particulate matter (PM2.5) estimates. PM2.5 is the mass of solid and liquid particles with diameters less than or equal to 2.5 µm suspended in the gas phase. OI was employed by combining model estimates with satellite and surface measurements. The satellite data assimilation combined 36 x 36 km aerosol concentrations from CMAQ with aerosol optical depth (AOD) measured by MODIS and AERONET over the continental United States for 2002. Posterior model concentrations generated by the OI algorithm were compared with surface PM2.5 measurements to evaluate a number of possible data assimilation parameters, including model error, observation error, and temporal averaging assumptions. Evaluation was conducted separately for six geographic U.S. regions in 2002. Variability in model error and MODIS biases limited the effectiveness of a single data assimilation system for the entire continental domain. The best combinations of four settings and three averaging schemes led to a domain-averaged improvement in fractional error from 1.2 to 0.97 and from 0.99 to 0.89 at respective IMPROVE and STN monitoring sites. For 38% of OI results, MODIS OI degraded the forward model skill due to biases and outliers in MODIS AOD. Surface data assimilation combined 36 × 36 km aerosol concentrations from the CMAQ model with surface PM2.5 measurements over the continental United States for 2002. The model error covariance matrix was constructed by using the observational method. The observation error covariance matrix included site representation that

  14. An improved geographically weighted regression model for PM2.5 concentration estimation in large areas

    Science.gov (United States)

    Zhai, Liang; Li, Shuang; Zou, Bin; Sang, Huiyong; Fang, Xin; Xu, Shan

    2018-05-01

    Considering the spatial non-stationary contributions of environment variables to PM2.5 variations, the geographically weighted regression (GWR) modeling method has been using to estimate PM2.5 concentrations widely. However, most of the GWR models in reported studies so far were established based on the screened predictors through pretreatment correlation analysis, and this process might cause the omissions of factors really driving PM2.5 variations. This study therefore developed a best subsets regression (BSR) enhanced principal component analysis-GWR (PCA-GWR) modeling approach to estimate PM2.5 concentration by fully considering all the potential variables' contributions simultaneously. The performance comparison experiment between PCA-GWR and regular GWR was conducted in the Beijing-Tianjin-Hebei (BTH) region over a one-year-period. Results indicated that the PCA-GWR modeling outperforms the regular GWR modeling with obvious higher model fitting- and cross-validation based adjusted R2 and lower RMSE. Meanwhile, the distribution map of PM2.5 concentration from PCA-GWR modeling also clearly depicts more spatial variation details in contrast to the one from regular GWR modeling. It can be concluded that the BSR enhanced PCA-GWR modeling could be a reliable way for effective air pollution concentration estimation in the coming future by involving all the potential predictor variables' contributions to PM2.5 variations.

  15. GIS-based models for ambient PM exposure and health impact assessment for the UK

    International Nuclear Information System (INIS)

    Stedman, John R; Grice, Susannah; Kent, Andrew; Cooke, Sally

    2009-01-01

    GIS-based models have been developed to map ambient PM 10 and PM 25 mass concentrations across the UK. The resulting maps are used for the assessments of air quality required by the EU ambient air quality directives, health impact assessment and the development of UK air quality policy. Maps are presented for 2006 along with projections to 2020. The largest single contribution to the UK population-weighted mean annual mean background concentrations of PM 10 in 2006 is estimated to be from secondary PM (43%), followed by the contribution from primary PM (24%). Concentrations are predicted to decline by 15% for PM 10 and 13% for PM 25 over the period from 2006 to 2020. The extent of exceedence of the 24-hour limit value is predicted to decline from 1.9% to 0.1% of urban major roads over the same period. The potential health benefits of reductions in ambient PM are large. A reduction in concentration of 0.93 μg m -3 as a result of a possible package of measures has been estimated within the UK Air Quality Strategy to result in a reduction in life years lost of approximately 2 - 4 million over a period of 100 years.

  16. Quantifying PM2.5-Meteorology Sensitivities in a Global Climate Model

    Science.gov (United States)

    Westervelt, D. M.; Horowitz, L. W.; Naik, V.; Tai, A. P. K.; Fiore, A. M.; Mauzerall, D. L.

    2016-01-01

    Climate change can influence fine particulate matter concentrations (PM2.5) through changes in air pollution meteorology. Knowledge of the extent to which climate change can exacerbate or alleviate air pollution in the future is needed for robust climate and air pollution policy decision-making. To examine the influence of climate on PM2.5, we use the Geophysical Fluid Dynamics Laboratory Coupled Model version 3 (GFDL CM3), a fully-coupled chemistry-climate model, combined with future emissions and concentrations provided by the four Representative Concentration Pathways (RCPs). For each of the RCPs, we conduct future simulations in which emissions of aerosols and their precursors are held at 2005 levels while other climate forcing agents evolve in time, such that only climate (and thus meteorology) can influence PM2.5 surface concentrations. We find a small increase in global, annual mean PM2.5 of about 0.21 micro-g/cu m3 (5%) for RCP8.5, a scenario with maximum warming. Changes in global mean PM2.5 are at a maximum in the fall and are mainly controlled by sulfate followed by organic aerosol with minimal influence of black carbon. RCP2.6 is the only scenario that projects a decrease in global PM2.5 with future climate changes, albeit only by -0.06 micro-g/cu m (1.5%) by the end of the 21st century. Regional and local changes in PM2.5 are larger, reaching upwards of 2 micro-g/cu m for polluted (eastern China) and dusty (western Africa) locations on an annually averaged basis in RCP8.5. Using multiple linear regression, we find that future PM2.5 concentrations are most sensitive to local temperature, followed by surface wind and precipitation. PM2.5 concentrations are robustly positively associated with temperature, while negatively related with precipitation and wind speed. Present-day (2006-2015) modeled sensitivities of PM2.5 to meteorological variables are evaluated against observations and found to agree reasonably well with observed sensitivities (within 10e50

  17. Modelling street level PM10 concentrations across Europe: source apportionment and possible futures

    Directory of Open Access Journals (Sweden)

    G. Kiesewetter

    2015-02-01

    Full Text Available Despite increasing emission controls, particulate matter (PM has remained a critical issue for European air quality in recent years. The various sources of PM, both from primary particulate emissions as well as secondary formation from precursor gases, make this a complex problem to tackle. In order to allow for credible predictions of future concentrations under policy assumptions, a modelling approach is needed that considers all chemical processes and spatial dimensions involved, from long-range transport of pollution to local emissions in street canyons. Here we describe a modelling scheme which has been implemented in the GAINS integrated assessment model to assess compliance with PM10 (PM with aerodynamic diameter 10 across Europe. Furthermore, we analyse the predicted evolution of PM10 concentrations in the European Union until 2030 under different policy scenarios. Significant improvements in ambient PM10 concentrations are expected assuming successful implementation of already agreed legislation; however, these will not be large enough to ensure attainment of PM10 limit values in hot spot locations such as Southern Poland and major European cities. Remaining issues are largely eliminated in a scenario applying the best available emission control technologies to the maximal technically feasible extent.

  18. Receptor modeling of PM2.5, PM10 and TSP in different seasons and long-range transport analysis at a coastal site of Tianjin, China.

    Science.gov (United States)

    Kong, Shaofei; Han, Bin; Bai, Zhipeng; Chen, Li; Shi, Jianwu; Xu, Zhun

    2010-09-15

    Atmospheric particulate matter (PM(2.5), PM(10) and TSP) were sampled synchronously during three monitoring campaigns from June 2007 to February 2008 at a coastal site in TEDA of Tianjin, China. Chemical compositions including 19 elements, 6 water-solubility ions, organic and elemental carbon were determined. principle components analysis (PCA) and chemical mass balance modeling (CMB) were applied to determine the PM sources and their contributions with the assistance of NSS SO(4)(2)(-), the mass ratios of NO(3)(-) to SO(4)(2)(-) and OC to EC. Air mass backward trajectory model was compared with source apportionment results to evaluate the origin of PM. Results showed that NSS SO(4)(2)(-) values for PM(2.5) were 2147.38, 1701.26 and 239.80 ng/m(3) in summer, autumn and winter, reflecting the influence of sources from local emissions. Most of it was below zero in summer for PM(10) indicating the influence of sea salt. The ratios of NO(3)(-) to SO(4)(2)(-) was 0.19 for PM(2.5), 0.18 for PM(10) and 0.19 for TSP in winter indicating high amounts of coal consumed for heating purpose. Higher OC/EC values (mostly larger than 2.5) demonstrated that secondary organic aerosol was abundant at this site. The major sources were construction activities, road dust, vehicle emissions, marine aerosol, metal manufacturing, secondary sulfate aerosols, soil dust, biomass burning, some pharmaceutics industries and fuel-oil combustion according to PCA. Coal combustion, marine aerosol, vehicular emission and soil dust explained 5-31%, 1-13%, 13-44% and 3-46% for PM(2.5), PM(10) and TSP, respectively. Backward trajectory analysis showed air parcels originating from sea accounted for 39% in summer, while in autumn and winter the air parcels were mainly related to continental origin. Copyright 2010 Elsevier B.V. All rights reserved.

  19. Modelling the long-range transport of secondary PM 10 to the UK

    Science.gov (United States)

    Malcolm, A. L.; Derwent, R. G.; Maryon, R. H.

    The fine fraction of airborne particulate matter (PM 10) is known to be harmful to human health. In order to establish how current air quality standards can best be met now and in the future, it is necessary to understand the cause of PM 10 episodes. The UK Met Office's dispersion model, NAME, has been used to model hourly concentrations of sulphate aerosol for 1996 at a number of UK locations. The model output has been compared with measured values of PM 10 or sulphate aerosol at these sites and used to provide attribution information. In particular two large PM 10 episodes in March and July 1996 have been studied. The March episode has been shown to be the result of imported pollution from outside the UK, whereas the July case was dominated by UK emissions. This work highlights the need to consider trans-boundary pollution when setting air quality standards and when making policy decisions on emissions.

  20. Modelling street level PM10 concentrations across Europe: source apportionment and possible futures

    Science.gov (United States)

    Kiesewetter, G.; Borken-Kleefeld, J.; Schöpp, W.; Heyes, C.; Thunis, P.; Bessagnet, B.; Terrenoire, E.; Fagerli, H.; Nyiri, A.; Amann, M.

    2015-02-01

    Despite increasing emission controls, particulate matter (PM) has remained a critical issue for European air quality in recent years. The various sources of PM, both from primary particulate emissions as well as secondary formation from precursor gases, make this a complex problem to tackle. In order to allow for credible predictions of future concentrations under policy assumptions, a modelling approach is needed that considers all chemical processes and spatial dimensions involved, from long-range transport of pollution to local emissions in street canyons. Here we describe a modelling scheme which has been implemented in the GAINS integrated assessment model to assess compliance with PM10 (PM with aerodynamic diameter dispersion calculations, and a traffic increment calculation wherever applicable. At each monitoring station fulfilling a few data coverage criteria, measured concentrations in the base year 2009 are explained to the extent possible and then modelled for the past and future. More than 1850 monitoring stations are covered, including more than 300 traffic stations and 80% of the stations which exceeded the EU air quality limit values in 2009. As a validation, we compare modelled trends in the period 2000-2008 to observations, which are well reproduced. The modelling scheme is applied here to quantify explicitly source contributions to ambient concentrations at several critical monitoring stations, displaying the differences in spatial origin and chemical composition of urban roadside PM10 across Europe. Furthermore, we analyse the predicted evolution of PM10 concentrations in the European Union until 2030 under different policy scenarios. Significant improvements in ambient PM10 concentrations are expected assuming successful implementation of already agreed legislation; however, these will not be large enough to ensure attainment of PM10 limit values in hot spot locations such as Southern Poland and major European cities. Remaining issues are

  1. Random forest meteorological normalisation models for Swiss PM10 trend analysis

    Science.gov (United States)

    Grange, Stuart K.; Carslaw, David C.; Lewis, Alastair C.; Boleti, Eirini; Hueglin, Christoph

    2018-05-01

    Meteorological normalisation is a technique which accounts for changes in meteorology over time in an air quality time series. Controlling for such changes helps support robust trend analysis because there is more certainty that the observed trends are due to changes in emissions or chemistry, not changes in meteorology. Predictive random forest models (RF; a decision tree machine learning technique) were grown for 31 air quality monitoring sites in Switzerland using surface meteorological, synoptic scale, boundary layer height, and time variables to explain daily PM10 concentrations. The RF models were used to calculate meteorologically normalised trends which were formally tested and evaluated using the Theil-Sen estimator. Between 1997 and 2016, significantly decreasing normalised PM10 trends ranged between -0.09 and -1.16 µg m-3 yr-1 with urban traffic sites experiencing the greatest mean decrease in PM10 concentrations at -0.77 µg m-3 yr-1. Similar magnitudes have been reported for normalised PM10 trends for earlier time periods in Switzerland which indicates PM10 concentrations are continuing to decrease at similar rates as in the past. The ability for RF models to be interpreted was leveraged using partial dependence plots to explain the observed trends and relevant physical and chemical processes influencing PM10 concentrations. Notably, two regimes were suggested by the models which cause elevated PM10 concentrations in Switzerland: one related to poor dispersion conditions and a second resulting from high rates of secondary PM generation in deep, photochemically active boundary layers. The RF meteorological normalisation process was found to be robust, user friendly and simple to implement, and readily interpretable which suggests the technique could be useful in many air quality exploratory data analysis situations.

  2. Simulation And Forecasting of Daily Pm10 Concentrations Using Autoregressive Models In Kagithane Creek Valley, Istanbul

    Science.gov (United States)

    Ağaç, Kübra; Koçak, Kasım; Deniz, Ali

    2015-04-01

    A time series approach using autoregressive model (AR), moving average model (MA) and seasonal autoregressive integrated moving average model (SARIMA) were used in this study to simulate and forecast daily PM10 concentrations in Kagithane Creek Valley, Istanbul. Hourly PM10 concentrations have been measured in Kagithane Creek Valley between 2010 and 2014 periods. Bosphorus divides the city in two parts as European and Asian parts. The historical part of the city takes place in Golden Horn. Our study area Kagithane Creek Valley is connected with this historical part. The study area is highly polluted because of its topographical structure and industrial activities. Also population density is extremely high in this site. The dispersion conditions are highly poor in this creek valley so it is necessary to calculate PM10 levels for air quality and human health. For given period there were some missing PM10 concentration values so to make an accurate calculations and to obtain exact results gap filling method was applied by Singular Spectrum Analysis (SSA). SSA is a new and efficient method for gap filling and it is an state-of-art modeling. SSA-MTM Toolkit was used for our study. SSA is considered as a noise reduction algorithm because it decomposes an original time series to trend (if exists), oscillatory and noise components by way of a singular value decomposition. The basic SSA algorithm has stages of decomposition and reconstruction. For given period daily and monthly PM10 concentrations were calculated and episodic periods are determined. Long term and short term PM10 concentrations were analyzed according to European Union (EU) standards. For simulation and forecasting of high level PM10 concentrations, meteorological data (wind speed, pressure and temperature) were used to see the relationship between daily PM10 concentrations. Fast Fourier Transformation (FFT) was also applied to the data to see the periodicity and according to these periods models were built

  3. Autoregressive spatially varying coefficients model for predicting daily PM2.5 using VIIRS satellite AOT

    Science.gov (United States)

    Schliep, E. M.; Gelfand, A. E.; Holland, D. M.

    2015-12-01

    There is considerable demand for accurate air quality information in human health analyses. The sparsity of ground monitoring stations across the United States motivates the need for advanced statistical models to predict air quality metrics, such as PM2.5, at unobserved sites. Remote sensing technologies have the potential to expand our knowledge of PM2.5 spatial patterns beyond what we can predict from current PM2.5 monitoring networks. Data from satellites have an additional advantage in not requiring extensive emission inventories necessary for most atmospheric models that have been used in earlier data fusion models for air pollution. Statistical models combining monitoring station data with satellite-obtained aerosol optical thickness (AOT), also referred to as aerosol optical depth (AOD), have been proposed in the literature with varying levels of success in predicting PM2.5. The benefit of using AOT is that satellites provide complete gridded spatial coverage. However, the challenges involved with using it in fusion models are (1) the correlation between the two data sources varies both in time and in space, (2) the data sources are temporally and spatially misaligned, and (3) there is extensive missingness in the monitoring data and also in the satellite data due to cloud cover. We propose a hierarchical autoregressive spatially varying coefficients model to jointly model the two data sources, which addresses the foregoing challenges. Additionally, we offer formal model comparison for competing models in terms of model fit and out of sample prediction of PM2.5. The models are applied to daily observations of PM2.5 and AOT in the summer months of 2013 across the conterminous United States. Most notably, during this time period, we find small in-sample improvement incorporating AOT into our autoregressive model but little out-of-sample predictive improvement.

  4. Source Apportionment of PM2.5 in Delhi, India Using PMF Model.

    Science.gov (United States)

    Sharma, S K; Mandal, T K; Jain, Srishti; Saraswati; Sharma, A; Saxena, Mohit

    2016-08-01

    Chemical characterization of PM2.5 [organic carbon, elemental carbon, water soluble inorganic ionic components, and major and trace elements] was carried out for a source apportionment study of PM2.5 at an urban site of Delhi, India from January, 2013, to December, 2014. The annual average mass concentration of PM2.5 was 122 ± 94.1 µg m(-3). Strong seasonal variation was observed in PM2.5 mass concentration and its chemical composition with maxima during winter and minima during monsoon. A receptor model, positive matrix factorization (PMF) was applied for source apportionment of PM2.5 mass concentration. The PMF model resolved the major sources of PM2.5 as secondary aerosols (21.3 %), followed by soil dust (20.5 %), vehicle emissions (19.7 %), biomass burning (14.3 %), fossil fuel combustion (13.7 %), industrial emissions (6.2 %) and sea salt (4.3 %).

  5. Sparkle/PM7 Lanthanide Parameters for the Modeling of Complexes and Materials.

    Science.gov (United States)

    Dutra, José Diogo L; Filho, Manoel A M; Rocha, Gerd B; Freire, Ricardo O; Simas, Alfredo M; Stewart, James J P

    2013-08-13

    The recently published Parametric Method number 7, PM7, is the first semiempirical method to be successfully tested by modeling crystal structures and heats of formation of solids. PM7 is thus also capable of producing results of useful accuracy for materials science, and constitutes a great improvement over its predecessor, PM6. In this article, we present Sparkle Model parameters to be used with PM7 that allow the prediction of geometries of metal complexes and materials which contain lanthanide trications. Accordingly, we considered the geometries of 224 high-quality crystallographic structures of complexes for the parameterization set and 395 more for the validation of the parameterization for the whole lanthanide series, from La(III) to Lu(III). The average unsigned error for Sparkle/PM7 for the distances between the metal ion and its coordinating atoms is 0.063Å for all lanthanides, ranging from a minimum of 0.052Å for Tb(III) to 0.088Å for Ce(III), comparable to the equivalent errors in the distances predicted by PM7 for other metals. These distance deviations follow a gamma distribution within a 95% level of confidence, signifying that they appear to be random around a mean, confirming that Sparkle/PM7 is a well-tempered method. We conclude by carrying out a Sparkle/PM7 full geometry optimization of two spatial groups of the same thulium-containing metal organic framework, with unit cells accommodating 376 atoms, of which 16 are Tm(III) cations; the optimized geometries were in good agreement with the crystallographic ones. These results emphasize the capability of the use of the Sparkle Model for the prediction of geometries of compounds containing lanthanide trications within the PM7 semiempirical model, as well as the usefulness of such semiempirical calculations for materials modeling. Sparkle/PM7 is available in the software package MOPAC2012, at no cost for academics and can be obtained from http://openmopac.net.

  6. Eddy Current Loss Modeling for Design of PM Generators for Wind Turbines

    NARCIS (Netherlands)

    Jassal, A.

    2014-01-01

    This thesis deals with analysis, calculation and validation of eddy current loss models for Permanent Magnet (PM) direct drive generators for wind turbines. The modelling approach is a mixed use of analytical and Finite Element (FE) methods. The models are validated experimentally and design

  7. A model-independent Dalitz plot analysis of $B^\\pm \\to D K^\\pm$ with $D \\to K^0_{\\rm S} h^+h^-$ ($h=\\pi, K$) decays and constraints on the CKM angle $\\gamma$

    CERN Document Server

    Aaij, R.; Adametz, A.; Adeva, B.; Adinolfi, M.; Adrover, C.; Affolder, A.; Ajaltouni, Z.; Albrecht, J.; Alessio, F.; Alexander, M.; Ali, S.; Alkhazov, G.; Alvarez Cartelle, P.; Alves Jr, A.A.; Amato, S.; Amhis, Y.; Anderlini, L.; Anderson, J.; Appleby, R.B.; Aquines Gutierrez, O.; Archilli, F.; Artamonov, A.; Artuso, M.; Aslanides, E.; Auriemma, G.; Bachmann, S.; Back, J.J.; Baesso, C.; Baldini, W.; Barlow, R.J.; Barschel, C.; Barsuk, S.; Barter, W.; Bates, A.; Bauer, Th.; Bay, A.; Beddow, J.; Bediaga, I.; Belogurov, S.; Belous, K.; Belyaev, I.; Ben-Haim, E.; Benayoun, M.; Bencivenni, G.; Benson, S.; Benton, J.; Berezhnoy, A.; Bernet, R.; Bettler, M.O.; van Beuzekom, M.; Bien, A.; Bifani, S.; Bird, T.; Bizzeti, A.; Bjornstad, P.M.; Blake, T.; Blanc, F.; Blanks, C.; Blouw, J.; Blusk, S.; Bobrov, A.; Bocci, V.; Bondar, A.; Bondar, N.; Bonivento, W.; Borghi, S.; Borgia, A.; Bowcock, T.J.V.; Bozzi, C.; Brambach, T.; van den Brand, J.; Bressieux, J.; Brett, D.; Britsch, M.; Britton, T.; Brook, N.H.; Brown, H.; Buchler-Germann, A.; Burducea, I.; Bursche, A.; Buytaert, J.; Cadeddu, S.; Callot, O.; Calvi, M.; Calvo Gomez, M.; Camboni, A.; Campana, P.; Carbone, A.; Carboni, G.; Cardinale, R.; Cardini, A.; Carson, L.; Carvalho Akiba, K.; Casse, G.; Cattaneo, M.; Cauet, Ch.; Charles, M.; Charpentier, Ph.; Chen, P.; Chiapolini, N.; Chrzaszcz, M.; Ciba, K.; Cid Vidal, X.; Ciezarek, G.; Clarke, P.E.L.; Clemencic, M.; Cliff, H.V.; Closier, J.; Coca, C.; Coco, V.; Cogan, J.; Cogneras, E.; Collins, P.; Comerma-Montells, A.; Contu, A.; Cook, A.; Coombes, M.; Corti, G.; Couturier, B.; Cowan, G.A.; Craik, D.; Cunliffe, S.; Currie, R.; D'Ambrosio, C.; David, P.; David, P.N.Y.; De Bonis, I.; De Bruyn, K.; De Capua, S.; De Cian, M.; de Miranda, J.M.; De Paula, L.; De Simone, P.; Decamp, D.; Deckenhoff, M.; Degaudenzi, H.; Del Buono, L.; Deplano, C.; Derkach, D.; Deschamps, O.; Dettori, F.; Di Canto, A.; Dickens, J.; Dijkstra, H.; Diniz Batista, P.; Domingo Bonal, F.; Donleavy, S.; Dordei, F.; Dosil Suarez, A.; Dossett, D.; Dovbnya, A.; Dupertuis, F.; Dzhelyadin, R.; Dziurda, A.; Dzyuba, A.; Easo, S.; Egede, U.; Egorychev, V.; Eidelman, S.; van Eijk, D.; Eisenhardt, S.; Ekelhof, R.; Eklund, L.; El Rifai, I.; Elsasser, Ch.; Elsby, D.; Esperante Pereira, D.; Falabella, A.; Farber, C.; Fardell, G.; Farinelli, C.; Farry, S.; Fave, V.; Fernandez Albor, V.; Ferreira Rodrigues, F.; Ferro-Luzzi, M.; Filippov, S.; Fitzpatrick, C.; Fontana, M.; Fontanelli, F.; Forty, R.; Francisco, O.; Frank, M.; Frei, C.; Frosini, M.; Furcas, S.; Gallas Torreira, A.; Galli, D.; Gandelman, M.; Gandini, P.; Gao, Y.; Garnier, J-C.; Garofoli, J.; Garosi, P.; Garra Tico, J.; Garrido, L.; Gaspar, C.; Gauld, R.; Gersabeck, E.; Gersabeck, M.; Gershon, T.; Ghez, Ph.; Gibson, V.; Gligorov, V.V.; Gobel, C.; Golubkov, D.; Golutvin, A.; Gomes, A.; Gordon, H.; Grabalosa Gandara, M.; Graciani Diaz, R.; Granado Cardoso, L.A.; Grauges, E.; Graziani, G.; Grecu, A.; Greening, E.; Gregson, S.; Grunberg, O.; Gui, B.; Gushchin, E.; Guz, Yu.; Gys, T.; Hadjivasiliou, C.; Haefeli, G.; Haen, C.; Haines, S.C.; Hall, S.; Hampson, T.; Hansmann-Menzemer, S.; Harnew, N.; Harnew, S.T.; Harrison, J.; Harrison, P.F.; Hartmann, T.; He, J.; Heijne, V.; Hennessy, K.; Henrard, P.; Hernando Morata, J.A.; van Herwijnen, E.; Hicks, E.; Hill, D.; Hoballah, M.; Hopchev, P.; Hulsbergen, W.; Hunt, P.; Huse, T.; Hussain, N.; Hutchcroft, D.; Hynds, D.; Iakovenko, V.; Ilten, P.; Imong, J.; Jacobsson, R.; Jaeger, A.; Jahjah Hussein, M.; Jans, E.; Jansen, F.; Jaton, P.; Jean-Marie, B.; Jing, F.; John, M.; Johnson, D.; Jones, C.R.; Jost, B.; Kaballo, M.; Kandybei, S.; Karacson, M.; Karbach, T.M.; Keaveney, J.; Kenyon, I.R.; Kerzel, U.; Ketel, T.; Keune, A.; Khanji, B.; Kim, Y.M.; Kochebina, O.; Komarov, V.; Koopman, R.F.; Koppenburg, P.; Korolev, M.; Kozlinskiy, A.; Kravchuk, L.; Kreplin, K.; Kreps, M.; Krocker, G.; Krokovny, P.; Kruse, F.; Kucharczyk, M.; Kudryavtsev, V.; Kvaratskheliya, T.; La Thi, V.N.; Lacarrere, D.; Lafferty, G.; Lai, A.; Lambert, D.; Lambert, R.W.; Lanciotti, E.; Lanfranchi, G.; Langenbruch, C.; Latham, T.; Lazzeroni, C.; Le Gac, R.; van Leerdam, J.; Lees, J.P.; Lefevre, R.; Leflat, A.; Lefrancois, J.; Leroy, O.; Lesiak, T.; Li, Y.; Li Gioi, L.; Liles, M.; Lindner, R.; Linn, C.; Liu, B.; Liu, G.; von Loeben, J.; Lopes, J.H.; Lopez Asamar, E.; Lopez-March, N.; Lu, H.; Luisier, J.; Raighne, A.Mac; Machefert, F.; Machikhiliyan, I.V.; Maciuc, F.; Maev, O.; Magnin, J.; Maino, M.; Malde, S.; Manca, G.; Mancinelli, G.; Mangiafave, N.; Marconi, U.; Marki, R.; Marks, J.; Martellotti, G.; Martens, A.; Martin, L.; Martin Sanchez, A.; Martinelli, M.; Martinez Santos, D.; Massafferri, A.; Mathe, Z.; Matteuzzi, C.; Matveev, M.; Maurice, E.; Mazurov, A.; McCarthy, J.; McGregor, G.; McNulty, R.; Meissner, M.; Merk, M.; Merkel, J.; Milanes, D.A.; Minard, M.N.; Molina Rodriguez, J.; Monteil, S.; Moran, D.; Morawski, P.; Mountain, R.; Mous, I.; Muheim, F.; Muller, K.; Muresan, R.; Muryn, B.; Muster, B.; Mylroie-Smith, J.; Naik, P.; Nakada, T.; Nandakumar, R.; Nasteva, I.; Needham, M.; Neufeld, N.; Nguyen, A.D.; Nguyen-Mau, C.; Nicol, M.; Niess, V.; Nikitin, N.; Nikodem, T.; Nomerotski, A.; Novoselov, A.; Oblakowska-Mucha, A.; Obraztsov, V.; Oggero, S.; Ogilvy, S.; Okhrimenko, O.; Oldeman, R.; Orlandea, M.; Otalora Goicochea, J.M.; Owen, P.; Pal, B.K.; Palano, A.; Palutan, M.; Panman, J.; Papanestis, A.; Pappagallo, M.; Parkes, C.; Parkinson, C.J.; Passaleva, G.; Patel, G.D.; Patel, M.; Patrick, G.N.; Patrignani, C.; Pavel-Nicorescu, C.; Pazos Alvarez, A.; Pellegrino, A.; Penso, G.; Pepe Altarelli, M.; Perazzini, S.; Perego, D.L.; Perez Trigo, E.; Perez-Calero Yzquierdo, A.; Perret, P.; Perrin-Terrin, M.; Pessina, G.; Petridis, K.; Petrolini, A.; Phan, A.; Picatoste Olloqui, E.; Pie Valls, B.; Pietrzyk, B.; Pilar, T.; Pinci, D.; Playfer, S.; Plo Casasus, M.; Polci, F.; Polok, G.; Poluektov, A.; Polycarpo, E.; Popov, D.; Popovici, B.; Potterat, C.; Powell, A.; Prisciandaro, J.; Pugatch, V.; Puig Navarro, A.; Qian, W.; Rademacker, J.H.; Rakotomiaramanana, B.; Rangel, M.S.; Raniuk, I.; Rauschmayr, N.; Raven, G.; Redford, S.; Reid, M.M.; Reis, A.C.dos; Ricciardi, S.; Richards, A.; Rinnert, K.; Rives Molina, V.; Roa Romero, D.A.; Robbe, P.; Rodrigues, E.; Rodriguez Perez, P.; Rogers, G.J.; Roiser, S.; Romanovsky, V.; Romero Vidal, A.; Rouvinet, J.; Ruf, T.; Ruiz, H.; Sabatino, G.; Saborido Silva, J.J.; Sagidova, N.; Sail, P.; Saitta, B.; Salzmann, C.; Sanmartin Sedes, B.; Sannino, M.; Santacesaria, R.; Santamarina Rios, C.; Santinelli, R.; Santovetti, E.; Sapunov, M.; Sarti, A.; Satriano, C.; Satta, A.; Savrie, M.; Schaack, P.; Schiller, M.; Schindler, H.; Schleich, S.; Schlupp, M.; Schmelling, M.; Schmidt, B.; Schneider, O.; Schopper, A.; Schune, M.H.; Schwemmer, R.; Sciascia, B.; Sciubba, A.; Seco, M.; Semennikov, A.; Senderowska, K.; Sepp, I.; Serra, N.; Serrano, J.; Seyfert, P.; Shapkin, M.; Shapoval, I.; Shatalov, P.; Shcheglov, Y.; Shears, T.; Shekhtman, L.; Shevchenko, O.; Shevchenko, V.; Shires, A.; Silva Coutinho, R.; Skwarnicki, T.; Smith, N.A.; Smith, E.; Smith, M.; Sobczak, K.; Soler, F.J.P.; Soomro, F.; Souza, D.; Souza De Paula, B.; Spaan, B.; Sparkes, A.; Spradlin, P.; Stagni, F.; Stahl, S.; Steinkamp, O.; Stoica, S.; Stone, S.; Storaci, B.; Straticiuc, M.; Straumann, U.; Subbiah, V.K.; Swientek, S.; Szczekowski, M.; Szczypka, P.; Szumlak, T.; T'Jampens, S.; Teklishyn, M.; Teodorescu, E.; Teubert, F.; Thomas, C.; Thomas, E.; van Tilburg, J.; Tisserand, V.; Tobin, M.; Tolk, S.; Tonelli, D.; Topp-Joergensen, S.; Torr, N.; Tournefier, E.; Tourneur, S.; Tran, M.T.; Tsaregorodtsev, A.; Tsopelas, P.; Tuning, N.; Garcia, M.Ubeda; Ukleja, A.; Urner, D.; Uwer, U.; Vagnoni, V.; Valenti, G.; Vazquez Gomez, R.; Vazquez Regueiro, P.; Vecchi, S.; Velthuis, J.J.; Veltri, M.; Veneziano, G.; Vesterinen, M.; Viaud, B.; Videau, I.; Vieira, D.; Vilasis-Cardona, X.; Visniakov, J.; Vollhardt, A.; Volyanskyy, D.; Voong, D.; Vorobyev, A.; Vorobyev, V.; Voss, H.; Voss, C.; Waldi, R.; Wallace, R.; Wandernoth, S.; Wang, J.; Ward, D.R.; Watson, N.K.; Webber, A.D.; Websdale, D.; Whitehead, M.; Wicht, J.; Wiedner, D.; Wiggers, L.; Wilkinson, G.; Williams, M.P.; Williams, M.; Wilson, F.F.; Wishahi, J.; Witek, M.; Witzeling, W.; Wotton, S.A.; Wright, S.; Wu, S.; Wyllie, K.; Xie, Y.; Xing, F.; Xing, Z.; Yang, Z.; Young, R.; Yuan, X.; Yushchenko, O.; Zangoli, M.; Zavertyaev, M.; Zhang, F.; Zhang, L.; Zhang, W.C.; Zhang, Y.; Zhelezov, A.; Zhong, L.; Zvyagin, A.

    2013-07-16

    A binned Dalitz plot analysis of $B^\\pm \\to D K^\\pm$ decays, with $D \\to K^0_{\\rm S} \\pi^+\\pi^-$ and $D \\to K^0_{\\rm S} K^+ K^-$, is performed to measure the $CP$-violating observables $x_{\\pm}$ and $y_{\\pm}$ which are sensitive to the CKM angle $\\gamma$. The analysis exploits 1.0~$\\rm fb^{-1}$ of data collected by the LHCb experiment. The study makes no model-based assumption on the variation of the strong phase of the $D$ decay amplitude over the Dalitz plot, but uses measurements of this quantity from CLEO-c as input. The values of the parameters are found to be $x_- = (0.0 \\pm 4.3 \\pm 1.5 \\pm 0.6) \\times 10^{-2}$, $y_- = (2.7 \\pm 5.2 \\pm 0.8 \\pm 2.3) \\times 10^{-2}$, $x_+ = ( -10.3 \\pm 4.5 \\pm 1.8 \\pm 1.4 )\\times 10^{-2}$ and $y_+ = (-0.9 \\pm 3.7 \\pm 0.8 \\pm 3.0)\\times 10^{-2}$. The first, second, and third uncertainties are the statistical, the experimental systematic, and the error associated with the precision of the strong-phase parameters measured at CLEO-c, respectively. These results correspond to ...

  8. Multiple linear regression and regression with time series error models in forecasting PM10 concentrations in Peninsular Malaysia.

    Science.gov (United States)

    Ng, Kar Yong; Awang, Norhashidah

    2018-01-06

    Frequent haze occurrences in Malaysia have made the management of PM 10 (particulate matter with aerodynamic less than 10 μm) pollution a critical task. This requires knowledge on factors associating with PM 10 variation and good forecast of PM 10 concentrations. Hence, this paper demonstrates the prediction of 1-day-ahead daily average PM 10 concentrations based on predictor variables including meteorological parameters and gaseous pollutants. Three different models were built. They were multiple linear regression (MLR) model with lagged predictor variables (MLR1), MLR model with lagged predictor variables and PM 10 concentrations (MLR2) and regression with time series error (RTSE) model. The findings revealed that humidity, temperature, wind speed, wind direction, carbon monoxide and ozone were the main factors explaining the PM 10 variation in Peninsular Malaysia. Comparison among the three models showed that MLR2 model was on a same level with RTSE model in terms of forecasting accuracy, while MLR1 model was the worst.

  9. Regression trees modeling and forecasting of PM10 air pollution in urban areas

    Science.gov (United States)

    Stoimenova, M.; Voynikova, D.; Ivanov, A.; Gocheva-Ilieva, S.; Iliev, I.

    2017-10-01

    Fine particulate matter (PM10) air pollution is a serious problem affecting the health of the population in many Bulgarian cities. As an example, the object of this study is the pollution with PM10 of the town of Pleven, Northern Bulgaria. The measured concentrations of this air pollutant for this city consistently exceeded the permissible limits set by European and national legislation. Based on data for the last 6 years (2011-2016), the analysis shows that this applies both to the daily limit of 50 micrograms per cubic meter and the allowable number of daily concentration exceedances to 35 per year. Also, the average annual concentration of PM10 exceeded the prescribed norm of no more than 40 micrograms per cubic meter. The aim of this work is to build high performance mathematical models for effective prediction and forecasting the level of PM10 pollution. The study was conducted with the powerful flexible data mining technique Classification and Regression Trees (CART). The values of PM10 were fitted with respect to meteorological data such as maximum and minimum air temperature, relative humidity, wind speed and direction and others, as well as with time and autoregressive variables. As a result the obtained CART models demonstrate high predictive ability and fit the actual data with up to 80%. The best models were applied for forecasting the level pollution for 3 to 7 days ahead. An interpretation of the modeling results is presented.

  10. Eigenvector Spatial Filtering Regression Modeling of Ground PM2.5 Concentrations Using Remotely Sensed Data

    Directory of Open Access Journals (Sweden)

    Jingyi Zhang

    2018-06-01

    Full Text Available This paper proposes a regression model using the Eigenvector Spatial Filtering (ESF method to estimate ground PM2.5 concentrations. Covariates are derived from remotely sensed data including aerosol optical depth, normal differential vegetation index, surface temperature, air pressure, relative humidity, height of planetary boundary layer and digital elevation model. In addition, cultural variables such as factory densities and road densities are also used in the model. With the Yangtze River Delta region as the study area, we constructed ESF-based Regression (ESFR models at different time scales, using data for the period between December 2015 and November 2016. We found that the ESFR models effectively filtered spatial autocorrelation in the OLS residuals and resulted in increases in the goodness-of-fit metrics as well as reductions in residual standard errors and cross-validation errors, compared to the classic OLS models. The annual ESFR model explained 70% of the variability in PM2.5 concentrations, 16.7% more than the non-spatial OLS model. With the ESFR models, we performed detail analyses on the spatial and temporal distributions of PM2.5 concentrations in the study area. The model predictions are lower than ground observations but match the general trend. The experiment shows that ESFR provides a promising approach to PM2.5 analysis and prediction.

  11. Eigenvector Spatial Filtering Regression Modeling of Ground PM2.5 Concentrations Using Remotely Sensed Data.

    Science.gov (United States)

    Zhang, Jingyi; Li, Bin; Chen, Yumin; Chen, Meijie; Fang, Tao; Liu, Yongfeng

    2018-06-11

    This paper proposes a regression model using the Eigenvector Spatial Filtering (ESF) method to estimate ground PM 2.5 concentrations. Covariates are derived from remotely sensed data including aerosol optical depth, normal differential vegetation index, surface temperature, air pressure, relative humidity, height of planetary boundary layer and digital elevation model. In addition, cultural variables such as factory densities and road densities are also used in the model. With the Yangtze River Delta region as the study area, we constructed ESF-based Regression (ESFR) models at different time scales, using data for the period between December 2015 and November 2016. We found that the ESFR models effectively filtered spatial autocorrelation in the OLS residuals and resulted in increases in the goodness-of-fit metrics as well as reductions in residual standard errors and cross-validation errors, compared to the classic OLS models. The annual ESFR model explained 70% of the variability in PM 2.5 concentrations, 16.7% more than the non-spatial OLS model. With the ESFR models, we performed detail analyses on the spatial and temporal distributions of PM 2.5 concentrations in the study area. The model predictions are lower than ground observations but match the general trend. The experiment shows that ESFR provides a promising approach to PM 2.5 analysis and prediction.

  12. Modeling PM2.5 Urban Pollution Using Machine Learning and Selected Meteorological Parameters

    Directory of Open Access Journals (Sweden)

    Jan Kleine Deters

    2017-01-01

    Full Text Available Outdoor air pollution costs millions of premature deaths annually, mostly due to anthropogenic fine particulate matter (or PM2.5. Quito, the capital city of Ecuador, is no exception in exceeding the healthy levels of pollution. In addition to the impact of urbanization, motorization, and rapid population growth, particulate pollution is modulated by meteorological factors and geophysical characteristics, which complicate the implementation of the most advanced models of weather forecast. Thus, this paper proposes a machine learning approach based on six years of meteorological and pollution data analyses to predict the concentrations of PM2.5 from wind (speed and direction and precipitation levels. The results of the classification model show a high reliability in the classification of low (25 µg/m3 and low (<10 µg/m3 versus moderate (10–25 µg/m3 concentrations of PM2.5. A regression analysis suggests a better prediction of PM2.5 when the climatic conditions are getting more extreme (strong winds or high levels of precipitation. The high correlation between estimated and real data for a time series analysis during the wet season confirms this finding. The study demonstrates that the use of statistical models based on machine learning is relevant to predict PM2.5 concentrations from meteorological data.

  13. Advanced receptor modelling for the apportionment of road dust resuspension to atmospheric PM

    Science.gov (United States)

    Amato, F.; Pandolfi, M.; Escrig, A.; Querol, X.; Alastuey, A.; Pey, J.; Perez, N.; Hopke, P. K.

    2009-04-01

    Fugitive emissions from traffic resuspension can often represent an important source of atmospheric particulate matter in urban environments, especially when the scarce precipitations favour the accumulation of road dust. Resuspension of road dust can lead to high exposures to heavy metals, metalloids and mineral matter. Knowing the amount of its contribution to atmospheric PM is a key task for establishing eventual mitigation or preventive measures. Factor analysis techniques are widely used tools for atmospheric aerosol source apportionment, based on the mass conservation principle. Paatero and Tapper (1993) suggested the use of a Weighted Least Squares scheme with the aim of obtaining a minimum variance solution. Additionally they proposed to incorporate the basic physical constraint of non negativity, calling their approach Positive Matrix Factorization (PMF), which can be performed by the program PMF2 released by Paatero (1997). Nevertheless, Positive Matrix Factorization can be either solved with the Multilinear Engine (ME-2), a more flexible program, also developed by Paatero (1999), which can solve any model consisting in sum of products of unknowns. The main difference with PMF2 is that ME-2 does not solve only well-defined tasks, but its actions are defined in a "script file" written in a special-purpose programming language, allowing incorporating additional tasks such as data processing etc. Thus in ME-2 a priori information, e.g. chemical fingerprints can be included as auxiliary terms of the object function to be minimized. This feature of ME-2 make it especially suitable for source apportionment studies where some knowledge (chemical ratios, profiles, mass conservation etc) of involved sources is available. The aim of this study was to quantify the contribution of road dust resuspension in PM10, PM2.5 and PM1 data set from Barcelona (Spain). Given that recently the emission profile of local road dust was characterized (Amato et al., in press

  14. Modeling extreme PM10 concentration in Malaysia using generalized extreme value distribution

    Science.gov (United States)

    Hasan, Husna; Mansor, Nadiah; Salleh, Nur Hanim Mohd

    2015-05-01

    Extreme PM10 concentration from the Air Pollutant Index (API) at thirteen monitoring stations in Malaysia is modeled using the Generalized Extreme Value (GEV) distribution. The data is blocked into monthly selection period. The Mann-Kendall (MK) test suggests a non-stationary model so two models are considered for the stations with trend. The likelihood ratio test is used to determine the best fitted model and the result shows that only two stations favor the non-stationary model (Model 2) while the other eleven stations favor stationary model (Model 1). The return level of PM10 concentration that is expected to exceed the maximum once within a selected period is obtained.

  15. PM Synchronous Motor Dynamic Modeling with Genetic Algorithm ...

    African Journals Online (AJOL)

    Adel

    This paper proposes dynamic modeling simulation for ac Surface Permanent Magnet Synchronous ... Simulations are implemented using MATLAB with its genetic algorithm toolbox. .... selection, the process that drives biological evolution.

  16. Inter-comparison of receptor models for PM source apportionment: Case study in an industrial area

    Science.gov (United States)

    Viana, M.; Pandolfi, M.; Minguillón, M. C.; Querol, X.; Alastuey, A.; Monfort, E.; Celades, I.

    2008-05-01

    Receptor modelling techniques are used to identify and quantify the contributions from emission sources to the levels and major and trace components of ambient particulate matter (PM). A wide variety of receptor models are currently available, and consequently the comparability between models should be evaluated if source apportionment data are to be used as input in health effects studies or mitigation plans. Three of the most widespread receptor models (principal component analysis, PCA; positive matrix factorization, PMF; chemical mass balance, CMB) were applied to a single PM10 data set (n=328 samples, 2002-2005) obtained from an industrial area in NE Spain, dedicated to ceramic production. Sensitivity and temporal trend analyses (using the Mann-Kendall test) were applied. Results evidenced the good overall performance of the three models (r2>0.83 and α>0.91×between modelled and measured PM10 mass), with a good agreement regarding source identification and high correlations between input (CMB) and output (PCA, PMF) source profiles. Larger differences were obtained regarding the quantification of source contributions (up to a factor of 4 in some cases). The combined application of different types of receptor models would solve the limitations of each of the models, by constructing a more robust solution based on their strengths. The authors suggest the combined use of factor analysis techniques (PCA, PMF) to identify and interpret emission sources, and to obtain a first quantification of their contributions to the PM mass, and the subsequent application of CMB. Further research is needed to ensure that source apportionment methods are robust enough for application to PM health effects assessments.

  17. Technical evaluation of Tom Scurry Associates: Model PM-203 doorway monitor

    International Nuclear Information System (INIS)

    1978-07-01

    Under a basic assignment by the Office of Safeguards and Security, the Tom Scurry Associates Model PM-203 Personnel Doorway SNM Monitor manufactured by Tom Scurry Associates was evaluated by LASL Group Q-2 against the DOE Personnel Doorway Monitor standards. During the evaluation, a small change was required in detector shielding to eliminate low sensitivity areas at the portal sides. With the modified shielding described, the PM-203 meets the Office of Safeguards and Security SNM doorway monitor specifications for detecting either 235 U or 239 Pu-- 233 U. This system is also capable of monitoring 238 Pu

  18. Spatial modelling of population at risk and PM 2.5 exposure index: A ...

    African Journals Online (AJOL)

    However, monitoring, spatial representation and development of associated risk indicators have been major problems undermining formulation of relevant policy on air quality. This study used ... to environmental health. Key Words: Population at risk, PM2.5; Spatial modeling, GIS, Exposure index, environmental health ...

  19. Displaced calibration of PM10 measurements using spatio-temporal models

    Directory of Open Access Journals (Sweden)

    Daniela Cocchi

    2007-12-01

    Full Text Available PM10 monitoring networks are equipped with heterogeneous samplers. Some of these samplers are known to underestimate true levels of concentrations (non-reference samplers. In this paper we propose a hierarchical spatio-temporal Bayesian model for the calibration of measurements recorded using non-reference samplers, by borrowing strength from non co-located reference sampler measurements.

  20. Modeling the contributions of emission, meteorology, and chemistry to high PM2.5 levels over China

    Science.gov (United States)

    Wang, Y.; Zhang, Q.; Jia, B.; Jiang, J.; Zhou, W.; Wang, B.

    2014-12-01

    PM2.5 is known to harm health and public welfare. In recent years, regional haze with PM2.5 levels exceeding ten folds of WHO's air quality guideline has become the largest air quality concern in China. To better protect the health of millions of people, the key question is whether we understand the formation mechanism of high PM2.5 episodes well enough to guide the formation of effective control strategies. Here we present a modeling analysis in conjunction of observational constraints to estimate the contribution of emissions, meteorology, and secondary chemical formation to changes in PM2.5 levels over China. Certain meteorological conditions are found particularly conducive to trigger fast increases of secondary PM under current emissions mixtures in China. While the nested-grid GEOS-Chem model reproduces the distribution of PM2.5 and simulates up to ~400 μg/m3 of daily maximum PM2.5, it fails to capture the large sulfate enhancement during haze. We propose heterogeneous oxidation of SO2 on deliquesced aerosols as an additional source of sulfate under high relative humidity conditions. Parameterizing this process in the model improves the simulated spatial distribution and results in significant increases of sulfate enhancement ratio and sulfate fraction in PM2.5 during haze episodes. Implications of our modeling analysis for PM2.5 pollution control policies will also be discussed.

  1. Research on Fault Diagnosis of HTR-PM Based on Multilevel Flow Model

    International Nuclear Information System (INIS)

    Zhang Yong; Zhou Yangping

    2014-01-01

    In this paper, we focus on the application of Multilevel Flow Model (MFM) in the automatic real-time fault diagnosis of High Temperature Gas-cooled Reactor Pebble-bed Module (HTR-PM) accidents. In the MFM, the plant process is described abstractly in function level by mass, energy and information flows, which reveal the interaction between different components and capacitate the causal reasoning between functions according to the flow properties. Thus, in the abnormal status, a goal-function-component oriented fault diagnosis can be performed with the model at a very quick speed and abnormal alarms can be also precisely explained by the reasoning relationship of the model. By using MFM, a fault diagnosis model of HTR-PM plant is built, and the detailed process of fault diagnosis is also shown by the flowcharts. Due to lack of simulation data about HTR-PM, experiments are not conducted to evaluate the fault diagnosis performance, but analysis of algorithm feasibility and complexity shows that the diagnosis system will have a good ability to detect and diagnosis accidents timely. (author)

  2. Analytical model of SiPM time resolution and order statistics with crosstalk

    International Nuclear Information System (INIS)

    Vinogradov, S.

    2015-01-01

    Time resolution is the most important parameter of photon detectors in a wide range of time-of-flight and time correlation applications within the areas of high energy physics, medical imaging, and others. Silicon photomultipliers (SiPM) have been initially recognized as perfect photon-number-resolving detectors; now they also provide outstanding results in the scintillator timing resolution. However, crosstalk and afterpulsing introduce false secondary non-Poissonian events, and SiPM time resolution models are experiencing significant difficulties with that. This study presents an attempt to develop an analytical model of the timing resolution of an SiPM taking into account statistics of secondary events resulting from a crosstalk. Two approaches have been utilized to derive an analytical expression for time resolution: the first one based on statistics of independent identically distributed detection event times and the second one based on order statistics of these times. The first approach is found to be more straightforward and “analytical-friendly” to model analog SiPMs. Comparisons of coincidence resolving times predicted by the model with the known experimental results from a LYSO:Ce scintillator and a Hamamatsu MPPC are presented

  3. Analytical model of SiPM time resolution and order statistics with crosstalk

    Energy Technology Data Exchange (ETDEWEB)

    Vinogradov, S., E-mail: Sergey.Vinogradov@liverpool.ac.uk [University of Liverpool and Cockcroft Institute, Sci-Tech Daresbury, Keckwick Lane, Warrington WA4 4AD (United Kingdom); P.N. Lebedev Physical Institute of the Russian Academy of Sciences, 119991 Leninskiy Prospekt 53, Moscow (Russian Federation)

    2015-07-01

    Time resolution is the most important parameter of photon detectors in a wide range of time-of-flight and time correlation applications within the areas of high energy physics, medical imaging, and others. Silicon photomultipliers (SiPM) have been initially recognized as perfect photon-number-resolving detectors; now they also provide outstanding results in the scintillator timing resolution. However, crosstalk and afterpulsing introduce false secondary non-Poissonian events, and SiPM time resolution models are experiencing significant difficulties with that. This study presents an attempt to develop an analytical model of the timing resolution of an SiPM taking into account statistics of secondary events resulting from a crosstalk. Two approaches have been utilized to derive an analytical expression for time resolution: the first one based on statistics of independent identically distributed detection event times and the second one based on order statistics of these times. The first approach is found to be more straightforward and “analytical-friendly” to model analog SiPMs. Comparisons of coincidence resolving times predicted by the model with the known experimental results from a LYSO:Ce scintillator and a Hamamatsu MPPC are presented.

  4. Complex time series analysis of PM10 and PM2.5 for a coastal site using artificial neural network modelling and k-means clustering

    Science.gov (United States)

    Elangasinghe, M. A.; Singhal, N.; Dirks, K. N.; Salmond, J. A.; Samarasinghe, S.

    2014-09-01

    This paper uses artificial neural networks (ANN), combined with k-means clustering, to understand the complex time series of PM10 and PM2.5 concentrations at a coastal location of New Zealand based on data from a single site. Out of available meteorological parameters from the network (wind speed, wind direction, solar radiation, temperature, relative humidity), key factors governing the pattern of the time series concentrations were identified through input sensitivity analysis performed on the trained neural network model. The transport pathways of particulate matter under these key meteorological parameters were further analysed through bivariate concentration polar plots and k-means clustering techniques. The analysis shows that the external sources such as marine aerosols and local sources such as traffic and biomass burning contribute equally to the particulate matter concentrations at the study site. These results are in agreement with the results of receptor modelling by the Auckland Council based on Positive Matrix Factorization (PMF). Our findings also show that contrasting concentration-wind speed relationships exist between marine aerosols and local traffic sources resulting in very noisy and seemingly large random PM10 concentrations. The inclusion of cluster rankings as an input parameter to the ANN model showed a statistically significant (p advanced air dispersion models.

  5. Magnetic Circuit Model of PM Motor-Generator to Predict Radial Forces

    Science.gov (United States)

    McLallin, Kerry (Technical Monitor); Kascak, Peter E.; Dever, Timothy P.; Jansen, Ralph H.

    2004-01-01

    A magnetic circuit model is developed for a PM motor for flywheel applications. A sample motor is designed and modeled. Motor configuration and selection of materials is discussed, and the choice of winding configuration is described. A magnetic circuit model is described, which includes the stator back iron, rotor yoke, permanent magnets, air gaps and the stator teeth. Iterative solution of this model yields flux linkages, back EMF, torque, power, and radial force at the rotor caused by eccentricity. Calculated radial forces are then used to determine motor negative stiffness.

  6. The association of LUR modeled PM2.5 elemental composition with personal exposure

    International Nuclear Information System (INIS)

    Montagne, Denise; Hoek, Gerard; Nieuwenhuijsen, Mark; Lanki, Timo; Pennanen, Arto; Portella, Meritxell; Meliefste, Kees; Wang, Meng; Eeftens, Marloes; Yli-Tuomi, Tarja; Cirach, Marta; Brunekreef, Bert

    2014-01-01

    Background and aims: Land use regression (LUR) models predict spatial variation of ambient concentrations, but little is known about the validity in predicting personal exposures. In this study, the association of LUR modeled concentrations of PM 2.5 components with measured personal concentrations was determined. The elements of interest were copper (Cu), iron (Fe), potassium (K), nickel (Ni), sulfur (S), silicon (Si), vanadium (V) and zinc (Zn). Methods: In Helsinki (Finland), Utrecht (the Netherlands) and Barcelona (Spain) five participants from urban background, five from suburban background and five from busy street sites were selected in each city (15 participants per city). Outdoor, indoor and personal 96-hour PM 2.5 samples were collected by the participants over periods of two weeks in three different seasons (winter, summer and spring/autumn) and the overall average was calculated. Elemental composition was measured by ED-XRF spectrometry. The LUR models for the average ambient concentrations of each element were developed by the ESCAPE project. Results: LUR models predicted the within-city variation of average outdoor Cu and Fe concentrations moderately well (range in R 2 27–67% for Cu and 24–54% for Fe). The outdoor concentrations of the other elements were not well predicted. The LUR modeled concentration only significantly correlated with measured personal Fe exposure in Utrecht and Ni and V in Helsinki. The LUR model predictions did not correlate with measured personal Cu exposure. After excluding observations with an indoor/outdoor ratio of > 1.5, modeled Cu outdoor concentrations correlated with indoor concentrations in Helsinki and Utrecht and personal concentrations in Utrecht. The LUR model predictions were associated with measured outdoor, indoor and personal concentrations for all elements when the data for the three cities was pooled. Conclusions: Within-city modeled variation of elemental composition of PM 2.5 did not predict measured

  7. Developing a Hierarchical Model for the Spatial Analysis of PM10 Pollution Extremes in the Mexico City Metropolitan Area

    Directory of Open Access Journals (Sweden)

    Alejandro Ivan Aguirre-Salado

    2017-07-01

    Full Text Available We implemented a spatial model for analysing PM 10 maxima across the Mexico City metropolitan area during the period 1995–2016. We assumed that these maxima follow a non-identical generalized extreme value (GEV distribution and modeled the trend by introducing multivariate smoothing spline functions into the probability GEV distribution. A flexible, three-stage hierarchical Bayesian approach was developed to analyse the distribution of the PM 10 maxima in space and time. We evaluated the statistical model’s performance by using a simulation study. The results showed strong evidence of a positive correlation between the PM 10 maxima and the longitude and latitude. The relationship between time and the PM 10 maxima was negative, indicating a decreasing trend over time. Finally, a high risk of PM 10 maxima presenting levels above 1000 μ g/m 3 (return period: 25 yr was observed in the northwestern region of the study area.

  8. Receptor modeling studies for the characterization of PM10 pollution sources in Belgrade

    Directory of Open Access Journals (Sweden)

    Mijić Zoran

    2012-01-01

    Full Text Available The objective of this study is to determine the major sources and potential source regions of PM10 over Belgrade, Serbia. The PM10 samples were collected from July 2003 to December 2006 in very urban area of Belgrade and concentrations of Al, V, Cr, Mn, Fe, Ni, Cu, Zn, Cd and Pb were analyzed by atomic absorption spectrometry. The analysis of seasonal variations of PM10 mass and some element concentrations reported relatively higher concentrations in winter, what underlined the importance of local emission sources. The Unmix model was used for source apportionment purpose and the four main source profiles (fossil fuel combustion; traffic exhaust/regional transport from industrial centers; traffic related particles/site specific sources and mineral/crustal matter were identified. Among the resolved factors the fossil fuel combustion was the highest contributor (34% followed by traffic/regional industry (26%. Conditional probability function (CPF results identified possible directions of local sources. The potential source contribution function (PSCF and concentration weighted trajectory (CWT receptor models were used to identify spatial source distribution and contribution of regional-scale transported aerosols. [Projekat Ministarstva nauke Republike Srbije, br. III43007 i br. III41011

  9. Improving spatio-temporal model estimation of satellite-derived PM2.5 concentrations: Implications for public health

    Science.gov (United States)

    Barik, M. G.; Al-Hamdan, M. Z.; Crosson, W. L.; Yang, C. A.; Coffield, S. R.

    2017-12-01

    Satellite-derived environmental data, available in a range of spatio-temporal scales, are contributing to the growing use of health impact assessments of air pollution in the public health sector. Models developed using correlation of Moderate Resolution Imaging Spectrometer (MODIS) Aerosol Optical Depth (AOD) with ground measurements of fine particulate matter less than 2.5 microns (PM2.5) are widely applied to measure PM2.5 spatial and temporal variability. In the public health sector, associations of PM2.5 with respiratory and cardiovascular diseases are often investigated to quantify air quality impacts on these health concerns. In order to improve predictability of PM2.5 estimation using correlation models, we have included meteorological variables, higher-resolution AOD products and instantaneous PM2.5 observations into statistical estimation models. Our results showed that incorporation of high-resolution (1-km) Multi-Angle Implementation of Atmospheric Correction (MAIAC)-generated MODIS AOD, meteorological variables and instantaneous PM2.5 observations improved model performance in various parts of California (CA), USA, where single variable AOD-based models showed relatively weak performance. In this study, we further asked whether these improved models actually would be more successful for exploring associations of public health outcomes with estimated PM2.5. To answer this question, we geospatially investigated model-estimated PM2.5's relationship with respiratory and cardiovascular diseases such as asthma, high blood pressure, coronary heart disease, heart attack and stroke in CA using health data from the Centers for Disease Control and Prevention (CDC)'s Wide-ranging Online Data for Epidemiologic Research (WONDER) and the Behavioral Risk Factor Surveillance System (BRFSS). PM2.5 estimation from these improved models have the potential to improve our understanding of associations between public health concerns and air quality.

  10. Development of West-European PM2.5 and NO2 land use regression models incorporating satellite-derived and chemical transport modelling data

    NARCIS (Netherlands)

    de Hoogh, Kees; Gulliver, John; Donkelaar, Aaron van; Martin, Randall V; Marshall, Julian D; Bechle, Matthew J; Cesaroni, Giulia; Pradas, Marta Cirach; Dedele, Audrius; Eeftens, Marloes|info:eu-repo/dai/nl/315028300; Forsberg, Bertil; Galassi, Claudia; Heinrich, Joachim; Hoffmann, Barbara; Jacquemin, Bénédicte; Katsouyanni, Klea; Korek, Michal; Künzli, Nino; Lindley, Sarah J; Lepeule, Johanna; Meleux, Frederik; de Nazelle, Audrey; Nieuwenhuijsen, Mark; Nystad, Wenche; Raaschou-Nielsen, Ole; Peters, Annette; Peuch, Vincent-Henri; Rouil, Laurence; Udvardy, Orsolya; Slama, Rémy; Stempfelet, Morgane; Stephanou, Euripides G; Tsai, Ming Y; Yli-Tuomi, Tarja; Weinmayr, Gudrun; Brunekreef, Bert|info:eu-repo/dai/nl/067548180; Vienneau, Danielle; Hoek, Gerard|info:eu-repo/dai/nl/069553475

    2016-01-01

    Satellite-derived (SAT) and chemical transport model (CTM) estimates of PM2.5 and NO2 are increasingly used in combination with Land Use Regression (LUR) models. We aimed to compare the contribution of SAT and CTM data to the performance of LUR PM2.5 and NO2 models for Europe. Four sets of models,

  11. Modeling spatial effects of PM{sub 2.5} on term low birth weight in Los Angeles County

    Energy Technology Data Exchange (ETDEWEB)

    Coker, Eric, E-mail: cokerer@onid.orst.edu [College of Public Health and Human Sciences, Oregon State University, Corvallis, OR (United States); Ghosh, Jokay [School of Public Health, University of California, Los Angeles, Los Angeles, CA (United States); Jerrett, Michael [School of Public Health, University of California, Berkeley, Berkeley, CA (United States); Gomez-Rubio, Virgilio [Department of Mathematics, Universidad De Castilla-La Mancha, Albacete (Spain); Beckerman, Bernardo [School of Public Health, University of California, Berkeley, Berkeley, CA (United States); Cockburn, Myles [Preventive Medicine and Spatial Sciences, University of Southern California, Los Angeles, CA (United States); Liverani, Silvia [Department of Mathematics, Brunel University, London (United Kingdom); Su, Jason [School of Public Health, University of California, Berkeley, Berkeley, CA (United States); Li, Arthur [Department of Information Science, City of Hope National Cancer Center, Duarte, CA (United States); Kile, Molly L [College of Public Health and Human Sciences, Oregon State University, Corvallis, OR (United States); Ritz, Beate [School of Public Health, University of California, Los Angeles, Los Angeles, CA (United States); Molitor, John [College of Public Health and Human Sciences, Oregon State University, Corvallis, OR (United States)

    2015-10-15

    Air pollution epidemiological studies suggest that elevated exposure to fine particulate matter (PM{sub 2.5}) is associated with higher prevalence of term low birth weight (TLBW). Previous studies have generally assumed the exposure–response of PM{sub 2.5} on TLBW to be the same throughout a large geographical area. Health effects related to PM{sub 2.5} exposures, however, may not be uniformly distributed spatially, creating a need for studies that explicitly investigate the spatial distribution of the exposure–response relationship between individual-level exposure to PM{sub 2.5} and TLBW. Here, we examine the overall and spatially varying exposure–response relationship between PM{sub 2.5} and TLBW throughout urban Los Angeles (LA) County, California. We estimated PM{sub 2.5} from a combination of land use regression (LUR), aerosol optical depth from remote sensing, and atmospheric modeling techniques. Exposures were assigned to LA County individual pregnancies identified from electronic birth certificates between the years 1995-2006 (N=1,359,284) provided by the California Department of Public Health. We used a single pollutant multivariate logistic regression model, with multilevel spatially structured and unstructured random effects set in a Bayesian framework to estimate global and spatially varying pollutant effects on TLBW at the census tract level. Overall, increased PM{sub 2.5} level was associated with higher prevalence of TLBW county-wide. The spatial random effects model, however, demonstrated that the exposure–response for PM{sub 2.5} and TLBW was not uniform across urban LA County. Rather, the magnitude and certainty of the exposure–response estimates for PM{sub 2.5} on log odds of TLBW were greatest in the urban core of Central and Southern LA County census tracts. These results suggest that the effects may be spatially patterned, and that simply estimating global pollutant effects obscures disparities suggested by spatial patterns of

  12. Modelling of PM10 concentration for industrialized area in Malaysia: A case study in Shah Alam

    Science.gov (United States)

    N, Norazian Mohamed; Abdullah, M. M. A.; Tan, Cheng-yau; Ramli, N. A.; Yahaya, A. S.; Fitri, N. F. M. Y.

    In Malaysia, the predominant air pollutants are suspended particulate matter (SPM) and nitrogen dioxide (NO2). This research is on PM10 as they may trigger harm to human health as well as environment. Six distributions, namely Weibull, log-normal, gamma, Rayleigh, Gumbel and Frechet were chosen to model the PM10 observations at the chosen industrial area i.e. Shah Alam. One-year period hourly average data for 2006 and 2007 were used for this research. For parameters estimation, method of maximum likelihood estimation (MLE) was selected. Four performance indicators that are mean absolute error (MAE), root mean squared error (RMSE), coefficient of determination (R2) and prediction accuracy (PA), were applied to determine the goodness-of-fit criteria of the distributions. The best distribution that fits with the PM10 observations in Shah Alamwas found to be log-normal distribution. The probabilities of the exceedences concentration were calculated and the return period for the coming year was predicted from the cumulative density function (cdf) obtained from the best-fit distributions. For the 2006 data, Shah Alam was predicted to exceed 150 μg/m3 for 5.9 days in 2007 with a return period of one occurrence per 62 days. For 2007, the studied area does not exceed the MAAQG of 150 μg/m3

  13. The value of using seasonality and meteorological variables to model intra-urban PM2.5 variation

    Science.gov (United States)

    Olvera Alvarez, Hector A.; Myers, Orrin B.; Weigel, Margaret; Armijos, Rodrigo X.

    2018-06-01

    A yearlong air monitoring campaign was conducted to assess the impact of local temperature, relative humidity, and wind speed on the temporal and spatial variability of PM2.5 in El Paso, Texas. Monitoring was conducted at four sites purposely selected to capture the local traffic variability. Effects of meteorological events on seasonal PM2.5 variability were identified. For instance, in winter low-wind and low-temperature conditions were associated with high PM2.5 events that contributed to elevated seasonal PM2.5 levels. Similarly, in spring, high PM2.5 events were associated with high-wind and low-relative humidity conditions. Correlation coefficients between meteorological variables and PM2.5 fluctuated drastically across seasons. Specifically, it was observed that for most sites correlations between PM2.5 and meteorological variables either changed from positive to negative or dissolved depending on the season. Overall, the results suggest that mixed effects analysis with season and site as fixed factors and meteorological variables as covariates could increase the explanatory value of LUR models for PM2.5.

  14. Non-chemistry coupled PM10 modeling in Chiang Mai City, Northern Thailand: A fast operational approach for aerosol forecasts

    Science.gov (United States)

    Macatangay, Ronald; Bagtasa, Gerry; Sonkaew, Thiranan

    2017-09-01

    The Weather Research and Forecasting (WRF v. 3.7) model was applied to model PM10 data in Chiang Mai city for 10-days during a high haze event utilizing updated land use categories from the Moderate Resolution Imaging Spectroradiometer (MODIS). A higher resolution meteorological lateral boundary condition (from 1 degree to 0.25 degree) was also used from the NCEP GDAS/FNL Global Tropospheric Analyses and Forecast Grid system. A 3-category urban canopy model was also added and the Thompson aerosol-aware microphysics parameterization scheme was used to model the aerosol number concentrations that were later converted to PM10 concentrations. Aerosol number concentration monthly climatology was firstly used as initial and lateral boundary conditions to model PM10 concentrations. These were compared to surface data obtained from two stations of the Pollution Control Department (PCD) of Thailand. The results from the modeled PM10 concentrations could not capture the variability (r = 0.29; 0.27 for each site) and underestimated a high PM10 spike during the period studied. The authors then added satellite data to the aerosol climatology that improved the comparison with observations (r = 0.45; 43). However, both model runs still were not able to capture the high PM10 concentration event. This requires further investigation.

  15. Modelling PM 10 concentrations and carrying capacity associated with woodheater emissions in Launceston, Tasmania

    Science.gov (United States)

    Luhar, Ashok K.; Galbally, Ian E.; Keywood, Melita

    Launceston is one of the Australian cities most affected by particle pollution due to the use of woodheaters in the winter months, with frequent exceedences of the national standard, the National Environment Protection Measure for Ambient Air Quality (or Air NEPM in short), of 50 micrograms per cubic metre for daily PM 10 (particulate matter with an aerodynamic diameter of 10 μm or less). The main objective of the present study was to determine the woodheater carrying capacity for Launceston—the number of woodheaters that can operate in the city without exceeding the Air NEPM. For this purpose, a prognostic meteorological and air pollution model called TAPM is used, coupled to a gridded woodheater PM 10 emissions inventory. The latter was derived using information on dwelling density, the percentage of dwellings with woodheaters, woodheater emission rates and their diurnal and seasonal variations, and the proportions of compliant/non-compliant woodheaters and open fireplaces. The model simulations are performed for the year 1998, and the concentrations are scaled for previous and subsequent years using trends in woodheater numbers and types. The modelled number of exceedences of the Air NEPM for the period 1997-2004 is in good agreement with the observations. The modelling indicates that the PM 10 Air NEPM would be met in Launceston when the total number of woodheaters is 20% of the total number of dwellings, of which 76%, 18%, 6% would be compliant woodheaters, non-compliant woodheaters and open fireplaces, respectively. With the present trends in the regional woodheater profile, this should occur in the year 2007.

  16. Modeling PM10 in Ho Chi Minh City, Vietnam and evaluation of its impact on human health

    Directory of Open Access Journals (Sweden)

    Bang Quoc Ho

    2017-03-01

    Full Text Available According to World Health Organization (WHO and Global Burden of Disease, ambient air pollution is estimated to be responsible for 3.7 million premature deaths in 2012 [1]. Therefore, it is urgent to estimate the impact of air pollution on public health and economic damage. The objectives of this research are: study the distribution of PM10 concentration over Ho Chi Minh city (HCMC and relationship to public health and for proposing solutions of diseases prevention in HCM, Vietnam. EMIssion SENSitivity model was applied to conduct air emission inventory for transportation sector. Then, Finite Volume Model and Transport and Photochemistry Mesoscale Model were used to simulate the meteorology and the spatial distribution of PM10 in HCMC. Together with disease data obtained, the US Environmental Benefits Mapping and Analysis Model was applied for calculating the number of deaths and estimating economic losses due to PM10 pollution. Finally, solutions to reduce PM10 pollution and protect public health are proposed. The results showed that the highest 1-h average concentration of PM10 is 240 μg m−3 in North Eastern of HCMC. The concentration of PM10 for annual average in District 5 ranged from 17 to 49 μg m−3. There are 12 wards of District 5 with PM10 concentration exceeding the WHO guidelines (20 μg m−3 for annual average of PM10 and 50 μg m−3 for 24-h average. The high concentration of PM10 causes 5 deaths yr−1 in District 5 and 204 deaths yr−1 in HCMC, and it causes economic losses of 1.84 billion of USD.

  17. PM10 modeling in the Oviedo urban area (Northern Spain) by using multivariate adaptive regression splines

    Science.gov (United States)

    Nieto, Paulino José García; Antón, Juan Carlos Álvarez; Vilán, José Antonio Vilán; García-Gonzalo, Esperanza

    2014-10-01

    The aim of this research work is to build a regression model of the particulate matter up to 10 micrometers in size (PM10) by using the multivariate adaptive regression splines (MARS) technique in the Oviedo urban area (Northern Spain) at local scale. This research work explores the use of a nonparametric regression algorithm known as multivariate adaptive regression splines (MARS) which has the ability to approximate the relationship between the inputs and outputs, and express the relationship mathematically. In this sense, hazardous air pollutants or toxic air contaminants refer to any substance that may cause or contribute to an increase in mortality or serious illness, or that may pose a present or potential hazard to human health. To accomplish the objective of this study, the experimental dataset of nitrogen oxides (NOx), carbon monoxide (CO), sulfur dioxide (SO2), ozone (O3) and dust (PM10) were collected over 3 years (2006-2008) and they are used to create a highly nonlinear model of the PM10 in the Oviedo urban nucleus (Northern Spain) based on the MARS technique. One main objective of this model is to obtain a preliminary estimate of the dependence between PM10 pollutant in the Oviedo urban area at local scale. A second aim is to determine the factors with the greatest bearing on air quality with a view to proposing health and lifestyle improvements. The United States National Ambient Air Quality Standards (NAAQS) establishes the limit values of the main pollutants in the atmosphere in order to ensure the health of healthy people. Firstly, this MARS regression model captures the main perception of statistical learning theory in order to obtain a good prediction of the dependence among the main pollutants in the Oviedo urban area. Secondly, the main advantages of MARS are its capacity to produce simple, easy-to-interpret models, its ability to estimate the contributions of the input variables, and its computational efficiency. Finally, on the basis of

  18. “Skill of Generalized Additive Model to Detect PM2.5 Health ...

    Science.gov (United States)

    Summary. Measures of health outcomes are collinear with meteorology and air quality, making analysis of connections between human health and air quality difficult. The purpose of this analysis was to determine time scales and periods shared by the variables of interest (and by implication scales and periods that are not shared). Hospital admissions, meteorology (temperature and relative humidity), and air quality (PM2.5 and daily maximum ozone) for New York City during the period 2000-2006 were decomposed into temporal scales ranging from 2 days to greater than two years using a complex wavelet transform. Health effects were modeled as functions of the wavelet components of meteorology and air quality using the generalized additive model (GAM) framework. This simulation study showed that GAM is extremely successful at extracting and estimating a health effect embedded in a dataset. It also shows that, if the objective in mind is to estimate the health signal but not to fully explain this signal, a simple GAM model with a single confounder (calendar time) whose smooth representation includes a sufficient number of constraints is as good as a more complex model.Introduction. In the context of wavelet regression, confounding occurs when two or more independent variables interact with the dependent variable at the same frequency. Confounding also acts on a variety of time scales, changing the PM2.5 coefficient (magnitude and sign) and its significance ac

  19. Modeling and experimental evaluation of the diffusion bonding of the oxide dispersion strengthened steel PM2000

    International Nuclear Information System (INIS)

    Sittel, Wiebke; Basuki, Widodo W.; Aktaa, Jarir

    2015-01-01

    A modeling based optimization process of the solid state diffusion bonding is presented for joining ferritic oxide dispersion strengthened steels PM2000. An optimization study employing varying bonding temperatures and pressures results in almost the same strength and toughness of the bonded compared to the as received material. TEM investigations of diffusion bonded samples show a homogeneous distribution of oxide particles at the bonding seam similar to that in the bulk. Hence, no loss in strength or creep resistance due to oxide particle agglomeration is found, as verified by the mechanical properties observed for the joint.

  20. Prediction of hourly PM2.5 using a space-time support vector regression model

    Science.gov (United States)

    Yang, Wentao; Deng, Min; Xu, Feng; Wang, Hang

    2018-05-01

    Real-time air quality prediction has been an active field of research in atmospheric environmental science. The existing methods of machine learning are widely used to predict pollutant concentrations because of their enhanced ability to handle complex non-linear relationships. However, because pollutant concentration data, as typical geospatial data, also exhibit spatial heterogeneity and spatial dependence, they may violate the assumptions of independent and identically distributed random variables in most of the machine learning methods. As a result, a space-time support vector regression model is proposed to predict hourly PM2.5 concentrations. First, to address spatial heterogeneity, spatial clustering is executed to divide the study area into several homogeneous or quasi-homogeneous subareas. To handle spatial dependence, a Gauss vector weight function is then developed to determine spatial autocorrelation variables as part of the input features. Finally, a local support vector regression model with spatial autocorrelation variables is established for each subarea. Experimental data on PM2.5 concentrations in Beijing are used to verify whether the results of the proposed model are superior to those of other methods.

  1. Improving satellite-based PM2.5 estimates in China using Gaussian processes modeling in a Bayesian hierarchical setting.

    Science.gov (United States)

    Yu, Wenxi; Liu, Yang; Ma, Zongwei; Bi, Jun

    2017-08-01

    Using satellite-based aerosol optical depth (AOD) measurements and statistical models to estimate ground-level PM 2.5 is a promising way to fill the areas that are not covered by ground PM 2.5 monitors. The statistical models used in previous studies are primarily Linear Mixed Effects (LME) and Geographically Weighted Regression (GWR) models. In this study, we developed a new regression model between PM 2.5 and AOD using Gaussian processes in a Bayesian hierarchical setting. Gaussian processes model the stochastic nature of the spatial random effects, where the mean surface and the covariance function is specified. The spatial stochastic process is incorporated under the Bayesian hierarchical framework to explain the variation of PM 2.5 concentrations together with other factors, such as AOD, spatial and non-spatial random effects. We evaluate the results of our model and compare them with those of other, conventional statistical models (GWR and LME) by within-sample model fitting and out-of-sample validation (cross validation, CV). The results show that our model possesses a CV result (R 2  = 0.81) that reflects higher accuracy than that of GWR and LME (0.74 and 0.48, respectively). Our results indicate that Gaussian process models have the potential to improve the accuracy of satellite-based PM 2.5 estimates.

  2. Evaluation for Long Term PM10 Concentration Forecasting using Multi Linear Regression (MLR and Principal Component Regression (PCR Models

    Directory of Open Access Journals (Sweden)

    Samsuri Abdullah

    2016-07-01

    Full Text Available Air pollution in Peninsular Malaysia is dominated by particulate matter which is demonstrated by having the highest Air Pollution Index (API value compared to the other pollutants at most part of the country. Particulate Matter (PM10 forecasting models development is crucial because it allows the authority and citizens of a community to take necessary actions to limit their exposure to harmful levels of particulates pollution and implement protection measures to significantly improve air quality on designated locations. This study aims in improving the ability of MLR using PCs inputs for PM10 concentrations forecasting. Daily observations for PM10 in Kuala Terengganu, Malaysia from January 2003 till December 2011 were utilized to forecast PM10 concentration levels. MLR and PCR (using PCs input models were developed and the performance was evaluated using RMSE, NAE and IA. Results revealed that PCR performed better than MLR due to the implementation of PCA which reduce intricacy and eliminate data multi-collinearity.

  3. Assessing the accuracy of ANFIS, EEMD-GRNN, PCR, and MLR models in predicting PM2.5

    Science.gov (United States)

    Ausati, Shadi; Amanollahi, Jamil

    2016-10-01

    Since Sanandaj is considered one of polluted cities of Iran, prediction of any type of pollution especially prediction of suspended particles of PM2.5, which are the cause of many diseases, could contribute to health of society by timely announcements and prior to increase of PM2.5. In order to predict PM2.5 concentration in the Sanandaj air the hybrid models consisting of an ensemble empirical mode decomposition and general regression neural network (EEMD-GRNN), Adaptive Neuro-Fuzzy Inference System (ANFIS), principal component regression (PCR), and linear model such as multiple liner regression (MLR) model were used. In these models the data of suspended particles of PM2.5 were the dependent variable and the data related to air quality including PM2.5, PM10, SO2, NO2, CO, O3 and meteorological data including average minimum temperature (Min T), average maximum temperature (Max T), average atmospheric pressure (AP), daily total precipitation (TP), daily relative humidity level of the air (RH) and daily wind speed (WS) for the year 2014 in Sanandaj were the independent variables. Among the used models, EEMD-GRNN model with values of R2 = 0.90, root mean square error (RMSE) = 4.9218 and mean absolute error (MAE) = 3.4644 in the training phase and with values of R2 = 0.79, RMSE = 5.0324 and MAE = 3.2565 in the testing phase, exhibited the best function in predicting this phenomenon. It can be concluded that hybrid models have accurate results to predict PM2.5 concentration compared with linear model.

  4. Model development for spatial variation of PM2.5 emissions from residential wood burning

    International Nuclear Information System (INIS)

    Yong Q, Tian; Peng Gong; Qian Yu; Radke, John D.

    2004-01-01

    This paper presents a preliminary research result of spatially quantifying and allocating the potential activity of residential wood burning (RWB) by using demographic, hypsographic, climatic and topographic information as independent variables. We also introduce the method for calculating PM 2.5 emission from residential wood combustion with the potential activity as primary variable. A linear regression model was generated to describe spatial and temporal distribution of the potential activity of wood burning as primary heating source. In order to improve the estimation, the classifications of urban, suburban and rural were redefined to meet the specifications of this application. Also, a unique way of defining forest accessibility is found useful in estimating the activity potential of RWB. The results suggest that the potential activity of wood burning is mostly determined by elevation of a location, forest accessibility, urban/non-urban position, climatic conditions and several demographic variables. The analysis results were validated using survey data collected through face-to-face and telephone interviews over the study area in central California. The linear regression model can explain approximately 86% of the variation of surveyed wood burning activity potential. The total PM 2.5 emitted from woodstoves and fireplaces is analyzed for the study region at county level. (Author)

  5. A Five-Year CMAQ PM2.5 Model Performance for Wildfires and Prescribed Fires

    Science.gov (United States)

    Wilkins, J. L.; Pouliot, G.; Foley, K.; Rappold, A.; Pierce, T. E.

    2016-12-01

    Biomass burning has been identified as an important contributor to the degradation of air quality because of its impact on ozone and particulate matter. Two components of the biomass burning inventory, wildfires and prescribed fires are routinely estimated in the national emissions inventory. However, there is a large amount of uncertainty in the development of these emission inventory sectors. We have completed a 5 year set of CMAQ model simulations (2008-2012) in which we have simulated regional air quality with and without the wildfire and prescribed fire inventory. We will examine CMAQ model performance over regions with significant PM2.5 and Ozone contribution from prescribed fires and wildfires. We will also review plume rise to see how it affects model bias and compare CMAQ current fire emissions input to an hourly dataset from FLAMBE.

  6. Long- and short-term exposure to PM2.5 and mortality: using novel exposure models.

    Science.gov (United States)

    Kloog, Itai; Ridgway, Bill; Koutrakis, Petros; Coull, Brent A; Schwartz, Joel D

    2013-07-01

    Many studies have reported associations between ambient particulate matter (PM) and adverse health effects, focused on either short-term (acute) or long-term (chronic) PM exposures. For chronic effects, the studied cohorts have rarely been representative of the population. We present a novel exposure model combining satellite aerosol optical depth and land-use data to investigate both the long- and short-term effects of PM2.5 exposures on population mortality in Massachusetts, United States, for the years 2000-2008. All deaths were geocoded. We performed two separate analyses: a time-series analysis (for short-term exposure) where counts in each geographic grid cell were regressed against cell-specific short-term PM2.5 exposure, temperature, socioeconomic data, lung cancer rates (as a surrogate for smoking), and a spline of time (to control for season and trends). In addition, for long-term exposure, we performed a relative incidence analysis using two long-term exposure metrics: regional 10 × 10 km PM2.5 predictions and local deviations from the cell average based on land use within 50 m of the residence. We tested whether these predicted the proportion of deaths from PM-related causes (cardiovascular and respiratory diseases). For short-term exposure, we found that for every 10-µg/m increase in PM 2.5 exposure there was a 2.8% increase in PM-related mortality (95% confidence interval [CI] = 2.0-3.5). For the long-term exposure at the grid cell level, we found an odds ratio (OR) for every 10-µg/m increase in long-term PM2.5 exposure of 1.6 (CI = 1.5-1.8) for particle-related diseases. Local PM2.5 had an OR of 1.4 (CI = 1.3-1.5), which was independent of and additive to the grid cell effect. We have developed a novel PM2.5 exposure model based on remote sensing data to assess both short- and long-term human exposures. Our approach allows us to gain spatial resolution in acute effects and an assessment of long-term effects in the entire population rather than a

  7. Development of nonlinear empirical models to forecast daily PM2.5 and ozone levels in three large Chinese cities

    Science.gov (United States)

    Lv, Baolei; Cobourn, W. Geoffrey; Bai, Yuqi

    2016-12-01

    Empirical regression models for next-day forecasting of PM2.5 and O3 air pollution concentrations have been developed and evaluated for three large Chinese cities, Beijing, Nanjing and Guangzhou. The forecast models are empirical nonlinear regression models designed for use in an automated data retrieval and forecasting platform. The PM2.5 model includes an upwind air quality variable, PM24, to account for regional transport of PM2.5, and a persistence variable (previous day PM2.5 concentration). The models were evaluated in the hindcast mode with a two-year air quality and meteorological data set using a leave-one-month-out cross validation method, and in the forecast mode with a one-year air quality and forecasted weather dataset that included forecasted air trajectories. The PM2.5 models performed well in the hindcast mode, with coefficient of determination (R2) values of 0.54, 0.65 and 0.64, and normalized mean error (NME) values of 0.40, 0.26 and 0.23 respectively, for the three cities. The O3 models also performed well in the hindcast mode, with R2 values of 0.75, 0.55 and 0.73, and NME values of 0.29, 0.26 and 0.24 in the three cities. The O3 models performed better in summertime than in winter in Beijing and Guangzhou, and captured the O3 variations well all the year round in Nanjing. The overall forecast performance of the PM2.5 and O3 models during the test year varied from fair to good, depending on location. The forecasts were somewhat degraded compared with hindcasts from the same year, depending on the accuracy of the forecasted meteorological input data. For the O3 models, the model forecast accuracy was strongly dependent on the maximum temperature forecasts. For the critical forecasts, involving air quality standard exceedences, the PM2.5 model forecasts were fair to good, and the O3 model forecasts were poor to fair.

  8. Source apportionment of PM10 and PM2.5 in major urban Greek agglomerations using a hybrid source-receptor modeling process.

    Science.gov (United States)

    Argyropoulos, G; Samara, C; Diapouli, E; Eleftheriadis, K; Papaoikonomou, K; Kungolos, A

    2017-12-01

    A hybrid source-receptor modeling process was assembled, to apportion and infer source locations of PM 10 and PM 2.5 in three heavily-impacted urban areas of Greece, during the warm period of 2011, and the cold period of 2012. The assembled process involved application of an advanced computational procedure, the so-called Robotic Chemical Mass Balance (RCMB) model. Source locations were inferred using two well-established probability functions: (a) the Conditional Probability Function (CPF), to correlate the output of RCMB with local wind directional data, and (b) the Potential Source Contribution Function (PSCF), to correlate the output of RCMB with 72h air-mass back-trajectories, arriving at the receptor sites, during sampling. Regarding CPF, a higher-level conditional probability function was defined as well, from the common locus of CPF sectors derived for neighboring receptor sites. With respect to PSCF, a non-parametric bootstrapping method was applied to discriminate the statistically significant values. RCMB modeling showed that resuspended dust is actually one of the main barriers for attaining the European Union (EU) limit values in Mediterranean urban agglomerations, where the drier climate favors build-up. The shift in the energy mix of Greece (caused by the economic recession) was also evidenced, since biomass burning was found to contribute more significantly to the sampling sites belonging to the coldest climatic zone, particularly during the cold period. The CPF analysis showed that short-range transport of anthropogenic emissions from urban traffic to urban background sites was very likely to have occurred, within all the examined urban agglomerations. The PSCF analysis confirmed that long-range transport of primary and/or secondary aerosols may indeed be possible, even from distances over 1000km away from study areas. Copyright © 2017 Elsevier B.V. All rights reserved.

  9. Modelling of particulate matter pollution (PM10) over the Etang de Berre area Determination of areas of homogeneous pollution

    International Nuclear Information System (INIS)

    Brocheton, F.; Poulet, D.; Mesbah, B.; Hourdin, G.

    2010-01-01

    AIRFOBEP is the association in charge of the air quality monitoring in the Etang de Berre area. AIRFOBEP is managing a network of ten sensors to monitor the PMI (particulate matter index) particulate pollution. This network is updated once a year according to the Air Quality Monitoring Plan (PSQA). Optimizing this network needs to know how the particulate pollution is distributed in the area. In other words, to determine the limits of homogeneous zones of PM 10 pollution. The aim of the project presented in this article is to produce a map of homogeneous zones of PM 10 pollution in the Etang de Berre area. The project was carried out in two steps: - PM 10 atmospheric dispersion modeling, using a ADMS-URBAN software, - Statistic classification, based on the well known Hierarchical Ascending Classification (HAC) technique. Results of the atmospheric dispersion modeling was namely adjusted using an original technique for the 'background PM 10 pollution' computation. Good performances have been obtained when comparing modeling and measurements data. Finally, a set of five homogeneous zones was found to well describe the PM 10 pollution level distribution in the Etang de Berre area. (author)

  10. Modelling Seasonal GWR of Daily PM2.5 with Proper Auxiliary Variables for the Yangtze River Delta

    Directory of Open Access Journals (Sweden)

    Man Jiang

    2017-04-01

    Full Text Available Over the past decades, regional haze episodes have frequently occurred in eastern China, especially in the Yangtze River Delta (YRD. Satellite derived Aerosol Optical Depth (AOD has been used to retrieve the spatial coverage of PM2.5 concentrations. To improve the retrieval accuracy of the daily AOD-PM2.5 model, various auxiliary variables like meteorological or geographical factors have been adopted into the Geographically Weighted Regression (GWR model. However, these variables are always arbitrarily selected without deep consideration of their potentially varying temporal or spatial contributions in the model performance. In this manuscript, we put forward an automatic procedure to select proper auxiliary variables from meteorological and geographical factors and obtain their optimal combinations to construct four seasonal GWR models. We employ two different schemes to comprehensively test the performance of our proposed GWR models: (1 comparison with other regular GWR models by varying the number of auxiliary variables; and (2 comparison with observed ground-level PM2.5 concentrations. The result shows that our GWR models of “AOD + 3” with three common meteorological variables generally perform better than all the other GWR models involved. Our models also show powerful prediction capabilities in PM2.5 concentrations with only slight overfitting. The determination coefficients R2 of our seasonal models are 0.8259 in spring, 0.7818 in summer, 0.8407 in autumn, and 0.7689 in winter. Also, the seasonal models in summer and autumn behave better than those in spring and winter. The comparison between seasonal and yearly models further validates the specific seasonal pattern of auxiliary variables of the GWR model in the YRD. We also stress the importance of key variables and propose a selection process in the AOD-PM2.5 model. Our work validates the significance of proper auxiliary variables in modelling the AOD-PM2.5 relationships and

  11. PM(10) episodes in Greece: Local sources versus long-range transport-observations and model simulations.

    Science.gov (United States)

    Matthaios, Vasileios N; Triantafyllou, Athanasios G; Koutrakis, Petros

    2017-01-01

    Periods of abnormally high concentrations of atmospheric pollutants, defined as air pollution episodes, can cause adverse health effects. Southern European countries experience high particulate matter (PM) levels originating from local and distant sources. In this study, we investigated the occurrence and nature of extreme PM 10 (PM with an aerodynamic diameter ≤10 μm) pollution episodes in Greece. We examined PM 10 concentration data from 18 monitoring stations located at five sites across the country: (1) an industrial area in northwestern Greece (Western Macedonia Lignite Area, WMLA), which includes sources such as lignite mining operations and lignite power plants that generate a high percentage of the energy in Greece; (2) the greater Athens area, the most populated area of the country; and (3) Thessaloniki, (4) Patra, and (5) Volos, three large cities in Greece. We defined extreme PM 10 pollution episodes (EEs) as days during which PM 10 concentrations at all five sites exceeded the European Union (EU) 24-hr PM 10 standards. For each EE, we identified the corresponding prevailing synoptic and local meteorological conditions, including wind surface data, for the period from January 2009 through December 2011. We also analyzed data from remote sensing and model simulations. We recorded 14 EEs that occurred over 49 days and could be grouped into two categories: (1) Local Source Impact (LSI; 26 days, 53%) and (2) African Dust Impact (ADI; 23 days, 47%). Our analysis suggested that the contribution of local sources to ADI EEs was relatively small. LSI EEs were observed only in the cold season, whereas ADI EEs occurred throughout the year, with a higher frequency during the cold season. The EEs with the highest intensity were recorded during African dust intrusions. ADI episodes were found to contribute more than local sources in Greece, with ADI and LSI fraction contribution ranging from 1.1 to 3.10. The EE contribution during ADI fluctuated from 41 to 83

  12. MLP based models to predict PM10, O3 concentrations, in Sines industrial area

    Science.gov (United States)

    Durao, R.; Pereira, M. J.

    2012-04-01

    Sines is an important Portuguese industrial area located southwest cost of Portugal with important nearby protected natural areas. The main economical activities are related with this industrial area, the deep-water port, petrochemical and thermo-electric industry. Nevertheless, tourism is also an important economic activity especially in summer time with potential to grow. The aim of this study is to develop prediction models of pollutant concentration categories (e.g. low concentration and high concentration) in order to provide early warnings to the competent authorities who are responsible for the air quality management. The knowledge in advanced of pollutant high concentrations occurrence will allow the implementation of mitigation actions and the release of precautionary alerts to population. The regional air quality monitoring network consists in three monitoring stations where a set of pollutants' concentrations are registered on a continuous basis. From this set stands out the tropospheric ozone (O3) and particulate matter (PM10) due to the high concentrations occurring in the region and their adverse effects on human health. Moreover, the major industrial plants of the region monitor SO2, NO2 and particles emitted flows at the principal chimneys (point sources), also on a continuous basis,. Therefore Artificial neuronal networks (ANN) were the applied methodology to predict next day pollutant concentrations; due to the ANNs structure they have the ability to capture the non-linear relationships between predictor variables. Hence the first step of this study was to apply multivariate exploratory techniques to select the best predictor variables. The classification trees methodology (CART) was revealed to be the most appropriate in this case.. Results shown that pollutants atmospheric concentrations are mainly dependent on industrial emissions and a complex combination of meteorological factors and the time of the year. In the second step, the Multi

  13. Gaseous VOCs rapidly modify particulate matter and its biological effects - Part 1: Simple VOCs and model PM

    Science.gov (United States)

    Ebersviller, S.; Lichtveld, K.; Sexton, K. G.; Zavala, J.; Lin, Y.-H.; Jaspers, I.; Jeffries, H. E.

    2012-12-01

    This is the first of a three-part study designed to demonstrate dynamic entanglements among gaseous organic compounds (VOC), particulate matter (PM), and their subsequent potential biological effects. We study these entanglements in increasingly complex VOC and PM mixtures in urban-like conditions in a large outdoor chamber. To the traditional chemical and physical characterizations of gas and PM, we added new measurements of biological effects, using cultured human lung cells as model indicators. These biological effects are assessed here as increases in cellular damage or expressed irritation (i.e., cellular toxic effects) from cells exposed to chamber air relative to cells exposed to clean air. The exposure systems permit virtually gas-only- or PM-only-exposures from the same air stream containing both gases and PM in equilibria, i.e., there are no extractive operations prior to cell exposure. Our simple experiments in this part of the study were designed to eliminate many competing atmospheric processes to reduce ambiguity in our results. Simple volatile and semi-volatile organic gases that have inherent cellular toxic properties were tested individually for biological effect in the dark (at constant humidity). Airborne mixtures were then created with each compound to which we added PM that has no inherent cellular toxic properties for another cellular exposure. Acrolein and p-tolualdehyde were used as model VOCs and mineral oil aerosol (MOA) was selected as a surrogate for organic-containing PM. MOA is appropriately complex in composition to represent ambient PM, and exhibits no inherent cellular toxic effects and thus did not contribute any biological detrimental effects on its own. Chemical measurements, combined with the responses of our biological exposures, clearly demonstrate that gas-phase pollutants can modify the composition of PM (and its resulting detrimental effects on lung cells). We observed that, even if the gas-phase pollutants are not

  14. Modeling wet deposition and concentration of inorganics over Northeast Asia with MRI-PM/c

    Directory of Open Access Journals (Sweden)

    M. Kajino

    2012-11-01

    Full Text Available We conducted a regional-scale simulation over Northeast Asia for the year 2006 using an aerosol chemical transport model, with time-varying lateral and upper boundary concentrations of gaseous species predicted by a global stratospheric and tropospheric chemistry-climate model. The present one-way nested global-through-regional-scale model is named the Meteorological Research Institute–Passive-tracers Model system for atmospheric Chemistry (MRI-PM/c. We evaluated the model's performance with respect to the major anthropogenic and natural inorganic components, SO42−, NH4+, NO3, Na+ and Ca2+ in the air, rain and snow measured at the Acid Deposition Monitoring Network in East Asia (EANET stations. Statistical analysis showed that approximately 40–50 % and 70–80 % of simulated concentration and wet deposition of SO42−, NH4+, NO3and Ca2+ are within factors of 2 and 5 of the observations, respectively. The prediction of the sea-salt originated component Na+ was not successful at near-coastal stations (where the distance from the coast ranged from 150 to 700 m, because the model grid resolution (Δx=60 km is too coarse to resolve it. The simulated Na+ in precipitation was significantly underestimated by up to a factor of 30.

  15. Metallurgical source-contribution analysis of PM10 annual average concentration: A dispersion modeling approach in moravian-silesian region

    Directory of Open Access Journals (Sweden)

    P. Jančík

    2013-10-01

    Full Text Available The goal of the article is to present analysis of metallurgical industry contribution to annual average PM10 concentrations in Moravian-Silesian based on means of the air pollution modelling in accord with the Czech reference methodology SYMOS´97.

  16. A Comparison on Function of Kriging and Inverse Distance Weighting Models in PM10 Zoning in Urban Area

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    Mohammad Hassan Ehrampoush

    2017-12-01

    Conclusion: According to higher concentration of PM10 compared to WHO standard values particularly in spring, necessary actions and solutions should be taken for the pollution reduction. This study indicated that Kriging model has a better efficiency for spatial analysis of suspended particles, compared to IDW method.

  17. Estimation of health effects (morbidity and mortality attributed to PM10 and PM2.5 exposure using an Air Quality model in Bukan city, from 2015-2016 exposure using air quality model

    Directory of Open Access Journals (Sweden)

    Bahram Kamarehie

    2017-08-01

    Full Text Available Background: Air Quality software is a useful tool for assessing the health risks associated with air pollutants. Quantifying the effects of exposure to air pollutants in terms of public health has become a critical component of policy discussion. The present study purposed to quantify the health effects of particulate matters on mortality and morbidity in a Bukan city hospital from 2015-2016. Methods: Information regarding coordinates, exposed population, number of stations used in profiling, mean and maximum concentrations (annual, winter, and summer, annual 98th percentile, baseline incidence (BI per 100 000 per year, and relative risk was needed for use with the software. Results: The average particulate matter concentration was higher in summer than in winter. The concentrations of PM10 in summer and winter were 84.37 and 74.86 μg m-3, respectively. The Air Quality model predicted that total mortality rates related to PM10 and PM2.5 were 33.3 and 49.8 deaths, respectively. As a result, 3.79% of the total mortality was due to PM10. In Bukan city, 2.004% of total deaths were due to cardiovascular mortality. The Air Quality model predicted that the deaths of 92.2 people were related to hospital admissions for respiratory disease. Conclusion: The continual evaluation of air quality data is necessary for investigating the effect of pollutants on human health.

  18. A system dynamics model of China's electric power structure adjustment with constraints of PM10 emission reduction.

    Science.gov (United States)

    Guo, Xiaopeng; Ren, Dongfang; Guo, Xiaodan

    2018-06-01

    Recently, Chinese state environmental protection administration has brought out several PM10 reduction policies to control the coal consumption strictly and promote the adjustment of power structure. Under this new policy environment, a suitable analysis method is required to simulate the upcoming major shift of China's electric power structure. Firstly, a complete system dynamics model is built to simulate China's evolution path of power structure with constraints of PM10 reduction considering both technical and economical factors. Secondly, scenario analyses are conducted under different clean-power capacity growth rates to seek applicable policy guidance for PM10 reduction. The results suggest the following conclusions. (1) The proportion of thermal power installed capacity will decrease to 67% in 2018 with a dropping speed, and there will be an accelerated decline in 2023-2032. (2) The system dynamics model can effectively simulate the implementation of the policy, for example, the proportion of coal consumption in the forecast model is 63.3% (the accuracy rate is 95.2%), below policy target 65% in 2017. (3) China should promote clean power generation such as nuclear power to meet PM10 reduction target.

  19. Accuracy issues involved in modeling in vivo protein structures using PM7.

    Science.gov (United States)

    Martin, Benjamin P; Brandon, Christopher J; Stewart, James J P; Braun-Sand, Sonja B

    2015-08-01

    Using the semiempirical method PM7, an attempt has been made to quantify the error in prediction of the in vivo structure of proteins relative to X-ray structures. Three important contributory factors are the experimental limitations of X-ray structures, the difference between the crystal and solution environments, and the errors due to PM7. The geometries of 19 proteins from the Protein Data Bank that had small R values, that is, high accuracy structures, were optimized and the resulting drop in heat of formation was calculated. Analysis of the changes showed that about 10% of this decrease in heat of formation was caused by faults in PM7, the balance being attributable to the X-ray structure and the difference between the crystal and solution environments. A previously unknown fault in PM7 was revealed during tests to validate the geometries generated using PM7. Clashscores generated by the Molprobity molecular mechanics structure validation program showed that PM7 was predicting unrealistically close contacts between nonbonding atoms in regions where the local geometry is dominated by very weak noncovalent interactions. The origin of this fault was traced to an underestimation of the core-core repulsion between atoms at distances smaller than the equilibrium distance. © 2015 The Authors. Proteins: Structure, Function, and Bioinformatics Published By Wiley Periodicals, Inc.

  20. Source apportionment of PM2.5 in North India using source-oriented air quality models

    International Nuclear Information System (INIS)

    Guo, Hao; Kota, Sri Harsha; Sahu, Shovan Kumar; Hu, Jianlin; Ying, Qi; Gao, Aifang; Zhang, Hongliang

    2017-01-01

    In recent years, severe pollution events were observed frequently in India especially at its capital, New Delhi. However, limited studies have been conducted to understand the sources to high pollutant concentrations for designing effective control strategies. In this work, source-oriented versions of the Community Multi-scale Air Quality (CMAQ) model with Emissions Database for Global Atmospheric Research (EDGAR) were applied to quantify the contributions of eight source types (energy, industry, residential, on-road, off-road, agriculture, open burning and dust) to fine particulate matter (PM 2.5 ) and its components including primary PM (PPM) and secondary inorganic aerosol (SIA) i.e. sulfate, nitrate and ammonium ions, in Delhi and three surrounding cities, Chandigarh, Lucknow and Jaipur in 2015. PPM mass is dominated by industry and residential activities (>60%). Energy (∼39%) and industry (∼45%) sectors contribute significantly to PPM at south of Delhi, which reach a maximum of 200 μg/m 3 during winter. Unlike PPM, SIA concentrations from different sources are more heterogeneous. High SIA concentrations (∼25 μg/m 3 ) at south Delhi and central Uttar Pradesh were mainly attributed to energy, industry and residential sectors. Agriculture is more important for SIA than PPM and contributions of on-road and open burning to SIA are also higher than to PPM. Residential sector contributes highest to total PM 2.5 (∼80 μg/m 3 ), followed by industry (∼70 μg/m 3 ) in North India. Energy and agriculture contribute ∼25 μg/m 3 and ∼16 μg/m 3 to total PM 2.5 , while SOA contributes <5 μg/m 3 . In Delhi, industry and residential activities contribute to 80% of total PM 2.5 . - Highlights: • Sources of PM 2.5 in North India were quantified by source-oriented CMAQ. • Industrial/residential activities are the dominating sources (60–70%) for PPM. • Energy/agriculture are the most important sources (30–40%) for SIA. • Strong seasonal

  1. Source contributions to PM2.5 in Guangdong province, China by numerical modeling: Results and implications

    Science.gov (United States)

    Yin, Xiaohong; Huang, Zhijiong; Zheng, Junyu; Yuan, Zibing; Zhu, Wenbo; Huang, Xiaobo; Chen, Duohong

    2017-04-01

    As one of the most populous and developed provinces in China, Guangdong province (GD) has been experiencing regional haze problems. Identification of source contributions to ambient PM2.5 level is essential for developing effective control strategies. In this study, using the most up-to-date emission inventory and validated numerical model, source contributions to ambient PM2.5 from eight emission source sectors (agriculture, biogenic, dust, industry, power plant, residential, mobile and others) in GD in 2012 were quantified. Results showed that mobile sources are the dominant contributors to the ambient PM2.5 (24.0%) in the Pearl River Delta (PRD) region, the central and most developed area of GD, while industry sources are the major contributors (21.5% 23.6%) to those in the Northeastern GD (NE-GD) region and the Southwestern GD (SW-GD) region. Although many industries have been encouraged to move from the central GD to peripheral areas such as NE-GD and SW-GD, their emissions still have an important impact on the PM2.5 level in the PRD. In addition, agriculture sources are responsible for 17.5% to ambient PM2.5 in GD, indicating the importance of regulations on agricultural activities, which has been largely ignored in the current air quality management. Super-regional contributions were also quantified and their contributions to the ambient PM2.5 in GD are significant with notable seasonal differences. But they might be overestimated and further studies are needed to better quantify the transport impacts.

  2. Development of a source oriented version of the WRF/Chem model and its application to the California regional PM10 / PM2.5 air quality study

    Directory of Open Access Journals (Sweden)

    H. Zhang

    2014-01-01

    Full Text Available A source-oriented version of the Weather Research and Forecasting model with chemistry (SOWC, hereinafter was developed. SOWC separately tracks primary particles with different hygroscopic properties rather than instantaneously combining them into an internal mixture. This approach avoids artificially mixing light absorbing black + brown carbon particles with materials such as sulfate that would encourage the formation of additional coatings. Source-oriented particles undergo coagulation and gas-particle conversion, but these processes are considered in a dynamic framework that realistically "ages" primary particles over hours and days in the atmosphere. SOWC more realistically predicts radiative feedbacks from anthropogenic aerosols compared to models that make internal mixing or other artificial mixing assumptions. A three-week stagnation episode (15 December 2000 to 6 January 2001 in the San Joaquin Valley (SJV during the California Regional PM10 / PM2.5 Air Quality Study (CRPAQS was chosen for the initial application of the new modeling system. Primary particles emitted from diesel engines, wood smoke, high-sulfur fuel combustion, food cooking, and other anthropogenic sources were tracked separately throughout the simulation as they aged in the atmosphere. Differences were identified between predictions from the source oriented vs. the internally mixed representation of particles with meteorological feedbacks in WRF/Chem for a number of meteorological parameters: aerosol extinction coefficients, downward shortwave flux, planetary boundary layer depth, and primary and secondary particulate matter concentrations. Comparisons with observations show that SOWC predicts particle scattering coefficients more accurately than the internally mixed model. Downward shortwave radiation predicted by SOWC is enhanced by ~1% at ground level chiefly because diesel engine particles in the source-oriented mixture are not artificially coated with material that

  3. Modeled PM2.5 removal by trees in ten U.S. cities and associated health effects

    International Nuclear Information System (INIS)

    Nowak, David J.; Hirabayashi, Satoshi; Bodine, Allison; Hoehn, Robert

    2013-01-01

    Urban particulate air pollution is a serious health issue. Trees within cities can remove fine particles from the atmosphere and consequently improve air quality and human health. Tree effects on PM 2.5 concentrations and human health are modeled for 10 U.S. cities. The total amount of PM 2.5 removed annually by trees varied from 4.7 tonnes in Syracuse to 64.5 tonnes in Atlanta, with annual values varying from $1.1 million in Syracuse to $60.1 million in New York City. Most of these values were from the effects of reducing human mortality. Mortality reductions were typically around 1 person yr −1 per city, but were as high as 7.6 people yr −1 in New York City. Average annual percent air quality improvement ranged between 0.05% in San Francisco and 0.24% in Atlanta. Understanding the impact of urban trees on air quality can lead to improved urban forest management strategies to sustain human health in cities. -- Highlights: •Paper provides the first broad-scale estimates of city-wide tree impacts on PM 2.5 . •Trees improve overall air quality by intercepting particulate matter. •Particle resuspension can lead to short-term increases in pollutant concentrations. •Urban trees produce substantial health improvements and values. -- Air pollution modeling reveals broad-scale impacts of pollution removal by urban trees on PM 2.5 concentrations and human health

  4. Quantification of source impact to PM using three-dimensional weighted factor model analysis on multi-site data

    Science.gov (United States)

    Shi, Guoliang; Peng, Xing; Huangfu, Yanqi; Wang, Wei; Xu, Jiao; Tian, Yingze; Feng, Yinchang; Ivey, Cesunica E.; Russell, Armistead G.

    2017-07-01

    Source apportionment technologies are used to understand the impacts of important sources of particulate matter (PM) air quality, and are widely used for both scientific studies and air quality management. Generally, receptor models apportion speciated PM data from a single sampling site. With the development of large scale monitoring networks, PM speciation are observed at multiple sites in an urban area. For these situations, the models should account for three factors, or dimensions, of the PM, including the chemical species concentrations, sampling periods and sampling site information, suggesting the potential power of a three-dimensional source apportionment approach. However, the principle of three-dimensional Parallel Factor Analysis (Ordinary PARAFAC) model does not always work well in real environmental situations for multi-site receptor datasets. In this work, a new three-way receptor model, called "multi-site three way factor analysis" model is proposed to deal with the multi-site receptor datasets. Synthetic datasets were developed and introduced into the new model to test its performance. Average absolute error (AAE, between estimated and true contributions) for extracted sources were all less than 50%. Additionally, three-dimensional ambient datasets from a Chinese mega-city, Chengdu, were analyzed using this new model to assess the application. Four factors are extracted by the multi-site WFA3 model: secondary source have the highest contributions (64.73 and 56.24 μg/m3), followed by vehicular exhaust (30.13 and 33.60 μg/m3), crustal dust (26.12 and 29.99 μg/m3) and coal combustion (10.73 and 14.83 μg/m3). The model was also compared to PMF, with general agreement, though PMF suggested a lower crustal contribution.

  5. Bioaccessibility of selected trace metals in urban PM{sub 2.5} and PM{sub 10} samples: a model study

    Energy Technology Data Exchange (ETDEWEB)

    Falta, Thomas; Koellensperger, Gunda; Hann, Stephan [University of Natural Resources and Applied Life Sciences, Division of Analytical Chemistry, Vienna (Austria); Limbeck, Andreas [Vienna University of Technology, Institute of Chemical Technologies and Analytics, Vienna (Austria)

    2008-02-15

    Bioaccessibility of trace metals originating from urban particulate matter was assessed in a worst case scenario to evaluate the uptake and thus the hazardous potential of these metals via gastric juice. Sampling was performed over a period of about two months at the Getreidemarkt in downtown Vienna. Concentrations of the assayed trace metals (Ti, Cr, Mn, Co, Ni, Cu, Zn, Mo, Ag, Cd, Sn, Sb, Tl and Pb) were determined in PM{sub 2.5} and PM{sub 10} samples by ICP-MS. The metal concentrations in sampled air were in the low picogram to high nanogram per cubic metre range. The concentrations in PM{sub 2.5} samples were generally lower than those in PM{sub 10} samples. The average daily intake of these metals by inhalation for a healthy adult was estimated to be in the range of <1 ng (Tl) to >1,000 ng (Zn). To estimate the accessibility of the inhaled and subsequently ingested metals (i.e. after lung clearance had taken place) in the size range from 2.5- to 10-{mu}m aerodynamic equivalent diameter, a batch-extraction with synthetic gastric juice was performed. The data were used to calculate the bioaccessibility of the investigated trace metals. Extractable fractions ranged from 2.10% (Ti in PM{sub 2.5}) to 91.0% (Cd in PM{sub 2.5}), thus yielding bioaccessible fractions (PM{sub 2.5-10}) from 0.16 ng (Ag) to 178 ng (Cu). (orig.)

  6. Neural network model for the prediction of PM10 daily concentrations in two sites in the Western Mediterranean.

    Science.gov (United States)

    de Gennaro, Gianluigi; Trizio, Livia; Di Gilio, Alessia; Pey, Jorge; Pérez, Noemi; Cusack, Michael; Alastuey, Andrés; Querol, Xavier

    2013-10-01

    An artificial neural network (ANN) was developed and tested to forecast PM10 daily concentration in two contrasted environments in NE Spain, a regional background site (Montseny), and an urban background site (Barcelona-CSIC), which was highly influenced by vehicular emissions. In order to predict 24-h average PM10 concentrations, the artificial neural network previously developed by Caselli et al. (2009) was improved by using hourly PM concentrations and deterministic factors such as a Saharan dust alert. In particular, the model input data for prediction were the hourly PM10 concentrations 1-day in advance, local meteorological data and information about air masses origin. The forecasted performance indexes for both sites were calculated and they showed better results for the regional background site in Montseny (R(2)=0.86, SI=0.75) than for urban site in Barcelona (R(2)=0.73, SI=0.58), influenced by local and sometimes unexpected sources. Moreover, a sensitivity analysis conducted to understand the importance of the different variables included among the input data, showed that local meteorology and air masses origin are key factors in the model forecasts. This result explains the reason for the improvement of ANN's forecasting performance at the Montseny site with respect to the Barcelona site. Moreover, the artificial neural network developed in this work could prove useful to predict PM10 concentrations, especially, at regional background sites such as those on the Mediterranean Basin which are primarily affected by long-range transports. Hence, the artificial neural network presented here could be a powerful tool for obtaining real time information on air quality status and could aid stakeholders in their development of cost-effective control strategies. © 2013 Elsevier B.V. All rights reserved.

  7. Chemical characterization and receptor modeling of PM10 in the surroundings of the opencast lignite mines of Western Macedonia, Greece.

    Science.gov (United States)

    Samara, Constantini; Argyropoulos, George; Grigoratos, Theodoros; Kouras, Αthanasios; Manoli, Εvangelia; Andreadou, Symela; Pavloudakis, Fragkiskos; Sahanidis, Chariton

    2018-05-01

    The Western Macedonian Lignite Center (WMLC) in northwestern Greece is the major lignite center in the Balkans feeding four major power plants of total power exceeding 4 GW. Concentrations of PM 10 (i.e., particulate matters with diameters ≤10 μm) are the main concern in the region, and the high levels observed are often attributed to the activities related to power generation. In this study, the contribution of fugitive dust emissions from the opencast lignite mines to the ambient levels of PM 10 in the surroundings was estimated by performing chemical mass balance (CMB) receptor modeling. For this purpose, PM 10 samples were concurrently collected at four receptor sites located in the periphery of the mine area during the cold and the warm periods of the year (November-December 2011 and August-September 2012), and analyzed for a total of 26 macro- and trace elements and ionic species (sulfate, nitrate, chloride). The robotic chemical mass balance (RCMB) model was employed for source identification/apportionment of PM 10 at each receptor site using as inputs the ambient concentrations and the chemical profiles of various sources including the major mine operations, the fly ash escaping the electrostatic filters of the power plants, and other primary and secondary sources. Mean measured PM 10 concentrations at the different sites ranged from 38 to 72 μg m -3 . The estimated total contribution of mines ranged between 9 and 22% in the cold period increasing to 36-42% in the dry warm period. Other significant sources were vehicular traffic, biomass burning, and secondary sulfate and nitrate aerosol. These results imply that more efficient measures to prevent and suppress fugitive dust emissions from the mines are needed.

  8. Indoor PM2.5 exposure in London's domestic stock: Modelling current and future exposures following energy efficient refurbishment

    Science.gov (United States)

    Shrubsole, C.; Ridley, I.; Biddulph, P.; Milner, J.; Vardoulakis, S.; Ucci, M.; Wilkinson, P.; Chalabi, Z.; Davies, M.

    2012-12-01

    Simulations using CONTAM (a validated multi-zone indoor air quality (IAQ) model) are employed to predict indoor exposure to PM2.5 in London dwellings in both the present day housing stock and the same stock following energy efficient refurbishments to meet greenhouse gas emissions reduction targets for 2050. We modelled interventions that would contribute to the achievement of these targets by reducing the permeability of the dwellings to 3 m3 m-2 h-1 at 50 Pa, combined with the introduction of mechanical ventilation and heat recovery (MVHR) systems. It is assumed that the current mean outdoor PM2.5 concentration of 13 μg m-3 decreased to 9 μg m-3 by 2050 due to emission control policies. Our primary finding was that installation of (assumed perfectly functioning) MVHR systems with permeability reduction are associated with appreciable reductions in PM2.5 exposure in both smoking and non-smoking dwellings. Modelling of the future scenario for non-smoking dwellings show a reduction in annual average indoor exposure to PM2.5 of 18.8 μg m-3 (from 28.4 to 9.6 μg m-3) for a typical household member. Also of interest is that a larger reduction of 42.6 μg m-3 (from 60.5 to 17.9 μg m-3) was shown for members exposed primarily to cooking-related particle emissions in the kitchen (cooks). Reductions in envelope permeability without mechanical ventilation produced increases in indoor PM2.5 concentrations; 5.4 μg m-3 for typical household members and 9.8 μg m-3 for cooks. These estimates of changes in PM2.5 exposure are sensitive to assumptions about occupant behaviour, ventilation system usage and the distributions of input variables (±72% for non-smoking and ±107% in smoking residences). However, if realised, they would result in significant health benefits.

  9. WRF modeling of PM2.5 remediation by SALSCS and its clean air flow over Beijing terrain.

    Science.gov (United States)

    Cao, Qingfeng; Shen, Lian; Chen, Sheng-Chieh; Pui, David Y H

    2018-06-01

    Atmospheric simulations were carried out over the terrain of entire Beijing, China, to investigate the effectiveness of an air-pollution cleaning system named Solar-Assisted Large-Scale Cleaning System (SALSCS) for PM 2.5 mitigation by using the Weather Research and Forecasting (WRF) model. SALSCS was proposed to utilize solar energy to generate airflow therefrom the airborne particulate pollution of atmosphere was separated by filtration elements. Our model used a derived tendency term in the potential temperature equation to simulate the buoyancy effect of SALSCS created with solar radiation on its nearby atmosphere. PM 2.5 pollutant and SALSCS clean air were simulated in the model domain by passive tracer scalars. Simulation conditions with two system flow rates of 2.64 × 10 5  m 3 /s and 3.80 × 10 5  m 3 /s were tested for seven air pollution episodes of Beijing during the winters of 2015-2017. The numerical results showed that with eight SALSCSs installed along the 6 th Ring Road of the city, 11.2% and 14.6% of PM 2.5 concentrations were reduced under the two flow-rate simulation conditions, respectively. Copyright © 2018 Elsevier B.V. All rights reserved.

  10. Sparkle/PM3 for the modeling of europium(III), gadolinium(III), and terbium(III) complexes

    International Nuclear Information System (INIS)

    Freire, Ricardo O.; Rocha, Gerd B.; Simas, Alfredo M.

    2009-01-01

    The Sparkle/PM3 model is extended to europium(III), gadolinium(III), and terbium(III) complexes. The validation procedure was carried out using only high quality crystallographic structures, for a total of ninety-six Eu(III) complexes, seventy Gd(III) complexes, and forty-two Tb(III) complexes. The Sparkle/PM3 unsigned mean error, for all interatomic distances between the trivalent lanthanide ion and the ligand atoms of the first sphere of coordination, is: 0.080 A for Eu(III); 0.063 A for Gd(III); and 0.070 A for Tb(III). These figures are similar to the Sparkle/AM1 ones of 0.082 A, 0.061 A, and 0.068 A respectively, indicating they are all comparable parameterizations. Moreover, their accuracy is similar to what can be obtained by present-day ab initio effective core potential full geometry optimization calculations on such lanthanide complexes. Finally, we report a preliminary attempt to show that Sparkle/PM3 geometry predictions are reliable. For one of the Eu(III) complexes, BAFZEO, we created hundreds of different input geometries by randomly varying the distances and angles of the ligands to the central Eu(III) ion, which were all subsequently fully optimized. A significant trend was unveiled, indicating that more accurate local minima geometries cluster at lower total energies, thus reinforcing the validity of sparkle model calculations. (author)

  11. Association between adverse cardiovascular outcomes and PM2.5 data obtained from monitors, CMAQ models, and satellite models.

    Data.gov (United States)

    U.S. Environmental Protection Agency — Background: Adverse cardiovascular events have been linked with PM2.5 exposure obtained primarily from air quality monitors, which rarely co-locate with participant...

  12. Improving Satellite-Driven PM2.5 Models with VIIRS Nighttime Light Data in the Beijing–Tianjin–Hebei Region, China

    Directory of Open Access Journals (Sweden)

    Xiya Zhang

    2017-08-01

    Full Text Available Previous studies have estimated ground-level concentrations of particulate matter 2.5 (PM2.5 using satellite-derived aerosol optical depth (AOD in conjunction with meteorological and land use variables. However, the impacts of urbanization on air pollution for predicting PM2.5 are seldom considered. Nighttime light (NTL data, acquired with the Visible Infrared Imaging Radiometer Suite (VIIRS aboard the Suomi National Polar-orbiting Partnership (S-NPP satellite, could be useful for predictions because they have been shown to be good indicators of the urbanization and human activity that can affect PM2.5 concentrations. This study investigated the potential of incorporating VIIRS NTL data in statistical models for PM2.5 concentration predictions. We developed a mixed-effects model to derive daily estimations of surface PM2.5 levels in the Beijing–Tianjin–Hebei region using 3 km resolution satellite AOD and VIIRS NTL data. The results showed the addition of NTL information could improve the performance of the PM2.5 prediction model. The NTL data revealed additional details for predication results in areas with low PM2.5 concentrations and greater apparent seasonal variation due to the seasonal variability of human activity. Comparison showed prediction accuracy was improved more substantially for the model using NTL directly than for the model using the vegetation-adjusted NTL urban index that included NTL. Our findings indicate that VIIRS NTL data have potential for predicting PM2.5 and that they could constitute a useful supplemental data source for estimating ground-level PM2.5 distributions.

  13. Source apportionment of PM2.5 at the Lin'an regional background site in China with three receptor models

    Science.gov (United States)

    Deng, Junjun; Zhang, Yanru; Qiu, Yuqing; Zhang, Hongliang; Du, Wenjiao; Xu, Lingling; Hong, Youwei; Chen, Yanting; Chen, Jinsheng

    2018-04-01

    Source apportionment of fine particulate matter (PM2.5) were conducted at the Lin'an Regional Atmospheric Background Station (LA) in the Yangtze River Delta (YRD) region in China from July 2014 to April 2015 with three receptor models including principal component analysis combining multiple linear regression (PCA-MLR), UNMIX and Positive Matrix Factorization (PMF). The model performance, source identification and source contribution of the three models were analyzed and inter-compared. Source apportionment of PM2.5 was also conducted with the receptor models. Good correlations between the reconstructed and measured concentrations of PM2.5 and its major chemical species were obtained for all models. PMF resolved almost all masses of PM2.5, while PCA-MLR and UNMIX explained about 80%. Five, four and seven sources were identified by PCA-MLR, UNMIX and PMF, respectively. Combustion, secondary source, marine source, dust and industrial activities were identified by all the three receptor models. Combustion source and secondary source were the major sources, and totally contributed over 60% to PM2.5. The PMF model had a better performance on separating the different combustion sources. These findings improve the understanding of PM2.5 sources in background region.

  14. Using Lagrangian Chemical Transport Modeling to Assess the Impact of Biomass Burning on Ozone and PM2.5

    Science.gov (United States)

    Alvarado, M. J.; Lonsdale, C. R.; Brodowski, C. M.

    2017-12-01

    One of the challenges of using in situ measurements to study the air quality and climate impacts of biomass burning is correctly determining the contribution of biomass burning sources to the measured ambient concentrations. This is especially important for policy purposes, as the ozone (O3) and fine particulate matter (PM2.5) from natural wildfires should not be confused with that from controllable anthropogenic sources. We have developed a Lagrangian chemical transport model called STILT-ASP that is able to quantify the impact of wildfire events on O3 and PM2.5 measurements made at surface monitoring sites, by mobile laboratories, or by aircraft. STILT-ASP is built by coupling the Stochastic Time Inverted Lagrangian Transport (STILT) model with AER's Aerosol Simulation Program (ASP), which has been used in many studies of the gas and aerosol chemistry of biomass burning smoke. Here we present recent revisions made in STILT-ASP v2.0, including the use of more detailed chemical speciation of fire emissions and biogenic emissions calculated using the MEGAN model with meteorological inputs consistent with those used to drive STILT. We will present the results of an evaluation of the performance of STILT-ASP v2.0 using surface, mobile lab, and aircraft data from the 2013 Houston DISCOVER-AQ campaign. STILT-ASP v2.0 showed good average performance for O3 during the peak of the high O3 episodes on Sept. 25-26, 2013, with a mean bias of -4 ppbv. We will also demonstrate the use of STILT-ASP to evaluate the impact of biomass burning on O3 and PM2.5 in urban areas and to assess the impact of remote fires on the boundary conditions used in Eulerian chemical transport models like CAMx.

  15. Identification and elucidation of anthropogenic source contribution in PM10 pollutant: Insight gain from dispersion and receptor models.

    Science.gov (United States)

    Roy, Debananda; Singh, Gurdeep; Yadav, Pankaj

    2016-10-01

    Source apportionment study of PM 10 (Particulate Matter) in a critically polluted area of Jharia coalfield, India has been carried out using Dispersion model, Principle Component Analysis (PCA) and Chemical Mass Balance (CMB) techniques. Dispersion model Atmospheric Dispersion Model (AERMOD) was introduced to simplify the complexity of sources in Jharia coalfield. PCA and CMB analysis indicates that monitoring stations near the mining area were mainly affected by the emission from open coal mining and its associated activities such as coal transportation, loading and unloading of coal. Mine fire emission also contributed a considerable amount of particulate matters in monitoring stations. Locations in the city area were mostly affected by vehicular, Liquid Petroleum Gas (LPG) & Diesel Generator (DG) set emissions, residential, and commercial activities. The experimental data sampling and their analysis could aid understanding how dispersion based model technique along with receptor model based concept can be strategically used for quantitative analysis of Natural and Anthropogenic sources of PM 10 . Copyright © 2016. Published by Elsevier B.V.

  16. Quantifying the decadal changes of PM2.5 over New York through a combination of satellite, model and in-situ measurements

    Science.gov (United States)

    Jin, X.; Fiore, A. M.; Curci, G.; Lyapustin, A.; Wang, Y.; Civerolo, K.; Ku, M.; van Donkelaar, A.; Martin, R.

    2017-12-01

    Ambient exposure to fine particulate matter (PM2.5) is one of the top global health concerns. Efforts have been made to regulate PM2.5 precursor emissions across the U.S.A, which are expected to mitigate the air pollution related health impacts. However, quantifying the health outcomes from emission controls requires robust estimates of PM2.5 exposures that accurately describe the spatial and temporal variability of PM2.5. Satellite remote sensing offers the potential to fill the gaps of the sparse, limited sampling of in situ measurement networks and is increasingly being used in health assessments. We provide new estimates of PM2.5 over New York State with 1 km spatial resolution that use Multi-Angle Implementation of Atmospheric Correction (MAIAC) AOD and a regional air quality model (CMAQ) to estimate the AOD-PM2.5 scaling factors. Next, we evaluate three major sources of uncertainties of satellite-derived PM2.5 data and their impacts on the derived decadal changes: 1) satellite retrieval of AOD, 2) optical properties of the particles, 3) relationships between the aerosol burden in the planetary boundary layer and full atmospheric column. Finally, we analyze the decadal changes of PM2.5 over New York State using the newly developed PM2.5 data, alongside four other PM2.5 estimates including satellite-derived PM2.5 developed by van Donkelaar et al. (2015), statistical land use regression developed by Beckerman et al. (2013), CMAQ simulations, and a Bayesian fusion of CMAQ and ground-based measurements. By evaluating the decadal changes of PM2.5 from multiple datasets over areas with dense (e.g. New York City area) and sparse ground-based measurements (e.g. upstate New York), we evaluate the extent to which satellite remote sensing could help better quantify the health outcomes of emission controls. References: Beckerman et al., (2013), A Hybrid Approach to Estimating National Scale Spatiotemporal Variability of PM2.5 in the Contiguous United States, Environ. Sci

  17. An investigation into the applicability of the semiempirical method PM7 for modeling the catalytic mechanism in the enzyme chymotrypsin.

    Science.gov (United States)

    Stewart, James J P

    2017-05-01

    The catalytic cycle for the serine protease α-chymotrypsin was investigated in an attempt to determine the suitability of using the semiempirical method PM7 in the program MOPAC for investigating enzyme-catalyzed reactions. All six classical intermediates were modeled using standard methods, and were characterized as stable minima on the potential energy surface. Using a modified saddle point optimization method, five transition states were located and verified both by vibrational and by intrinsic reaction coordinate analysis. Some individual features, such as the hydrogen bonds in the oxyanion hole, the nature of various electrostatic interactions, and the role of Met192, were examined. This involved designing and running computational experiments to model mutations that would allow features of interest, in particular the energies involved, to be isolated. Three features within the enzyme were examined in detail: the reaction site itself, where covalent bonds were made and broken, the electrostatic effects of the buried aspartate anion, a passive but essential component of the catalytic triad, and the oxyanion hole, where hydrogen bonds help stabilize charged intermediates. With one minor exception, all phenomena investigated agreed with previously-reported descriptions. This result, along with the fact that all the techniques used were relatively straightforward, leads to the recommendation that PM7 and related methods, such as PM6-D3H4, are appropriate for modeling similar enzyme-catalyzed reactions. Graphical abstract Fifth of six transition states, showing water splitting into hydroxyl anion and a proton, to form the second tetrahedral intermediate and histidinium ion. Atoms of the water molecule involved in the hydrolysis are indicated by halos.

  18. Source apportionment of population representative samples of PM(2.5) in three European cities using structural equation modelling.

    Science.gov (United States)

    Ilacqua, Vito; Hänninen, Otto; Saarela, Kristina; Katsouyanni, Klea; Künzli, Nino; Jantunen, Matti

    2007-10-01

    Apportionment of urban particulate matter (PM) to sources is central for air quality management and efficient reduction of the substantial public health risks associated with fine particles (PM(2.5)). Traffic is an important source combustion particles, but also a significant source of resuspended particles that chemically resemble Earth's crust and that are not affected by development of cleaner motor technologies. A substantial fraction of urban ambient PM originates from long-range transport outside the immediate urban environment including secondary particles formed from gaseous emissions of mainly sulphur, nitrogen oxides and ammonia. Most source apportionment studies are based on small number of fixed monitoring sites and capture well population exposures to regional and long-range transported particles. However, concentrations from local sources are very unevenly distributed and the results from such studies are therefore poorly representative of the actual exposures. The current study uses PM(2.5) data observed at population based random sampled residential locations in Athens, Basle and Helsinki with 17 elemental constituents, selected VOCs (xylenes, trimethylbenzenes, nonane and benzene) and light absorbance (black smoke). The major sources identified across the three cities included crustal, salt, long-range transported inorganic and traffic sources. Traffic was associated separately with source categories with crustal (especially Athens and Helsinki) and long-range transported chemical composition (all cities). Remarkably high fractions of the variability of elemental (R(2)>0.6 except for Ca in Basle 0.38) and chemical concentrations (R(2)>0.5 except benzene in Basle 0.22 and nonane in Athens 0.39) are explained by the source factors of an SEM model. The RAINS model that is currently used as the main tool in developing European air quality management policies seems to capture the local urban fraction (the city delta term) quite well, but underestimates

  19. Development of a stacked ensemble model for forecasting and analyzing daily average PM2.5 concentrations in Beijing, China.

    Science.gov (United States)

    Zhai, Binxu; Chen, Jianguo

    2018-04-18

    A stacked ensemble model is developed for forecasting and analyzing the daily average concentrations of fine particulate matter (PM 2.5 ) in Beijing, China. Special feature extraction procedures, including those of simplification, polynomial, transformation and combination, are conducted before modeling to identify potentially significant features based on an exploratory data analysis. Stability feature selection and tree-based feature selection methods are applied to select important variables and evaluate the degrees of feature importance. Single models including LASSO, Adaboost, XGBoost and multi-layer perceptron optimized by the genetic algorithm (GA-MLP) are established in the level 0 space and are then integrated by support vector regression (SVR) in the level 1 space via stacked generalization. A feature importance analysis reveals that nitrogen dioxide (NO 2 ) and carbon monoxide (CO) concentrations measured from the city of Zhangjiakou are taken as the most important elements of pollution factors for forecasting PM 2.5 concentrations. Local extreme wind speeds and maximal wind speeds are considered to extend the most effects of meteorological factors to the cross-regional transportation of contaminants. Pollutants found in the cities of Zhangjiakou and Chengde have a stronger impact on air quality in Beijing than other surrounding factors. Our model evaluation shows that the ensemble model generally performs better than a single nonlinear forecasting model when applied to new data with a coefficient of determination (R 2 ) of 0.90 and a root mean squared error (RMSE) of 23.69μg/m 3 . For single pollutant grade recognition, the proposed model performs better when applied to days characterized by good air quality than when applied to days registering high levels of pollution. The overall classification accuracy level is 73.93%, with most misclassifications made among adjacent categories. The results demonstrate the interpretability and generalizability of

  20. Application Study of Comprehensive Forecasting Model Based on Entropy Weighting Method on Trend of PM2.5 Concentration in Guangzhou, China

    Science.gov (United States)

    Liu, Dong-jun; Li, Li

    2015-01-01

    For the issue of haze-fog, PM2.5 is the main influence factor of haze-fog pollution in China. The trend of PM2.5 concentration was analyzed from a qualitative point of view based on mathematical models and simulation in this study. The comprehensive forecasting model (CFM) was developed based on the combination forecasting ideas. Autoregressive Integrated Moving Average Model (ARIMA), Artificial Neural Networks (ANNs) model and Exponential Smoothing Method (ESM) were used to predict the time series data of PM2.5 concentration. The results of the comprehensive forecasting model were obtained by combining the results of three methods based on the weights from the Entropy Weighting Method. The trend of PM2.5 concentration in Guangzhou China was quantitatively forecasted based on the comprehensive forecasting model. The results were compared with those of three single models, and PM2.5 concentration values in the next ten days were predicted. The comprehensive forecasting model balanced the deviation of each single prediction method, and had better applicability. It broadens a new prediction method for the air quality forecasting field. PMID:26110332

  1. A national prediction model for PM2.5 component exposures and measurement error-corrected health effect inference.

    Science.gov (United States)

    Bergen, Silas; Sheppard, Lianne; Sampson, Paul D; Kim, Sun-Young; Richards, Mark; Vedal, Sverre; Kaufman, Joel D; Szpiro, Adam A

    2013-09-01

    Studies estimating health effects of long-term air pollution exposure often use a two-stage approach: building exposure models to assign individual-level exposures, which are then used in regression analyses. This requires accurate exposure modeling and careful treatment of exposure measurement error. To illustrate the importance of accounting for exposure model characteristics in two-stage air pollution studies, we considered a case study based on data from the Multi-Ethnic Study of Atherosclerosis (MESA). We built national spatial exposure models that used partial least squares and universal kriging to estimate annual average concentrations of four PM2.5 components: elemental carbon (EC), organic carbon (OC), silicon (Si), and sulfur (S). We predicted PM2.5 component exposures for the MESA cohort and estimated cross-sectional associations with carotid intima-media thickness (CIMT), adjusting for subject-specific covariates. We corrected for measurement error using recently developed methods that account for the spatial structure of predicted exposures. Our models performed well, with cross-validated R2 values ranging from 0.62 to 0.95. Naïve analyses that did not account for measurement error indicated statistically significant associations between CIMT and exposure to OC, Si, and S. EC and OC exhibited little spatial correlation, and the corrected inference was unchanged from the naïve analysis. The Si and S exposure surfaces displayed notable spatial correlation, resulting in corrected confidence intervals (CIs) that were 50% wider than the naïve CIs, but that were still statistically significant. The impact of correcting for measurement error on health effect inference is concordant with the degree of spatial correlation in the exposure surfaces. Exposure model characteristics must be considered when performing two-stage air pollution epidemiologic analyses because naïve health effect inference may be inappropriate.

  2. Fine particulates over South Asia: Review and meta-analysis of PM2.5 source apportionment through receptor model.

    Science.gov (United States)

    Singh, Nandita; Murari, Vishnu; Kumar, Manish; Barman, S C; Banerjee, Tirthankar

    2017-04-01

    Fine particulates (PM 2.5 ) constitute dominant proportion of airborne particulates and have been often associated with human health disorders, changes in regional climate, hydrological cycle and more recently to food security. Intrinsic properties of particulates are direct function of sources. This initiates the necessity of conducting a comprehensive review on PM 2.5 sources over South Asia which in turn may be valuable to develop strategies for emission control. Particulate source apportionment (SA) through receptor models is one of the existing tool to quantify contribution of particulate sources. Review of 51 SA studies were performed of which 48 (94%) were appeared within a span of 2007-2016. Almost half of SA studies (55%) were found concentrated over few typical urban stations (Delhi, Dhaka, Mumbai, Agra and Lahore). Due to lack of local particulate source profile and emission inventory, positive matrix factorization and principal component analysis (62% of studies) were the primary choices, followed by chemical mass balance (CMB, 18%). Metallic species were most regularly used as source tracers while use of organic molecular markers and gas-to-particle conversion were minimum. Among all the SA sites, vehicular emissions (mean ± sd: 37 ± 20%) emerged as most dominating PM 2.5 source followed by industrial emissions (23 ± 16%), secondary aerosols (22 ± 12%) and natural sources (20 ± 15%). Vehicular emissions (39 ± 24%) also identified as dominating source for highly polluted sites (PM 2.5 >100 μgm -3 , n = 15) while site specific influence of either or in combination of industrial, secondary aerosols and natural sources were recognized. Source specific trends were considerably varied in terms of region and seasonality. Both natural and industrial sources were most influential over Pakistan and Afghanistan while over Indo-Gangetic plain, vehicular, natural and industrial emissions appeared dominant. Influence of vehicular emission was

  3. Efektifitas penggunaan model pembelajaran kooperatif dengan presentasi untuk meningkatkan kemampuan belajar menulis surat kelas XI PM di SMK Negeri 48

    Directory of Open Access Journals (Sweden)

    Rahayu Retno Pujiastuti

    2016-08-01

    Full Text Available This study aims to Know Efekifitas use Cooperative Learning Model Type Presentation Can Improve Learning Ability Letter Writing Class XI PM at SMK Negeri 48 2014. The experiment was conducted in July to / with December 2014, in SMK Negeri 48 Jakarta. Learning Model for this use is Cooperative Presentation mode, Class Action Research, which consists of two cycles, each cycle of meetings held three times and at the third meeting done Post Test. Indicators of success seen improved understanding of the concept, kreaktifan, and decrease the negative habits of students during the learning process. The instrument used in this study was a test sheet and Observation class observing aspects of liveliness and negative habits. Research has achieved the expected indicators in the second cycle, the number of students who scored to complete the boundary or over by 97, 45% and the average reached 80, 79%.

  4. Compilation of Published PM2.5 Emission Rates for Cooking, Candles and Incense for Use in Modeling of Exposures in Residences

    Energy Technology Data Exchange (ETDEWEB)

    Hu, Tianchao [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Singer, Brett C. [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Logue, Jennifer M. [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

    2012-08-01

    recent analysis of health impacts from air pollutant inhalation in homes found that PM2.5 is the most damaging at the population level. Chronic exposure to elevated PM2.5 has the potential to damage human respiratory systems, and may result in premature death. PM2.5 exposures in homes can be mitigated through various approaches including kitchen exhaust ventilation, filtration, indoor pollutant source reduction and designing ventilation systems to reduce the entry of PM2.5 from outdoors. Analysis of the potential benefits and costs of various approaches can be accomplished using computer codes that simulate the key physical processes including emissions, dilution and ventilation. The largest sources of PM2.5 in residences broadly are entry from outdoors and emissions from indoor combustion. The largest indoor sources are tobacco combustion (smoking), cooking and the burning of candles and incense. Data on the magnitude of PM2.5 and other pollutant emissions from these events and processes are required to conduct simulations for analysis. The goal of this study was to produce a database of pollutant emission rates associated with cooking and the burning of candles and incense. The target use of these data is for indoor air quality modeling.

  5. Impact of 2000–2050 climate change on fine particulate matter (PM2.5 air quality inferred from a multi-model analysis of meteorological modes

    Directory of Open Access Journals (Sweden)

    D. J. Jacob

    2012-12-01

    Full Text Available Studies of the effect of climate change on fine particulate matter (PM2.5 air quality using general circulation models (GCMs show inconsistent results including in the sign of the effect. This reflects uncertainty in the GCM simulations of the regional meteorological variables affecting PM2.5. Here we use the CMIP3 archive of data from fifteen different IPCC AR4 GCMs to obtain improved statistics of 21st-century trends in the meteorological modes driving PM2.5 variability over the contiguous US. We analyze 1999–2010 observations to identify the dominant meteorological modes driving interannual PM2.5 variability and their synoptic periods T. We find robust correlations (r > 0.5 of annual mean PM2.5 with T, especially in the eastern US where the dominant modes represent frontal passages. The GCMs all have significant skill in reproducing present-day statistics for T and we show that this reflects their ability to simulate atmospheric baroclinicity. We then use the local PM2.5-to-period sensitivity (dPM2.5/dT from the 1999–2010 observations to project PM2.5 changes from the 2000–2050 changes in T simulated by the 15 GCMs following the SRES A1B greenhouse warming scenario. By weighted-average statistics of GCM results we project a likely 2000–2050 increase of ~ 0.1 μg m−3 in annual mean PM2.5 in the eastern US arising from less frequent frontal ventilation, and a likely decrease albeit with greater inter-GCM variability in the Pacific Northwest due to more frequent maritime inflows. Potentially larger regional effects of 2000–2050 climate change on PM2.5 may arise from changes in temperature, biogenic emissions, wildfires, and vegetation, but are still unlikely to affect annual PM2.5 by more than 0.5 μg m−3.

  6. Simulating the meteorology and PM10 concentrations in Arizona dust storms using the Weather Research and Forecasting model with Chemistry (Wrf-Chem).

    Science.gov (United States)

    Hyde, Peter; Mahalov, Alex; Li, Jialun

    2018-03-01

    Nine dust storms in south-central Arizona were simulated with the Weather Research and Forecasting with Chemistry model (WRF-Chem) at 2 km resolution. The windblown dust emission algorithm was the Air Force Weather Agency model. In comparison with ground-based PM 10 observations, the model unevenly reproduces the dust-storm events. The model adequately estimates the location and timing of the events, but it is unable to precisely replicate the magnitude and timing of the elevated hourly concentrations of particles 10 µm and smaller ([PM 10 ]).Furthermore, the model underestimated [PM 10 ] in highly agricultural Pinal County because it underestimated surface wind speeds and because the model's erodible fractions of the land surface data were too coarse to effectively resolve the active and abandoned agricultural lands. In contrast, the model overestimated [PM 10 ] in western Arizona along the Colorado River because it generated daytime sea breezes (from the nearby Gulf of California) for which the surface-layer speeds were too strong. In Phoenix, AZ, the model's performance depended on the event, with both under- and overestimations partly due to incorrect representation of urban features. Sensitivity tests indicate that [PM 10 ] highly relies on meteorological forcing. Increasing the fraction of erodible surfaces in the Pinal County agricultural areas improved the simulation of [PM 10 ] in that region. Both 24-hr and 1-hr measured [PM 10 ] were, for the most part, and especially in Pinal County, extremely elevated, with the former exceeding the health standard by as much as 10-fold and the latter exceeding health-based guidelines by as much as 70-fold. Monsoonal thunderstorms not only produce elevated [PM 10 ], but also cause urban flash floods and disrupt water resource deliveries. Given the severity and frequency of these dust storms, and conceding that the modeling system applied in this work did not produce the desired agreement between simulations and

  7. Detection of critical PM2.5 emission sources and their contributions to a heavy haze episode in Beijing, China, using an adjoint model

    Science.gov (United States)

    Zhai, Shixian; An, Xingqin; Zhao, Tianliang; Sun, Zhaobin; Wang, Wei; Hou, Qing; Guo, Zengyuan; Wang, Chao

    2018-05-01

    Air pollution sources and their regional transport are important issues for air quality control. The Global-Regional Assimilation and Prediction System coupled with the China Meteorological Administration Unified Atmospheric Chemistry Environment (GRAPES-CUACE) aerosol adjoint model was applied to detect the sensitive primary emission sources of a haze episode in Beijing occurring between 19 and 21 November 2012. The high PM2.5 concentration peaks occurring at 05:00 and 23:00 LT (GMT+8) over Beijing on 21 November 2012 were set as the cost functions for the aerosol adjoint model. The critical emission regions of the first PM2.5 concentration peak were tracked to the west and south of Beijing, with 2 to 3 days of cumulative transport of air pollutants to Beijing. The critical emission regions of the second peak were mainly located to the south of Beijing, where southeasterly moist air transport led to the hygroscopic growth of particles and pollutant convergence in front of the Taihang Mountains during the daytime on 21 November. The temporal variations in the sensitivity coefficients for the two PM2.5 concentration peaks revealed that the response time of the onset of Beijing haze pollution from the local primary emissions is approximately 1-2 h and that from the surrounding primary emissions it is approximately 7-12 h. The upstream Hebei province has the largest impact on the two PM2.5 concentration peaks, and the contribution of emissions from Hebei province to the first PM2.5 concentration peak (43.6 %) is greater than that to the second PM2.5 concentration peak (41.5 %). The second most influential province for the 05:00 LT PM2.5 concentration peak is Beijing (31.2 %), followed by Shanxi (9.8 %), Tianjin (9.8 %), and Shandong (5.7 %). The second most influential province for the 23:00 LT PM2.5 concentration peak is Beijing (35.7 %), followed by Shanxi (8.1 %), Shandong (8.0 %), and Tianjin (6.7 %). The adjoint model results were compared with the forward

  8. Development and assessment of a physics-based simulation model to investigate residential PM2.5 infiltration across the US housing stock

    Science.gov (United States)

    The Lawrence Berkeley National Laboratory Population Impact Assessment Modeling Framework (PIAMF) was expanded to enable determination of indoor PM2.5 concentrations and exposures in a set of 50,000 homes representing the US housing stock. A mass-balance model is used to calculat...

  9. Modelling of a linear PM machine including magnetic saturation and end effects : maximum force to current ratio

    NARCIS (Netherlands)

    Polinder, H.; Slootweg, J.G.; Hoeijmakers, M.J.; Compter, J.C.

    2003-01-01

    The use of linear permanent-magnet (PM) actuators increases in a wide variety of applications because of their high force density, robustness and accuracy. These linear PM motors are often heavily loaded during short intervals of high acceleration, so that magnetic saturation occurs. This paper

  10. Modeling individual exposures to ambient PM2.5 in the diabetes and the environment panel study (DEPS)

    Science.gov (United States)

    Air pollution epidemiology studies of ambient fine particulate matter (PM2.5) often use outdoor concentrations as exposure surrogates, which can induce exposure error. The goal of this study was to improve ambient PM2.5 exposure assessments for a repeated measurements study with ...

  11. Acute Exposure to Particulate Matter (PM) Alters Physiologic and Toxicologic Endpoints in a Rat Model of Heart Failure

    Science.gov (United States)

    Human exposure to ambient PM from fossil-fuel emissions is linked to cardiovascular disease and death. This association strengthens in people with preexisting cardiopulmonary diseases—especially heart failure (HF). We previously examined the effects of PM on HF by exposing Sponta...

  12. Assessment and statistical modeling of the relationship between remotely sensed aerosol optical depth and PM2.5 in the eastern United States.

    Science.gov (United States)

    Paciorek, Christopher J; Liu, Yang

    2012-05-01

    Research in scientific, public health, and policy disciplines relating to the environment increasingly makes use of high-dimensional remote sensing and the output of numerical models in conjunction with traditional observations. Given the public health and resultant public policy implications of the potential health effects of particulate matter (PM*) air pollution, specifically fine PM with an aerodynamic diameter work has been rare, in part because discrepancies necessarily exist between satellite-retrieved estimates of AOD, which is an atmospheric-column average, and ground-level PM2.5. In this report, we summarize the results of a number of empirical analyses and of the development of statistical models for the use of proxy information, in particular satellite AOD, in predicting PM2.5 concentrations in the eastern United States. We analyzed the spatiotemporal structure of the relationship between PM2.5 and AOD, first using simple correlations both before and after calibration based on meteorology, as well as large-scale spatial and temporal calibration to account for discrepancies between AOD and PM2.5. We then used both raw and calibrated AOD retrievals in statistical models to predict PM2.5 concentrations, accounting for AOD in two ways: primarily as a separate data source contributing a second likelihood to a Bayesian statistical model, as well as a data source on which we could directly regress. Previous consideration of satellite AOD has largely focused on the National Aeronautics and Space Administration (NASA) moderate resolution imaging spectroradiometer (MODIS) and multiangle imaging spectroradiometer (MISR) instruments. One contribution of our work is more extensive consideration of AOD derived from the Geostationary Operational Environmental Satellite East Aerosol/Smoke Product (GOES GASP) AOD and its relationship with PM2.5. In addition to empirically assessing the spatiotemporal relationship between GASP AOD and PM2.5, we considered new statistical

  13. Estimating PM2.5 Concentrations in Xi'an City Using a Generalized Additive Model with Multi-Source Monitoring Data.

    Directory of Open Access Journals (Sweden)

    Yong-Ze Song

    Full Text Available Particulate matter with an aerodynamic diameter <2.5 μm (PM2.5 represents a severe environmental problem and is of negative impact on human health. Xi'an City, with a population of 6.5 million, is among the highest concentrations of PM2.5 in China. In 2013, in total, there were 191 days in Xi'an City on which PM2.5 concentrations were greater than 100 μg/m3. Recently, a few studies have explored the potential causes of high PM2.5 concentration using remote sensing data such as the MODIS aerosol optical thickness (AOT product. Linear regression is a commonly used method to find statistical relationships among PM2.5 concentrations and other pollutants, including CO, NO2, SO2, and O3, which can be indicative of emission sources. The relationships of these variables, however, are usually complicated and non-linear. Therefore, a generalized additive model (GAM is used to estimate the statistical relationships between potential variables and PM2.5 concentrations. This model contains linear functions of SO2 and CO, univariate smoothing non-linear functions of NO2, O3, AOT and temperature, and bivariate smoothing non-linear functions of location and wind variables. The model can explain 69.50% of PM2.5 concentrations, with R2 = 0.691, which improves the result of a stepwise linear regression (R2 = 0.582 by 18.73%. The two most significant variables, CO concentration and AOT, represent 20.65% and 19.54% of the deviance, respectively, while the three other gas-phase concentrations, SO2, NO2, and O3 account for 10.88% of the total deviance. These results show that in Xi'an City, the traffic and other industrial emissions are the primary source of PM2.5. Temperature, location, and wind variables also non-linearly related with PM2.5.

  14. Estimation of excess mortality due to long-term exposure to PM2.5 in Japan using a high-resolution model for present and future scenarios

    Science.gov (United States)

    Goto, Daisuke; Ueda, Kayo; Ng, Chris Fook Sheng; Takami, Akinori; Ariga, Toshinori; Matsuhashi, Keisuke; Nakajima, Teruyuki

    2016-09-01

    Particulate matter with a diameter of less than 2.5 μm, known as PM2.5, can affect human health, especially in elderly people. Because of the imminent aging of society in the near future in most developed countries, the human health impacts of PM2.5 must be evaluated. In this study, we used a global-to-regional atmospheric transport model to simulate PM2.5 in Japan with a high-resolution stretched grid system (∼10 km for the high-resolution model, HRM) for the present (the 2000) and the future (the 2030, as proposed by the Representative Concentrations Pathway 4.5, RCP4.5). We also used the same model with a low-resolution uniform grid system (∼100 km for the low-resolution model, LRM). These calculations were conducted by nudging meteorological fields obtained from an atmosphere-ocean coupled model and providing emission inventories used in the coupled model. After correcting for bias, we calculated the excess mortality due to long-term exposure to PM2.5 among the elderly (over 65 years old) based on different minimum PM2.5 concentration (MINPM) levels to account for uncertainty using the simulated PM2.5 distributions to express the health effect as a concentration-response function. As a result, we estimated the excess mortality for all of Japan to be 31,300 (95% confidence intervals: 20,700 to 42,600) people in 2000 and 28,600 (95% confidence intervals: 19,000 to 38,700) people in 2030 using the HRM with a MINPM of 5.8 μg/m3. In contrast, the LRM resulted in underestimates of approximately 30% (for PM2.5 concentrations in the 2000 and 2030), approximately 60% (excess mortality in the 2000) and approximately 90% (excess mortality in 2030) compared to the HRM results. We also found that the uncertainty in the MINPM value, especially for low PM2.5 concentrations in the future (2030) can cause large variability in the estimates, ranging from 0 (MINPM of 15 μg/m3 in both HRM and LRM) to 95,000 (MINPM of 0 μg/m3 in HRM) people.

  15. A Computational Fluid Dynamic (CFD) Simulation of PM10 Dispersion Caused by Rail Transit Construction Activity: A Real Urban Street Canyon Model

    Science.gov (United States)

    Wang, Yang; Zhou, Ying; Zuo, Jian

    2018-01-01

    Particle emissions derived from construction activities have a significant impact on the local air quality, while the canyon effect with reduced natural ventilation contributes to the highest particulate pollution in urban environments. This study attempted to examine the effect of PM10 emissions derived from the construction of a rail transit system in an urban street canyon. Using a 3D computational fluid dynamic (CFD) model based on a real street canyon with different height ratios, this study formulates the impact of height ratio and wind directions on the dispersion and concentration of PM10. The results indicate that parallel flow would cause the concentration of PM10 at the end of the street canyons in all height ratios, and the trends in horizontal, vertical and lateral planes in all street canyons are similar. While in the condition of perpendicular flow, double-eddy circulations occur and lead to the concentration of PM10 in the middle part of the street canyon and leeward of backwind buildings in all height ratios. Furthermore, perpendicular flow will cause the concentration of PM10 to increase if the upwind buildings are higher than the backwind ones. This study also shows that the dispersion of PM10 is strongly associated with wind direction in and the height ratios of the street canyons. Certain measures could, therefore, be taken to prevent the impact on people in terms of the PM10 concentration and the heights of street canyons identified in this research. Potential mitigation strategies are suggested, include measurements below 4 m according to governmental regulations, dust shields, and atomized water. PMID:29522495

  16. A Computational Fluid Dynamic (CFD Simulation of PM10 Dispersion Caused by Rail Transit Construction Activity: A Real Urban Street Canyon Model

    Directory of Open Access Journals (Sweden)

    Yang Wang

    2018-03-01

    Full Text Available Particle emissions derived from construction activities have a significant impact on the local air quality, while the canyon effect with reduced natural ventilation contributes to the highest particulate pollution in urban environments. This study attempted to examine the effect of PM10 emissions derived from the construction of a rail transit system in an urban street canyon. Using a 3D computational fluid dynamic (CFD model based on a real street canyon with different height ratios, this study formulates the impact of height ratio and wind directions on the dispersion and concentration of PM10. The results indicate that parallel flow would cause the concentration of PM10 at the end of the street canyons in all height ratios, and the trends in horizontal, vertical and lateral planes in all street canyons are similar. While in the condition of perpendicular flow, double-eddy circulations occur and lead to the concentration of PM10 in the middle part of the street canyon and leeward of backwind buildings in all height ratios. Furthermore, perpendicular flow will cause the concentration of PM10 to increase if the upwind buildings are higher than the backwind ones. This study also shows that the dispersion of PM10 is strongly associated with wind direction in and the height ratios of the street canyons. Certain measures could, therefore, be taken to prevent the impact on people in terms of the PM10 concentration and the heights of street canyons identified in this research. Potential mitigation strategies are suggested, include measurements below 4 m according to governmental regulations, dust shields, and atomized water.

  17. A Computational Fluid Dynamic (CFD) Simulation of PM10 Dispersion Caused by Rail Transit Construction Activity: A Real Urban Street Canyon Model.

    Science.gov (United States)

    Wang, Yang; Zhou, Ying; Zuo, Jian; Rameezdeen, Raufdeen

    2018-03-09

    Particle emissions derived from construction activities have a significant impact on the local air quality, while the canyon effect with reduced natural ventilation contributes to the highest particulate pollution in urban environments. This study attempted to examine the effect of PM 10 emissions derived from the construction of a rail transit system in an urban street canyon. Using a 3D computational fluid dynamic (CFD) model based on a real street canyon with different height ratios, this study formulates the impact of height ratio and wind directions on the dispersion and concentration of PM 10 . The results indicate that parallel flow would cause the concentration of PM 10 at the end of the street canyons in all height ratios, and the trends in horizontal, vertical and lateral planes in all street canyons are similar. While in the condition of perpendicular flow, double-eddy circulations occur and lead to the concentration of PM 10 in the middle part of the street canyon and leeward of backwind buildings in all height ratios. Furthermore, perpendicular flow will cause the concentration of PM 10 to increase if the upwind buildings are higher than the backwind ones. This study also shows that the dispersion of PM 10 is strongly associated with wind direction in and the height ratios of the street canyons. Certain measures could, therefore, be taken to prevent the impact on people in terms of the PM 10 concentration and the heights of street canyons identified in this research. Potential mitigation strategies are suggested, include measurements below 4 m according to governmental regulations, dust shields, and atomized water.

  18. Modelling the Effects of Surface Residual Stresses on Fatigue Behavior of PM Disk Alloys, Phase I

    Data.gov (United States)

    National Aeronautics and Space Administration — A finite element based model will be developed and validated to capture the evolution of residual stresses and cold work at machined features of compressor and...

  19. Source apportionment of the carcinogenic potential of polycyclic aromatic hydrocarbons (PAH) associated to airborne PM10 by a PMF model.

    Science.gov (United States)

    Callén, M S; Iturmendi, A; López, J M; Mastral, A M

    2014-02-01

    In order to perform a study of the carcinogenic potential of polycyclic aromatic hydrocarbons (PAH), benzo(a)pyrene equivalent (BaP-eq) concentration was calculated and modelled by a receptor model based on positive matrix factorization (PMF). Nineteen PAH associated to airborne PM10 of Zaragoza, Spain, were quantified during the sampling period 2001-2009 and used as potential variables by the PMF model. Afterwards, multiple linear regression analysis was used to quantify the potential sources of BaP-eq. Five sources were obtained as the optimal solution and vehicular emission was identified as the main carcinogenic source (35 %) followed by heavy-duty vehicles (28 %), light-oil combustion (18 %), natural gas (10 %) and coal combustion (9 %). Two of the most prevailing directions contributing to this carcinogenic character were the NE and N directions associated with a highway, industrial parks and a paper factory. The lifetime lung cancer risk exceeded the unit risk of 8.7 x 10(-5) per ng/m(3) BaP in both winter and autumn seasons and the most contributing source was the vehicular emission factor becoming an important issue in control strategies.

  20. Texture evolution in upset-forged P/M and wrought tantalum: Experimentation and modeling

    International Nuclear Information System (INIS)

    Bingert, J.F.; Desch, P.B.; Bingert, S.R.; Maudlin, P.J.; Tome, C.N.

    1997-11-01

    Preferred orientations in polycrystalline materials can significantly affect their physical and mechanical response through the retention of anisotropic properties inherent to the single crystal. In this study the texture evolution in upset-forged PIM and wrought tantalum was measured as a function of initial texture, compressive strain, and relative position in the pressing. A / duplex fiber texture parallel to the compression axis was generally observed, with varying degrees of a radial component evident in the wrought material. The development of deformation textures derives from restricted crystallographic slip conditions that generate lattice rotations, and these grain reorientations can be modeled as a function of the prescribed deformation gradient. Texture development was simulated for equivalent deformations using both a modified Taylor approach and a viscoplastic self-consistent (VPSC) model. A comparison between the predicted evolution and experimental results shows a good correlation with the texture components, but an overly sharp prediction at large strains from both the Taylor and VPSC models

  1. A statistical model for determining impact of wildland fires on Particulate Matter (PM2.5) in Central California aided by satellite imagery of smoke

    International Nuclear Information System (INIS)

    Preisler, Haiganoush K.; Schweizer, Donald; Cisneros, Ricardo; Procter, Trent; Ruminski, Mark; Tarnay, Leland

    2015-01-01

    As the climate in California warms and wildfires become larger and more severe, satellite-based observational tools are frequently used for studying impact of those fires on air quality. However little objective work has been done to quantify the skill these satellite observations of smoke plumes have in predicting impacts to PM 2.5 concentrations at ground level monitors, especially those monitors used to determine attainment values for air quality under the Clean Air Act. Using PM 2.5 monitoring data from a suite of monitors throughout the Central California area, we found a significant, but weak relationship between satellite-observed smoke plumes and PM 2.5 concentrations measured at the surface. However, when combined with an autoregressive statistical model that uses weather and seasonal factors to identify thresholds for flagging unusual events at these sites, we found that the presence of smoke plumes could reliably identify periods of wildfire influence with 95% accuracy. - Highlights: • Satellite observed smoke is useful for predicting wildfire impacts on Particulate Matter. • A metric was developed to flag ‘exceptional events’ days as defined by EPA. • We found significant impact of wildfires on PM 2.5 at various sites in Central California. • Fires in most years had no significant impact on compliance with EPA standards. - This work quantifies the skill of satellite observations of smoke plumes in predicting wildfire impacts on PM 2.5 concentrations at ground level monitors

  2. Lagged PM2.5 effects in mortality time series: Critical impact of covariate model

    Science.gov (United States)

    The two most common approaches to modeling the effects of air pollution on mortality are the Harvard and the Johns Hopkins (NMMAPS) approaches. These two approaches, which use different sets of covariates, result in dissimilar estimates of the effect of lagged fine particulate ma...

  3. Land-use regression with long-term satellite-based greenness index and culture-specific sources to model PM2.5 spatial-temporal variability.

    Science.gov (United States)

    Wu, Chih-Da; Chen, Yu-Cheng; Pan, Wen-Chi; Zeng, Yu-Ting; Chen, Mu-Jean; Guo, Yue Leon; Lung, Shih-Chun Candice

    2017-05-01

    This study utilized a long-term satellite-based vegetation index, and considered culture-specific emission sources (temples and Chinese restaurants) with Land-use Regression (LUR) modelling to estimate the spatial-temporal variability of PM 2.5 using data from Taipei metropolis, which exhibits typical Asian city characteristics. Annual average PM 2.5 concentrations from 2006 to 2012 of 17 air quality monitoring stations established by Environmental Protection Administration of Taiwan were used for model development. PM 2.5 measurements from 2013 were used for external data verification. Monthly Normalized Difference Vegetation Index (NDVI) images coupled with buffer analysis were used to assess the spatial-temporal variations of greenness surrounding the monitoring sites. The distribution of temples and Chinese restaurants were included to represent the emission contributions from incense and joss money burning, and gas cooking, respectively. Spearman correlation coefficient and stepwise regression were used for LUR model development, and 10-fold cross-validation and external data verification were applied to verify the model reliability. The results showed a strongly negative correlation (r: -0.71 to -0.77) between NDVI and PM 2.5 while temples (r: 0.52 to 0.66) and Chinese restaurants (r: 0.31 to 0.44) were positively correlated to PM 2.5 concentrations. With the adjusted model R 2 of 0.89, a cross-validated adj-R 2 of 0.90, and external validated R 2 of 0.83, the high explanatory power of the resultant model was confirmed. Moreover, the averaged NDVI within a 1750 m circular buffer (p < 0.01), the number of Chinese restaurants within a 1750 m buffer (p < 0.01), and the number of temples within a 750 m buffer (p = 0.06) were selected as important predictors during the stepwise selection procedures. According to the partial R 2 , NDVI explained 66% of PM 2.5 variation and was the dominant variable in the developed model. We suggest future studies

  4. Modeling PM10 gravimetric data from the Qalabotjha low-smoke fuels macro-scale experiment in South Africa

    International Nuclear Information System (INIS)

    Engelbrecht, J.P.; Swanepoel, L.; Zunckel, M.; Chow, J.C.

    1998-01-01

    D-grade domestic coal is being widely used for household cooking and heating purposes by the poorer urban communities in South Africa. The smoke from the combustion of coal has had a severe impact on the health of communities living in the rural townships and cities. To alleviate this escalating problem, the Department of Minerals and Energy of South Africa evaluated low-smoke fuels as an alternative source of energy. The technical and social implications of such fuels were investigated in the course of the Qalabotjha Low-Smoke Fuels Macro-Scale Experiment. Three low-smoke fuels (Chartech, African Fine Carbon (AFC) and Flame Africa) were tested in Qalabotjha over a 10 to 20 day period. This paper presents results from a PM10 TEOM continuous monitor at the Clinic site in Qalabotjha over the mentioned monitoring period. Both the fuel-type and the wind were found to have an effect on the air particulate concentrations. An exponential model which incorporates both these variables is proposed. This model allows for all measured particulate concentrations to be re-calculated to zero wind values. From the analysis of variance (ANOVA) calculations on the zero wind concentrations, it is concluded that the combustion of low-smoke fuels did make a significant improvement to the air quality in Qalabotjha over the period when these were used

  5. Characterizing and sourcing ambient PM2.5 over key emission regions in China II: Organic molecular markers and CMB modeling

    Science.gov (United States)

    Zhou, Jiabin; Xiong, Ying; Xing, Zhenyu; Deng, Junjun; Du, Ke

    2017-08-01

    From November 2012 to July 2013, a sampling campaign was completed for comprehensive characterization of PM2.5 over four key emission regions in China: Beijing-Tianjin-Hebei (BTH), Yangzi River Delta (YRD), Pearl River Delta (PRD), and Sichuan Basin (SB). A multi-method approach, adopting different analytical and receptor modeling methods, was employed to determine the relative abundances of region-specific air pollution constituents and contributions of emission sources. This paper is focused on organic molecular marker based source apportionment using chemical mass balance (CMB) receptor modeling. Analyses of the organic molecular markers revealed that vehicle emission, coal combustion, biomass burning, meat cooking and natural gas combustion were the major contributors to organic carbon (OC) in PM2.5. The vehicle emission dominated the sources contributing to OC in spring at four sampling sites. During wintertime, the coal combustion had highest contribution to OC at BTH site, while the major source contributing to OC at YRD and PRD sites was vehicle emission. In addition, the relative contributions of different emission sources to PM2.5 mass at a specific location site and in a specific season revealed seasonal and spatial variations across all four sampling locations. The largest contributor to PM2.5 mass was secondary sulfate (14-17%) in winter at the four sites. The vehicle emission was found to be the major source (14-21%) for PM2.5 mass at PRD site. The secondary ammonium has minor variation (4-5%) across the sites, confirming the influences of regional emission sources on these sites. The distinct patterns of seasonal and spatial variations of source apportionment observed in this study were consistent with the findings in our previous paper based upon water-soluble ions and carbonaceous fractions. This makes it essential for the local government to make season- and region-specific mitigation strategies for abating PM2.5 pollution in China.

  6. Spatiotemporal modeling of PM2.5 concentrations at the national scale combining land use regression and Bayesian maximum entropy in China.

    Science.gov (United States)

    Chen, Li; Gao, Shuang; Zhang, Hui; Sun, Yanling; Ma, Zhenxing; Vedal, Sverre; Mao, Jian; Bai, Zhipeng

    2018-05-03

    Concentrations of particulate matter with aerodynamic diameter Bayesian Maximum Entropy (BME) interpolation of the LUR space-time residuals were developed to estimate the PM 2.5 concentrations on a national scale in China. This hybrid model could potentially provide more valid predictions than a commonly-used LUR model. The LUR/BME model had good performance characteristics, with R 2  = 0.82 and root mean square error (RMSE) of 4.6 μg/m 3 . Prediction errors of the LUR/BME model were reduced by incorporating soft data accounting for data uncertainty, with the R 2 increasing by 6%. The performance of LUR/BME is better than OK/BME. The LUR/BME model is the most accurate fine spatial scale PM 2.5 model developed to date for China. Copyright © 2018. Published by Elsevier Ltd.

  7. A novel hybrid decomposition-and-ensemble model based on CEEMD and GWO for short-term PM2.5 concentration forecasting

    Science.gov (United States)

    Niu, Mingfei; Wang, Yufang; Sun, Shaolong; Li, Yongwu

    2016-06-01

    To enhance prediction reliability and accuracy, a hybrid model based on the promising principle of "decomposition and ensemble" and a recently proposed meta-heuristic called grey wolf optimizer (GWO) is introduced for daily PM2.5 concentration forecasting. Compared with existing PM2.5 forecasting methods, this proposed model has improved the prediction accuracy and hit rates of directional prediction. The proposed model involves three main steps, i.e., decomposing the original PM2.5 series into several intrinsic mode functions (IMFs) via complementary ensemble empirical mode decomposition (CEEMD) for simplifying the complex data; individually predicting each IMF with support vector regression (SVR) optimized by GWO; integrating all predicted IMFs for the ensemble result as the final prediction by another SVR optimized by GWO. Seven benchmark models, including single artificial intelligence (AI) models, other decomposition-ensemble models with different decomposition methods and models with the same decomposition-ensemble method but optimized by different algorithms, are considered to verify the superiority of the proposed hybrid model. The empirical study indicates that the proposed hybrid decomposition-ensemble model is remarkably superior to all considered benchmark models for its higher prediction accuracy and hit rates of directional prediction.

  8. Implementing the PM Programming Language using MPI and OpenMP - a New Tool for Programming Geophysical Models on Parallel Systems

    Science.gov (United States)

    Bellerby, Tim

    2015-04-01

    PM (Parallel Models) is a new parallel programming language specifically designed for writing environmental and geophysical models. The language is intended to enable implementers to concentrate on the science behind the model rather than the details of running on parallel hardware. At the same time PM leaves the programmer in control - all parallelisation is explicit and the parallel structure of any given program may be deduced directly from the code. This paper describes a PM implementation based on the Message Passing Interface (MPI) and Open Multi-Processing (OpenMP) standards, looking at issues involved with translating the PM parallelisation model to MPI/OpenMP protocols and considering performance in terms of the competing factors of finer-grained parallelisation and increased communication overhead. In order to maximise portability, the implementation stays within the MPI 1.3 standard as much as possible, with MPI-2 MPI-IO file handling the only significant exception. Moreover, it does not assume a thread-safe implementation of MPI. PM adopts a two-tier abstract representation of parallel hardware. A PM processor is a conceptual unit capable of efficiently executing a set of language tasks, with a complete parallel system consisting of an abstract N-dimensional array of such processors. PM processors may map to single cores executing tasks using cooperative multi-tasking, to multiple cores or even to separate processing nodes, efficiently sharing tasks using algorithms such as work stealing. While tasks may move between hardware elements within a PM processor, they may not move between processors without specific programmer intervention. Tasks are assigned to processors using a nested parallelism approach, building on ideas from Reyes et al. (2009). The main program owns all available processors. When the program enters a parallel statement then either processors are divided out among the newly generated tasks (number of new tasks number of processors

  9. Using the Community Multiscale Air Quality (CMAQ) model to estimate public health impacts of PM2.5 from individual power plants.

    Science.gov (United States)

    Buonocore, Jonathan J; Dong, Xinyi; Spengler, John D; Fu, Joshua S; Levy, Jonathan I

    2014-07-01

    We estimated PM2.5-related public health impacts/ton emitted of primary PM2.5, SO2, and NOx for a set of power plants in the Mid-Atlantic and Lower Great Lakes regions of the United States, selected to include varying emission profiles and broad geographic representation. We then developed a regression model explaining variability in impacts per ton emitted using the population distributions around each plant. We linked outputs from the Community Multiscale Air Quality (CMAQ) model v 4.7.1 with census data and concentration-response functions for PM2.5-related mortality, and monetized health estimates using the value-of-statistical-life. The median impacts for the final set of plants were $130,000/ton for primary PM2.5 (range: $22,000-230,000), $28,000/ton for SO2 (range: $19,000-33,000), and $16,000/ton for NOx (range: $7100-26,000). Impacts of NOx were a median of 34% (range: 20%-75%) from ammonium nitrate and 66% (range: 25%-79%) from ammonium sulfate. The latter pathway is likely from NOx enhancing atmospheric oxidative capacity and amplifying sulfate formation, and is often excluded. Our regression models explained most of the variation in impact/ton estimates using basic population covariates, and can aid in estimating impacts averted from interventions such as pollution controls, alternative energy installations, or demand-side management. Copyright © 2014 Elsevier Ltd. All rights reserved.

  10. Metabolic Activation of the Organic Fraction Coated Onto Air Pollution PM2.5 and its Genotoxicity in a Co Culture Model of Human Lung Cells

    International Nuclear Information System (INIS)

    Abbas, I; Garcon, G; Billet, S.; Verdin, A.; Escande, F.; Saint-Georges, F.; Mulliez, Ph.; Gosset, P.; Shirali, P.

    2011-01-01

    Air pollution Particulate Matter (PM 2 .5) is described as one of the major risk factors affecting human health. Hence, the objective of our research project was to evaluate the lung toxicity of PM 2 .5 collected in Dunkerque (France), through the study of the metabolic activation of its organic fraction (e.g. Polycyclic Aromatic Hydrocarbons, PAHs; Volatile Organic Compounds, VOCs) and its genotoxicity in two human cell models: embryonic lung epithelial L132 cells and Alveolar Macrophages (AM) isolated from bronchiolo-alveolar lavages of healthy outpatients, in mono- and/or coculture. The coculture system we used allowed the direct exposure of AM to PM 2 .5, and the interaction between the two cell types only through soluble factor diffusion. Exposure to Dunkerque City's PM 2 .5 induced the gene expression of phase I and phase II enzymes (e.g. CYP1A1, CYP2E1, CYP2F1, NQO1, GSTπ1, GSTμ3) involved in the metabolic activation of PAHS and/or VOCS, in AM, in mono- and coculture, and in L132 cells, only in monoculture. Taken together, these results reinforced the key role of AM in lung defenses, and indicated that particles, as physical vector of the penetration and retention of coated-PAHS and/or VOCS within cells, enabled them to exert a durable toxicity. DNA bulky adduct formation was also reported not only in Dunkerque City's PM 2 .5-exposed AM, in mono- and coculture, but also in L132 cells from PAH-exposed coculture. Loss of Heterozygosity (LOH) and/or MicroSatellite Instability (MSI) of some MicroSatellites (MS) located in multiple critical regions of chromosome 3 were reported in L132 cells from Dunkerque City's PM 2 .5-exposed mono- or cocultures. (author)

  11. Parameter and model uncertainty in a life-table model for fine particles (PM2.5): a statistical modeling study.

    Science.gov (United States)

    Tainio, Marko; Tuomisto, Jouni T; Hänninen, Otto; Ruuskanen, Juhani; Jantunen, Matti J; Pekkanen, Juha

    2007-08-23

    The estimation of health impacts involves often uncertain input variables and assumptions which have to be incorporated into the model structure. These uncertainties may have significant effects on the results obtained with model, and, thus, on decision making. Fine particles (PM2.5) are believed to cause major health impacts, and, consequently, uncertainties in their health impact assessment have clear relevance to policy-making. We studied the effects of various uncertain input variables by building a life-table model for fine particles. Life-expectancy of the Helsinki metropolitan area population and the change in life-expectancy due to fine particle exposures were predicted using a life-table model. A number of parameter and model uncertainties were estimated. Sensitivity analysis for input variables was performed by calculating rank-order correlations between input and output variables. The studied model uncertainties were (i) plausibility of mortality outcomes and (ii) lag, and parameter uncertainties (iii) exposure-response coefficients for different mortality outcomes, and (iv) exposure estimates for different age groups. The monetary value of the years-of-life-lost and the relative importance of the uncertainties related to monetary valuation were predicted to compare the relative importance of the monetary valuation on the health effect uncertainties. The magnitude of the health effects costs depended mostly on discount rate, exposure-response coefficient, and plausibility of the cardiopulmonary mortality. Other mortality outcomes (lung cancer, other non-accidental and infant mortality) and lag had only minor impact on the output. The results highlight the importance of the uncertainties associated with cardiopulmonary mortality in the fine particle impact assessment when compared with other uncertainties. When estimating life-expectancy, the estimates used for cardiopulmonary exposure-response coefficient, discount rate, and plausibility require careful

  12. Parameter and model uncertainty in a life-table model for fine particles (PM2.5: a statistical modeling study

    Directory of Open Access Journals (Sweden)

    Jantunen Matti J

    2007-08-01

    Full Text Available Abstract Background The estimation of health impacts involves often uncertain input variables and assumptions which have to be incorporated into the model structure. These uncertainties may have significant effects on the results obtained with model, and, thus, on decision making. Fine particles (PM2.5 are believed to cause major health impacts, and, consequently, uncertainties in their health impact assessment have clear relevance to policy-making. We studied the effects of various uncertain input variables by building a life-table model for fine particles. Methods Life-expectancy of the Helsinki metropolitan area population and the change in life-expectancy due to fine particle exposures were predicted using a life-table model. A number of parameter and model uncertainties were estimated. Sensitivity analysis for input variables was performed by calculating rank-order correlations between input and output variables. The studied model uncertainties were (i plausibility of mortality outcomes and (ii lag, and parameter uncertainties (iii exposure-response coefficients for different mortality outcomes, and (iv exposure estimates for different age groups. The monetary value of the years-of-life-lost and the relative importance of the uncertainties related to monetary valuation were predicted to compare the relative importance of the monetary valuation on the health effect uncertainties. Results The magnitude of the health effects costs depended mostly on discount rate, exposure-response coefficient, and plausibility of the cardiopulmonary mortality. Other mortality outcomes (lung cancer, other non-accidental and infant mortality and lag had only minor impact on the output. The results highlight the importance of the uncertainties associated with cardiopulmonary mortality in the fine particle impact assessment when compared with other uncertainties. Conclusion When estimating life-expectancy, the estimates used for cardiopulmonary exposure

  13. Changes in gene expression in chronic allergy mouse model exposed to natural environmental PM2.5-rich ambient air pollution.

    Science.gov (United States)

    Ouyang, Yuhui; Xu, Zhaojun; Fan, Erzhong; Li, Ying; Miyake, Kunio; Xu, Xianyan; Zhang, Luo

    2018-04-20

    Particulate matter (PM) air pollution has been associated with an increase in the incidence of chronic allergic diseases; however, the mechanisms underlying the effect of exposure to natural ambient air pollution in chronic allergic diseases have not been fully elucidated. In the present study, we aimed to investigate the cellular responses induced by exposure to natural ambient air pollution, employing a mouse model of chronic allergy. The results indicated that exposure to ambient air pollution significantly increased the number of eosinophils in the nasal mucosa. The modulation of gene expression profile identified a set of regulated genes, and the Triggering Receptor Expressed on Myeloid cells1(TREM1) signaling canonical pathway was increased after exposure to ambient air pollution. In vitro, PM2.5 increased Nucleotide-binding oligomerization domain-containing protein 1 (Nod1) and nuclear factor (NF)-κB signaling pathway activation in A549 and HEK293 cell cultures. These results suggest a novel mechanism by which, PM2.5 in ambient air pollution may stimulate the innate immune system through the PM2.5-Nod1-NF-κB axis in chronic allergic disease.

  14. Retrieve Aerosol Concentration Based On Surface Model and Distribution of Concentration of PM2.5 ——A Case Study of Beijing

    Science.gov (United States)

    Cui, H.

    2017-12-01

    As China's economy continues to grow, urbanization continues to advance, along with growth in all areas to pollutant emissions in the air industry, air quality also continued to deteriorate. Aerosol concentrations as a measure of air quality of the most important part of are more and more people's attention. Traditional monitoring stations measuring aerosol concentration method is accurate, but time-consuming and can't be done simultaneously measure a large area, can only rely on data from several monitoring sites to predict the concentration of the panorama. Remote Sensing Technology retrieves aerosol concentrations being by virtue of their efficient, fast advantages gradually into sight. In this paper, by the method of surface model to start with the physical processes of atmospheric transport, innovative aerosol concentration coefficient proposed to replace the traditional aerosol concentrations, pushed to a set of retrieval of aerosol concentration coefficient method, enabling fast and efficient Get accurate air pollution target area. At the same paper also monitoring data for PM2.5 in Beijing were analyzed from different angles, from the perspective of the data summarized in Beijing PM2.5 concentration of time, space, geographical distribution and concentration of PM2.5 and explored the relationship between aerosol concentration coefficient and concentration of PM2.5.

  15. Land use regression modeling of oxidative potential of fine particles, NO2, PM2.5 mass and association to type two diabetes mellitus

    Science.gov (United States)

    Hellack, Bryan; Sugiri, Dorothea; Schins, Roel P. F.; Schikowski, Tamara; Krämer, Ursula; Kuhlbusch, Thomas A. J.; Hoffmann, Barbara

    2017-12-01

    While land use regression models (LUR) are commonly used, e.g. for the prediction of spatially variable air pollutant mass concentrations, they are scarcely used for predicting the oxidative potential (OP), a suggested unifying predictor of health effects. Therefore a LUR model was developed to examine if long-term OP of fine particulate exposure can be reasonably predicted by LUR modeling and whether it is related to health effects in a study region comprised of urban and rural areas. Four 14-day sampling periods over 1 year at 40 sites in the western Ruhr Area and adjacent northern rural area, Germany, in 2002/2003 were conducted and annual Nitrogen Dioxide (NO2), fine particles (PM2.5), and OP were calculated. LUR models were developed to estimate spatially-resolved annual OP, NO2 and PM2.5 concentrations. The model performance was checked by leave-one-out cross validation (LOOCV) and cox regression was used to analyze the association of modeled residential OP and NO2 with incident type 2 diabetes mellitus (T2DM) in 1784 elderly women during a mean follow-up of 16 years (baseline 1985-1994). The measured OP and NO2 concentrations were moderately correlated (rSpearman 0.57). The LUR models explained 62% and 92% of the OP and NO2 variance (adjusted LOOCV R2 57% and 90%). PM10 emission from combustion in a 5000 m buffer was the most important predictor for OP and NO2. Modeled pollutants were highly correlated (rSpearman 0.87). Model quality for OP was sensitive to the inclusion of a single influential measurement site. For PM2.5 mass only an insufficient model with a low explained variance of 22% (adjusted R2) was developed so no health effects analyses were conducted with estimated PM2.5. Increases in OP and NO2 were associated with an increase in risk of T2DM by a hazard ratio of 1.38 (95% CI 1.06-1.80) and 1.39 (95% CI 1.07-1.81) per interquartile range of OP and NO2, respectively. We conclude that spatially-resolved OP can be predicted by LUR modeling, but

  16. A study of health effect estimates using competing methods to model personal exposures to ambient PM2.5.

    Science.gov (United States)

    Strand, Matthew; Hopke, Philip K; Zhao, Weixiang; Vedal, Sverre; Gelfand, Erwin; Rabinovitch, Nathan

    2007-09-01

    Various methods have been developed recently to estimate personal exposures to ambient particulate matter less than 2.5 microm in diameter (PM2.5) using fixed outdoor monitors as well as personal exposure monitors. One class of estimators involves extrapolating values using ambient-source components of PM2.5, such as sulfate and iron. A key step in extrapolating these values is to correct for differences in infiltration characteristics of the component used in extrapolation (such as sulfate within PM2.5) and PM2.5. When this is not done, resulting health effect estimates will be biased. Another class of approaches involves factor analysis methods such as positive matrix factorization (PMF). Using either an extrapolation or a factor analysis method in conjunction with regression calibration allows one to estimate the direct effects of ambient PM2.5 on health, eliminating bias caused by using fixed outdoor monitors and estimated personal ambient PM2.5 concentrations. Several forms of the extrapolation method are defined, including some new ones. Health effect estimates that result from the use of these methods are compared with those from an expanded PMF analysis using data collected from a health study of asthmatic children conducted in Denver, Colorado. Examining differences in health effect estimates among the various methods using a measure of lung function (forced expiratory volume in 1 s) as the health indicator demonstrated the importance of the correction factor(s) in the extrapolation methods and that PMF yielded results comparable with the extrapolation methods that incorporated correction factors.

  17. Chemical data assimilation of geostationary aerosol optical depth and PM surface observations on regional aerosol modeling over the Korean Peninsula during KORUS-AQ campaign

    Science.gov (United States)

    Jung, J.; Choi, Y.; Souri, A.; Jeon, W.

    2017-12-01

    Particle matter(PM) has played a significantly deleterious role in affecting human health and climate. Recently, continuous high concentrations of PM in Korea attracted public attention to this critical issue, and the Korea-United States Air Quality Study(KORUS-AQ) campaign in 2016 was conducted to investigate the causes. For this study, we adjusted the initial conditions in the chemical transport model(CTM) to improve its performance over Korean Peninsula during KORUS-AQ period, using the campaign data to evaluate our model performance. We used the Optimal Interpolation(OI) approach and used hourly surface air quality measurement data from the Air Quality Monitoring Station(AQMS) by NIER and the aerosol optical depth(AOD) measured by a GOCI sensor from the geostationary orbit onboard the Communication Ocean and Meteorological Satellite(COMS). The AOD at 550nm has a 6km spatial resolution and broad coverage over East Asia. After assimilating the surface air quality observation data, the model accuracy significantly improved compared to base model result (without assimilation). It reported very high correlation value (0.98) and considerably decreased mean bias. Especially, it well captured some high peaks which was underpredicted by the base model. To assimilate satellite data, we applied AOD scaling factors to quantify each specie's contribution to total PM concentration and find-mode fraction(FMF) to define vertical distribution. Finally, the improvement showed fairly good agreement.

  18. Chemical composition and sources of PM1 and PM2.5 in Beijing in autumn.

    Science.gov (United States)

    Zhang, Yanyun; Lang, Jianlei; Cheng, Shuiyuan; Li, Shengyue; Zhou, Ying; Chen, Dongsheng; Zhang, Hanyu; Wang, Haiyan

    2018-02-20

    Beijing, the capital of China, suffers from severe atmospheric aerosol pollution; nevertheless, a comprehensive study of the constituents and sources of PM 1 is still lacking, and the differences between PM 1 and PM 2.5 are still unclear. In this study, an intensive observation was conducted to reveal the pollution characteristics of PM 1 and PM 2.5 in Beijing in autumn. Positive matrix factorization (PMF), backward trajectories and a potential source contribution function (PSCF) model were used to identify the source categories and source areas of PM 1 and PM 2.5 . The results showed that the average concentrations of PM 1 and PM 2.5 reached 78.20μg/m 3 and 95.47μg/m 3 during the study period, respectively. PM 1 contributed greatly to PM 2.5 . The PM 1 /PM 2.5 value increased from 73.6% to 90.1% with PM 1 concentration growing from 150μg/m 3 . Higher secondary inorganic aerosol (SIA) proportions (31.3%-70.8%) were found in PM 1 . The higher fraction of SIA, OC, EC and typical elements in PM 1 illustrated that anthropogenic components accumulated more in smaller size particles. Three typical weather patterns causing the heavy pollution in autumn were found as follows: (1) Siberian high and uniform high pressure field, (2) cold front and low-voltage system, and (3) uniform low pressure field. A PMF analysis indicated that secondary aerosols and coal combustion, vehicle, industry, biomass burning, and dust were the important sources of PM, accounting for 53.8%, 8.0%, 13.0%, 13.2% and 12.0% of PM 1 , respectively, and for 47.5%, 9.9%, 12.4%, 8.4% and 21.8% of PM 2.5 , respectively. The HYSPLIT and chemical components analysis indicated the potential contribution from biomass burning and fertilization ammonia emissions to PM 1 in autumn. The source areas were similar for PM 1 and PM 1-2.5 under general polluted conditions, but during the heavily polluted periods, the source areas were distributed in farther regions from Beijing for PM 1 than for PM 1-2.5 . Copyright

  19. Contribution of ship emissions to the concentration of PM2.5: A comprehensive study using AIS data and WRF/Chem model in Bohai Rim Region, China.

    Science.gov (United States)

    Chen, Dongsheng; Zhao, Na; Lang, Jianlei; Zhou, Ying; Wang, Xiaotong; Li, Yue; Zhao, Yuehua; Guo, Xiurui

    2018-01-01

    Compared with on-road vehicles, emission from ships is one of the least-regulated anthropogenic emission sources and non-negligible source of primary aerosols and gas-phase precursors of PM 2.5 . The Bohai Rim Region in China hosts dozens of large ports, two of which ranked among the top ten ports in the world. To determine the impact of ship emissions on the PM 2.5 concentrations over this region, two parts of works have been conducted in this study. First, a detailed ship emission inventory with high spatiotemporal resolution was developed based on Automatic Identification System (AIS) data. Then the WRF/Chem model was applied to modeling the impact of ship emissions by comparing two scenarios: with and without ship emissions. The results indicate that the total estimated ship emissions of SO 2 , NO X , PM 10 , PM 2.5 , CO, HC, and CO 2 from Bohai Rim Region in 2014 are 1.9×10 5 , 2.9×10 5 , 2.6×10 4 , 2.4×10 4 , 2.5×10 4 , 1.2×10 4 , and 1.3×10 7 tonnes, respectively. The modeling results indicate that the annual PM 2.5 concentrations increased by 5.9% on land areas of Bohai Rim Region (the continent within 115.2°E-124.3°E and 36.1°N-41.6°N) due to ship emissions. The contributions show distinctive seasonal variations of contributions, presenting highest in summer (12.5%) followed by spring (6.9%) and autumn (3.3%), and lowest in winter (0.9%). The contribution reaches up to 10.7% along the shoreline and down to 1.0% 200km inland. After examining the statistics of the modeling results during heavy and non-heavy haze days in July, it was found that 6 out of 9 cities around the Bohai Rim Region were observed with higher contributions from ship emissions during heavy haze days compared with non-heavy haze days. These results indicate that the impacts of ship emissions on the ambient PM 2.5 are non-negligible, especially for heavy haze days for most coastal cities in the Bohai Rim Region. Copyright © 2017 Elsevier B.V. All rights reserved.

  20. Comparison of lidar-derived PM10 with regional modeling and ground-based observations in the frame of MEGAPOLI experiment

    Directory of Open Access Journals (Sweden)

    J.-C. Raut

    2011-10-01

    Full Text Available An innovative approach using mobile lidar measurements was implemented to test the performances of chemistry-transport models in simulating mass concentrations (PM10 predicted by chemistry-transport models. A ground-based mobile lidar (GBML was deployed around Paris onboard a van during the MEGAPOLI (Megacities: Emissions, urban, regional and Global Atmospheric POLlution and climate effects, and Integrated tools for assessment and mitigation summer experiment in July 2009. The measurements performed with this Rayleigh-Mie lidar are converted into PM10 profiles using optical-to-mass relationships previously established from in situ measurements performed around Paris for urban and peri-urban aerosols. The method is described here and applied to the 10 measurements days (MD. MD of 1, 15, 16 and 26 July 2009, corresponding to different levels of pollution and atmospheric conditions, are analyzed here in more details. Lidar-derived PM10 are compared with results of simulations from POLYPHEMUS and CHIMERE chemistry-transport models (CTM and with ground-based observations from the AIRPARIF network. GBML-derived and AIRPARIF in situ measurements have been found to be in good agreement with a mean Root Mean Square Error RMSE (and a Mean Absolute Percentage Error MAPE of 7.2 μg m−3 (26.0% and 8.8 μg m−3 (25.2% with relationships assuming peri-urban and urban-type particles, respectively. The comparisons between CTMs and lidar at ~200 m height have shown that CTMs tend to underestimate wet PM10 concentrations as revealed by the mean wet PM10 observed during the 10 MD of 22.4, 20.0 and 17.5 μg m−3 for lidar with peri-urban relationship, and POLYPHEMUS and CHIMERE models, respectively. This leads to a RMSE (and a MAPE of 6.4 μg m−3 (29.6% and 6.4 μg m−3 (27.6% when considering POLYPHEMUS and CHIMERE CTMs, respectively. Wet integrated PM10 computed (between the ground and 1 km above the ground level from lidar, POLYPHEMUS and CHIMERE results

  1. An Integrated WRF-CAMx Modeling Approach for Impact Analysis of Implementing the Emergency PM2.5 Control Measures during Red Alerts in Beijing in December 2015

    Science.gov (United States)

    Jia, J.; Cheng, S.; Lei, L.; Lang, J.

    2017-12-01

    In December 2015, the Beijing-Tianjin-Hebei (BTH) region experienced several episodes of heavy air pollution. Beijing municipal government therefore issued 2 red alerts on December 7 and 19, respectively, and also implemented emergency control measures to alleviate the negative effects of pollution. It is estimated that the heavy pollutions in 2 red alert periods in Beijing were due mainly to the accumulation of air pollutants from local emission sources and the transboundary transport of pollutants from surrounding areas. The collected meteorological and PM2.5 data indicate that the severity of air pollutions were enlarged by the poor meteorological conditions along with lower mixing layer height. In this study, the WRF-CAMx modeling system was utilized not only for analyzing the contributions of PM2.5 from different sources, but also for quantitatively assessing the effects of implementing various emergency control measures on PM2.5 pollution control during the red alert periods. The modeling results show that local emissions were the most dominant contributors (64.8%-83.5%) among all emission sources, while the main external contributions came from the city of Baoding (3.4%-9.3%). In addition, among 5 different emission source categories, coal and traffic were the two dominant contributors to PM2.5 concentration in urban area of Beijing. Then four pollution control scenarios were designed particularly to investigate the effectiveness of the emergency control measures, and the results show that, generally these emergency control measures have positive effects on air pollution reduction. In particular, restrictive measures of traffic volume control and industrial activity shutdown/suspension have been found as the most effective measures in comparison to other emergency control measures. It is recommended that such effective measures should be considered to implement when next time similar heavy air pollutions occur in the city of Beijing.

  2. Investigation of Air Quality Index and PM10 and PM2.5 in Arak

    Directory of Open Access Journals (Sweden)

    Fatemeh Fazelinia

    2013-12-01

    Full Text Available Background and purpose: In this study, the air quality index and concentration of particles such as PM10 and PM2.5 were investigated in Arak. Materials and Methods: To determine the concentration of PM10 and PM2.5, 60 samples were collected by laser TSI model 8520 in summer and winter 2012. The collection site was around Arak city center. Results: during the sampling period, as a matter of PM10, the cleanest and the most polluted month were December and June with the average of 34.33 µg m-3 and 100.1 µg m-3, respectively. The concentration of PM2.5 was 12.93 and 53.17 µg m-3 for December and June, respectively. Meanwhile, in terms of air quality index (AQI, in 98.3% and 70% of cases, the concentrations of PM10 and PM2.5, respectively were less than normal (AQI100. Conclusion: The concentration of PM10 in the study period was less than Environmental Protection Agency (EPA 2006 guideline. Meanwhile, the concentrations of PM2.5 in 30% of air samples were greater than EPA guideline. The average PM2.5/PM10 ratio during the sampling period was 0.41 compared to range 0.15 to 0.25 reported by EPA.

  3. Estimating representative background PM2.5 concentration in heavily polluted areas using baseline separation technique and chemical mass balance model

    Science.gov (United States)

    Gao, Shuang; Yang, Wen; Zhang, Hui; Sun, Yanling; Mao, Jian; Ma, Zhenxing; Cong, Zhiyuan; Zhang, Xian; Tian, Shasha; Azzi, Merched; Chen, Li; Bai, Zhipeng

    2018-02-01

    The determination of background concentration of PM2.5 is important to understand the contribution of local emission sources to total PM2.5 concentration. The purpose of this study was to exam the performance of baseline separation techniques to estimate PM2.5 background concentration. Five separation methods, which included recursive digital filters (Lyne-Hollick, one-parameter algorithm, and Boughton two-parameter algorithm), sliding interval and smoothed minima, were applied to one-year PM2.5 time-series data in two heavily polluted cities, Tianjin and Jinan. To obtain the proper filter parameters and recession constants for the separation techniques, we conducted regression analysis at a background site during the emission reduction period enforced by the Government for the 2014 Asia-Pacific Economic Cooperation (APEC) meeting in Beijing. Background concentrations in Tianjin and Jinan were then estimated by applying the determined filter parameters and recession constants. The chemical mass balance (CMB) model was also applied to ascertain the effectiveness of the new approach. Our results showed that the contribution of background PM concentration to ambient pollution was at a comparable level to the contribution obtained from the previous study. The best performance was achieved using the Boughton two-parameter algorithm. The background concentrations were estimated at (27 ± 2) μg/m3 for the whole year, (34 ± 4) μg/m3 for the heating period (winter), (21 ± 2) μg/m3 for the non-heating period (summer), and (25 ± 2) μg/m3 for the sandstorm period in Tianjin. The corresponding values in Jinan were (30 ± 3) μg/m3, (40 ± 4) μg/m3, (24 ± 5) μg/m3, and (26 ± 2) μg/m3, respectively. The study revealed that these baseline separation techniques are valid for estimating levels of PM2.5 air pollution, and that our proposed method has great potential for estimating the background level of other air pollutants.

  4. Spatiotemporal variation of PM1 pollution in China

    Science.gov (United States)

    Chen, Gongbo; Morawska, Lidia; Zhang, Wenyi; Li, Shanshan; Cao, Wei; Ren, Hongyan; Wang, Boguang; Wang, Hao; Knibbs, Luke D.; Williams, Gail; Guo, Jianping; Guo, Yuming

    2018-04-01

    Understanding spatiotemporal variation of PM1 (mass concentrations of particles with aerodynamic diameter health, which is potentially more severe for its deeper penetrating capability into human bodies compared with larger particles. This study aimed to quantify the spatial and temporal distribution of PM1 across China as well as its ratio with PM2.5 (additive models were employed to examine the relationships between PM1 and meteorological parameters. We showed that PM1 concentrations were the lowest in summer and the highest in winter. Across China, the PM1/PM2.5 ratios ranged from 0.75-0.88, reaching higher levels in January and lower in August. For spatial distribution, higher PM1/PM2.5 ratios (>0.9) were observed in North-Eastern China, North China Plain, coastal areas of Eastern China and Sichuan Basin while lower ratios (<0.7) were present in remote areas in North-Western and Northern China (e.g., Xinjiang, Tibet and Inner Mongolia). Higher PM1/PM2.5 ratios were observed on heavily polluted days and lower ratios on clean days. The high PM1/PM2.5 ratios observed in China suggest that smaller particles, PM1 fraction, are key drivers of air pollution, and that they effectively account for the majority of PM2.5 concentrations. This emphasised the role of combustion process and secondary particle formation, the sources of PM1, and the significance of controlling them.

  5. Analysis of coupled model uncertainties in source-to-dose modeling of human exposures to ambient air pollution: A PM 2.5 case study

    Science.gov (United States)

    Özkaynak, Halûk; Frey, H. Christopher; Burke, Janet; Pinder, Robert W.

    Quantitative assessment of human exposures and health effects due to air pollution involve detailed characterization of impacts of air quality on exposure and dose. A key challenge is to integrate these three components on a consistent spatial and temporal basis taking into account linkages and feedbacks. The current state-of-practice for such assessments is to exercise emission, meteorology, air quality, exposure, and dose models separately, and to link them together by using the output of one model as input to the subsequent downstream model. Quantification of variability and uncertainty has been an important topic in the exposure assessment community for a number of years. Variability refers to differences in the value of a quantity (e.g., exposure) over time, space, or among individuals. Uncertainty refers to lack of knowledge regarding the true value of a quantity. An emerging challenge is how to quantify variability and uncertainty in integrated assessments over the source-to-dose continuum by considering contributions from individual as well as linked components. For a case study of fine particulate matter (PM 2.5) in North Carolina during July 2002, we characterize variability and uncertainty associated with each of the individual concentration, exposure and dose models that are linked, and use a conceptual framework to quantify and evaluate the implications of coupled model uncertainties. We find that the resulting overall uncertainties due to combined effects of both variability and uncertainty are smaller (usually by a factor of 3-4) than the crudely multiplied model-specific overall uncertainty ratios. Future research will need to examine the impact of potential dependencies among the model components by conducting a truly coupled modeling analysis.

  6. Studying the effect of meteorological factors on the SO2 and PM10 pollution levels with refined versions of the SARIMA model

    Energy Technology Data Exchange (ETDEWEB)

    Voynikova, D. S., E-mail: desi-sl2000@yahoo.com; Gocheva-Ilieva, S. G., E-mail: snegocheva@yahoo.com; Ivanov, A. V., E-mail: aivanov-99@yahoo.com [Department of Applied Mathematics and Modeling, Faculty of Mathematics and Informatics, Paisii Hilendarski University of Plovdiv, 24 Tzar Assen str., 4000 Plovdiv (Bulgaria); Iliev, I. P., E-mail: iliev55@abv.bg [Department of Physics, Technical University – Plovdiv, 25 Tzanko Djusstabanov str., 4000 Plovdiv (Bulgaria)

    2015-10-28

    Numerous time series methods are used in environmental sciences allowing the detailed investigation of air pollution processes. The goal of this study is to present the empirical analysis of various aspects of stochastic modeling and in particular the ARIMA/SARIMA methods. The subject of investigation is air pollution in the town of Kardzhali, Bulgaria with 2 problematic pollutants – sulfur dioxide (SO2) and particulate matter (PM10). Various SARIMA Transfer Function models are built taking into account meteorological factors, data transformations and the use of different horizons selected to predict future levels of concentrations of the pollutants.

  7. Seasonal variation of benzo(a)pyrene in the Spanish airborne PM10. Multivariate linear regression model applied to estimate BaP concentrations.

    Science.gov (United States)

    Callén, M S; López, J M; Mastral, A M

    2010-08-15

    The estimation of benzo(a)pyrene (BaP) concentrations in ambient air is very important from an environmental point of view especially with the introduction of the Directive 2004/107/EC and due to the carcinogenic character of this pollutant. A sampling campaign of particulate matter less or equal than 10 microns (PM10) carried out during 2008-2009 in four locations of Spain was collected to determine experimentally BaP concentrations by gas chromatography mass-spectrometry mass-spectrometry (GC-MS-MS). Multivariate linear regression models (MLRM) were used to predict BaP air concentrations in two sampling places, taking PM10 and meteorological variables as possible predictors. The model obtained with data from two sampling sites (all sites model) (R(2)=0.817, PRESS/SSY=0.183) included the significant variables like PM10, temperature, solar radiation and wind speed and was internally and externally validated. The first validation was performed by cross validation and the last one by BaP concentrations from previous campaigns carried out in Zaragoza from 2001-2004. The proposed model constitutes a first approximation to estimate BaP concentrations in urban atmospheres with very good internal prediction (Q(CV)(2)=0.813, PRESS/SSY=0.187) and with the maximal external prediction for the 2001-2002 campaign (Q(ext)(2)=0.679 and PRESS/SSY=0.321) versus the 2001-2004 campaign (Q(ext)(2)=0.551, PRESS/SSY=0.449). Copyright 2010 Elsevier B.V. All rights reserved.

  8. Seasonal variation of benzo(a)pyrene in the Spanish airborne PM10. Multivariate linear regression model applied to estimate BaP concentrations

    International Nuclear Information System (INIS)

    Callen, M.S.; Lopez, J.M.; Mastral, A.M.

    2010-01-01

    The estimation of benzo(a)pyrene (BaP) concentrations in ambient air is very important from an environmental point of view especially with the introduction of the Directive 2004/107/EC and due to the carcinogenic character of this pollutant. A sampling campaign of particulate matter less or equal than 10 microns (PM10) carried out during 2008-2009 in four locations of Spain was collected to determine experimentally BaP concentrations by gas chromatography mass-spectrometry mass-spectrometry (GC-MS-MS). Multivariate linear regression models (MLRM) were used to predict BaP air concentrations in two sampling places, taking PM10 and meteorological variables as possible predictors. The model obtained with data from two sampling sites (all sites model) (R 2 = 0.817, PRESS/SSY = 0.183) included the significant variables like PM10, temperature, solar radiation and wind speed and was internally and externally validated. The first validation was performed by cross validation and the last one by BaP concentrations from previous campaigns carried out in Zaragoza from 2001-2004. The proposed model constitutes a first approximation to estimate BaP concentrations in urban atmospheres with very good internal prediction (Q CV 2 =0.813, PRESS/SSY = 0.187) and with the maximal external prediction for the 2001-2002 campaign (Q ext 2 =0.679 and PRESS/SSY = 0.321) versus the 2001-2004 campaign (Q ext 2 =0.551, PRESS/SSY = 0.449).

  9. The role of perceived air pollution and health risk perception in health symptoms and disease: a population-based study combined with modelled levels of PM10.

    Science.gov (United States)

    Orru, Kati; Nordin, Steven; Harzia, Hedi; Orru, Hans

    2018-03-31

    Adverse health impact of air pollution on health may not only be associated with the level of exposure, but rather mediated by perception of the pollution and by top-down processing (e.g. beliefs of the exposure being hazardous), especially in areas with relatively low levels of pollutants. The aim of this study was to test a model that describes interrelations between air pollution (particles pollution, health risk perception, health symptoms and diseases. A population-based questionnaire study was conducted among 1000 Estonian residents (sample was stratified by age, sex, and geographical location) about health risk perception and coping. The PM 10 levels were modelled in 1 × 1 km grids using a Eulerian air quality dispersion model. Respondents were ascribed their annual mean PM 10 exposure according to their home address. Path analysis was performed to test the validity of the model. The data refute the model proposing that exposure level significantly influences symptoms and disease. Instead, the perceived exposure influences symptoms and the effect of perceived exposure on disease is mediated by health risk perception. This relationship is more pronounced in large cities compared to smaller towns or rural areas. Perceived pollution and health risk perception, in particular in large cities, play important roles in understanding and predicting environmentally induced symptoms and diseases at relatively low levels of air pollution.

  10. An optimized data fusion method and its application to improve lateral boundary conditions in winter for Pearl River Delta regional PM2.5 modeling, China

    Science.gov (United States)

    Huang, Zhijiong; Hu, Yongtao; Zheng, Junyu; Zhai, Xinxin; Huang, Ran

    2018-05-01

    Lateral boundary conditions (LBCs) are essential for chemical transport models to simulate regional transport; however they often contain large uncertainties. This study proposes an optimized data fusion approach to reduce the bias of LBCs by fusing gridded model outputs, from which the daughter domain's LBCs are derived, with ground-level measurements. The optimized data fusion approach follows the framework of a previous interpolation-based fusion method but improves it by using a bias kriging method to correct the spatial bias in gridded model outputs. Cross-validation shows that the optimized approach better estimates fused fields in areas with a large number of observations compared to the previous interpolation-based method. The optimized approach was applied to correct LBCs of PM2.5 concentrations for simulations in the Pearl River Delta (PRD) region as a case study. Evaluations show that the LBCs corrected by data fusion improve in-domain PM2.5 simulations in terms of the magnitude and temporal variance. Correlation increases by 0.13-0.18 and fractional bias (FB) decreases by approximately 3%-15%. This study demonstrates the feasibility of applying data fusion to improve regional air quality modeling.

  11. VIIRS satellite and ground pm2.5 monitoring data

    Data.gov (United States)

    U.S. Environmental Protection Agency — contains all satellite, pm2.5, and meteorological data used in statistical modeling effort to improve prediction of pm2.5. This dataset is associated with the...

  12. Inverse modeling and mapping US air quality influences of inorganic PM2.5 precursor emissions using the adjoint of GEOS-Chem

    Science.gov (United States)

    Henze, D. K.; Seinfeld, J. H.; Shindell, D. T.

    2009-08-01

    Influences of specific sources of inorganic PM2.5 on peak and ambient aerosol concentrations in the US are evaluated using a combination of inverse modeling and sensitivity analysis. First, sulfate and nitrate aerosol measurements from the IMPROVE network are assimilated using the four-dimensional variational (4D-Var) method into the GEOS-Chem chemical transport model in order to constrain emissions estimates in four separate month-long inversions (one per season). Of the precursor emissions, these observations primarily constrain ammonia (NH3). While the net result is a decrease in estimated US~NH3 emissions relative to the original inventory, there is considerable variability in adjustments made to NH3 emissions in different locations, seasons and source sectors, such as focused decreases in the midwest during July, broad decreases throughout the US~in January, increases in eastern coastal areas in April, and an effective redistribution of emissions from natural to anthropogenic sources. Implementing these constrained emissions, the adjoint model is applied to quantify the influences of emissions on representative PM2.5 air quality metrics within the US. The resulting sensitivity maps display a wide range of spatial, sectoral and seasonal variability in the susceptibility of the air quality metrics to absolute emissions changes and the effectiveness of incremental emissions controls of specific source sectors. NH3 emissions near sources of sulfur oxides (SOx) are estimated to most influence peak inorganic PM2.5 levels in the East; thus, the most effective controls of NH3 emissions are often disjoint from locations of peak NH3 emissions. Controls of emissions from industrial sectors of SOx and NOx are estimated to be more effective than surface emissions, and changes to NH3 emissions in regions dominated by natural sources are disproportionately more effective than regions dominated by anthropogenic sources. NOx controls are most effective in northern states in

  13. Impacts of 2000-2050 Climate Change on Fine Particulate Matter (PM2.5) Air Quality in China Based on Statistical Projections Using an Ensemble of Global Climate Models

    Science.gov (United States)

    Leung, D. M.; Tai, A. P. K.; Shen, L.; Moch, J. M.; van Donkelaar, A.; Mickley, L. J.

    2017-12-01

    Fine particulate matter (PM2.5) air quality is strongly dependent on not only on emissions but also meteorological conditions. Here we examine the dominant synoptic circulation patterns that control day-to-day PM2.5 variability over China. We perform principal component (PC) analysis on 1998-2016 NCEP/NCAR Reanalysis I daily meteorological fields to diagnose distinct synoptic meteorological modes, and perform PC regression on spatially interpolated 2014-2016 daily mean PM2.5 concentrations in China to identify modes dominantly explaining PM2.5 variability. We find that synoptic systems, e.g., cold-frontal passages, maritime inflow and frontal precipitation, can explain up to 40% of the day-to-day PM2.5 variability in major metropolitan regions in China. We further investigate how annually changing frequencies of synoptic systems, as well as changing local meteorology, drive interannual PM2.5 variability. We apply a spectral analysis on the PC time series to obtain the 1998-2016 annual median synoptic frequency, and use a forward-selection multiple linear regression (MLR) model of satellite-derived 1998-2015 annual mean PM2.5 concentrations on local meteorology and synoptic frequency, selecting predictors that explain the highest fraction of interannual PM2.5 variability while guarding against multicollinearity. To estimate the effect of climate change on future PM2.5 air quality, we project a multimodel ensemble of 15 CMIP5 models under the RCP8.5 scenario on the PM2.5-to-meteorology sensitivities derived for the present-day from the MLR model. Our results show that climate change could be responsible for increases in PM2.5 of more than 25 μg m-3 in northwestern China and 10 mg m-3 in northeastern China by the 2050s. Increases in synoptic frequency of cold-frontal passages cause only a modest 1 μg m-3 decrease in PM2.5 in North China Plain. Our analyses show that climate change imposes a significant penalty on air quality over China and poses serious threat on

  14. Contribution of regional-scale fire events to ozone and PM2.5 air quality estimated by photochemical modeling approaches

    Science.gov (United States)

    Baker, K. R.; Woody, M. C.; Tonnesen, G. S.; Hutzell, W.; Pye, H. O. T.; Beaver, M. R.; Pouliot, G.; Pierce, T.

    2016-09-01

    Two specific fires from 2011 are tracked for local to regional scale contribution to ozone (O3) and fine particulate matter (PM2.5) using a freely available regulatory modeling system that includes the BlueSky wildland fire emissions tool, Spare Matrix Operator Kernel Emissions (SMOKE) model, Weather and Research Forecasting (WRF) meteorological model, and Community Multiscale Air Quality (CMAQ) photochemical grid model. The modeling system was applied to track the contribution from a wildfire (Wallow) and prescribed fire (Flint Hills) using both source sensitivity and source apportionment approaches. The model estimated fire contribution to primary and secondary pollutants are comparable using source sensitivity (brute-force zero out) and source apportionment (Integrated Source Apportionment Method) approaches. Model estimated O3 enhancement relative to CO is similar to values reported in literature indicating the modeling system captures the range of O3 inhibition possible near fires and O3 production both near the fire and downwind. O3 and peroxyacetyl nitrate (PAN) are formed in the fire plume and transported downwind along with highly reactive VOC species such as formaldehyde and acetaldehyde that are both emitted by the fire and rapidly produced in the fire plume by VOC oxidation reactions. PAN and aldehydes contribute to continued downwind O3 production. The transport and thermal decomposition of PAN to nitrogen oxides (NOX) enables O3 production in areas limited by NOX availability and the photolysis of aldehydes to produce free radicals (HOX) causes increased O3 production in NOX rich areas. The modeling system tends to overestimate hourly surface O3 at routine rural monitors in close proximity to the fires when the model predicts elevated fire impacts on O3 and Hazard Mapping System (HMS) data indicates possible fire impact. A sensitivity simulation in which solar radiation and photolysis rates were more aggressively attenuated by aerosol in the plume

  15. Simulating indoor concentrations of NO(2) and PM(2.5) in multifamily housing for use in health-based intervention modeling.

    Science.gov (United States)

    Fabian, P; Adamkiewicz, G; Levy, J I

    2012-02-01

    Residents of low-income multifamily housing can have elevated exposures to multiple environmental pollutants known to influence asthma. Simulation models can characterize the health implications of changing indoor concentrations, but quantifying the influence of interventions on concentrations is challenging given complex airflow and source characteristics. In this study, we simulated concentrations in a prototype multifamily building using CONTAM, a multizone airflow and contaminant transport program. Contaminants modeled included PM(2.5) and NO(2) , and parameters included stove use, presence and operability of exhaust fans, smoking, unit level, and building leakiness. We developed regression models to explain variability in CONTAM outputs for individual sources, in a manner that could be utilized in simulation modeling of health outcomes. To evaluate our models, we generated a database of 1000 simulated households with characteristics consistent with Boston public housing developments and residents and compared the predicted levels of NO(2) and PM(2.5) and their correlates with the literature. Our analyses demonstrated that CONTAM outputs could be readily explained by available parameters (R(2) between 0.89 and 0.98 across models), but that one-compartment box models would mischaracterize concentrations and source contributions. Our study quantifies the key drivers for indoor concentrations in multifamily housing and helps to identify opportunities for interventions. Many low-income urban asthmatics live in multifamily housing that may be amenable to ventilation-related interventions such as weatherization or air sealing, wall and ceiling hole repairs, and exhaust fan installation or repair, but such interventions must be designed carefully given their cost and their offsetting effects on energy savings as well as indoor and outdoor pollutants. We developed models to take into account the complex behavior of airflow patterns in multifamily buildings, which can

  16. A New Hybrid Spatio-temporal Model for Estimating Daily Multi-year PM2.5 Concentrations Across Northeastern USA Using High Resolution Aerosol Optical Depth Data

    Science.gov (United States)

    Kloog, Itai; Chudnovsky, Alexandra A.; Just, Allan C.; Nordio, Francesco; Koutrakis, Petros; Coull, Brent A.; Lyapustin, Alexei; Wang, Yujie; Schwartz, Joel

    2014-01-01

    The use of satellite-based aerosol optical depth (AOD) to estimate fine particulate matter PM(sub 2.5) for epidemiology studies has increased substantially over the past few years. These recent studies often report moderate predictive power, which can generate downward bias in effect estimates. In addition, AOD measurements have only moderate spatial resolution, and have substantial missing data. We make use of recent advances in MODIS satellite data processing algorithms (Multi-Angle Implementation of Atmospheric Correction (MAIAC), which allow us to use 1 km (versus currently available 10 km) resolution AOD data.We developed and cross validated models to predict daily PM(sub 2.5) at a 1X 1 km resolution across the northeastern USA (New England, New York and New Jersey) for the years 2003-2011, allowing us to better differentiate daily and long term exposure between urban, suburban, and rural areas. Additionally, we developed an approach that allows us to generate daily high-resolution 200 m localized predictions representing deviations from the area 1 X 1 km grid predictions. We used mixed models regressing PM(sub 2.5) measurements against day-specific random intercepts, and fixed and random AOD and temperature slopes. We then use generalized additive mixed models with spatial smoothing to generate grid cell predictions when AOD was missing. Finally, to get 200 m localized predictions, we regressed the residuals from the final model for each monitor against the local spatial and temporal variables at each monitoring site. Our model performance was excellent (mean out-of-sample R(sup 2) = 0.88). The spatial and temporal components of the out-of-sample results also presented very good fits to the withheld data (R(sup 2) = 0.87, R(sup)2 = 0.87). In addition, our results revealed very little bias in the predicted concentrations (Slope of predictions versus withheld observations = 0.99). Our daily model results show high predictive accuracy at high spatial resolutions

  17. Agreement of central site measurements and land use regression modeled oxidative potential of PM{sub 2.5} with personal exposure

    Energy Technology Data Exchange (ETDEWEB)

    Yang, Aileen, E-mail: Yang@uu.nl [National Institute for Public Health and the Environment (RIVM), P.O. Box 1, 3720BA Bilthoven (Netherlands); Institute for Risk Assessment Sciences, Division Environmental Epidemiology, Utrecht University, P.O. Box 80.178, 3508TD Utrecht (Netherlands); Hoek, Gerard; Montagne, Denise [Institute for Risk Assessment Sciences, Division Environmental Epidemiology, Utrecht University, P.O. Box 80.178, 3508TD Utrecht (Netherlands); Leseman, Daan L.A.C. [National Institute for Public Health and the Environment (RIVM), P.O. Box 1, 3720BA Bilthoven (Netherlands); Hellack, Bryan [Air Quality & Sustainable Nanotechnology, Institute of Energy and Environmental Technology (IUTA), e.V., Blierheimer Str. 58-60, 47229 Duisburg (Germany); Kuhlbusch, Thomas A.J. [Air Quality & Sustainable Nanotechnology, Institute of Energy and Environmental Technology (IUTA), e.V., Blierheimer Str. 58-60, 47229 Duisburg (Germany); Center for Nanointegration Duisburg-Essen (CENIDE), University Duisburg-Essen, Carl-Benz-Straße 199, 47057 Duisburg (Germany); Cassee, Flemming R. [National Institute for Public Health and the Environment (RIVM), P.O. Box 1, 3720BA Bilthoven (Netherlands); Institute for Risk Assessment Sciences, Division Environmental Epidemiology, Utrecht University, P.O. Box 80.178, 3508TD Utrecht (Netherlands); Brunekreef, Bert [Institute for Risk Assessment Sciences, Division Environmental Epidemiology, Utrecht University, P.O. Box 80.178, 3508TD Utrecht (Netherlands); Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht (Netherlands); Janssen, Nicole A.H. [National Institute for Public Health and the Environment (RIVM), P.O. Box 1, 3720BA Bilthoven (Netherlands)

    2015-07-15

    Oxidative potential (OP) of ambient particulate matter (PM) has been suggested as a health-relevant exposure metric. In order to use OP for exposure assessment, information is needed about how well central site OP measurements and modeled average OP at the home address reflect temporal and spatial variation of personal OP. We collected 96-hour personal, home outdoor and indoor PM{sub 2.5} samples from 15 volunteers living either at traffic, urban or regional background locations in Utrecht, the Netherlands. OP was also measured at one central reference site to account for temporal variations. OP was assessed using electron spin resonance (OP{sup ESR}) and dithiothreitol (OP{sup DTT}). Spatial variation of average OP at the home address was modeled using land use regression (LUR) models. For both OP{sup ESR} and OP{sup DTT}, temporal correlations of central site measurements with home outdoor measurements were high (R>0.75), and moderate to high (R=0.49–0.70) with personal measurements. The LUR model predictions for OP correlated significantly with the home outdoor concentrations for OP{sup DTT} and OP{sup ESR} (R=0.65 and 0.62, respectively). LUR model predictions were moderately correlated with personal OP{sup DTT} measurements (R=0.50). Adjustment for indoor sources, such as vacuum cleaning and absence of fume-hood, improved the temporal and spatial agreement with measured personal exposure for OP{sup ESR}. OP{sup DTT} was not associated with any indoor sources. Our study results support the use of central site OP for exposure assessment of epidemiological studies focusing on short-term health effects. - Highlights: • Oxidative potential (OP) of PM was proposed as a health-relevant exposure metric. • We evaluated the relationship between measured and modeled outdoor and personal OP. • Temporal correlations of central site with personal OP are moderate to high. • Adjusting for indoor sources improved the agreement with personal OP. • Our results

  18. Hourly elemental concentrations in PM2.5 aerosols sampled simultaneously at urban background and road site during SAPUSS - diurnal variations and PMF receptor modelling

    Science.gov (United States)

    Dall'Osto, M.; Querol, X.; Amato, F.; Karanasiou, A.; Lucarelli, F.; Nava, S.; Calzolai, G.; Chiari, M.

    2013-04-01

    Hourly-resolved aerosol chemical speciation data can be a highly powerful tool to determine the source origin of atmospheric pollutants in urban environments. Aerosol mass concentrations of seventeen elements (Na, Mg, Al, S, Cl, K, Ca, Ti, V, Cr, Mn, Fe, Ni, Cu, Zn, Sr and Pb) were obtained by time (1 h) and size (PM2.5 particulate matter Spain) during September-October 2010: an urban background site (UB) and a street canyon traffic road site (RS). Elements related to primary non-exhaust traffic emission (Fe, Cu), dust resuspension (Ca) and anthropogenic Cl were found enhanced at the RS, whereas industrial related trace metals (Zn, Pb, Mn) were found at higher concentrations at the more ventilated UB site. When receptor modelling was performed with positive matrix factorization (PMF), nine different aerosol sources were identified at both sites: three types of regional aerosols (regional sulphate (S) - 27%, biomass burning (K) - 5%, sea salt (Na-Mg) - 17%), three types of dust aerosols (soil dust (Al-Ti) - 17%, urban crustal dust (Ca) - 6%, and primary traffic non-exhaust brake dust (Fe-Cu) - 7%), and three types of industrial aerosol plumes-like events (shipping oil combustion (V-Ni) - 17%, industrial smelters (Zn-Mn) - 3%, and industrial combustion (Pb-Cl) - 5%, percentages presented are average source contributions to the total elemental mass measured). The validity of the PMF solution of the PIXE data is supported by very good correlations with external single particle mass spectrometry measurements. Some important conclusions can be drawn about the PM2.5 mass fraction simultaneously measured at the UB and RS sites: (1) the regional aerosol sources impact both monitoring sites at similar concentrations regardless their different ventilation conditions; (2) by contrast, local industrial aerosol plumes associated with shipping oil combustion and smelters activities have a higher impact on the more ventilated UB site; (3) a unique source of Pb-Cl (associated with

  19. PM 10 Nonattainment Areas

    Data.gov (United States)

    U.S. Environmental Protection Agency — This data layer identifies areas in the U.S. where air pollution levels have not met the National Ambient Air Quality Standards (NAAQS) for PM 10 and have been...

  20. Source Apportionment and Elemental Composition of PM2.5 and PM10 in Jeddah City, Saudi Arabia.

    Science.gov (United States)

    Khodeir, Mamdouh; Shamy, Magdy; Alghamdi, Mansour; Zhong, Mianhua; Sun, Hong; Costa, Max; Chen, Lung-Chi; Maciejczyk, Polina

    2012-07-01

    This paper presents the first comprehensive investigation of PM2.5 and PM10 composition and sources in Saudi Arabia. We conducted a multi-week multiple sites sampling campaign in Jeddah between June and September, 2011, and analyzed samples by XRF. The overall mean mass concentration was 28.4 ± 25.4 μg/m 3 for PM2.5 and 87.3 ± 47.3 μg/m 3 for PM10, with significant temporal and spatial variability. The average ratio of PM2.5/PM10 was 0.33. Chemical composition data were modeled using factor analysis with varimax orthogonal rotation to determine five and four particle source categories contributing significant amount of for PM2.5 and PM10 mass, respectively. In both PM2.5 and PM10 sources were (1) heavy oil combustion characterized by high Ni and V; (2) resuspended soil characterized by high concentrations of Ca, Fe, Al, and Si; and (3) marine aerosol. The two other sources in PM2.5 were (4) Cu/Zn source; (5) traffic source identified by presence of Pb, Br, and Se; while in PM10 it was a mixed industrial source. To estimate the mass contributions of each individual source category, the CAPs mass concentration was regressed against the factor scores. Cumulatively, resuspended soil and oil combustion contributed 77 and 82% mass of PM2.5 and PM10, respectively.

  1. Asthma and PM10

    Directory of Open Access Journals (Sweden)

    Gilmour M Ian

    2000-07-01

    Full Text Available Abstract PM10 (the mass of particles present in the air having a 50% cutoff for particles with an aerodynamic diameter of 10 μm is the standard measure of particulate air pollution used worldwide. Epidemiological studies suggest that asthma symptoms can be worsened by increases in the levels of PM10. Epidemiological evidence at present indicates that PM10 increases do not raise the chances of initial sensitisation and induction of disease, although further research is warranted. PM10 is a complex mixture of particle types and has many components and there is no general agreement regarding which component(s could lead to exacerbations of asthma. However pro-inflammatory effects of transition metals, hydrocarbons, ultrafine particles and endotoxin, all present to varying degrees in PM10, could be important. An understanding of the role of the different components of PM10 in exacerbating asthma is essential before proper risk assessment can be undertaken leading to advice on risk management for the many asthmatics who are exposed to air pollution particles.

  2. Leadership PM: Theory and practice

    International Nuclear Information System (INIS)

    Misumi, J.

    1997-01-01

    A leadership concept which is designed to overcome the limitations of the commonly used behavioral classification scheme is presented. In this PM concept, P stands for performance and M for maintenance. Measuring each characteristic on an axis between ''high'' and ''low'', four distinct types of leadership could be identified. The model was tested in laboratory studies and field surveys of different organizations. (author). 8 refs, 1 fig., 2 tabs

  3. The HTR-PM Plant Full Scope Training Simulator

    International Nuclear Information System (INIS)

    Wang Junsan; Wang Yuding; Zhou Shuyong; Cai Ruizhong; Cao Jianting

    2014-01-01

    This paper describes the technical aspects of the Full Scope Training Simulator developed for HTR-PM Plant in Shidao Bay, Shandong Province, China. An overview of the HTR-PM plant and simulator structure is presented. The models developed for the simulator are discussed in detail. Some important verification tests have been conducted on the HTR-PM Plant Training Simulator. (author)

  4. Preliminary comparative assessment of PM10 hourly measurement results from new monitoring stations type using stochastic and exploratory methodology and models

    Science.gov (United States)

    Czechowski, Piotr Oskar; Owczarek, Tomasz; Badyda, Artur; Majewski, Grzegorz; Rogulski, Mariusz; Ogrodnik, Paweł

    2018-01-01

    The paper presents selected preliminary stage key issues proposed extended equivalence measurement results assessment for new portable devices - the comparability PM10 concentration results hourly series with reference station measurement results with statistical methods. In article presented new portable meters technical aspects. The emphasis was placed on the comparability the results using the stochastic and exploratory methods methodology concept. The concept is based on notice that results series simple comparability in the time domain is insufficient. The comparison of regularity should be done in three complementary fields of statistical modeling: time, frequency and space. The proposal is based on model's results of five annual series measurement results new mobile devices and WIOS (Provincial Environmental Protection Inspectorate) reference station located in Nowy Sacz city. The obtained results indicate both the comparison methodology completeness and the high correspondence obtained new measurements results devices with reference.

  5. De onde vem o endividamento feminino?: construção e validação de um modelo PLS-PM Where does the women debt come from?: construction and validation of a PLS-PM model

    Directory of Open Access Journals (Sweden)

    Larissa de Lima Trindade

    2012-12-01

    Full Text Available O consumo exacerbado pode levar muitos indivíduos a contraírem dívidas comprometendo uma parcela significativa de suas rendas e, em muitos casos, acabando por ser tornarem inadimplentes. A inadimplência trás consigo efeitos muitas vezes arrasadores tanto do ponto de vista macroeconômico, aumentando o risco das operações e produtos financeiros, como do ponto de vista do indivíduo, ao afetar suas relações sociais, seu estado psicológico e sua vida familiar. Por outro lado, a maior participação da mulher no mercado de trabalho trouxe uma maior independência financeira e consequentente maior poder na decisão de consumo e ao mesmo tempo, maiores responsabilidades sobre o gerenciamento financeiro e nas decisões de endividamento. Neste sentido, este estudo centrou-se na identificação e análise dos fatores que afetam na propensão ao endividamento, nas mulheres da Mesorregião Centro Ocidental Rio-grandense. Assim, este trabalho propõe um modelo estrutural para explorar as relações entre os fatores determinantes da propensão ao endividamento junto às mulheres da referida Mesorregião, considerando variáveis que compõem os construtos de STATUS SOCIAL, PREOCUPAÇÃO, ESTABILIDADE, PRAZER, PODER, ORÇAMENTO, ILUSÃO e MATERIALISMO. Para isso, foram aplicados 2.500 questionários espalhados estatisticamente entre os 31 municípios que compõem esta Mesorregião. Os dados foram analisados através da metodologia Partial Least Squares - Path Modeling (PLS-PM. Sumariamente, os resultados sugerem que o construto ENDIVIDAMENTO está associado aos construtos STATUS, PREOCUPAÇÃO e MATERIALISMO, corroborando com as teorias das Finanças Comportamentais, ao sugerir que as decisões que envolvem endividamento vão além da simples relação consumo e renda, ou seja, existem outras variáveis comportamentais que são importantes na hora do indivíduo contrair dívidas, tais como, o significado que o indivíduo atribui ao dinheiro e o n

  6. Characteristics of PM1.0, PM2.5, and PM10, and Their Relation to Black Carbon in Wuhan, Central China

    Directory of Open Access Journals (Sweden)

    Wei Gong

    2015-09-01

    Full Text Available Hourly average monitoring data for mass concentrations of PM1, PM2.5, PM10, and black carbon (BC were measured in Wuhan from December 2013 to December 2014, which has a flourishing steel industry, to analyze the characteristics of PM and their relation to BC, using statistical methods. The results indicate that variations in the monthly average mass concentrations of PM have similar concave parabolic shapes, with the highest values occurring in January and the lowest values appearing in August or September. The correlation coefficient of the linear regression model between PM1 and PM2.5 is quite high, reaching 0.99. Furthermore, the proportion of PM1 contained within PM2.5 is roughly 90%, directly proving that ultrafine particles whose diameter less than 1 μm may be a primary component of PM2.5 in Wuhan. Additionally, better seasonal correlation between PM and BC occurs only in summer and autumn, due to multiple factors such as topography, temperature, and the atmosphere in winter and spring. Finally, analysis of the diurnal variation of PM and BC demonstrates that the traffic emissions during rush hour, exogenous pollutants, and the shallow PBLH with stagnant atmosphere, all contribute to the severe pollution of Wuhan in winter.

  7. Towards the geophysical regime in numerical dynamo models: studies of rapidly-rotating convection driven dynamos with low Pm and constant heat flux boundary conditions

    DEFF Research Database (Denmark)

    Sheyko, A.A.; Finlay, Chris; Marti, P.

    We present a set of numerical dynamo models with the convection strength varied by a factor of 30 and the ratio of magnetic to viscous diffusivities by a factor of 20 at rapid rotation rates (E =nu/(2 Omega d^2 ) = 10-6 and 10-7 ) using a heat flux outer BC. This regime has been little explored...... on the structure of the dynamos and how this changes in relation to the selection of control parameters, a comparison with the proposed rotating convection and dynamo scaling laws, energy spectra of steady solutions and inner core rotation rates. Magnetic field on the CMB. E=2.959*10-7, Ra=6591.0, Pm=0.05, Pr=1....

  8. Modelos lineares aplicados à estimativa da concentração do material particulado (PM10 na cidade do Rio de Janeiro, RJ Linear models applied to the assessment of daily concentration of particulate matter (PM10 in Rio de Janeiro city, RJ, Brazil

    Directory of Open Access Journals (Sweden)

    Gustavo Bastos Lyra

    2011-09-01

    Full Text Available Regressão linear múltipla foi aplicada para ajustar dois modelos à concentração média de 24 h do material particulado com diâmetro inferior a 10 µm (PM10. As variáveis explanatórias no primeiro modelo (M1 foram os elementos meteorológicos (temperatura e umidade do ar, precipitação pluvial, velocidade do vento e pressão atmosférica e o índice de direção do vento (IDV. No segundo (M2, além dos elementos meteorológicos e do IDV, foi incluído como variável explanatória, a concentração de PM10 do dia anterior (PM10,i-1. Para a seleção das variáveis explanatórias a serem incluídas no modelo, utilizou-se a técnica stepwise. Medidas da concentração de PM10 e dos elementos meteorológicos foram realizadas entre 01/05/02 e 31/08/03 em São Cristóvão (22º 53´ S; 43º 13´ W e 24 m na cidade do Rio de Janeiro. O coeficiente de determinação (r² para o ajuste dos modelos foi razoável, sendo que o modelo M2 (r² = 0,557 mostrou ajuste superior ao modelo M1 (r² = 0,334. Os elementos meteorológicos tiveram correlação negativa com PM10, com exceção do índice de direção do vento, que da mesma forma de PM10,i-1, apresentou correlação positiva. A umidade relativa do ar e a precipitação pluvial mostraram-se os elementos meteorológicos mais significativos nos modelos. Contudo, quando PM10,i-1 é considerada, esta variável se mostrou a mais significativa no modelo. Independente do modelo, a inclusão da temperatura do ar não foi significativa (p > 0,05. O modelo M2 teve concordância entre os valores estimados e observados e precisão superior ao modelo M1. Em termos de previsão da qualidade do ar, os modelos mostraram resultados satisfatórios, sobressaindo-se o modelo M2.Multiple linear regression was used to fit two models to the daily average concentration of particulate matter with diameter lower than 10 µm (PM10. The explanatory variables in the first model (M1 were the weather variables (air temperature

  9. Simultaneous statistical bias correction of multiplePM2.5 species from a regional photochemical grid model

    Science.gov (United States)

    In recent years environmental epidemiologists have begun utilizing regionalscale air quality computer models to predict ambient air pollution concentrations in health studies instead of or in addition to monitoring data from central sites. The advantages of using such models i...

  10. Using a latent variable model with non-constant factor loadings to examine PM2.5 constituents related to secondary inorganic aerosols.

    Science.gov (United States)

    Zhang, Zhenzhen; O'Neill, Marie S; Sánchez, Brisa N

    2016-04-01

    Factor analysis is a commonly used method of modelling correlated multivariate exposure data. Typically, the measurement model is assumed to have constant factor loadings. However, from our preliminary analyses of the Environmental Protection Agency's (EPA's) PM 2.5 fine speciation data, we have observed that the factor loadings for four constituents change considerably in stratified analyses. Since invariance of factor loadings is a prerequisite for valid comparison of the underlying latent variables, we propose a factor model that includes non-constant factor loadings that change over time and space using P-spline penalized with the generalized cross-validation (GCV) criterion. The model is implemented using the Expectation-Maximization (EM) algorithm and we select the multiple spline smoothing parameters by minimizing the GCV criterion with Newton's method during each iteration of the EM algorithm. The algorithm is applied to a one-factor model that includes four constituents. Through bootstrap confidence bands, we find that the factor loading for total nitrate changes across seasons and geographic regions.

  11. PENINGKATAN KETERAMPILAN MEMBERI SOLUSI TERHADAP KELUHAN DAN KEBERATAN PELANGGAN DENGAN MODEL PEMBELAJARAN PROJECT BASED LEARNING (PjBL PADA SISWA KELAS XI PM 2 SMK PGRI BATANG

    Directory of Open Access Journals (Sweden)

    Rakhima An Naafi Solekha

    2015-06-01

    Full Text Available Subyek penelitian adalah siswa kelas XI PM 2 SMK PGRI Batang yang berjumlah 41 siswa. Latar belakang penelitian ini adalah karena kurangnya keterampilan siswa dalam memberikan solusi terhadap keluhan dan keberatan pelanggan, selain itu metode pembelajaran yang digunakan kurang tepat yaitu menggunakan ceramah sedangkan karakter materinya adalah analistik dan praktik. Hasil penelitian ini diperoleh persentase aktivitas siswa pada pembelajaran siklus I yaitu sebesar 63,7% dengan katagori baik dan pada siklus II meningkat menjadi 87,25% dengan katagori sangat baik, persentase aktivitas guru pada pembelajaran siklus I yaitu sebesar 70,84% dengan katagori baik dan pada siklus II meningkat menjadi 83,34% dengan katagori sangat baik. Persentase keterampilan siswa pada siklus I sebesar 65,85% dengan katagori baik dan pada siklus II meningkat menjadi 90,24 dengan katagori sangat baik. Untuk hasil belajar siswa berupa post test keterampilan memberi solusi terhadap keluhan dan keberatan pelanggan dengan rata-rata kelas yang dicapai pada siklus I adalah 74,88 dengan ketercapaian ketuntasan klasikal yaitu sebesar 65,85% dan pada siklus II rata-rata kelas meningkat menjadi 80,37 dan ketercapaian ketuntasan klasikal yaitu sebesar 90,24%. Berdasarkan hasil penelitian di atas dapat diambil kesimpulan bahwa, adanya peningkatan keterampilan siswa dalam memberikan solusi terhadap keluhan dan keberatan pelanggan kelas XI PM 2 SMK PGRI Batang dengan menggunakan model pembelajaran Project Based Learning pada materi memberi solusi terhadap keluhan dan keberatan pelanggan. Subjects were students of class XI PM 2 SMK PGRI Trunk totaling 41 students. The background of this study is due to lack of students' skills in providing solutions to customer complaints and objections, besides learning methods used quite right that using lecture material while the character is analytics and practice. The results of this study showed the percentage of students in the learning

  12. Corrigendum to 'A novel model evaluation approach focusing on local and advected contributions to urban PM2.5 levels - application to Paris, France' published in Geosci. Model Dev., 7, 1483-1505, 2014

    International Nuclear Information System (INIS)

    Petetin, H.; Beekmann, M.; Sciare, J.; Bressi, M.; Rosso, A.; Sanchez, O.; Ghersi, V.

    2014-01-01

    Complete text of publication follows: Due to an oversight in the production process, an essential word (overestimation) was left out of the abstract. The correct version of the abstract can be seen below. Aerosol simulations in chemistry transport models (CTMs) still suffer from numerous uncertainties, and diagnostic evaluations are required to point out major error sources. This paper presents an original approach to evaluate CTMs based on local and imported contributions in a large mega-city rather than urban background concentrations. The study is applied to the CHIMERE model in the Paris region (France) and considers the fine particulate matter (PM2.5) and its main chemical constituents (elemental and organic carbon, nitrate, sulfate and ammonium), for which daily measurements are available during a whole year at various stations (PARTICULES project). Back-trajectory data are used to locate the upwind station, from which the concentration is identified as the import, the local production being deduced from the urban concentration by subtraction. Uncertainties on these contributions are quantified. Small biases in urban background PM2.5 simulations (bias of +16 %) hide significant error compensations between local and advected contributions, as well as in PM2.5 chemical compounds. In particular, winter time organic matter (OM) imports appear strongly underestimated while local OM and elemental carbon (EC) production is overestimated all along the year. Erroneous continental wood burning emissions and missing secondary organic aerosol (SOA) pathways may explain errors on advected OM, while the carbonaceous compounds overestimation is likely to be related to errors in emissions and dynamics. A statistically significant local formation of nitrate is also highlighted from observations, but missed by the model. Together with the overestimation of nitrate imports, it leads to a bias of +51% on the local PM2.5 contribution. Such an evaluation finally gives more

  13. Temporal and Spatial Simulation of Atmospheric Pollutant PM2.5 Changes and Risk Assessment of Population Exposure to Pollution Using Optimization Algorithms of the Back Propagation-Artificial Neural Network Model and GIS

    Directory of Open Access Journals (Sweden)

    Ping Zhang

    2015-09-01

    Full Text Available PM2.5 pollution has become of increasing public concern because of its relative importance and sensitivity to population health risks. Accurate predictions of PM2.5 pollution and population exposure risks are crucial to developing effective air pollution control strategies. We simulated and predicted the temporal and spatial changes of PM2.5 concentration and population exposure risks, by coupling optimization algorithms of the Back Propagation-Artificial Neural Network (BP-ANN model and a geographical information system (GIS in Xi’an, China, for 2013, 2020, and 2025. Results indicated that PM2.5 concentration was positively correlated with GDP, SO2, and NO2, while it was negatively correlated with population density, average temperature, precipitation, and wind speed. Principal component analysis of the PM2.5 concentration and its influencing factors’ variables extracted four components that accounted for 86.39% of the total variance. Correlation coefficients of the Levenberg-Marquardt (trainlm and elastic (trainrp algorithms were more than 0.8, the index of agreement (IA ranged from 0.541 to 0.863 and from 0.502 to 0.803 by trainrp and trainlm algorithms, respectively; mean bias error (MBE and Root Mean Square Error (RMSE indicated that the predicted values were very close to the observed values, and the accuracy of trainlm algorithm was better than the trainrp. Compared to 2013, temporal and spatial variation of PM2.5 concentration and risk of population exposure to pollution decreased in 2020 and 2025. The high-risk areas of population exposure to PM2.5 were mainly distributed in the northern region, where there is downtown traffic, abundant commercial activity, and more exhaust emissions. A moderate risk zone was located in the southern region associated with some industrial pollution sources, and there were mainly low-risk areas in the western and eastern regions, which are predominantly residential and educational areas.

  14. Temporal and Spatial Simulation of Atmospheric Pollutant PM2.5 Changes and Risk Assessment of Population Exposure to Pollution Using Optimization Algorithms of the Back Propagation-Artificial Neural Network Model and GIS.

    Science.gov (United States)

    Zhang, Ping; Hong, Bo; He, Liang; Cheng, Fei; Zhao, Peng; Wei, Cailiang; Liu, Yunhui

    2015-09-29

    PM2.5 pollution has become of increasing public concern because of its relative importance and sensitivity to population health risks. Accurate predictions of PM2.5 pollution and population exposure risks are crucial to developing effective air pollution control strategies. We simulated and predicted the temporal and spatial changes of PM2.5 concentration and population exposure risks, by coupling optimization algorithms of the Back Propagation-Artificial Neural Network (BP-ANN) model and a geographical information system (GIS) in Xi'an, China, for 2013, 2020, and 2025. Results indicated that PM2.5 concentration was positively correlated with GDP, SO₂, and NO₂, while it was negatively correlated with population density, average temperature, precipitation, and wind speed. Principal component analysis of the PM2.5 concentration and its influencing factors' variables extracted four components that accounted for 86.39% of the total variance. Correlation coefficients of the Levenberg-Marquardt (trainlm) and elastic (trainrp) algorithms were more than 0.8, the index of agreement (IA) ranged from 0.541 to 0.863 and from 0.502 to 0.803 by trainrp and trainlm algorithms, respectively; mean bias error (MBE) and Root Mean Square Error (RMSE) indicated that the predicted values were very close to the observed values, and the accuracy of trainlm algorithm was better than the trainrp. Compared to 2013, temporal and spatial variation of PM2.5 concentration and risk of population exposure to pollution decreased in 2020 and 2025. The high-risk areas of population exposure to PM2.5 were mainly distributed in the northern region, where there is downtown traffic, abundant commercial activity, and more exhaust emissions. A moderate risk zone was located in the southern region associated with some industrial pollution sources, and there were mainly low-risk areas in the western and eastern regions, which are predominantly residential and educational areas.

  15. Mapping the annual exceedance frequencies of the PM10 air quality standard - Comparing kriging to a generalized linear spatial model

    CSIR Research Space (South Africa)

    Khuluse, S

    2013-11-01

    Full Text Available . Monestiez P., Dubroca L., Bonnin E., Durbec J.-P., Guinet C. (2004). Comparison of model based geostatistical methods in ecology: Application to fin whale distribution in northwestern Mediterranean sea. In proceedings of Geostatistics Banff, Leuangthong...

  16. Sensitivity of a Chemical Mass Balance model for PM2.5 to source profiles for differing styles of cooking

    Science.gov (United States)

    Abdullahi, K. L.; Delgado-Saborit, J. M.; Harrison, Roy M.

    2018-04-01

    Use of a Chemical Mass Balance model is one of the two most commonly used approaches to estimating atmospheric concentrations of cooking aerosol. Such models require the input of chemical profiles for each of the main sources contributing to particulate matter mass and there is appreciable evidence from the literature that not only the mass emission but also the chemical composition of particulate matter varies according to the food being prepared and the style of cooking. In this study, aerosol has been sampled in the laboratory from four different styles of cooking, i.e. Indian, Chinese, Western and African cooking. The chemical profiles of molecular markers have been quantified and are used individually within a Chemical Mass Balance model applied to air samples collected in a multi-ethnic area of Birmingham, UK. The model results give a source contribution estimate for cooking aerosol which is consistent with other comparable UK studies, but also shows a very low sensitivity of the model to the cooking aerosol profile utilised. A survey of local restaurants suggested a wide range of cooking styles taking place which may explain why no one profile gives an appreciably better fit in the CMB model.

  17. New measurement of the $K^{\\pm} \\to \\pi^{\\pm}\\mu^{+}\\mu^{-}$ decay

    CERN Document Server

    Batley, J.R.; Lazzeroni, C.; Munday, D.J.; Slater, M.W.; Wotton, S.A.; Arcidiacono, R.; Bocquet, G.; Cabibbo, N.; Ceccucci, A.; Cundy, D.; Falaleev, V.; Fidecaro, M.; Gatignon, L.; Gonidec, A.; Kubischta, W.; Norton, A.; Maier, A.; Patel, M.; Peters, A.; Balev, S.; Frabetti, P.L.; Goudzovski, E.; Hristov, P.; Kekelidze, V.; Kozhuharov, V.; Litov, L.; Madigozhin, D.; Marinova, E.; Molokanova, N.; Polenkevich, I.; Potrebenikov, Yu.; Stoynev, S.; Zinchenko, A.; Monnier, E.; Swallow, E.; Winston, R.; Rubin, P.; Walker, A.; Baldini, W.; Cotta Ramusino, A.; Dalpiaz, P.; Damiani, C.; Fiorini, M.; Gianoli, A.; Martini, M.; Petrucci, F.; Savrie, M.; Scarpa, M.; Wahl, H.; Bizzeti, A.; Lenti, M.; Veltri, M.; Calvetti, M.; Celeghini, E.; Iacopini, E.; Ruggiero, G.; Behler, M.; Eppard, K.; Kleinknecht, K.; Marouelli, P.; Masetti, L.; Moosbrugger, U.; Morales Morales, C.; Renk, B.; Wache, M.; Wanke, R.; Winhart, A.; Coward, D.; Dabrowski, A.; Fonseca Martin, T.; Shieh, M.; Szleper, M.; Velasco, M.; Wood, M.D.; Cenci, P.; Pepe, M.; Petrucci, M.C.; Anzivino, G.; Imbergamo, E.; Nappi, A.; Piccini, M.; Raggi, M.; Valdata-Nappi, M.; Cerri, C.; Fantechi, R.; Collazuol, G.; Di Lella, L.; Lamanna, G.; Mannelli, I.; Michetti, A.; Costantini, F.; Doble, N.; Fiorini, L.; Giudici, S.; Pierazzini, G.; Sozzi, M.; Venditti, S.; Bloch-Devaux, B.; Cheshkov, C.; Cheze, J.B.; De Beer, M.; Derre, J.; Marel, G.; Mazzucato, E.; Peyaud, B.; Vallage, B.; Holder, M.; Ziolkowski, M.; Biino, C.; Cartiglia, N.; Marchetto, F.; Bifani, S.; Clemencic, M.; Goy Lopez, S.; Dibon, H.; Jeitler, M.; Markytan, M.; Mikulec, I.; Neuhofer, G.; Widhalm, L.

    2011-01-01

    A sample of 3120 $K^\\pm\\to\\pi^\\pm\\mu^+\\mu^-$ decay candidates with $(3.3\\pm0.7)$% background contamination has been collected by the NA48/2 experiment at the CERN SPS, allowing a detailed study of the decay properties. The branching ratio was measured to be ${\\rm BR}=(9.62\\pm0.25)\\times 10^{-8}$. The form factor $W(z)$, where $z=(M_{\\mu\\mu}/M_K)^2$, was parameterized according to several models. In particular, the slope of the linear form factor $W(z)=W_0(1+\\delta z)$ was measured to be $\\delta=3.11\\pm0.57$. Upper limits of $2.9\\times 10^{-2}$ and $2.3\\times 10^{-2}$ on possible charge asymmetry and forward-backward asymmetry were established at 90% CL. An upper limit ${\\rm BR}(K^\\pm\\to\\pi^\\mp\\mu^\\pm\\mu^\\pm)<1.1\\times 10^{-9}$ was established at 90% CL for the rate of the lepton number violating decay.

  18. Correcting Measurement Error in Satellite Aerosol Optical Depth with Machine Learning for Modeling PM2.5 in the Northeastern USA

    Directory of Open Access Journals (Sweden)

    Allan C. Just

    2018-05-01

    Full Text Available Satellite-derived estimates of aerosol optical depth (AOD are key predictors in particulate air pollution models. The multi-step retrieval algorithms that estimate AOD also produce quality control variables but these have not been systematically used to address the measurement error in AOD. We compare three machine-learning methods: random forests, gradient boosting, and extreme gradient boosting (XGBoost to characterize and correct measurement error in the Multi-Angle Implementation of Atmospheric Correction (MAIAC 1 × 1 km AOD product for Aqua and Terra satellites across the Northeastern/Mid-Atlantic USA versus collocated measures from 79 ground-based AERONET stations over 14 years. Models included 52 quality control, land use, meteorology, and spatially-derived features. Variable importance measures suggest relative azimuth, AOD uncertainty, and the AOD difference in 30–210 km moving windows are among the most important features for predicting measurement error. XGBoost outperformed the other machine-learning approaches, decreasing the root mean squared error in withheld testing data by 43% and 44% for Aqua and Terra. After correction using XGBoost, the correlation of collocated AOD and daily PM2.5 monitors across the region increased by 10 and 9 percentage points for Aqua and Terra. We demonstrate how machine learning with quality control and spatial features substantially improves satellite-derived AOD products for air pollution modeling.

  19. Respiratory hospitalizations in association with fine PM and its ...

    Science.gov (United States)

    Despite observed geographic and temporal variation in particulate matter (PM)-related health morbidities, only a small number of epidemiologic studies have evaluated the relation between PM2.5 chemical constituents and respiratory disease. Most assessments are limited by inadequate spatial and temporal resolution of ambient PM measurements and/or by their approaches to examine the role of specific PM components on health outcomes. In a case-crossover analysis using daily average ambient PM2.5 total mass and species estimates derived from the Community Multiscale Air Quality (CMAQ) model and available observations, we examined the association between the chemical components of PM (including elemental and organic carbon, sulfate, nitrate, ammonium, and other remaining) and respiratory hospitalizations in New York State. We evaluated relationships between levels (low, medium, high) of PM constituent mass fractions, and assessed modification of the PM2.5–hospitalization association via models stratified by mass fractions of both primary and secondary PM components. In our results, average daily PM2.5 concentrations in New York State were generally lower than the 24-hr average National Ambient Air Quality Standard (NAAQS). Year-round analyses showed statistically significant positive associations between respiratory hospitalizations and PM2.5 total mass, sulfate, nitrate, and ammonium concentrations at multiple exposure lags (0.5–2.0% per interquartile range [IQR

  20. Chemical characterization of PM2.5 from a southern coastal city of China: applications of modeling and chemical tracers in demonstration of regional transport.

    Science.gov (United States)

    Zhou, Jiamao; Ho, Steven Sai Hang; Cao, Junji; Zhao, Zhuzi; Zhao, Shuyu; Zhu, Chongshu; Wang, Qiyuan; Liu, Suixin; Zhang, Ting; Zhao, Youzhi; Wang, Ping; Tie, Xuexi

    2018-05-11

    An intensive sampling campaign of airborne fine particles (PM 2.5 ) was conducted at Sanya, a coastal city in Southern China, from January to February 2012. Chemical analyses and mass reconstruction were used identify potential pollution sources and investigate atmospheric reaction mechanisms. A thermodynamic model indicated that low ammonia and high relative humidity caused the aerosols be acidic and that drove heterogeneous reactions which led to the formation of secondary inorganic aerosol. Relationships among neutralization ratios, free acidity, and air-mass trajectories suggest that the atmosphere at Sanya was impacted by both local and regional emissions. Three major transport pathways were identified, and flow from the northeast (from South China) typically brought the most polluted air to Sanya. A case study confirmed strong impact from South China (e.g., Pearl River Delta region) (contributed 76.8% to EC, and then this result can be extended to primary pollutants) when the northeast winds were dominant. The Weather Research Forecasting Black carbon model and trace organic markers were used to apportion local pollution versus regional contributions. Results of the study offer new insights into the atmospheric conditions and air pollution at this coastal city.

  1. Impact of Highly Reflective Materials on Meteorology, PM10 and Ozone in Urban Areas: A Modeling Study with WRF-CHIMERE at High Resolution over Milan (Italy

    Directory of Open Access Journals (Sweden)

    Serena Falasca

    2018-02-01

    Full Text Available The Urban Heat Island (UHI is a well-known phenomenon concerning an increasing percentage of the world’s population due to the growth rates of metropolitan areas. Given the health and economic implications of UHIs, several mitigation techniques are being evaluated and tested. In this study, we consider the use of highly reflective materials for urban surfaces, and we carried out numerical experiments using the Weather Research and Forecasting model coupled with the CHIMERE model in order to investigate the effects of these materials on the meteorology and air quality in the urban area of Milan (Italy. Results show that an increase in albedo from 0.2 to 0.7 for urban roofs, walls and streets leads to a decrease in UHI intensity by up to 2–3 °C and of the planetary boundary layer (PBL height of about 500 m. However, the difference of PM10 and ozone between urban and surrounding areas increases by a factor of about 2, attributable to the reduction of PBL height and wind speed and to the increased reflected solar radiation that may enhance photochemical production during the daytime. Therefore, if anthropogenic emissions are held at the same levels, the potential benefit to the UHI in terms of thermal discomfort may have negative repercussions on air quality.

  2. Using support vector regression to predict PM10 and PM2.5

    International Nuclear Information System (INIS)

    Weizhen, Hou; Zhengqiang, Li; Yuhuan, Zhang; Hua, Xu; Ying, Zhang; Kaitao, Li; Donghui, Li; Peng, Wei; Yan, Ma

    2014-01-01

    Support vector machine (SVM), as a novel and powerful machine learning tool, can be used for the prediction of PM 10 and PM 2.5 (particulate matter less or equal than 10 and 2.5 micrometer) in the atmosphere. This paper describes the development of a successive over relaxation support vector regress (SOR-SVR) model for the PM 10 and PM 2.5 prediction, based on the daily average aerosol optical depth (AOD) and meteorological parameters (atmospheric pressure, relative humidity, air temperature, wind speed), which were all measured in Beijing during the year of 2010–2012. The Gaussian kernel function, as well as the k-fold crosses validation and grid search method, are used in SVR model to obtain the optimal parameters to get a better generalization capability. The result shows that predicted values by the SOR-SVR model agree well with the actual data and have a good generalization ability to predict PM 10 and PM 2.5 . In addition, AOD plays an important role in predicting particulate matter with SVR model, which should be included in the prediction model. If only considering the meteorological parameters and eliminating AOD from the SVR model, the prediction results of predict particulate matter will be not satisfying

  3. Analytical modeling and analysis of magnetic field and torque for novel axial flux eddy current couplers with PM excitation

    Science.gov (United States)

    Li, Zhao; Wang, Dazhi; Zheng, Di; Yu, Linxin

    2017-10-01

    Rotational permanent magnet eddy current couplers are promising devices for torque and speed transmission without any mechanical contact. In this study, flux-concentration disk-type permanent magnet eddy current couplers with double conductor rotor are investigated. Given the drawback of the accurate three-dimensional finite element method, this paper proposes a mixed two-dimensional analytical modeling approach. Based on this approach, the closed-form expressions of magnetic field, eddy current, electromagnetic force and torque for such devices are obtained. Finally, a three-dimensional finite element method is employed to validate the analytical results. Besides, a prototype is manufactured and tested for the torque-speed characteristic.

  4. Effect of the microstructure on the creep behavior of PM Udimet 720 superalloy--experiments and modeling

    International Nuclear Information System (INIS)

    Dubiez-Le Goff, Sophie; Couturier, Raphaeel; Guetaz, Laure; Burlet, Helene

    2004-01-01

    Powder metallurgy processed Udimet 720 is a high creep strength nickel-based superalloy considered for high temperature turbine disks for nuclear gas cooled reactors working under 700 deg. C. Both fine-grained and coarse-grained microstructures have been obtained by applying respectively a subsolvus or a supersolvus solution treatments, followed by ageing treatments. In both microstructures, the distribution of the strengthening γ' precipitates has been characterized by transmission electron microscopy (TEM). The creep curves of the coarse-grained microstructure show the three usual creep stages. On the contrary, the creep curves of the fine-grained microstructure show a transition directly from primary to apparent tertiary creep without any obvious steady state. According to TEM analyses, Orowan loops surround Udimet 720 CR γ' and U720 HS γ' at high stress whereas U720 HS γ' are sheared at low stress. To describe the behavior of the superalloy Udimet 720, a specific creep model is developed on the basis of McLean and Dyson models including physical damage parameters

  5. In vitro short-term exposure to air pollution PM{sub 2.5-0.3} induced cell cycle alterations and genetic instability in a human lung cell coculture model

    Energy Technology Data Exchange (ETDEWEB)

    Abbas, Imane [Université de Lille, Lille (France); EA4492-UCEIV, Université du Littoral-Côte d’Opale, Dunkerque (France); Lebanese Atomic Energy Commission – CNRS, Beirut (Lebanon); Verdin, Anthony [Université de Lille, Lille (France); EA4492-UCEIV, Université du Littoral-Côte d’Opale, Dunkerque (France); Escande, Fabienne [Centre de Biologie Pathologie, Centre Hospitalier Régional et Universitaire, Lille (France); Saint-Georges, Françoise [Université de Lille, Lille (France); Groupement Hospitalier de l’Institut Catholique de Lille, Lille (France); Cazier, Fabrice [Université de Lille, Lille (France); Centre Commun de Mesures, Université du Littoral-Côte d’Opale, Dunkerque (France); Mulliez, Philippe [Université de Lille, Lille (France); Groupement Hospitalier de l’Institut Catholique de Lille, Lille (France); Courcot, Dominique; Shirali, Pirouz [Université de Lille, Lille (France); EA4492-UCEIV, Université du Littoral-Côte d’Opale, Dunkerque (France); Gosset, Pierre [Université de Lille, Lille (France); Groupement Hospitalier de l’Institut Catholique de Lille, Lille (France); and others

    2016-05-15

    Although its adverse health effects of air pollution particulate matter (PM2.5) are well-documented and often related to oxidative stress and pro-inflammatory response, recent evidence support the role of the remodeling of the airway epithelium involving the regulation of cell death processes. Hence, the overarching goals of the present study were to use an in vitro coculture model, based on human AM and L132 cells to study the possible alteration of TP53-RB gene signaling pathways (i.e. cell cycle phases, gene expression of TP53, BCL2, BAX, P21, CCND1, and RB, and protein concentrations of their active forms), and genetic instability (i.e. LOH and/or MSI) in the PM{sub 2.5-0.3}-exposed coculture model. PM{sub 2.5-0.3} exposure of human AM from the coculture model induced marked cell cycle alterations after 24 h, as shown by increased numbers of L132 cells in subG1 and S+G2 cell cycle phases, indicating apoptosis and proliferation. Accordingly, activation of the TP53-RB gene signaling pathways after the coculture model exposure to PM{sub 2.5-0.3} was reported in the L132 cells. Exposure of human AM from the coculture model to PM{sub 2.5-0.3} resulted in MS alterations in 3p chromosome multiple critical regions in L132 cell population. Hence, in vitro short-term exposure of the coculture model to PM{sub 2.5-0.3} induced cell cycle alterations relying on the sequential occurrence of molecular abnormalities from TP53-RB gene signaling pathway activation and genetic instability. - Highlights: • Better knowledge on health adverse effects of air pollution PM{sub 2.5}. • Human alveolar macrophage and normal human epithelial lung cell coculture. • Molecular abnormalities from TP53-RB gene signaling pathway. • Loss of heterozygosity and microsatellite instability. • Pathologic changes in morphology and number of cells in relation to airway remodeling.

  6. Characterising an intense PM pollution episode in March 2015 in France from multi-site approach and near real time data: Climatology, variabilities, geographical origins and model evaluation

    Science.gov (United States)

    Petit, J.-E.; Amodeo, T.; Meleux, F.; Bessagnet, B.; Menut, L.; Grenier, D.; Pellan, Y.; Ockler, A.; Rocq, B.; Gros, V.; Sciare, J.; Favez, O.

    2017-04-01

    During March 2015, a severe and large-scale particulate matter (PM) pollution episode occurred in France. Measurements in near real-time of the major chemical composition at four different urban background sites across the country (Paris, Creil, Metz and Lyon) allowed the investigation of spatiotemporal variabilities during this episode. A climatology approach showed that all sites experienced clear unusual rain shortage, a pattern that is also found on a longer timescale, highlighting the role of synoptic conditions over Wester-Europe. This episode is characterized by a strong predominance of secondary pollution, and more particularly of ammonium nitrate, which accounted for more than 50% of submicron aerosols at all sites during the most intense period of the episode. Pollution advection is illustrated by similar variabilities in Paris and Creil (distant of around 100 km), as well as trajectory analyses applied on nitrate and sulphate. Local sources, especially wood burning, are however found to contribute to local/regional sub-episodes, notably in Metz. Finally, simulated concentrations from Chemistry-Transport model CHIMERE were compared to observed ones. Results highlighted different patterns depending on the chemical components and the measuring site, reinforcing the need of such exercises over other pollution episodes and sites.

  7. Precise measurement of the $K^{\\pm} \\to \\pi^{\\pm}e^{+}e^{−}$ decay

    CERN Document Server

    Batley, J.R.; Kalmus, G.; Lazzeroni, C.; Munday, D.J.; Slater, M.W.; Wotton, S.A.; Arcidiacono, R.; Bocquet, G.; Cabibbo, N.; Ceccucci, A.; Cundy, D.; Falaleev, V.; Fidecaro, M.; Gatignon, L.; Gonidec, A.; Kubischta, W.; Norton, A.; Maier, A.; Patel, M.; Peters, A.; Balev, S.; Frabetti, P.L.; Goudzovski, E.; Hristov, P.; Kekelidze, V.; Kozhuharov, V.; Litov, L.; Madigozhin, D.; Marinova, E.; Molokanova, N.; Polenkevich, I.; Potrebenikov, Yu.; Stoynev, S.; Zinchenko, A.; Monnier, E.; Swallow, E.; Winston, R.; Rubin, P.; Walker, A.; Baldini, W.; Cotta Ramusino, A.; Dalpiaz, P.; Damiani, C.; Fiorini, M.; Gianoli, A.; Martini, M.; Petrucci, F.; Savrie, M.; Scarpa, M.; Wahl, H.; Bizzeti, A.; Calvetti, M.; Celeghini, E.; Iacopini, E.; Lenti, M.; Martelli, F.; Ruggiero, G.; Veltri, M.; Behler, M.; Eppard, K.; Kleinknecht, K.; Marouelli, P.; Masetti, L.; Moosbrugger, U.; Morales Morales, C.; Renk, B.; Wache, M.; Wanke, R.; Winhart, A.; Coward, D.; Dabrowski, A.; Fonseca Martin, T.; Shieh, M.; Szleper, M.; Velasco, M.; Wood, M.D.; Anzivino, G.; Cenci, P.; Imbergamo, E.; Nappi, A.; Pepe, M.; Petrucci, M.C.; Piccini, M.; Raggi, M.; Valdata-Nappi, M.; Cerri, C.; Fantechi, R.; Collazuol, G.; DiLella, L.; Lamanna, G.; Mannelli, I.; Michetti, A.; Costantini, F.; Doble, N.; Fiorini, L.; Giudici, S.; Pierazzini, G.; Sozzi, M.; Venditti, S.; Bloch-Devaux, B.; Cheshkov, C.; Cheze, J.B.; De Beer, M.; Derre, J.; Marel, G.; Mazzucato, E.; Peyaud, B.; Vallage, B.; Holder, M.; Ziolkowski, M.; Bifani, S.; Biino, C.; Cartiglia, N.; Clemencic, M.; Goy Lopez, S.; Marchetto, F.; Dibon, H.; Jeitler, M.; Markytan, M.; Mikulec, I.; Neuhofer, G.; Widhalm, L.

    2009-01-01

    A sample of 7253 $K^\\pm\\to\\pi^\\pm e^+e^-(\\gamma)$ decay candidates with 1.0% background contamination has been collected by the NA48/2 experiment at the CERN SPS, allowing a precise measurement of the decay properties. The branching ratio in the full kinematic range was measured to be ${\\rm BR}=(3.11\\pm0.12)\\times 10^{-7}$, where the uncertainty includes also the model dependence. The shape of the form factor $W(z)$, where $z=(M_{ee}/M_K)^2$, was parameterized according to several models, and, in particular, the slope $\\delta$ of the linear form factor $W(z)=W_0(1+\\delta z)$ was determined to be $\\delta=2.32\\pm0.18$. A possible CP violating asymmetry of $K^+$ and $K^-$ decay widths was investigated, and a conservative upper limit of $2.1\\times 10^{-2}$ at 90% CL was established.

  8. LHCb: Evidence of CP violation in charmless three-body decays $B^\\pm\\rightarrow K^\\pm\\pi^+\\pi^-$, $B^\\pm\\rightarrow K^\\pm K^+K^-$, $B^\\pm\\rightarrow K^+ K^-\\pi^\\pm$ and $B^\\pm\\rightarrow \\pi^\\pm\\pi^+\\pi^-$

    CERN Multimedia

    Lopes, J H

    2013-01-01

    Evidence of CP violation in charmless three-body decays $B^\\pm\\rightarrow K^\\pm\\pi^+\\pi^-$, $B^\\pm\\rightarrow K^\\pm K^+K^-$, $B^\\pm\\rightarrow K^+ K^-\\pi^\\pm$ and $B^\\pm\\rightarrow \\pi^\\pm\\pi^+\\pi^-$

  9. Simultaneous monitoring and compositions analysis of PM1 and PM2.5 in Shanghai: Implications for characterization of haze pollution and source apportionment.

    Science.gov (United States)

    Qiao, Ting; Zhao, Mengfei; Xiu, Guangli; Yu, Jianzhen

    2016-07-01

    A year-long simultaneous observation of PM1 and PM2.5 were conducted at ECUST campus in Shanghai, the compositions were analyzed and compared. Results showed that PM2.5 was dominated by PM1 on clear days while the contribution of PM1-2.5 to PM2.5 increased on haze days, indicating that PM2.5 should be given priority to characterize or predict haze pollution. On haze days, accumulation of organic carbon (OC), elemental carbon (EC) and primary organic carbon (POC) in PM1-2.5 was faster than that in PM1. Humic-like substances carbon (Hulis-C) in both PM2.5 and PM1 formed faster than water soluble organic carbon (WSOC) on haze days, hence Hulis-C/WSOC increased with the intensification of haze pollution. In terms of water soluble ions, NO3(-)/SO4(2-) in PM1 increased with the aggravation of haze pollution, implying that mobile sources dominated on haze days, so is nitrogen oxidation ratio (NOR). Liquid water content (LWC) in both PM1 and PM2.5 had positive correlations with relative humidity (RH) but negative correlations with visibility, implying that hygroscopic growth might be a factor for visibility impairment, especially LWC in PM1. By comparison with multi-linear equations of LWC in PM1 and PM2.5, NO3(-) exerted a higher influence on hygroscopicity of PM1 than PM2.5, while RH, WSOC, SO4(2-) and NH4(+) had higher effects on PM2.5, especially WSOC. Source apportionment of PM2.5 was also investigated to provide reference for policy making. Cluster analysis by HYSPLIT (HYbrid Single Particle Lagrangian Integrated Trajectory) model showed that PM2.5 originated from marine aerosols, middle-scale transportation and large-scale transportation. Furthermore, PM2.5 on haze days was dominated by middle-scale transportation. In line with source apportionment by positive matrix factorization (PMF) model, PM2.5 was attributed to secondary inorganics, aged sea salt, combustion emissions, hygroscopic growth and secondary organics. Secondary formation was the principle source of

  10. Identifying Optimal Temporal Scale for the Correlation of AOD and Ground Measurements of PM2.5 to Improve the Model Performance in a Real-time Air Quality Estimation System

    Science.gov (United States)

    Li, Hui; Faruque, Fazlay; Williams, Worth; Al-Hamdan, Mohammad; Luvall, Jeffrey C.; Crosson, William; Rickman, Douglas; Limaye, Ashutosh

    2009-01-01

    Aerosol optical depth (AOD), an indirect estimate of particle matter using satellite observations, has shown great promise in improving estimates of PM 2.5 air quality surface. Currently, few studies have been conducted to explore the optimal way to apply AOD data to improve the model accuracy of PM 2.5 surface estimation in a real-time air quality system. We believe that two major aspects may be worthy of consideration in that area: 1) the approach to integrate satellite measurements with ground measurements in the pollution estimation, and 2) identification of an optimal temporal scale to calculate the correlation of AOD and ground measurements. This paper is focused on the second aspect on the identifying the optimal temporal scale to correlate AOD with PM2.5. Five following different temporal scales were chosen to evaluate their impact on the model performance: 1) within the last 3 days, 2) within the last 10 days, 3) within the last 30 days, 4) within the last 90 days, and 5) the time period with the highest correlation in a year. The model performance is evaluated for its accuracy, bias, and errors based on the following selected statistics: the Mean Bias, the Normalized Mean Bias, the Root Mean Square Error, Normalized Mean Error, and the Index of Agreement. This research shows that the model with the temporal scale of within the last 30 days displays the best model performance in this study area using 2004 and 2005 data sets.

  11. Modelación de episodios críticos de contaminación por material particulado (PM10 en Santiago de Chile: Comparación de la eficiencia predictiva de los modelos paramétricos y no paramétricos Modeling critical episodes of air pollution by PM10 in Santiago, Chile: Comparison of the predictive efficiency of parametric and non-parametric statistical models

    Directory of Open Access Journals (Sweden)

    Sergio A. Alvarado

    2010-12-01

    Full Text Available Objetivo: Evaluar la eficiencia predictiva de modelos estadísticos paramétricos y no paramétricos para predecir episodios críticos de contaminación por material particulado PM10 del día siguiente, que superen en Santiago de Chile la norma de calidad diaria. Una predicción adecuada de tales episodios permite a la autoridad decretar medidas restrictivas que aminoren la gravedad del episodio, y consecuentemente proteger la salud de la comunidad. Método: Se trabajó con las concentraciones de material particulado PM10 registradas en una estación asociada a la red de monitorización de la calidad del aire MACAM-2, considerando 152 observaciones diarias de 14 variables, y con información meteorológica registrada durante los años 2001 a 2004. Se ajustaron modelos estadísticos paramétricos Gamma usando el paquete estadístico STATA v11, y no paramétricos usando una demo del software estadístico MARS v 2.0 distribuida por Salford-Systems. Resultados: Ambos métodos de modelación presentan una alta correlación entre los valores observados y los predichos. Los modelos Gamma presentan mejores aciertos que MARS para las concentraciones de PM10 con valores Objective: To evaluate the predictive efficiency of two statistical models (one parametric and the other non-parametric to predict critical episodes of air pollution exceeding daily air quality standards in Santiago, Chile by using the next day PM10 maximum 24h value. Accurate prediction of such episodes would allow restrictive measures to be applied by health authorities to reduce their seriousness and protect the community´s health. Methods: We used the PM10 concentrations registered by a station of the Air Quality Monitoring Network (152 daily observations of 14 variables and meteorological information gathered from 2001 to 2004. To construct predictive models, we fitted a parametric Gamma model using STATA v11 software and a non-parametric MARS model by using a demo version of Salford

  12. Multi-year objective analyses of warm season ground-level ozone and PM2.5 over North America using real-time observations and Canadian operational air quality models

    Science.gov (United States)

    Robichaud, A.; Ménard, R.

    2014-02-01

    Multi-year objective analyses (OA) on a high spatiotemporal resolution for the warm season period (1 May to 31 October) for ground-level ozone and for fine particulate matter (diameter less than 2.5 microns (PM2.5)) are presented. The OA used in this study combines model outputs from the Canadian air quality forecast suite with US and Canadian observations from various air quality surface monitoring networks. The analyses are based on an optimal interpolation (OI) with capabilities for adaptive error statistics for ozone and PM2.5 and an explicit bias correction scheme for the PM2.5 analyses. The estimation of error statistics has been computed using a modified version of the Hollingsworth-Lönnberg (H-L) method. The error statistics are "tuned" using a χ2 (chi-square) diagnostic, a semi-empirical procedure that provides significantly better verification than without tuning. Successful cross-validation experiments were performed with an OA setup using 90% of data observations to build the objective analyses and with the remainder left out as an independent set of data for verification purposes. Furthermore, comparisons with other external sources of information (global models and PM2.5 satellite surface-derived or ground-based measurements) show reasonable agreement. The multi-year analyses obtained provide relatively high precision with an absolute yearly averaged systematic error of less than 0.6 ppbv (parts per billion by volume) and 0.7 μg m-3 (micrograms per cubic meter) for ozone and PM2.5, respectively, and a random error generally less than 9 ppbv for ozone and under 12 μg m-3 for PM2.5. This paper focuses on two applications: (1) presenting long-term averages of OA and analysis increments as a form of summer climatology; and (2) analyzing long-term (decadal) trends and inter-annual fluctuations using OA outputs. The results show that high percentiles of ozone and PM2.5 were both following a general decreasing trend in North America, with the eastern

  13. Spatiotemporal estimation of historical PM2.5 concentrations using PM10, meteorological variables, and spatial effect

    Science.gov (United States)

    Li, Lianfa; Wu, Anna H.; Cheng, Iona; Chen, Jiu-Chiuan; Wu, Jun

    2017-10-01

    Monitoring of fine particulate matter with diameter health outcomes such as cancer. In this study, we aimed to design a flexible approach to reliably estimate historical PM2.5 concentrations by incorporating spatial effect and the measurements of existing co-pollutants such as particulate matter with diameter additive non-linear model. The spatiotemporal model was evaluated, using leaving-one-site-month-out cross validation. Our final daily model had an R2 of 0.81, with PM10, meteorological variables, and spatial autocorrelation, explaining 55%, 10%, and 10% of the variance in PM2.5 concentrations, respectively. The model had a cross-validation R2 of 0.83 for monthly PM2.5 concentrations (N = 8170) and 0.79 for daily PM2.5 concentrations (N = 51,421) with few extreme values in prediction. Further, the incorporation of spatial effects reduced bias in predictions. Our approach achieved a cross validation R2 of 0.61 for the daily model when PM10 was replaced by total suspended particulate. Our model can robustly estimate historical PM2.5 concentrations in California when PM2.5 measurements were not available.

  14. A statistical model for determining impact of wildland fires on Particulate Matter (PM2.5) in Central California aided by satellite imagery of smoke

    Science.gov (United States)

    Haiganoush K. Preisler; Donald Schweizer; Ricardo Cisneros; Trent Procter; Mark Ruminski; Leland Tarnay

    2015-01-01

    As the climate in California warms and wildfires become larger and more severe, satellite-based observational tools are frequently used for studying impact of those fires on air quality. However little objective work has been done to quantify the skill these satellite observations of smoke plumes have in predicting impacts to PM2.5 concentrations...

  15. Search for $CP$ violation in $D^{\\pm}\\rightarrow K^0_S K^{\\pm}$ and $D^{\\pm}_{s}\\rightarrow K^0_S \\pi^{\\pm}$ decays

    CERN Document Server

    Aaij, R.; Adinolfi, M.; Affolder, A.; Ajaltouni, Z.; Akar, S.; Albrecht, J.; Alessio, F.; Alexander, M.; Ali, S.; Alkhazov, G.; Alvarez Cartelle, P.; Alves Jr, A.A.; Amato, S.; Amerio, S.; Amhis, Y.; An, L.; Anderlini, L.; Anderson, J.; Andreassen, R.; Andreotti, M.; Andrews, J.E.; Appleby, R.B.; Aquines Gutierrez, O.; Archilli, F.; Artamonov, A.; Artuso, M.; Aslanides, E.; Auriemma, G.; Baalouch, M.; Bachmann, S.; Back, J.J.; Badalov, A.; Balagura, V.; Baldini, W.; Barlow, R.J.; Barschel, C.; Barsuk, S.; Barter, W.; Batozskaya, V.; Battista, V.; Bay, A.; Beaucourt, L.; Beddow, J.; Bedeschi, F.; Bediaga, I.; Belogurov, S.; Belous, K.; Belyaev, I.; Ben-Haim, E.; Bencivenni, G.; Benson, S.; Benton, J.; Berezhnoy, A.; Bernet, R.; Bettler, M.O.; van Beuzekom, M.; Bien, A.; Bifani, S.; Bird, T.; Bizzeti, A.; Bjornstad, P.M.; Blake, T.; Blanc, F.; Blouw, J.; Blusk, S.; Bocci, V.; Bondar, A.; Bondar, N.; Bonivento, W.; Borghi, S.; Borgia, A.; Borsato, M.; Bowcock, T.J.V.; Bowen, E.; Bozzi, C.; Brambach, T.; van den Brand, J.; Bressieux, J.; Brett, D.; Britsch, M.; Britton, T.; Brodzicka, J.; Brook, N.H.; Brown, H.; Bursche, A.; Busetto, G.; Buytaert, J.; Cadeddu, S.; Calabrese, R.; Calvi, M.; Calvo Gomez, M.; Camboni, A.; Campana, P.; Campora Perez, D.; Carbone, A.; Carboni, G.; Cardinale, R.; Cardini, A.; Carranza-Mejia, H.; Carson, L.; Carvalho Akiba, K.; Casse, G.; Cassina, L.; Garcia, L.Castillo; Cattaneo, M.; Cauet, Ch.; Cenci, R.; Charles, M.; Charpentier, Ph.; Chen, S.; Cheung, S.F.; Chiapolini, N.; Chrzaszcz, M.; Ciba, K.; Cid Vidal, X.; Ciezarek, G.; Clarke, P.E.L.; Clemencic, M.; Cliff, H.V.; Closier, J.; Coco, V.; Cogan, J.; Cogneras, E.; Collins, P.; Comerma-Montells, A.; Contu, A.; Cook, A.; Coombes, M.; Coquereau, S.; Corti, G.; Corvo, M.; Counts, I.; Couturier, B.; Cowan, G.A.; Craik, D.C.; Cruz Torres, M.; Cunliffe, S.; Currie, R.; D'Ambrosio, C.; Dalseno, J.; David, P.; David, P.N.Y.; Davis, A.; De Bruyn, K.; De Capua, S.; De Cian, M.; de Miranda, J.M.; De Paula, L.; De Silva, W.; De Simone, P.; Decamp, D.; Deckenhoff, M.; Del Buono, L.; Deleage, N.; Derkach, D.; Deschamps, O.; Dettori, F.; Di Canto, A.; Dijkstra, H.; Donleavy, S.; Dordei, F.; Dorigo, M.; Dosil Suarez, A.; Dossett, D.; Dovbnya, A.; Dreimanis, K.; Dujany, G.; Dupertuis, F.; Durante, P.; Dzhelyadin, R.; Dziurda, A.; Dzyuba, A.; Easo, S.; Egede, U.; Egorychev, V.; Eidelman, S.; Eisenhardt, S.; Eitschberger, U.; Ekelhof, R.; Eklund, L.; El Rifai, I.; Elsasser, Ch.; Ely, S.; Esen, S.; Evans, T.; Falabella, A.; Farber, C.; Farinelli, C.; Farley, N.; Farry, S.; Fay, RF.; Ferguson, D.; Fernandez Albor, V.; Ferreira Rodrigues, F.; Ferro-Luzzi, M.; Filippov, S.; Fiore, M.; Fiorini, M.; Firlej, M.; Fitzpatrick, C.; Fiutowski, T.; Fontana, M.; Fontanelli, F.; Forty, R.; Francisco, O.; Frank, M.; Frei, C.; Frosini, M.; Fu, J.; Furfaro, E.; Gallas Torreira, A.; Galli, D.; Gallorini, S.; Gambetta, S.; Gandelman, M.; Gandini, P.; Gao, Y.; Garofoli, J.; Garra Tico, J.; Garrido, L.; Gaspar, C.; Gauld, R.; Gavardi, L.; Gavrilov, G.; Gersabeck, E.; Gersabeck, M.; Gershon, T.; Ghez, Ph.; Gianelle, A.; Giani', S.; Gibson, V.; Giubega, L.; Gligorov, V.V.; Gobel, C.; Golubkov, D.; Golutvin, A.; Gomes, A.; Gordon, H.; Gotti, C.; Grabalosa Gandara, M.; Graciani Diaz, R.; Granado Cardoso, L.A.; Grauges, E.; Graziani, G.; Grecu, A.; Greening, E.; Gregson, S.; Griffith, P.; Grillo, L.; Grunberg, O.; Gui, B.; Gushchin, E.; Guz, Yu.; Gys, T.; Hadjivasiliou, C.; Haefeli, G.; Haen, C.; Haines, S.C.; Hall, S.; Hamilton, B.; Hampson, T.; Han, X.; Hansmann-Menzemer, S.; Harnew, N.; Harnew, S.T.; Harrison, J.; Hartmann, T.; He, J.; Head, T.; Heijne, V.; Hennessy, K.; Henrard, P.; Henry, L.; Hernando Morata, J.A.; van Herwijnen, E.; Hess, M.; Hicheur, A.; Hill, D.; Hoballah, M.; Hombach, C.; Hulsbergen, W.; Hunt, P.; Hussain, N.; Hutchcroft, D.; Hynds, D.; Idzik, M.; Ilten, P.; Jacobsson, R.; Jaeger, A.; Jalocha, J.; Jans, E.; Jaton, P.; Jawahery, A.; Jing, F.; John, M.; Johnson, D.; Jones, C.R.; Joram, C.; Jost, B.; Jurik, N.; Kaballo, M.; Kandybei, S.; Kanso, W.; Karacson, M.; Karbach, T.M.; Karodia, S.; Kelsey, M.; Kenyon, I.R.; Ketel, T.; Khanji, B.; Khurewathanakul, C.; Klaver, S.; Kochebina, O.; Kolpin, M.; Komarov, I.; Koopman, R.F.; Koppenburg, P.; Korolev, M.; Kozlinskiy, A.; Kravchuk, L.; Kreplin, K.; Kreps, M.; Krocker, G.; Krokovny, P.; Kruse, F.; Kucewicz, W.; Kucharczyk, M.; Kudryavtsev, V.; Kurek, K.; Kvaratskheliya, T.; La Thi, V.N.; Lacarrere, D.; Lafferty, G.; Lai, A.; Lambert, D.; Lambert, R.W.; Lanciotti, E.; Lanfranchi, G.; Langenbruch, C.; Langhans, B.; Latham, T.; Lazzeroni, C.; Le Gac, R.; van Leerdam, J.; Lees, J.P.; Lefevre, R.; Leflat, A.; Lefrancois, J.; Leo, S.; Leroy, O.; Lesiak, T.; Leverington, B.; Li, Y.; Liles, M.; Lindner, R.; Linn, C.; Lionetto, F.; Liu, B.; Liu, G.; Lohn, S.; Longstaff, I.; Lopes, J.H.; Lopez-March, N.; Lowdon, P.; Lu, H.; Lucchesi, D.; Luo, H.; Lupato, A.; Luppi, E.; Lupton, O.; Machefert, F.; Machikhiliyan, I.V.; Maciuc, F.; Maev, O.; Malde, S.; Manca, G.; Mancinelli, G.; Maratas, J.; Marchand, J.F.; Marconi, U.; Benito, C.Marin; Marino, P.; Marki, R.; Marks, J.; Martellotti, G.; Martens, A.; Martin Sanchez, A.; Martinelli, M.; Martinez Santos, D.; Martinez Vidal, F.; Martins Tostes, D.; Massafferri, A.; Matev, R.; Mathe, Z.; Matteuzzi, C.; Mazurov, A.; McCann, M.; McCarthy, J.; McNab, A.; McNulty, R.; McSkelly, B.; Meadows, B.; Meier, F.; Meissner, M.; Merk, M.; Milanes, D.A.; Minard, M.N.; Moggi, N.; Molina Rodriguez, J.; Monteil, S.; Morandin, M.; Morawski, P.; Morda, A.; Morello, M.J.; Moron, J.; Morris, A.B.; Mountain, R.; Muheim, F.; Muller, K.; Muresan, R.; Mussini, M.; Muster, B.; Naik, P.; Nakada, T.; Nandakumar, R.; Nasteva, I.; Needham, M.; Neri, N.; Neubert, S.; Neufeld, N.; Neuner, M.; Nguyen, A.D.; Nguyen, T.D.; Nguyen-Mau, C.; Nicol, M.; Niess, V.; Niet, R.; Nikitin, N.; Nikodem, T.; Novoselov, A.; O'Hanlon, D.P.; Oblakowska-Mucha, A.; Obraztsov, V.; Oggero, S.; Ogilvy, S.; Okhrimenko, O.; Oldeman, R.; Onderwater, G.; Orlandea, M.; Otalora Goicochea, J.M.; Owen, P.; Oyanguren, A.; Pal, B.K.; Palano, A.; Palombo, F.; Palutan, M.; Panman, J.; Papanestis, A.; Pappagallo, M.; Parkes, C.; Parkinson, C.J.; Passaleva, G.; Patel, G.D.; Patel, M.; Patrignani, C.; Pazos Alvarez, A.; Pearce, A.; Pellegrino, A.; Pepe Altarelli, M.; Perazzini, S.; Perez Trigo, E.; Perret, P.; Perrin-Terrin, M.; Pescatore, L.; Pesen, E.; Petridis, K.; Petrolini, A.; Picatoste Olloqui, E.; Pietrzyk, B.; Pilar, T.; Pinci, D.; Pistone, A.; Playfer, S.; Plo Casasus, M.; Polci, F.; Poluektov, A.; Polycarpo, E.; Popov, A.; Popov, D.; Popovici, B.; Potterat, C.; Prisciandaro, J.; Pritchard, A.; Prouve, C.; Pugatch, V.; Puig Navarro, A.; Punzi, G.; Qian, W.; Rachwal, B.; Rademacker, J.H.; Rakotomiaramanana, B.; Rama, M.; Rangel, M.S.; Raniuk, I.; Rauschmayr, N.; Raven, G.; Reichert, S.; Reid, M.M.; dos Reis, A.C.; Ricciardi, S.; Richards, A.; Rihl, M.; Rinnert, K.; Rives Molina, V.; Roa Romero, D.A.; Robbe, P.; Rodrigues, A.B.; Rodrigues, E.; Rodriguez Perez, P.; Roiser, S.; Romanovsky, V.; Vidal, A.Romero; Rotondo, M.; Rouvinet, J.; Ruf, T.; Ruffini, F.; Ruiz, H.; Valls, P.Ruiz; Sabatino, G.; Saborido Silva, J.J.; Sagidova, N.; Sail, P.; Saitta, B.; Salustino Guimaraes, V.; Sanchez Mayordomo, C.; Sanmartin Sedes, B.; Santacesaria, R.; Santamarina Rios, C.; Santovetti, E.; Sapunov, M.; Sarti, A.; Satriano, C.; Satta, A.; Savrie, M.; Savrina, D.; Schiller, M.; Schindler, H.; Schlupp, M.; Schmelling, M.; Schmidt, B.; Schneider, O.; Schopper, A.; Schune, M.H.; Schwemmer, R.; Sciascia, B.; Sciubba, A.; Seco, M.; Semennikov, A.; Sepp, I.; Serra, N.; Serrano, J.; Sestini, L.; Seyfert, P.; Shapkin, M.; Shapoval, I.; Shcheglov, Y.; Shears, T.; Shekhtman, L.; Shevchenko, V.; Shires, A.; Coutinho, R.Silva; Simi, G.; Sirendi, M.; Skidmore, N.; Skwarnicki, T.; Smith, N.A.; Smith, E.; Smith, E.; Smith, J.; Smith, M.; Snoek, H.; Sokoloff, M.D.; Soler, F.J.P.; Soomro, F.; Souza, D.; Souza De Paula, B.; Spaan, B.; Sparkes, A.; Spradlin, P.; Stagni, F.; Stahl, M.; Stahl, S.; Steinkamp, O.; Stenyakin, O.; Stevenson, S.; Stoica, S.; Stone, S.; Storaci, B.; Stracka, S.; Straticiuc, M.; Straumann, U.; Stroili, R.; Subbiah, V.K.; Sun, L.; Sutcliffe, W.; Swientek, K.; Swientek, S.; Syropoulos, V.; Szczekowski, M.; Szczypka, P.; Szilard, D.; Szumlak, T.; T'Jampens, S.; Teklishyn, M.; Tellarini, G.; Teubert, F.; Thomas, C.; Thomas, E.; van Tilburg, J.; Tisserand, V.; Tobin, M.; Tolk, S.; Tomassetti, L.; Tonelli, D.; Topp-Joergensen, S.; Torr, N.; Tournefier, E.; Tourneur, S.; Tran, M.T.; Tresch, M.; Tsaregorodtsev, A.; Tsopelas, P.; Tuning, N.; Garcia, M.Ubeda; Ukleja, A.; Ustyuzhanin, A.; Uwer, U.; Vagnoni, V.; Valenti, G.; Vallier, A.; Vazquez Gomez, R.; Vazquez Regueiro, P.; Vazquez Sierra, C.; Vecchi, S.; Velthuis, J.J.; Veltri, M.; Veneziano, G.; Vesterinen, M.; Viaud, B.; Vieira, D.; Vieites Diaz, M.; Vilasis-Cardona, X.; Vollhardt, A.; Volyanskyy, D.; Voong, D.; Vorobyev, A.; Vorobyev, V.; Voss, C.; Voss, H.; de Vries, J.A.; Waldi, R.; Wallace, C.; Wallace, R.; Walsh, J.; Wandernoth, S.; Wang, J.; Ward, D.R.; Watson, N.K.; Websdale, D.; Whitehead, M.; Wicht, J.; Wiedner, D.; Wilkinson, G.; Williams, M.P.; Williams, M.; Wilson, F.F.; Wimberley, J.; Wishahi, J.; Wislicki, W.; Witek, M.; Wormser, G.; Wotton, S.A.; Wright, S.; Wu, S.; Wyllie, K.; Xie, Y.; Xing, Z.; Xu, Z.; Yang, Z.; Yuan, X.; Yushchenko, O.; Zangoli, M.; Zavertyaev, M.; Zhang, L.; Zhang, W.C.; Zhang, Y.; Zhelezov, A.; Zhokhov, A.; Zhong, L.; Zvyagin, A.

    2014-10-03

    A search for $CP$ violation in Cabibbo-suppressed $D^{\\pm}\\rightarrow K^0_S K^{\\pm}$ and $D^{\\pm}_{s}\\rightarrow K^0_S \\pi^{\\pm}$ decays is performed using $pp$ collision data, corresponding to an integrated luminosity of 3~fb$^{-1}$, recorded by the LHCb experiment. The individual $CP$-violating asymmetries are measured to be \\begin{eqnarray*} \\mathcal{A}_{CP}^{D^{\\pm}\\rightarrow K^0_S K^{\\pm}} & = & (+0.03 \\pm 0.17 \\pm 0.14) \\% \\\\ \\mathcal{A}_{CP}^{D^{\\pm}_s\\rightarrow K^0_S \\pi^{\\pm}} & = & (+0.38 \\pm 0.46 \\pm 0.17) \\%, \\end{eqnarray*} assuming that $CP$ violation in the Cabibbo-favoured decays is negligible. A combination of the measured asymmetries for the four decay modes $D^{\\pm}_{(s)}\\rightarrow K^0_S K^{\\pm}$ and $D^{\\pm}_{(s)}\\rightarrow K^0_S \\pi^{\\pm}$ gives the sum \\[ \\mathcal{A}_{CP}^{D^{\\pm}\\rightarrow K^0_S K^{\\pm}}+ \\mathcal{A}_{CP}^{D^{\\pm}_s\\rightarrow K^0_S \\pi^{\\pm}} = (+0.41 \\pm 0.49 \\pm 0.26) \\%. \\] In all cases, the first uncertainties are statistical and the second sys...

  16. A study of diurnal variations of PM2.5 acidity and related chemical species using a new thermodynamic equilibrium model

    International Nuclear Information System (INIS)

    Behera, Sailesh N.; Betha, Raghu; Liu, Ping; Balasubramanian, Rajasekhar

    2013-01-01

    Aerosol acidity is one of the most important parameters that can influence atmospheric visibility, climate change and human health. Based on continuous field measurements of inorganic aerosol species and their thermodynamic modeling on a time resolution of 1 h, this study has investigated the acidic properties of PM 2.5 and their relation with the formation of secondary inorganic aerosols (SIA). The study was conducted by taking into account the prevailing ambient temperature (T) and relative humidity (RH) in a tropical urban atmosphere. The in-situ aerosol pH (pH IS ) on a 12 h basis ranged from − 0.20 to 1.46 during daytime with an average value of 0.48 and 0.23 to 1.53 during nighttime with an average value of 0.72. These diurnal variations suggest that the daytime aerosol was more acidic than that caused by the nighttime aerosol. The hourly values of pH IS showed a reverse trend as compared to that of in-situ aerosol acidity ([H + ] Ins ). The pH IS had its maximum values at 3:00 and at 20:00 and its minimum during 11:00 to 12:00. Correlation analyses revealed that the molar concentration ratio of ammonium to sulfate (R N/S ), equivalent concentration ratio of cations to anions (R C/A ), T and RH can be used as independent variables for prediction of pH IS . A multi-linear regression model consisting of R N/S , R C/A, T and RH was developed to estimate aerosol pH IS. - Highlights: • Fine aerosol acidic characteristics were evaluated on an hourly basis. • Diurnal variations of in-situ acidity, water content and pH of aerosols were investigated. • Aerosols were more acidic during daytime than during nighttime. • The molar ratio of ammonium to sulfate and equivalent ratio of cations to anions were good indicators of aerosol acidity. • Meteorology had a significant effect on the hygroscopic nature of aerosol

  17. Measurement of polynuclear aromatic hydrocarbons (PAHs) in epiphytic lichens and from PM 2.5 filters for receptor modeling in the Alberta Oil Sands Region (Invited)

    Science.gov (United States)

    Studabaker, W. B.; Jayanty, J.; Raymer, J. H.; Krupa, S.

    2013-12-01

    As mining and refinery operations in the Alberta Oil Sands Region (AOSR) have expanded, there has been increasing concern for the impacts of air pollution generated by those operations on both human and ecosystem health. The inaccessibility of much of the AOSR makes it difficult to establish conventional air quality monitoring stations to the extent needed to model long-range impacts of emissions from the AOSR operations. Epiphytic lichens are important markers of ecosystem health, are well-established bioaccumulators of trace metals, and are potentially useful biomonitors of air pollution. However, their ability to take up organic pollutants has not been extensively explored, and only recently have they been used for biomonitoring of pollution by PAHs. Here we describe the determination of polynuclear aromatic hydrocarbons (PAHs) in lichens, collected from sites throughout the AOSR, for modeling emissions associated with mining and oil extraction operations. We also describe preliminary results of the determination of PAHs in PM 2.5 filters from dichotomous samplers stationed in the AOSR, in the context of the biological sample data. Lichens (Hypogymnia physodes) were collected on two separate occasions. During the summer of 2009, single samples were taken from 200 sites in the AOSR; a subset of 20 of these was selected for determination of PAHs. During the summer of 2011, triplicate samples (from separate trees within a site) were collected from 20 sites representing similar locations to the 2008 sites. Lichens were milled in a cryogenic impactor, then were extracted with cyclohexane. Extracts were purified on silica gel using automated solid phase extraction and analyzed by gas chromatography with mass selective detection. Method detection limits for individual PAHs were 2-4 ng/g. Total PAHs in the samples from both collection events ranged from 50 ng/g to 350 ng/g, and declined with increasing distance from the mining and refinery operations. The relative

  18. Influence of PM1 and PM2.5 on lung function parameters in healthy schoolchildren-a panel study.

    Science.gov (United States)

    Zwozdziak, A; Sówka, I; Willak-Janc, E; Zwozdziak, J; Kwiecińska, K; Balińska-Miśkiewicz, W

    2016-12-01

    To evaluate lung function responses to short-term indoor PM 1 and PM 2.5 concentrations, we conducted a panel study of healthy schoolchildren aged 13-14 years. The following lung function parameters FVC, FEV 1 , PEF, and mid expiratory flows MEF 25 , MEF 50 , and MEF 75 were measured in 141 schoolchildren of the secondary school in Wroclaw, Poland in years 2009-2010. On days when spirometry tests were conducted, simultaneously, PM 1 and PM 2.5 samples were collected inside the school premises. Information about differentiating factors for children including smoking parents, sex, living close to busy streets, dust, mold, and pollen allergies were collected by means of questionnaires. To account for repeated measurements, the method of generalized estimating equations (GEE) was used. The GEE models for the entire group of children revealed the adverse effects (p < 0.05) of PM 1 and PM 2.5 . Small differences in effects estimates per interquartile range (IQR) of PM 1 and PM 2.5 on MEF 25 (5.1 and 4.8 %), MEF 50 (3.7 and 3.9 %), MEF 75 (3.5 and 3.6 %) and FEV 1 (1.3 and 1.0 %) imply that PM 1 was likely the component of PM 2.5 that might have a principal health effect on these lung function parameters. However, the reduction of FVC and PEF per IQR for PM 2.5 (2.1 and 5.2 %, respectively) was higher than for PM 1 (1.0 and 4.4 %, respectively). Adjustment for potential confounders did not change the unadjusted analysis.

  19. Effects of Boundary Layer Height on the Model of Ground-Level PM2.5 Concentrations from AOD: Comparison of Stable and Convective Boundary Layer Heights from Different Methods

    Directory of Open Access Journals (Sweden)

    Zengliang Zang

    2017-06-01

    Full Text Available The aerosol optical depth (AOD from satellites or ground-based sun photometer spectral observations has been widely used to estimate ground-level PM2.5 concentrations by regression methods. The boundary layer height (BLH is a popular factor in the regression model of AOD and PM2.5, but its effect is often uncertain. This may result from the structures between the stable and convective BLHs and from the calculation methods of the BLH. In this study, the boundary layer is divided into two types of stable and convective boundary layer, and the BLH is calculated using different methods from radiosonde data and National Centers for Environmental Prediction (NCEP reanalysis data for the station in Beijing, China during 2014–2015. The BLH values from these methods show significant differences for both the stable and convective boundary layer. Then, these BLHs were introduced into the regression model of AOD-PM2.5 to seek the respective optimal BLH for the two types of boundary layer. It was found that the optimal BLH for the stable boundary layer is determined using the method of surface-based inversion, and the optimal BLH for the convective layer is determined using the method of elevated inversion. Finally, the optimal BLH and other meteorological parameters were combined to predict the PM2.5 concentrations using the stepwise regression method. The results indicate that for the stable boundary layer, the optimal stepwise regression model includes the factors of surface relative humidity, BLH, and surface temperature. These three factors can significantly enhance the prediction accuracy of ground-level PM2.5 concentrations, with an increase of determination coefficient from 0.50 to 0.68. For the convective boundary layer, however, the optimal stepwise regression model includes the factors of BLH and surface wind speed. These two factors improve the determination coefficient, with a relatively low increase from 0.65 to 0.70. It is found that the

  20. Hourly elemental concentrations in PM2.5 aerosols sampled simultaneously at urban background and road site during SAPUSS – diurnal variations and PMF receptor modelling

    Directory of Open Access Journals (Sweden)

    M. Dall'Osto

    2013-04-01

    Full Text Available Hourly-resolved aerosol chemical speciation data can be a highly powerful tool to determine the source origin of atmospheric pollutants in urban environments. Aerosol mass concentrations of seventeen elements (Na, Mg, Al, S, Cl, K, Ca, Ti, V, Cr, Mn, Fe, Ni, Cu, Zn, Sr and Pb were obtained by time (1 h and size (PM2.5 particulate matter 2.5 mass fraction simultaneously measured at the UB and RS sites: (1 the regional aerosol sources impact both monitoring sites at similar concentrations regardless their different ventilation conditions; (2 by contrast, local industrial aerosol plumes associated with shipping oil combustion and smelters activities have a higher impact on the more ventilated UB site; (3 a unique source of Pb-Cl (associated with combustion emissions is found to be the major (82% source of fine Cl in the urban agglomerate; (4 the mean diurnal variation of PM2.5 primary traffic non-exhaust brake dust (Fe-Cu suggests that this source is mainly emitted and not resuspended, whereas PM2.5 urban dust (Ca is found mainly resuspended by both traffic vortex and sea breeze; (5 urban dust (Ca is found the aerosol source most affected by land wetness, reduced by a factor of eight during rainy days and suggesting that wet roads may be a solution for reducing urban dust concentrations.

  1. Winter mass concentrations of carbon species in PM10, PM 2.5 and PM1 in Zagreb air, Croatia.

    Science.gov (United States)

    Godec, Ranka; Čačković, Mirjana; Šega, Krešimir; Bešlić, Ivan

    2012-11-01

    The purpose of our investigation was to examine the mass concentrations of EC, OC and TC (EC + OC) in PM(10), PM(2.5) and PM(1) particle fractions. Daily PM(10), PM(2.5) and PM(1) samples were collected at an urban background monitoring site in Zagreb during winter 2009. Average OC and EC mass concentrations were 11.9 and 1.8 μg m(-3) in PM(10), 9.0 and 1.4 μg m(-3) in PM(2.5), and 5.5 and 1.1 μg m(-3) in PM(1). Average OC/EC ratios in PM(10), PM(2.5), and PM(1) were 7.4, 6.9 and 5.4, respectively.

  2. 75 FR 29537 - Draft Transportation Conformity Guidance for Quantitative Hot-spot Analyses in PM2.5

    Science.gov (United States)

    2010-05-26

    ... Quantitative Hot- spot Analyses in PM 2.5 and PM 10 Nonattainment and Maintenance Areas AGENCY: Environmental... finalized, this guidance would help state and local agencies complete quantitative PM 2.5 and PM 10 hot-spot... projects. A hot-spot analysis includes an estimation of project-level emissions, air quality modeling, and...

  3. Observation of $C\\!P$ violation in $B^\\pm \\to D K^\\pm$ decays

    CERN Document Server

    Aaij, R; Adeva, B; Adinolfi, M; Adrover, C; Affolder, A; Ajaltouni, Z; Albrecht, J; Alessio, F; Alexander, M; Ali, S; Alkhazov, G; Alvarez Cartelle, P; Alves Jr, A A; Amato, S; Amhis, Y; Anderson, J; Appleby, R B; Aquines Gutierrez, O; Archilli, F; Artamonov, A; Artuso, M; Aslanides, E; Auriemma, G; Bachmann, S; Back, J J; Balagura, V; Baldini, W; Barlow, R J; Barschel, C; Barsuk, S; Barter, W; Bates, A; Bauer, C; Bauer, Th; Bay, A; Bediaga, I; Belogurov, S; Belous, K; Belyaev, I; Ben-Haim, E; Benayoun, M; Bencivenni, G; Benson, S; Benton, J; Bernet, R; Bettler, M-O; van Beuzekom, M; Bien, A; Bifani, S; Bird, T; Bizzeti, A; Bjørnstad, P M; Blake, T; Blanc, F; Blanks, C; Blouw, J; Blusk, S; Bobrov, A; Bocci, V; Bondar, A; Bondar, N; Bonivento, W; Borghi, S; Borgia, A; Bowcock, T J V; Bozzi, C; Brambach, T; van den Brand, J; Bressieux, J; Brett, D; Britsch, M; Britton, T; Brook, N H; Brown, H; Büchler-Germann, A; Burducea, I; Bursche, A; Buytaert, J; Cadeddu, S; Callot, O; Calvi, M; Calvo Gomez, M; Camboni, A; Campana, P; Carbone, A; Carboni, G; Cardinale, R; Cardini, A; Carson, L; Carvalho Akiba, K; Casse, G; Cattaneo, M; Cauet, Ch; Charles, M; Charpentier, Ph; Chiapolini, N; Ciba, K; Cid Vidal, X; Ciezarek, G; Clarke, P E L; Clemencic, M; Cliff, H V; Closier, J; Coca, C; Coco, V; Cogan, J; Collins, P; Comerma-Montells, A; Contu, A; Cook, A; Coombes, M; Corti, G; Couturier, B; Cowan, G A; Currie, R; D'Ambrosio, C; David, P; David, P N Y; De Bonis, I; De Bruyn, K; De Capua, S; De Cian, M; De Miranda, J M; De Paula, L; De Simone, P; Decamp, D; Deckenhoff, M; Degaudenzi, H; Del Buono, L; Deplano, C; Derkach, D; Deschamps, O; Dettori, F; Dickens, J; Dijkstra, H; Diniz Batista, P; Domingo Bonal, F; Donleavy, S; Dordei, F; Dosil Suárez, A; Dossett, D; Dovbnya, A; Dupertuis, F; Dzhelyadin, R; Dziurda, A; Easo, S; Egede, U; Egorychev, V; Eidelman, S; van Eijk, D; Eisele, F; Eisenhardt, S; Ekelhof, R; Eklund, L; Elsasser, Ch; Elsby, D; Esperante Pereira, D; Falabella, A; Färber, C; Fardell, G; Farinelli, C; Farry, S; Fave, V; Fernandez Albor, V; Ferro-Luzzi, M; Filippov, S; Fitzpatrick, C; Fontana, M; Fontanelli, F; Forty, R; Francisco, O; Frank, M; Frei, C; Frosini, M; Furcas, S; Gallas Torreira, A; Galli, D; Gandelman, M; Gandini, P; Gao, Y; Garnier, J-C; Garofoli, J; Garra Tico, J; Garrido, L; Gascon, D; Gaspar, C; Gauld, R; Gauvin, N; Gersabeck, M; Gershon, T; Ghez, Ph; Gibson, V; Gligorov, V V; Göbel, C; Golubkov, D; Golutvin, A; Gomes, A; Gordon, H; Grabalosa Gándara, M; Graciani Diaz, R; Granado Cardoso, L A; Graugés, E; Graziani, G; Grecu, A; Greening, E; Gregson, S; Gui, B; Gushchin, E; Guz, Yu; Gys, T; Hadjivasiliou, C; Haefeli, G; Haen, C; Haines, S C; Hampson, T; Hansmann-Menzemer, S; Harji, R; Harnew, N; Harrison, J; Harrison, P F; Hartmann, T; He, J; Heijne, V; Hennessy, K; Henrard, P; Hernando Morata, J A; van Herwijnen, E; Hicks, E; Holubyev, K; Hopchev, P; Hulsbergen, W; Hunt, P; Huse, T; Huston, R S; Hutchcroft, D; Hynds, D; Iakovenko, V; Ilten, P; Imong, J; Jacobsson, R; Jaeger, A; Jahjah Hussein, M; Jans, E; Jansen, F; Jaton, P; Jean-Marie, B; Jing, F; John, M; Johnson, D; Jones, C R; Jost, B; Kaballo, M; Kandybei, S; Karacson, M; Karbach, T M; Keaveney, J; Kenyon, I R; Kerzel, U; Ketel, T; Keune, A; Khanji, B; Kim, Y M; Knecht, M; Koopman, R F; Koppenburg, P; Korolev, M; Kozlinskiy, A; Kravchuk, L; Kreplin, K; Kreps, M; Krocker, G; Krokovny, P; Kruse, F; Kruzelecki, K; Kucharczyk, M; Kudryavtsev, V; Kvaratskheliya, T; La Thi, V N; Lacarrere, D; Lafferty, G; Lai, A; Lambert, D; Lambert, R W; Lanciotti, E; Lanfranchi, G; Langenbruch, C; Latham, T; Lazzeroni, C; Le Gac, R; van Leerdam, J; Lees, J-P; Lefèvre, R; Leflat, A; Lefrançois, J; Leroy, O; Lesiak, T; Li, L; Li Gioi, L; Lieng, M; Liles, M; Lindner, R; Linn, C; Liu, B; Liu, G; von Loeben, J; Lopes, J H; Lopez Asamar, E; Lopez-March, N; Lu, H; Luisier, J; Mac Raighne, A; Machefert, F; Machikhiliyan, I V; Maciuc, F; Maev, O; Magnin, J; Malde, S; Mamunur, R M D; Manca, G; Mancinelli, G; Mangiafave, N; Marconi, U; Märki, R; Marks, J; Martellotti, G; Martens, A; Martin, L; Martín Sánchez, A; Martinelli, M; Martinez Santos, D; Massafferri, A; Mathe, Z; Matteuzzi, C; Matveev, M; Maurice, E; Maynard, B; Mazurov, A; McGregor, G; McNulty, R; Meissner, M; Merk, M; Merkel, J; Miglioranzi, S; Milanes, D A; Minard, M-N; Molina Rodriguez, J; Monteil, S; Moran, D; Morawski, P; Mountain, R; Mous, I; Muheim, F; Müller, K; Muresan, R; Muryn, B; Muster, B; Mylroie-Smith, J; Naik, P; Nakada, T; Nandakumar, R; Nasteva, I; Needham, M; Neufeld, N; Nguyen, A D; Nguyen-Mau, C; Nicol, M; Niess, V; Nikitin, N; Nikodem, T; Nomerotski, A; Novoselov, A; Oblakowska-Mucha, A; Obraztsov, V; Oggero, S; Ogilvy, S; Okhrimenko, O; Oldeman, R; Orlandea, M; Otalora Goicochea, J M; Owen, P; Pal, B K; Palacios, J; Palano, A; Palutan, M; Panman, J; Papanestis, A; Pappagallo, M; Parkes, C; Parkinson, C J; Passaleva, G; Patel, G D; Patel, M; Paterson, S K; Patrick, G N; Patrignani, C; Pavel-Nicorescu, C; Pazos Alvarez, A; Pellegrino, A; Penso, G; Pepe Altarelli, M; Perazzini, S; Perego, D L; Perez Trigo, E; Pérez-Calero Yzquierdo, A; Perret, P; Perrin-Terrin, M; Pessina, G; Petrolini, A; Phan, A; Picatoste Olloqui, E; Pie Valls, B; Pietrzyk, B; Pilař, T; Pinci, D; Plackett, R; Playfer, S; Plo Casasus, M; Polok, G; Poluektov, A; Polycarpo, E; Popov, D; Popovici, B; Potterat, C; Powell, A; Prisciandaro, J; Pugatch, V; Puig Navarro, A; Qian, W; Rademacker, J H; Rakotomiaramanana, B; Rangel, M S; Raniuk, I; Raven, G; Redford, S; Reid, M M; dos Reis, A C; Ricciardi, S; Richards, A; Rinnert, K; Roa Romero, D A; Robbe, P; Rodrigues, E; Rodrigues, F; Rodriguez Perez, P; Rogers, G J; Roiser, S; Romanovsky, V; Rosello, M; Rouvinet, J; Ruf, T; Ruiz, H; Sabatino, G; Saborido Silva, J J; Sagidova, N; Sail, P; Saitta, B; Salzmann, C; Sannino, M; Santacesaria, R; Santamarina Rios, C; Santinelli, R; Santovetti, E; Sapunov, M; Sarti, A; Satriano, C; Satta, A; Savrie, M; Savrina, D; Schaack, P; Schiller, M; Schindler, H; Schleich, S; Schlupp, M; Schmelling, M; Schmidt, B; Schneider, O; Schopper, A; Schune, M-H; Schwemmer, R; Sciascia, B; Sciubba, A; Seco, M; Semennikov, A; Senderowska, K; Sepp, I; Serra, N; Serrano, J; Seyfert, P; Shapkin, M; Shapoval, I; Shatalov, P; Shcheglov, Y; Shears, T; Shekhtman, L; Shevchenko, O; Shevchenko, V; Shires, A; Silva Coutinho, R; Skwarnicki, T; Smith, N A; Smith, E; Sobczak, K; Soler, F J P; Solomin, A; Soomro, F; Souza De Paula, B; Spaan, B; Sparkes, A; Spradlin, P; Stagni, F; Stahl, S; Steinkamp, O; Stoica, S; Stone, S; Storaci, B; Straticiuc, M; Straumann, U; Subbiah, V K; Swientek, S; Szczekowski, M; Szczypka, P; Szumlak, T; T'Jampens, S; Teodorescu, E; Teubert, F; Thomas, C; Thomas, E; van Tilburg, J; Tisserand, V; Tobin, M; Tolk, S; Topp-Joergensen, S; Torr, N; Tournefier, E; Tourneur, S; Tran, M T; Tsaregorodtsev, A; Tuning, N; Ubeda Garcia, M; Ukleja, A; Uwer, U; Vagnoni, V; Valenti, G; Vazquez Gomez, R; Vazquez Regueiro, P; Vecchi, S; Velthuis, J J; Veltri, M; Viaud, B; Videau, I; Vieira, D; Vilasis-Cardona, X; Visniakov, J; Vollhardt, A; Volyanskyy, D; Voong, D; Vorobyev, A; Vorobyev, V; Voss, H; Waldi, R; Wandernoth, S; Wang, J; Ward, D R; Watson, N K; Webber, A D; Websdale, D; Whitehead, M; Wiedner, D; Wiggers, L; Wilkinson, G; Williams, M P; Williams, M; Wilson, F F; Wishahi, J; Witek, M; Witzeling, W; Wotton, S A; Wyllie, K; Xie, Y; Xing, F; Xing, Z; Yang, Z; Young, R; Yushchenko, O; Zangoli, M; Zavertyaev, M; Zhang, F; Zhang, L; Zhang, W C; Zhang, Y; Zhelezov, A; Zhong, L; Zvyagin, A

    2012-01-01

    An analysis of $B^{\\pm}\\to DK^{\\pm}$ and $B^{\\pm}\\to D\\pi^{\\pm}$ decays is presented where the $D$ meson is reconstructed in the two-body final states:$K^{\\pm}\\pi^{\\mp}$, $K^+K^-$, $\\pi^+\\pi^-$ and $\\pi^{\\pm}K^{\\mp}$. Using $1.0{\\rm \\,fb}^{-1}$ of LHCb data, measurements of several observables are made including the first observation of the suppressed mode $B^{\\pm}\\to[\\pi^{\\pm}K^{\\mp}]_DK^{\\pm}$. $C\\!P$ violation in $B^{\\pm}\\to DK^{\\pm}$ decays is observed with $5.8\\,\\sigma$ significance.

  4. Lifetime measurement in 136Pm

    International Nuclear Information System (INIS)

    Toney, D.; Zhong, Q.; De Angelis, G.

    2005-01-01

    The aim of the present work is to investigate the electromagnetic transition probabilities in the doublet bands of 136 Pm. These two bands have been observed up to Iπ = (21 + ). Contrary to the case of 134 Pr, the B(M1)/B(E2) ratios take similar values within the error bars in 136 Pm. This is a strong indication that there is considerable difference between the two nuclei. However, a lifetime measurement in 136 Pm is needed to shed light on the scale and the origin of the difference

  5. Measurement of the charge asymmetry in $B^{\\pm}\\rightarrow \\phi K^{\\pm}$ and search for $B^{\\pm}\\rightarrow \\phi \\pi^{\\pm}$ decays

    CERN Document Server

    Aaij, R; Adinolfi, M; Adrover, C; Affolder, A; Ajaltouni, Z; Albrecht, J; Alessio, F; Alexander, M; Ali, S; Alkhazov, G; Alvarez Cartelle, P; Alves Jr, A A; Amato, S; Amerio, S; Amhis, Y; Anderlini, L; Anderson, J; Andreassen, R; Andrews, J E; Appleby, R B; Aquines Gutierrez, O; Archilli, F; Artamonov, A; Artuso, M; Aslanides, E; Auriemma, G; Baalouch, M; Bachmann, S; Back, J J; Badalov, A; Baesso, C; Balagura, V; Baldini, W; Barlow, R J; Barschel, C; Barsuk, S; Barter, W; Bauer, Th; Bay, A; Beddow, J; Bedeschi, F; Bediaga, I; Belogurov, S; Belous, K; Belyaev, I; Ben-Haim, E; Bencivenni, G; Benson, S; Benton, J; Berezhnoy, A; Bernet, R; Bettler, M -O; van Beuzekom, M; Bien, A; Bifani, S; Bird, T; Bizzeti, A; Bjørnstad, P M; Blake, T; Blanc, F; Blouw, J; Blusk, S; Bocci, V; Bondar, A; Bondar, N; Bonivento, W; Borghi, S; Borgia, A; Bowcock, T J V; Bowen, E; Bozzi, C; Brambach, T; van den Brand, J; Bressieux, J; Brett, D; Britsch, M; Britton, T; Brook, N H; Brown, H; Bursche, A; Busetto, G; Buytaert, J; Cadeddu, S; Callot, O; Calvi, M; Calvo Gomez, M; Camboni, A; Campana, P; Campora Perez, D; Carbone, A; Carboni, G; Cardinale, R; Cardini, A; Carranza-Mejia, H; Carson, L; Carvalho Akiba, K; Casse, G; Castillo Garcia, L; Cattaneo, M; Cauet, Ch; Cenci, R; Charles, M; Charpentier, Ph; Chen, P; Cheung, S -F; Chiapolini, N; Chrzaszcz, M; Ciba, K; Cid Vidal, X; Ciezarek, G; Clarke, P E L; Clemencic, M; Cliff, H V; Closier, J; Coca, C; Coco, V; Cogan, J; Cogneras, E; Collins, P; Comerma-Montells, A; Contu, A; Cook, A; Coombes, M; Coquereau, S; Corti, G; Couturier, B; Cowan, G A; Craik, D C; Cruz Torres, M; Cunliffe, S; Currie, R; D'Ambrosio, C; David, P; David, P N Y; Davis, A; De Bonis, I; De Bruyn, K; De Capua, S; De Cian, M; De Miranda, J M; De Paula, L; De Silva, W; De Simone, P; Decamp, D; Deckenhoff, M; Del Buono, L; Déléage, N; Derkach, D; Deschamps, O; Dettori, F; Di Canto, A; Dijkstra, H; Dogaru, M; Donleavy, S; Dordei, F; Dosil Suárez, A; Dossett, D; Dovbnya, A; Dupertuis, F; Durante, P; Dzhelyadin, R; Dziurda, A; Dzyuba, A; Easo, S; Egede, U; Egorychev, V; Eidelman, S; van Eijk, D; Eisenhardt, S; Eitschberger, U; Ekelhof, R; Eklund, L; El Rifai, I; Elsasser, Ch; Falabella, A; Färber, C; Farinelli, C; Farry, S; Ferguson, D; Fernandez Albor, V; Ferreira Rodrigues, F; Ferro-Luzzi, M; Filippov, S; Fiore, M; Fitzpatrick, C; Fontana, M; Fontanelli, F; Forty, R; Francisco, O; Frank, M; Frei, C; Frosini, M; Furfaro, E; Gallas Torreira, A; Galli, D; Gandelman, M; Gandini, P; Gao, Y; Garofoli, J; Garosi, P; Garra Tico, J; Garrido, L; Gaspar, C; Gauld, R; Gersabeck, E; Gersabeck, M; Gershon, T; Ghez, Ph; Gibson, V; Giubega, L; Gligorov, V V; Göbel, C; Golubkov, D; Golutvin, A; Gomes, A; Gorbounov, P; Gordon, H; Grabalosa Gándara, M; Graciani Diaz, R; Granado Cardoso, L A; Graugés, E; Graziani, G; Grecu, A; Greening, E; Gregson, S; Griffith, P; Grünberg, O; Gui, B; Gushchin, E; Guz, Yu; Gys, T; Hadjivasiliou, C; Haefeli, G; Haen, C; Haines, S C; Hall, S; Hamilton, B; Hampson, T; Hansmann-Menzemer, S; Harnew, N; Harnew, S T; Harrison, J; Hartmann, T; He, J; Head, T; Heijne, V; Hennessy, K; Henrard, P; Hernando Morata, J A; van Herwijnen, E; Heß, M; Hicheur, A; Hicks, E; Hill, D; Hoballah, M; Hombach, C; Hulsbergen, W; Hunt, P; Huse, T; Hussain, N; Hutchcroft, D; Hynds, D; Iakovenko, V; Idzik, M; Ilten, P; Jacobsson, R; Jaeger, A; Jans, E; Jaton, P; Jawahery, A; Jing, F; John, M; Johnson, D; Jones, C R; Joram, C; Jost, B; Kaballo, M; Kandybei, S; Kanso, W; Karacson, M; Karbach, T M; Kenyon, I R; Ketel, T; Khanji, B; Kochebina, O; Komarov, I; Koopman, R F; Koppenburg, P; Korolev, M; Kozlinskiy, A; Kravchuk, L; Kreplin, K; Kreps, M; Krocker, G; Krokovny, P; Kruse, F; Kucharczyk, M; Kudryavtsev, V; Kurek, K; Kvaratskheliya, T; La Thi, V N; Lacarrere, D; Lafferty, G; Lai, A; Lambert, D; Lambert, R W; Lanciotti, E; Lanfranchi, G; Langenbruch, C; Latham, T; Lazzeroni, C; Le Gac, R; van Leerdam, J; Lees, J -P; Lefèvre, R; Leflat, A; Lefrançois, J; Leo, S; Leroy, O; Lesiak, T; Leverington, B; Li, Y; Li Gioi, L; Liles, M; Lindner, R; Linn, C; Liu, B; Liu, G; Lohn, S; Longstaff, I; Lopes, J H; Lopez-March, N; Lu, H; Lucchesi, D; Luisier, J; Luo, H; Lupton, O; Machefert, F; Machikhiliyan, I V; Maciuc, F; Maev, O; Malde, S; Manca, G; Mancinelli, G; Maratas, J; Marconi, U; Marino, P; Märki, R; Marks, J; Martellotti, G; Martens, A; Martín Sánchez, A; Martinelli, M; Martinez Santos, D; Martins Tostes, D; Martynov, A; Massafferri, A; Matev, R; Mathe, Z; Matteuzzi, C; Maurice, E; Mazurov, A; McCarthy, J; McNab, A; McNulty, R; McSkelly, B; Meadows, B; Meier, F; Meissner, M; Merk, M; Milanes, D A; Minard, M -N; Molina Rodriguez, J; Monteil, S; Moran, D; Morawski, P; Mordà, A; Morello, M J; Mountain, R; Mous, I; Muheim, F; Müller, K; Muresan, R; Muryn, B; Muster, B; Naik, P; Nakada, T; Nandakumar, R; Nasteva, I; Needham, M; Neubert, S; Neufeld, N; Nguyen, A D; Nguyen, T D; Nguyen-Mau, C; Nicol, M; Niess, V; Niet, R; Nikitin, N; Nikodem, T; Nomerotski, A; Novoselov, A; Oblakowska-Mucha, A; Obraztsov, V; Oggero, S; Ogilvy, S; Okhrimenko, O; Oldeman, R; Orlandea, M; Otalora Goicochea, J M; Owen, P; Oyanguren, A; Pal, B K; Palano, A; Palutan, M; Panman, J; Papanestis, A; Pappagallo, M; Parkes, C; Parkinson, C J; Passaleva, G; Patel, G D; Patel, M; Patrick, G N; Patrignani, C; Pavel-Nicorescu, C; Pazos Alvarez, A; Pearce, A; Pellegrino, A; Penso, G; Pepe Altarelli, M; Perazzini, S; Perez Trigo, E; Pérez-Calero Yzquierdo, A; Perret, P; Perrin-Terrin, M; Pescatore, L; Pesen, E; Pessina, G; Petridis, K; Petrolini, A; Phan, A; Picatoste Olloqui, E; Pietrzyk, B; Pilař, T; Pinci, D; Playfer, S; Plo Casasus, M; Polci, F; Polok, G; Poluektov, A; Polycarpo, E; Popov, A; Popov, D; Popovici, B; Potterat, C; Powell, A; Prisciandaro, J; Pritchard, A; Prouve, C; Pugatch, V; Puig Navarro, A; Punzi, G; Qian, W; Rachwal, B; Rademacker, J H; Rakotomiaramanana, B; Rangel, M S; Raniuk, I; Rauschmayr, N; Raven, G; Redford, S; Reichert, S; Reid, M M; dos Reis, A C; Ricciardi, S; Richards, A; Rinnert, K; Rives Molina, V; Roa Romero, D A; Robbe, P; Roberts, D A; Rodrigues, A B; Rodrigues, E; Rodriguez Perez, P; Roiser, S; Romanovsky, V; Romero Vidal, A; Rouvinet, J; Ruf, T; Ruffini, F; Ruiz, H; Ruiz Valls, P; Sabatino, G; Saborido Silva, J J; Sagidova, N; Sail, P; Saitta, B; Salustino Guimaraes, V; Sanmartin Sedes, B; Santacesaria, R; Santamarina Rios, C; Santovetti, E; Sapunov, M; Sarti, A; Satriano, C; Satta, A; Savrie, M; Savrina, D; Schiller, M; Schindler, H; Schlupp, M; Schmelling, M; Schmidt, B; Schneider, O; Schopper, A; Schune, M -H; Schwemmer, R; Sciascia, B; Sciubba, A; Seco, M; Semennikov, A; Senderowska, K; Sepp, I; Serra, N; Serrano, J; Seyfert, P; Shapkin, M; Shapoval, I; Shcheglov, Y; Shears, T; Shekhtman, L; Shevchenko, O; Shevchenko, V; Shires, A; Silva Coutinho, R; Sirendi, M; Skidmore, N; Skwarnicki, T; Smith, N A; Smith, E; Smith, E; Smith, J; Smith, M; Sokoloff, M D; Soler, F J P; Soomro, F; Souza, D; Souza De Paula, B; Spaan, B; Sparkes, A; Spradlin, P; Stagni, F; Stahl, S; Steinkamp, O; Stevenson, S; Stoica, S; Stone, S; Storaci, B; Straticiuc, M; Straumann, U; Subbiah, V K; Sun, L; Sutcliffe, W; Swientek, S; Syropoulos, V; Szczekowski, M; Szczypka, P; Szilard, D; Szumlak, T; T'Jampens, S; Teklishyn, M; Teodorescu, E; Teubert, F; Thomas, C; Thomas, E; van Tilburg, J; Tisserand, V; Tobin, M; Tolk, S; Tonelli, D; Topp-Joergensen, S; Torr, N; Tournefier, E; Tourneur, S; Tran, M T; Tresch, M; Tsaregorodtsev, A; Tsopelas, P; Tuning, N; Ubeda Garcia, M; Ukleja, A; Ustyuzhanin, A; Uwer, U; Vagnoni, V; Valenti, G; Vallier, A; Vazquez Gomez, R; Vazquez Regueiro, P; Vázquez Sierra, C; Vecchi, S; Velthuis, J J; Veltri, M; Veneziano, G; Vesterinen, M; Viaud, B; Vieira, D; Vilasis-Cardona, X; Vollhardt, A; Volyanskyy, D; Voong, D; Vorobyev, A; Vorobyev, V; Voß, C; Voss, H; Waldi, R; Wallace, C; Wallace, R; Wandernoth, S; Wang, J; Ward, D R; Watson, N K; Webber, A D; Websdale, D; Whitehead, M; Wicht, J; Wiechczynski, J; Wiedner, D; Wiggers, L; Wilkinson, G; Williams, M P; Williams, M; Wilson, F F; Wimberley, J; Wishahi, J; Wislicki, W; Witek, M; Wormser, G; Wotton, S A; Wright, S; Wu, S; Wyllie, K; Xie, Y; Xing, Z; Yang, Z; Yuan, X; Yushchenko, O; Zangoli, M; Zavertyaev, M; Zhang, F; Zhang, L; Zhang, W C; Zhang, Y; Zhelezov, A; Zhokhov, A; Zhong, L; Zvyagin, A

    2014-01-01

    The CP-violating charge asymmetry in $B^{\\pm}\\rightarrow \\phi K^{\\pm}$ decays is measured in a sample of $pp$ collisions at 7 TeV centre-of-mass energy, corresponding to an integrated luminosity of 1.0 fb$^{-1}$ collected by the LHCb experiment. The result is $\\mathcal{A}_{CP}(B^{\\pm}\\rightarrow \\phi K^{\\pm}) = \\rm 0.022\\pm 0.021 \\pm 0.009$, where the first uncertainty is statistical and the second systematic. In addition, a search for the $B^{\\pm}\\rightarrow \\phi \\pi^{\\pm}$ decay mode is performed, using the $B^{\\pm}\\rightarrow \\phi K^{\\pm}$ decay rate for normalization. An upper limit on the branching fraction $\\mathcal{B}(B^{\\pm}\\rightarrow \\phi \\pi^{\\pm})< 1.5\\times 10^{-7}$ is set at 90% confidence level.

  6. Historical Trends in Pm2.5-Related Premature Mortality ...

    Science.gov (United States)

    Background: Air quality across the northern hemisphere over the past two decades has witnessed dramatic changes, with continuous improvement in developed countries in North America and Europe, but a contrasting sharp deterioration in developing regions of Asia. Objective: This study investigates the historical trend in the long-term exposure to PM2.5 and PM2.5-related premature mortality (PM2.5-mortality) and its response to changes in emission that occurred during 1990-2010 across the northern hemisphere. Implications for future trends in human exposure to air pollution in both developed and developing regions of the world are discussed. Methods: We employed the integrated exposure-response model developed by Health Effects Institute to estimate the PM2.5-mortality. The 1990-2010 annual-average PM2.5 concentrations were obtained from the simulations using WRF-CMAQ model. Emission mitigation efficiencies of SO2, NOx, NH3 and primary PM are estimated from the PM2.5-mortality responses to the emission variations. Results: Estimated PM2.5-mortalities in East Asia and South Asia increased by 21% and 85% respectively, from 866,000 and 578,000 in 1990, to 1,048,000 and 1,068,000 in 2010. PM2.5-mortalities in developed regions, i.e., Europe and high-income North America decreased substantially by 67% and 58% respectively. Conclusions: Over the past two decades, correlations between population and PM2.5 have become weaker in Europe and North America due to air pollu

  7. Development of Positive Matrix Factorization Model (PMF) to Annual Study of the PM2.5 Organic Composition in ChapinerIa

    International Nuclear Information System (INIS)

    Pindado, O.; Perez, R. M.; Garcia, S.

    2013-01-01

    The Positive Matrix Factorization (PMF) application to a set of PM2.5 data collected in a rural area of Madrid for a period of 1 year has been developed. Results has let describing the particulate faction of atmospheric aerosol collected in Chapineria according to 7 factor, among them fossil fuel combustion by traffic, wax plants, primary emissions of organic carbon, crustal material, and secondary organic aerosol. Five of these factors are related to primary particles; meanwhile only one factor has been associated to secondary particles. Finally, a factor has not been associated to any known source of particulate matter. (Author)

  8. Seasonal variation of benzo(a) pyrene in the Spanish airborne PM10. Multivariate linear regression model applied to estimate BaP concentrations

    OpenAIRE

    Callén Romero, Mª Soledad; López Sebastián, José Manuel; Mastral Lamarca, Ana María

    2010-01-01

    The estimation of benzo(a)pyrene (BaP) concentrations in ambient air is very important from an environmental point of view especially with the introduction of the Directive 2004/107/EC and due to the carcinogenic character of this pollutant. A sampling campaign of particulate matter less or equal than 10 microns (PM10) carried out during 2008-2009 in four locations of Spain was collected to determine experimentally BaP concentrations by gas chromatography-mass spectrometry-mass spectrometry (...

  9. Using a developed PM in order to optimize the production ...

    African Journals Online (AJOL)

    Michael Horsfall

    production productivity is preventive maintenance (PM). It helps to protect assets, increase the useful life of equipment ... Production planning models seek typically to balance the costs of .... clinker. In this work the effect of reactions contained.

  10. Study of the $K^{\\pm} \\rightarrow \\pi^{\\pm} \\gamma \\gamma$ decay by the NA62 experiment

    CERN Document Server

    INSPIRE-00100355; Romano, A.; Ceccucci, A.; Danielsson, H.; Falaleev, V.; Gatignon, L.; Goy Lopez, S.; Hallgren, B.; Maier, A.; Peters, A.; Piccini, M.; Riedler, P.; Frabetti, P.L.; Gersabeck, E.; Kekelidze, V.; Madigozhin, D.; Misheva, M.; Molokanova, N.; Movchan, S.; Shkarovskiy, S.; Zinchenko, A.; Rubin, P.; Baldini, W.; Cotta Ramusino, A.; Dalpiaz, P.; Fiorini, M.; Gianoli, A.; Norton, A.; Petrucci, F.; Savrie, M.; Wahl, H.; Bizzeti, A.; Bucci, F.; Iacopini, E.; Lenti, M.; Veltri, M.; Antonelli, A.; Moulson, M.; Raggi, M.; Spadaro, T.; Eppard, K.; Hita-Hochgesand, M.; Kleinknecht, K.; Renk, B.; Wanke, R.; Winhart, A.; Winston, R.; Bolotov, V.; Duk, V.; Gushchin, E.; Ambrosino, F.; Di Filippo, D.; Massarotti, P.; Napolitano, M.; Palladino, V.; Saracino, G.; Anzivino, G.; Imbergamo, E.; Piandani, R.; Sergi, A.; Cenci, P.; Pepe, M.; Costantini, F.; Doble, N.; Giudici, S.; Pierazzini, G.; Sozzi, M.; Venditti, S.; Balev, S.; Collazuol, G.; Di, L.; Gallorini, S.; Goudzovski, E.; Lamanna, G.; Mannelli, I.; Ruggiero, G.; Cerri, C.; Fantechi, R.; Kholodenko, S.; Kurshetsov, V.; Obraztsov, V.; Semenov, V.; Yushchenko, O.; D'Agostini, G.; Leonardi, E.; Serra, M.; Valente, P.; Fucci, A.; Salamon, A.; Bloch-Devaux, B.; Peyaud, B.; Engelfried, J.; Coward, D.; Kozhuharov, V.; Litov, L.; Arcidiacono, R.; Bifani, S.; Biino, C.; Dellacasa, G.; Marchetto, F.; Numao, T.; Retiere, F.

    2014-05-01

    A study of the dynamics of the rare decay $K^\\pm\\to\\pi^\\pm\\gamma\\gamma$ has been performed on a sample of 232 decay candidates, with an estimated background of $17.4\\pm1.1$ events, collected by the NA62 experiment at CERN in 2007. The results are combined with those from a measurement conducted by the NA48/2 collaboration at CERN. The combined model-independent branching ratio in the kinematic range $z=(m_{\\gamma\\gamma}/m_K)^2>0.2$ is ${\\cal B}_{\\rm MI}(z>0.2) = (0.965 \\pm 0.063) \\times 10^{-6}$, and the combined branching ratio in the full kinematic range assuming a Chiral Perturbation Theory description is ${\\cal B}(K_{\\pi\\gamma\\gamma}) = (1.003 \\pm 0.056) \\times 10^{-6}$. A detailed comparison of the results with the previous measurements is performed.

  11. Evidence of electroweak production of $W^{\\pm}W^{\\pm}jj$ in $pp$ collisions at $\\sqrt{s}=8$ TeV with the ATLAS detector

    CERN Document Server

    Aad, Georges; Abdallah, Jalal; Abdel Khalek, Samah; Abdinov, Ovsat; Aben, Rosemarie; Abi, Babak; Abolins, Maris; AbouZeid, Ossama; Abramowicz, Halina; Abreu, Henso; Abreu, Ricardo; Abulaiti, Yiming; Acharya, Bobby Samir; Adamczyk, Leszek; Adams, David; Adelman, Jahred; Adomeit, Stefanie; Adye, Tim; Agatonovic-Jovin, Tatjana; Aguilar-Saavedra, Juan Antonio; Agustoni, Marco; Ahlen, Steven; Ahmadov, Faig; Aielli, Giulio; Akerstedt, Henrik; Åkesson, Torsten Paul Ake; Akimoto, Ginga; Akimov, Andrei; Alberghi, Gian Luigi; Albert, Justin; Albrand, Solveig; Alconada Verzini, Maria Josefina; Aleksa, Martin; Aleksandrov, Igor; Alexa, Calin; Alexander, Gideon; Alexandre, Gauthier; Alexopoulos, Theodoros; Alhroob, Muhammad; Alimonti, Gianluca; Alio, Lion; Alison, John; Allbrooke, Benedict; Allison, Lee John; Allport, Phillip; Almond, John; Aloisio, Alberto; Alonso, Alejandro; Alonso, Francisco; Alpigiani, Cristiano; Altheimer, Andrew David; Alvarez Gonzalez, Barbara; Alviggi, Mariagrazia; Amako, Katsuya; Amaral Coutinho, Yara; Amelung, Christoph; Amidei, Dante; Amor Dos Santos, Susana Patricia; Amorim, Antonio; Amoroso, Simone; Amram, Nir; Amundsen, Glenn; Anastopoulos, Christos; Ancu, Lucian Stefan; Andari, Nansi; Andeen, Timothy; Anders, Christoph Falk; Anders, Gabriel; Anderson, Kelby; Andreazza, Attilio; Andrei, George Victor; Anduaga, Xabier; Angelidakis, Stylianos; Angelozzi, Ivan; Anger, Philipp; Angerami, Aaron; Anghinolfi, Francis; Anisenkov, Alexey; Anjos, Nuno; Annovi, Alberto; Antonaki, Ariadni; Antonelli, Mario; Antonov, Alexey; Antos, Jaroslav; Anulli, Fabio; Aoki, Masato; Aperio Bella, Ludovica; Apolle, Rudi; Arabidze, Giorgi; Aracena, Ignacio; Arai, Yasuo; Araque, Juan Pedro; Arce, Ayana; Arguin, Jean-Francois; Argyropoulos, Spyridon; Arik, Metin; Armbruster, Aaron James; Arnaez, Olivier; Arnal, Vanessa; Arnold, Hannah; Arratia, Miguel; Arslan, Ozan; Artamonov, Andrei; Artoni, Giacomo; Asai, Shoji; Asbah, Nedaa; Ashkenazi, Adi; Åsman, Barbro; Asquith, Lily; Assamagan, Ketevi; Astalos, Robert; Atkinson, Markus; Atlay, Naim Bora; Auerbach, Benjamin; Augsten, Kamil; Aurousseau, Mathieu; Avolio, Giuseppe; Azuelos, Georges; Azuma, Yuya; Baak, Max; Bacci, Cesare; Bachacou, Henri; Bachas, Konstantinos; Backes, Moritz; Backhaus, Malte; Backus Mayes, John; Badescu, Elisabeta; Bagiacchi, Paolo; Bagnaia, Paolo; Bai, Yu; Bain, Travis; Baines, John; Baker, Oliver Keith; Baker, Sarah; Balek, Petr; Balli, Fabrice; Banas, Elzbieta; Banerjee, Swagato; Bannoura, Arwa A E; Bansal, Vikas; Bansil, Hardeep Singh; Barak, Liron; Baranov, Sergei; Barberio, Elisabetta Luigia; Barberis, Dario; Barbero, Marlon; Barillari, Teresa; Barisonzi, Marcello; Barklow, Timothy; Barlow, Nick; Barnett, Bruce; Barnett, Michael; Barnovska, Zuzana; Baroncelli, Antonio; Barone, Gaetano; Barr, Alan; Barreiro, Fernando; Barreiro Guimarães da Costa, João; Bartoldus, Rainer; Barton, Adam Edward; Bartos, Pavol; Bartsch, Valeria; Bassalat, Ahmed; Basye, Austin; Bates, Richard; Batkova, Lucia; Batley, Richard; Battaglia, Marco; Battistin, Michele; Bauer, Florian; Bawa, Harinder Singh; Beau, Tristan; Beauchemin, Pierre-Hugues; Beccherle, Roberto; Bechtle, Philip; Beck, Hans Peter; Becker, Anne Kathrin; Becker, Sebastian; Beckingham, Matthew; Becot, Cyril; Beddall, Andrew; Beddall, Ayda; Bedikian, Sourpouhi; Bednyakov, Vadim; Bee, Christopher; Beemster, Lars; Beermann, Thomas; Begel, Michael; Behr, Katharina; Belanger-Champagne, Camille; Bell, Paul; Bell, William; Bella, Gideon; Bellagamba, Lorenzo; Bellerive, Alain; Bellomo, Massimiliano; Belotskiy, Konstantin; Beltramello, Olga; Benary, Odette; Benchekroun, Driss; Bendtz, Katarina; Benekos, Nektarios; Benhammou, Yan; Benhar Noccioli, Eleonora; Benitez Garcia, Jorge-Armando; Benjamin, Douglas; Bensinger, James; Benslama, Kamal; Bentvelsen, Stan; Berge, David; Bergeaas Kuutmann, Elin; Berger, Nicolas; Berghaus, Frank; Berglund, Elina; Beringer, Jürg; Bernard, Clare; Bernat, Pauline; Bernius, Catrin; Bernlochner, Florian Urs; Berry, Tracey; Berta, Peter; Bertella, Claudia; Bertoli, Gabriele; Bertolucci, Federico; Bertsche, David; Besana, Maria Ilaria; Besjes, Geert-Jan; Bessidskaia, Olga; Bessner, Martin Florian; Besson, Nathalie; Betancourt, Christopher; Bethke, Siegfried; Bhimji, Wahid; Bianchi, Riccardo-Maria; Bianchini, Louis; Bianco, Michele; Biebel, Otmar; Bieniek, Stephen Paul; Bierwagen, Katharina; Biesiada, Jed; Biglietti, Michela; Bilbao De Mendizabal, Javier; Bilokon, Halina; Bindi, Marcello; Binet, Sebastien; Bingul, Ahmet; Bini, Cesare; Black, Curtis; Black, James; Black, Kevin; Blackburn, Daniel; Blair, Robert; Blanchard, Jean-Baptiste; Blazek, Tomas; Bloch, Ingo; Blocker, Craig; Blum, Walter; Blumenschein, Ulrike; Bobbink, Gerjan; Bobrovnikov, Victor; Bocchetta, Simona Serena; Bocci, Andrea; Bock, Christopher; Boddy, Christopher Richard; Boehler, Michael; Boek, Jennifer; Boek, Thorsten Tobias; Bogaerts, Joannes Andreas; Bogdanchikov, Alexander; Bogouch, Andrei; Bohm, Christian; Bohm, Jan; Boisvert, Veronique; Bold, Tomasz; Boldea, Venera; Boldyrev, Alexey; Bomben, Marco; Bona, Marcella; Boonekamp, Maarten; Borisov, Anatoly; Borissov, Guennadi; Borri, Marcello; Borroni, Sara; Bortfeldt, Jonathan; Bortolotto, Valerio; Bos, Kors; Boscherini, Davide; Bosman, Martine; Boterenbrood, Hendrik; Boudreau, Joseph; Bouffard, Julian; Bouhova-Thacker, Evelina Vassileva; Boumediene, Djamel Eddine; Bourdarios, Claire; Bousson, Nicolas; Boutouil, Sara; Boveia, Antonio; Boyd, James; Boyko, Igor; Bozovic-Jelisavcic, Ivanka; Bracinik, Juraj; Brandt, Andrew; Brandt, Gerhard; Brandt, Oleg; Bratzler, Uwe; Brau, Benjamin; Brau, James; Braun, Helmut; Brazzale, Simone Federico; Brelier, Bertrand; Brendlinger, Kurt; Brennan, Amelia Jean; Brenner, Richard; Bressler, Shikma; Bristow, Kieran; Bristow, Timothy Michael; Britton, Dave; Brochu, Frederic; Brock, Ian; Brock, Raymond; Bromberg, Carl; Bronner, Johanna; Brooijmans, Gustaaf; Brooks, Timothy; Brooks, William; Brosamer, Jacquelyn; Brost, Elizabeth; Brown, Gareth; Brown, Jonathan; Bruckman de Renstrom, Pawel; Bruncko, Dusan; Bruneliere, Renaud; Brunet, Sylvie; Bruni, Alessia; Bruni, Graziano; Bruschi, Marco; Bryngemark, Lene; Buanes, Trygve; Buat, Quentin; Bucci, Francesca; Buchholz, Peter; Buckingham, Ryan; Buckley, Andrew; Buda, Stelian Ioan; Budagov, Ioulian; Buehrer, Felix; Bugge, Lars; Bugge, Magnar Kopangen; Bulekov, Oleg; Bundock, Aaron Colin; Burckhart, Helfried; Burdin, Sergey; Burghgrave, Blake; Burke, Stephen; Burmeister, Ingo; Busato, Emmanuel; Büscher, Daniel; Büscher, Volker; Bussey, Peter; Buszello, Claus-Peter; Butler, Bart; Butler, John; Butt, Aatif Imtiaz; Buttar, Craig; Butterworth, Jonathan; Butti, Pierfrancesco; Buttinger, William; Buzatu, Adrian; Byszewski, Marcin; Cabrera Urbán, Susana; Caforio, Davide; Cakir, Orhan; Calafiura, Paolo; Calandri, Alessandro; Calderini, Giovanni; Calfayan, Philippe; Calkins, Robert; Caloba, Luiz; Calvet, David; Calvet, Samuel; Camacho Toro, Reina; Camarda, Stefano; Cameron, David; Caminada, Lea Michaela; Caminal Armadans, Roger; Campana, Simone; Campanelli, Mario; Campoverde, Angel; Canale, Vincenzo; Canepa, Anadi; Cano Bret, Marc; Cantero, Josu; Cantrill, Robert; Cao, Tingting; Capeans Garrido, Maria Del Mar; Caprini, Irinel; Caprini, Mihai; Capua, Marcella; Caputo, Regina; Cardarelli, Roberto; Carli, Tancredi; Carlino, Gianpaolo; Carminati, Leonardo; Caron, Sascha; Carquin, Edson; Carrillo-Montoya, German D; Carter, Janet; Carvalho, João; Casadei, Diego; Casado, Maria Pilar; Casolino, Mirkoantonio; Castaneda-Miranda, Elizabeth; Castelli, Angelantonio; Castillo Gimenez, Victoria; Castro, Nuno Filipe; Catastini, Pierluigi; Catinaccio, Andrea; Catmore, James; Cattai, Ariella; Cattani, Giordano; Caughron, Seth; Cavaliere, Viviana; Cavalli, Donatella; Cavalli-Sforza, Matteo; Cavasinni, Vincenzo; Ceradini, Filippo; Cerio, Benjamin; Cerny, Karel; Santiago Cerqueira, Augusto; Cerri, Alessandro; Cerrito, Lucio; Cerutti, Fabio; Cerv, Matevz; Cervelli, Alberto; Cetin, Serkant Ali; Chafaq, Aziz; Chakraborty, Dhiman; Chalupkova, Ina; Chan, Kevin; Chang, Philip; Chapleau, Bertrand; Chapman, John Derek; Charfeddine, Driss; Charlton, Dave; Chau, Chav Chhiv; Chavez Barajas, Carlos Alberto; Cheatham, Susan; Chegwidden, Andrew; Chekanov, Sergei; Chekulaev, Sergey; Chelkov, Gueorgui; Chelstowska, Magda Anna; Chen, Chunhui; Chen, Hucheng; Chen, Karen; Chen, Liming; Chen, Shenjian; Chen, Xin; Chen, Yujiao; Cheng, Hok Chuen; Cheng, Yangyang; Cheplakov, Alexander; Cherkaoui El Moursli, Rajaa; Chernyatin, Valeriy; Cheu, Elliott; Chevalier, Laurent; Chiarella, Vitaliano; Chiefari, Giovanni; Childers, John Taylor; Chilingarov, Alexandre; Chiodini, Gabriele; Chisholm, Andrew; Chislett, Rebecca Thalatta; Chitan, Adrian; Chizhov, Mihail; Chouridou, Sofia; Chow, Bonnie Kar Bo; Chromek-Burckhart, Doris; Chu, Ming-Lee; Chudoba, Jiri; Chwastowski, Janusz; Chytka, Ladislav; Ciapetti, Guido; Ciftci, Abbas Kenan; Ciftci, Rena; Cinca, Diane; Cindro, Vladimir; Ciocio, Alessandra; Cirkovic, Predrag; Citron, Zvi Hirsh; Citterio, Mauro; Ciubancan, Mihai; Clark, Allan G; Clark, Philip James; Clarke, Robert; Cleland, Bill; Clemens, Jean-Claude; Clement, Christophe; Coadou, Yann; Cobal, Marina; Coccaro, Andrea; Cochran, James H; Coffey, Laurel; Cogan, Joshua Godfrey; Coggeshall, James; Cole, Brian; Cole, Stephen; Colijn, Auke-Pieter; Collot, Johann; Colombo, Tommaso; Colon, German; Compostella, Gabriele; Conde Muiño, Patricia; Coniavitis, Elias; Conidi, Maria Chiara; Connell, Simon Henry; Connelly, Ian; Consonni, Sofia Maria; Consorti, Valerio; Constantinescu, Serban; Conta, Claudio; Conti, Geraldine; Conventi, Francesco; Cooke, Mark; Cooper, Ben; Cooper-Sarkar, Amanda; Cooper-Smith, Neil; Copic, Katherine; Cornelissen, Thijs; Corradi, Massimo; Corriveau, Francois; Corso-Radu, Alina; Cortes-Gonzalez, Arely; Cortiana, Giorgio; Costa, Giuseppe; Costa, María José; Costanzo, Davide; Côté, David; Cottin, Giovanna; Cowan, Glen; Cox, Brian; Cranmer, Kyle; Cree, Graham; Crépé-Renaudin, Sabine; Crescioli, Francesco; Cribbs, Wayne Allen; Crispin Ortuzar, Mireia; Cristinziani, Markus; Croft, Vince; Crosetti, Giovanni; Cuciuc, Constantin-Mihai; Cuhadar Donszelmann, Tulay; Cummings, Jane; Curatolo, Maria; Cuthbert, Cameron; Czirr, Hendrik; Czodrowski, Patrick; Czyczula, Zofia; D'Auria, Saverio; D'Onofrio, Monica; Da Cunha Sargedas De Sousa, Mario Jose; Da Via, Cinzia; Dabrowski, Wladyslaw; Dafinca, Alexandru; Dai, Tiesheng; Dale, Orjan; Dallaire, Frederick; Dallapiccola, Carlo; Dam, Mogens; Daniells, Andrew Christopher; Dano Hoffmann, Maria; Dao, Valerio; Darbo, Giovanni; Darmora, Smita; Dassoulas, James; Dattagupta, Aparajita; Davey, Will; David, Claire; Davidek, Tomas; Davies, Eleanor; Davies, Merlin; Davignon, Olivier; Davison, Adam; Davison, Peter; Davygora, Yuriy; Dawe, Edmund; Dawson, Ian; Daya-Ishmukhametova, Rozmin; De, Kaushik; de Asmundis, Riccardo; De Castro, Stefano; De Cecco, Sandro; De Groot, Nicolo; de Jong, Paul; De la Torre, Hector; De Lorenzi, Francesco; De Nooij, Lucie; De Pedis, Daniele; De Salvo, Alessandro; De Sanctis, Umberto; De Santo, Antonella; De Vivie De Regie, Jean-Baptiste; Dearnaley, William James; Debbe, Ramiro; Debenedetti, Chiara; Dechenaux, Benjamin; Dedovich, Dmitri; Deigaard, Ingrid; Del Peso, Jose; Del Prete, Tarcisio; Deliot, Frederic; Delitzsch, Chris Malena; Deliyergiyev, Maksym; Dell'Acqua, Andrea; Dell'Asta, Lidia; Dell'Orso, Mauro; Della Pietra, Massimo; della Volpe, Domenico; Delmastro, Marco; Delsart, Pierre-Antoine; Deluca, Carolina; Demers, Sarah; Demichev, Mikhail; Demilly, Aurelien; Denisov, Sergey; Derendarz, Dominik; Derkaoui, Jamal Eddine; Derue, Frederic; Dervan, Paul; Desch, Klaus Kurt; Deterre, Cecile; Deviveiros, Pier-Olivier; Dewhurst, Alastair; Dhaliwal, Saminder; Di Ciaccio, Anna; Di Ciaccio, Lucia; Di Domenico, Antonio; Di Donato, Camilla; Di Girolamo, Alessandro; Di Girolamo, Beniamino; Di Mattia, Alessandro; Di Micco, Biagio; Di Nardo, Roberto; Di Simone, Andrea; Di Sipio, Riccardo; Di Valentino, David; Diaz, Marco Aurelio; Diehl, Edward; Dietrich, Janet; Dietzsch, Thorsten; Diglio, Sara; Dimitrievska, Aleksandra; Dingfelder, Jochen; Dionisi, Carlo; Dita, Petre; Dita, Sanda; Dittus, Fridolin; Djama, Fares; Djobava, Tamar; Barros do Vale, Maria Aline; Do Valle Wemans, André; Doan, Thi Kieu Oanh; Dobos, Daniel; Doglioni, Caterina; Doherty, Tom; Dohmae, Takeshi; Dolejsi, Jiri; Dolezal, Zdenek; Dolgoshein, Boris; Donadelli, Marisilvia; Donati, Simone; Dondero, Paolo; Donini, Julien; Dopke, Jens; Doria, Alessandra; Dova, Maria-Teresa; Doyle, Tony; Dris, Manolis; Dubbert, Jörg; Dube, Sourabh; Dubreuil, Emmanuelle; Duchovni, Ehud; Duckeck, Guenter; Ducu, Otilia Anamaria; Duda, Dominik; Dudarev, Alexey; Dudziak, Fanny; Duflot, Laurent; Duguid, Liam; Dührssen, Michael; Dunford, Monica; Duran Yildiz, Hatice; Düren, Michael; Durglishvili, Archil; Dwuznik, Michal; Dyndal, Mateusz; Ebke, Johannes; Edson, William; Edwards, Nicholas Charles; Ehrenfeld, Wolfgang; Eifert, Till; Eigen, Gerald; Einsweiler, Kevin; Ekelof, Tord; El Kacimi, Mohamed; Ellert, Mattias; Elles, Sabine; Ellinghaus, Frank; Ellis, Nicolas; Elmsheuser, Johannes; Elsing, Markus; Emeliyanov, Dmitry; Enari, Yuji; Endner, Oliver Chris; Endo, Masaki; Engelmann, Roderich; Erdmann, Johannes; Ereditato, Antonio; Eriksson, Daniel; Ernis, Gunar; Ernst, Jesse; Ernst, Michael; Ernwein, Jean; Errede, Deborah; Errede, Steven; Ertel, Eugen; Escalier, Marc; Esch, Hendrik; Escobar, Carlos; Esposito, Bellisario; Etienvre, Anne-Isabelle; Etzion, Erez; Evans, Hal; Ezhilov, Alexey; Fabbri, Laura; Facini, Gabriel; Fakhrutdinov, Rinat; Falciano, Speranza; Falla, Rebecca Jane; Faltova, Jana; Fang, Yaquan; Fanti, Marcello; Farbin, Amir; Farilla, Addolorata; Farooque, Trisha; Farrell, Steven; Farrington, Sinead; Farthouat, Philippe; Fassi, Farida; Fassnacht, Patrick; Fassouliotis, Dimitrios; Favareto, Andrea; Fayard, Louis; Federic, Pavol; Fedin, Oleg; Fedorko, Wojciech; Fehling-Kaschek, Mirjam; Feigl, Simon; Feligioni, Lorenzo; Feng, Cunfeng; Feng, Eric; Feng, Haolu; Fenyuk, Alexander; Fernandez Perez, Sonia; Ferrag, Samir; Ferrando, James; Ferrari, Arnaud; Ferrari, Pamela; Ferrari, Roberto; Ferreira de Lima, Danilo Enoque; Ferrer, Antonio; Ferrere, Didier; Ferretti, Claudio; Ferretto Parodi, Andrea; Fiascaris, Maria; Fiedler, Frank; Filipčič, Andrej; Filipuzzi, Marco; Filthaut, Frank; Fincke-Keeler, Margret; Finelli, Kevin Daniel; Fiolhais, Miguel; Fiorini, Luca; Firan, Ana; Fischer, Julia; Fisher, Wade Cameron; Fitzgerald, Eric Andrew; Flechl, Martin; Fleck, Ivor; Fleischmann, Philipp; Fleischmann, Sebastian; Fletcher, Gareth Thomas; Fletcher, Gregory; Flick, Tobias; Floderus, Anders; Flores Castillo, Luis; Florez Bustos, Andres Carlos; Flowerdew, Michael; Formica, Andrea; Forti, Alessandra; Fortin, Dominique; Fournier, Daniel; Fox, Harald; Fracchia, Silvia; Francavilla, Paolo; Franchini, Matteo; Franchino, Silvia; Francis, David; Franklin, Melissa; Franz, Sebastien; Fraternali, Marco; French, Sky; Friedrich, Conrad; Friedrich, Felix; Froidevaux, Daniel; Frost, James; Fukunaga, Chikara; Fullana Torregrosa, Esteban; Fulsom, Bryan Gregory; Fuster, Juan; Gabaldon, Carolina; Gabizon, Ofir; Gabrielli, Alessandro; Gabrielli, Andrea; Gadatsch, Stefan; Gadomski, Szymon; Gagliardi, Guido; Gagnon, Pauline; Galea, Cristina; Galhardo, Bruno; Gallas, Elizabeth; Gallo, Valentina Santina; Gallop, Bruce; Gallus, Petr; Galster, Gorm Aske Gram Krohn; Gan, KK; Gandrajula, Reddy Pratap; Gao, Jun; Gao, Yongsheng; Garay Walls, Francisca; Garberson, Ford; García, Carmen; García Navarro, José Enrique; Garcia-Sciveres, Maurice; Gardner, Robert; Garelli, Nicoletta; Garonne, Vincent; Gatti, Claudio; Gaudio, Gabriella; Gaur, Bakul; Gauthier, Lea; Gauzzi, Paolo; Gavrilenko, Igor; Gay, Colin; Gaycken, Goetz; Gazis, Evangelos; Ge, Peng; Gecse, Zoltan; Gee, Norman; Geerts, Daniël Alphonsus Adrianus; Geich-Gimbel, Christoph; Gellerstedt, Karl; Gemme, Claudia; Gemmell, Alistair; Genest, Marie-Hélène; Gentile, Simonetta; George, Matthias; George, Simon; Gerbaudo, Davide; Gershon, Avi; Ghazlane, Hamid; Ghodbane, Nabil; Giacobbe, Benedetto; Giagu, Stefano; Giangiobbe, Vincent; Giannetti, Paola; Gianotti, Fabiola; Gibbard, Bruce; Gibson, Stephen; Gilchriese, Murdock; Gillam, Thomas; Gillberg, Dag; Gilles, Geoffrey; Gingrich, Douglas; Giokaris, Nikos; Giordani, MarioPaolo; Giordano, Raffaele; Giorgi, Filippo Maria; Giorgi, Francesco Michelangelo; Giraud, Pierre-Francois; Giugni, Danilo; Giuliani, Claudia; Giulini, Maddalena; Gjelsten, Børge Kile; Gkaitatzis, Stamatios; Gkialas, Ioannis; Gladilin, Leonid; Glasman, Claudia; Glatzer, Julian; Glaysher, Paul; Glazov, Alexandre; Glonti, George; Goblirsch-Kolb, Maximilian; Goddard, Jack Robert; Godfrey, Jennifer; Godlewski, Jan; Goeringer, Christian; Goldfarb, Steven; Golling, Tobias; Golubkov, Dmitry; Gomes, Agostinho; Gomez Fajardo, Luz Stella; Gonçalo, Ricardo; Goncalves Pinto Firmino Da Costa, Joao; Gonella, Laura; González de la Hoz, Santiago; Gonzalez Parra, Garoe; Gonzalez Silva, Laura; Gonzalez-Sevilla, Sergio; Goossens, Luc; Gorbounov, Petr Andreevich; Gordon, Howard; Gorelov, Igor; Gorini, Benedetto; Gorini, Edoardo; Gorišek, Andrej; Gornicki, Edward; Goshaw, Alfred; Gössling, Claus; Gostkin, Mikhail Ivanovitch; Gouighri, Mohamed; Goujdami, Driss; Goulette, Marc Phillippe; Goussiou, Anna; Goy, Corinne; Gozpinar, Serdar; Grabas, Herve Marie Xavier; Graber, Lars; Grabowska-Bold, Iwona; Grafström, Per; Grahn, Karl-Johan; Gramling, Johanna; Gramstad, Eirik; Grancagnolo, Sergio; Grassi, Valerio; Gratchev, Vadim; Gray, Heather; Graziani, Enrico; Grebenyuk, Oleg; Greenwood, Zeno Dixon; Gregersen, Kristian; Gregor, Ingrid-Maria; Grenier, Philippe; Griffiths, Justin; Grillo, Alexander; Grimm, Kathryn; Grinstein, Sebastian; Gris, Philippe Luc Yves; Grishkevich, Yaroslav; Grivaz, Jean-Francois; Grohs, Johannes Philipp; Grohsjean, Alexander; Gross, Eilam; Grosse-Knetter, Joern; Grossi, Giulio Cornelio; Groth-Jensen, Jacob; Grout, Zara Jane; Guan, Liang; Guescini, Francesco; Guest, Daniel; Gueta, Orel; Guicheney, Christophe; Guido, Elisa; Guillemin, Thibault; Guindon, Stefan; Gul, Umar; Gumpert, Christian; Gunther, Jaroslav; Guo, Jun; Gupta, Shaun; Gutierrez, Phillip; Gutierrez Ortiz, Nicolas Gilberto; Gutschow, Christian; Guttman, Nir; Guyot, Claude; Gwenlan, Claire; Gwilliam, Carl; Haas, Andy; Haber, Carl; Hadavand, Haleh Khani; Haddad, Nacim; Haefner, Petra; Hageböck, Stephan; Hajduk, Zbigniew; Hakobyan, Hrachya; Haleem, Mahsana; Hall, David; Halladjian, Garabed; Hamacher, Klaus; Hamal, Petr; Hamano, Kenji; Hamer, Matthias; Hamilton, Andrew; Hamilton, Samuel; Hamnett, Phillip George; Han, Liang; Hanagaki, Kazunori; Hanawa, Keita; Hance, Michael; Hanke, Paul; Hanna, Remie; Hansen, Jørgen Beck; Hansen, Jorn Dines; Hansen, Peter Henrik; Hara, Kazuhiko; Hard, Andrew; Harenberg, Torsten; Hariri, Faten; Harkusha, Siarhei; Harper, Devin; Harrington, Robert; Harris, Orin; Harrison, Paul Fraser; Hartjes, Fred; Hasegawa, Satoshi; Hasegawa, Yoji; Hasib, A; Hassani, Samira; Haug, Sigve; Hauschild, Michael; Hauser, Reiner; Havranek, Miroslav; Hawkes, Christopher; Hawkings, Richard John; Hawkins, Anthony David; Hayashi, Takayasu; Hayden, Daniel; Hays, Chris; Hayward, Helen; Haywood, Stephen; Head, Simon; Heck, Tobias; Hedberg, Vincent; Heelan, Louise; Heim, Sarah; Heim, Timon; Heinemann, Beate; Heinrich, Lukas; Heisterkamp, Simon; Hejbal, Jiri; Helary, Louis; Heller, Claudio; Heller, Matthieu; Hellman, Sten; Hellmich, Dennis; Helsens, Clement; Henderson, James; Henderson, Robert; Hengler, Christopher; Henrichs, Anna; Henriques Correia, Ana Maria; Henrot-Versille, Sophie; Hensel, Carsten; Herbert, Geoffrey Henry; Hernández Jiménez, Yesenia; Herrberg-Schubert, Ruth; Herten, Gregor; Hertenberger, Ralf; Hervas, Luis; Hesketh, Gavin Grant; Hessey, Nigel; Hickling, Robert; Higón-Rodriguez, Emilio; Hill, Ewan; Hill, John; Hiller, Karl Heinz; Hillert, Sonja; Hillier, Stephen; Hinchliffe, Ian; Hines, Elizabeth; Hirose, Minoru; Hirschbuehl, Dominic; Hobbs, John; Hod, Noam; Hodgkinson, Mark; Hodgson, Paul; Hoecker, Andreas; Hoeferkamp, Martin; Hoffman, Julia; Hoffmann, Dirk; Hofmann, Julia Isabell; Hohlfeld, Marc; Holmes, Tova Ray; Hong, Tae Min; Hooft van Huysduynen, Loek; Hostachy, Jean-Yves; Hou, Suen; Hoummada, Abdeslam; Howard, Jacob; Howarth, James; Hrabovsky, Miroslav; Hristova, Ivana; Hrivnac, Julius; Hryn'ova, Tetiana; Hsu, Pai-hsien Jennifer; Hsu, Shih-Chieh; Hu, Diedi; Hu, Xueye; Huang, Yanping; Hubacek, Zdenek; Hubaut, Fabrice; Huegging, Fabian; Huffman, Todd Brian; Hughes, Emlyn; Hughes, Gareth; Huhtinen, Mika; Hülsing, Tobias Alexander; Hurwitz, Martina; Huseynov, Nazim; Huston, Joey; Huth, John; Iacobucci, Giuseppe; Iakovidis, Georgios; Ibragimov, Iskander; Iconomidou-Fayard, Lydia; Ideal, Emma; Iengo, Paolo; Igonkina, Olga; Iizawa, Tomoya; Ikegami, Yoichi; Ikematsu, Katsumasa; Ikeno, Masahiro; Ilchenko, Iurii; Iliadis, Dimitrios; Ilic, Nikolina; Inamaru, Yuki; Ince, Tayfun; Ioannou, Pavlos; Iodice, Mauro; Iordanidou, Kalliopi; Ippolito, Valerio; Irles Quiles, Adrian; Isaksson, Charlie; Ishino, Masaya; Ishitsuka, Masaki; Ishmukhametov, Renat; Issever, Cigdem; Istin, Serhat; Iturbe Ponce, Julia Mariana; Iuppa, Roberto; Ivarsson, Jenny; Iwanski, Wieslaw; Iwasaki, Hiroyuki; Izen, Joseph; Izzo, Vincenzo; Jackson, Brett; Jackson, Matthew; Jackson, Paul; Jaekel, Martin; Jain, Vivek; Jakobs, Karl; Jakobsen, Sune; Jakoubek, Tomas; Jakubek, Jan; Jamin, David Olivier; Jana, Dilip; Jansen, Eric; Jansen, Hendrik; Janssen, Jens; Janus, Michel; Jarlskog, Göran; Javadov, Namig; Javůrek, Tomáš; Jeanty, Laura; Jejelava, Juansher; Jeng, Geng-yuan; Jennens, David; Jenni, Peter; Jentzsch, Jennifer; Jeske, Carl; Jézéquel, Stéphane; Ji, Haoshuang; Ji, Weina; Jia, Jiangyong; Jiang, Yi; Jimenez Belenguer, Marcos; Jin, Shan; Jinaru, Adam; Jinnouchi, Osamu; Joergensen, Morten Dam; Johansson, Erik; Johansson, Per; Johns, Kenneth; Jon-And, Kerstin; Jones, Graham; Jones, Roger; Jones, Tim; Jongmanns, Jan; Jorge, Pedro; Joshi, Kiran Daniel; Jovicevic, Jelena; Ju, Xiangyang; Jung, Christian; Jungst, Ralph Markus; Jussel, Patrick; Juste Rozas, Aurelio; Kaci, Mohammed; Kaczmarska, Anna; Kado, Marumi; Kagan, Harris; Kagan, Michael; Kajomovitz, Enrique; Kalderon, Charles William; Kama, Sami; Kanaya, Naoko; Kaneda, Michiru; Kaneti, Steven; Kanno, Takayuki; Kantserov, Vadim; Kanzaki, Junichi; Kaplan, Benjamin; Kapliy, Anton; Kar, Deepak; Karakostas, Konstantinos; Karastathis, Nikolaos; Karnevskiy, Mikhail; Karpov, Sergey; Karthik, Krishnaiyengar; Kartvelishvili, Vakhtang; Karyukhin, Andrey; Kashif, Lashkar; Kasieczka, Gregor; Kass, Richard; Kastanas, Alex; Kataoka, Yousuke; Katre, Akshay; Katzy, Judith; Kaushik, Venkatesh; Kawagoe, Kiyotomo; Kawamoto, Tatsuo; Kawamura, Gen; Kazama, Shingo; Kazanin, Vassili; Kazarinov, Makhail; Keeler, Richard; Kehoe, Robert; Keil, Markus; Keller, John; Kempster, Jacob Julian; Keoshkerian, Houry; Kepka, Oldrich; Kerševan, Borut Paul; Kersten, Susanne; Kessoku, Kohei; Keung, Justin; Khalil-zada, Farkhad; Khandanyan, Hovhannes; Khanov, Alexander; Khodinov, Alexander; Khomich, Andrei; Khoo, Teng Jian; Khoriauli, Gia; Khoroshilov, Andrey; Khovanskiy, Valery; Khramov, Evgeniy; Khubua, Jemal; Kim, Hee Yeun; Kim, Hyeon Jin; Kim, Shinhong; Kimura, Naoki; Kind, Oliver; King, Barry; King, Matthew; King, Robert Steven Beaufoy; King, Samuel Burton; Kirk, Julie; Kiryunin, Andrey; Kishimoto, Tomoe; Kisielewska, Danuta; Kiss, Florian; Kitamura, Takumi; Kittelmann, Thomas; Kiuchi, Kenji; Kladiva, Eduard; Klein, Max; Klein, Uta; Kleinknecht, Konrad; Klimek, Pawel; Klimentov, Alexei; Klingenberg, Reiner; Klinger, Joel Alexander; Klioutchnikova, Tatiana; Klok, Peter; Kluge, Eike-Erik; Kluit, Peter; Kluth, Stefan; Kneringer, Emmerich; Knoops, Edith; Knue, Andrea; Kobayashi, Tomio; Kobel, Michael; Kocian, Martin; Kodys, Peter; Koevesarki, Peter; Koffas, Thomas; Koffeman, Els; Kogan, Lucy Anne; Kohlmann, Simon; Kohout, Zdenek; Kohriki, Takashi; Koi, Tatsumi; Kolanoski, Hermann; Koletsou, Iro; Koll, James; Komar, Aston; Komori, Yuto; Kondo, Takahiko; Kondrashova, Nataliia; Köneke, Karsten; König, Adriaan; König, Sebastian; Kono, Takanori; Konoplich, Rostislav; Konstantinidis, Nikolaos; Kopeliansky, Revital; Koperny, Stefan; Köpke, Lutz; Kopp, Anna Katharina; Korcyl, Krzysztof; Kordas, Kostantinos; Korn, Andreas; Korol, Aleksandr; Korolkov, Ilya; Korolkova, Elena; Korotkov, Vladislav; Kortner, Oliver; Kortner, Sandra; Kostyukhin, Vadim; Kotov, Vladislav; Kotwal, Ashutosh; Kourkoumelis, Christine; Kouskoura, Vasiliki; Koutsman, Alex; Kowalewski, Robert Victor; Kowalski, Tadeusz; Kozanecki, Witold; Kozhin, Anatoly; Kral, Vlastimil; Kramarenko, Viktor; Kramberger, Gregor; Krasnopevtsev, Dimitriy; Krasny, Mieczyslaw Witold; Krasznahorkay, Attila; Kraus, Jana; Kravchenko, Anton; Kreiss, Sven; Kretz, Moritz; Kretzschmar, Jan; Kreutzfeldt, Kristof; Krieger, Peter; Kroeninger, Kevin; Kroha, Hubert; Kroll, Joe; Kroseberg, Juergen; Krstic, Jelena; Kruchonak, Uladzimir; Krüger, Hans; Kruker, Tobias; Krumnack, Nils; Krumshteyn, Zinovii; Kruse, Amanda; Kruse, Mark; Kruskal, Michael; Kubota, Takashi; Kuday, Sinan; Kuehn, Susanne; Kugel, Andreas; Kuhl, Andrew; Kuhl, Thorsten; Kukhtin, Victor; Kulchitsky, Yuri; Kuleshov, Sergey; Kuna, Marine; Kunkle, Joshua; Kupco, Alexander; Kurashige, Hisaya; Kurochkin, Yurii; Kurumida, Rie; Kus, Vlastimil; Kuwertz, Emma Sian; Kuze, Masahiro; Kvita, Jiri; La Rosa, Alessandro; La Rotonda, Laura; Lacasta, Carlos; Lacava, Francesco; Lacey, James; Lacker, Heiko; Lacour, Didier; Lacuesta, Vicente Ramón; Ladygin, Evgueni; Lafaye, Remi; Laforge, Bertrand; Lagouri, Theodota; Lai, Stanley; Laier, Heiko; Lambourne, Luke; Lammers, Sabine; Lampen, Caleb; Lampl, Walter; Lançon, Eric; Landgraf, Ulrich; Landon, Murrough; Lang, Valerie Susanne; Lange, Clemens; Lankford, Andrew; Lanni, Francesco; Lantzsch, Kerstin; Laplace, Sandrine; Lapoire, Cecile; Laporte, Jean-Francois; Lari, Tommaso; Lassnig, Mario; Laurelli, Paolo; Lavrijsen, Wim; Law, Alexander; Laycock, Paul; Le, Bao Tran; Le Dortz, Olivier; Le Guirriec, Emmanuel; Le Menedeu, Eve; LeCompte, Thomas; Ledroit-Guillon, Fabienne Agnes Marie; Lee, Claire Alexandra; Lee, Hurng-Chun; Lee, Jason; Lee, Shih-Chang; Lee, Lawrence; Lefebvre, Guillaume; Lefebvre, Michel; Legger, Federica; Leggett, Charles; Lehan, Allan; Lehmacher, Marc; Lehmann Miotto, Giovanna; Lei, Xiaowen; Leight, William Axel; Leisos, Antonios; Leister, Andrew Gerard; Leite, Marco Aurelio Lisboa; Leitner, Rupert; Lellouch, Daniel; Lemmer, Boris; Leney, Katharine; Lenz, Tatjana; Lenzen, Georg; Lenzi, Bruno; Leone, Robert; Leonhardt, Kathrin; Leontsinis, Stefanos; Leroy, Claude; Lester, Christopher; Lester, Christopher Michael; Levchenko, Mikhail; Levêque, Jessica; Levin, Daniel; Levinson, Lorne; Levy, Mark; Lewis, Adrian; Lewis, George; Leyko, Agnieszka; Leyton, Michael; Li, Bing; Li, Bo; Li, Haifeng; Li, Ho Ling; Li, Lei; Li, Liang; Li, Shu; Li, Yichen; Liang, Zhijun; Liao, Hongbo; Liberti, Barbara; Lichard, Peter; Lie, Ki; Liebal, Jessica; Liebig, Wolfgang; Limbach, Christian; Limosani, Antonio; Lin, Simon; Lin, Tai-Hua; Linde, Frank; Lindquist, Brian Edward; Linnemann, James; Lipeles, Elliot; Lipniacka, Anna; Lisovyi, Mykhailo; Liss, Tony; Lissauer, David; Lister, Alison; Litke, Alan; Liu, Bo; Liu, Dong; Liu, Jianbei; Liu, Kun; Liu, Lulu; Liu, Miaoyuan; Liu, Minghui; Liu, Yanwen; Livan, Michele; Livermore, Sarah; Lleres, Annick; Llorente Merino, Javier; Lloyd, Stephen; Lo Sterzo, Francesco; Lobodzinska, Ewelina; Loch, Peter; Lockman, William; Loddenkoetter, Thomas; Loebinger, Fred; Loevschall-Jensen, Ask Emil; Loginov, Andrey; Loh, Chang Wei; Lohse, Thomas; Lohwasser, Kristin; Lokajicek, Milos; Lombardo, Vincenzo Paolo; Long, Brian Alexander; Long, Jonathan; Long, Robin Eamonn; Lopes, Lourenco; Lopez Mateos, David; Lopez Paredes, Brais; Lopez Paz, Ivan; Lorenz, Jeanette; Lorenzo Martinez, Narei; Losada, Marta; Loscutoff, Peter; Lou, XinChou; Lounis, Abdenour; Love, Jeremy; Love, Peter; Lowe, Andrew; Lu, Feng; Lubatti, Henry; Luci, Claudio; Lucotte, Arnaud; Luehring, Frederick; Lukas, Wolfgang; Luminari, Lamberto; Lundberg, Olof; Lund-Jensen, Bengt; Lungwitz, Matthias; Lynn, David; Lysak, Roman; Lytken, Else; Ma, Hong; Ma, Lian Liang; Maccarrone, Giovanni; Macchiolo, Anna; Machado Miguens, Joana; Macina, Daniela; Madaffari, Daniele; Madar, Romain; Maddocks, Harvey Jonathan; Mader, Wolfgang; Madsen, Alexander; Maeno, Mayuko; Maeno, Tadashi; Magradze, Erekle; Mahboubi, Kambiz; Mahlstedt, Joern; Mahmoud, Sara; Maiani, Camilla; Maidantchik, Carmen; Maio, Amélia; Majewski, Stephanie; Makida, Yasuhiro; Makovec, Nikola; Mal, Prolay; Malaescu, Bogdan; Malecki, Pawel; Maleev, Victor; Malek, Fairouz; Mallik, Usha; Malon, David; Malone, Caitlin; Maltezos, Stavros; Malyshev, Vladimir; Malyukov, Sergei; Mamuzic, Judita; Mandelli, Beatrice; Mandelli, Luciano; Mandić, Igor; Mandrysch, Rocco; Maneira, José; Manfredini, Alessandro; Manhaes de Andrade Filho, Luciano; Manjarres Ramos, Joany Andreina; Mann, Alexander; Manning, Peter; Manousakis-Katsikakis, Arkadios; Mansoulie, Bruno; Mantifel, Rodger; Mapelli, Livio; March, Luis; Marchand, Jean-Francois; Marchiori, Giovanni; Marcisovsky, Michal; Marino, Christopher; Marjanovic, Marija; Marques, Carlos; Marroquim, Fernando; Marsden, Stephen Philip; Marshall, Zach; Marti, Lukas Fritz; Marti-Garcia, Salvador; Martin, Brian; Martin, Brian Thomas; Martin, Tim; Martin, Victoria Jane; Martin dit Latour, Bertrand; Martinez, Homero; Martinez, Mario; Martin-Haugh, Stewart; Martyniuk, Alex; Marx, Marilyn; Marzano, Francesco; Marzin, Antoine; Masetti, Lucia; Mashimo, Tetsuro; Mashinistov, Ruslan; Masik, Jiri; Maslennikov, Alexey; Massa, Ignazio; Massol, Nicolas; Mastrandrea, Paolo; Mastroberardino, Anna; Masubuchi, Tatsuya; Matsushita, Takashi; Mättig, Peter; Mättig, Stefan; Mattmann, Johannes; Maurer, Julien; Maxfield, Stephen; Maximov, Dmitriy; Mazini, Rachid; Mazzaferro, Luca; Mc Goldrick, Garrin; Mc Kee, Shawn Patrick; McCarn, Allison; McCarthy, Robert; McCarthy, Tom; McCubbin, Norman; McFarlane, Kenneth; Mcfayden, Josh; Mchedlidze, Gvantsa; McMahon, Steve; McPherson, Robert; Meade, Andrew; Mechnich, Joerg; Medinnis, Michael; Meehan, Samuel; Mehlhase, Sascha; Mehta, Andrew; Meier, Karlheinz; Meineck, Christian; Meirose, Bernhard; Melachrinos, Constantinos; Mellado Garcia, Bruce Rafael; Meloni, Federico; Mengarelli, Alberto; Menke, Sven; Meoni, Evelin; Mercurio, Kevin Michael; Mergelmeyer, Sebastian; Meric, Nicolas; Mermod, Philippe; Merola, Leonardo; Meroni, Chiara; Merritt, Frank; Merritt, Hayes; Messina, Andrea; Metcalfe, Jessica; Mete, Alaettin Serhan; Meyer, Carsten; Meyer, Christopher; Meyer, Jean-Pierre; Meyer, Jochen; Middleton, Robin; Migas, Sylwia; Mijović, Liza; Mikenberg, Giora; Mikestikova, Marcela; Mikuž, Marko; Miller, David; Mills, Corrinne; Milov, Alexander; Milstead, David; Milstein, Dmitry; Minaenko, Andrey; Minashvili, Irakli; Mincer, Allen; Mindur, Bartosz; Mineev, Mikhail; Ming, Yao; Mir, Lluisa-Maria; Mirabelli, Giovanni; Mitani, Takashi; Mitrevski, Jovan; Mitsou, Vasiliki A; Mitsui, Shingo; Miucci, Antonio; Miyagawa, Paul; Mjörnmark, Jan-Ulf; Moa, Torbjoern; Mochizuki, Kazuya; Moeller, Victoria; Mohapatra, Soumya; Mohr, Wolfgang; Molander, Simon; Moles-Valls, Regina; Mönig, Klaus; Monini, Caterina; Monk, James; Monnier, Emmanuel; Montejo Berlingen, Javier; Monticelli, Fernando; Monzani, Simone; Moore, Roger; Moraes, Arthur; Morange, Nicolas; Moreno, Deywis; Moreno Llácer, María; Morettini, Paolo; Morgenstern, Marcus; Morii, Masahiro; Moritz, Sebastian; Morley, Anthony Keith; Mornacchi, Giuseppe; Morris, John; Morvaj, Ljiljana; Moser, Hans-Guenther; Mosidze, Maia; Moss, Josh; Mount, Richard; Mountricha, Eleni; Mouraviev, Sergei; Moyse, Edward; Muanza, Steve; Mudd, Richard; Mueller, Felix; Mueller, James; Mueller, Klemens; Mueller, Thibaut; Mueller, Timo; Muenstermann, Daniel; Munwes, Yonathan; Murillo Quijada, Javier Alberto; Murray, Bill; Musheghyan, Haykuhi; Musto, Elisa; Myagkov, Alexey; Myska, Miroslav; Nackenhorst, Olaf; Nadal, Jordi; Nagai, Koichi; Nagai, Ryo; Nagai, Yoshikazu; Nagano, Kunihiro; Nagarkar, Advait; Nagasaka, Yasushi; Nagel, Martin; Nairz, Armin Michael; Nakahama, Yu; Nakamura, Koji; Nakamura, Tomoaki; Nakano, Itsuo; Namasivayam, Harisankar; Nanava, Gizo; Narayan, Rohin; Nattermann, Till; Naumann, Thomas; Navarro, Gabriela; Nayyar, Ruchika; Neal, Homer; Nechaeva, Polina; Neep, Thomas James; Negri, Andrea; Negri, Guido; Negrini, Matteo; Nektarijevic, Snezana; Nelson, Andrew; Nelson, Timothy Knight; Nemecek, Stanislav; Nemethy, Peter; Nepomuceno, Andre Asevedo; Nessi, Marzio; Neubauer, Mark; Neumann, Manuel; Neves, Ricardo; Nevski, Pavel; Newman, Paul; Nguyen, Duong Hai; Nickerson, Richard; Nicolaidou, Rosy; Nicquevert, Bertrand; Nielsen, Jason; Nikiforou, Nikiforos; Nikiforov, Andriy; Nikolaenko, Vladimir; Nikolic-Audit, Irena; Nikolics, Katalin; Nikolopoulos, Konstantinos; Nilsson, Paul; Ninomiya, Yoichi; Nisati, Aleandro; Nisius, Richard; Nobe, Takuya; Nodulman, Lawrence; Nomachi, Masaharu; Nomidis, Ioannis; Norberg, Scarlet; Nordberg, Markus; Nowak, Sebastian; Nozaki, Mitsuaki; Nozka, Libor; Ntekas, Konstantinos; Nunes Hanninger, Guilherme; Nunnemann, Thomas; Nurse, Emily; Nuti, Francesco; O'Brien, Brendan Joseph; O'grady, Fionnbarr; O'Neil, Dugan; O'Shea, Val; Oakham, Gerald; Oberlack, Horst; Obermann, Theresa; Ocariz, Jose; Ochi, Atsuhiko; Ochoa, Ines; Oda, Susumu; Odaka, Shigeru; Ogren, Harold; Oh, Alexander; Oh, Seog; Ohm, Christian; Ohman, Henrik; Ohshima, Takayoshi; Okamura, Wataru; Okawa, Hideki; Okumura, Yasuyuki; Okuyama, Toyonobu; Olariu, Albert; Olchevski, Alexander; Olivares Pino, Sebastian Andres; Oliveira Damazio, Denis; Oliver Garcia, Elena; Olszewski, Andrzej; Olszowska, Jolanta; Onofre, António; Onyisi, Peter; Oram, Christopher; Oreglia, Mark; Oren, Yona; Orestano, Domizia; Orlando, Nicola; Oropeza Barrera, Cristina; Orr, Robert; Osculati, Bianca; Ospanov, Rustem; Otero y Garzon, Gustavo; Otono, Hidetoshi; Ouchrif, Mohamed; Ouellette, Eric; Ould-Saada, Farid; Ouraou, Ahmimed; Oussoren, Koen Pieter; Ouyang, Qun; Ovcharova, Ana; Owen, Mark; Ozcan, Veysi Erkcan; Ozturk, Nurcan; Pachal, Katherine; Pacheco Pages, Andres; Padilla Aranda, Cristobal; Pagáčová, Martina; Pagan Griso, Simone; Paganis, Efstathios; Pahl, Christoph; Paige, Frank; Pais, Preema; Pajchel, Katarina; Palacino, Gabriel; Palestini, Sandro; Palka, Marek; Pallin, Dominique; Palma, Alberto; Palmer, Jody; Pan, Yibin; Panagiotopoulou, Evgenia; Panduro Vazquez, William; Pani, Priscilla; Panikashvili, Natalia; Panitkin, Sergey; Pantea, Dan; Paolozzi, Lorenzo; Papadopoulou, Theodora; Papageorgiou, Konstantinos; Paramonov, Alexander; Paredes Hernandez, Daniela; Parker, Michael Andrew; Parodi, Fabrizio; Parsons, John; Parzefall, Ulrich; Pasqualucci, Enrico; Passaggio, Stefano; Passeri, Antonio; Pastore, Fernanda; Pastore, Francesca; Pásztor, Gabriella; Pataraia, Sophio; Patel, Nikhul; Pater, Joleen; Patricelli, Sergio; Pauly, Thilo; Pearce, James; Pedersen, Maiken; Pedraza Lopez, Sebastian; Pedro, Rute; Peleganchuk, Sergey; Pelikan, Daniel; Peng, Haiping; Penning, Bjoern; Penwell, John; Perepelitsa, Dennis; Perez Codina, Estel; Pérez García-Estañ, María Teresa; Perez Reale, Valeria; Perini, Laura; Pernegger, Heinz; Perrino, Roberto; Peschke, Richard; Peshekhonov, Vladimir; Peters, Krisztian; Peters, Yvonne; Petersen, Brian; Petersen, Troels; Petit, Elisabeth; Petridis, Andreas; Petridou, Chariclia; Petrolo, Emilio; Petrucci, Fabrizio; Petteni, Michele; Pettersson, Nora Emilia; Pezoa, Raquel; Phillips, Peter William; Piacquadio, Giacinto; Pianori, Elisabetta; Picazio, Attilio; Piccaro, Elisa; Piccinini, Maurizio; Piegaia, Ricardo; Pignotti, David; Pilcher, James; Pilkington, Andrew; Pina, João Antonio; Pinamonti, Michele; Pinder, Alex; Pinfold, James; Pingel, Almut; Pinto, Belmiro; Pires, Sylvestre; Pitt, Michael; Pizio, Caterina; Plazak, Lukas; Pleier, Marc-Andre; Pleskot, Vojtech; Plotnikova, Elena; Plucinski, Pawel; Poddar, Sahill; Podlyski, Fabrice; Poettgen, Ruth; Poggioli, Luc; Pohl, David-leon; Pohl, Martin; Polesello, Giacomo; Policicchio, Antonio; Polifka, Richard; Polini, Alessandro; Pollard, Christopher Samuel; Polychronakos, Venetios; Pommès, Kathy; Pontecorvo, Ludovico; Pope, Bernard; Popeneciu, Gabriel Alexandru; Popovic, Dragan; Poppleton, Alan; Portell Bueso, Xavier; Pospelov, Guennady; Pospisil, Stanislav; Potamianos, Karolos; Potrap, Igor; Potter, Christina; Potter, Christopher; Poulard, Gilbert; Poveda, Joaquin; Pozdnyakov, Valery; Pralavorio, Pascal; Pranko, Aliaksandr; Prasad, Srivas; Pravahan, Rishiraj; Prell, Soeren; Price, Darren; Price, Joe; Price, Lawrence; Prieur, Damien; Primavera, Margherita; Proissl, Manuel; Prokofiev, Kirill; Prokoshin, Fedor; Protopapadaki, Eftychia-sofia; Protopopescu, Serban; Proudfoot, James; Przybycien, Mariusz; Przysiezniak, Helenka; Ptacek, Elizabeth; Pueschel, Elisa; Puldon, David; Purohit, Milind; Puzo, Patrick; Qian, Jianming; Qin, Gang; Qin, Yang; Quadt, Arnulf; Quarrie, David; Quayle, William; Queitsch-Maitland, Michaela; Quilty, Donnchadha; Qureshi, Anum; Radeka, Veljko; Radescu, Voica; Radhakrishnan, Sooraj Krishnan; Radloff, Peter; Rados, Pere; Ragusa, Francesco; Rahal, Ghita; Rajagopalan, Srinivasan; Rammensee, Michael; Randle-Conde, Aidan Sean; Rangel-Smith, Camila; Rao, Kanury; Rauscher, Felix; Rave, Tobias Christian; Ravenscroft, Thomas; Raymond, Michel; Read, Alexander Lincoln; Readioff, Nathan Peter; Rebuzzi, Daniela; Redelbach, Andreas; Redlinger, George; Reece, Ryan; Reeves, Kendall; Rehnisch, Laura; Reisin, Hernan; Relich, Matthew; Rembser, Christoph; Ren, Huan; Ren, Zhongliang; Renaud, Adrien; Rescigno, Marco; Resconi, Silvia; Rezanova, Olga; Reznicek, Pavel; Rezvani, Reyhaneh; Richter, Robert; Ridel, Melissa; Rieck, Patrick; Rieger, Julia; Rijssenbeek, Michael; Rimoldi, Adele; Rinaldi, Lorenzo; Ritsch, Elmar; Riu, Imma; Rizatdinova, Flera; Rizvi, Eram; Robertson, Steven; Robichaud-Veronneau, Andree; Robinson, Dave; Robinson, James; Robson, Aidan; Roda, Chiara; Rodrigues, Luis; Roe, Shaun; Røhne, Ole; Rolli, Simona; Romaniouk, Anatoli; Romano, Marino; Romeo, Gaston; Romero Adam, Elena; Rompotis, Nikolaos; Roos, Lydia; Ros, Eduardo; Rosati, Stefano; Rosbach, Kilian; Rose, Matthew; Rosendahl, Peter Lundgaard; Rosenthal, Oliver; Rossetti, Valerio; Rossi, Elvira; Rossi, Leonardo Paolo; Rosten, Rachel; Rotaru, Marina; Roth, Itamar; Rothberg, Joseph; Rousseau, David; Royon, Christophe; Rozanov, Alexandre; Rozen, Yoram; Ruan, Xifeng; Rubbo, Francesco; Rubinskiy, Igor; Rud, Viacheslav; Rudolph, Christian; Rudolph, Matthew Scott; Rühr, Frederik; Ruiz-Martinez, Aranzazu; Rurikova, Zuzana; Rusakovich, Nikolai; Ruschke, Alexander; Rutherfoord, John; Ruthmann, Nils; Ryabov, Yury; Rybar, Martin; Rybkin, Grigori; Ryder, Nick; Saavedra, Aldo; Sacerdoti, Sabrina; Saddique, Asif; Sadeh, Iftach; Sadrozinski, Hartmut; Sadykov, Renat; Safai Tehrani, Francesco; Sakamoto, Hiroshi; Sakurai, Yuki; Salamanna, Giuseppe; Salamon, Andrea; Saleem, Muhammad; Salek, David; Sales De Bruin, Pedro Henrique; Salihagic, Denis; Salnikov, Andrei; Salt, José; Salvachua Ferrando, Belén; Salvatore, Daniela; Salvatore, Pasquale Fabrizio; Salvucci, Antonio; Salzburger, Andreas; Sampsonidis, Dimitrios; Sanchez, Arturo; Sánchez, Javier; Sanchez Martinez, Victoria; Sandaker, Heidi; Sandbach, Ruth Laura; Sander, Heinz Georg; Sanders, Michiel; Sandhoff, Marisa; Sandoval, Tanya; Sandoval, Carlos; Sandstroem, Rikard; Sankey, Dave; Sansoni, Andrea; Santoni, Claudio; Santonico, Rinaldo; Santos, Helena; Santoyo Castillo, Itzebelt; Sapp, Kevin; Sapronov, Andrey; Saraiva, João; Sarrazin, Bjorn; Sartisohn, Georg; Sasaki, Osamu; Sasaki, Yuichi; Sauvage, Gilles; Sauvan, Emmanuel; Savard, Pierre; Savu, Dan Octavian; Sawyer, Craig; Sawyer, Lee; Saxon, David; Saxon, James; Sbarra, Carla; Sbrizzi, Antonio; Scanlon, Tim; Scannicchio, Diana; Scarcella, Mark; Schaarschmidt, Jana; Schacht, Peter; Schaefer, Douglas; Schaefer, Ralph; Schaepe, Steffen; Schaetzel, Sebastian; Schäfer, Uli; Schaffer, Arthur; Schaile, Dorothee; Schamberger, R. Dean; Scharf, Veit; Schegelsky, Valery; Scheirich, Daniel; Schernau, Michael; Scherzer, Max; Schiavi, Carlo; Schieck, Jochen; Schillo, Christian; Schioppa, Marco; Schlenker, Stefan; Schmidt, Evelyn; Schmieden, Kristof; Schmitt, Christian; Schmitt, Christopher; Schmitt, Sebastian; Schneider, Basil; Schnellbach, Yan Jie; Schnoor, Ulrike; Schoeffel, Laurent; Schoening, Andre; Schoenrock, Bradley Daniel; Schorlemmer, Andre Lukas; Schott, Matthias; Schouten, Doug; Schovancova, Jaroslava; Schramm, Steven; Schreyer, Manuel; Schroeder, Christian; Schuh, Natascha; Schultens, Martin Johannes; Schultz-Coulon, Hans-Christian; Schulz, Holger; Schumacher, Markus; Schumm, Bruce; Schune, Philippe; Schwanenberger, Christian; Schwartzman, Ariel; Schwegler, Philipp; Schwemling, Philippe; Schwienhorst, Reinhard; Schwindling, Jerome; Schwindt, Thomas; Schwoerer, Maud; Sciacca, Gianfranco; Scifo, Estelle; Sciolla, Gabriella; Scott, Bill; Scuri, Fabrizio; Scutti, Federico; Searcy, Jacob; Sedov, George; Sedykh, Evgeny; Seidel, Sally; Seiden, Abraham; Seifert, Frank; Seixas, José; Sekhniaidze, Givi; Sekula, Stephen; Selbach, Karoline Elfriede; Seliverstov, Dmitry; Sellers, Graham; Semprini-Cesari, Nicola; Serfon, Cedric; Serin, Laurent; Serkin, Leonid; Serre, Thomas; Seuster, Rolf; Severini, Horst; Sforza, Federico; Sfyrla, Anna; Shabalina, Elizaveta; Shamim, Mansoora; Shan, Lianyou; Shang, Ruo-yu; Shank, James; Shao, Qi Tao; Shapiro, Marjorie; Shatalov, Pavel; Shaw, Kate; Shehu, Ciwake Yusufu; Sherwood, Peter; Shi, Liaoshan; Shimizu, Shima; Shimmin, Chase Owen; Shimojima, Makoto; Shiyakova, Mariya; Shmeleva, Alevtina; Shochet, Mel; Short, Daniel; Shrestha, Suyog; Shulga, Evgeny; Shupe, Michael; Shushkevich, Stanislav; Sicho, Petr; Sidiropoulou, Ourania; Sidorov, Dmitri; Sidoti, Antonio; Siegert, Frank; Sijacki, Djordje; Silva, José; Silver, Yiftah; Silverstein, Daniel; Silverstein, Samuel; Simak, Vladislav; Simard, Olivier; Simic, Ljiljana; Simion, Stefan; Simioni, Eduard; Simmons, Brinick; Simoniello, Rosa; Simonyan, Margar; Sinervo, Pekka; Sinev, Nikolai; Sipica, Valentin; Siragusa, Giovanni; Sircar, Anirvan; Sisakyan, Alexei; Sivoklokov, Serguei; Sjölin, Jörgen; Sjursen, Therese; Skottowe, Hugh Philip; Skovpen, Kirill; Skubic, Patrick; Slater, Mark; Slavicek, Tomas; Sliwa, Krzysztof; Smakhtin, Vladimir; Smart, Ben; Smestad, Lillian; Smirnov, Sergei; Smirnov, Yury; Smirnova, Lidia; Smirnova, Oxana; Smith, Kenway; Smizanska, Maria; Smolek, Karel; Snesarev, Andrei; Snidero, Giacomo; Snyder, Scott; Sobie, Randall; Socher, Felix; Soffer, Abner; Soh, Dart-yin; Solans, Carlos; Solar, Michael; Solc, Jaroslav; Soldatov, Evgeny; Soldevila, Urmila; Solfaroli Camillocci, Elena; Solodkov, Alexander; Soloshenko, Alexei; Solovyanov, Oleg; Solovyev, Victor; Sommer, Philip; Song, Hong Ye; Soni, Nitesh; Sood, Alexander; Sopczak, Andre; Sopko, Bruno; Sopko, Vit; Sorin, Veronica; Sosebee, Mark; Soualah, Rachik; Soueid, Paul; Soukharev, Andrey; South, David; Spagnolo, Stefania; Spanò, Francesco; Spearman, William Robert; Spighi, Roberto; Spigo, Giancarlo; Spousta, Martin; Spreitzer, Teresa; Spurlock, Barry; St Denis, Richard Dante; Staerz, Steffen; Stahlman, Jonathan; Stamen, Rainer; Stanecka, Ewa; Stanek, Robert; Stanescu, Cristian; Stanescu-Bellu, Madalina; Stanitzki, Marcel Michael; Stapnes, Steinar; Starchenko, Evgeny; Stark, Jan; Staroba, Pavel; Starovoitov, Pavel; Staszewski, Rafal; Stavina, Pavel; Steinberg, Peter; Stelzer, Bernd; Stelzer, Harald Joerg; Stelzer-Chilton, Oliver; Stenzel, Hasko; Stern, Sebastian; Stewart, Graeme; Stillings, Jan Andre; Stockton, Mark; Stoebe, Michael; Stoicea, Gabriel; Stolte, Philipp; Stonjek, Stefan; Stradling, Alden; Straessner, Arno; Stramaglia, Maria Elena; Strandberg, Jonas; Strandberg, Sara; Strandlie, Are; Strauss, Emanuel; Strauss, Michael; Strizenec, Pavol; Ströhmer, Raimund; Strom, David; Stroynowski, Ryszard; Stucci, Stefania Antonia; Stugu, Bjarne; Styles, Nicholas Adam; Su, Dong; Su, Jun; Subramania, Halasya Siva; Subramaniam, Rajivalochan; Succurro, Antonella; Sugaya, Yorihito; Suhr, Chad; Suk, Michal; Sulin, Vladimir; Sultansoy, Saleh; Sumida, Toshi; Sun, Xiaohu; Sundermann, Jan Erik; Suruliz, Kerim; Susinno, Giancarlo; Sutton, Mark; Suzuki, Yu; Svatos, Michal; Swedish, Stephen; Swiatlowski, Maximilian; Sykora, Ivan; Sykora, Tomas; Ta, Duc; Tackmann, Kerstin; Taenzer, Joe; Taffard, Anyes; Tafirout, Reda; Taiblum, Nimrod; Takahashi, Yuta; Takai, Helio; Takashima, Ryuichi; Takeda, Hiroshi; Takeshita, Tohru; Takubo, Yosuke; Talby, Mossadek; Talyshev, Alexey; Tam, Jason; Tan, Kong Guan; Tanaka, Junichi; Tanaka, Reisaburo; Tanaka, Satoshi; Tanaka, Shuji; Tanasijczuk, Andres Jorge; Tani, Kazutoshi; Tannoury, Nancy; Tapprogge, Stefan; Tarem, Shlomit; Tarrade, Fabien; Tartarelli, Giuseppe Francesco; Tas, Petr; Tasevsky, Marek; Tashiro, Takuya; Tassi, Enrico; Tavares Delgado, Ademar; Tayalati, Yahya; Taylor, Frank; Taylor, Geoffrey; Taylor, Wendy; Teischinger, Florian Alfred; Teixeira Dias Castanheira, Matilde; Teixeira-Dias, Pedro; Temming, Kim Katrin; Ten Kate, Herman; Teng, Ping-Kun; Teoh, Jia Jian; Terada, Susumu; Terashi, Koji; Terron, Juan; Terzo, Stefano; Testa, Marianna; Teuscher, Richard; Therhaag, Jan; Theveneaux-Pelzer, Timothée; Thomas, Juergen; Thomas-Wilsker, Joshuha; Thompson, Emily; Thompson, Paul; Thompson, Peter; Thompson, Stan; Thomsen, Lotte Ansgaard; Thomson, Evelyn; Thomson, Mark; Thong, Wai Meng; Thun, Rudolf; Tian, Feng; Tibbetts, Mark James; Tikhomirov, Vladimir; Tikhonov, Yury; Timoshenko, Sergey; Tiouchichine, Elodie; Tipton, Paul; Tisserant, Sylvain; Todorov, Theodore; Todorova-Nova, Sharka; Toggerson, Brokk; Tojo, Junji; Tokár, Stanislav; Tokushuku, Katsuo; Tollefson, Kirsten; Tomlinson, Lee; Tomoto, Makoto; Tompkins, Lauren; Toms, Konstantin; Topilin, Nikolai; Torrence, Eric; Torres, Heberth; Torró Pastor, Emma; Toth, Jozsef; Touchard, Francois; Tovey, Daniel; Tran, Huong Lan; Trefzger, Thomas; Tremblet, Louis; Tricoli, Alessandro; Trigger, Isabel Marian; Trincaz-Duvoid, Sophie; Tripiana, Martin; Triplett, Nathan; Trischuk, William; Trocmé, Benjamin; Troncon, Clara; Trottier-McDonald, Michel; Trovatelli, Monica; True, Patrick; Trzebinski, Maciej; Trzupek, Adam; Tsarouchas, Charilaos; Tseng, Jeffrey; Tsiareshka, Pavel; Tsionou, Dimitra; Tsipolitis, Georgios; Tsirintanis, Nikolaos; Tsiskaridze, Shota; Tsiskaridze, Vakhtang; Tskhadadze, Edisher; Tsukerman, Ilya; Tsulaia, Vakhtang; Tsuno, Soshi; Tsybychev, Dmitri; Tudorache, Alexandra; Tudorache, Valentina; Tuna, Alexander Naip; Tupputi, Salvatore; Turchikhin, Semen; Turecek, Daniel; Turk Cakir, Ilkay; Turra, Ruggero; Tuts, Michael; Tykhonov, Andrii; Tylmad, Maja; Tyndel, Mike; Uchida, Kirika; Ueda, Ikuo; Ueno, Ryuichi; Ughetto, Michael; Ugland, Maren; Uhlenbrock, Mathias; Ukegawa, Fumihiko; Unal, Guillaume; Undrus, Alexander; Unel, Gokhan; Ungaro, Francesca; Unno, Yoshinobu; Urbaniec, Dustin; Urquijo, Phillip; Usai, Giulio; Usanova, Anna; Vacavant, Laurent; Vacek, Vaclav; Vachon, Brigitte; Valencic, Nika; Valentinetti, Sara; Valero, Alberto; Valery, Loic; Valkar, Stefan; Valladolid Gallego, Eva; Vallecorsa, Sofia; Valls Ferrer, Juan Antonio; Van Der Deijl, Pieter; van der Geer, Rogier; van der Graaf, Harry; Van Der Leeuw, Robin; van der Ster, Daniel; van Eldik, Niels; van Gemmeren, Peter; Van Nieuwkoop, Jacobus; van Vulpen, Ivo; van Woerden, Marius Cornelis; Vanadia, Marco; Vandelli, Wainer; Vanguri, Rami; Vaniachine, Alexandre; Vankov, Peter; Vannucci, Francois; Vardanyan, Gagik; Vari, Riccardo; Varnes, Erich; Varol, Tulin; Varouchas, Dimitris; Vartapetian, Armen; Varvell, Kevin; Vazeille, Francois; Vazquez Schroeder, Tamara; Veatch, Jason; Veloso, Filipe; Veneziano, Stefano; Ventura, Andrea; Ventura, Daniel; Venturi, Manuela; Venturi, Nicola; Venturini, Alessio; Vercesi, Valerio; Verducci, Monica; Verkerke, Wouter; Vermeulen, Jos; Vest, Anja; Vetterli, Michel; Viazlo, Oleksandr; Vichou, Irene; Vickey, Trevor; Vickey Boeriu, Oana Elena; Viehhauser, Georg; Viel, Simon; Vigne, Ralph; Villa, Mauro; Villaplana Perez, Miguel; Vilucchi, Elisabetta; Vincter, Manuella; Vinogradov, Vladimir; Virzi, Joseph; Vivarelli, Iacopo; Vives Vaque, Francesc; Vlachos, Sotirios; Vladoiu, Dan; Vlasak, Michal; Vogel, Adrian; Vogel, Marcelo; Vokac, Petr; Volpi, Guido; Volpi, Matteo; von der Schmitt, Hans; von Radziewski, Holger; von Toerne, Eckhard; Vorobel, Vit; Vorobev, Konstantin; Vos, Marcel; Voss, Rudiger; Vossebeld, Joost; Vranjes, Nenad; Vranjes Milosavljevic, Marija; Vrba, Vaclav; Vreeswijk, Marcel; Vu Anh, Tuan; Vuillermet, Raphael; Vukotic, Ilija; Vykydal, Zdenek; Wagner, Peter; Wagner, Wolfgang; Wahlberg, Hernan; Wahrmund, Sebastian; Wakabayashi, Jun; Walder, James; Walker, Rodney; Walkowiak, Wolfgang; Wall, Richard; Waller, Peter; Walsh, Brian; Wang, Chao; Wang, Chiho; Wang, Fuquan; Wang, Haichen; Wang, Hulin; Wang, Jike; Wang, Jin; Wang, Kuhan; Wang, Rui; Wang, Song-Ming; Wang, Tan; Wang, Xiaoxiao; Wanotayaroj, Chaowaroj; Warburton, Andreas; Ward, Patricia; Wardrope, David Robert; Warsinsky, Markus; Washbrook, Andrew; Wasicki, Christoph; Watanabe, Ippei; Watkins, Peter; Watson, Alan; Watson, Ian; Watson, Miriam; Watts, Gordon; Watts, Stephen; Waugh, Ben; Webb, Samuel; Weber, Michele; Weber, Stefan Wolf; Webster, Jordan S; Weidberg, Anthony; Weigell, Philipp; Weinert, Benjamin; Weingarten, Jens; Weiser, Christian; Weits, Hartger; Wells, Phillippa; Wenaus, Torre; Wendland, Dennis; Weng, Zhili; Wengler, Thorsten; Wenig, Siegfried; Wermes, Norbert; Werner, Matthias; Werner, Per; Wessels, Martin; Wetter, Jeffrey; Whalen, Kathleen; White, Andrew; White, Martin; White, Ryan; White, Sebastian; Whiteson, Daniel; Wicke, Daniel; Wickens, Fred; Wiedenmann, Werner; Wielers, Monika; Wienemann, Peter; Wiglesworth, Craig; Wiik-Fuchs, Liv Antje Mari; Wijeratne, Peter Alexander; Wildauer, Andreas; Wildt, Martin Andre; Wilkens, Henric George; Will, Jonas Zacharias; Williams, Hugh; Williams, Sarah; Willis, Christopher; Willocq, Stephane; Wilson, Alan; Wilson, John; Wingerter-Seez, Isabelle; Winklmeier, Frank; Winter, Benedict Tobias; Wittgen, Matthias; Wittig, Tobias; Wittkowski, Josephine; Wollstadt, Simon Jakob; Wolter, Marcin Wladyslaw; Wolters, Helmut; Wosiek, Barbara; Wotschack, Jorg; Woudstra, Martin; Wozniak, Krzysztof; Wright, Michael; Wu, Mengqing; Wu, Sau Lan; Wu, Xin; Wu, Yusheng; Wulf, Evan; Wyatt, Terry Richard; Wynne, Benjamin; Xella, Stefania; Xiao, Meng; Xu, Da; Xu, Lailin; Yabsley, Bruce; Yacoob, Sahal; Yamada, Miho; Yamaguchi, Hiroshi; Yamaguchi, Yohei; Yamamoto, Akira; Yamamoto, Kyoko; Yamamoto, Shimpei; Yamamura, Taiki; Yamanaka, Takashi; Yamauchi, Katsuya; Yamazaki, Yuji; Yan, Zhen; Yang, Haijun; Yang, Hongtao; Yang, Un-Ki; Yang, Yi; Yanush, Serguei; Yao, Liwen; Yao, Weiming; Yasu, Yoshiji; Yatsenko, Elena; Yau Wong, Kaven Henry; Ye, Jingbo; Ye, Shuwei; Yen, Andy L; Yildirim, Eda; Yilmaz, Metin; Yoosoofmiya, Reza; Yorita, Kohei; Yoshida, Rikutaro; Yoshihara, Keisuke; Young, Charles; Young, Christopher John; Youssef, Saul; Yu, David Ren-Hwa; Yu, Jaehoon; Yu, Jiaming; Yu, Jie; Yuan, Li; Yurkewicz, Adam; Zabinski, Bartlomiej; Zaidan, Remi; Zaitsev, Alexander; Zaman, Aungshuman; Zambito, Stefano; Zanello, Lucia; Zanzi, Daniele; Zeitnitz, Christian; Zeman, Martin; Zemla, Andrzej; Zengel, Keith; Zenin, Oleg; Ženiš, Tibor; Zerwas, Dirk; Zevi della Porta, Giovanni; Zhang, Dongliang; Zhang, Fangzhou; Zhang, Huaqiao; Zhang, Jinlong; Zhang, Lei; Zhang, Xueyao; Zhang, Zhiqing; Zhao, Zhengguo; Zhemchugov, Alexey; Zhong, Jiahang; Zhou, Bing; Zhou, Lei; Zhou, Ning; Zhu, Cheng Guang; Zhu, Hongbo; Zhu, Junjie; Zhu, Yingchun; Zhuang, Xuai; Zhukov, Konstantin; Zibell, Andre; Zieminska, Daria; Zimine, Nikolai; Zimmermann, Christoph; Zimmermann, Robert; Zimmermann, Simone; Zimmermann, Stephanie; Zinonos, Zinonas; Ziolkowski, Michael; Zobernig, Georg; Zoccoli, Antonio; zur Nedden, Martin; Zurzolo, Giovanni; Zutshi, Vishnu; Zwalinski, Lukasz

    2014-10-03

    This Letter presents the first study of $W^{\\pm}W^{\\pm}jj$, same-electric-charge diboson production in association with two jets, using 20.3~fb$^{-1}$ of proton--proton collision data at $\\sqrt{s}=8$ TeV recorded by the ATLAS detector at the Large Hadron Collider. Events with two reconstructed same-charge leptons ($e^\\pm e^\\pm$, $e^\\pm \\mu^\\pm$, and $\\mu^\\pm \\mu^\\pm$) and two or more jets are analyzed. Production cross sections are measured in two fiducial regions, with different sensitivities to the electroweak and strong production mechanisms. First evidence for $W^{\\pm}W^{\\pm}jj$ production and electroweak-only $W^{\\pm}W^{\\pm}jj$ production is observed with a significance of $4.5$ and $3.6$ standard deviations respectively. The measured production cross sections are in agreement with Standard Model predictions. Limits at 95\\% confidence level are set on anomalous quartic gauge couplings.

  12. Isomeric structures in neutron-rich odd-odd Pm (Z = 61) isotopes

    International Nuclear Information System (INIS)

    Sood, P.C.; Singh, B.; Jain, A.K.

    2008-01-01

    Each of the heavier odd-odd isotopes, namely, 152 Pm, 154 Pm and 156 Pm, have multiple low-lying isomers, almost all of them with undefined configuration and also undefined energy placement. Present investigations attempt credible characterization of the isomers using a simplified two-quasiparticle rotor model which has been widely employed for description of odd-odd deformed nuclei

  13. Psychological.pm6

    African Journals Online (AJOL)

    Adele

    predisposition to the experience of pain; anxiety, depression and their relationship to chronic pain; and the cognitive mechanisms by which ... pain within the biopsychosocial model and diathesis-stress framework. Within these paradigms the ...

  14. Measurement of the CKM angle $\\gamma$ using $B^\\pm\\to DK^\\pm$ with $D\\to K_\\text{S}^0\\pi^+\\pi^-$, $K_\\text{S}^0K^+K^-$ decays

    CERN Document Server

    Aaij, Roel; LHCb Collaboration; Adinolfi, Marco; Aidala, Christine Angela; Ajaltouni, Ziad; Akar, Simon; Albicocco, Pietro; Albrecht, Johannes; Alessio, Federico; Alexander, Michael; Alfonso Albero, Alejandro; Ali, Suvayu; Alkhazov, Georgy; Alvarez Cartelle, Paula; Alves Jr, Antonio Augusto; Amato, Sandra; Amerio, Silvia; Amhis, Yasmine; An, Liupan; Anderlini, Lucio; Andreassi, Guido; Andreotti, Mirco; Andrews, Jason; Appleby, Robert; Archilli, Flavio; d'Argent, Philippe; Arnau Romeu, Joan; Artamonov, Alexander; Artuso, Marina; Arzymatov, Kenenbek; Aslanides, Elie; Atzeni, Michele; Bachmann, Sebastian; Back, John; Baker, Sophie; Balagura, Vladislav; Baldini, Wander; Baranov, Alexander; Barlow, Roger; Barsuk, Sergey; Barter, William; Baryshnikov, Fedor; Batozskaya, Varvara; Batsukh, Baasansuren; Battista, Vincenzo; Bay, Aurelio; Beddow, John; Bedeschi, Franco; Bediaga, Ignacio; Beiter, Andrew; Bel, Lennaert; Beliy, Nikita; Bellee, Violaine; Belloli, Nicoletta; Belous, Konstantin; Belyaev, Ivan; Ben-Haim, Eli; Bencivenni, Giovanni; Benson, Sean; Beranek, Sarah; Berezhnoy, Alexander; Bernet, Roland; Berninghoff, Daniel; Bertholet, Emilie; Bertolin, Alessandro; Betancourt, Christopher; Betti, Federico; Bettler, Marc-Olivier; van Beuzekom, Martinus; Bezshyiko, Iaroslava; Bhasin, Srishti; Bhom, Jihyun; Bian, Lingzhu; Bifani, Simone; Billoir, Pierre; Birnkraut, Alex; Bizzeti, Andrea; Bjørn, Mikkel; Blago, Michele Piero; Blake, Thomas; Blanc, Frederic; Blusk, Steven; Bobulska, Dana; Bocci, Valerio; Boente Garcia, Oscar; Boettcher, Thomas; Bondar, Alexander; Bondar, Nikolay; Borghi, Silvia; Borisyak, Maxim; Borsato, Martino; Bossu, Francesco; Boubdir, Meriem; Bowcock, Themistocles; Bozzi, Concezio; Braun, Svende; Brodski, Michael; Brodzicka, Jolanta; Brundu, Davide; Buchanan, Emma; Buonaura, Annarita; Burr, Christopher; Bursche, Albert; Buytaert, Jan; Byczynski, Wiktor; Cadeddu, Sandro; Cai, Hao; Calabrese, Roberto; Calladine, Ryan; Calvi, Marta; Calvo Gomez, Miriam; Camboni, Alessandro; Campana, Pierluigi; Campora Perez, Daniel Hugo; Capriotti, Lorenzo; Carbone, Angelo; Carboni, Giovanni; Cardinale, Roberta; Cardini, Alessandro; Carniti, Paolo; Carson, Laurence; Carvalho Akiba, Kazuyoshi; Casse, Gianluigi; Cassina, Lorenzo; Cattaneo, Marco; Cavallero, Giovanni; Cenci, Riccardo; Chamont, David; Chapman, Matthew George; Charles, Matthew; Charpentier, Philippe; Chatzikonstantinidis, Georgios; Chefdeville, Maximilien; Chekalina, Viktoriia; Chen, Chen; Chen, Shanzhen; Chitic, Stefan-Gabriel; Chobanova, Veronika; Chrzaszcz, Marcin; Chubykin, Alexsei; Ciambrone, Paolo; Cid Vidal, Xabier; Ciezarek, Gregory; Clarke, Peter; Clemencic, Marco; Cliff, Harry; Closier, Joel; Coco, Victor; Cogan, Julien; Cogneras, Eric; Cojocariu, Lucian; Collins, Paula; Colombo, Tommaso; Comerma-Montells, Albert; Contu, Andrea; Coombs, George; Coquereau, Samuel; Corti, Gloria; Corvo, Marco; Costa Sobral, Cayo Mar; Couturier, Benjamin; Cowan, Greig; Craik, Daniel Charles; Crocombe, Andrew; Cruz Torres, Melissa Maria; Currie, Robert; D'Ambrosio, Carmelo; Da Cunha Marinho, Franciole; Da Silva, Cesar Luiz; Dall'Occo, Elena; Dalseno, Jeremy; Danilina, Anna; Davis, Adam; De Aguiar Francisco, Oscar; De Bruyn, Kristof; De Capua, Stefano; De Cian, Michel; De Miranda, Jussara; De Paula, Leandro; De Serio, Marilisa; De Simone, Patrizia; Dean, Cameron Thomas; Decamp, Daniel; Del Buono, Luigi; Delaney, Blaise; Dembinski, Hans Peter; Demmer, Moritz; Dendek, Adam; Derkach, Denis; Deschamps, Olivier; Desse, Fabrice; Dettori, Francesco; Dey, Biplab; Di Canto, Angelo; Di Nezza, Pasquale; Didenko, Sergey; Dijkstra, Hans; Dordei, Francesca; Dorigo, Mirco; Dosil Suárez, Alvaro; Douglas, Lauren; Dovbnya, Anatoliy; Dreimanis, Karlis; Dufour, Laurent; Dujany, Giulio; Durante, Paolo; Durham, John Matthew; Dutta, Deepanwita; Dzhelyadin, Rustem; Dziewiecki, Michal; Dziurda, Agnieszka; Dzyuba, Alexey; Easo, Sajan; Egede, Ulrik; Egorychev, Victor; Eidelman, Semen; Eisenhardt, Stephan; Eitschberger, Ulrich; Ekelhof, Robert; Eklund, Lars; Ely, Scott; Ene, Alexandru; Escher, Stephan; Esen, Sevda; Evans, Timothy; Falabella, Antonio; Farley, Nathanael; Farry, Stephen; Fazzini, Davide; Federici, Luca; Fernandez, Gerard; Fernandez Declara, Placido; Fernandez Prieto, Antonio; Ferrari, Fabio; Ferreira Lopes, Lino; Ferreira Rodrigues, Fernando; Ferro-Luzzi, Massimiliano; Filippov, Sergey; Fini, Rosa Anna; Fiorini, Massimiliano; Firlej, Miroslaw; Fitzpatrick, Conor; Fiutowski, Tomasz; Fleuret, Frederic; Fontana, Marianna; Fontanelli, Flavio; Forty, Roger; Franco Lima, Vinicius; Frank, Markus; Frei, Christoph; Fu, Jinlin; Funk, Wolfgang; Färber, Christian; Féo Pereira Rivello Carvalho, Mauricio; Gabriel, Emmy; Gallas Torreira, Abraham; Galli, Domenico; Gallorini, Stefano; Gambetta, Silvia; Gandelman, Miriam; Gandini, Paolo; Gao, Yuanning; Garcia Martin, Luis Miguel; Garcia Plana, Beatriz; García Pardiñas, Julián; Garra Tico, Jordi; Garrido, Lluis; Gascon, David; Gaspar, Clara; Gavardi, Laura; Gazzoni, Giulio; Gerick, David; Gersabeck, Evelina; Gersabeck, Marco; Gershon, Timothy; Gerstel, Dawid; Ghez, Philippe; Gianì, Sebastiana; Gibson, Valerie; Girard, Olivier Göran; Giubega, Lavinia-Helena; Gizdov, Konstantin; Gligorov, Vladimir; Golubkov, Dmitry; Golutvin, Andrey; Gomes, Alvaro; Gorelov, Igor Vladimirovich; Gotti, Claudio; Govorkova, Ekaterina; Grabowski, Jascha Peter; Graciani Diaz, Ricardo; Granado Cardoso, Luis Alberto; Graugés, Eugeni; Graverini, Elena; Graziani, Giacomo; Grecu, Alexandru; Greim, Roman; Griffith, Peter; Grillo, Lucia; Gruber, Lukas; Gruberg Cazon, Barak Raimond; Grünberg, Oliver; Gu, Chenxi; Gushchin, Evgeny; Guz, Yury; Gys, Thierry; Göbel, Carla; Hadavizadeh, Thomas; Hadjivasiliou, Christos; Haefeli, Guido; Haen, Christophe; Haines, Susan; Hamilton, Brian; Han, Xiaoxue; Hancock, Thomas Henry; Hansmann-Menzemer, Stephanie; Harnew, Neville; Harnew, Samuel; Harrison, Thomas; Hasse, Christoph; Hatch, Mark; He, Jibo; Hecker, Malte; Heinicke, Kevin; Heister, Arno; Hennessy, Karol; Henry, Louis; van Herwijnen, Eric; Heß, Miriam; Hicheur, Adlène; Hill, Donal; Hilton, Martha; Hopchev, Plamen Hristov; Hu, Wenhua; Huang, Wenqian; Huard, Zachary; Hulsbergen, Wouter; Humair, Thibaud; Hushchyn, Mikhail; Hutchcroft, David; Ibis, Philipp; Idzik, Marek; Ilten, Philip; Ivshin, Kuzma; Jacobsson, Richard; Jalocha, Pawel; Jans, Eddy; Jawahery, Abolhassan; Jiang, Feng; John, Malcolm; Johnson, Daniel; Jones, Christopher; Joram, Christian; Jost, Beat; Jurik, Nathan; Kandybei, Sergii; Karacson, Matthias; Kariuki, James Mwangi; Karodia, Sarah; Kazeev, Nikita; Kecke, Matthieu; Keizer, Floris; Kelsey, Matthew; Kenzie, Matthew; Ketel, Tjeerd; Khairullin, Egor; Khanji, Basem; Khurewathanakul, Chitsanu; Kim, Kyung Eun; Kirn, Thomas; Klaver, Suzanne; Klimaszewski, Konrad; Klimkovich, Tatsiana; Koliiev, Serhii; Kolpin, Michael; Kopecna, Renata; Koppenburg, Patrick; Kostiuk, Igor; Kotriakhova, Sofia; Kozeiha, Mohamad; Kravchuk, Leonid; Kreps, Michal; Kress, Felix Johannes; Krokovny, Pavel; Krupa, Wojciech; Krzemien, Wojciech; Kucewicz, Wojciech; Kucharczyk, Marcin; Kudryavtsev, Vasily; Kuonen, Axel Kevin; Kvaratskheliya, Tengiz; Lacarrere, Daniel; Lafferty, George; Lai, Adriano; Lancierini, Davide; Lanfranchi, Gaia; Langenbruch, Christoph; Latham, Thomas; Lazzeroni, Cristina; Le Gac, Renaud; Leflat, Alexander; Lefrançois, Jacques; Lefèvre, Regis; Lemaitre, Florian; Leroy, Olivier; Lesiak, Tadeusz; Leverington, Blake; Li, Pei-Rong; Li, Tenglin; Li, Zhuoming; Liang, Xixin; Likhomanenko, Tatiana; Lindner, Rolf; Lionetto, Federica; Lisovskyi, Vitalii; Liu, Xuesong; Loh, David; Loi, Angelo; Longstaff, Iain; Lopes, Jose; Lovell, George Holger; Lucchesi, Donatella; Lucio Martinez, Miriam; Lupato, Anna; Luppi, Eleonora; Lupton, Oliver; Lusiani, Alberto; Lyu, Xiao-Rui; Machefert, Frederic; Maciuc, Florin; Macko, Vladimir; Mackowiak, Patrick; Maddrell-Mander, Samuel; Maev, Oleg; Maguire, Kevin; Maisuzenko, Dmitrii; Majewski, Maciej Witold; Malde, Sneha; Malecki, Bartosz; Malinin, Alexander; Maltsev, Timofei; Manca, Giulia; Mancinelli, Giampiero; Marangotto, Daniele; Maratas, Jan; Marchand, Jean François; Marconi, Umberto; Marin Benito, Carla; Marinangeli, Matthieu; Marino, Pietro; Marks, Jörg; Martellotti, Giuseppe; Martin, Morgan; Martinelli, Maurizio; Martinez Santos, Diego; Martinez Vidal, Fernando; Massafferri, André; Matev, Rosen; Mathad, Abhijit; Mathe, Zoltan; Matteuzzi, Clara; Mauri, Andrea; Maurice, Emilie; Maurin, Brice; Mazurov, Alexander; McCann, Michael; McNab, Andrew; McNulty, Ronan; Mead, James Vincent; Meadows, Brian; Meaux, Cedric; Meier, Frank; Meinert, Nis; Melnychuk, Dmytro; Merk, Marcel; Merli, Andrea; Michielin, Emanuele; Milanes, Diego Alejandro; Millard, Edward James; Minard, Marie-Noelle; Minzoni, Luca; Mitzel, Dominik Stefan; Mogini, Andrea; Molina Rodriguez, Josue; Mombächer, Titus; Monroy, Igancio Alberto; Monteil, Stephane; Morandin, Mauro; Morello, Gianfranco; Morello, Michael Joseph; Morgunova, Olga; Moron, Jakub; Morris, Adam Benjamin; Mountain, Raymond; Muheim, Franz; Mulder, Mick; Murphy, Colm Harold; Murray, Donal; Müller, Dominik; Müller, Janine; Müller, Katharina; Müller, Vanessa; Naik, Paras; Nakada, Tatsuya; Nandakumar, Raja; Nandi, Anita; Nanut, Tara; Nasteva, Irina; Needham, Matthew; Neri, Nicola; Neubert, Sebastian; Neufeld, Niko; Neuner, Max; Nguyen, Thi Dung; Nguyen-Mau, Chung; Nieswand, Simon; Niet, Ramon; Nikitin, Nikolay; Nogay, Alla; O'Hanlon, Daniel Patrick; Oblakowska-Mucha, Agnieszka; Obraztsov, Vladimir; Ogilvy, Stephen; Oldeman, Rudolf; Onderwater, Gerco; Ossowska, Anna; Otalora Goicochea, Juan Martin; Owen, Patrick; Oyanguren, Maria Aranzazu; Pais, Preema Rennee; Palano, Antimo; Palutan, Matteo; Panshin, Gennady; Papanestis, Antonios; Pappagallo, Marco; Pappalardo, Luciano; Parker, William; Parkes, Christopher; Passaleva, Giovanni; Pastore, Alessandra; Patel, Mitesh; Patrignani, Claudia; Pearce, Alex; Pellegrino, Antonio; Penso, Gianni; Pepe Altarelli, Monica; Perazzini, Stefano; Pereima, Dmitrii; Perret, Pascal; Pescatore, Luca; Petridis, Konstantinos; Petrolini, Alessandro; Petrov, Aleksandr; Petrucci, Stefano; Petruzzo, Marco; Pietrzyk, Boleslaw; Pietrzyk, Guillaume; Pikies, Malgorzata; Pili, Martina; Pinci, Davide; Pinzino, Jacopo; Pisani, Flavio; Piucci, Alessio; Placinta, Vlad-Mihai; Playfer, Stephen; Plews, Jonathan; Plo Casasus, Maximo; Polci, Francesco; Poli Lener, Marco; Poluektov, Anton; Polukhina, Natalia; Polyakov, Ivan; Polycarpo, Erica; Pomery, Gabriela Johanna; Ponce, Sebastien; Popov, Alexander; Popov, Dmitry; Poslavskii, Stanislav; Potterat, Cédric; Price, Eugenia; Prisciandaro, Jessica; Prouve, Claire; Pugatch, Valery; Puig Navarro, Albert; Pullen, Hannah Louise; Punzi, Giovanni; Qian, Wenbin; Qin, Jia-Jia; Quagliani, Renato; Quintana, Boris; Rachwal, Bartlomiej; Rademacker, Jonas; Rama, Matteo; Ramos Pernas, Miguel; Rangel, Murilo; Ratnikov, Fedor; Raven, Gerhard; Ravonel Salzgeber, Melody; Reboud, Meril; Redi, Federico; Reichert, Stefanie; dos Reis, Alberto; Reiss, Florian; Remon Alepuz, Clara; Ren, Zan; Renaudin, Victor; Ricciardi, Stefania; Richards, Sophie; Rinnert, Kurt; Robbe, Patrick; Robert, Arnaud; Rodrigues, Ana Barbara; Rodrigues, Eduardo; Rodriguez Lopez, Jairo Alexis; Roehrken, Markus; Rogozhnikov, Alexey; Roiser, Stefan; Rollings, Alexandra Paige; Romanovskiy, Vladimir; Romero Vidal, Antonio; Rotondo, Marcello; Rudolph, Matthew Scott; Ruf, Thomas; Ruiz Vidal, Joan; Saborido Silva, Juan Jose; Sagidova, Naylya; Saitta, Biagio; Salustino Guimaraes, Valdir; Sanchez Gras, Cristina; Sanchez Mayordomo, Carlos; Sanmartin Sedes, Brais; Santacesaria, Roberta; Santamarina Rios, Cibran; Santimaria, Marco; Santovetti, Emanuele; Sarpis, Gediminas; Sarti, Alessio; Satriano, Celestina; Satta, Alessia; Saur, Miroslav; Savrina, Darya; Schael, Stefan; Schellenberg, Margarete; Schiller, Manuel; Schindler, Heinrich; Schmelling, Michael; Schmelzer, Timon; Schmidt, Burkhard; Schneider, Olivier; Schopper, Andreas; Schreiner, HF; Schubiger, Maxime; Schune, Marie Helene; Schwemmer, Rainer; Sciascia, Barbara; Sciubba, Adalberto; Semennikov, Alexander; Sepulveda, Eduardo Enrique; Sergi, Antonino; Serra, Nicola; Serrano, Justine; Sestini, Lorenzo; Seyfert, Paul; Shapkin, Mikhail; Shcheglov, Yury; Shears, Tara; Shekhtman, Lev; Shevchenko, Vladimir; Shmanin, Evgenii; Siddi, Benedetto Gianluca; Silva Coutinho, Rafael; Silva de Oliveira, Luiz Gustavo; Simi, Gabriele; Simone, Saverio; Skidmore, Nicola; Skwarnicki, Tomasz; Smeaton, John Gordon; Smith, Eluned; Smith, Iwan Thomas; Smith, Mark; Soares, Marcelo; Soares Lavra, Lais; Sokoloff, Michael; Soler, Paul; Souza De Paula, Bruno; Spaan, Bernhard; Spradlin, Patrick; Stagni, Federico; Stahl, Marian; Stahl, Sascha; Stefko, Pavol; Stefkova, Slavomira; Steinkamp, Olaf; Stemmle, Simon; Stenyakin, Oleg; Stepanova, Margarita; Stevens, Holger; Stone, Sheldon; Storaci, Barbara; Stracka, Simone; Stramaglia, Maria Elena; Straticiuc, Mihai; Straumann, Ulrich; Strokov, Sergey; Sun, Jiayin; Sun, Liang; Swientek, Krzysztof; Syropoulos, Vasileios; Szumlak, Tomasz; Szymanski, Maciej Pawel; T'Jampens, Stephane; Tang, Zhipeng; Tayduganov, Andrey; Tekampe, Tobias; Tellarini, Giulia; Teubert, Frederic; Thomas, Eric; van Tilburg, Jeroen; Tilley, Matthew James; Tisserand, Vincent; Tolk, Siim; Tomassetti, Luca; Tonelli, Diego; Tou, Da Yu; Tourinho Jadallah Aoude, Rafael; Tournefier, Edwige; Traill, Murdo; Tran, Minh Tâm; Trisovic, Ana; Tsaregorodtsev, Andrei; Tully, Alison; Tuning, Niels; Ukleja, Artur; Usachov, Andrii; Ustyuzhanin, Andrey; Uwer, Ulrich; Vacca, Claudia; Vagner, Alexander; Vagnoni, Vincenzo; Valassi, Andrea; Valat, Sebastien; Valenti, Giovanni; Vazquez Gomez, Ricardo; Vazquez Regueiro, Pablo; Vecchi, Stefania; van Veghel, Maarten; Velthuis, Jaap; Veltri, Michele; Veneziano, Giovanni; Venkateswaran, Aravindhan; Verlage, Tobias Anton; Vernet, Maxime; Vesterinen, Mika; Viana Barbosa, Joao Vitor; Vieira, Daniel; Vieites Diaz, Maria; Viemann, Harald; Vilasis-Cardona, Xavier; Vitkovskiy, Arseniy; Vitti, Marcela; Volkov, Vladimir; Vollhardt, Achim; Voneki, Balazs; Vorobyev, Alexey; Vorobyev, Vitaly; de Vries, Jacco; Vázquez Sierra, Carlos; Waldi, Roland; Walsh, John; Wang, Jianchun; Wang, Mengzhen; Wang, Yilong; Wang, Zhenzi; Ward, David; Wark, Heather Mckenzie; Watson, Nigel; Websdale, David; Weiden, Andreas; Weisser, Constantin; Whitehead, Mark; Wicht, Jean; Wilkinson, Guy; Wilkinson, Michael; Williams, Ifan; Williams, Mark Richard James; Williams, Mike; Williams, Timothy; Wilson, Fergus; Wimberley, Jack; Winn, Michael Andreas; Wishahi, Julian; Wislicki, Wojciech; Witek, Mariusz; Wormser, Guy; Wotton, Stephen; Wyllie, Kenneth; Xiao, Dong; Xie, Yuehong; Xu, Ao; Xu, Menglin; Xu, Qingnian; Xu, Zehua; Xu, Zhirui; Yang, Zhenwei; Yang, Zishuo; Yao, Yuezhe; Yeomans, Lauren Emma; Yin, Hang; Yu, Jiesheng; Yuan, Xuhao; Yushchenko, Oleg; Zarebski, Kristian Alexander; Zavertyaev, Mikhail; Zhang, Dongliang; Zhang, Liming; Zhang, Wen Chao; Zhang, Yanxi; Zhelezov, Alexey; Zheng, Yangheng; Zhu, Xianglei; Zhukov, Valery; Zonneveld, Jennifer Brigitta; Zucchelli, Stefano

    2018-01-01

    A binned Dalitz plot analysis of $B^\\pm \\to D K^\\pm$ decays, with $D\\to K_\\text{S}^0\\pi^+\\pi^-$ and $D\\to K_\\text{S}^0K^+K^-$, is used to perform a measurement of the CP-violating observables $x_{\\pm}$ and $y_{\\pm}$, which are sensitive to the Cabibbo-Kobayashi-Maskawa angle $\\gamma$. The analysis is performed without assuming any $D$ decay model, through the use of information on the strong-phase variation over the Dalitz plot from the CLEO collaboration. Using a sample of proton-proton collision data collected with the LHCb experiment in 2015 and 2016, and corresponding to an integrated luminosity of 2.0$\\,\\text{fb}^{-1}$, the values of the CP violation parameters are found to be $x_- = ( 9.0 \\pm 1.7 \\pm 0.7 \\pm 0.4) \\times 10^{-2}$, $y_- = ( 2.1 \\pm 2.2 \\pm 0.5 \\pm 1.1) \\times 10^{-2}$, $x_+ = (- 7.7 \\pm 1.9 \\pm 0.7 \\pm 0.4) \\times 10^{-2}$, and $y_+ = (- 1.0 \\pm 1.9 \\pm 0.4 \\pm 0.9) \\times 10^{-2}$. The first uncertainty is statistical, the second is systematic, and the third is due to the uncertainty on ...

  15. Indoor PM1, PM2.5, PM10 and outdoor PM2.5 concentrations in primary schools in Sari, Iran.

    Science.gov (United States)

    Mohammadyan, Mahmoud; Shabankhani, Bijan

    2013-09-01

    This study was carried out to determine the distribution of particles in classrooms in primary schools located in the centre of the city of Sari, Iran and identify the relationship between indoor classroom particle levels and outdoor PM2.5 concentrations. Outdoor PM2.5 and indoor PM1, PM2.5, and PM10 were monitored using a real-time Micro Dust Pro monitor and a GRIMM monitor, respectively. Both monitors were calibrated by gravimetric method using filters. The Kolmogorov-Smirnov test showed that all indoor and outdoor data fitted normal distribution. Mean indoor PM1, PM2.5, PM10 and outdoor PM2.5 concentrations for all of the classrooms were 17.6 μg m(-3), 46.6 μg m(-3), 400.9 μg m(-3), and 36.9 μg m(-3), respectively. The highest levels of indoor and outdoor PM2.5 concentrations were measured at the Shahed Boys School (69.1 μg m(-3) and 115.8 μg m(-3), respectively). The Kazemi school had the lowest levels of indoor and outdoor PM2.5 (29.1 μg m(-3) and 15.5 μg m(-3), respectively). In schools located near both main and small roads, the association between indoor fine particle (PM2.5 and PM1) and outdoor PM2.5 levels was stronger than that between indoor PM10 and outdoor PM2.5 levels. Mean indoor PM2.5 and PM10 and outdoor PM2.5 were higher than the standards for PM2.5 and PM10, and there was a good correlation between indoor and outdoor fine particle concentrations.

  16. Reduction of PM emissions from specific sources reflected on key components concentrations of ambient PM10

    Science.gov (United States)

    Minguillon, M. C.; Querol, X.; Monfort, E.; Alastuey, A.; Escrig, A.; Celades, I.; Miro, J. V.

    2009-04-01

    The relationship between specific particulate emission control and ambient levels of some PM10 components (Zn, As, Pb, Cs, Tl) was evaluated. To this end, the industrial area of Castellón (Eastern Spain) was selected, where around 40% of the EU glazed ceramic tiles and a high proportion of EU ceramic frits (middle product for the manufacture of ceramic glaze) are produced. The PM10 emissions from the ceramic processes were calculated over the period 2000 to 2007 taking into account the degree of implementation of corrective measures throughout the study period. Abatement systems (mainly bag filters) were implemented in the majority of the fusion kilns for frit manufacture in the area as a result of the application of the Directive 1996/61/CE, leading to a marked decrease in PM10 emissions. On the other hand, ambient PM10 sampling was carried out from April 2002 to July 2008 at three urban sites and one suburban site of the area and a complete chemical analysis was made for about 35 % of the collected samples, by means of different techniques (ICP-AES, ICP-MS, Ion Chromatography, selective electrode and elemental analyser). The series of chemical composition of PM10 allowed us to apply a source contribution model (Principal Component Analysis), followed by a multilinear regression analysis, so that PM10 sources were identified and their contribution to bulk ambient PM10 was quantified on a daily basis, as well as the contribution to bulk ambient concentrations of the identified key components (Zn, As, Pb, Cs, Tl). The contribution of the sources identified as the manufacture and use of ceramic glaze components, including the manufacture of ceramic frits, accounted for more than 65, 75, 58, 53, and 53% of ambient Zn, As, Pb, Cs and Tl levels, respectively (with the exception of Tl contribution at one of the sites). The important emission reductions of these sources during the study period had an impact on ambient key components levels, such that there was a high

  17. Will PM control undermine China's efforts to reduce soil acidification?

    International Nuclear Information System (INIS)

    Zhao Yu; Duan Lei; Lei Yu; Xing Jia; Nielsen, Chris P.; Hao Jiming

    2011-01-01

    China's strategies to control acidifying pollutants and particulate matter (PM) may be in conflict for soil acidification abatement. Acidifying pollutant emissions are estimated for 2005 and 2020 with anticipated control policies. PM emissions including base cations (BCs) are evaluated with two scenarios, a base case applying existing policy to 2020, and a control case including anticipated tightened measures. Depositions of sulfur (S), nitrogen (N) and BCs are simulated and their acidification risks are evaluated with critical load (CL). In 2005, the area exceeding CL covered 15.6% of mainland China, with total exceedance of 2.2 Mt S. These values decrease in the base scenario 2020, implying partial recovery from acidification. Under more realistic PM control, the respective estimates are 17.9% and 2.4 Mt S, indicating increased acidification risks due to abatement of acid-neutralizing BCs. China's anthropogenic PM abatement will have potentially stronger chemical implications for acidification than developed countries. - Highlights: → We model the emission and deposition of base cations and acid precursors in China. → Soil acidification in China is analyzed with exceedance of critical load. → China's PM control increases the acidification risk even with reduced SO 2 emission. → The impact of PM for acidification is stronger than that in developed countries. - The control of anthropogenic PM emission in China will increase the risk of soil acidification even with reduced SO 2 emission. Such implication is stronger than that in developed countries.

  18. A study on experimental toxicology of 147Pm in rat

    International Nuclear Information System (INIS)

    Zhao Jingyong; Lao Qinhua

    1988-11-01

    The absorb law of 147 Pm by pulmonary compartment, intestines and skin of rat is reported. The metabolic pattern of 147 Pm in rat and its dose model are worked out on the basis of measured data. The injurious effects of the nuclide were observed by using LD 50/60 , micro-nucleus rates of lymphocytes in peripheral blood and activity of SGPT in serum as indices

  19. Modelling of the urban concentrations of PM2.5 on a high resolution for a period of 35 years, for the assessment of lifetime exposure and health effects

    Directory of Open Access Journals (Sweden)

    J. Kukkonen

    2018-06-01

    Full Text Available Reliable and self-consistent data on air quality are needed for an extensive period of time for conducting long-term, or even lifetime health impact assessments. We have modelled the urban-scale concentrations of fine particulate matter (PM2.5 in the Helsinki Metropolitan Area for a period of 35 years, from 1980 to 2014. The regional background concentrations were evaluated based on reanalyses of the atmospheric composition on global and European scales, using the SILAM model. The high-resolution urban computations included both the emissions originated from vehicular traffic (separately exhaust and suspension emissions and those from small-scale combustion, and were conducted using the road network dispersion model CAR-FMI and the multiple-source Gaussian dispersion model UDM-FMI. The modelled concentrations of PM2.5 agreed fairly well with the measured data at a regional background station and at four urban measurement stations, during 1999–2014. The modelled concentration trends were also evaluated for earlier years, until 1988, using proxy analyses. There was no systematic deterioration of the agreement of predictions and data for earlier years (the 1980s and 1990s, compared with the results for more recent years (2000s and early 2010s. The local vehicular emissions were about 5 times higher in the 1980s, compared with the emissions during the latest considered years. The local small-scale combustion emissions increased slightly over time. The highest urban concentrations of PM2.5 occurred in the 1980s; these have since decreased to about to a half of the highest values. In general, regional background was the largest contribution in this area. Vehicular exhaust has been the most important local source, but the relative shares of both small-scale combustion and vehicular non-exhaust emissions have increased in time. The study has provided long-term, high-resolution concentration databases on regional and urban scales that can be used for

  20. PM levels in urban area of Bejaia

    Science.gov (United States)

    Benaissa, Fatima; Maesano, Cara Nichole; Alkama, Rezak; Annesi-Maesano, Isabella

    2017-04-01

    Air pollution is not routinely measured in Bejaia City, Algeria, an urban area of around 200,000 inhabitants. We present first time measurements of particulate matter (PM) mass concentrations for this city (PM10, PM7, PM4, PM2.5 and PM1) over the course of one week, from July 8 to July 14, 2015. This study covered eight urban sampling sites and 169 measurements were obtained to determine mass concentration levels. Air pollution is not routinely measured in Bejaia City, Algeria, an urban area of around 200,000 inhabitants. We present first time measurements of particulate matter (PM) mass concentrations for this city (PM10, PM7, PM4, PM2.5 and PM1) over the course of one week, from July 8 to July 14, 2015. This study covered eight urban sampling sites and 169 measurements were obtained to determine mass concentration levels. The average city-wide PM10 and PM2.5 concentrations measured during this sampling were 87.8 ± 33.9 and 28.7 ± 10.6 µg/m3 respectively. These results show that particulate matter levels are high and exceed Algerian ambient air quality standards (maximum 80 µg/m3, without specifying the particle size). Further, PM10 and PM2.5 averages were well above the prescribed 24-hour average World Health Organization Air Quality Guidelines (WHO AQG) (50 µg/m3 for PM10 and 25 µg/m3 for PM2.5). The PM1, PM2,5, PM4 and PM7 fractions accounted for 15%, 32 %, 56% and 78% respectively of the PM10 measurements. Our analysis reveals that PM concentration variations in the study region were influenced primarily by traffic. In fact, lower PM10 concentrations (21.7 and 33.1 µg/m3) were recorded in residential sites while higher values (53.1, and 45.2 µg/m3) were registered in city centers. Keywords: Particulate matter, Urban area, vehicle fleet, Bejaia.

  1. Human health risk due to variations in PM10-PM2.5 and associated PAHs levels

    Science.gov (United States)

    Sosa, Beatriz S.; Porta, Andrés; Colman Lerner, Jorge Esteban; Banda Noriega, Roxana; Massolo, Laura

    2017-07-01

    WHO (2012) reports that chronic exposure to air pollutants, including particulate matter (PM), causes the death of 7 million people, constituting the most important environmental risk for health in the world. IARC classifies contaminated outdoor air as carcinogenic, Group 1 category. However, in our countries there are few studies regarding air pollution levels and possible associated effects on public health. The current study determined PM and associated polycyclic aromatic hydrocarbons (PAHs) levels in outdoor air, identified their possible emission sources and analysed health risks in the city of Tandil (Argentina). PM10 and PM2.5 samples were collected using a low volume sampler (MiniVol TAS) in three areas: city centre, industrial and residential. Concentrations were determined by gravimetric methods and the content of the US EPA 16 priority PAHs was found by high performance liquid chromatography (HPLC). Description of the main emission sources and selection of monitoring sites resulted from spatial analysis and the IVE (International Vehicle Emissions) model was used in the characterisation of the traffic flow. Median values of 35.7 μgm-3 and 9.6 μgm-3 in PM10 and PM2.5 respectively and characteristic profiles were found for each area. Local values PAHs associated to PM10 and PM2.5, in general, were lower than 10ngm-3. The estimated Unit Risk for the three areas exceeds US EPA standards (9 × 10-5). The number of deaths attributable to short term exposure to outdoor PM10 was 4 cases in children under 5 years of age, and 21 cases in total population, for a relative risk of 1.037.

  2. Anomalous elevated radiocarbon measurements of PM2.5

    International Nuclear Information System (INIS)

    Buchholz, Bruce A.; Fallon, Stewart J.; Zermeño, Paula; Bench, Graham; Schichtel, Bret A.

    2013-01-01

    Two-component models are often used to determine the contributions made by fossil fuel and natural sources of carbon in airborne particulate matter (PM). The models reduce thousands of actual sources to two end members based on isotopic signature. Combustion of fossil fuels produces PM free of carbon-14 ( 14 C). Wood or charcoal smoke, restaurant fryer emissions, and natural emissions from plants produce PM with the contemporary concentration of 14 C approximately 1.2 × 10 −1214 C/C. Such data can be used to estimate the relative contributions of fossil fuels and biogenic aerosols to the total aerosol loading and radiocarbon analysis is becoming a popular source apportionment method. Emissions from incinerators combusting medical or biological wastes containing tracer 14 C can skew the 14 C/C ratio of PM, however, so critical analysis of sampling sites for possible sources of elevated PM needs to be completed prior to embarking on sampling campaigns. Results are presented for two ambient monitoring sites in different areas of the United States where 14 C contamination is apparent. Our experience suggests that such contamination is uncommon but is also not rare (∼10%) for PM sampling sites.

  3. Implementation and evaluation of PM2.5 source contribution ...

    Science.gov (United States)

    Source culpability assessments are useful for developing effective emissions control programs. The Integrated Source Apportionment Method (ISAM) has been implemented in the Community Multiscale Air Quality (CMAQ) model to track contributions from source groups and regions to ambient levels and deposited amounts of primary and secondary inorganic PM2.5. Confidence in this approach is established by comparing ISAM source contribution estimates to emissions zero-out simulations recognizing that these approaches are not always expected to provide the same answer. The comparisons are expected to be most similar for more linear processes such as those involving primary emissions of PM2.5 and most different for non-linear systems like ammonium nitrate formation. Primarily emitted PM2.5 (e.g. elemental carbon), sulfur dioxide, ammonia, and nitrogen oxide contribution estimates compare well to zero-out estimates for ambient concentration and deposition. PM2.5 sulfate ion relationships are strong, but nonlinearity is evident and shown to be related to aqueous phase oxidation reactions in the host model. ISAM and zero-out contribution estimates are less strongly related for PM2.5 ammonium nitrate, resulting from instances of non-linear chemistry and negative responses (increases in PM2.5 due to decreases in emissions). ISAM is demonstrated in the context of an annual simulation tracking well characterized emissions source sectors and boundary conditions shows source contri

  4. Differentiating the effects of characteristics of PM pollution on mortality from ischemic and hemorrhagic strokes.

    Science.gov (United States)

    Lin, Hualiang; Tao, Jun; Du, Yaodong; Liu, Tao; Qian, Zhengmin; Tian, Linwei; Di, Qian; Zeng, Weilin; Xiao, Jianpeng; Guo, Lingchuan; Li, Xing; Xu, Yanjun; Ma, Wenjun

    2016-03-01

    Though increasing evidence supports significant association between particulate matter (PM) air pollution and stroke, it remains unclear what characteristics, such as particle size and chemical constituents, are responsible for this association. A time-series model with quasi-Poisson function was applied to assess the association of PM pollution with different particle sizes and chemical constituents with mortalities from ischemic and hemorrhagic strokes in Guangzhou, China, we controlled for potential confounding factors in the model, such as temporal trends, day of the week, public holidays, meteorological factors and influenza epidemic. We found significant association between stroke mortality and various PM fractions, such as PM10, PM2.5 and PM1, with generally larger magnitudes for smaller particles. For the PM2.5 chemical constituents, we found that organic carbon (OC), elemental carbon (EC), sulfate, nitrate and ammonium were significantly associated with stroke mortality. The analysis for specific types of stroke suggested that it was hemorrhagic stroke, rather than ischemic stroke, that was significantly associated with PM pollution. Our study shows that various PM pollution fractions are associated with stroke mortality, and constituents primarily from combustion and secondary aerosols might be the harmful components of PM2.5 in Guangzhou, and this study suggests that PM pollution is more relevant to hemorrhagic stroke in the study area, however, more studies are warranted due to the underlying limitations of this study. Copyright © 2015 Elsevier GmbH. All rights reserved.

  5. Source apportionment of PM10 and PM2.5 in a desert region in northern Chile

    International Nuclear Information System (INIS)

    Jorquera, Héctor; Barraza, Francisco

    2013-01-01

    Estimating contributions of anthropogenic sources to ambient particulate matter (PM) in desert regions is a challenging issue because wind erosion contributions are ubiquitous, significant and difficult to quantify by using source-oriented, dispersion models. A receptor modeling analysis has been applied to ambient PM 10 and PM 2.5 measured in an industrial zone ∼ 20 km SE of Antofagasta (23.63°S, 70.39°W), a midsize coastal city in northern Chile; the monitoring site is within a desert region that extends from northern Chile to southern Perú. Integrated 24-hour ambient samples of PM 10 and PM 2.5 were taken with Harvard Impactors; samples were analyzed by X Ray Fluorescence, ionic chromatography (NO 3 − and SO 4 = ), atomic absorption (Na + , K + ) and thermal optical transmission for elemental and organic carbon determination. Receptor modeling was carried out using Positive Matrix Factorization (US EPA Version 3.0); sources were identified by looking at specific tracers, tracer ratios, local winds and wind trajectories computed from NOAA's HYSPLIT model. For the PM 2.5 fraction, six contributions were found — cement plant, 33.7 ± 1.3%; soil dust, 22.4 ± 1.6%; sulfates, 17.8 ± 1.7%; mineral stockpiles and brine plant, 12.4 ± 1.2%; Antofagasta, 8.5 ± 1.3% and copper smelter, 5.3 ± 0.8%. For the PM 10 fraction five sources were identified — cement plant, 38.2 ± 1.5%; soil dust, 31.2 ± 2.3%; mineral stockpiles and brine plant, 12.7 ± 1.7%; copper smelter, 11.5 ± 1.6% and marine aerosol, 6.5 ± 2.4%. Therefore local sources contribute to ambient PM concentrations more than distant sources (Antofagasta, marine aerosol) do. Soil dust is enriched with deposition of marine aerosol and calcium, sulfates and heavy metals from surrounding industrial activities. The mean contribution of suspended soil dust to PM 10 is 50 μg/m 3 and the peak daily value is 104 μg/m 3 . For the PM 2.5 fraction, suspended soil dust contributes with an average of 9.3

  6. Elemental characterization and source apportionment of PM{sub 10} and PM{sub 2.5} in the western coastal area of central Taiwan

    Energy Technology Data Exchange (ETDEWEB)

    Hsu, Chin-Yu; Chiang, Hung-Che [National Environmental Health Research Center, National Health Research Institutes, 35 Keyan Road, Zhunan Town, Miaoli 35053, Taiwan (China); Lin, Sheng-Lun [Super Micro Mass Research and Technology Center, Cheng Shiu University, No. 840, Chengcing Rd., Kaohsiung 83347, Taiwan (China); Chen, Mu-Jean; Lin, Tzu-Yu [National Environmental Health Research Center, National Health Research Institutes, 35 Keyan Road, Zhunan Town, Miaoli 35053, Taiwan (China); Chen, Yu-Cheng, E-mail: yucheng@nhri.org.tw [National Environmental Health Research Center, National Health Research Institutes, 35 Keyan Road, Zhunan Town, Miaoli 35053, Taiwan (China); Department of Occupational Safety and Health, China Medical University, 91 Hsueh-Shih Road, Taichung 40402, Taiwan (China)

    2016-01-15

    ABSTRACT: This study investigated seasonal variations in PM{sub 10} and PM{sub 2.5} mass and associated trace metal concentrations in a residential area in proximity to the crude oil refinery plants and industrial parks of central Taiwan. Particle measurements were conducted during winter, spring and summer in 2013 and 2014. Twenty-six trace metals in PM{sub 10} and PM{sub 2.5} were analyzed using ICP-MS. Multiple approaches of the backward trajectory model, enrichment factor (EF), Lanthanum enrichment and positive matrix fraction (PMF) were used to identify potential sources of particulate metals. Mean concentrations of PM{sub 10} in winter, spring and summer were 76.4 ± 22.6, 33.2 ± 9.9 and 37.4 ± 17.0 μg m{sup −3}, respectively, while mean levels of PM{sub 2.5} in winter, spring and summer were 47.8 ± 20.0, 23.9 ± 11.2 and 16.3 ± 8.2 μg m{sup −3}, respectively. The concentrations of carcinogenic metals (Ni, As and adjusted Cr(VI)) in PM{sub 10} and PM{sub 2.5} exceeded the guideline limits published by WHO. The result of EF analysis confirmed that Mo, Sb, Cd, Zn, Mg, Cr, As, Pb, Cu, Ni and V were attributable to anthropogenic emission. PMF analysis demonstrated that trace metals in PM{sub 10} and PM{sub 2.5} were from the similar sources, such as coal combustion, oil combustion and traffic-related emission, except for soil dust and crustal element emissions only observed in PM{sub 10} and secondary aluminum smelter only observed in PM{sub 2.5}. Considering health-related particulate metals, the traffic-related emission and coal combustion for PM{sub 10} and PM{sub 2.5}, respectively, are important to control for reducing potential carcinogenic risk. The results could aid efforts to clarify the impact of source-specific origins on human health. - Highlights: • Multiple approaches to identify sources of PM{sub 10} and PM{sub 2.5} metals were used. • Four similar sources contributed to metals in PM{sub 10} and PM{sub 2.5} in the study area. • Six

  7. [Preliminary study of source apportionment of PM10 and PM2.5 in three cities of China during spring].

    Science.gov (United States)

    Gao, Shen; Pan, Xiao-chuan; Madaniyazi, Li-na; Xie, Juan; He, Ya-hui

    2013-09-01

    To study source apportionment of atmospheric PM10 (particle matter ≤ 10 µm in aerodynamic diameter) and PM2.5 (particle matter ≤ 2.5 µm in aerodynamic diameter) in Beijing,Urumqi and Qingdao, China. The atmospheric particle samples of PM10 and PM2.5 collected from Beijing between May 17th and June 18th, 2005, from Urumqi between April 20th and June 1st, 2006 and from Qingdao between April 4th and May 15th, 2005, were detected to trace the source apportionment by factor analysis and enrichment factor methods. In Beijing, the source apportionment results derived from factor analysis model for PM10 were construction dust and soil sand dust (contributing rate of variance at 45.35%), industry dust, coal-combusted smoke and vehicle emissions (contributing rate at 31.83%), and biomass burning dust (13.57%). The main pollution element was Pb, while the content (median (minimum value-maximum value)was 0.216 (0.040-0.795) µg/m(3)) . As for PM2.5, the sources were construction dust and soil sand dust (38.86%), industry dust, coal-combusted smoke and vehicle emissions (25.73%), biomass burning dust (13.10%) and burning oil dust (11.92%). The main pollution element was Zn (0.365(0.126-0.808) µg/m(3)).In Urumqi, source apportionment results for PM10 were soil sand dust and coal-combusted dust(49.75%), industry dust, vehicle emissions and secondary particles dust (30.65%). The main characteristic pollution element was Cd (0.463(0.033-1.351) ng/m(3)). As for PM2.5, the sources were soil sand dust and coal-combusted dust (43.26%), secondary particles dust (22.29%), industry dust and vehicle emissions (20.50%). The main characteristic pollution element was As (14.599 (1.696-36.741) µg/m(3)).In Qingdao, source apportionment results for PM10 were construction dust (30.91%), vehicle emissions and industry dust (29.65%) and secondary particles dust (28.99%). The main characteristic pollution element was Pb (64.071 (5.846-346.831) µg/m(3)). As for PM2.5, the sources were

  8. Safety and efficacy assessment of standardized herbal formula PM012

    Science.gov (United States)

    2012-01-01

    Background This study was conducted to evaluate the efficacy of the herbal formula PM012 on an Alzheimer's disease model, human presenilin 2 mutant transgenic mice (hPS2m), and also to evaluate the toxicity of PM012 in Sprague-Dawely rats after 4 or 26 weeks treatment with repeated oral administration. Methods Spatial learning and memory capacities of hPS2m transgenic mice were evaluated using the Morris Water Maze. Simultaneously, PM012 was repeatedly administered orally to male and female SD rats (15/sex/group) at doses of 0 (vehicle control), 500, 1,000 and 2,000 mg/kg/day for 4 or 26 weeks. To evaluate the recovery potential, 5 animals of each sex were assigned to vehicle control and 2,000 mg/kg/day groups during the 4-week recovery period. Results The results showed that PM012-treated hPS2m transgenic mice showed significantly reduced escape latency when compared with the hPS2m transgenic mice. The repeated oral administration of PM012 over 26 weeks in male and female rats induced an increase and increasing trend in thymus weight in the female treatment groups (main and recovery groups), but the change was judged to be toxicologically insignificant. In addition, the oral administration of the herbal medicine PM012 did not cause adverse effects as assessed by clinical signs, mortality, body weight, food and water consumption, ophthalmology, urinalysis, hematology, serum biochemistry, blood clotting time, organ weights and histopathology. The No Observed Adverse Effects Levels of PM012 was determined to be 2,000 mg/kg/day for both sexes, and the target organ was not identified. Conclusion These results suggest that PM012 has potential for use in the treatment of the Alzheimer's disease without serious adverse effects. PMID:22458507

  9. PM Raman fiber laser at 1679 nm

    DEFF Research Database (Denmark)

    Svane, Ask Sebastian; Rottwitt, Karsten

    2012-01-01

    We demonstrate a PM Raman fiber laser emitting light at 1679 nm. The laser has an slope efficiency of 67 % and an output power of more than 275mWwith a 27 pm linewidth.......We demonstrate a PM Raman fiber laser emitting light at 1679 nm. The laser has an slope efficiency of 67 % and an output power of more than 275mWwith a 27 pm linewidth....

  10. Analysis of PM10, PM2.5, and PM2 5-10 concentrations in Santiago, Chile, from 1989 to 2001.

    Science.gov (United States)

    Koutrakis, Petros; Sax, Sonja N; Sarnat, Jeremy A; Coull, Brent; Demokritou, Phil; Oyola, Pedro; Garcia, Javier; Gramsch, Ernesto

    2005-03-01

    Daily particle samples were collected in Santiago, Chile, at four urban locations from January 1, 1989, through December 31, 2001. Both fine PM with da Ambient Air Quality Standards and the European Union concentration limits. Mean PM2.5 levels during the cold season (April through September) were more than twice as high as those observed in the warm season (October through March); whereas coarse particle levels were similar in both seasons. PM concentration trends were investigated using regression models, controlling for site, weekday, month, wind speed, temperature, and RH. Results showed that PM2.5 concentrations decreased substantially, 52% over the 12-year period (1989-2000), whereas PM2.5-10 concentrations increased by approximately 50% in the first 5 years and then decreased by a similar percentage over the following 7 years. These decreases were evident even after controlling for significant climatic effects. These results suggest that the pollution reduction programs developed and implemented by the Comisión Nacional del Medio Ambiente (CONAMA) have been effective in reducing particle levels in the Santiago Metropolitan region. However, particle levels remain high and it is thus imperative that efforts to improve air quality continue.

  11. Monetary Valuation of PM10-Related Health Risks in Beijing China: The Necessity for PM10 Pollution Indemnity.

    Science.gov (United States)

    Yin, Hao; Xu, Linyu; Cai, Yanpeng

    2015-08-21

    Severe health risks caused by PM10 (particulate matter with an aerodynamic diameter ≤10 μm) pollution have induced inevitable economic losses and have rendered pressure on the sustainable development of society as a whole. In China, with the "Polluters Pay Principle", polluters should pay for the pollution they have caused, but how much they should pay remains an intractable problem for policy makers. This paper integrated an epidemiological exposure-response model with economics methods, including the Amended Human Capital (AHC) approach and the Cost of Illness (COI) method, to value the economic loss of PM10-related health risks in 16 districts and also 4 functional zones in Beijing from 2008 to 2012. The results show that from 2008 to 2012 the estimated annual deaths caused by PM10 in Beijing are around 56,000, 58,000, 63,000, 61,000 and 59,000, respectively, while the economic losses related to health damage increased from around 23 to 31 billion dollars that PM10 polluters should pay for pollution victims between 2008 and 2012. It is illustrated that not only PM10 concentration but also many other social economic factors influence PM10-related health economic losses, which makes health economic losses show a time lag discrepancy compared with the decline of PM10 concentration. In conclusion, health economic loss evaluation is imperative in the pollution indemnity system establishment and should be considered for the urban planning and policy making to control the burgeoning PM10 health economic loss.

  12. Measurement of $CP$ violation and constraints on the CKM angle $\\gamma$ in $B^{\\pm}\\rightarrow D K^{\\pm}$ with $D \\rightarrow K_S^0 \\pi^+ \\pi^-$ decays

    CERN Document Server

    Aaij, Roel; Adinolfi, Marco; Affolder, Anthony; Ajaltouni, Ziad; Albrecht, Johannes; Alessio, Federico; Alexander, Michael; Ali, Suvayu; Alkhazov, Georgy; Alvarez Cartelle, Paula; Alves Jr, Antonio; Amato, Sandra; Amerio, Silvia; Amhis, Yasmine; An, Liupan; Anderlini, Lucio; Anderson, Jonathan; Andreassen, Rolf; Andreotti, Mirco; Andrews, Jason; Appleby, Robert; Aquines Gutierrez, Osvaldo; Archilli, Flavio; Artamonov, Alexander; Artuso, Marina; Aslanides, Elie; Auriemma, Giulio; Baalouch, Marouen; Bachmann, Sebastian; Back, John; Badalov, Alexey; Balagura, Vladislav; Baldini, Wander; Barlow, Roger; Barschel, Colin; Barsuk, Sergey; Barter, William; Batozskaya, Varvara; Bay, Aurelio; Beaucourt, Leo; Beddow, John; Bedeschi, Franco; Bediaga, Ignacio; Belogurov, Sergey; Belous, Konstantin; Belyaev, Ivan; Ben-Haim, Eli; Bencivenni, Giovanni; Benson, Sean; Benton, Jack; Berezhnoy, Alexander; Bernet, Roland; Bettler, Marc-Olivier; van Beuzekom, Martinus; Bien, Alexander; Bifani, Simone; Bird, Thomas; Bizzeti, Andrea; Bjørnstad, Pål Marius; Blake, Thomas; Blanc, Frédéric; Blouw, Johan; Blusk, Steven; Bocci, Valerio; Bondar, Alexander; Bondar, Nikolay; Bonivento, Walter; Borghi, Silvia; Borgia, Alessandra; Borsato, Martino; Bowcock, Themistocles; Bowen, Espen Eie; Bozzi, Concezio; Brambach, Tobias; van den Brand, Johannes; Bressieux, Joël; Brett, David; Britsch, Markward; Britton, Thomas; Brodzicka, Jolanta; Brook, Nicholas; Brown, Henry; Bursche, Albert; Busetto, Giovanni; Buytaert, Jan; Cadeddu, Sandro; Calabrese, Roberto; Calvi, Marta; Calvo Gomez, Miriam; Camboni, Alessandro; Campana, Pierluigi; Campora Perez, Daniel; Carbone, Angelo; Carboni, Giovanni; Cardinale, Roberta; Cardini, Alessandro; Carranza-Mejia, Hector; Carson, Laurence; Carvalho Akiba, Kazuyoshi; Casse, Gianluigi; Cassina, Lorenzo; Castillo Garcia, Lucia; Cattaneo, Marco; Cauet, Christophe; Cenci, Riccardo; Charles, Matthew; Charpentier, Philippe; Chen, Shanzhen; Cheung, Shu-Faye; Chiapolini, Nicola; Chrzaszcz, Marcin; Ciba, Krzystof; Cid Vidal, Xabier; Ciezarek, Gregory; Clarke, Peter; Clemencic, Marco; Cliff, Harry; Closier, Joel; Coco, Victor; Cogan, Julien; Cogneras, Eric; Collins, Paula; Comerma-Montells, Albert; Contu, Andrea; Cook, Andrew; Coombes, Matthew; Coquereau, Samuel; Corti, Gloria; Corvo, Marco; Counts, Ian; Couturier, Benjamin; Cowan, Greig; Craik, Daniel Charles; Cruz Torres, Melissa Maria; Cunliffe, Samuel; Currie, Robert; D'Ambrosio, Carmelo; Dalseno, Jeremy; David, Pascal; David, Pieter; Davis, Adam; De Bruyn, Kristof; De Capua, Stefano; De Cian, Michel; De Miranda, Jussara; De Paula, Leandro; De Silva, Weeraddana; De Simone, Patrizia; Decamp, Daniel; Deckenhoff, Mirko; Del Buono, Luigi; Déléage, Nicolas; Derkach, Denis; Deschamps, Olivier; Dettori, Francesco; Di Canto, Angelo; Dijkstra, Hans; Donleavy, Stephanie; Dordei, Francesca; Dorigo, Mirco; Dosil Suárez, Alvaro; Dossett, David; Dovbnya, Anatoliy; Dujany, Giulio; Dupertuis, Frederic; Durante, Paolo; Dzhelyadin, Rustem; Dziurda, Agnieszka; Dzyuba, Alexey; Easo, Sajan; Egede, Ulrik; Egorychev, Victor; Eidelman, Semen; Eisenhardt, Stephan; Eitschberger, Ulrich; Ekelhof, Robert; Eklund, Lars; El Rifai, Ibrahim; Elsasser, Christian; Ely, Scott; Esen, Sevda; Falabella, Antonio; Färber, Christian; Farinelli, Chiara; Farley, Nathanael; Farry, Stephen; Fay, Robert; Ferguson, Dianne; Fernandez Albor, Victor; Ferreira Rodrigues, Fernando; Ferro-Luzzi, Massimiliano; Filippov, Sergey; Fiore, Marco; Fiorini, Massimiliano; Firlej, Miroslaw; Fitzpatrick, Conor; Fiutowski, Tomasz; Fontana, Marianna; Fontanelli, Flavio; Forty, Roger; Francisco, Oscar; Frank, Markus; Frei, Christoph; Frosini, Maddalena; Fu, Jinlin; Furfaro, Emiliano; Gallas Torreira, Abraham; Galli, Domenico; Gallorini, Stefano; Gambetta, Silvia; Gandelman, Miriam; Gandini, Paolo; Gao, Yuanning; Garofoli, Justin; Garra Tico, Jordi; Garrido, Lluis; Gaspar, Clara; Gauld, Rhorry; Gavardi, Laura; Geraci, Angelo; Gersabeck, Evelina; Gersabeck, Marco; Gershon, Timothy; Ghez, Philippe; Gianelle, Alessio; Giani', Sebastiana; Gibson, Valerie; Giubega, Lavinia-Helena; Gligorov, V.V.; Göbel, Carla; Golubkov, Dmitry; Golutvin, Andrey; Gomes, Alvaro; Gordon, Hamish; Gotti, Claudio; Grabalosa Gándara, Marc; Graciani Diaz, Ricardo; Granado Cardoso, Luis Alberto; Graugés, Eugeni; Graziani, Giacomo; Grecu, Alexandru; Greening, Edward; Gregson, Sam; Griffith, Peter; Grillo, Lucia; Grünberg, Oliver; Gui, Bin; Gushchin, Evgeny; Guz, Yury; Gys, Thierry; Hadjivasiliou, Christos; Haefeli, Guido; Haen, Christophe; Haines, Susan; Hall, Samuel; Hamilton, Brian; Hampson, Thomas; Han, Xiaoxue; Hansmann-Menzemer, Stephanie; Harnew, Neville; Harnew, Samuel; Harrison, Jonathan; Hartmann, Thomas; He, Jibo; Head, Timothy; Heijne, Veerle; Hennessy, Karol; Henrard, Pierre; Henry, Louis; Hernando Morata, Jose Angel; van Herwijnen, Eric; Heß, Miriam; Hicheur, Adlène; Hill, Donal; Hoballah, Mostafa; Hombach, Christoph; Hulsbergen, Wouter; Hunt, Philip; Hussain, Nazim; Hutchcroft, David; Hynds, Daniel; Idzik, Marek; Ilten, Philip; Jacobsson, Richard; Jaeger, Andreas; Jalocha, Pawel; Jans, Eddy; Jaton, Pierre; Jawahery, Abolhassan; Jing, Fanfan; John, Malcolm; Johnson, Daniel; Jones, Christopher; Joram, Christian; Jost, Beat; Jurik, Nathan; Kaballo, Michael; Kandybei, Sergii; Kanso, Walaa; Karacson, Matthias; Karbach, Moritz; Kelsey, Matthew; Kenyon, Ian; Ketel, Tjeerd; Khanji, Basem; Khurewathanakul, Chitsanu; Klaver, Suzanne; Kochebina, Olga; Kolpin, Michael; Komarov, Ilya; Koopman, Rose; Koppenburg, Patrick; Korolev, Mikhail; Kozlinskiy, Alexandr; Kravchuk, Leonid; Kreplin, Katharina; Kreps, Michal; Krocker, Georg; Krokovny, Pavel; Kruse, Florian; Kucharczyk, Marcin; Kudryavtsev, Vasily; Kurek, Krzysztof; Kvaratskheliya, Tengiz; La Thi, Viet Nga; Lacarrere, Daniel; Lafferty, George; Lai, Adriano; Lambert, Dean; Lambert, Robert W; Lanciotti, Elisa; Lanfranchi, Gaia; Langenbruch, Christoph; Langhans, Benedikt; Latham, Thomas; Lazzeroni, Cristina; Le Gac, Renaud; van Leerdam, Jeroen; Lees, Jean-Pierre; Lefèvre, Regis; Leflat, Alexander; Lefrançois, Jacques; Leo, Sabato; Leroy, Olivier; Lesiak, Tadeusz; Leverington, Blake; Li, Yiming; Liles, Myfanwy; Lindner, Rolf; Linn, Christian; Lionetto, Federica; Liu, Bo; Liu, Guoming; Lohn, Stefan; Longstaff, Iain; Lopes, Jose; Lopez-March, Neus; Lowdon, Peter; Lu, Haiting; Lucchesi, Donatella; Luo, Haofei; Lupato, Anna; Luppi, Eleonora; Lupton, Oliver; Machefert, Frederic; Machikhiliyan, Irina V; Maciuc, Florin; Maev, Oleg; Malde, Sneha; Manca, Giulia; Mancinelli, Giampiero; Mapelli, Alessandro; Maratas, Jan; Marchand, Jean François; Marconi, Umberto; Marin Benito, Carla; Marino, Pietro; Märki, Raphael; Marks, Jörg; Martellotti, Giuseppe; Martens, Aurelien; Martín Sánchez, Alexandra; Martinelli, Maurizio; Martinez Santos, Diego; Martinez Vidal, Fernando; Martins Tostes, Danielle; Massafferri, André; Matev, Rosen; Mathe, Zoltan; Matteuzzi, Clara; Mazurov, Alexander; McCann, Michael; McCarthy, James; McNab, Andrew; McNulty, Ronan; McSkelly, Ben; Meadows, Brian; Meier, Frank; Meissner, Marco; Merk, Marcel; Milanes, Diego Alejandro; Minard, Marie-Noelle; Moggi, Niccolò; Molina Rodriguez, Josue; Monteil, Stephane; Moran, Dermot; Morandin, Mauro; Morawski, Piotr; Mordà, Alessandro; Morello, Michael Joseph; Moron, Jakub; Morris, Adam Benjamin; Mountain, Raymond; Muheim, Franz; Müller, Katharina; Muresan, Raluca; Mussini, Manuel; Muster, Bastien; Naik, Paras; Nakada, Tatsuya; Nandakumar, Raja; Nasteva, Irina; Needham, Matthew; Neri, Nicola; Neubert, Sebastian; Neufeld, Niko; Neuner, Max; Nguyen, Anh Duc; Nguyen, Thi-Dung; Nguyen-Mau, Chung; Nicol, Michelle; Niess, Valentin; Niet, Ramon; Nikitin, Nikolay; Nikodem, Thomas; Novoselov, Alexey; Oblakowska-Mucha, Agnieszka; Obraztsov, Vladimir; Oggero, Serena; Ogilvy, Stephen; Okhrimenko, Oleksandr; Oldeman, Rudolf; Onderwater, Gerco; Orlandea, Marius; Otalora Goicochea, Juan Martin; Owen, Patrick; Oyanguren, Maria Arantza; Pal, Bilas Kanti; Palano, Antimo; Palombo, Fernando; Palutan, Matteo; Panman, Jacob; Papanestis, Antonios; Pappagallo, Marco; Parkes, Christopher; Parkinson, Christopher John; Passaleva, Giovanni; Patel, Girish; Patel, Mitesh; Patrignani, Claudia; Pazos Alvarez, Antonio; Pearce, Alex; Pellegrino, Antonio; Pepe Altarelli, Monica; Perazzini, Stefano; Perez Trigo, Eliseo; Perret, Pascal; Perrin-Terrin, Mathieu; Pescatore, Luca; Pesen, Erhan; Petridis, Konstantin; Petrolini, Alessandro; Picatoste Olloqui, Eduardo; Pietrzyk, Boleslaw; Pilař, Tomas; Pinci, Davide; Pistone, Alessandro; Playfer, Stephen; Plo Casasus, Maximo; Polci, Francesco; Poluektov, Anton; Polycarpo, Erica; Popov, Alexander; Popov, Dmitry; Popovici, Bogdan; Potterat, Cédric; Powell, Andrew; Prisciandaro, Jessica; Pritchard, Adrian; Prouve, Claire; Pugatch, Valery; Puig Navarro, Albert; Punzi, Giovanni; Qian, Wenbin; Rachwal, Bartolomiej; Rademacker, Jonas; Rakotomiaramanana, Barinjaka; Rama, Matteo; Rangel, Murilo; Raniuk, Iurii; Rauschmayr, Nathalie; Raven, Gerhard; Reichert, Stefanie; Reid, Matthew; dos Reis, Alberto; Ricciardi, Stefania; Richards, Alexander; Rihl, Mariana; Rinnert, Kurt; Rives Molina, Vincente; Roa Romero, Diego; Robbe, Patrick; Rodrigues, Ana Barbara; Rodrigues, Eduardo; Rodriguez Perez, Pablo; Roiser, Stefan; Romanovsky, Vladimir; Romero Vidal, Antonio; Rotondo, Marcello; Rouvinet, Julien; Ruf, Thomas; Ruffini, Fabrizio; Ruiz, Hugo; Ruiz Valls, Pablo; Sabatino, Giovanni; Saborido Silva, Juan Jose; Sagidova, Naylya; Sail, Paul; Saitta, Biagio; Salustino Guimaraes, Valdir; Sanchez Mayordomo, Carlos; Sanmartin Sedes, Brais; Santacesaria, Roberta; Santamarina Rios, Cibran; Santovetti, Emanuele; Sapunov, Matvey; Sarti, Alessio; Satriano, Celestina; Satta, Alessia; Savrie, Mauro; Savrina, Darya; Schiller, Manuel; Schindler, Heinrich; Schlupp, Maximilian; Schmelling, Michael; Schmidt, Burkhard; Schneider, Olivier; Schopper, Andreas; Schune, Marie Helene; Schwemmer, Rainer; Sciascia, Barbara; Sciubba, Adalberto; Seco, Marcos; Semennikov, Alexander; Senderowska, Katarzyna; Sepp, Indrek; Serra, Nicola; Serrano, Justine; Sestini, Lorenzo; Seyfert, Paul; Shapkin, Mikhail; Shapoval, Illya; Shcheglov, Yury; Shears, Tara; Shekhtman, Lev; Shevchenko, Vladimir; Shires, Alexander; Silva Coutinho, Rafael; Simi, Gabriele; Sirendi, Marek; Skidmore, Nicola; Skwarnicki, Tomasz; Smith, Anthony; Smith, Edmund; Smith, Eluned; Smith, Jackson; Smith, Mark; Snoek, Hella; Sokoloff, Michael; Soler, Paul; Soomro, Fatima; Souza, Daniel; Souza De Paula, Bruno; Spaan, Bernhard; Sparkes, Ailsa; Spradlin, Patrick; Stagni, Federico; Stahl, Sascha; Steinkamp, Olaf; Stenyakin, Oleg; Stevenson, Scott; Stoica, Sabin; Stone, Sheldon; Storaci, Barbara; Stracka, Simone; Straticiuc, Mihai; Straumann, Ulrich; Stroili, Roberto; Subbiah, Vijay Kartik; Sun, Liang; Sutcliffe, William; Swientek, Krzysztof; Swientek, Stefan; Syropoulos, Vasileios; Szczekowski, Marek; Szczypka, Paul; Szilard, Daniela; Szumlak, Tomasz; T'Jampens, Stephane; Teklishyn, Maksym; Tellarini, Giulia; Teubert, Frederic; Thomas, Christopher; Thomas, Eric; van Tilburg, Jeroen; Tisserand, Vincent; Tobin, Mark; Tolk, Siim; Tomassetti, Luca; Tonelli, Diego; Topp-Joergensen, Stig; Torr, Nicholas; Tournefier, Edwige; Tourneur, Stephane; Tran, Minh Tâm; Tresch, Marco; Tsaregorodtsev, Andrei; Tsopelas, Panagiotis; Tuning, Niels; Ubeda Garcia, Mario; Ukleja, Artur; Ustyuzhanin, Andrey; Uwer, Ulrich; Vagnoni, Vincenzo; Valenti, Giovanni; Vallier, Alexis; Vazquez Gomez, Ricardo; Vazquez Regueiro, Pablo; Vázquez Sierra, Carlos; Vecchi, Stefania; Velthuis, Jaap; Veltri, Michele; Veneziano, Giovanni; Vesterinen, Mika; Viaud, Benoit; Vieira, Daniel; Vieites Diaz, Maria; Vilasis-Cardona, Xavier; Vollhardt, Achim; Volyanskyy, Dmytro; Voong, David; Vorobyev, Alexey; Vorobyev, Vitaly; Voß, Christian; Voss, Helge; de Vries, Jacco; Waldi, Roland; Wallace, Charlotte; Wallace, Ronan; Walsh, John; Wandernoth, Sebastian; Wang, Jianchun; Ward, David; Watson, Nigel; Websdale, David; Whitehead, Mark; Wicht, Jean; Wiedner, Dirk; Wilkinson, Guy; Williams, Matthew; Williams, Mike; Wilson, Fergus; Wimberley, Jack; Wishahi, Julian; Wislicki, Wojciech; Witek, Mariusz; Wormser, Guy; Wotton, Stephen; Wright, Simon; Wu, Suzhi; Wyllie, Kenneth; Xie, Yuehong; Xing, Zhou; Xu, Zhirui; Yang, Zhenwei; Yuan, Xuhao; Yushchenko, Oleg; Zangoli, Maria; Zavertyaev, Mikhail; Zhang, Feng; Zhang, Liming; Zhang, Wen Chao; Zhang, Yanxi; Zhelezov, Alexey; Zhokhov, Anatoly; Zhong, Liang; Zvyagin, Alexander

    A model-dependent amplitude analysis of $B^{\\pm} \\rightarrow D K^{\\pm}$ with $D \\rightarrow K_S^0 \\pi^+ \\pi^-$ decays is performed using proton-proton collision data, corresponding to an integrated luminosity of $1$ fb$^{-1}$, recorded by LHCb at a centre-of-mass energy of $7$ TeV in $2011$. Values of the $CP$ violation observables $x_{\\pm}$ and $y_{\\pm}$, which are sensitive to the CKM angle $\\gamma$, are measured to be \\begin{align*} x_- &= +0.027 \\pm 0.044 ^{+0.010}_{-0.008} \\pm 0.001, \\\\ y_- &= +0.013 \\pm 0.048 ^{+0.009}_{-0.007} \\pm 0.003, \\\\ x_+ &= -0.084 \\pm 0.045 \\pm 0.009 \\pm 0.005, \\\\ y_+ &= -0.032 \\pm 0.048 ^{+0.010}_{-0.009} \\pm 0.008, \\end{align*} where the first uncertainty is statistical, the second systematic and the third arises from the uncertainty of the $D \\rightarrow K_S^0 \\pi^+ \\pi^-$ amplitude model. The value of $\\gamma$ is determined to be $(84^{+49}_{-42})^\\circ$, including all sources of uncertainty. Neutral $D$ meson mixing is found to have negligible effect.

  13. Measurement of $CP$ violation and constraints on the CKM angle $\\gamma$ in $B^{\\pm}\\rightarrow D K^{\\pm}$ with $D \\rightarrow K_S^0 \\pi^+ \\pi^-$ decays

    CERN Document Server

    Aaij, Roel; Adinolfi, Marco; Affolder, Anthony; Ajaltouni, Ziad; Albrecht, Johannes; Alessio, Federico; Alexander, Michael; Ali, Suvayu; Alkhazov, Georgy; Alvarez Cartelle, Paula; Alves Jr, Antonio; Amato, Sandra; Amerio, Silvia; Amhis, Yasmine; An, Liupan; Anderlini, Lucio; Anderson, Jonathan; Andreassen, Rolf; Andreotti, Mirco; Andrews, Jason; Appleby, Robert; Aquines Gutierrez, Osvaldo; Archilli, Flavio; Artamonov, Alexander; Artuso, Marina; Aslanides, Elie; Auriemma, Giulio; Baalouch, Marouen; Bachmann, Sebastian; Back, John; Badalov, Alexey; Balagura, Vladislav; Baldini, Wander; Barlow, Roger; Barschel, Colin; Barsuk, Sergey; Barter, William; Batozskaya, Varvara; Bay, Aurelio; Beaucourt, Leo; Beddow, John; Bedeschi, Franco; Bediaga, Ignacio; Belogurov, Sergey; Belous, Konstantin; Belyaev, Ivan; Ben-Haim, Eli; Bencivenni, Giovanni; Benson, Sean; Benton, Jack; Berezhnoy, Alexander; Bernet, Roland; Bettler, Marc-Olivier; van Beuzekom, Martinus; Bien, Alexander; Bifani, Simone; Bird, Thomas; Bizzeti, Andrea; Bjørnstad, Pål Marius; Blake, Thomas; Blanc, Frédéric; Blouw, Johan; Blusk, Steven; Bocci, Valerio; Bondar, Alexander; Bondar, Nikolay; Bonivento, Walter; Borghi, Silvia; Borgia, Alessandra; Borsato, Martino; Bowcock, Themistocles; Bowen, Espen Eie; Bozzi, Concezio; Brambach, Tobias; van den Brand, Johannes; Bressieux, Joël; Brett, David; Britsch, Markward; Britton, Thomas; Brodzicka, Jolanta; Brook, Nicholas; Brown, Henry; Bursche, Albert; Busetto, Giovanni; Buytaert, Jan; Cadeddu, Sandro; Calabrese, Roberto; Calvi, Marta; Calvo Gomez, Miriam; Camboni, Alessandro; Campana, Pierluigi; Campora Perez, Daniel; Carbone, Angelo; Carboni, Giovanni; Cardinale, Roberta; Cardini, Alessandro; Carranza-Mejia, Hector; Carson, Laurence; Carvalho Akiba, Kazuyoshi; Casse, Gianluigi; Cassina, Lorenzo; Castillo Garcia, Lucia; Cattaneo, Marco; Cauet, Christophe; Cenci, Riccardo; Charles, Matthew; Charpentier, Philippe; Chen, Shanzhen; Cheung, Shu-Faye; Chiapolini, Nicola; Chrzaszcz, Marcin; Ciba, Krzystof; Cid Vidal, Xabier; Ciezarek, Gregory; Clarke, Peter; Clemencic, Marco; Cliff, Harry; Closier, Joel; Coco, Victor; Cogan, Julien; Cogneras, Eric; Collins, Paula; Comerma-Montells, Albert; Contu, Andrea; Cook, Andrew; Coombes, Matthew; Coquereau, Samuel; Corti, Gloria; Corvo, Marco; Counts, Ian; Couturier, Benjamin; Cowan, Greig; Craik, Daniel Charles; Cruz Torres, Melissa Maria; Cunliffe, Samuel; Currie, Robert; D'Ambrosio, Carmelo; Dalseno, Jeremy; David, Pascal; David, Pieter; Davis, Adam; De Bruyn, Kristof; De Capua, Stefano; De Cian, Michel; De Miranda, Jussara; De Paula, Leandro; De Silva, Weeraddana; De Simone, Patrizia; Decamp, Daniel; Deckenhoff, Mirko; Del Buono, Luigi; Déléage, Nicolas; Derkach, Denis; Deschamps, Olivier; Dettori, Francesco; Di Canto, Angelo; Dijkstra, Hans; Donleavy, Stephanie; Dordei, Francesca; Dorigo, Mirco; Dosil Suárez, Alvaro; Dossett, David; Dovbnya, Anatoliy; Dujany, Giulio; Dupertuis, Frederic; Durante, Paolo; Dzhelyadin, Rustem; Dziurda, Agnieszka; Dzyuba, Alexey; Easo, Sajan; Egede, Ulrik; Egorychev, Victor; Eidelman, Semen; Eisenhardt, Stephan; Eitschberger, Ulrich; Ekelhof, Robert; Eklund, Lars; El Rifai, Ibrahim; Elsasser, Christian; Ely, Scott; Esen, Sevda; Falabella, Antonio; Färber, Christian; Farinelli, Chiara; Farley, Nathanael; Farry, Stephen; Fay, Robert; Ferguson, Dianne; Fernandez Albor, Victor; Ferreira Rodrigues, Fernando; Ferro-Luzzi, Massimiliano; Filippov, Sergey; Fiore, Marco; Fiorini, Massimiliano; Firlej, Miroslaw; Fitzpatrick, Conor; Fiutowski, Tomasz; Fontana, Marianna; Fontanelli, Flavio; Forty, Roger; Francisco, Oscar; Frank, Markus; Frei, Christoph; Frosini, Maddalena; Fu, Jinlin; Furfaro, Emiliano; Gallas Torreira, Abraham; Galli, Domenico; Gallorini, Stefano; Gambetta, Silvia; Gandelman, Miriam; Gandini, Paolo; Gao, Yuanning; Garofoli, Justin; Garra Tico, Jordi; Garrido, Lluis; Gaspar, Clara; Gauld, Rhorry; Gavardi, Laura; Geraci, Angelo; Gersabeck, Evelina; Gersabeck, Marco; Gershon, Timothy; Ghez, Philippe; Gianelle, Alessio; Giani', Sebastiana; Gibson, Valerie; Giubega, Lavinia-Helena; Gligorov, Vladimir; Göbel, Carla; Golubkov, Dmitry; Golutvin, Andrey; Gomes, Alvaro; Gordon, Hamish; Gotti, Claudio; Grabalosa Gándara, Marc; Graciani Diaz, Ricardo; Granado Cardoso, Luis Alberto; Graugés, Eugeni; Graziani, Giacomo; Grecu, Alexandru; Greening, Edward; Gregson, Sam; Griffith, Peter; Grillo, Lucia; Grünberg, Oliver; Gui, Bin; Gushchin, Evgeny; Guz, Yury; Gys, Thierry; Hadjivasiliou, Christos; Haefeli, Guido; Haen, Christophe; Haines, Susan; Hall, Samuel; Hamilton, Brian; Hampson, Thomas; Han, Xiaoxue; Hansmann-Menzemer, Stephanie; Harnew, Neville; Harnew, Samuel; Harrison, Jonathan; Hartmann, Thomas; He, Jibo; Head, Timothy; Heijne, Veerle; Hennessy, Karol; Henrard, Pierre; Henry, Louis; Hernando Morata, Jose Angel; van Herwijnen, Eric; Heß, Miriam; Hicheur, Adlène; Hill, Donal; Hoballah, Mostafa; Hombach, Christoph; Hulsbergen, Wouter; Hunt, Philip; Hussain, Nazim; Hutchcroft, David; Hynds, Daniel; Idzik, Marek; Ilten, Philip; Jacobsson, Richard; Jaeger, Andreas; Jalocha, Pawel; Jans, Eddy; Jaton, Pierre; Jawahery, Abolhassan; Jing, Fanfan; John, Malcolm; Johnson, Daniel; Jones, Christopher; Joram, Christian; Jost, Beat; Jurik, Nathan; Kaballo, Michael; Kandybei, Sergii; Kanso, Walaa; Karacson, Matthias; Karbach, Moritz; Kelsey, Matthew; Kenyon, Ian; Ketel, Tjeerd; Khanji, Basem; Khurewathanakul, Chitsanu; Klaver, Suzanne; Kochebina, Olga; Kolpin, Michael; Komarov, Ilya; Koopman, Rose; Koppenburg, Patrick; Korolev, Mikhail; Kozlinskiy, Alexandr; Kravchuk, Leonid; Kreplin, Katharina; Kreps, Michal; Krocker, Georg; Krokovny, Pavel; Kruse, Florian; Kucharczyk, Marcin; Kudryavtsev, Vasily; Kurek, Krzysztof; Kvaratskheliya, Tengiz; La Thi, Viet Nga; Lacarrere, Daniel; Lafferty, George; Lai, Adriano; Lambert, Dean; Lambert, Robert W; Lanciotti, Elisa; Lanfranchi, Gaia; Langenbruch, Christoph; Langhans, Benedikt; Latham, Thomas; Lazzeroni, Cristina; Le Gac, Renaud; van Leerdam, Jeroen; Lees, Jean-Pierre; Lefèvre, Regis; Leflat, Alexander; Lefrançois, Jacques; Leo, Sabato; Leroy, Olivier; Lesiak, Tadeusz; Leverington, Blake; Li, Yiming; Liles, Myfanwy; Lindner, Rolf; Linn, Christian; Lionetto, Federica; Liu, Bo; Liu, Guoming; Lohn, Stefan; Longstaff, Iain; Lopes, Jose; Lopez-March, Neus; Lowdon, Peter; Lu, Haiting; Lucchesi, Donatella; Luo, Haofei; Lupato, Anna; Luppi, Eleonora; Lupton, Oliver; Machefert, Frederic; Machikhiliyan, Irina V; Maciuc, Florin; Maev, Oleg; Malde, Sneha; Manca, Giulia; Mancinelli, Giampiero; Mapelli, Alessandro; Maratas, Jan; Marchand, Jean François; Marconi, Umberto; Marin Benito, Carla; Marino, Pietro; Märki, Raphael; Marks, Jörg; Martellotti, Giuseppe; Martens, Aurelien; Martín Sánchez, Alexandra; Martinelli, Maurizio; Martinez Santos, Diego; Martinez Vidal, Fernando; Martins Tostes, Danielle; Massafferri, André; Matev, Rosen; Mathe, Zoltan; Matteuzzi, Clara; Mazurov, Alexander; McCann, Michael; McCarthy, James; McNab, Andrew; McNulty, Ronan; McSkelly, Ben; Meadows, Brian; Meier, Frank; Meissner, Marco; Merk, Marcel; Milanes, Diego Alejandro; Minard, Marie-Noelle; Moggi, Niccolò; Molina Rodriguez, Josue; Monteil, Stephane; Moran, Dermot; Morandin, Mauro; Morawski, Piotr; Mordà, Alessandro; Morello, Michael Joseph; Moron, Jakub; Morris, Adam Benjamin; Mountain, Raymond; Muheim, Franz; Müller, Katharina; Muresan, Raluca; Mussini, Manuel; Muster, Bastien; Naik, Paras; Nakada, Tatsuya; Nandakumar, Raja; Nasteva, Irina; Needham, Matthew; Neri, Nicola; Neubert, Sebastian; Neufeld, Niko; Neuner, Max; Nguyen, Anh Duc; Nguyen, Thi-Dung; Nguyen-Mau, Chung; Nicol, Michelle; Niess, Valentin; Niet, Ramon; Nikitin, Nikolay; Nikodem, Thomas; Novoselov, Alexey; Oblakowska-Mucha, Agnieszka; Obraztsov, Vladimir; Oggero, Serena; Ogilvy, Stephen; Okhrimenko, Oleksandr; Oldeman, Rudolf; Onderwater, Gerco; Orlandea, Marius; Otalora Goicochea, Juan Martin; Owen, Patrick; Oyanguren, Maria Arantza; Pal, Bilas Kanti; Palano, Antimo; Palombo, Fernando; Palutan, Matteo; Panman, Jacob; Papanestis, Antonios; Pappagallo, Marco; Parkes, Christopher; Parkinson, Christopher John; Passaleva, Giovanni; Patel, Girish; Patel, Mitesh; Patrignani, Claudia; Pazos Alvarez, Antonio; Pearce, Alex; Pellegrino, Antonio; Pepe Altarelli, Monica; Perazzini, Stefano; Perez Trigo, Eliseo; Perret, Pascal; Perrin-Terrin, Mathieu; Pescatore, Luca; Pesen, Erhan; Petridis, Konstantin; Petrolini, Alessandro; Picatoste Olloqui, Eduardo; Pietrzyk, Boleslaw; Pilař, Tomas; Pinci, Davide; Pistone, Alessandro; Playfer, Stephen; Plo Casasus, Maximo; Polci, Francesco; Poluektov, Anton; Polycarpo, Erica; Popov, Alexander; Popov, Dmitry; Popovici, Bogdan; Potterat, Cédric; Powell, Andrew; Prisciandaro, Jessica; Pritchard, Adrian; Prouve, Claire; Pugatch, Valery; Puig Navarro, Albert; Punzi, Giovanni; Qian, Wenbin; Rachwal, Bartolomiej; Rademacker, Jonas; Rakotomiaramanana, Barinjaka; Rama, Matteo; Rangel, Murilo; Raniuk, Iurii; Rauschmayr, Nathalie; Raven, Gerhard; Reichert, Stefanie; Reid, Matthew; dos Reis, Alberto; Ricciardi, Stefania; Richards, Alexander; Rihl, Mariana; Rinnert, Kurt; Rives Molina, Vincente; Roa Romero, Diego; Robbe, Patrick; Rodrigues, Ana Barbara; Rodrigues, Eduardo; Rodriguez Perez, Pablo; Roiser, Stefan; Romanovsky, Vladimir; Romero Vidal, Antonio; Rotondo, Marcello; Rouvinet, Julien; Ruf, Thomas; Ruffini, Fabrizio; Ruiz, Hugo; Ruiz Valls, Pablo; Sabatino, Giovanni; Saborido Silva, Juan Jose; Sagidova, Naylya; Sail, Paul; Saitta, Biagio; Salustino Guimaraes, Valdir; Sanchez Mayordomo, Carlos; Sanmartin Sedes, Brais; Santacesaria, Roberta; Santamarina Rios, Cibran; Santovetti, Emanuele; Sapunov, Matvey; Sarti, Alessio; Satriano, Celestina; Satta, Alessia; Savrie, Mauro; Savrina, Darya; Schiller, Manuel; Schindler, Heinrich; Schlupp, Maximilian; Schmelling, Michael; Schmidt, Burkhard; Schneider, Olivier; Schopper, Andreas; Schune, Marie Helene; Schwemmer, Rainer; Sciascia, Barbara; Sciubba, Adalberto; Seco, Marcos; Semennikov, Alexander; Senderowska, Katarzyna; Sepp, Indrek; Serra, Nicola; Serrano, Justine; Sestini, Lorenzo; Seyfert, Paul; Shapkin, Mikhail; Shapoval, Illya; Shcheglov, Yury; Shears, Tara; Shekhtman, Lev; Shevchenko, Vladimir; Shires, Alexander; Silva Coutinho, Rafael; Simi, Gabriele; Sirendi, Marek; Skidmore, Nicola; Skwarnicki, Tomasz; Smith, Anthony; Smith, Edmund; Smith, Eluned; Smith, Jackson; Smith, Mark; Snoek, Hella; Sokoloff, Michael; Soler, Paul; Soomro, Fatima; Souza, Daniel; Souza De Paula, Bruno; Spaan, Bernhard; Sparkes, Ailsa; Spradlin, Patrick; Stagni, Federico; Stahl, Sascha; Steinkamp, Olaf; Stenyakin, Oleg; Stevenson, Scott; Stoica, Sabin; Stone, Sheldon; Storaci, Barbara; Stracka, Simone; Straticiuc, Mihai; Straumann, Ulrich; Stroili, Roberto; Subbiah, Vijay Kartik; Sun, Liang; Sutcliffe, William; Swientek, Krzysztof; Swientek, Stefan; Syropoulos, Vasileios; Szczekowski, Marek; Szczypka, Paul; Szilard, Daniela; Szumlak, Tomasz; T'Jampens, Stephane; Teklishyn, Maksym; Tellarini, Giulia; Teubert, Frederic; Thomas, Christopher; Thomas, Eric; van Tilburg, Jeroen; Tisserand, Vincent; Tobin, Mark; Tolk, Siim; Tomassetti, Luca; Tonelli, Diego; Topp-Joergensen, Stig; Torr, Nicholas; Tournefier, Edwige; Tourneur, Stephane; Tran, Minh Tâm; Tresch, Marco; Tsaregorodtsev, Andrei; Tsopelas, Panagiotis; Tuning, Niels; Ubeda Garcia, Mario; Ukleja, Artur; Ustyuzhanin, Andrey; Uwer, Ulrich; Vagnoni, Vincenzo; Valenti, Giovanni; Vallier, Alexis; Vazquez Gomez, Ricardo; Vazquez Regueiro, Pablo; Vázquez Sierra, Carlos; Vecchi, Stefania; Velthuis, Jaap; Veltri, Michele; Veneziano, Giovanni; Vesterinen, Mika; Viaud, Benoit; Vieira, Daniel; Vieites Diaz, Maria; Vilasis-Cardona, Xavier; Vollhardt, Achim; Volyanskyy, Dmytro; Voong, David; Vorobyev, Alexey; Vorobyev, Vitaly; Voß, Christian; Voss, Helge; de Vries, Jacco; Waldi, Roland; Wallace, Charlotte; Wallace, Ronan; Walsh, John; Wandernoth, Sebastian; Wang, Jianchun; Ward, David; Watson, Nigel; Websdale, David; Whitehead, Mark; Wicht, Jean; Wiedner, Dirk; Wilkinson, Guy; Williams, Matthew; Williams, Mike; Wilson, Fergus; Wimberley, Jack; Wishahi, Julian; Wislicki, Wojciech; Witek, Mariusz; Wormser, Guy; Wotton, Stephen; Wright, Simon; Wu, Suzhi; Wyllie, Kenneth; Xie, Yuehong; Xing, Zhou; Xu, Zhirui; Yang, Zhenwei; Yuan, Xuhao; Yushchenko, Oleg; Zangoli, Maria; Zavertyaev, Mikhail; Zhang, Feng; Zhang, Liming; Zhang, Wen Chao; Zhang, Yanxi; Zhelezov, Alexey; Zhokhov, Anatoly; Zhong, Liang; Zvyagin, Alexander

    2014-01-01

    A model-dependent amplitude analysis of $B^{\\pm} \\rightarrow D K^{\\pm}$ with $D \\rightarrow K_S^0 \\pi^+ \\pi^-$ decays is performed using proton-proton collision data, corresponding to an integrated luminosity of $1$ fb$^{-1}$, recorded by LHCb at a centre-of-mass energy of $7$ TeV in $2011$. Values of the $CP$ violation observables $x_{\\pm}$ and $y_{\\pm}$, which are sensitive to the CKM angle $\\gamma$, are measured to be \\begin{align*} x_- &= +0.027 \\pm 0.044 ^{+0.010}_{-0.008} \\pm 0.001, \\\\ y_- &= +0.013 \\pm 0.048 ^{+0.009}_{-0.007} \\pm 0.003, \\\\ x_+ &= -0.084 \\pm 0.045 \\pm 0.009 \\pm 0.005, \\\\ y_+ &= -0.032 \\pm 0.048 ^{+0.010}_{-0.009} \\pm 0.008, \\end{align*} where the first uncertainty is statistical, the second systematic and the third arises from the uncertainty of the $D \\rightarrow K_S^0 \\pi^+ \\pi^-$ amplitude model. The value of $\\gamma$ is determined to be $(84^{+49}_{-42})^\\circ$, including all sources of uncertainty. Neutral $D$ meson mixing is found to have negligible effect.

  14. CARDIOVASCULAR MORTALITY IN PHOENIX: PM1 IS A BETTER INDICATOR THAN PM2.5.

    Science.gov (United States)

    EPA has obtained a 3-year database of particulate matter (PM) in Phoenix, AZ from 1995 - 1997 that includes elemental analysis by XRF of daily PM2.5. During this time period PM1 and PM2.5 TEOMs were run simultaneously for about 7 months during two periods of the year. Regressio...

  15. Indoor pollution: PM2.5 and PM10 from cigarette smoke

    International Nuclear Information System (INIS)

    Chianese, E.; Barone, G.; Castaldo, R.M.; Riccio, A.

    2009-01-01

    This work is aimed to establishing the temporal and spatial dispersion of PM 10 and PM 2.5 particulate matter fractions generated by cigarettes smoking in an indoor ambient. To this purpose, PM 10 and PM 2.5 concentrations were collected with a mobile instrument positioned in a room accommodating a smoking machine. [it

  16. Particle reduction strategies - PAREST. Evaluation of emission reduction scenarios using chemical transport calculations. PM10- and PM2.5-reduction potentials by package of measures for further immission reduction in Germany. Sub-report

    International Nuclear Information System (INIS)

    Stern, Rainer

    2013-01-01

    This report documents the effects of additional emission control measures the PM10 and PM2.5 air quality in Germany (PM = particulate matter). The immission effects of the planned measures were calculated with the Chemistry-Aerosol-Transport Model REM CALGRID (RCG). [de

  17. Predictability Analysis of PM10 Concentrations in Budapest

    Science.gov (United States)

    Ferenczi, Zita

    2013-04-01

    Climate, weather and air quality may have harmful effects on human health and environment. Over the past few hundred years we had to face the changes in climate in parallel with the changes in air quality. These observed changes in climate, weather and air quality continuously interact with each other: pollutants are changing the climate, thus changing the weather, but climate also has impacts on air quality. The increasing number of extreme weather situations may be a result of climate change, which could create favourable conditions for rising of pollutant concentrations. Air quality in Budapest is determined by domestic and traffic emissions combined with the meteorological conditions. In some cases, the effect of long-range transport could also be essential. While the time variability of the industrial and traffic emissions is not significant, the domestic emissions increase in winter season. In recent years, PM10 episodes have caused the most critical air quality problems in Budapest, especially in winter. In Budapest, an air quality network of 11 stations detects the concentration values of different pollutants hourly. The Hungarian Meteorological Service has developed an air quality prediction model system for the area of Budapest. The system forecasts the concentration of air pollutants (PM10, NO2, SO2 and O3) for two days in advance. In this work we used meteorological parameters and PM10 data detected by the stations of the air quality network, as well as the forecasted PM10 values of the air quality prediction model system. In this work we present the evaluation of PM10 predictions in the last two years and the most important meteorological parameters affecting PM10 concentration. The results of this analysis determine the effect of the meteorological parameters and the emission of aerosol particles on the PM10 concentration values as well as the limits of this prediction system.

  18. Model Integrasi Penjadwalan Batch dan Penjadwalan Preventive Maintenance dengan Kriteria Minimisasi Biaya Simpan, Biaya Setup, Biaya Pm, serta Biaya Rework pada Mesin Stabil

    Directory of Open Access Journals (Sweden)

    Zahedi Zahedi

    2014-06-01

    Full Text Available This study developed a model of batch scheduling involving the unavailability machine to minimize setup costs, cost of preventive maintenance and the cost of rework in a stable machine. This model is considered necessary in order to understand the effect of the unavailability machine for production runs and to understand the effect on the batch production schedule. The results of this study indicate that the first and last run will not give single batch. Given a hypothetical example of how the model and algorithm developed solve the problem instance. 

  19. Revealing driving factors of China's PM2.5 pollution

    Science.gov (United States)

    Zheng, Y.; Zhao, H.; Zhang, Q.; Geng, G.; Tong, D.; Peng, L.; He, K.

    2017-12-01

    China's rapid economic development and intensive energy consumption are deteriorating the air quality significantly. Understanding the key driving factors behind China's growing emissions of air pollutants and the accompanying PM2.5 pollution is critical for the development of China's clean air policies and also provides insight into how other emerging economies may develop a clear sky future. Here we reveal the socioeconomic drivers of the variations of China's PM2.5 concentrations during 2002-2012 by using an interdisciplinary framework that integrates an emission inventory model, an index decomposition analysis model, and a regional air quality model. The decomposition results demostrate that the improvements in emission efficiency and energy efficiency failed to offset the increased emissions of both primary PM2.5 and gaseous PM2.5 precursors (including SO2 NOx, and volatile organic compounds) triggered by the surging economic growth during 2002-2012. During the same time, the effects of energy structure, production structure and population growth were relatively less significant to all pollutants, which indicates the potential of large emission abatements through energy structure and production structure adjustment. Sensitivity simulations by the air quality model based on the provincial decomposition results also show that the economic growth have outpaced efficiency improvements in the increments of PM2.5 concentrations during the study years. As China continues to develop rapidly, future policies should promote further improvements in efficiency and accelerate the adjustments toward clean energy and production structures, which are critical for reducing China's emissions and alleviating the severe PM2.5 pollution.

  20. Characterization of Fine Particulate Matter (PM) and Secondary PM Precursor Gases in Mexico City

    Energy Technology Data Exchange (ETDEWEB)

    Dr. Charles E. Kolb

    2008-03-31

    This project was one of three collaborating grants designed to understand the atmospheric chemistry and aerosol particle microphysics impacting air quality in the Mexico City Metropolitan Area (MCMA) and its urban plume. The overall effort, titled MCMA- 2006, focused on: 1) the primary emissions of fine particles and precursor gases leading to photochemical production of atmospheric oxidants and secondary aerosol particles and 2) the measurement and analysis of secondary oxidants and secondary fine particular matter (PM) production, with particular emphasis on secondary organic aerosol (SOA). MCAM-2006 pursued it goals through three main activities: 1) performance and publication of detailed analyses of extensive MCMA trace gas and fine PM measurements made by the collaborating groups and others during earlier MCMA field campaigns in 2002 and 2003; 2) deployment and utilization of extensive real-time trace gas and fine PM instrumentation at urban and downwind MCMA sites in support of the MAX-Mex/MILAGRO field measurements in March, 2006; and, 3) analyses of the 2006 MCMA data sets leading to further publications that are based on new data as well as insights from analysis and publication of the 2002/2003 field data. Thirteen archival publications were coauthored with other MCMA-2003 participants. Documented findings included a significantly improved speciated emissions inventory from on-road vehicles, a greatly enhanced understanding of the sources and atmospheric loadings of volatile organic compounds, a unique analysis of the high fraction of ambient formaldehyde from primary emission sources, a much more extensive knowledge of the composition, size distributions and atmospheric mass loadings of both primary and secondary fine PM, including the fact that the rate of MCMA SOA production greatly exceeded that predicted by current atmospheric models, and evaluations of significant errors that can arise from standard air quality monitors for ozone and nitrogen

  1. Search for direct CP violating charge asymmetries in $K^\\pm\\to\\pi^\\pm\\pi^+\\pi^-$ and $K^\\pm\\to\\pi^\\pm\\pi^0\\pi^0$ decays

    CERN Document Server

    Batley, J Richard; Kalmus, George Ernest; Lazzeroni, C; Munday, D J; Slater, M W; Wotton, S A; Arcidiacono, R; Bocquet, G; Cabibbo, Nicola; Ceccucci, A; Cundy, Donald C; Falaleev, V; Fidecaro, Maria; Gatignon, L; Gonidec, A; Kubischta, Werner; Norton, A; Maier, A; Patel, M; Peters, A; Balev, S; Frabetti, P L; Goudzovski, E; Khristov, P Z; Kekelidze, V D; Kozhuharov, V; Litov, L; Madigozhin, D T; Marinova, E; Molokanova, N A; Polenkevich, I; Potrebenikov, Yu K; Stoynev, S; Zinchenko, A I; Monnier, E; Swallow, E; Winston, R; Rubin, P; Walker, A; Baldini, W; Cotta-Ramusino, A; Dalpiaz, P; Damiani, C; Fiorini, M; Gianoli, A; Martini, M; Petrucci, F; Savrié, M; Scarpa, M; Wahle, H; Bizzeti, A; Calvetti, M; Celeghini, E; Iacopini, E; Lenti, M; Martelli, F; Ruggiero, G; Veltri, M; Behler, M; Eppard, K; Kleinknecht, K; Marouelli, P; Masetti, L; Moosbrugger, U; Morales-Morales, C; Renk, B; Wache, M; Wanke, R; Winhart, A; Coward, D; Dabrowski, A; Fonseca-Martin, T; Shieh, M; Szleper, M; Velasco, M; Wood, M D; Anzivino, Giuseppina; Cenci, P; Imbergamo, E; Nappi, A; Pepé, M; Petrucci, M C; Piccini, M; Raggi, M; Valdata-Nappi, M; Cerri, C; Collazuol, G; Costantini, F; Di Lella, L; Doble, N; Fantechi, R; Fiorini, L; Giudici, S; Lamanna, G; Mannelli, I; Michetti, A; Pierazzini, G M; Sozzi, M; Bloch-Devaux, B; Cheshkov, C; Chèze, J B; De Beer, M; Derré, J; Marel, Gérard; Mazzucato, E; Peyaud, B; Vallage, B; Holder, M; Ziolkowski, M; Bifani, S; Biino, C; Cartiglia, N; Clemencic, M; Goy-Lopez, S; Marchetto, F; Dibon, Heinz; Jeitler, Manfred; Markytan, Manfred; Mikulec, I; Neuhofer, G; Widhalm, L

    2007-01-01

    A measurement of the direct CP violating charge asymmetries of the Dalitz plot linear slopes $A_g=(g^+-g^-)/(g^++g^-)$ in $K^\\pm\\to\\pi^\\pm\\pi^+\\pi^-$ and $K^\\pm\\to\\pi^\\pm\\pi^0\\pi^0$ decays by the NA48/2 experiment at CERN SPS is presented. A new technique of asymmetry measurement involving simultaneous $K^+$ and $K^-$ beams and a large data sample collected allowed a result of an unprecedented precision. The charge asymmetries were measured to be $A^c_g=(-1.5\\pm2.1)\\times10^{-4}$ with $3.11\\times 10^9$ $K^{\\pm}\\to\\pi^\\pm\\pi^+\\pi^-$ decays, and $A^n_g=(1.8\\pm1.8)\\times10^{-4}$ with $9.13\\times 10^7$ $K^{\\pm}\\to\\pi^\\pm\\pi^0\\pi^0$ decays. The precision of the results is limited mainly by the size of the data sample.

  2. Meteorological factors for PM10 concentration levels in Northern Spain

    Science.gov (United States)

    Santurtún, Ana; Mínguez, Roberto; Villar-Fernández, Alejandro; González Hidalgo, Juan Carlos; Zarrabeitia, María Teresa

    2013-04-01

    models, iii) fitting of a times series model (Autoregressive moving average, ARMA) to the transformed historical values in order to eliminate the temporal autocorrelation structure of each stochastic process, obtaining a white noise for each variable, and finally, iv) the calculation of cross correlations between white noises at different time lags. These cross correlations allow characterization of the true correlation between signals, avoiding the problems induced by data scaling or autocorrelations inherent to each signal. Results provide the relationship and possible contribution to PM10 concentration levels associated with each meteorological variable. This information can be used to improve PM10 concentration levels forecasting using existing meteorological forecasts.

  3. Urban air quality modeling with full O 3-NO x-VOC chemistry: Implications for O 3 and PM air quality in a street canyon

    Science.gov (United States)

    Kim, Minjoong J.; Park, Rokjin J.; Kim, Jae-Jin

    2012-02-01

    We examine transport and chemical transformation of reactive pollutants on an urban street using a computation fluid dynamics (CFD) model coupled with full photochemistry of reactive pollutants. An extensive comparison between simulated results and observations is conducted to evaluate the model, focusing on a field campaign occurred in Dongfeng Middle Street in Guangzhou, China. Observed CO and NO concentrations vary diurnally following traffic volumes. The model captures this observed diurnal variation and magnitudes of CO concentrations successfully. However, simulated NO concentration is three times higher than observation. This high bias is significantly reduced in the sensitivity simulation with lower NO x emissions. We find that oxidation products of O 3 photochemistry such as NO 2 and O 3 vary differently from primary pollutants, indicating important effects of photochemical reactions on their fates. The model appears to reproduce observed O 3 and NO 2 variability with time and altitude. Our analysis shows that high NO x concentrations in the urban street canyon may efficiently produce aerosol nitrate in the presence of NH 3. Simulated inorganic NO 3- aerosol concentration reaches up to 0.3 μg m -3 in July but increases an order of magnitude higher at lower temperature that favors partitioning of gas-phase HNO 3 to aerosol-phase, implying a serious concern for urban air quality in winter.

  4. Evaluation and intercomparison of Ozone and PM10 simulations by several chemistry transport models over four European cities within the CityDelta project

    NARCIS (Netherlands)

    Vautard, R.; Builtjes, P.H.J.; Thunis, P.; Cuvelier, C.; Bedogni, M.; Bessagnet, B.; Honoré, C.; Moussiopoulos, N.; Pirovano, G.; Schaap, M.; Stern, R.; Tarrason, L.; Wind, P.

    2007-01-01

    The CityDelta project Cuvelier et al. [2006. CityDelta: a model intercomparison study to explore the impact of emission reductions in European cities in 2010. Atmospheric Environment] is designed to evaluate the air quality response of several emission abatement scenarios for 2010 at the scale of

  5. Transient thermal analysis of flux switching PM machines

    NARCIS (Netherlands)

    Ilhan, E.; Kremers, M.F.J.; Motoasca, T.E.; Paulides, J.J.H.; Lomonova, E.

    2013-01-01

    Flux switching permanent magnet (FSPM) machines bring together the merits of switched reluctance and PM synchronous motors. FSPM employs armature windings and PMs together in the stator region, therefore the proximity of the windings PMs makes a thermal model mandatory. In literature, thermal

  6. Development of ambient PM 2.5 management strategies.

    Science.gov (United States)

    2009-10-01

    "Using analyzed and modeled field data on air quality and meteorology, researchers identified major contributors of fine particulate matter (PM2.5) in Fairbanks. This : project was an effort to help the city meet U.S. Environmental Protection Agency ...

  7. PM2.5 constituents and hospital emergency-room visits in Shanghai, China.

    Science.gov (United States)

    Qiao, Liping; Cai, Jing; Wang, Hongli; Wang, Weibing; Zhou, Min; Lou, Shengrong; Chen, Renjie; Dai, Haixia; Chen, Changhong; Kan, Haidong

    2014-09-02

    Although ambient PM2.5 has been linked to adverse health effects, the chemical constituents that cause harm are largely unclear. Few prior studies in a developing country have reported the health impacts of PM2.5 constituents. In this study, we examined the short-term association between PM2.5 constituents and emergency room visits in Shanghai, China. We measured daily concentrations of PM2.5, organic carbon (OC), elemental carbon (EC), and eight water-soluble ions between January 1, 2011 and December 31, 2012. We analyzed the data using overdispersed generalized linear Poisson models. During our study period, the mean daily average concentration of PM2.5 in Shanghai was 55 μg/m(3). Major contributors to PM2.5 mass included OC, EC, sulfate, nitrate, and ammonium. For a 1-day lag, an interquartile range increment in PM2.5 mass (36.47 μg/m(3)) corresponded to 0.57% [95% confidence interval (CI): 0.13%, 1.01%] increase of emergency room visits. In all the three models used, we found significant positive associations of emergency room visits with OC and EC. Our findings suggest that PM2.5 constituents from the combustion of fossil fuel (e.g., OC and EC) may have an appreciable influence on the health impact attributable to PM2.5.

  8. EBIT spectroscopy of Pm-like tungsten

    International Nuclear Information System (INIS)

    Hutton, R.; Zou, Y.; Reyna Almandos, J.; Biedermann, C.; Radtke, R.; Greier, A.; Neu, R.

    2003-01-01

    Methods of VUV electron beam ion trap (EBIT) spectroscopy are applied to the study of Pm-like tungsten (W 13+ ). These data show that theory appears well capable of dealing with these multi-electron (61) ions, at least for high ionization stages. A comparison of other spectroscopic methods applied to the study of other ions of the Pm I sequence is also given, and finally a search for the Pm-like W lines at the ASDEX Upgrade Tokamak is mentioned

  9. Development of a continuous monitoring system for PM10 and components of PM2.5.

    Science.gov (United States)

    Lippmann, M; Xiong, J Q; Li, W

    2000-01-01

    While particulate matter with aerodynamic diameters below 10 and 2.5 microns (PM10 and PM2.5) correlate with excess mortality and morbidity, there is evidence for still closer epidemiological associations with sulfate ion, and experimental exposure-response studies suggest that the hydrogen ion and ultrafine (PM0.15) concentrations may be important risk factors. Also, there are measurement artifacts in current methods used to measure ambient PM10 and PM2.5, including negative artifacts because of losses of sampled semivolatile components (ammonium nitrate and some organics) and positive artifacts due to particle-bound water. To study such issues, we are developing a semi-continuous monitoring system for PM10, PM2.5, semivolatiles (organic compounds and NH4NO3), particle-bound water, and other PM2.5 constituents that may be causal factors. PM10 is aerodynamically sorted into three size-fractions: (1) coarse (PM10-PM2.5); (2) accumulation mode (PM2.5-PM0.15); and (3) ultrafine (PM0.15). The mass concentration of each fraction is measured in terms of the linear relation between accumulated mass and pressure drop on polycarbonate pore filters. The PM0.15 mass, being highly correlated with the ultrafine number concentration, provides a good index of the total number concentration in ambient air. For the accumulation mode (PM2.5-PM0.15), which contains nearly all of the semivolatiles and particle-bound water by mass, aliquots of the aerosol stream flow into system components that continuously monitor sulfur (by flame photometry), ammonium and nitrate (by chemiluminescence following catalytic transformations to NO), organics (by thermal-optical analysis) and particle-bound water (by electrolytic hygrometer after vacuum evaporation of sampled particles). The concentration of H+ can be calculated (by ion balance using the monitoring data on NO3-, NH4+, and SO4=).

  10. Local contribution of wood combustion to PM10 and PM2.5; Lokale bijdrage van houtverbranding aan PM10 en PM2,5

    Energy Technology Data Exchange (ETDEWEB)

    Kos, G.; Weijers, E. [ECN Biomassa, Kolen en Milieuonderzoek, Petten (Netherlands)

    2011-04-15

    In February 2009 the concentration of wood smoke in a residential area in Schoorl (Noord-Holland, Netherlands) was investigated over a period of three weeks. The aim was to assess the effect of local particulate matter (PM) emissions - caused by heating with wood stoves in this area - on local PM concentration. [Dutch] In februari 2009 zijn in Schoorl in Noord-Holland concentraties houtrook bepaald door levoglucosanmetingen (een voor houtrook kenmerkende koolwaterstofverbinding). Lokale houtrook draagt daar significant bij aan de concentratie fijn stof: tussen 9% en 27% voor PM10 en tussen 30% en 39% voor PM2,5.

  11. The local contribution of wood burning to PM10 and PM2.5; De lokale bijdrage van houtverbranding aan PM10 en PM2,5

    Energy Technology Data Exchange (ETDEWEB)

    Kos, G.; Weijers, E. [ECN Biomassa, Kolen en Milieuonderzoek, Petten (Netherlands)

    2011-04-15

    In January 2009, the concentrations of wood smoke in Schoorl, the Netherlands, were established by means of levoglucosan measurements (a hydrocarbon compound that is characteristic for wood smoke). Local wood smoke contributes significantly to the concentration of particulate matter: between 9% and 27% for PM10 and between 30% and 29% for PM2.5. [Dutch] In februari 2009 zijn in Schoorl in Noord-Holland concentraties houtrook bepaald door levoglucosanmetingen (een voor houtrook kenmerkende koolwaterstofverbinding). Lokale houtrook draagt daar significant bij aan de concentratie fijn stof: tussen 9% en 27% voor PM10 en tussen 30% en 39% voor PM2,5.

  12. Research on PM2.5 emission reduction path of China ‘s electric power industry based on DEA model

    Science.gov (United States)

    Jin, Yanming; Yang, Fan; Liu, Jun

    2018-02-01

    Based on the theory of data envelopment analysis, this study constructs the environmental performance evaluation model of the power industry, analyzes the performance of development of clean energy, the implementation of electricity replacement, and the development of coal-fired energy-saving and emission-reducing measures. Put forward technology path to reduce emission in the future. The results show that (1) improving the proportion of coal for power generation, speeding up the replacement of electricity is the key to solve the haze in China. (2) With the photovoltaic and other new energy power generation costs gradually reduced and less limit from thermal energy, by final of “thirteenth five-years plan”, the economy of clean energy will surpass thermal energy-saving emission reduction. (3) After 2025, the economy of the electricity replacement will be able to show.

  13. Modelos lineares aplicados à estimativa da concentração do material particulado (PM10) na cidade do Rio de Janeiro, RJ Linear models applied to the assessment of daily concentration of particulate matter (PM10) in Rio de Janeiro city, RJ, Brazil

    OpenAIRE

    Gustavo Bastos Lyra; Melissa Oda-Souza; Denise Nunes Viola

    2011-01-01

    Regressão linear múltipla foi aplicada para ajustar dois modelos à concentração média de 24 h do material particulado com diâmetro inferior a 10 µm (PM10). As variáveis explanatórias no primeiro modelo (M1) foram os elementos meteorológicos (temperatura e umidade do ar, precipitação pluvial, velocidade do vento e pressão atmosférica) e o índice de direção do vento (IDV). No segundo (M2), além dos elementos meteorológicos e do IDV, foi incluído como variável explanatória, a concentração de PM1...

  14. A new measurement of the $K^{\\pm} \\rightarrow \\pi^{\\pm} \\gamma \\gamma$ decay at the NA48/2 experiment

    CERN Document Server

    Batley, J.R.; Lazzeroni, C.; Munday, D.J.; Slater, M.W.; Wotton, S.A.; Arcidiacono, R.; Bocquet, G.; Cabibbo, N.; Ceccucci, A.; Cundy, D.; Falaleev, V.; Fidecaro, M.; Gatignon, L.; Gonidec, A.; Kubischta, W.; Norton, A.; Maier, A.; Patel, M.; Peters, A.; Balev, S.; Frabetti, P.L.; Gersabeck, E.; Goudzovski, E.; Hristov, P.; Kekelidze, V.; Kozhuharov, V.; Litov, L.; Madigozhin, D.; Molokanova, N.; Polenkevich, I.; Potrebenikov, Yu.; Stoynev, S.; Zinchenko, A.; Monnier, E.; Swallow, E.; Winston, R.; Rubin, P.; Walker, A.; Baldini, W.; Cotta Ramusino, A.; Dalpiaz, P.; Damiani, C.; Fiorini, M.; Gianoli, A.; Martini, M.; Petrucci, F.; Savrie, M.; Scarpa, M.; Wahl, H.; Bizzeti, A.; Lenti, M.; Veltri, M.; Calvetti, M.; Celeghini, E.; Iacopini, E.; Ruggiero, G.; Behler, M.; Eppard, K.; Kleinknecht, K.; Marouelli, P.; Masetti, L.; Moosbrugger, U.; Morales Morales, C.; Renk, B.; Wache, M.; Wanke, R.; Winhart, A.; Coward, D.; Dabrowski, A.; Fonseca Martin, T.; Shieh, M.; Szleper, M.; Velasco, M.; Wood, M.D.; Cenci, P.; Pepe, M.; Petrucci, M.C.; Anzivino, G.; Imbergamo, E.; Nappi, A.; Piccini, M.; Raggi, M.; Valdata-Nappi, M.; Cerri, C.; Fantechi, R.; Collazuol, G.; DiLella, L.; Lamanna, G.; Mannelli, I.; Michetti, A.; Costantini, F.; Doble, N.; Fiorini, L.; Giudici, S.; Pierazzini, G.; Sozzi, M.; Venditti, S.; Bloch-Devaux, B.; Cheshkov, C.; Cheze, J.B.; De Beer, M.; Derre, J.; Marel, G.; Mazzucato, E.; Peyaud, B.; Vallage, B.; Holder, M.; Ziolkowski, M.; Biino, C.; Cartiglia, N.; Marchetto, F.; Bifani, S.; Clemencic, M.; Goy Lopez, S.; Dibon, H.; Jeitler, M.; Markytan, M.; Mikulec, I.; Neuhofer, G.; Widhalm, L.

    2014-01-01

    The NA48/2 experiment at CERN collected two data samples with minimum bias trigger conditions in 2003 and 2004. A measurement of the rate and dynamic properties of the rare decay $K^\\pm\\to\\pi^\\pm\\gamma\\gamma$ from these data sets based on 149 decay candidates with an estimated background of $15.5\\pm0.7$ events is reported. The model-independent branching ratio in the kinematic range $z=(m_{\\gamma\\gamma}/m_K)^2>0.2$ is measured to be ${\\cal B}_{\\rm MI}(z>0.2) = (0.877 \\pm 0.089) \\times 10^{-6}$, and the branching ratio in the full kinematic range assuming a particular Chiral Perturbation Theory description to be ${\\cal B}(K_{\\pi\\gamma\\gamma}) = (0.910 \\pm 0.075) \\times 10^{-6}$.

  15. A novel PM motor with hybrid PM excitation and asymmetric rotor structure for high torque performance

    Directory of Open Access Journals (Sweden)

    Gaohong Xu

    2017-05-01

    Full Text Available This paper proposes a novel permanent magnet (PM motor for high torque performance, in which hybrid PM material and asymmetric rotor design are applied. The hybrid PM material is adopted to reduce the consumption of rare-earth PM because ferrite PM is assisted to enhance the torque production. Meanwhile, the rotor structure is designed to be asymmetric by shifting the surface-insert PM (SPM, which is used to improve the torque performance, including average torque and torque ripple. Moreover, the reasons for improvement of the torque performance are explained by evaluation and analysis of the performances of the proposed motor. Compared with SPM motor and V-type motor, the merit of high utilization ratio of rare-earth PM is also confirmed, showing that the proposed motor can offer higher torque density and lower torque ripple simultaneously with less consumption of rare-earth PM.

  16. Health risks zonation in megacities vis-à-vis PM using GIS-based ...

    African Journals Online (AJOL)

    Since the air quality, considering PM2.5 varies over space and time, in this paper, RBF method in a based GIS model was utilized to zone air quality and its health risks upon PM2.5 concentrations dispersion over Tehran, during one year, from 21 March 2013 to 20 March 2014. The RBF method was applied to obtain the ...

  17. A novel calibration approach of MODIS AOD data to predict PM2.5 concentrations

    Directory of Open Access Journals (Sweden)

    P. Koutrakis

    2011-08-01

    Full Text Available Epidemiological studies investigating the human health effects of PM2.5 are susceptible to exposure measurement errors, a form of bias in exposure estimates, since they rely on data from a limited number of PM2.5 monitors within their study area. Satellite data can be used to expand spatial coverage, potentially enhancing our ability to estimate location- or subject-specific exposures to PM2.5, but some have reported poor predictive power. A new methodology was developed to calibrate aerosol optical depth (AOD data obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS. Subsequently, this method was used to predict ground daily PM2.5 concentrations in the New England region. 2003 MODIS AOD data corresponding to the New England region were retrieved, and PM2.5 concentrations measured at 26 US Environmental Protection Agency (EPA PM2.5 monitoring sites were used to calibrate the AOD data. A mixed effects model which allows day-to-day variability in daily PM2.5-AOD relationships was used to predict location-specific PM2.5 levels. PM2.5 concentrations measured at the monitoring sites were compared to those predicted for the corresponding grid cells. Both cross-sectional and longitudinal comparisons between the observed and predicted concentrations suggested that the proposed new calibration approach renders MODIS AOD data a potentially useful predictor of PM2.5 concentrations. Furthermore, the estimated PM2.5 levels within the study domain were examined in relation to air pollution sources. Our approach made it possible to investigate the spatial patterns of PM2.5 concentrations within the study domain.

  18. Analysis of Particulate matter (PM 10 and PM 2.5 concentration in Khorramabad city

    Directory of Open Access Journals (Sweden)

    Seyed Hamed Mirhosseini

    2013-01-01

    Full Text Available Aims: In this study, the concentration of PM10 and PM2.5 in eight station of Khorramabad city was analyzed. Materials and Methods: For this study, the data were taken from April 2010 to March 2011. The eight sampling point were chosen in account to Khorramabad maps. During this period, 240 daily PM samples including coarse particle (PM 10 and fine particle (PM 2.5 were collected. A two-part sampler was used to collect samples of PM. According to one-way ANOVA, multiple comparisons Scheffe, the obtained data were analyzed and then compared with the Environment protection organization standard rates. Khorramabad Results: The results revealed that during measuring the maximum concentration of PM 10 and PM 2.5 was respectively 120.9 and 101.09 μ/m 3 at Shamshirabad station. There was a significant difference between the mean values of PM 10 concentration (μg/m 3 in the seasons of summer. In addition, the mean concentrations of PM 10 in warmer months exceeded to the maximum permissible concentration. Conclusions: Year comparison of PM 10 and PM 2.5 concentration with standard were revealed particle matter concentration in summer season was higher than standard. Although total mean of particle matter was less than standard concentration.

  19. Spatiotemporal patterns of particulate matter (PM and associations between PM and mortality in Shenzhen, China

    Directory of Open Access Journals (Sweden)

    Fengying Zhang

    2016-03-01

    Full Text Available Abstract Background Most studies on air pollution exposure and its associations with human health in China have focused on the heavily polluted industrial areas and/or mega-cities, and studies on cities with comparatively low air pollutant concentrations are still rare. Only a few studies have attempted to analyse particulate matter (PM for the vibrant economic centre Shenzhen in the Pearl River Delta. So far no systematic investigation of PM spatiotemporal patterns in Shenzhen has been undertaken and the understanding of pollution exposure in urban agglomerations with comparatively low pollution is still limited. Methods We analyze daily and hourly particulate matter concentrations and all-cause mortality during 2013 in Shenzhen, China. Temporal patterns of PM (PM2.5 and PM10 with aerodynamic diameters of 2.5 (10 μm or less (or less (including particles with a diameter that equals to 2.5 (10 μm are studied, along with the ratio of PM2.5 to PM10. Spatial distributions of PM10 and PM2.5 are addressed and associations of PM10 or PM2.5 and all-cause mortality are analyzed. Results Annual average PM10 and PM2.5 concentrations were 61.3 and 39.6 μg/m3 in 2013. PM2.5 failed to meet the Class 2 annual limit of the National Ambient Air Quality Standard. PM2.5 was the primary air pollutant, with 8.8 % of days having heavy PM2.5 pollution. The daily PM2.5/PM10 ratios were high. Hourly PM2.5 concentrations in the tourist area were lower than downtown throughout the day. PM10 and PM2.5 concentrations were higher in western parts of Shenzhen than in eastern parts. Excess risks in the number of all-cause mortality with a 10 μg/m3 increase of PM were 0.61 % (95 % confidence interval [CI]: 0.50–0.72 for PM10, and 0.69 % (95 % CI: 0.55–0.83 for PM2.5, respectively. The greatest ERs of PM10 and PM2.5 were in 2-day cumulative measures for the all-cause mortality, 2-day lag for females and the young (0–65 years, and L02 for males and the elder (>65

  20. 2005-2014 trends of PM10 source contributions in an industrialized area of southern Spain.

    Science.gov (United States)

    Li, Jiwei; Chen, Bing; de la Campa, Ana M Sánchez; Alastuey, Andrés; Querol, Xavier; de la Rosa, Jesus D

    2018-05-01

    Particulate matter with a diameter of 10 μm or less (PM10) using receptor modelling was determined at an urban (La Linea, LL) and an industrial area (Puente Mayorga, PMY) in Southern Spain with samples collected during 2005-2014. The concentrations of PM10 had been decreasing at both sites in three distinctive periods: 1) the initial PM10 levels approached or exceeded the Spain and EU PM10 annual guidelines of 40 μg/m 3 during 2005-2007 at LL and 2005-2009 at PMY; 2) then PM10 dropped by 25%-∼30 μg/m 3 during 2008-2011 at LL and during 2010-2011 at PMY; 3) since 2012, the PM10 concentrations gradually decreased to major elements. These PM components generally showed a decrease trend, in accord with the trend of PM10 reduction. A PMF model identified seven sources to PM10 contributions. Secondary sulfate, soil/urban/construction dust, and secondary nitrate showed significantly decreasing trends with reduction of 40-60% comparing to the initial levels. The road traffic contribution decreased by 14% from the first to third period. However, sea salt, oil combustion, and industrial metallurgical process had relative stable contributions. These source contribution changes are reasonably governed by the PM emission abatement actions implemented during the past decade, as well as the financial crisis, that accounted for a significant decrease of PM pollution in Southern Spain. We identified that the mitigation efforts on industry, fossil fuel combustion, and urban transportation during the past decade were successful for air quality improvement in a highly industrialized area in Southern Spain. Copyright © 2018 Elsevier Ltd. All rights reserved.

  1. The Lancet Countdown on PM2·5 pollution-related health impacts of China's projected carbon dioxide mitigation in the electric power generation sector under the Paris Agreement: a modelling study

    Directory of Open Access Journals (Sweden)

    Wenjia Cai, PhD

    2018-04-01

    Full Text Available Summary: Background: Except for comparing the implementation costs of the Paris Agreement with potential health benefits at the national levels, previous studies have not explored the health impacts of the nationally determined contributions (NDCs by countries and in regional details. In this Lancet Countdown study, we aimed to estimate and monetise the health benefits of China's NDCs in the electric power generation sector, and then compare them with the implementation costs, both at the national and regional levels. Methods: In this modelling study, we linked the Multi-regional model for Energy Supply system and their Environmental ImpaCts, the Multi-resolution Emission Inventory for China model, the offline-coupled Weather Research and Forecasting model, the Community Multiscale Air Quality model, and the Integrated Health Impact Assessment model with a time scope from 2010 to 2050. We calculated the PM2·5 concentrations and compared the health impacts and implementation costs between two scenarios that reflect CO2 and air pollutant emissions—the reference (REF scenario (no climate policy and the NDC scenario (100% realisation of NDC targets: CO2 emission intensity needs to be about 40% below 2010 emissions by 2030 [roughly 35% below 2030 emissions in REF], and about 90% below 2010 emissions by 2050 [roughly 96% below 2050 emissions in REF]. Findings: Under a comparatively optimistic health benefits valuation condition, at the national level, 18–62% of implementation costs could be covered by the health benefits in 2030. In 2050, the overall health benefits would substantially increase to 3–9 times of the implementation costs. However, northwest China would require the highest implementation costs and will also have more premature deaths because of a more carbon-intensive energy structure than business as usual. By 2030, people in northwest China (especially in Gansu, Shaanxi, and Xinjiang provinces would need to bear worse air quality

  2. Observation of photon polarization in $B^\\pm \\to K^\\pm\\pi^\\mp\\pi^\\pm\\gamma$ decays

    CERN Multimedia

    Veneziano, G

    2014-01-01

    A study of the flavor-changing neutral current radiative $B^{\\pm} \\to K^{\\pm}\\pi^{\\mp}\\pi^{\\pm}\\gamma$ decays performed using data collected in proton-proton collisions with the LHCb detector at $7$ and $8\\,$TeV center-of-mass energies is presented. In this sample, corresponding to an integrated luminosity of $3\\,\\text{fb}^{-1}$, nearly $14\\,000$ signal events are reconstructed and selected, containing all possible intermediate resonances with a $K^{\\pm}\\pi^{\\mp}\\pi^{\\pm}$ final state in the $[1.1, 1.9]$\\,GeV/$c^{2}$ mass range. The distribution of the angle of the photon direction with respect to the plane defined by the final-state hadrons in their rest frame is studied in intervals of $K^{\\pm}\\pi^{\\mp}\\pi^{\\pm}$ mass and the asymmetry between the number of signal events found on each side of the plane is obtained. The first direct observation of the photon polarization in the $b \\to s\\gamma$ transition is reported with a significance of $5.2\\,\\sigma$.

  3. Development of Positive Matrix Factorization Model (PMF) to Annual Study of the PM2.5 Organic Composition in ChapinerIa; Desarrollo del Modelo de Factorizacion de la Matriz Positiva (PMF) al Estudio Anual de la Composicion Organica del PM2.5 en Chapineria

    Energy Technology Data Exchange (ETDEWEB)

    Pindado, O.; Perez, R. M.; Garcia, S.

    2013-05-01

    The Positive Matrix Factorization (PMF) application to a set of PM2.5 data collected in a rural area of Madrid for a period of 1 year has been developed. Results has let describing the particulate faction of atmospheric aerosol collected in Chapineria according to 7 factor, among them fossil fuel combustion by traffic, wax plants, primary emissions of organic carbon, crustal material, and secondary organic aerosol. Five of these factors are related to primary particles; meanwhile only one factor has been associated to secondary particles. Finally, a factor has not been associated to any known source of particulate matter. (Author)

  4. Measurement of $C\\!P$ violation in the phase space of $B^{\\pm} \\to K^{\\pm} \\pi^{+} \\pi^{-}$ and $B^{\\pm} \\to K^{\\pm} K^{+} K^{-}$ decays

    CERN Document Server

    INSPIRE-00258707; Adeva, B; Adinolfi, M; Adrover, C; Affolder, A; Ajaltouni, Z; Albrecht, J; Alessio, F; Alexander, M; Ali, S; Alkhazov, G; Alvarez Cartelle, P; Alves Jr, A A; Amato, S; Amerio, S; Amhis, Y; Anderlini, L; Anderson, J; Andreassen, R; Andrews, J E; Appleby, R B; Aquines Gutierrez, O; Archilli, F; Artamonov, A; Artuso, M; Aslanides, E; Auriemma, G; Baalouch, M; Bachmann, S; Back, J J; Baesso, C; Balagura, V; Baldini, W; Barlow, R J; Barschel, C; Barsuk, S; Barter, W; Bauer, Th; Bay, A; Beddow, J; Bedeschi, F; Bediaga, I; Belogurov, S; Belous, K; Belyaev, I; Ben-Haim, E; Bencivenni, G; Benson, S; Benton, J; Berezhnoy, A; Bernet, R; Bettler, M -O; van Beuzekom, M; Bien, A; Bifani, S; Bird, T; Bizzeti, A; Bjørnstad, P M; Blake, T; Blanc, F; Blouw, J; Blusk, S; Bocci, V; Bondar, A; Bondar, N; Bonivento, W; Borghi, S; Borgia, A; Bowcock, T J V; Bowen, E; Bozzi, C; Brambach, T; van den Brand, J; Bressieux, J; Brett, D; Britsch, M; Britton, T; Brook, N H; Brown, H; Burducea, I; Bursche, A; Busetto, G; Buytaert, J; Cadeddu, S; Callot, O; Calvi, M; Calvo Gomez, M; Camboni, A; Campana, P; Campora Perez, D; Carbone, A; Carboni, G; Cardinale, R; Cardini, A; Carranza-Mejia, H; Carson, L; Carvalho Akiba, K; Casse, G; Castillo Garcia, L; Cattaneo, M; Cauet, Ch; Cenci, R; Charles, M; Charpentier, Ph; Chen, P; Chiapolini, N; Chrzaszcz, M; Ciba, K; Cid Vidal, X; Ciezarek, G; Clarke, P E L; Clemencic, M; Cliff, H V; Closier, J; Coca, C; Coco, V; Cogan, J; Cogneras, E; Collins, P; Comerma-Montells, A; Contu, A; Cook, A; Coombes, M; Coquereau, S; Corti, G; Couturier, B; Cowan, G A; Craik, D C; Cunliffe, S; Currie, R; D'Ambrosio, C; David, P; David, P N Y; Davis, A; De Bonis, I; De Bruyn, K; De Capua, S; De Cian, M; De Miranda, J M; De Paula, L; De Silva, W; De Simone, P; Decamp, D; Deckenhoff, M; Del Buono, L; Déléage, N; Derkach, D; Deschamps, O; Dettori, F; Di Canto, A; Di Ruscio, F; Dijkstra, H; Dogaru, M; Donleavy, S; Dordei, F; Dosil Suárez, A; Dossett, D; Dovbnya, A; Dupertuis, F; Dzhelyadin, R; Dziurda, A; Dzyuba, A; Easo, S; Egede, U; Egorychev, V; Eidelman, S; van Eijk, D; Eisenhardt, S; Eitschberger, U; Ekelhof, R; Eklund, L; El Rifai, I; Elsasser, Ch; Elsby, D; Falabella, A; Färber, C; Fardell, G; Farinelli, C; Farry, S; Fave, V; Ferguson, D; Fernandez Albor, V; Ferreira Rodrigues, F; Ferro-Luzzi, M; Filippov, S; Fiore, M; Fitzpatrick, C; Fontana, M; Fontanelli, F; Forty, R; Francisco, O; Frank, M; Frei, C; Frosini, M; Furcas, S; Furfaro, E; Gallas Torreira, A; Galli, D; Gandelman, M; Gandini, P; Gao, Y; Garofoli, J; Garosi, P; Garra Tico, J; Garrido, L; Gaspar, C; Gauld, R; Gersabeck, E; Gersabeck, M; Gershon, T; Ghez, Ph; Gibson, V; Giubega, L; Gligorov, V V; Göbel, C; Golubkov, D; Golutvin, A; Gomes, A; Gordon, H; Grabalosa Gándara, M; Graciani Diaz, R; Granado Cardoso, L A; Graugés, E; Graziani, G; Grecu, A; Greening, E; Gregson, S; Griffith, P; Grünberg, O; Gui, B; Gushchin, E; Guz, Yu; Gys, T; Hadjivasiliou, C; Haefeli, G; Haen, C; Haines, S C; Hall, S; Hamilton, B; Hampson, T; Hansmann-Menzemer, S; Harnew, N; Harnew, S T; Harrison, J; Hartmann, T; He, J; Head, T; Heijne, V; Hennessy, K; Henrard, P; Hernando Morata, J A; van Herwijnen, E; Hicheur, A; Hicks, E; Hill, D; Hoballah, M; Holtrop, M; Hombach, C; Hopchev, P; Hulsbergen, W; Hunt, P; Huse, T; Hussain, N; Hutchcroft, D; Hynds, D; Iakovenko, V; Idzik, M; Ilten, P; Jacobsson, R; Jaeger, A; Jans, E; Jaton, P; Jawahery, A; Jing, F; John, M; Johnson, D; Jones, C R; Joram, C; Jost, B; Kaballo, M; Kandybei, S; Kanso, W; Karacson, M; Karbach, T M; Kenyon, I R; Ketel, T; Keune, A; Khanji, B; Kochebina, O; Komarov, I; Koopman, R F; Koppenburg, P; Korolev, M; Kozlinskiy, A; Kravchuk, L; Kreplin, K; Kreps, M; Krocker, G; Krokovny, P; Kruse, F; Kucharczyk, M; Kudryavtsev, V; Kvaratskheliya, T; La Thi, V N; Lacarrere, D; Lafferty, G; Lai, A; Lambert, D; Lambert, R W; Lanciotti, E; Lanfranchi, G; Langenbruch, C; Latham, T; Lazzeroni, C; Le Gac, R; van Leerdam, J; Lees, J -P; Lefèvre, R; Leflat, A; Lefrançois, J; Leo, S; Leroy, O; Lesiak, T; Leverington, B; Li, Y; Li Gioi, L; Liles, M; Lindner, R; Linn, C; Liu, B; Liu, G; Lohn, S; Longstaff, I; Lopes, J H; Lopez-March, N; Lu, H; Lucchesi, D; Luisier, J; Luo, H; Machefert, F; Machikhiliyan, I V; Maciuc, F; Maev, O; Malde, S; Manca, G; Mancinelli, G; Marconi, U; Märki, R; Marks, J; Martellotti, G; Martens, A; Martín Sánchez, A; Martinelli, M; Martinez Santos, D; Martins Tostes, D; Massafferri, A; Matev, R; Mathe, Z; Matteuzzi, C; Maurice, E; Mazurov, A; Mc Skelly, B; McCarthy, J; McNab, A; McNulty, R; Meadows, B; Meier, F; Meissner, M; Merk, M; Milanes, D A; Minard, M -N; Molina Rodriguez, J; Monteil, S; Moran, D; Morawski, P; Mordà, A; Morello, M J; Mountain, R; Mous, I; Muheim, F; Müller, K; Muresan, R; Muryn, B; Muster, B; Naik, P; Nakada, T; Nandakumar, R; Nasteva, I; Needham, M; Neubert, S; Neufeld, N; Nguyen, A D; Nguyen, T D; Nguyen-Mau, C; Nicol, M; Niess, V; Niet, R; Nikitin, N; Nikodem, T; Nomerotski, A; Novoselov, A; Oblakowska-Mucha, A; Obraztsov, V; Oggero, S; Ogilvy, S; Okhrimenko, O; Oldeman, R; Orlandea, M; Otalora Goicochea, J M; Owen, P; Oyanguren, A; Pal, B K; Palano, A; Palutan, M; Panman, J; Papanestis, A; Pappagallo, M; Parkes, C; Parkinson, C J; Passaleva, G; Patel, G D; Patel, M; Patrick, G N; Patrignani, C; Pavel-Nicorescu, C; Pazos Alvarez, A; Pellegrino, A; Penso, G; Pepe Altarelli, M; Perazzini, S; Perez Trigo, E; Pérez-Calero Yzquierdo, A; Perret, P; Perrin-Terrin, M; Pessina, G; Petridis, K; Petrolini, A; Phan, A; Picatoste Olloqui, E; Pietrzyk, B; Pilař, T; Pinci, D; Playfer, S; Plo Casasus, M; Polci, F; Polok, G; Poluektov, A; Polycarpo, E; Popov, A; Popov, D; Popovici, B; Potterat, C; Powell, A; Prisciandaro, J; Pritchard, A; Prouve, C; Pugatch, V; Puig Navarro, A; Punzi, G; Qian, W; Rademacker, J H; Rakotomiaramanana, B; Rangel, M S; Raniuk, I; Rauschmayr, N; Raven, G; Redford, S; Reid, M M; dos Reis, A C; Ricciardi, S; Richards, A; Rinnert, K; Rives Molina, V; Roa Romero, D A; Robbe, P; Roberts, D A; Rodrigues, E; Rodriguez Perez, P; Roiser, S; Romanovsky, V; Romero Vidal, A; Rouvinet, J; Ruf, T; Ruffini, F; Ruiz, H; Ruiz Valls, P; Sabatino, G; Saborido Silva, J J; Sagidova, N; Sail, P; Saitta, B; Salustino Guimaraes, V; Salzmann, C; Sanmartin Sedes, B; Sannino, M; Santacesaria, R; Santamarina Rios, C; Santovetti, E; Sapunov, M; Sarti, A; Satriano, C; Satta, A; Savrie, M; Savrina, D; Schaack, P; Schiller, M; Schindler, H; Schlupp, M; Schmelling, M; Schmidt, B; Schneider, O; Schopper, A; Schune, M -H; Schwemmer, R; Sciascia, B; Sciubba, A; Seco, M; Semennikov, A; Sepp, I; Serra, N; Serrano, J; Seyfert, P; Shapkin, M; Shapoval, I; Shatalov, P; Shcheglov, Y; Shears, T; Shekhtman, L; Shevchenko, O; Shevchenko, V; Shires, A; Silva Coutinho, R; Sirendi, M; Skwarnicki, T; Smith, N A; Smith, E; Smith, J; Smith, M; Sokoloff, M D; Soler, F J P; Soomro, F; Souza, D; Souza De Paula, B; Spaan, B; Sparkes, A; Spradlin, P; Stagni, F; Stahl, S; Steinkamp, O; Stoica, S; Stone, S; Storaci, B; Straticiuc, M; Straumann, U; Subbiah, V K; Sun, L; Swientek, S; Syropoulos, V; Szczekowski, M; Szczypka, P; Szumlak, T; T'Jampens, S; Teklishyn, M; Teodorescu, E; Teubert, F; Thomas, C; Thomas, E; van Tilburg, J; Tisserand, V; Tobin, M; Tolk, S; Tonelli, D; Topp-Joergensen, S; Torr, N; Tournefier, E; Tourneur, S; Tran, M T; Tresch, M; Tsaregorodtsev, A; Tsopelas, P; Tuning, N; Ubeda Garcia, M; Ukleja, A; Urner, D; Ustyuzhanin, A; Uwer, U; Vagnoni, V; Valenti, G; Vallier, A; Van Dijk, M; Vazquez Gomez, R; Vazquez Regueiro, P; Vázquez Sierra, C; Vecchi, S; Velthuis, J J; Veltri, M; Veneziano, G; Vesterinen, M; Viaud, B; Vieira, D; Vilasis-Cardona, X; Vollhardt, A; Volyanskyy, D; Voong, D; Vorobyev, A; Vorobyev, V; Voß, C; Voss, H; Waldi, R; Wallace, C; Wallace, R; Wandernoth, S; Wang, J; Ward, D R; Watson, N K; Webber, A D; Websdale, D; Whitehead, M; Wicht, J; Wiechczynski, J; Wiedner, D; Wiggers, L; Wilkinson, G; Williams, M P; Williams, M; Wilson, F F; Wimberley, J; Wishahi, J; Witek, M; Wotton, S A; Wright, S; Wu, S; Wyllie, K; Xie, Y; Xing, Z; Yang, Z; Young, R; Yuan, X; Yushchenko, O; Zangoli, M; Zavertyaev, M; Zhang, F; Zhang, L; Zhang, W C; Zhang, Y; Zhelezov, A; Zhokhov, A; Zhong, L; Zvyagin, A

    2013-01-01

    The charmless decays $B^{\\pm}\\to K^{\\pm}\\pi^+\\pi^-$ and $B^{\\pm}\\to K^{\\pm}K^+K^-$ are reconstructed using data, corresponding to an integrated luminosity of 1.0 fb$^{-1}$, collected by LHCb in 2011. The inclusive charge asymmetries of these modes are measured as $A_{C\\!P}(B^{\\pm}\\to K^{\\pm}\\pi^+\\pi^-) = 0.032 \\pm 0.008 {\\mathrm{\\,(stat)}} \\pm 0.004 {\\mathrm{\\,(syst)}} \\pm 0.007 (J/\\psi K^{\\pm})$ and $A_{C\\!P}(B^{\\pm}\\to K^{\\pm}K^+K^-) = -0.043 \\pm 0.009 {\\mathrm{\\,(stat)}} \\pm 0.003 {\\mathrm{\\,(syst)}} \\pm 0.007 (J/\\psi K^{\\pm})$, where the third uncertainty is due to the $C\\!P$ asymmetry of the $B^{\\pm}\\to J/\\psi K^{\\pm}$ reference mode. The significance of $A_{C\\!P}(B^{\\pm}\\to K^{\\pm}K^+K^-)$ exceeds three standard deviations and is the first evidence of an inclusive $C\\!P$ asymmetry in charmless three-body $B$ decays. In addition to the inclusive $C\\!P$ asymmetries, larger asymmetries are observed in localised regions of phase space.

  5. Amplitude and timing properties of a Geiger discharge in a SiPM cell

    Energy Technology Data Exchange (ETDEWEB)

    Popova, E., E-mail: elenap73@mail.ru [National Research Nuclear University MEPhI (Moscow Engineering Physics Institute), 115409, Kashirskoe Shosse 31 (Russian Federation); Buzhan, P.; Pleshko, A. [National Research Nuclear University MEPhI (Moscow Engineering Physics Institute), 115409, Kashirskoe Shosse 31 (Russian Federation); Vinogradov, S. [University of Liverpool and Cockcroft Institute, Sci-Tech Daresbury, Keckwick Lane, Warrington WA4 4AD, Cheshire (United Kingdom); P.N. Lebedev Physical Institute of the Russian Academy of Sciences, Leninskiy Prospect 53, Moscow 119991 (Russian Federation); Stifutkin, A.; Ilyin, A. [National Research Nuclear University MEPhI (Moscow Engineering Physics Institute), 115409, Kashirskoe Shosse 31 (Russian Federation); Besson, D. [National Research Nuclear University MEPhI (Moscow Engineering Physics Institute), 115409, Kashirskoe Shosse 31 (Russian Federation); Department of Physics and Astronomy, University of Kansas, Lawrence, KS 66045-2151 (United States); Mirzoyan, R. [Max-Planck-Institute for Physics, Föhringer Ring 6, 80805 München (Germany)

    2015-07-01

    The amplitude and timing properties of a Geiger discharge in a stand-alone SiPM cell have been investigated in detail. Use of a single stand-alone SiPM cell allows us to perform measurements with better accuracy than the multicell structure of conventional SiPMs. We have studied the dependence of the output charge and amplitude from an SiPM cell illuminated by focused light vs the number of primary photoelectrons. We propose a SPICE model which explains the amplitude over saturation (when the SiPM's amplitude is greater than the sum over all cells) characteristics of SiPM signals for more than one initial photoelectrons. The time resolutions of a SiPM cell have been measured for the case of single (SPTR) and multiphoton light pulses. The Full Width Half Max (FWHM) for SPTR has been found to be at the level of 30 ps for focused and 40 ps for unfocused light (100 μm cell size). - Highlights: • A stand-alone SiPM cell has been investigated in detail. • Amplitude and time properties have been measured with femtosecond 660 nm laser. • SPICE model for a Geiger discharge development has been proposed. • SPTR for a stand-alone 100 μm size SiPM cell has been found to be 40 ps FWHM.

  6. Variations of PM2.5, PM10 mass concentration and health assessment in Islamabad, Pakistan

    Science.gov (United States)

    Memhood, Tariq; Tianle, Z.; Ahmad, I.; Li, X.; Shen, F.; Akram, W.; Dong, L.

    2018-04-01

    Sparse information appears in lack of awareness among the people regarding the linkage between particulate matter (PM) and mortality in Pakistan. The current study is aimed to investigate the seasonal mass concentration level of PM2.5 and PM10 in ambient air of Islamabad to assess the health risk of PM pollution. The sampling was carried out with two parallel medium volume air samplers on Whatman 47 mm quartz filter at a flow rate of 100L/min. Mass concentration was obtained by gravimetric analysis. A noticeable seasonal change in PM10 and PM2.5 mass concentration was observed. In case of PM2.5, the winter was a most polluted and spring was the cleanest season of 2017 in Islamabad with 69.97 and 40.44 μgm‑3 mean concentration. Contrary, highest (152.42 μgm‑3) and lowest (74.90 μgm‑3) PM10 mass concentration was observed in autumn and summer respectively. Air Quality index level for PM2.5 and PM10 was remained moderated to unhealthy and good to sensitive respectively. Regarding health risk assessment, using national data for mortality rates, the excess mortality due to PM2.5 and PM10 exposure has been calculated and amounts to over 198 and 98 deaths annually for Islamabad. Comparatively estimated lifetime risk for PM2.5 (1.16×10-6) was observed higher than PM10 (7.32×10-8).

  7. Industrial PM2.5 cause pulmonary adverse effect through RhoA/ROCK pathway.

    Science.gov (United States)

    Yan, Junyan; Lai, Chia-Hsiang; Lung, Shih-Chun Candice; Chen, Chongjun; Wang, Wen-Cheng; Huang, Pin-I; Lin, Chia-Hua

    2017-12-01

    According to the Chinese Ministry of Health, industrial pollution-induced health impacts have been the leading cause of death in China. While industrial fine particulate matter (PM 2.5 ) is associated with adverse health effects, the major action mechanisms of different compositions of PM 2.5 are currently unclear. In this study, we treated normal human lung epithelial BEAS-2B cells with industrial organic and water-soluble PM 2.5 extracts under daily alveolar deposition dose to elucidate the molecular mechanisms underlying adverse pulmonary effects induced by PM 2.5 , including oxidative damage, inflammatory response, lung epithelial barrier dysfunction, and the recruitment of macrophages. We found that water-soluble PM 2.5 extracts caused more severe cytotoxic effects on BEAS-2B cells compared with that of organic extracts. Both organic and water-soluble PM 2.5 extracts induced activation of the RhoA/ROCK pathway. Inflammatory response, epithelial barrier dysfunction, and the activation of NF-кB caused by both PM 2.5 extracts were attenuated by ROCK inhibitor Y-27632. This indicated that both PM 2.5 extracts could cause damage to epithelial cells through RhoA/ROCK-dependent NF-кB activation. Furthermore, the upregulation of macrophage adhesion induced by both PM 2.5 extracts was also attenuated by Y-27632 in a co-culture model of macrophages and the epithelial cells. Therefore, our results support that industrial PM 2.5 extracts-induced activation of the RhoA/ROCK-dependent NF-кB pathway induces pulmonary adverse effect. Thus, pharmacological inhibition of ROCK activation might have therapeutic potential in preventing lung disease associated with PM 2.5 . Copyright © 2017 Elsevier B.V. All rights reserved.

  8. Meteorological modes of variability for fine particulate matter (PM2.5 air quality in the United States: implications for PM2.5 sensitivity to climate change

    Directory of Open Access Journals (Sweden)

    J. A. Fisher

    2012-03-01

    Full Text Available We applied a multiple linear regression model to understand the relationships of PM2.5 with meteorological variables in the contiguous US and from there to infer the sensitivity of PM2.5 to climate change. We used 2004–2008 PM2.5 observations from ~1000 sites (~200 sites for PM2.5 components and compared to results from the GEOS-Chem chemical transport model (CTM. All data were deseasonalized to focus on synoptic-scale correlations. We find strong positive correlations of PM2.5 components with temperature in most of the US, except for nitrate in the Southeast where the correlation is negative. Relative humidity (RH is generally positively correlated with sulfate and nitrate but negatively correlated with organic carbon. GEOS-Chem results indicate that most of the correlations of PM2.5 with temperature and RH do not arise from direct dependence but from covariation with synoptic transport. We applied principal component analysis and regression to identify the dominant meteorological modes controlling PM2.5 variability, and show that 20–40% of the observed PM2.5 day-to-day variability can be explained by a single dominant meteorological mode: cold frontal passages in the eastern US and maritime inflow in the West. These and other synoptic transport modes drive most of the overall correlations of PM2.5 with temperature and RH except in the Southeast. We show that interannual variability of PM2.5 in the US Midwest is strongly correlated with cyclone frequency as diagnosed from a spectral-autoregressive analysis of the dominant meteorological mode. An ensemble of five realizations of 1996–2050 climate change with the GISS general circulation model (GCM using the same climate forcings shows inconsistent trends in cyclone frequency over the Midwest (including in sign, with a likely decrease in cyclone frequency implying an increase in PM2.5. Our results demonstrate the need for multiple GCM realizations (because of climate chaos when diagnosing

  9. Contingent valuation of health and mood impacts of PM2.5 in Beijing, China.

    Science.gov (United States)

    Yin, Hao; Pizzol, Massimo; Jacobsen, Jette Bredahl; Xu, Linyu

    2018-07-15

    Air pollution from PM 2 . 5 affects many cities worldwide, causing both health impacts and mood depression. One of the obstacles to implementing environmental regulations for PM 2 . 5 reduction is that there are limited studies of PM 2 . 5 welfare loss and few investigations of mood depression caused by PM 2 . 5 . This article describes a survey study conducted in Beijing, China to estimate the welfare loss due to PM 2 . 5 . In total, 1709 participants completed either a face-to-face or online survey. A contingent valuation method was applied to elicit people's willingness to pay to avoid PM 2 . 5 pollution and willingness to accept a compensation for such pollution. The payment/compensation was evaluated for two outcome variables: perceived health impacts and mood depression caused by PM 2 . 5 pollution. This is one of few papers that explicitly studies the effects of PM 2 . 5 on subjective well-being, and to the authors' knowledge, the first to estimate welfare loss from PM 2 . 5 using a random forest model. Compared to the standard Turnbull, probit, and two-part models, the random forest model gave the best fit to the data, suggesting that this may be a useful tool for future studies too. The welfare loss due to health impacts and mood depression is CNY 1388.4/person/year and CNY 897.7/person/year respectively, indicating that the public attaches great importance to mood, feelings and happiness. The study provides scientific support to the development of economic policy instruments for PM 2 . 5 control in China. Copyright © 2018. Published by Elsevier B.V.

  10. Partitioning of magnetic particles in PM10, PM2.5 and PM1 aerosols in the urban atmosphere of Barcelona (Spain)

    International Nuclear Information System (INIS)

    Revuelta, María Aránzazu; McIntosh, Gregg; Pey, Jorge; Pérez, Noemi; Querol, Xavier; Alastuey, Andrés

    2014-01-01

    A combined magnetic-chemical study of 15 daily, simultaneous PM 10 –PM 2.5 –PM 1 urban background aerosol samples has been carried out. The magnetic properties are dominated by non-stoichiometric magnetite, with highest concentrations seen in PM 10 . Low temperature magnetic analyses showed that the superparamagnetic fraction is more abundant when coarse, multidomain particles are present, confirming that they may occur as an oxidized outer shell around coarser grains. A strong association of the magnetic parameters with a vehicular PM 10 source has been identified. Strong correlations found with Cu and Sb suggests that this association is related to brake abrasion emissions rather than exhaust emissions. For PM 1 the magnetic remanence parameters are more strongly associated with crustal sources. Two crustal sources are identified in PM 1 , one of which is of North African origin. The magnetic particles are related to this source and so may be used to distinguish North African dust from other sources in PM 1 . - Highlights: • Magnetic properties of PM 10 , PM 2.5 and PM 1 defined for a Mediterranean urban site. • Vehicular source of magnetic particles dominates in PM 10 . • Crustal source of magnetic particles dominates in PM 1 . • Magnetic remanence may distinguish between North African and regional dust in PM 1 . - Capsule abstract two sources of magnetic atmospheric particles have been identified in Barcelona, a vehicular source which dominates in PM 10 and a crustal source that dominates in PM 1

  11. Estimating PM2.5 concentrations in China from 1957 to 2014 using meteorological visibility data

    Science.gov (United States)

    Ma, Z.; Liu, M.; Wen, T.; Bi, J.

    2017-12-01

    PM2.5 is a major air pollutant that has caused severe adverse health impacts in China. It was not until late 2012 that China established its ground PM2.5 monitoring network. The lack of ground PM2.5 measurements before 2013 makes it difficult to assess the long-term trends of PM2.5 and its health impacts in China. PM2.5 has been widely recognized as an air pollutant that would cause visibility degradation. Given the facts that the visibility data has been available since 1950s in most major cities in China, it provides a potential way to figure out the long-term ground PM2.5 concentrations. In this work, we developed a national-scale spatiotemporal linear mixed effects model to estimate the long-term PM2.5 concentrations in China from 1957 to 2014 using ground visibility monitoring data as the primary predictor. We used the 2014 data to develop the model. The overall model-fitting and cross-validation R2 is 0.74 and 0.72, suggesting that the model is not over-fitted. Validation beyond the model year (2014) indicated that the model could generate accurate historical PM2.5 concentrations at the monthly (R2 = 0.72) level. Results show that air pollution is not a new environmental issue that occurs in the recent decades but a problem existing in a longer time before 1980. The PM2.5 concentrations have reached 60-80 µg/m3 in the north part of North China Plain during 1950s-1960s and increased to generally higher than 90 µg/m3 during 1970s. The results also show that the entire China experienced an overall increasing trend (0.20 µg/m3/yr, Ppollution in China in a longer time scale when ground monitoring and satellite remote sensing data are unavailable.

  12. PM EMISSIONS PRODUCED BY AIRCRAFT UNDER THE OPERATIONS AT THE AIRPORT

    Directory of Open Access Journals (Sweden)

    Oleksandr Zaporozhets

    2016-12-01

    the emission of aircraft at airports, APU emissions are not certificated by ICAO or any other responsible for that authority.It is quite actual task for local air quality to development model and find measurement techniques to identify aircraft engine and APU contribution to total airport PM pollution.

  13. Identification of the sources of PM10 in a subway tunnel using positive matrix factorization.

    Science.gov (United States)

    Park, Duckshin; Lee, Taejeong; Hwang, Doyeon; Jung, Wonseok; Lee, Yongil; Cho, KiChul; Kim, Dongsool; Lees, Kiyoung

    2014-12-01

    The level of particulate matter of less than 10 μm diameter (PM10) at subway platforms can be significantly reduced by installing a platform screen-door system. However, both workers and passengers might be exposed to higher PM10 levels while the cars are within the tunnel because it is a more confined environment. This study determined the PM10 levels in a subway tunnel, and identified the sources of PM10 using elemental analysis and receptor modeling. Forty-four PM10 samples were collected in the tunnel between the Gireum and Mia stations on Line 4 in metropolitan Seoul and analyzed using inductively coupled plasma-atomic emission spectrometry and ion chromatography. The major PM10 sources were identified using positive matrix factorization (PMF). The average PM10 concentration in the tunnels was 200.8 ± 22.0 μg/m3. Elemental analysis indicated that the PM10 consisted of 40.4% inorganic species, 9.1% anions, 4.9% cations, and 45.6% other materials. Iron was the most abundant element, with an average concentration of 72.5 ± 10.4 μg/m3. The PM10 sources characterized by PMF included rail, wheel, and brake wear (59.6%), soil combustion (17.0%), secondary aerosols (10.0%), electric cable wear (8.1%), and soil and road dust (5.4%). Internal sources comprising rail, wheel, brake, and electric cable wear made the greatest contribution to the PM10 (67.7%) in tunnel air. Implications: With installation of a platform screen door, PM10 levels in subway tunnels were higher than those on platforms. Tunnel PM10 levels exceeded 150 µg/m3 of the Korean standard for subway platform. Elemental analysis of PM10 in a tunnel showed that Fe was the most abundant element. Five PM10 sources in tunnel were identified by positive matrix factorization. Railroad-related sources contributed 68% of PM10 in the subway tunnel.

  14. Development and Reliability Analysis of HTR-PM Reactor Protection System

    International Nuclear Information System (INIS)

    Li Duo; Guo Chao; Xiong Huasheng

    2014-01-01

    High Temperature Gas-Cooled Reactor-Pebble bed Module (HTR-PM) digital Reactor Protection System (RPS) is a dedicated system, which is designed and developed according to HTR-PM NPP protection specifications. To decrease the probability of accident trips and increase the system reliability, HTR-PM RPS has such features as a framework of four redundant channels, two diverse sub-systems in each channel, and two level two-out-of-four logic voters. Reliability analysis of HTR-PM RPS is based on fault tree model. A fault tree is built based on HTR-PM RPS Failure Modes and Effects Analysis (FMEA), and special analysis is focused on the sub-tree of redundant channel ''2-out-of-4'' logic and the fault tree under one channel is bypassed. The qualitative analysis of fault tree, such as RPS weakness according to minimal cut sets, is summarized in the paper. (author)

  15. Minor loop dependence of the magnetic forces and stiffness in a PM-HTS levitation system

    Directory of Open Access Journals (Sweden)

    Yong Yang

    2017-12-01

    Full Text Available Based upon the method of current vector potential and the critical state model of Bean, the vertical and lateral forces with different sizes of minor loop are simulated in two typical cooling conditions when a rectangular permanent magnet (PM above a cylindrical high temperature superconductor (HTS moves vertically and horizontally. The different values of average magnetic stiffness are calculated by various sizes of minor loop changing from 0.1 to 2 mm. The magnetic stiffness with zero traverse is obtained by using the method of linear extrapolation. The simulation results show that the extreme values of forces decrease with increasing size of minor loop. The magnetic hysteresis of the force curves also becomes small as the size of minor loop increases. This means that the vertical and lateral forces are significantly influenced by the size of minor loop because the forces intensely depend on the moving history of the PM. The vertical stiffness at every vertical position when the PM vertically descends to 1 mm is larger than that as the PM vertically ascents to 30 mm. When the PM moves laterally, the lateral stiffness during the PM passing through any horizontal position in the first time almost equal to the value during the PM passing through the same position in the second time in zero-field cooling (ZFC, however, the lateral stiffness in field cooling (FC and the cross stiffness in ZFC and FC are significantly affected by the moving history of the PM.

  16. Minor loop dependence of the magnetic forces and stiffness in a PM-HTS levitation system

    Science.gov (United States)

    Yang, Yong; Li, Chengshan

    2017-12-01

    Based upon the method of current vector potential and the critical state model of Bean, the vertical and lateral forces with different sizes of minor loop are simulated in two typical cooling conditions when a rectangular permanent magnet (PM) above a cylindrical high temperature superconductor (HTS) moves vertically and horizontally. The different values of average magnetic stiffness are calculated by various sizes of minor loop changing from 0.1 to 2 mm. The magnetic stiffness with zero traverse is obtained by using the method of linear extrapolation. The simulation results show that the extreme values of forces decrease with increasing size of minor loop. The magnetic hysteresis of the force curves also becomes small as the size of minor loop increases. This means that the vertical and lateral forces are significantly influenced by the size of minor loop because the forces intensely depend on the moving history of the PM. The vertical stiffness at every vertical position when the PM vertically descends to 1 mm is larger than that as the PM vertically ascents to 30 mm. When the PM moves laterally, the lateral stiffness during the PM passing through any horizontal position in the first time almost equal to the value during the PM passing through the same position in the second time in zero-field cooling (ZFC), however, the lateral stiffness in field cooling (FC) and the cross stiffness in ZFC and FC are significantly affected by the moving history of the PM.

  17. Bufei Huoxue Capsule Attenuates PM2.5-Induced Pulmonary Inflammation in Mice

    Directory of Open Access Journals (Sweden)

    Yue Jing

    2017-01-01

    Full Text Available Atmospheric fine particulate matter 2.5 (PM 2.5 may carry many toxic substances on its surface and this may pose a public health threat. Epidemiological research indicates that cumulative ambient PM2.5 is correlated to morbidity and mortality due to pulmonary and cardiovascular diseases and cancer. Mitigating the toxic effects of PM2.5 is therefore highly desired. Bufei Huoxue (BFHX capsules have been used in China to treat pulmonary heart disease (cor pulmonale. Thus, we assessed the effects of BFHX capsules on PM2.5-induced pulmonary inflammation and the underlying mechanisms of action. Using Polysearch and Cytoscape 3.2.1 software, pharmacological targets of BFHX capsules in atmospheric PM2.5-related respiratory disorders were predicted and found to be related to biological pathways of inflammation and immune function. In a mouse model of PM2.5-induced inflammation established with intranasal instillation of PM2.5 suspension, BFHX significantly reduced pathological response and inflammatory mediators including IL-4, IL-6, IL-10, IL-8, TNF-α, and IL-1β. BFHX also reduced keratinocyte growth factor (KGF, secretory immunoglobulin A (sIgA, and collagen fibers deposition in lung and improved lung function. Thus, BFHX reduced pathological responses induced by PM2.5, possibly via regulation of inflammatory mediators in mouse lungs.

  18. Assessing the impact of fine particulate matter (PM2.5) on respiratory-cardiovascular chronic diseases in the New York City Metropolitan area using Hierarchical Bayesian Model estimates

    Science.gov (United States)

    An enhanced research paradigm is presented to address the spatial and temporal gaps in fine particulate matter (PM2.5) measurements and generate realistic and representative concentration fields for use in epidemiological studies of human exposure to ambient air particulate conce...

  19. PM 2.5 Nonattainment Areas

    Data.gov (United States)

    U.S. Environmental Protection Agency — This data layer identifies areas in the U.S. where air pollution levels have not met the National Ambient Air Quality Standards (NAAQS) for PM 2.5 and have been...

  20. 2006 PM-2.5 Nonattainment Areas

    Data.gov (United States)

    U.S. Environmental Protection Agency — This data layer identifies areas in the U.S. where air pollution levels have not met the National Ambient Air Quality Standards (NAAQS) for 2006 PM-2.5 standards....

  1. The Effect of PM 10 on Ischemia- Reperfusion Induced Arrhythmias in Rats

    Directory of Open Access Journals (Sweden)

    Esmat Radmanesh

    Full Text Available ABSTRACT Epidemiological studies show that particulate matter (PM is the principal instigator of some adverse clinical symptoms involving cardiovascular diseases. PM exposure can increase experimental infarct size and potentiate myocardial ischemia and arrhythmias in experimental MI models such as ischemia-reperfusion (I/R injury.The present study was aimed to evaluate the effects of particulate matter (PM10 on ischemia- reperfusion induced arrhythmias with emphasis on the protective role of VA as an antioxidant on them. Male Wistar rats were divided into 8 groups (n=10: Control, VAc, Sham, VA, PM1 (0.5 mg/kg, PM2 (2.5 mg/kg, PM3 group (5 mg/kg, PM3 + VA group. Within 48 hours, PM10 was instilled into trachea in two stages. Then the hearts were isolated, transferred to a Langendorff apparatus, and subjected to global ischemia (30 minutes followed by reperfusion (60 minutes. The ischemia- reperfusion induced ventricular arrhythmias were assessed according to the Lambeth conventions.In the present study,the number, incidence and duration of arrhythmiasduring30 minutes ischemia were demonstrated to be more than those in the reperfusion stage. PM exposure increased significantly the number, incidence and duration of arrhythmias in the ischemia and reperfusion duration. Vanillic acid reduced significantly the number, incidence and duration of arrhythmias during the ischemia and reperfusion period.In summary, the results of this study demonstrated that the protective and dysrhythmic effects of VA in the PM exposure rats in I/R model are probably related to its antioxidant properties.

  2. 40 CFR Table C-4 to Subpart C of... - Test Specifications for PM10, PM2.5 and PM10-2.5 Candidate Equivalent Methods

    Science.gov (United States)

    2010-07-01

    ... 40 Protection of Environment 5 2010-07-01 2010-07-01 false Test Specifications for PM10, PM2.5 and PM10-2.5 Candidate Equivalent Methods C Table C-4 to Subpart C of Part 53 Protection of Environment... Pt. 53, Subpt. C, Table C-4 Table C-4 to Subpart C of Part 53—Test Specifications for PM10, PM2.5 and...

  3. Measurement of CP violation in the phase space of $B^{\\pm} \\rightarrow K^{+} K^{-} \\pi^{\\pm}$ and $B^{\\pm} \\rightarrow \\pi^{+} \\pi^{-} \\pi^{\\pm}$ decays

    CERN Document Server

    Aaij, Roel; Adinolfi, Marco; Adrover, Cosme; Affolder, Anthony; Ajaltouni, Ziad; Albrecht, Johannes; Alessio, Federico; Alexander, Michael; Ali, Suvayu; Alkhazov, Georgy; Alvarez Cartelle, Paula; Alves Jr, Antonio; Amato, Sandra; Amerio, Silvia; Amhis, Yasmine; Anderlini, Lucio; Anderson, Jonathan; Andreassen, Rolf; Andrews, Jason; Appleby, Robert; Aquines Gutierrez, Osvaldo; Archilli, Flavio; Artamonov, Alexander; Artuso, Marina; Aslanides, Elie; Auriemma, Giulio; Baalouch, Marouen; Bachmann, Sebastian; Back, John; Badalov, Alexey; Baesso, Clarissa; Balagura, Vladislav; Baldini, Wander; Barlow, Roger; Barschel, Colin; Barsuk, Sergey; Barter, William; Bauer, Thomas; Bay, Aurelio; Beddow, John; Bedeschi, Franco; Bediaga, Ignacio; Belogurov, Sergey; Belous, Konstantin; Belyaev, Ivan; Ben-Haim, Eli; Bencivenni, Giovanni; Benson, Sean; Benton, Jack; Berezhnoy, Alexander; Bernet, Roland; Bettler, Mar-Olivier; van Beuzekom, Martinus; Bien, Alexander; Bifani, Simone; Bird, Thomas; Bizzeti, Andrea; Bjørnstad, Pål Marius; Blake, Thomas; Blanc, Frédéric; Blouw, Johan; Blusk, Steven; Bocci, Valerio; Bondar, Alexander; Bondar, Nikolay; Bonivento, Walter; Borghi, Silvia; Borgia, Alessandra; Bowcock, Themistocles; Bowen, Espen Eie; Bozzi, Concezio; Brambach, Tobias; van den Brand, Johannes; Bressieux, Joël; Brett, David; Britsch, Markward; Britton, Thomas; Brook, Nicholas; Brown, Henry; Bursche, Albert; Busetto, Giovanni; Buytaert, Jan; Cadeddu, Sandro; Callot, Olivier; Calvi, Marta; Calvo Gomez, Miriam; Camboni, Alessandro; Campana, Pierluigi; Campora Perez, Daniel; Carbone, Angelo; Carboni, Giovanni; Cardinale, Roberta; Cardini, Alessandro; Carranza-Mejia, Hector; Carson, Laurence; Carvalho Akiba, Kazuyoshi; Casse, Gianluigi; Castillo Garcia, Lucia; Cattaneo, Marco; Cauet, Christophe; Cenci, Riccardo; Charles, Matthew; Charpentier, Philippe; Cheung, Shu-Faye; Chiapolini, Nicola; Chrzaszcz, Marcin; Ciba, Krzystof; Cid Vidal, Xabier; Ciezarek, Gregory; Clarke, Peter; Clemencic, Marco; Cliff, Harry; Closier, Joel; Coca, Cornelia; Coco, Victor; Cogan, Julien; Cogneras, Eric; Collins, Paula; Comerma-Montells, Albert; Contu, Andrea; Cook, Andrew; Coombes, Matthew; Coquereau, Samuel; Corti, Gloria; Couturier, Benjamin; Cowan, Greig; Craik, Daniel Charles; Cruz Torres, Melissa Maria; Cunliffe, Samuel; Currie, Robert; D'Ambrosio, Carmelo; David, Pascal; David, Pieter; Davis, Adam; De Bonis, Isabelle; De Bruyn, Kristof; De Capua, Stefano; De Cian, Michel; De Miranda, Jussara; De Paula, Leandro; De Silva, Weeraddana; De Simone, Patrizia; Decamp, Daniel; Deckenhoff, Mirko; Del Buono, Luigi; Déléage, Nicolas; Derkach, Denis; Deschamps, Olivier; Dettori, Francesco; Di Canto, Angelo; Dijkstra, Hans; Dogaru, Marius; Donleavy, Stephanie; Dordei, Francesca; Dosil Suárez, Alvaro; Dossett, David; Dovbnya, Anatoliy; Dupertuis, Frederic; Durante, Paolo; Dzhelyadin, Rustem; Dziurda, Agnieszka; Dzyuba, Alexey; Easo, Sajan; Egede, Ulrik; Egorychev, Victor; Eidelman, Semen; van Eijk, Daan; Eisenhardt, Stephan; Eitschberger, Ulrich; Ekelhof, Robert; Eklund, Lars; El Rifai, Ibrahim; Elsasser, Christian; Falabella, Antonio; Färber, Christian; Farinelli, Chiara; Farry, Stephen; Ferguson, Dianne; Fernandez Albor, Victor; Ferreira Rodrigues, Fernando; Ferro-Luzzi, Massimiliano; Filippov, Sergey; Fiore, Marco; Fitzpatrick, Conor; Fontana, Marianna; Fontanelli, Flavio; Forty, Roger; Francisco, Oscar; Frank, Markus; Frei, Christoph; Frosini, Maddalena; Furfaro, Emiliano; Gallas Torreira, Abraham; Galli, Domenico; Gandelman, Miriam; Gandini, Paolo; Gao, Yuanning; Garofoli, Justin; Garosi, Paola; Garra Tico, Jordi; Garrido, Lluis; Gaspar, Clara; Gauld, Rhorry; Gersabeck, Evelina; Gersabeck, Marco; Gershon, Timothy; Ghez, Philippe; Gibson, Valerie; Giubega, Lavinia-Helena; Gligorov, V.V.; Göbel, Carla; Golubkov, Dmitry; Golutvin, Andrey; Gomes, Alvaro; Gorbounov, Petr; Gordon, Hamish; Grabalosa Gándara, Marc; Graciani Diaz, Ricardo; Granado Cardoso, Luis Alberto; Graugés, Eugeni; Graziani, Giacomo; Grecu, Alexandru; Greening, Edward; Gregson, Sam; Griffith, Peter; Grillo, Lucia; Grünberg, Oliver; Gui, Bin; Gushchin, Evgeny; Guz, Yury; Gys, Thierry; Hadjivasiliou, Christos; Haefeli, Guido; Haen, Christophe; Haines, Susan; Hall, Samuel; Hamilton, Brian; Hampson, Thomas; Hansmann-Menzemer, Stephanie; Harnew, Neville; Harnew, Samuel; Harrison, Jonathan; Hartmann, Thomas; He, Jibo; Head, Timothy; Heijne, Veerle; Hennessy, Karol; Henrard, Pierre; Hernando Morata, Jose Angel; van Herwijnen, Eric; Heß, Miriam; Hicheur, Adlène; Hicks, Emma; Hill, Donal; Hoballah, Mostafa; Hombach, Christoph; Hulsbergen, Wouter; Hunt, Philip; Huse, Torkjell; Hussain, Nazim; Hutchcroft, David; Hynds, Daniel; Iakovenko, Viktor; Idzik, Marek; Ilten, Philip; Jacobsson, Richard; Jaeger, Andreas; Jans, Eddy; Jaton, Pierre; Jawahery, Abolhassan; Jing, Fanfan; John, Malcolm; Johnson, Daniel; Jones, Christopher; Joram, Christian; Jost, Beat; Kaballo, Michael; Kandybei, Sergii; Kanso, Wallaa; Karacson, Matthias; Karbach, Moritz; Kenyon, Ian; Ketel, Tjeerd; Khanji, Basem; Kochebina, Olga; Komarov, Ilya; Koopman, Rose; Koppenburg, Patrick; Korolev, Mikhail; Kozlinskiy, Alexandr; Kravchuk, Leonid; Kreplin, Katharina; Kreps, Michal; Krocker, Georg; Krokovny, Pavel; Kruse, Florian; Kucharczyk, Marcin; Kudryavtsev, Vasily; Kurek, Krzysztof; Kvaratskheliya, Tengiz; La Thi, Viet Nga; Lacarrere, Daniel; Lafferty, George; Lai, Adriano; Lambert, Dean; Lambert, Robert W; Lanciotti, Elisa; Lanfranchi, Gaia; Langenbruch, Christoph; Latham, Thomas; Lazzeroni, Cristina; Le Gac, Renaud; van Leerdam, Jeroen; Lees, Jean-Pierre; Lefèvre, Regis; Leflat, Alexander; Lefrançois, Jacques; Leo, Sabato; Leroy, Olivier; Lesiak, Tadeusz; Leverington, Blake; Li, Yiming; Li Gioi, Luigi; Liles, Myfanwy; Lindner, Rolf; Linn, Christian; Liu, Bo; Liu, Guoming; Lohn, Stefan; Longstaff, Ian; Lopes, Jose; Lopez-March, Neus; Lu, Haiting; Lucchesi, Donatella; Luisier, Johan; Luo, Haofei; Lupton, Oliver; Machefert, Frederic; Machikhiliyan, Irina V; Maciuc, Florin; Maev, Oleg; Malde, Sneha; Manca, Giulia; Mancinelli, Giampiero; Maratas, Jan; Marconi, Umberto; Marino, Pietro; Märki, Raphael; Marks, Jörg; Martellotti, Giuseppe; Martens, Aurelien; Martín Sánchez, Alexandra; Martinelli, Maurizio; Martinez Santos, Diego; Martins Tostes, Danielle; Martynov, Aleksandr; Massafferri, André; Matev, Rosen; Mathe, Zoltan; Matteuzzi, Clara; Maurice, Emilie; Mazurov, Alexander; McCarthy, James; McNab, Andrew; McNulty, Ronan; McSkelly, Ben; Meadows, Brian; Meier, Frank; Meissner, Marco; Merk, Marcel; Milanes, Diego Alejandro; Minard, Marie-Noelle; Molina Rodriguez, Josue; Monteil, Stephane; Moran, Dermot; Morawski, Piotr; Mordà, Alessandro; Morello, Michael Joseph; Mountain, Raymond; Mous, Ivan; Muheim, Franz; Müller, Katharina; Muresan, Raluca; Muryn, Bogdan; Muster, Bastien; Naik, Paras; Nakada, Tatsuya; Nandakumar, Raja; Nasteva, Irina; Needham, Matthew; Neubert, Sebastian; Neufeld, Niko; Nguyen, Anh Duc; Nguyen, Thi-Dung; Nguyen-Mau, Chung; Nicol, Michelle; Niess, Valentin; Niet, Ramon; Nikitin, Nikolay; Nikodem, Thomas; Nomerotski, Andrey; Novoselov, Alexey; Oblakowska-Mucha, Agnieszka; Obraztsov, Vladimir; Oggero, Serena; Ogilvy, Stephen; Okhrimenko, Oleksandr; Oldeman, Rudolf; Orlandea, Marius; Otalora Goicochea, Juan Martin; Owen, Patrick; Oyanguren, Maria Arantza; Pal, Bilas Kanti; Palano, Antimo; Palutan, Matteo; Panman, Jacob; Papanestis, Antonios; Pappagallo, Marco; Parkes, Christopher; Parkinson, Christopher John; Passaleva, Giovanni; Patel, Girish; Patel, Mitesh; Patrick, Glenn; Patrignani, Claudia; Pavel-Nicorescu, Carmen; Pazos Alvarez, Antonio; Pearce, Alex; Pellegrino, Antonio; Penso, Gianni; Pepe Altarelli, Monica; Perazzini, Stefano; Perez Trigo, Eliseo; Pérez-Calero Yzquierdo, Antonio; Perret, Pascal; Perrin-Terrin, Mathieu; Pescatore, Luca; Pesen, Erhan; Pessina, Gianluigi; Petridis, Konstantin; Petrolini, Alessandro; Phan, Anna; Picatoste Olloqui, Eduardo; Pietrzyk, Boleslaw; Pilař, Tomas; Pinci, Davide; Playfer, Stephen; Plo Casasus, Maximo; Polci, Francesco; Polok, Grzegorz; Poluektov, Anton; Polycarpo, Erica; Popov, Alexander; Popov, Dmitry; Popovici, Bogdan; Potterat, Cédric; Powell, Andrew; Prisciandaro, Jessica; Pritchard, Adrian; Prouve, Claire; Pugatch, Valery; Puig Navarro, Albert; Punzi, Giovanni; Qian, Wenbin; Rachwal, Bartolomiej; Rademacker, Jonas; Rakotomiaramanana, Barinjaka; Rangel, Murilo; Raniuk, Iurii; Rauschmayr, Nathalie; Raven, Gerhard; Redford, Sophie; Reichert, Stefanie; Reid, Matthew; dos Reis, Alberto; Ricciardi, Stefania; Richards, Alexander; Rinnert, Kurt; Rives Molina, Vincente; Roa Romero, Diego; Robbe, Patrick; Roberts, Douglas; Rodrigues, Ana Barbara; Rodrigues, Eduardo; Rodriguez Perez, Pablo; Roiser, Stefan; Romanovsky, Vladimir; Romero Vidal, Antonio; Rotondo, Marcello; Rouvinet, Julien; Ruf, Thomas; Ruffini, Fabrizio; Ruiz, Hugo; Ruiz Valls, Pablo; Sabatino, Giovanni; Saborido Silva, Juan Jose; Sagidova, Naylya; Sail, Paul; Saitta, Biagio; Salustino Guimaraes, Valdir; Sanmartin Sedes, Brais; Santacesaria, Roberta; Santamarina Rios, Cibran; Santovetti, Emanuele; Sapunov, Matvey; Sarti, Alessio; Satriano, Celestina; Satta, Alessia; Savrie, Mauro; Savrina, Darya; Schiller, Manuel; Schindler, Heinrich; Schlupp, Maximilian; Schmelling, Michael; Schmidt, Burkhard; Schneider, Olivier; Schopper, Andreas; Schune, Marie Helene; Schwemmer, Rainer; Sciascia, Barbara; Sciubba, Adalberto; Seco, Marcos; Semennikov, Alexander; Senderowska, Katarzyna; Sepp, Indrek; Serra, Nicola; Serrano, Justine; Seyfert, Paul; Shapkin, Mikhail; Shapoval, Illya; Shcheglov, Yury; Shears, Tara; Shekhtman, Lev; Shevchenko, Oksana; Shevchenko, Vladimir; Shires, Alexander; Silva Coutinho, Rafael; Sirendi, Marek; Skidmore, Nicola; Skwarnicki, Tomasz; Smith, Anthony; Smith, Edmund; Smith, Eluned; Smith, Jackson; Smith, Mark; Sokoloff, Michael; Soler, Paul; Soomro, Fatima; Souza, Daniel; Souza De Paula, Bruno; Spaan, Bernhard; Sparkes, Ailsa; Spradlin, Patrick; Stagni, Federico; Stahl, Sascha; Steinkamp, Olaf; Stevenson, Scott; Stoica, Sabin; Stone, Sheldon; Storaci, Barbara; Straticiuc, Mihai; Straumann, Ulrich; Subbiah, Vijay Kartik; Sun, Liang; Sutcliffe, William; Swientek, Stefan; Syropoulos, Vasileios; Szczekowski, Marek; Szczypka, Paul; Szilard, Daniela; Szumlak, Tomasz; T'Jampens, Stephane; Teklishyn, Maksym; Teodorescu, Eliza; Teubert, Frederic; Thomas, Christopher; Thomas, Eric; van Tilburg, Jeroen; Tisserand, Vincent; Tobin, Mark; Tolk, Siim; Tonelli, Diego; Topp-Joergensen, Stig; Torr, Nicholas; Tournefier, Edwige; Tourneur, Stephane; Tran, Minh Tâm; Tresch, Marco; Tsaregorodtsev, Andrei; Tsopelas, Panagiotis; Tuning, Niels; Ubeda Garcia, Mario; Ukleja, Artur; Ustyuzhanin, Andrey; Uwer, Ulrich; Vagnoni, Vincenzo; Valenti, Giovanni; Vallier, Alexis; Vazquez Gomez, Ricardo; Vazquez Regueiro, Pablo; Vázquez Sierra, Carlos; Vecchi, Stefania; Velthuis, Jaap; Veltri, Michele; Veneziano, Giovanni; Vesterinen, Mika; Viaud, Benoit; Vieira, Daniel; Vilasis-Cardona, Xavier; Vollhardt, Achim; Volyanskyy, Dmytro; Voong, David; Vorobyev, Alexey; Vorobyev, Vitaly; Voß, Christian; Voss, Helge; Waldi, Roland; Wallace, Charlotte; Wallace, Ronan; Wandernoth, Sebastian; Wang, Jianchun; Ward, David; Watson, Nigel; Webber, Adam Dane; Websdale, David; Whitehead, Mark; Wicht, Jean; Wiechczynski, Jaroslaw; Wiedner, Dirk; Wiggers, Leo; Wilkinson, Guy; Williams, Matthew; Williams, Mike; Wilson, Fergus; Wimberley, Jack; Wishahi, Julian; Wislicki, Wojciech; Witek, Mariusz; Wormser, Guy; Wotton, Stephen; Wright, Simon; Wu, Suzhi; Wyllie, Kenneth; Xie, Yuehong; Xing, Zhou; Yang, Zhenwei; Yuan, Xuhao; Yushchenko, Oleg; Zangoli, Maria; Zavertyaev, Mikhail; Zhang, Feng; Zhang, Liming; Zhang, Wen Chao; Zhang, Yanxi; Zhelezov, Alexey; Zhokhov, Anatoly; Zhong, Liang; Zvyagin, Alexander

    2014-01-01

    The charmless decays $B^{\\pm} \\rightarrow K^{+}K^{-}\\pi^{\\pm}$ and $B^{\\pm} \\rightarrow \\pi^{+}\\pi^{-}\\pi^{\\pm}$ are reconstructed in a data set, corresponding to an integrated luminosity of 1.0 fb$^{-1}$ of pp collisions at a center-of-mass energy of 7 TeV, collected by LHCb in 2011. The inclusive charge asymmetries of these modes are measured to be $A_{CP}(B^{\\pm} \\rightarrow K^{+}K^{-}\\pi^{\\pm}) =-0.141 \\pm 0.040 (stat) \\pm 0.018 (syst) \\pm 0.007 (J/\\psi K^{\\pm})$ and $A_{CP}(B^{\\pm} \\rightarrow \\pi^{+}\\pi^{-}\\pi^{\\pm}) = 0.117 \\pm 0.021 (stat) \\pm 0.009 (syst) \\pm 0.007 (J/\\psi K^{\\pm})$, where the third uncertainty is due to the CP asymmetry of the $B^{\\pm} \\rightarrow J/\\psi K^{\\pm}$ reference mode. In addition to the inclusive CP asymmetries, larger asymmetries are observed in localized regions of phase space.

  4. SPECIEUROPE: The European data base for PM source profiles

    OpenAIRE

    PERNIGOTTI DENISE; BELIS CLAUDIO; SPANO' LUCA

    2015-01-01

    A database of atmospheric particulate matter emission source profiles in Europe (SPECIEUROPE) was developed by the Joint Research Center in the framework of the Forum for air quality modeling in Europe (FAIRMODE, Working Group 3). It contains the chemical composition of particulate matter (PM) emission sources reported in the scientific literature and reports drafted by competent authorities. The first release of SPECIEUROPE consists of 151 measured profiles (original), 13 composite (merging ...

  5. MODELADO DE PARTÍCULAS PM10 Y PM2.5 MEDIANTE REDES NEURONALES ARTIFICIALES SOBRE CLIMA TROPICAL DE SAN FRANCISCO DE CAMPECHE, MÉXICO

    Directory of Open Access Journals (Sweden)

    Alberto Antonio Espinosa Guzmán

    Full Text Available In this paper, a computational methodology based on Artificial Neural Networks (ANN was developed to estimate the index of PM10 and PM2.5 concentration in air of San Francisco de Campeche city. A three layer ANN architecture was trained using an experimental database composed by days of the week, time of day, ambient temperature, atmospheric pressure, wind speed, wind direction, relative humidity, and solar radiation. The best ANN architecture, composed by 30 neurons in hidden layer, was obtained using the Levenberg-Marquardt (LM optimization algorithm, logarithmic sigmoid and linear transfer functions. Model results generate predictions with a determination coefficient of 93.01% and 90.10% for PM2.5 and PM10, respectively. The proposed methodology can be implemented in several studies as public health, environmental studies, urban development, and degradation of historical monuments.

  6. Validation of PM6 & PM7 semiempirical methods on polarizability calculations

    Science.gov (United States)

    Praveen, P. A.; Babu, R. Ramesh; Ramamurthi, K.

    2015-06-01

    Modern semiempirical methods such as PM6 and PM7 are often used to explore the electronic structure dependent properties of molecules. In this work we report the evaluation of PM6 and PM7 methods towards linear and nonlinear optical polarizability calculations for different molecules and solid nanoclusters. The results are compared with reported experimental results as well as theoretical results from other high level theories for the same systems. It is found that both methods produce accurate results for small molecules and the accuracy increases with the increase in asymmetry of the medium sized organic molecules and accuracy reduces for solid nanoclusters.

  7. Validation of PM6 & PM7 semiempirical methods on polarizability calculations

    Energy Technology Data Exchange (ETDEWEB)

    Praveen, P. A.; Babu, R. Ramesh, E-mail: rampap2k@yahoo.co.in [Crystal Growth and Thin film Laboratory, Department of Physics, Bharathidasan University, Tiruchirappalli-620024, Tamilnadu (India); Ramamurthi, K. [Department of Physics and Nanotechnology, Faculty of Engineering and Technology, SRM University, Kattankulathur – 603 203, Tamil Nadu (India)

    2015-06-24

    Modern semiempirical methods such as PM6 and PM7 are often used to explore the electronic structure dependent properties of molecules. In this work we report the evaluation of PM6 and PM7 methods towards linear and nonlinear optical polarizability calculations for different molecules and solid nanoclusters. The results are compared with reported experimental results as well as theoretical results from other high level theories for the same systems. It is found that both methods produce accurate results for small molecules and the accuracy increases with the increase in asymmetry of the medium sized organic molecules and accuracy reduces for solid nanoclusters.

  8. Validation of PM6 & PM7 semiempirical methods on polarizability calculations

    International Nuclear Information System (INIS)

    Praveen, P. A.; Babu, R. Ramesh; Ramamurthi, K.

    2015-01-01

    Modern semiempirical methods such as PM6 and PM7 are often used to explore the electronic structure dependent properties of molecules. In this work we report the evaluation of PM6 and PM7 methods towards linear and nonlinear optical polarizability calculations for different molecules and solid nanoclusters. The results are compared with reported experimental results as well as theoretical results from other high level theories for the same systems. It is found that both methods produce accurate results for small molecules and the accuracy increases with the increase in asymmetry of the medium sized organic molecules and accuracy reduces for solid nanoclusters

  9. The Lancet Countdown on PM2·5 pollution-related health impacts of China's projected carbon dioxide mitigation in the electric power generation sector under the Paris Agreement: a modelling study.

    Science.gov (United States)

    Cai, Wenjia; Hui, Jingxuan; Wang, Can; Zheng, Yixuan; Zhang, Xin; Zhang, Qiang; Gong, Peng

    2018-04-01

    Except for comparing the implementation costs of the Paris Agreement with potential health benefits at the national levels, previous studies have not explored the health impacts of the nationally determined contributions (NDCs) by countries and in regional details. In this Lancet Countdown study, we aimed to estimate and monetise the health benefits of China's NDCs in the electric power generation sector, and then compare them with the implementation costs, both at the national and regional levels. In this modelling study, we linked the Multi-regional model for Energy Supply system and their Environmental ImpaCts, the Multi-resolution Emission Inventory for China model, the offline-coupled Weather Research and Forecasting model, the Community Multiscale Air Quality model, and the Integrated Health Impact Assessment model with a time scope from 2010 to 2050. We calculated the PM 2·5 concentrations and compared the health impacts and implementation costs between two scenarios that reflect CO 2 and air pollutant emissions-the reference (REF) scenario (no climate policy) and the NDC scenario (100% realisation of NDC targets: CO 2 emission intensity needs to be about 40% below 2010 emissions by 2030 [roughly 35% below 2030 emissions in REF], and about 90% below 2010 emissions by 2050 [roughly 96% below 2050 emissions in REF]). Under a comparatively optimistic health benefits valuation condition, at the national level, 18-62% of implementation costs could be covered by the health benefits in 2030. In 2050, the overall health benefits would substantially increase to 3-9 times of the implementation costs. However, northwest China would require the highest implementation costs and will also have more premature deaths because of a more carbon-intensive energy structure than business as usual. By 2030, people in northwest China (especially in Gansu, Shaanxi, and Xinjiang provinces) would need to bear worse air quality, and 10 083 (95% CI 3419-16 138) more premature

  10. LHCb: Measurements of the relative branching fractions of the decay channel $B^{\\pm}\\to p \\bar{p} K^{\\pm}$ including charmonium contributions at LHCb

    CERN Multimedia

    Cardinale, Roberta

    2011-01-01

    The study of the $B^{\\pm}\\to p \\bar{p} K^{\\pm}$ decay channel at LHCb is of great interest since it gives the possibility to study different aspects of the Standard Model and possibly Beyond Standard Model physics. A measurement of the direct CP asymmetry can be performed. Moreover intermediate states such as charmonium and "charmonium-like" resonances in the $p \\bar{p}$ final state can be observed and studied along with their characteristics. A multivariate selection has been implemented to select the interesting events using kinematic and topological variables and the particle identification information using the Ring Imaging Cherenkov detectors. The selection has a high signal efficiency and high background rejection capability. The ratios of the branching fractions of the $B^{\\pm}\\to p \\bar{p} K^{\\pm}$ decay channel, of the charmless component with $M_{p \\bar{p}} < 2.85 \\,{\\rm GeV/}c^{2}$ and of the charmonium contribution $\\eta_{c}$, ${\\mathcal B} (B^{\\pm}\\to \\eta_{c} K^{\\pm})\\times {\\mathcal B} (\\eta...

  11. Evolution de la surveillance des PM10 en France : épisodes de pollution par les particules au printemps 2007

    OpenAIRE

    Aymoz , Gilles; Bessagnet , Bertrand; Rouil , Laurence; Le Bihan , Olivier

    2008-01-01

    National audience; Since the 1st January 2007, PM10 monitoring network in France has evolved, in order to account for volatile fraction of PM10. This evolution permitted the observation of high peaks of PM10 during spring 2007. Concentrations during these peaks would have been largely underestimated with measuring techniques used before 2007. A study, coupling chemical and modelling approach of the phenomenon has been launched by LCSQA (Laboratoire Central de Surveillance de la Qualité de l'A...

  12. Chemical characterization and mass closure of PM10 and PM2.5 at an urban site in Karachi - Pakistan

    Science.gov (United States)

    Shahid, Imran; Kistler, Magdalena; Mukhtar, Azam; Ghauri, Badar M.; Ramirez-Santa Cruz, Carlos; Bauer, Heidi; Puxbaum, Hans

    2016-03-01

    A mass balance method is applied to assess main source contributions to PM2.5 and PM10 levels in Karachi. Carbonaceous species (elemental carbon, organic carbon, carbonate carbon), soluble ions (Ca++, Mg++, Na+, K+, NH4+, Cl-, NO3-, SO4-), saccharides (levoglucosan, galactosan, mannosan, sucrose, fructose, glucose, arabitol and mannitol) were determined in atmospheric fine (PM2.5) and coarse (PM10) aerosol samples collected under pre-monsoon conditions (March-April 2009) at an urban site in Karachi (Pakistan). The concentrations of PM2.5 and PM10 were found to be 75 μg/m3 and 437 μg/m3 respectively. The large difference between PM10 and PM2.5 originated predominantly from mineral dust. "Calcareous dust" and "siliceous dust" were the over all dominating material in PM, with 46% contribution to PM2.5 and 78% to PM10-2.5. Combustion particles and secondary organics (EC + OM) comprised 23% of PM2.5 and 6% of PM10-2.5. EC, as well as OC ambient levels were higher (59% and 56%) in PM10-2.5 than in PM2.5. Biomass burning contributed about 3% to PM2.5, and had a share of about 13% of ;EC + OM; in PM2.5. The impact of bioaerosol (fungal spores) was minor and had a share of 1 and 2% of the OC in the PM2.5 and PM10-2.5 size fractions. In case of secondary inorganic aerosols, ammonium sulphate (NH4)2SO4 contributes 4.4% to PM2.5 and no detectable quantity were found in fraction PM10-2.5. The sea salt contribution is about 2% both to PM2.5 and PM10-2.5.

  13. A study of CP violation in $B^\\pm \\to D K^\\pm$ and $B^\\pm \\to D \\pi^\\pm$ decays with $D \\to K^0_{\\rm S} K^\\pm \\pi^\\mp$ final states

    CERN Document Server

    Aaij, Roel; Adinolfi, Marco; Affolder, Anthony; Ajaltouni, Ziad; Albrecht, Johannes; Alessio, Federico; Alexander, Michael; Ali, Suvayu; Alkhazov, Georgy; Alvarez Cartelle, Paula; Alves Jr, Antonio; Amato, Sandra; Amerio, Silvia; Amhis, Yasmine; Anderlini, Lucio; Anderson, Jonathan; Andreassen, Rolf; Andreotti, Mirco; Andrews, Jason; Appleby, Robert; Aquines Gutierrez, Osvaldo; Archilli, Flavio; Artamonov, Alexander; Artuso, Marina; Aslanides, Elie; Auriemma, Giulio; Baalouch, Marouen; Bachmann, Sebastian; Back, John; Badalov, Alexey; Balagura, Vladislav; Baldini, Wander; Barlow, Roger; Barschel, Colin; Barsuk, Sergey; Barter, William; Batozskaya, Varvara; Bauer, Thomas; Bay, Aurelio; Beddow, John; Bedeschi, Franco; Bediaga, Ignacio; Belogurov, Sergey; Belous, Konstantin; Belyaev, Ivan; Ben-Haim, Eli; Bencivenni, Giovanni; Benson, Sean; Benton, Jack; Berezhnoy, Alexander; Bernet, Roland; Bettler, Marc-Olivier; van Beuzekom, Martinus; Bien, Alexander; Bifani, Simone; Bird, Thomas; Bizzeti, Andrea; Bjørnstad, Pål Marius; Blake, Thomas; Blanc, Frédéric; Blouw, Johan; Blusk, Steven; Bocci, Valerio; Bondar, Alexander; Bondar, Nikolay; Bonivento, Walter; Borghi, Silvia; Borgia, Alessandra; Borsato, Martino; Bowcock, Themistocles; Bowen, Espen Eie; Bozzi, Concezio; Brambach, Tobias; van den Brand, Johannes; Bressieux, Joël; Brett, David; Britsch, Markward; Britton, Thomas; Brook, Nicholas; Brown, Henry; Bursche, Albert; Busetto, Giovanni; Buytaert, Jan; Cadeddu, Sandro; Calabrese, Roberto; Callot, Olivier; Calvi, Marta; Calvo Gomez, Miriam; Camboni, Alessandro; Campana, Pierluigi; Campora Perez, Daniel; Carbone, Angelo; Carboni, Giovanni; Cardinale, Roberta; Cardini, Alessandro; Carranza-Mejia, Hector; Carson, Laurence; Carvalho Akiba, Kazuyoshi; Casse, Gianluigi; Cassina, Lorenzo; Castillo Garcia, Lucia; Cattaneo, Marco; Cauet, Christophe; Cenci, Riccardo; Charles, Matthew; Charpentier, Philippe; Cheung, Shu-Faye; Chiapolini, Nicola; Chrzaszcz, Marcin; Ciba, Krzystof; Cid Vidal, Xabier; Ciezarek, Gregory; Clarke, Peter; Clemencic, Marco; Cliff, Harry; Closier, Joel; Coca, Cornelia; Coco, Victor; Cogan, Julien; Cogneras, Eric; Collins, Paula; Comerma-Montells, Albert; Contu, Andrea; Cook, Andrew; Coombes, Matthew; Coquereau, Samuel; Corti, Gloria; Counts, Ian; Couturier, Benjamin; Cowan, Greig; Craik, Daniel Charles; Cruz Torres, Melissa Maria; Cunliffe, Samuel; Currie, Robert; D'Ambrosio, Carmelo; Dalseno, Jeremy; David, Pascal; David, Pieter; Davis, Adam; De Bonis, Isabelle; De Bruyn, Kristof; De Capua, Stefano; De Cian, Michel; De Miranda, Jussara; De Paula, Leandro; De Silva, Weeraddana; De Simone, Patrizia; Decamp, Daniel; Deckenhoff, Mirko; Del Buono, Luigi; Déléage, Nicolas; Derkach, Denis; Deschamps, Olivier; Dettori, Francesco; Di Canto, Angelo; Dijkstra, Hans; Donleavy, Stephanie; Dordei, Francesca; Dorigo, Mirco; Dorosz, Piotr; Dosil Suárez, Alvaro; Dossett, David; Dovbnya, Anatoliy; Dupertuis, Frederic; Durante, Paolo; Dzhelyadin, Rustem; Dziurda, Agnieszka; Dzyuba, Alexey; Easo, Sajan; Egede, Ulrik; Egorychev, Victor; Eidelman, Semen; Eisenhardt, Stephan; Eitschberger, Ulrich; Ekelhof, Robert; Eklund, Lars; El Rifai, Ibrahim; Elsasser, Christian; Esen, Sevda; Falabella, Antonio; Färber, Christian; Farinelli, Chiara; Farry, Stephen; Ferguson, Dianne; Fernandez Albor, Victor; Ferreira Rodrigues, Fernando; Ferro-Luzzi, Massimiliano; Filippov, Sergey; Fiore, Marco; Fiorini, Massimiliano; Fitzpatrick, Conor; Fontana, Marianna; Fontanelli, Flavio; Forty, Roger; Francisco, Oscar; Frank, Markus; Frei, Christoph; Frosini, Maddalena; Fu, Jinlin; Furfaro, Emiliano; Gallas Torreira, Abraham; Galli, Domenico; Gandelman, Miriam; Gandini, Paolo; Gao, Yuanning; Garofoli, Justin; Garra Tico, Jordi; Garrido, Lluis; Gaspar, Clara; Gauld, Rhorry; Gavardi, Laura; Gersabeck, Evelina; Gersabeck, Marco; Gershon, Timothy; Ghez, Philippe; Gianelle, Alessio; Giani', Sebastiana; Gibson, Valerie; Giubega, Lavinia-Helena; Gligorov, Vladimir; Göbel, Carla; Golubkov, Dmitry; Golutvin, Andrey; Gomes, Alvaro; Gordon, Hamish; Grabalosa Gándara, Marc; Graciani Diaz, Ricardo; Granado Cardoso, Luis Alberto; Graugés, Eugeni; Graziani, Giacomo; Grecu, Alexandru; Greening, Edward; Gregson, Sam; Griffith, Peter; Grillo, Lucia; Grünberg, Oliver; Gui, Bin; Gushchin, Evgeny; Guz, Yury; Gys, Thierry; Hadjivasiliou, Christos; Haefeli, Guido; Haen, Christophe; Hafkenscheid, Tom; Haines, Susan; Hall, Samuel; Hamilton, Brian; Hampson, Thomas; Hansmann-Menzemer, Stephanie; Harnew, Neville; Harnew, Samuel; Harrison, Jonathan; Hartmann, Thomas; He, Jibo; Head, Timothy; Heijne, Veerle; Hennessy, Karol; Henrard, Pierre; Henry, Louis; Hernando Morata, Jose Angel; van Herwijnen, Eric; Heß, Miriam; Hicheur, Adlène; Hill, Donal; Hoballah, Mostafa; Hombach, Christoph; Hulsbergen, Wouter; Hunt, Philip; Hussain, Nazim; Hutchcroft, David; Hynds, Daniel; Iakovenko, Viktor; Idzik, Marek; Ilten, Philip; Jacobsson, Richard; Jaeger, Andreas; Jans, Eddy; Jaton, Pierre; Jawahery, Abolhassan; Jing, Fanfan; John, Malcolm; Johnson, Daniel; Jones, Christopher; Joram, Christian; Jost, Beat; Jurik, Nathan; Kaballo, Michael; Kandybei, Sergii; Kanso, Wallaa; Karacson, Matthias; Karbach, Moritz; Kelsey, Matthew; Kenyon, Ian; Ketel, Tjeerd; Khanji, Basem; Khurewathanakul, Chitsanu; Klaver, Suzanne; Kochebina, Olga; Komarov, Ilya; Koopman, Rose; Koppenburg, Patrick; Korolev, Mikhail; Kozlinskiy, Alexandr; Kravchuk, Leonid; Kreplin, Katharina; Kreps, Michal; Krocker, Georg; Krokovny, Pavel; Kruse, Florian; Kucharczyk, Marcin; Kudryavtsev, Vasily; Kurek, Krzysztof; Kvaratskheliya, Tengiz; La Thi, Viet Nga; Lacarrere, Daniel; Lafferty, George; Lai, Adriano; Lambert, Dean; Lambert, Robert W; Lanciotti, Elisa; Lanfranchi, Gaia; Langenbruch, Christoph; Latham, Thomas; Lazzeroni, Cristina; Le Gac, Renaud; van Leerdam, Jeroen; Lees, Jean-Pierre; Lefèvre, Regis; Leflat, Alexander; Lefrançois, Jacques; Leo, Sabato; Leroy, Olivier; Lesiak, Tadeusz; Leverington, Blake; Li, Yiming; Liles, Myfanwy; Lindner, Rolf; Linn, Christian; Lionetto, Federica; Liu, Bo; Liu, Guoming; Lohn, Stefan; Longstaff, Ian; Lopes, Jose; Lopez-March, Neus; Lowdon, Peter; Lu, Haiting; Lucchesi, Donatella; Luisier, Johan; Luo, Haofei; Luppi, Eleonora; Lupton, Oliver; Machefert, Frederic; Machikhiliyan, Irina V; Maciuc, Florin; Maev, Oleg; Malde, Sneha; Manca, Giulia; Mancinelli, Giampiero; Manzali, Matteo; Maratas, Jan; Marconi, Umberto; Marino, Pietro; Märki, Raphael; Marks, Jörg; Martellotti, Giuseppe; Martens, Aurelien; Martín Sánchez, Alexandra; Martinelli, Maurizio; Martinez Santos, Diego; Martinez Vidal, Fernando; Martins Tostes, Danielle; Massafferri, André; Matev, Rosen; Mathe, Zoltan; Matteuzzi, Clara; Mazurov, Alexander; McCann, Michael; McCarthy, James; McNab, Andrew; McNulty, Ronan; McSkelly, Ben; Meadows, Brian; Meier, Frank; Meissner, Marco; Merk, Marcel; Milanes, Diego Alejandro; Minard, Marie-Noelle; Molina Rodriguez, Josue; Monteil, Stephane; Moran, Dermot; Morandin, Mauro; Morawski, Piotr; Mordà, Alessandro; Morello, Michael Joseph; Mountain, Raymond; Muheim, Franz; Müller, Katharina; Muresan, Raluca; Muryn, Bogdan; Muster, Bastien; Naik, Paras; Nakada, Tatsuya; Nandakumar, Raja; Nasteva, Irina; Needham, Matthew; Neri, Nicola; Neubert, Sebastian; Neufeld, Niko; Nguyen, Anh Duc; Nguyen, Thi-Dung; Nguyen-Mau, Chung; Nicol, Michelle; Niess, Valentin; Niet, Ramon; Nikitin, Nikolay; Nikodem, Thomas; Novoselov, Alexey; Oblakowska-Mucha, Agnieszka; Obraztsov, Vladimir; Oggero, Serena; Ogilvy, Stephen; Okhrimenko, Oleksandr; Oldeman, Rudolf; Onderwater, Gerco; Orlandea, Marius; Otalora Goicochea, Juan Martin; Owen, Patrick; Oyanguren, Maria Arantza; Pal, Bilas Kanti; Palano, Antimo; Palombo, Fernando; Palutan, Matteo; Panman, Jacob; Papanestis, Antonios; Pappagallo, Marco; Pappalardo, Luciano; Parkes, Christopher; Parkinson, Christopher John; Passaleva, Giovanni; Patel, Girish; Patel, Mitesh; Patrignani, Claudia; Pavel-Nicorescu, Carmen; Pazos Alvarez, Antonio; Pearce, Alex; Pellegrino, Antonio; Penso, Gianni; Pepe Altarelli, Monica; Perazzini, Stefano; Perez Trigo, Eliseo; Perret, Pascal; Perrin-Terrin, Mathieu; Pescatore, Luca; Pesen, Erhan; Pessina, Gianluigi; Petridis, Konstantin; Petrolini, Alessandro; Picatoste Olloqui, Eduardo; Pietrzyk, Boleslaw; Pilař, Tomas; Pinci, Davide; Pistone, Alessandro; Playfer, Stephen; Plo Casasus, Maximo; Polci, Francesco; Polok, Grzegorz; Poluektov, Anton; Polycarpo, Erica; Popov, Alexander; Popov, Dmitry; Popovici, Bogdan; Potterat, Cédric; Powell, Andrew; Prisciandaro, Jessica; Pritchard, Adrian; Prouve, Claire; Pugatch, Valery; Puig Navarro, Albert; Punzi, Giovanni; Qian, Wenbin; Rachwal, Bartolomiej; Rademacker, Jonas; Rakotomiaramanana, Barinjaka; Rama, Matteo; Rangel, Murilo; Raniuk, Iurii; Rauschmayr, Nathalie; Raven, Gerhard; Redford, Sophie; Reichert, Stefanie; Reid, Matthew; dos Reis, Alberto; Ricciardi, Stefania; Richards, Alexander; Rinnert, Kurt; Rives Molina, Vincente; Roa Romero, Diego; Robbe, Patrick; Roberts, Douglas; Rodrigues, Ana Barbara; Rodrigues, Eduardo; Rodriguez Perez, Pablo; Roiser, Stefan; Romanovsky, Vladimir; Romero Vidal, Antonio; Rotondo, Marcello; Rouvinet, Julien; Ruf, Thomas; Ruffini, Fabrizio; Ruiz, Hugo; Ruiz Valls, Pablo; Sabatino, Giovanni; Saborido Silva, Juan Jose; Sagidova, Naylya; Sail, Paul; Saitta, Biagio; Salustino Guimaraes, Valdir; Sanmartin Sedes, Brais; Santacesaria, Roberta; Santamarina Rios, Cibran; Santovetti, Emanuele; Sapunov, Matvey; Sarti, Alessio; Satriano, Celestina; Satta, Alessia; Savrie, Mauro; Savrina, Darya; Schiller, Manuel; Schindler, Heinrich; Schlupp, Maximilian; Schmelling, Michael; Schmidt, Burkhard; Schneider, Olivier; Schopper, Andreas; Schune, Marie Helene; Schwemmer, Rainer; Sciascia, Barbara; Sciubba, Adalberto; Seco, Marcos; Semennikov, Alexander; Senderowska, Katarzyna; Sepp, Indrek; Serra, Nicola; Serrano, Justine; Seyfert, Paul; Shapkin, Mikhail; Shapoval, Illya; Shcheglov, Yury; Shears, Tara; Shekhtman, Lev; Shevchenko, Oksana; Shevchenko, Vladimir; Shires, Alexander; Silva Coutinho, Rafael; Simi, Gabriele; Sirendi, Marek; Skidmore, Nicola; Skwarnicki, Tomasz; Smith, Anthony; Smith, Edmund; Smith, Eluned; Smith, Jackson; Smith, Mark; Snoek, Hella; Sokoloff, Michael; Soler, Paul; Soomro, Fatima; Souza, Daniel; Souza De Paula, Bruno; Spaan, Bernhard; Sparkes, Ailsa; Spinella, Franco; Spradlin, Patrick; Stagni, Federico; Stahl, Sascha; Steinkamp, Olaf; Stevenson, Scott; Stoica, Sabin; Stone, Sheldon; Storaci, Barbara; Stracka, Simone; Straticiuc, Mihai; Straumann, Ulrich; Stroili, Roberto; Subbiah, Vijay Kartik; Sun, Liang; Sutcliffe, William; Swientek, Stefan; Syropoulos, Vasileios; Szczekowski, Marek; Szczypka, Paul; Szilard, Daniela; Szumlak, Tomasz; T'Jampens, Stephane; Teklishyn, Maksym; Tellarini, Giulia; Teodorescu, Eliza; Teubert, Frederic; Thomas, Christopher; Thomas, Eric; van Tilburg, Jeroen; Tisserand, Vincent; Tobin, Mark; Tolk, Siim; Tomassetti, Luca; Tonelli, Diego; Topp-Joergensen, Stig; Torr, Nicholas; Tournefier, Edwige; Tourneur, Stephane; Tran, Minh Tâm; Tresch, Marco; Tsaregorodtsev, Andrei; Tsopelas, Panagiotis; Tuning, Niels; Ubeda Garcia, Mario; Ukleja, Artur; Ustyuzhanin, Andrey; Uwer, Ulrich; Vagnoni, Vincenzo; Valenti, Giovanni; Vallier, Alexis; Vazquez Gomez, Ricardo; Vazquez Regueiro, Pablo; Vázquez Sierra, Carlos; Vecchi, Stefania; Velthuis, Jaap; Veltri, Michele; Veneziano, Giovanni; Vesterinen, Mika; Viaud, Benoit; Vieira, Daniel; Vilasis-Cardona, Xavier; Vollhardt, Achim; Volyanskyy, Dmytro; Voong, David; Vorobyev, Alexey; Vorobyev, Vitaly; Voß, Christian; Voss, Helge; de Vries, Jacco; Waldi, Roland; Wallace, Charlotte; Wallace, Ronan; Wandernoth, Sebastian; Wang, Jianchun; Ward, David; Watson, Nigel; Webber, Adam Dane; Websdale, David; Whitehead, Mark; Wicht, Jean; Wiechczynski, Jaroslaw; Wiedner, Dirk; Wiggers, Leo; Wilkinson, Guy; Williams, Matthew; Williams, Mike; Wilson, Fergus; Wimberley, Jack; Wishahi, Julian; Wislicki, Wojciech; Witek, Mariusz; Wormser, Guy; Wotton, Stephen; Wright, Simon; Wu, Suzhi; Wyllie, Kenneth; Xie, Yuehong; Xing, Zhou; Yang, Zhenwei; Yuan, Xuhao; Yushchenko, Oleg; Zangoli, Maria; Zavertyaev, Mikhail; Zhang, Feng; Zhang, Liming; Zhang, Wen Chao; Zhang, Yanxi; Zhelezov, Alexey; Zhokhov, Anatoly; Zhong, Liang; Zvyagin, Alexander

    2014-01-01

    A first study of CP violation in the decay modes $B^\\pm\\to [K^0_{\\rm S} K^\\pm \\pi^\\mp]_D h^\\pm$ and $B^\\pm\\to [K^0_{\\rm S} K^\\mp \\pi^\\pm]_D h^\\pm$, where $h$ labels a $K$ or $\\pi$ meson and $D$ labels a $D^0$ or $\\overline{D}^0$ meson, is performed. The analysis uses the LHCb data set collected in $pp$ collisions, corresponding to an integrated luminosity of 3 fb$^{-1}$. The analysis is sensitive to the CP-violating CKM phase $\\gamma$ through seven observables: one charge asymmetry in each of the four modes and three ratios of the charge-integrated yields. The results are consistent with measurements of $\\gamma$ using other decay modes.

  14. Impacts of Stratospheric Sulfate Geoengineering on PM2.5

    Science.gov (United States)

    Robock, A.; Xia, L.; Tilmes, S.; Mills, M. J.; Richter, J.; Kravitz, B.; MacMartin, D.

    2017-12-01

    Particulate matter (PM) includes sulfate, nitrate, organic carbon, elemental carbon, soil dust, and sea salt. The first four components are mostly present near the ground as fine particulate matter with a diameter less than 2.5 µm (PM2.5), and these are of the most concern for human health. PM is efficiently scavenged by precipitation, which is its main atmospheric sink. Here we examine the impact of stratospheric climate engineering on this important pollutant and health risk, taking advantage of two sets of climate model simulations conducted at the National Center for Atmospheric Research. We use the full tropospheric and stratospheric chemistry version of the Community Earth System Model - Community Atmospheric Model 4 (CESM CAM4-chem) with a horizontal resolution of 0.9° x 1.25° lat-lon to simulate a stratospheric sulfate injection climate intervention of 8 Tg SO2 yr-1 combined with an RCP6.0 global warming forcing, the G4 Specified Stratospheric Aerosol (G4SSA) scenario. We also analyze the output from a 20-member ensemble of Community Earth System Model, version 1 with the Whole Atmosphere Community Climate Model as its atmospheric component (CESM1(WACCM)) simulations, also at 0.9° x 1.25° lat-lon resolution, with sulfur dioxide injection at 15°N, 15°S, 30°N, and 30°S varying in time to balance RCP8.5 forcing. While the CESM CAM4-chem model has full tropospheric and stratospheric chemistry, CESM1(WACCM) has an internally generated quasi-biennial oscillation and a comprehensive tropospheric and stratospheric sulfate aerosol treatment, but only stratospheric chemistry. For G4SSA, there are a global temperature reduction of 0.8 K and global averaged precipitation decrease of 3% relative to RCP6.0. The global averaged surface PM2.5 reduces about 1% compared with RCP6.0, mainly over Eurasian and East Asian regions in Northern Hemisphere winter. The PM2.5 concentration change is a combination of effects from tropospheric chemistry and precipitation

  15. Wintertime indoor air levels of PM10, PM2.5 and PM1 at public places and their contributions to TSP.

    Science.gov (United States)

    Liu, Yangsheng; Chen, Rui; Shen, Xingxing; Mao, Xiaoling

    2004-04-01

    From 26 October 2002 to 8 March 2003, particulate matter (PM) concentrations (total suspended particles [TSP], PM10, PM2.5 and PM1) were measured at 49 public places representing different environments in the urban area of Beijing. The objectives of this study were (1) to characterize the indoor PM concentrations in public places, (2) to evaluate the potential indoor sources and (3) to investigate the contribution of PM10 to TSP and the contributions of PM2.5 and PM1 to PM10. Additionally, The indoor and outdoor particle concentrations in the same type of indoor environment were employed to investigate the I/O level, and comparison was made between I/O levels in different types of indoor environment. Construction activities and traffic condition were the major outdoor sources to influence the indoor particle levels. The contribution of PM10 to TSP was even up to 68.8%, while the contributions of PM2.5 and PM1 to PM10 were not as much as that of PM10 to TSP.

  16. Level structures in 156Pm from 156Nd β - decay

    International Nuclear Information System (INIS)

    Sood, P.C.; Gowrishankar, R.; Sainath, M.; Singh, B.

    2012-01-01

    Level energies in two-quasiparticle (2qp) structures in 156 61 Pm 95 are evaluated using the phenomenological rotor-particle model for odd-odd deformed nuclei with the inclusion of the residual p-n interaction contribution. Using these results as location guides, the experimental data from a recent 156 Nd decay study are used to construct a level scheme for 156 Pm with excitation energies upto 550 keV, wherein 26 (out of 30) γ's reported in 156 Nd decay are incorporated. Spin-parities and configuration assignments are suggested for 15 levels, in addition to the two isomers, in this energy domain. These investigations point to the need for information on higher-energy γ's and on β-γ and γ-γ coincidence data from 156 Nd β-decay to confirm these assignments. (orig.)

  17. Measurement of 147Pm in-vivo using phoswich detectors

    International Nuclear Information System (INIS)

    Johnson, J.R.

    1977-10-01

    Recently an individual was suspected of having inhaled significant amounts of the almost pure beta emitter, 147 Pm. Urine analysis confirmed that contaminations had occurred but these results could not be used to evaluate the amount of material deposited in the lungs because an acceptable model of promethium clearance from the lung to blood and hence to urine has not been developed. Therefore another method of evaluation, that of the measurement, using phoswich detectors, of the soft photons emitted by the deposited 147 Pm was used. This paper describes the calibrations and measurements that were done in order that an upper limit on the deposited activity, and hence limits for committed dose to the various organs, could be assigned. (author)

  18. A search for $W\\pm H \\to\\mu\

    CERN Document Server

    Anastasoaie, Mirunna; Filthaut, F

    2008-01-01

    This thesis describes a search for the Higgs boson in $D0$ data taken between April 2002 and February 2006. The search focuses on associated $W^{\\pm} H$ production, where the $W^{\\pm}$ decays to a muon and a neutrino and the Higgs boson into a $b\\overline{b}$ pair. Chapter 2 introduces the Standard Model and the Higgs mechanism. Chapter 3 describes the Tevatron particle accelerator and the $D0$ detector. The methods and algorithms used to acquire and reconstruct the data used in the analysis are presented in Chapter 4. Since the Higgs boson most often decays into a bb pair, the identification of jets originating from bottom quarks is very important. Chapter 5 describes in detail a Neural Net-based tool used for the identification of b-jets. The tool uses information from previously developed tagging algorithms used in $D0$ and improves the efficiency for finding b-jets.

  19. SiPM response to long and intense light pulses

    Energy Technology Data Exchange (ETDEWEB)

    Vinogradov, S., E-mail: Sergey.Vinogradov@liverpool.ac.uk [University of Liverpool and Cockcroft Institute, Sci-Tech Daresbury, Keckwick Lane, Warrington WA4 4AD (United Kingdom); P.N. Lebedev Physical Institute of the Russian Academy of Sciences, 119991 Leninskiy prospekt 53, Moscow (Russian Federation); Arodzero, A. [Department of Nuclear Science and Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139 (United States); RadiaBeam Technologies Inc., 1717 Stewart St., Santa Monica, CA 90404 (United States); Lanza, R.C. [Department of Nuclear Science and Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139 (United States); Welsch, C.P. [University of Liverpool and Cockcroft Institute, Sci-Tech Daresbury, Keckwick Lane, Warrington WA4 4AD (United Kingdom)

    2015-07-01

    Recently Silicon Photomultipliers (SiPMs) have become well recognized as the detector of choice for various applications which demand good photon number resolution and time resolution of short weak light pulses in the nanosecond time scale. In the case of longer and more intensive light pulses, SiPM performance gradually degrades due to dark noise, afterpulsing, and non-instant cell recovering. Nevertheless, SiPM benefits are expected to overbalance their drawbacks in applications such as X-ray cargo inspection using Scintillation-Cherenkov detectors and accelerator beam loss monitoring with Cherenkov fibres, where light pulses of a microsecond time scale have to be detected with good amplitude and timing resolution in a wide dynamic range of 10{sup 5}–10{sup 6}. This report is focused on transient characteristics of a SiPM response on a long rectangular light pulse with special attention to moderate and high light intensities above the linear dynamic range. An analytical model of the transient response and an initial consideration of experimental results in comparison with the model are presented.

  20. Application of XGBoost algorithm in hourly PM2.5 concentration prediction

    Science.gov (United States)

    Pan, Bingyue

    2018-02-01

    In view of prediction techniques of hourly PM2.5 concentration in China, this paper applied the XGBoost(Extreme Gradient Boosting) algorithm to predict hourly PM2.5 concentration. The monitoring data of air quality in Tianjin city was analyzed by using XGBoost algorithm. The prediction performance of the XGBoost method is evaluated by comparing observed and predicted PM2.5 concentration using three measures of forecast accuracy. The XGBoost method is also compared with the random forest algorithm, multiple linear regression, decision tree regression and support vector machines for regression models using computational results. The results demonstrate that the XGBoost algorithm outperforms other data mining methods.

  1. Estimating spatiotemporal distribution of PM1 concentrations in China with satellite remote sensing, meteorology, and land use information.

    Science.gov (United States)

    Chen, Gongbo; Knibbs, Luke D; Zhang, Wenyi; Li, Shanshan; Cao, Wei; Guo, Jianping; Ren, Hongyan; Wang, Boguang; Wang, Hao; Williams, Gail; Hamm, N A S; Guo, Yuming

    2018-02-01

    PM 1 might be more hazardous than PM 2.5 (particulate matter with an aerodynamic diameter ≤ 1 μm and ≤2.5 μm, respectively). However, studies on PM 1 concentrations and its health effects are limited due to a lack of PM 1 monitoring data. To estimate spatial and temporal variations of PM 1 concentrations in China during 2005-2014 using satellite remote sensing, meteorology, and land use information. Two types of Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 aerosol optical depth (AOD) data, Dark Target (DT) and Deep Blue (DB), were combined. Generalised additive model (GAM) was developed to link ground-monitored PM 1 data with AOD data and other spatial and temporal predictors (e.g., urban cover, forest cover and calendar month). A 10-fold cross-validation was performed to assess the predictive ability. The results of 10-fold cross-validation showed R 2 and Root Mean Squared Error (RMSE) for monthly prediction were 71% and 13.0 μg/m 3 , respectively. For seasonal prediction, the R 2 and RMSE were 77% and 11.4 μg/m 3 , respectively. The predicted annual mean concentration of PM 1 across China was 26.9 μg/m 3 . The PM 1 level was highest in winter while lowest in summer. Generally, the PM 1 levels in entire China did not substantially change during the past decade. Regarding local heavy polluted regions, PM 1 levels increased substantially in the South-Western Hebei and Beijing-Tianjin region. GAM with satellite-retrieved AOD, meteorology, and land use information has high predictive ability to estimate ground-level PM 1 . Ambient PM 1 reached high levels in China during the past decade. The estimated results can be applied to evaluate the health effects of PM 1 . Copyright © 2017 Elsevier Ltd. All rights reserved.

  2. Measurements of the branching fractions and $C\\!P$ asymmetries of $B^{\\pm} \\to J\\!/\\!\\psi\\, \\pi^{\\pm}$ and $B^{\\pm} \\to \\psi(2S) \\pi^{\\pm}$ decays

    CERN Document Server

    Aaij, R; Adeva, B; Adinolfi, M; Adrover, C; Affolder, A; Ajaltouni, Z; Albrecht, J; Alessio, F; Alexander, M; Ali, S; Alkhazov, G; Alvarez Cartelle, P; Alves Jr, A A; Amato, S; Amhis, Y; Anderson, J; Appleby, R B; Aquines Gutierrez, O; Archilli, F; Artamonov, A; Artuso, M; Aslanides, E; Auriemma, G; Bachmann, S; Back, J J; Balagura, V; Baldini, W; Barlow, R J; Barschel, C; Barsuk, S; Barter, W; Bates, A; Bauer, C; Bauer, Th; Bay, A; Bediaga, I; Belogurov, S; Belous, K; Belyaev, I; Ben-Haim, E; Benayoun, M; Bencivenni, G; Benson, S; Benton, J; Bernet, R; Bettler, M-O; van Beuzekom, M; Bien, A; Bifani, S; Bird, T; Bizzeti, A; Bjørnstad, P M; Blake, T; Blanc, F; Blanks, C; Blouw, J; Blusk, S; Bobrov, A; Bocci, V; Bondar, A; Bondar, N; Bonivento, W; Borghi, S; Borgia, A; Bowcock, T J V; Bozzi, C; Brambach, T; van den Brand, J; Bressieux, J; Brett, D; Britsch, M; Britton, T; Brook, N H; Brown, H; Büchler-Germann, A; Burducea, I; Bursche, A; Buytaert, J; Cadeddu, S; Callot, O; Calvi, M; Calvo Gomez, M; Camboni, A; Campana, P; Carbone, A; Carboni, G; Cardinale, R; Cardini, A; Carson, L; Carvalho Akiba, K; Casse, G; Cattaneo, M; Cauet, Ch; Charles, M; Charpentier, Ph; Chiapolini, N; Ciba, K; Cid Vidal, X; Ciezarek, G; Clarke, P E L; Clemencic, M; Cliff, H V; Closier, J; Coca, C; Coco, V; Cogan, J; Collins, P; Comerma-Montells, A; Contu, A; Cook, A; Coombes, M; Corti, G; Couturier, B; Cowan, G A; Currie, R; D'Ambrosio, C; David, P; David, P N Y; De Bonis, I; De Bruyn, K; De Capua, S; De Cian, M; De Miranda, J M; De Paula, L; De Simone, P; Decamp, D; Deckenhoff, M; Degaudenzi, H; Del Buono, L; Deplano, C; Derkach, D; Deschamps, O; Dettori, F; Dickens, J; Dijkstra, H; Diniz Batista, P; Domingo Bonal, F; Donleavy, S; Dordei, F; Dosil Suárez, A; Dossett, D; Dovbnya, A; Dupertuis, F; Dzhelyadin, R; Dziurda, A; Easo, S; Egede, U; Egorychev, V; Eidelman, S; van Eijk, D; Eisele, F; Eisenhardt, S; Ekelhof, R; Eklund, L; Elsasser, Ch; Elsby, D; Esperante Pereira, D; Falabella, A; Färber, C; Fardell, G; Farinelli, C; Farry, S; Fave, V; Fernandez Albor, V; Ferro-Luzzi, M; Filippov, S; Fitzpatrick, C; Fontana, M; Fontanelli, F; Forty, R; Francisco, O; Frank, M; Frei, C; Frosini, M; Furcas, S; Gallas Torreira, A; Galli, D; Gandelman, M; Gandini, P; Gao, Y; Garnier, J-C; Garofoli, J; Garra Tico, J; Garrido, L; Gascon, D; Gaspar, C; Gauld, R; Gauvin, N; Gersabeck, M; Gershon, T; Ghez, Ph; Gibson, V; Gligorov, V V; Göbel, C; Golubkov, D; Golutvin, A; Gomes, A; Gordon, H; Grabalosa Gándara, M; Graciani Diaz, R; Granado Cardoso, L A; Graugés, E; Graziani, G; Grecu, A; Greening, E; Gregson, S; Gui, B; Gushchin, E; Guz, Yu; Gys, T; Hadjivasiliou, C; Haefeli, G; Haen, C; Haines, S C; Hampson, T; Hansmann-Menzemer, S; Harji, R; Harnew, N; Harrison, J; Harrison, P F; Hartmann, T; He, J; Heijne, V; Hennessy, K; Henrard, P; Hernando Morata, J A; van Herwijnen, E; Hicks, E; Holubyev, K; Hopchev, P; Hulsbergen, W; Hunt, P; Huse, T; Huston, R S; Hutchcroft, D; Hynds, D; Iakovenko, V; Ilten, P; Imong, J; Jacobsson, R; Jaeger, A; Jahjah Hussein, M; Jans, E; Jansen, F; Jaton, P; Jean-Marie, B; Jing, F; John, M; Johnson, D; Jones, C R; Jost, B; Kaballo, M; Kandybei, S; Karacson, M; Karbach, T M; Keaveney, J; Kenyon, I R; Kerzel, U; Ketel, T; Keune, A; Khanji, B; Kim, Y M; Knecht, M; Koopman, R F; Koppenburg, P; Korolev, M; Kozlinskiy, A; Kravchuk, L; Kreplin, K; Kreps, M; Krocker, G; Krokovny, P; Kruse, F; Kruzelecki, K; Kucharczyk, M; Kudryavtsev, V; Kvaratskheliya, T; La Thi, V N; Lacarrere, D; Lafferty, G; Lai, A; Lambert, D; Lambert, R W; Lanciotti, E; Lanfranchi, G; Langenbruch, C; Latham, T; Lazzeroni, C; Le Gac, R; van Leerdam, J; Lees, J-P; Lefèvre, R; Leflat, A; Lefrançois, J; Leroy, O; Lesiak, T; Li, L; Li Gioi, L; Lieng, M; Liles, M; Lindner, R; Linn, C; Liu, B; Liu, G; von Loeben, J; Lopes, J H; Lopez Asamar, E; Lopez-March, N; Lu, H; Luisier, J; Mac Raighne, A; Machefert, F; Machikhiliyan, I V; Maciuc, F; Maev, O; Magnin, J; Malde, S; Mamunur, R M D; Manca, G; Mancinelli, G; Mangiafave, N; Marconi, U; Märki, R; Marks, J; Martellotti, G; Martens, A; Martin, L; Martín Sánchez, A; Martinelli, M; Martinez Santos, D; Massafferri, A; Mathe, Z; Matteuzzi, C; Matveev, M; Maurice, E; Maynard, B; Mazurov, A; McGregor, G; McNulty, R; Meissner, M; Merk, M; Merkel, J; Miglioranzi, S; Milanes, D A; Minard, M-N; Molina Rodriguez, J; Monteil, S; Moran, D; Morawski, P; Mountain, R; Mous, I; Muheim, F; Müller, K; Muresan, R; Muryn, B; Muster, B; Mylroie-Smith, J; Naik, P; Nakada, T; Nandakumar, R; Nasteva, I; Needham, M; Neufeld, N; Nguyen, A D; Nguyen-Mau, C; Nicol, M; Niess, V; Nikitin, N; Nikodem, T; Nomerotski, A; Novoselov, A; Oblakowska-Mucha, A; Obraztsov, V; Oggero, S; Ogilvy, S; Okhrimenko, O; Oldeman, R; Orlandea, M; Otalora Goicochea, J M; Owen, P; Pal, B K; Palacios, J; Palano, A; Palutan, M; Panman, J; Papanestis, A; Pappagallo, M; Parkes, C; Parkinson, C J; Passaleva, G; Patel, G D; Patel, M; Paterson, S K; Patrick, G N; Patrignani, C; Pavel-Nicorescu, C; Pazos Alvarez, A; Pellegrino, A; Penso, G; Pepe Altarelli, M; Perazzini, S; Perego, D L; Perez Trigo, E; Pérez-Calero Yzquierdo, A; Perret, P; Perrin-Terrin, M; Pessina, G; Petrolini, A; Phan, A; Picatoste Olloqui, E; Pie Valls, B; Pietrzyk, B; Pilař, T; Pinci, D; Plackett, R; Playfer, S; Plo Casasus, M; Polok, G; Poluektov, A; Polycarpo, E; Popov, D; Popovici, B; Potterat, C; Powell, A; Prisciandaro, J; Pugatch, V; Puig Navarro, A; Qian, W; Rademacker, J H; Rakotomiaramanana, B; Rangel, M S; Raniuk, I; Raven, G; Redford, S; Reid, M M; dos Reis, A C; Ricciardi, S; Richards, A; Rinnert, K; Roa Romero, D A; Robbe, P; Rodrigues, E; Rodrigues, F; Rodriguez Perez, P; Rogers, G J; Roiser, S; Romanovsky, V; Rosello, M; Rouvinet, J; Ruf, T; Ruiz, H; Sabatino, G; Saborido Silva, J J; Sagidova, N; Sail, P; Saitta, B; Salzmann, C; Sannino, M; Santacesaria, R; Santamarina Rios, C; Santinelli, R; Santovetti, E; Sapunov, M; Sarti, A; Satriano, C; Satta, A; Savrie, M; Savrina, D; Schaack, P; Schiller, M; Schindler, H; Schleich, S; Schlupp, M; Schmelling, M; Schmidt, B; Schneider, O; Schopper, A; Schune, M-H; Schwemmer, R; Sciascia, B; Sciubba, A; Seco, M; Semennikov, A; Senderowska, K; Sepp, I; Serra, N; Serrano, J; Seyfert, P; Shapkin, M; Shapoval, I; Shatalov, P; Shcheglov, Y; Shears, T; Shekhtman, L; Shevchenko, O; Shevchenko, V; Shires, A; Silva Coutinho, R; Skwarnicki, T; Smith, N A; Smith, E; Sobczak, K; Soler, F J P; Solomin, A; Soomro, F; Souza De Paula, B; Spaan, B; Sparkes, A; Spradlin, P; Stagni, F; Stahl, S; Steinkamp, O; Stoica, S; Stone, S; Storaci, B; Straticiuc, M; Straumann, U; Subbiah, V K; Swientek, S; Szczekowski, M; Szczypka, P; Szumlak, T; T'Jampens, S; Teodorescu, E; Teubert, F; Thomas, C; Thomas, E; van Tilburg, J; Tisserand, V; Tobin, M; Tolk, S; Topp-Joergensen, S; Torr, N; Tournefier, E; Tourneur, S; Tran, M T; Tsaregorodtsev, A; Tuning, N; Ubeda Garcia, M; Ukleja, A; Uwer, U; Vagnoni, V; Valenti, G; Vazquez Gomez, R; Vazquez Regueiro, P; Vecchi, S; Velthuis, J J; Veltri, M; Viaud, B; Videau, I; Vieira, D; Vilasis-Cardona, X; Visniakov, J; Vollhardt, A; Volyanskyy, D; Voong, D; Vorobyev, A; Vorobyev, V; Voss, H; Waldi, R; Wandernoth, S; Wang, J; Ward, D R; Watson, N K; Webber, A D; Websdale, D; Whitehead, M; Wiedner, D; Wiggers, L; Wilkinson, G; Williams, M P; Williams, M; Wilson, F F; Wishahi, J; Witek, M; Witzeling, W; Wotton, S A; Wyllie, K; Xie, Y; Xing, F; Xing, Z; Yang, Z; Young, R; Yushchenko, O; Zangoli, M; Zavertyaev, M; Zhang, F; Zhang, L; Zhang, W C; Zhang, Y; Zhelezov, A; Zhong, L; Zvyagin, A

    2012-01-01

    A study of $B^{\\pm} \\to J\\!/\\!\\psi\\, \\pi^{\\pm}$ and $B^{\\pm} \\to \\psi(2S) \\pi^{\\pm}$ decays is performed with data corresponding to $0.37\\,{\\rm fb}^{-1}$ of proton-proton collisions at $\\sqrt{s}=7\\,\\mathrm{Te\\kern -0.1em V}$. Their branching fractions are found to be \\begin{eqnarray*} \\mathcal{B}(B^{\\pm} \\to J\\!/\\!\\psi\\, \\pi^{\\pm}) &=& (3.88 \\pm 0.11 \\pm 0.15) \\times 10^{-5}\\ {\\rm and}\\\\ \\mathcal{B}(B^{\\pm} \\to \\psi(2S) \\pi^{\\pm}) &=& (2.52 \\pm 0.26 \\pm 0.15) \\times 10^{-5}, \\end{eqnarray*} where the first uncertainty is related to the statistical size of the sample and the second quantifies systematic effects. The measured $C\\!P$ asymmetries in these modes are \\begin{eqnarray*} A_{CP}^{J\\!/\\!\\psi\\, \\pi} &=& 0.005 \\pm 0.027 \\pm 0.011\\ {\\rm and} \\\\ A_{CP}^{\\psi(2S) \\pi} &=& 0.048 \\pm 0.090 \\pm 0.011 \\end{eqnarray*} with no evidence of direct $C\\!P$ violation seen.

  3. PM2.5 and PM10 Emission from agricultural soils by wind erosion

    Science.gov (United States)

    Soil tillage and wind erosion are a major source of particulate matter less than 2.5 and 10 µm (PM2.5 and PM10) emission from cultivated soil. Fifteen cultivated soils collected from 5 states were tested as crushed (<2.0 mm) and uncrushed (natural aggregation) at 8, 10, and 13 m s-1 wind velocity in...

  4. Remote sensing of PM2.5 during cloudy and nighttime periods using ceilometer backscatter

    Science.gov (United States)

    Li, Siwei; Joseph, Everette; Min, Qilong; Yin, Bangsheng; Sakai, Ricardo; Payne, Megan K.

    2017-06-01

    Monitoring PM2.5 (particulate matter with aerodynamic diameter d ≤ 2.5 µm) mass concentration has become of more importance recently because of the negative impacts of fine particles on human health. However, monitoring PM2.5 during cloudy and nighttime periods is difficult since nearly all the passive instruments used for aerosol remote sensing are not able to measure aerosol optical depth (AOD) under either cloudy or nighttime conditions. In this study, an empirical model based on the regression between PM2.5 and the near-surface backscatter measured by ceilometers was developed and tested using 6 years of data (2006 to 2011) from the Howard University Beltsville Campus (HUBC) site. The empirical model can explain ˜ 56, ˜ 34 and ˜ 42 % of the variability in the hourly average PM2.5 during daytime clear, daytime cloudy and nighttime periods, respectively. Meteorological conditions and seasons were found to influence the relationship between PM2.5 mass concentration and the surface backscatter. Overall the model can explain ˜ 48 % of the variability in the hourly average PM2.5 at the HUBC site when considering the seasonal variation. The model also was tested using 4 years of data (2012 to 2015) from the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site, which was geographically and climatologically different from the HUBC site. The results show that the empirical model can explain ˜ 66 and ˜ 82 % of the variability in the daily average PM2.5 at the ARM SGP site and HUBC site, respectively. The findings of this study illustrate the strong need for ceilometer data in air quality monitoring under cloudy and nighttime conditions. Since ceilometers are used broadly over the world, they may provide an important supplemental source of information of aerosols to determine surface PM2.5 concentrations.

  5. Hospital indoor PM10/PM2.5 and associated trace elements in Guangzhou, China

    International Nuclear Information System (INIS)

    Wang Xinhua; Bi Xinhui; Sheng Guoying; Fu Jiamo

    2006-01-01

    PM10 and PM2.5 samples were collected in the indoor environments of four hospitals and their adjacent outdoor environments in Guangzhou, China during the summertime. The concentrations of 18 target elements in particles were also quantified. The results showed that indoor PM2.5 levels with an average of 99 μg m -3 were significantly higher than outdoor PM2.5 standard of 65 μg m -3 recommended by USEPA [United States Environmental Protection Agency. Office of Air and Radiation, Office of Air Quality Planning and Standards, Fact Sheet. EPA's Revised Particulate Matter Standards, 17, July 1997] and PM2.5 constituted a large fraction of indoor respirable particles (PM10) by an average of 78% in four hospitals. High correlation between PM2.5 and PM10 (R 2 of 0.87 for indoors and 0.90 for outdoors) suggested that PM2.5 and PM10 came from similar particulate emission sources. The indoor particulate levels were correlated with the corresponding outdoors (R 2 of 0.78 for PM2.5 and 0.67 for PM10), demonstrating that outdoor infiltration could lead to direct transportation into indoors. In addition to outdoor infiltration, human activities and ventilation types could also influence indoor particulate levels in four hospitals. Total target elements accounted for 3.18-5.56% of PM2.5 and 4.38-9.20% of PM10 by mass, respectively. Na, Al, Ca, Fe, Mg, Mn and Ti were found in the coarse particles, while K, V, Cr, Ni, Cu, Zn, Cd, Sn, Pb, As and Se existed more in the fine particles. The average indoor concentrations of total elements were lower than those measured outdoors, suggesting that indoor elements originated mainly from outdoor emission sources. Enrichment factors (EF) for trace element were calculated to show that elements of anthropogenic origins (Zn, Pb, As, Se, V, Ni, Cu and Cd) were highly enriched with respect to crustal composition (Al, Fe, Ca, Ti and Mn). Factor analysis was used to identify possible pollution source-types, namely street dust, road traffic and

  6. Hospital indoor PM10/PM2.5 and associated trace elements in Guangzhou, China.

    Science.gov (United States)

    Wang, Xinhua; Bi, Xinhui; Sheng, Guoying; Fu, Jiamo

    2006-07-31

    PM10 and PM2.5 samples were collected in the indoor environments of four hospitals and their adjacent outdoor environments in Guangzhou, China during the summertime. The concentrations of 18 target elements in particles were also quantified. The results showed that indoor PM2.5 levels with an average of 99 microg m(-3) were significantly higher than outdoor PM2.5 standard of 65 microg m(-3) recommended by USEPA [United States Environmental Protection Agency. Office of Air and Radiation, Office of Air Quality Planning and Standards, Fact Sheet. EPA's Revised Particulate Matter Standards, 17, July 1997] and PM2.5 constituted a large fraction of indoor respirable particles (PM10) by an average of 78% in four hospitals. High correlation between PM2.5 and PM10 (R(2) of 0.87 for indoors and 0.90 for outdoors) suggested that PM2.5 and PM10 came from similar particulate emission sources. The indoor particulate levels were correlated with the corresponding outdoors (R(2) of 0.78 for PM2.5 and 0.67 for PM10), demonstrating that outdoor infiltration could lead to direct transportation into indoors. In addition to outdoor infiltration, human activities and ventilation types could also influence indoor particulate levels in four hospitals. Total target elements accounted for 3.18-5.56% of PM2.5 and 4.38-9.20% of PM10 by mass, respectively. Na, Al, Ca, Fe, Mg, Mn and Ti were found in the coarse particles, while K, V, Cr, Ni, Cu, Zn, Cd, Sn, Pb, As and Se existed more in the fine particles. The average indoor concentrations of total elements were lower than those measured outdoors, suggesting that indoor elements originated mainly from outdoor emission sources. Enrichment factors (EF) for trace element were calculated to show that elements of anthropogenic origins (Zn, Pb, As, Se, V, Ni, Cu and Cd) were highly enriched with respect to crustal composition (Al, Fe, Ca, Ti and Mn). Factor analysis was used to identify possible pollution source-types, namely street dust, road traffic

  7. Numerical Simulation of Two-branch Hot Gas Mixing at Reactor Outlet of HTR-PM

    International Nuclear Information System (INIS)

    Hao Pengefei; Zhou Yangping; Li Fu; Shi Lei; He Heng

    2014-01-01

    A series of two-branch model experiment has been finished to investigate the thermal mixing efficiency of the HTR-PM reactor outlet. This paper introduces the numerical simulation on the design of thermal mixing structure of HTR-PM and the test facility with Fluent software. The profiles of temperature, pressure and velocity in the mixing structure design and the test facility are discussed by comparing with the model experiment results. The numerical simulation results of the test facility have good agreement to the experiment results. In addition, the thermal-fluid characters obtained by numerical simulation show the thermal mixing structure of HTR-PM has similarity with the test facility. Finally, it is concluded that the thermal mixing design at HTR-PM reactor outlet can fulfilled the requirements for high thermal mixing efficiency and appropriate pressure drop. (author)

  8. The Interaction between Ambient PM10 and NO₂ on Mortality in Guangzhou, China.

    Science.gov (United States)

    Gu, Yuzhou; Lin, Hualiang; Liu, Tao; Xiao, Jianpeng; Zeng, Weilin; Li, Zhihao; Lv, Xiaojuan; Ma, Wenjun

    2017-11-13

    Air pollution is now a significant environmental issue in China. To better understand the health impacts of ambient air pollution, this study investigated the potential interaction between PM 10 and NO₂ on mortality in Guangzhou, China. Time series data of daily non-accidental mortality and concentrations of PM 10 and NO₂ from 2006 to 2010 were collected. Based on generalized additive model, we developed two models (bivariate model and stratified model) to explore the interaction both qualitatively and quantitatively. At lag of 0-2 days, greater interactive effects between PM 10 and NO₂ were presented in the graphs. Positive modified effects were also found between the two pollutants on total non-accidental death and cardiovascular death. When the NO₂ concentration was at a high level (>76.14 μg/m³), PM 10 showed the greatest excess relative risk percentage (ERR%) for total non-accidental mortality (0.46, 95% CI: 0.13-0.79) and cardiovascular disease mortality (0.61, 95% CI: 0.06-1.16) for each 10 μg/m³ increase. During the period of high PM 10 concentration (>89.82 μg/m³), NO₂ demonstrated its strongest effect for total non-accidental mortality (ERR%: 0.92, 95% CI: 0.42-1.42) and cardiovascular disease mortality (ERR%: 1.20, 95% CI: 0.38-2.03). Our results suggest a positive interaction between PM 10 and NO₂ on non-accidental mortality in Guangzhou.

  9. Correction of SiPM temperature dependencies

    International Nuclear Information System (INIS)

    Kaplan, A.

    2009-01-01

    The performance of a high granular analogue hadronic calorimeter (AHCAL) using scintillator tiles with built-in Silicon Photomultiplier (SiPM) readout is reported. A muon beam is used for the minimum ionizing particle (MIP) based calibration of the single cells. The calibration chain including corrections for the non-linearity of the SiPM is presented. The voltage and temperature dependencies of the SiPM signal have been investigated using the versatile LED system of the AHCAL. Monitoring and correction methods are discussed. Measurements from the operation 2006 and 2007 at the CERN SPS test beam and data provided by the Institute for Theoretical and Experimental Physics (ITEP) in Moscow are compared.

  10. High concentrations of heavy metals in PM from ceramic factories of Southern Spain

    Science.gov (United States)

    Sánchez de la Campa, Ana M.; de la Rosa, Jesús D.; González-Castanedo, Yolanda; Fernández-Camacho, Rocío; Alastuey, Andrés; Querol, Xavier; Pio, Casimiro

    2010-06-01

    In this study, physicochemical characterization of Atmospheric Particulate Matter (PM) was performed in an urban-industrial site background (Bailén, Southern Spain), highly influenced by the impact of emission plumes from ceramic factories. This area is considered one of the towns with the highest PM 10 levels and average SO 2 concentration in Spain. A three stages methodology was used: 1) real-time measurements of levels of PM 10 and gaseous pollutants, and sampling of PM; 2) chemical characterization using ICP-MS, ICP-OES, CI and TOT, and source apportionment analysis (receptor modelling) of PM; and 3) chemical characterization of emission plumes derived from representative factories. High ambient air concentrations were found for most major components and trace elements compared with other industrialized towns in Spain. V and Ni are considered fingerprints of PM derived from the emissions of brick factories in this area, and were shown to be of particular interest. This highlights the high V and Ni concentrations in PM 10 (122 ngV/m 3 and 23.4 ngNi/m 3), with Ni exceeding the 2013 annual target value for the European Directive 2004/107/EC (20 ng/m 3). The methodology of this work can be used by Government departments responsible for Environment and Epidemiology in planning control strategies for improving air quality.

  11. Source contributions to PM2.5 and PM10 at an urban background and a street location

    NARCIS (Netherlands)

    Keuken, M. P.; Moerman, M.; Voogt, M.; Blom, M.; Weijers, E. P.; Rockmann, T.; Dusek, U.

    The contribution of regional, urban and traffic sources to PM2.5 and PM10 in an urban area was investigated in this study. The chemical composition of PM2.5 and PM10 was measured over a year at a street location and up- and down-wind of the city of Rotterdam, the Netherlands. The C-14 content in EC

  12. Source contributions to PM2.5 and PM10 at an urban background and a street location

    NARCIS (Netherlands)

    Keuken, M.P.; Moerman, M.M.; Voogt, M.H.

    2013-01-01

    The contribution of regional, urban and traffic sources to PM2.5 and PM10 in an urban area was investigated in this study. The chemical composition of PM2.5 and PM10 was measured over a year at a street location and up- and down-wind of the city of Rotterdam, the Netherlands. The 14C content in EC

  13. Danish emission inventory for particular matter (PM)

    Energy Technology Data Exchange (ETDEWEB)

    Nielsen, M; Winther, M; Illerup, J B; Hjort Mikkelsen, M

    2003-11-01

    The first Danish emission inventory that was reported in 2002 was a provisional-estimate based on data presently available. This report documents methodology, emission factors and references used for an improved Danish emission inventory for particulate matter. Further results of the improved emission inventory for the year 2000 are shown. The particulate matter emission inventory includes TSP, PM,, and PM, The report covers emission inventories for transport and stationary combustion. An appendix covering emissions from agriculture is also included. For the transport sector, both exhaust and non-exhaust emission such as tyre and break wear and road abrasion are included. (au)

  14. LHCb: Observation of CP violation in $B^{\\pm} \\to D^0 K^{\\pm}$ decays at LHCb

    CERN Multimedia

    Johnson, Daniel

    2012-01-01

    An analysis of $B^\\pm \\to DK^\\pm$ and $B^\\pm \\to D\\pi^\\pm$ decays is presented where the D meson is reconstructed in the two-body final states: $K^\\pm \\pi^\\mp$, $K^+K^−$ and $\\pi^+\\pi^-$. Using 1.0 fb$^{−1}$ of LHCb data, measurements of several observables are made including the first observation of the suppressed mode $B^\\pm \\to [\\pi^\\pm K^\\mp] DK^\\pm$. CP violation in $B^\\pm \\to DK^\\pm$ decays is observed with $5.8\\sigma$ significance. We also comment on the prospects for similar measurements using different final states.

  15. Research and application of a novel hybrid decomposition-ensemble learning paradigm with error correction for daily PM10 forecasting

    Science.gov (United States)

    Luo, Hongyuan; Wang, Deyun; Yue, Chenqiang; Liu, Yanling; Guo, Haixiang

    2018-03-01

    In this paper, a hybrid decomposition-ensemble learning paradigm combining error correction is proposed for improving the forecast accuracy of daily PM10 concentration. The proposed learning paradigm is consisted of the following two sub-models: (1) PM10 concentration forecasting model; (2) error correction model. In the proposed model, fast ensemble empirical mode decomposition (FEEMD) and variational mode decomposition (VMD) are applied to disassemble original PM10 concentration series and error sequence, respectively. The extreme learning machine (ELM) model optimized by cuckoo search (CS) algorithm is utilized to forecast the components generated by FEEMD and VMD. In order to prove the effectiveness and accuracy of the proposed model, two real-world PM10 concentration series respectively collected from Beijing and Harbin located in China are adopted to conduct the empirical study. The results show that the proposed model performs remarkably better than all other considered models without error correction, which indicates the superior performance of the proposed model.

  16. Source identification of ambient PM2.5 during summer inhalation exposure studies in Detroit, MI

    Energy Technology Data Exchange (ETDEWEB)

    Morishita, M.; Keeler, G.J.; Wagner, J.G.; Harkema, J.R. [University of Michigan, Ann Arbor, MI (United States). Air Quality Laboratory

    2006-07-15

    Particulate air pollution is associated with cardiopulmonary morbidity and mortality in heavily populated urban centers of the United States. Because ambient fine particulate matter (aerodynamic diameter {<=} 2.5 {mu}m; PM2.5) is a complex mixture resulting from multiple sources and variable atmospheric conditions, it is difficult to identify specific components of PM2.5 that are responsible for adverse health effects. During four consecutive summers from 2000 to 2003 we characterized the ambient gaseous and PM2.5 air quality in an urban southwest Detroit community where childhood asthma hospitalization rates are more than twice the statewide average. Both integrated and continuous PM measurements together with gaseous air pollution measurements were performed using a mobile air research facility, AirCARE1, in which concurrent toxicological studies were being conducted. Chemical and physical characterizations of PM2.5 as well as receptor modeling using positive matrix factorization (PMF) were completed. Results from PMF indicated that six major sources contributed to the observed ambient PM2.5 mass during the summer months. Primary sources included (1) coal combustion/secondary sulfate aerosol, (2) motor vehicle/urban road dust, (3) municipal waste incinerators, (4) oil combustion/refineries, (5) sewage sludge incinerators, and (6) iron/steel manufacturing. Although the contribution of the coal/secondary sulfate aerosol source was greater than other factors, increased levels of urban PM2.5 from local combustion sources were also observed. In addition to characterization of ambient PM2.5 and their sources in southwest Detroit, this paper discusses possible associations of ambient PM2.5 from local combustion sources, specifically incinerator and refinery emissions and the observed adverse health effects during the inhalation exposure campaigns.

  17. Outdoor particulate matter (PM) and associated cardiovascular diseases in the Middle East.

    Science.gov (United States)

    Nasser, Zeina; Salameh, Pascale; Nasser, Wissam; Abou Abbas, Linda; Elias, Elias; Leveque, Alain

    2015-01-01

    Air pollution is a widespread environmental concern. Considerable epidemiological evidence indicates air pollution, particularly particulate matter (PM), as a major risk factor for cardiovascular diseases (CVD) in the developed countries. The main objective of our review is to assess the levels and sources of PM across the Middle East area and to search evidence for the relationship between PM exposure and CVD. An extensive review of the published literature pertaining to the subject (2000-2013) was conducted using PubMed, Medline and Google Scholar databases. We reveal that low utilization of public transport, ageing vehicle fleet and the increasing number of personal cars in the developing countries all contribute to the traffic congestion and aggravate the pollution problem. The annual average values of PM pollutants in the Middle East region are much higher than the World Health Organization 2006 guidelines (PM2.5 = 10 μg/m(3), PM10 = 20 μg/m(3)). We uncover evidence on the association between PM and CVD in 4 Middle East countries: Iran, Kingdom of Saudi Arabia, Qatar and the United Arab Emirates. The findings are in light of the international figures. Ambient PM pollution is considered a potential risk factor for platelet activation and atherosclerosis and has been found to be linked with an increased risk for mortality and hospital admissions due to CVD. This review highlights the importance of developing a strategy to improve air quality and reduce outdoor air pollution in the developing countries, particularly in the Middle East. Future studies should weigh the potential impact of PM on the overall burden of cardiac diseases. This work is available in Open Access model and licensed under a CC BY-NC 3.0 PL license.

  18. Predicting PM10 concentration in Seoul metropolitan subway stations using artificial neural network (ANN).

    Science.gov (United States)

    Park, Sechan; Kim, Minjeong; Kim, Minhae; Namgung, Hyeong-Gyu; Kim, Ki-Tae; Cho, Kyung Hwa; Kwon, Soon-Bark

    2018-01-05

    The indoor air quality of subway systems can significantly affect the health of passengers since these systems are widely used for short-distance transit in metropolitan urban areas in many countries. The particles generated by abrasion during subway operations and the vehicle-emitted pollutants flowing in from the street in particular affect the air quality in underground subway stations. Thus the continuous monitoring of particulate matter (PM) in underground station is important to evaluate the exposure level of PM to passengers. However, it is difficult to obtain indoor PM data because the measurement systems are expensive and difficult to install and operate for significant periods of time in spaces crowded with people. In this study, we predicted the indoor PM concentration using the information of outdoor PM, the number of subway trains running, and information on ventilation operation by the artificial neural network (ANN) model. As well, we investigated the relationship between ANN's performance and the depth of underground subway station. ANN model showed a high correlation between the predicted and actual measured values and it was able to predict 67∼80% of PM at 6 subway station. In addition, we found that platform shape and depth influenced the model performance. Copyright © 2017 Elsevier B.V. All rights reserved.

  19. PM1-Alpha ELISA: the assay of choice for the detection of anti-PM/Scl autoantibodies?

    Science.gov (United States)

    Mahler, Michael; Fritzler, Marvin J

    2009-03-01

    A characteristic serological feature of patients suffering from the overlap polymyositis and scleroderma (PM/Scl) syndrome are antibodies to the human counterpart of the yeast exosome referred to as the PM/Scl complex. Historically, the detection of anti-PM/Scl antibodies was laborious and relied largely on indirect immunofluorescence and immunodiffusion techniques. In 1992 the major autoantigen PM/Scl-100 was identified and cloned. Subsequently, the major epitopes were mapped and one of these, termed PM1-Alpha, became the antigen for a novel ELISA exhibiting high sensitivity and specificity for the detection of anti-PM/Scl antibodies. Comparative studies with other methods using other PM/Scl autoantigens have shown that the PM1-Alpha ELISA has higher sensitivity and specificity than assays that employed recombinant PM/Scl-75c and PM/Scl-100. Anti-PM1-Alpha antibodies were identified in 55.0% of sera from PM/Scl overlap syndrome patients, but were also seen in 7.9% of SSc and in 7.5% of PM patients. The frequency in other systemic autoimmune diseases and in infectious diseases was significant lower. In summary, the data derived from individual studies suggest that PM1-Alpha may become the "gold standard" for the detection of anti-PM/Scl antibodies.

  20. Decay of Pm151

    DEFF Research Database (Denmark)

    Nielsen, H. Loft; Bertelsen, U.; Ewan, G. T.

    1964-01-01

    The disintegration scheme of Pm151 has been studied by conversion electron-gamma and beta-gamma coincidence techniques using a six-gap β-ray spectrometer. The internal conversion electron spectrum has also been studied at 0.05% resolution in a 180° magnetic spectrograph. Fifty-seven transitions...

  1. High spin states of 141Pm

    Science.gov (United States)

    Bhattacharyya, Sarmishtha; Chanda, Somen; Bhattacharjee, Tumpa; Basu, Swapan Kumar; Bhowmik, R. K.; Muralithar, S.; Singh, R. P.; Ghugre, S. S.

    2004-01-01

    The high spin states in the N=80 odd- A141Pm nucleus have been investigated by in-beam γ-spectroscopic techniques following the reaction 133Cs( 12C, 4n) 141Pm at E=65 MeV using a modest γ detector array, consisting of seven Compton-suppressed high purity germanium detectors and a multiplicity ball of 14 bismuth germanate elements. Thirty new γ rays have been assigned to 141Pm on the basis of γ-ray singles and γγ-coincidence data. The level scheme of 141Pm has been extended upto an excitation energy of 5.2 MeV and spin {35}/{2}ℏ and 16 new levels have been proposed. Spin-parity assignments for most of the newly proposed levels have been made on the basis of the deduced directional correlation orientation ratios for strong transitions. The meanlives of a few excited states have been determined from the pulsed beam- γγ coincidence data using the generalised centroid-shift method. The level structure is discussed in the light of known systematics of neighbouring N=80 isotonic nuclei.

  2. Preparation of 147Pm ceramic source core

    International Nuclear Information System (INIS)

    Mielcarski, M.

    1989-01-01

    Preparation of ceramic pellets containing fixed promethium-147 is described. Incorporation rate of 147 Pm into the ceramic material was determined. The leachability and vaporization of promethium from the obtained ceramics was investigated. The ceramic pellets prepared by the described procedure, mounted in special holders, can be applied as point sources in beta backscatter thickness gauges. (author)

  3. Burden of mortality and years of life lost due to ambient PM10 pollution in Wuhan, China.

    Science.gov (United States)

    Zhang, Yunquan; Peng, Minjin; Yu, Chuanhua; Zhang, Lan

    2017-11-01

    Ambient particulate matter (PM) has been mainly linked with mortality and morbidity when assessing PM-associated health effects. Up-to-date epidemiologic evidence is very sparse regarding the relation between PM and years of life lost (YLL). The present study aimed to estimate the burden of YLL and mortality due to ambient PM pollution. Individual records of all registered deaths and daily data on PM 10 and meteorology during 2009-2012 were obtained in Wuhan, central China. Using a time-series study design, we applied generalized additive model to assess the short-term association of 10-μg/m 3 increase in PM 10 with daily YLL and mortality, adjusting for long-term trend and seasonality, mean temperature, relative humidity, public holiday, and day of the week. A linear-no-threshold dose-response association was observed between daily ambient PM 10 and mortality outcomes. PM 10 pollution along lag 0-1 days was found to be mostly strongly associated with mortality and YLL. The effects of PM 10 on cause-specific mortality and YLL showed generally similar seasonal patterns, with stronger associations consistently occurring in winter and/or autumn. Compared with males and younger persons, females and the elderly suffered more significantly from both increased YLL and mortality due to ambient PM 10 pollution. Stratified analyses by education level (0-6 and 7 + years) demonstrated great mortality impact on both subgroups, whereas only low-educated persons were strongly affected by PM 10 -associated burden of YLL. Our study confirmed that short-term PM 10 exposure was linearly associated with significant increases in both mortality incidence and years of life lost. Given the non-threshold adverse effects on mortality burden, the on-going efforts to reduce particulate air pollution would substantially benefit public health in China. Copyright © 2017 Elsevier Ltd. All rights reserved.

  4. Concentration distribution of NO2, PM10 and PM2,5 in severe pollution episodes in Oslo, Drammen, Bergen and Trondheim

    International Nuclear Information System (INIS)

    Sloerdal, Leiv Haavard; Toennesen, Dag

    1999-04-01

    Based on hourly model calculations of NO 2 , PM 1 0 and PM 2 ,5 through a 6 months winter season in the cities of Oslo, Drammen, Bergen and Trondheim, the 10 most severe pollution episodes have been analysed. Concentration distributions, calculated as the average of these episodes have been combined with the population distribution in order to reveal the exposure levels in such episodes. The model calculations have only been performed for the city background, i.e. on a km 2 grid system. (author)

  5. LHCb: The search for $D^0\\rightarrow e^\\pm \\mu^\\mp$

    CERN Multimedia

    Bird, T

    2013-01-01

    In 2011 and 2012 LHCb collected a total of $3\\,\\mathrm{fb}^{-1}$ of $pp$-collisions, making LHCb the perfect place to look for rare charm decays. The lepton flavour violating decay $D^0\\rightarrow{}e^\\pm\\mu^\\mp$ is forbidden in the Standard Model and so it's detection would be a clear sign of new physics. In this poster an overview of the method used measure $\\mathcal{B}\\left(D^0\\rightarrow{}e^\\pm\\mu^\\mp\\right)$ with respect to $\\mathcal{B}\\left(D^0\\rightarrow{}\\pi^\\pm\\pi^\\mp\\right)$ is presented. It is estimated that this analysis will be able to set a limit on $\\mathcal{B}\\left(D^0\\rightarrow{}e^\\pm\\mu^\\mp\\right) < 10^{-8}$ at a $90\\%$ confidence level.

  6. What Causes Haze Pollution? An Empirical Study of PM2.5 Concentrations in Chinese Cities

    Directory of Open Access Journals (Sweden)

    Jiannan Wu

    2016-01-01

    Full Text Available In recent years, many areas of China have suffered from serious haze pollution, which greatly affects human health and daily life. It is of policy importance to understand the factors that influence the spatial concentration of PM2.5. Based on data from 74 cities with PM2.5 monitoring stations in 2013 and 2014, this study presents the overall haze situation in China and explores the determinants of PM2.5 using a random-effects model, as well as a set of OLS regressions. The results indicate that PM2.5 is significantly correlated with the industrial proportion, the number of motor vehicles, and household gas consumption, while public financial expenditure on energy saving and environmental protection does not show statistically significant effects. The analysis implies that China should adjust its economic structure and optimizes environmental governance to effectively respond to haze pollution.

  7. NodePM: a remote monitoring alert system for energy consumption using probabilistic techniques.

    Science.gov (United States)

    Filho, Geraldo P R; Ueyama, Jó; Villas, Leandro A; Pinto, Alex R; Gonçalves, Vinícius P; Pessin, Gustavo; Pazzi, Richard W; Braun, Torsten

    2014-01-06

    In this paper, we propose an intelligent method, named the Novelty Detection Power Meter (NodePM), to detect novelties in electronic equipment monitored by a smart grid. Considering the entropy of each device monitored, which is calculated based on a Markov chain model, the proposed method identifies novelties through a machine learning algorithm. To this end, the NodePM is integrated into a platform for the remote monitoring of energy consumption, which consists of a wireless sensors network (WSN). It thus should be stressed that the experiments were conducted in real environments different from many related works, which are evaluated in simulated environments. In this sense, the results show that the NodePM reduces by 13.7% the power consumption of the equipment we monitored. In addition, the NodePM provides better efficiency to detect novelties when compared to an approach from the literature, surpassing it in different scenarios in all evaluations that were carried out.

  8. Experiment study on thermal mixing performance of HTR-PM reactor outlet

    Energy Technology Data Exchange (ETDEWEB)

    Zhou, Yangping, E-mail: zhouyp@mail.tsinghua.edu.cn [Institute of Nuclear and New Energy Technology, Collaborative Innovation Center of Advanced Nuclear Energy Technology, the Key Laboratory of Advanced Reactor Engineering and Safety, Ministry of Education, Tsinghua University, Beijing 100084 (China); Hao, Pengfei [School of Aerospace, Tsinghua University, Beijing 100084 (China); Li, Fu; Shi, Lei [Institute of Nuclear and New Energy Technology, Collaborative Innovation Center of Advanced Nuclear Energy Technology, the Key Laboratory of Advanced Reactor Engineering and Safety, Ministry of Education, Tsinghua University, Beijing 100084 (China); He, Feng [School of Aerospace, Tsinghua University, Beijing 100084 (China); Dong, Yujie; Zhang, Zuoyi [Institute of Nuclear and New Energy Technology, Collaborative Innovation Center of Advanced Nuclear Energy Technology, the Key Laboratory of Advanced Reactor Engineering and Safety, Ministry of Education, Tsinghua University, Beijing 100084 (China)

    2016-09-15

    A model experiment is proposed to investigate the thermal mixing performance of HTR-PM reactor outlet. The design of the test facility is introduced, which is set at a scale of 1:2.5 comparing with the design of thermal mixing structure at HTR-PM reactor outlet. The test facility using air as its flow media includes inlet pipe system, electric heaters, main mixing structure, hot gas duct, exhaust pipe system and I&C system. Experiments are conducted on the test facility and the values of thermal-fluid parameters are collected and analyzed, which include the temperature, pressure and velocity of the flow as well as the temperature of the tube wall. The analysis results show the mixing efficiency of the test facility is higher than that required by the steam generator of HTR-PM, which indicates that the thermal mixing structure of HTR-PM fulfills its design requirement.

  9. Evaluation of impacts of trees on PM2.5 dispersion in urban streets

    Science.gov (United States)

    Jin, Sijia; Guo, Jiankang; Wheeler, Stephen; Kan, Liyan; Che, Shengquan

    2014-12-01

    Reducing airborne particulate matter (PM), especially PM2.5 (PM with aerodynamic diameters of 2.5 μm or less), in urban street canyons is critical to the health of central city population. Tree-planting in urban street canyons is a double-edged sword, providing landscape benefits while inevitably resulting in PM2.5 concentrating at street level, thus showing negative environmental effects. Thereby, it is necessary to quantify the impact of trees on PM2.5 dispersion and obtain the optimum structure of street trees for minimizing the PM2.5 concentration in street canyons. However, most of the previous findings in this field were derived from wind tunnel or numerical simulation rather than on-site measuring data. In this study, a seasonal investigation was performed in six typical street canyons in the residential area of central Shanghai, which has been suffering from haze pollution while having large numbers of green streets. We monitored and measured PM2.5 concentrations at five heights, structural parameters of street trees and weather. For tree-free street canyons, declining PM2.5 concentrations were found with increasing height. However, in presence of trees the reduction rate of PM2.5 concentrations was less pronounced, and for some cases, the concentrations even increased at the top of street canyons, indicating tree canopies are trapping PM2.5. To quantify the decrease of PM2.5 reduction rate, we developed the attenuation coefficient of PM2.5 (PMAC). The wind speed was significantly lower in street canyons with trees than in tree-free ones. A mixed-effects model indicated that canopy density (CD), leaf area index (LAI), rate of change of wind speed were the most significant predictors influencing PMAC. Further regression analysis showed that in order to balance both environmental and landscape benefits of green streets, the optimum range of CD and LAI was 50%-60% and 1.5-2.0 respectively. We concluded by suggesting an optimized tree-planting pattern and

  10. The powdery mildew resistance gene Pm8 derived from rye is suppressed by its wheat ortholog Pm3.

    Science.gov (United States)

    Hurni, Severine; Brunner, Susanne; Stirnweis, Daniel; Herren, Gerhard; Peditto, David; McIntosh, Robert A; Keller, Beat

    2014-09-01

    The powdery mildew resistance gene Pm8 derived from rye is located on a 1BL.1RS chromosome translocation in wheat. However, some wheat lines with this translocation do not show resistance to isolates of the wheat powdery mildew pathogen avirulent to Pm8 due to an unknown genetically dominant suppression mechanism. Here we show that lines with suppressed Pm8 activity contain an intact and expressed Pm8 gene. Therefore, the absence of Pm8 function in certain 1BL.1RS-containing wheat lines is not the result of gene loss or mutation but is based on suppression. The wheat gene Pm3, an ortholog of rye Pm8, suppressed Pm8-mediated powdery mildew resistance in lines containing Pm8 in a transient single-cell expression assay. This result was further confirmed in transgenic lines with combined Pm8 and Pm3 transgenes. Expression analysis revealed that suppression is not the result of gene silencing, either in wheat 1BL.1RS translocation lines carrying Pm8 or in transgenic genotypes with both Pm8 and Pm3 alleles. In addition, a similar abundance of the PM8 and PM3 proteins in single or double homozygous transgenic lines suggested that a post-translational mechanism is involved in suppression of Pm8. Co-expression of Pm8 and Pm3 genes in Nicotiana benthamiana leaves followed by co-immunoprecipitation analysis showed that the two proteins interact. Therefore, the formation of a heteromeric protein complex might result in inefficient or absent signal transmission for the defense reaction. These data provide a molecular explanation for the suppression of resistance genes in certain genetic backgrounds and suggest ways to circumvent it in future plant breeding. © 2014 The Authors The Plant Journal © 2014 John Wiley & Sons Ltd.

  11. Anomalous elevated radiocarbon measurements of PM{sub 2.5}

    Energy Technology Data Exchange (ETDEWEB)

    Buchholz, Bruce A., E-mail: buchholz2@llnl.gov [Center for Accelerator Mass Spectrometry, Mail Stop L-397, Lawrence Livermore National Laboratory, P.O. Box 808 Livermore, CA 94551 (United States); Fallon, Stewart J. [Center for Accelerator Mass Spectrometry, Mail Stop L-397, Lawrence Livermore National Laboratory, P.O. Box 808 Livermore, CA 94551 (United States); Radiocarbon Dating Laboratory, Research School of Earth Sciences, Australian National University, Canberra, ACT 0200 (Australia); Zermeno, Paula; Bench, Graham [Center for Accelerator Mass Spectrometry, Mail Stop L-397, Lawrence Livermore National Laboratory, P.O. Box 808 Livermore, CA 94551 (United States); Schichtel, Bret A. [Cooperative Institute for Research in the Atmosphere, Colorado State University, 1375 Campus Delivery, Fort Collins, CO 80523 (United States)

    2013-01-15

    Two-component models are often used to determine the contributions made by fossil fuel and natural sources of carbon in airborne particulate matter (PM). The models reduce thousands of actual sources to two end members based on isotopic signature. Combustion of fossil fuels produces PM free of carbon-14 ({sup 14}C). Wood or charcoal smoke, restaurant fryer emissions, and natural emissions from plants produce PM with the contemporary concentration of {sup 14}C approximately 1.2 Multiplication-Sign 10{sup -1214}C/C. Such data can be used to estimate the relative contributions of fossil fuels and biogenic aerosols to the total aerosol loading and radiocarbon analysis is becoming a popular source apportionment method. Emissions from incinerators combusting medical or biological wastes containing tracer {sup 14}C can skew the {sup 14}C/C ratio of PM, however, so critical analysis of sampling sites for possible sources of elevated PM needs to be completed prior to embarking on sampling campaigns. Results are presented for two ambient monitoring sites in different areas of the United States where {sup 14}C contamination is apparent. Our experience suggests that such contamination is uncommon but is also not rare ({approx}10%) for PM sampling sites.

  12. Seasonal variation, risk assessment and source estimation of PM 10 and PM10-bound PAHs in the ambient air of Chiang Mai and Lamphun, Thailand.

    Science.gov (United States)

    Pengchai, Petch; Chantara, Somporn; Sopajaree, Khajornsak; Wangkarn, Sunanta; Tengcharoenkul, Urai; Rayanakorn, Mongkon

    2009-07-01

    /APCS) model and multiple regression analysis were applied to the PM10 and its constituents data. The results pointed to the vegetative burning as the largest PM10 contributor in Chiang Mai and Lamphun ambient air. Vegetative burning, natural gas burning & coke ovens, and secondary particle accounted for 46-82%, 12-49%, and 3-19% of the PM10 concentrations, respectively. However, natural gas burning & coke ovens as well as vehicle exhaust also deserved careful attention due to their large contributions to PAHs concentration. In the wet season and transition periods, 42-60% of the total PAHs concentrations originated from vehicle exhaust while 16-37% and 14-38% of them were apportioned to natural gas burning & coke ovens and vegetative burning, respectively. In the dry period, natural gas burning & coke ovens, vehicle exhaust, and vegetative burning accounted for 47-59%, 20-25%, and 19-28% of total PAHs concentrations. The close agreement between the measured and predicted concentrations data (R(2) > 0.8) assured enough capability of PCA/APCS receptor model to be used for the PM10 and PAHs source apportionment.

  13. Real-Time and Seamless Monitoring of Ground-Level PM2.5 Using Satellite Remote Sensing

    Science.gov (United States)

    Li, Tongwen; Zhang, Chengyue; Shen, Huanfeng; Yuan, Qiangqiang; Zhang, Liangpei

    2018-04-01

    Satellite remote sensing has been reported to be a promising approach for the monitoring of atmospheric PM2.5. However, the satellite-based monitoring of ground-level PM2.5 is still challenging. First, the previously used polar-orbiting satellite observations, which can be usually acquired only once per day, are hard to monitor PM2.5 in real time. Second, many data gaps exist in satellitederived PM2.5 due to the cloud contamination. In this paper, the hourly geostationary satellite (i.e., Harawari-8) observations were adopted for the real-time monitoring of PM2.5 in a deep learning architecture. On this basis, the satellite-derived PM2.5 in conjunction with ground PM2.5 measurements are incorporated into a spatio-temporal fusion model to fill the data gaps. Using Wuhan Urban Agglomeration as an example, we have successfully derived the real-time and seamless PM2.5 distributions. The results demonstrate that Harawari-8 satellite-based deep learning model achieves a satisfactory performance (out-of-sample cross-validation R2 = 0.80, RMSE = 17.49 μg/m3) for the estimation of PM2.5. The missing data in satellite-derive PM2.5 are accurately recovered, with R2 between recoveries and ground measurements of 0.75. Overall, this study has inherently provided an effective strategy for the realtime and seamless monitoring of ground-level PM2.5.

  14. Basic statistics of PM2.5 and PM10 in the atmosphere of Mexico City.

    Science.gov (United States)

    Vega, E; Reyes, E; Sánchez, G; Ortiz, E; Ruiz, M; Chow, J; Watson, J; Edgerton, S

    2002-03-27

    The high levels of fine particulate matter in Mexico City are of concern since they may induce severe public health effects as well as the attenuation of visible light. Sequential filter samplers were used at six different sites from 23 February to 22 March 1997. The sampling campaign was carried out as part of the project 'Investigación sobre Materia Particulada y Deterioro Atmosferico-Aerosol and Visibility Evaluation Research'. This research was a cooperative project sponsored by PEMEX and by the US Department of Energy. Sampling sites represent the different land uses along the city, the northwest station, Tlalnepantla, is located in a mixed medium income residential and industrial area. The northeast station, Xalostoc, is located in a highly industrialized area, Netzahualcoyotl is located in a mixed land use area, mainly commercial and residential. Station La Merced is located in the commercial and administrative district downtown. The southwest station is located in the Pedregal de San Angel, in a high-income neighborhood, and the southeast station located in Cerro de la Estrella is a mixed medium income residential and commercial area. Samples were collected four times a day in Cerro de la Estrella (CES), La Merced (MER) and Xalostoc (XAL) with sampling periods of 6 h. In Pedregal (PED), Tlalnepantla (TLA) and Netzahualcoyot1 (NEZ) sampling periods were every 24 h. In this paper the basic statistics of PM2.5 and PM10 mass concentrations are presented. The average results showed that 49, 61, 46, 57, 51 and 44% of the PM10 consisted of PM2.5 for CES, MER, XAL, PED, TLA and NEZ, respectively. The 24-h average highest concentrations of PM25 and PM10 were registered at NEZ (184 and 267 microg/m3) and the lowest at PED (22 and 39 microg/m3). The highest PM10 correlations were between XAL-CES (0.79), PED-TLA (0.80). In contrast, the highest PM2.5 correlations were between CES-PED (0.74), MER-CES (0.73) and TLA-PED (0.72), showing a lower correlation than the PM10

  15. Source profiles and contributions of biofuel combustion for PM2.5, PM10 and their compositions, in a city influenced by biofuel stoves.

    Science.gov (United States)

    Tian, Ying-Ze; Chen, Jia-Bao; Zhang, Lin-Lin; Du, Xin; Wei, Jin-Jin; Fan, Hui; Xu, Jiao; Wang, Hai-Ting; Guan, Liao; Shi, Guo-Liang; Feng, Yin-Chang

    2017-12-01

    Source and ambient samples were collected in a city in China that uses considerable biofuel, to assess influence of biofuel combustion and other sources on particulate matter (PM). Profiles and size distribution of biofuel combustion were investigated. Higher levels in source profiles, a significant increase in heavy-biomass ambient and stronger correlations of K + , Cl - , OC and EC suggest that they can be tracers of biofuel combustion. And char-EC/soot-EC (8.5 for PM 2.5 and 15.8 for PM 10 of source samples) can also be used to distinguish it. In source samples, water-soluble organic carbon (WSOC) were approximately 28.0%-68.8% (PM 2.5 ) and 27.2%-43.8% (PM 10 ) of OC. For size distribution, biofuel combustion mainly produces smaller particles. OC1, OC2, EC1 and EC2 abundances showed two peaks with one below 1 μm and one above 2 μm. An advanced three-way factory analysis model was applied to quantify source contributions to ambient PM 2.5 and PM 10 . Higher contributions of coal combustion, vehicular emission, nitrate and biofuel combustion occurred during the heavy-biomass period, and higher contributions of sulfate and crustal dust were observed during the light-biomass period. Mass and percentage contributions of biofuel combustion were significantly higher in heavy-biomass period. The biofuel combustion attributed above 45% of K + and Cl - , above 30% of EC and about 20% of OC. In addition, through analysis of source profiles and contributions, they were consistently evident that biofuel combustion and crustal dust contributed more to cation than to anion, while sulfate & SOC and nitrate showed stronger influence on anion than on cation. Copyright © 2017 Elsevier Ltd. All rights reserved.

  16. AIRUSE-LIFE+: a harmonized PM speciation and source apportionment in five southern European cities

    Science.gov (United States)

    Amato, Fulvio; Alastuey, Andrés; Karanasiou, Angeliki; Lucarelli, Franco; Nava, Silvia; Calzolai, Giulia; Severi, Mirko; Becagli, Silvia; Gianelle, Vorne L.; Colombi, Cristina; Alves, Celia; Custódio, Danilo; Nunes, Teresa; Cerqueira, Mario; Pio, Casimiro; Eleftheriadis, Konstantinos; Diapouli, Evangelia; Reche, Cristina; Cruz Minguillón, María; Manousakas, Manousos-Ioannis; Maggos, Thomas; Vratolis, Stergios; Harrison, Roy M.; Querol, Xavier

    2016-03-01

    The AIRUSE-LIFE+ project aims at characterizing similarities and heterogeneities in particulate matter (PM) sources and contributions in urban areas from southern Europe. Once the main PMx sources are identified, AIRUSE aims at developing and testing the efficiency of specific and non-specific measures to improve urban air quality. This article reports the results of the source apportionment of PM10 and PM2.5 conducted at three urban background sites (Barcelona, Florence and Milan, BCN-UB, FI-UB and MLN-UB), one suburban background site (Athens, ATH-SUB) and one traffic site (Porto, POR-TR). After collecting 1047 PM10 and 1116 PM2.5 24 h samples during 12 months (from January 2013 on) simultaneously at the five cities, these were analysed for the contents of OC, EC, anions, cations, major and trace elements and levoglucosan. The USEPA PMF5 receptor model was applied to these data sets in a harmonized way for each city. The sum of vehicle exhaust (VEX) and non-exhaust (NEX) contributes between 3.9 and 10.8 µg m-3 (16-32 %) to PM10 and 2.3 and 9.4 µg m-3 (15-36 %) to PM2.5, although a fraction of secondary nitrate is also traffic-related but could not be estimated. Important contributions arise from secondary particles (nitrate, sulfate and organics) in PM2.5 (37-82 %) but also in PM10 (40-71 %), mostly at background sites, revealing the importance of abating gaseous precursors in designing air quality plans. Biomass burning (BB) contributions vary widely, from 14-24 % of PM10 in POR-TR, MLN-UB and FI-UB, 7 % in ATH-SUB, to levels increase on an annual basis by 1-9 µg m-3 due to biomass burning influence. Other significant sources are the following. - Local dust, 7-12 % of PM10 at SUB and UB sites and 19 % at the TR site, revealing a contribution from road dust resuspension. In PM2.5 percentages decrease to 2-7 % at SUB-UB sites and 15 % at the TR site. - Industry, mainly metallurgy, contributing 4-11 % of PM10 (5-12 % in PM2.5), but only at BCN-UB, POR-TR and MLN

  17. AIRUSE-LIFE+: a harmonized PM speciation and source apportionment in 5 Southern European cities

    Science.gov (United States)

    Amato, F.; Alastuey, A.; Karanasiou, A.; Lucarelli, F.; Nava, S.; Calzolai, G.; Severi, M.; Becagli, S.; Gianelle, V. L.; Colombi, C.; Alves, C.; Custódio, D.; Nunes, T.; Cerqueira, M.; Pio, C.; Eleftheriadis, K.; Diapouli, E.; Reche, C.; Minguillón, M. C.; Manousakas, M.; Maggos, T.; Vratolis, S.; Harrison, R. M.; Querol, X.

    2015-09-01

    The AIRUSE-LIFE+ project aims at characterising similarities and heterogeneities in PM sources and contributions in urban areas from the Southern Europe. Once the main PMx sources are identified, AIRUSE aims at developing and testing the efficiency of specific and non-specific measures to improve urban air quality. This article reports the results of the source apportionment of PM10 and PM2.5 conducted at three urban background sites (Barcelona, Florence and Milan, BCN-UB, FI-UB, MLN-UB) one sub-urban background site (Athens, ATH-SUB) and one traffic site (Porto, POR-TR). After collecting 1047 PM10 and 1116 PM2.5 24 h samples from January 2013 to February 2014 simultaneously at the 5 cities, these were analysed for the contents of OC, EC, anions, cations, major and trace elements and levoglucosan. The USEPA PMF5 receptor model was applied to these datasets in a harmonised way for each city. The sum of vehicle exhaust and non-exhaust contributes within 3.9-10.8 μg m-3 (16-32 %) to PM10 and 2.3-9.4 μg m-3 (15-36 %) to PM2.5, although a fraction of secondary nitrate is also traffic-related but could not be estimated. Important contributions arise from secondary particles (nitrate, sulphate and organics) in PM2.5 (37-82 %) but also in PM10 (40-71 %) mostly at background sites, revealing the importance of abating gaseous precursors in designing air quality plans. Biomass burning (BB) contributions vary widely, from 14-24 % of PM10 in POR-TR, MLN-UB and FI-UB, 7 % in ATH-SUB to levels increase on an annual basis by 1-9 μg m-3 due to this source. Other significant sources are: - Local dust, 7-12 % of PM10 at SUB and UB sites and 19 % at the TR site, revealing a contribution from road dust resuspension. In PM2.5 percentages decrease to 2-7 % at SUB-UB sites and 15 % at the TR site. - Industries, mainly metallurgy, contributing 4-11 % of PM10 (5-12 % in PM2.5), but only at BCN-UB, POR-TR and MLN-UB. No clear impact of industrial emissions was found in FI-UB and ATH

  18. PM2.5 metal exposures and nocturnal heart rate variability: a panel study of boilermaker construction workers

    Directory of Open Access Journals (Sweden)

    Herrick Robert F

    2008-07-01

    Full Text Available Abstract Background To better understand the mechanism(s of particulate matter (PM associated cardiovascular effects, research priorities include identifying the responsible PM characteristics. Evidence suggests that metals play a role in the cardiotoxicity of fine PM (PM2.5 and in exposure-related decreases in heart rate variability (HRV. We examined the association between daytime exposure to the metal content of PM2.5 and night HRV in a panel study of boilermaker construction workers exposed to metal-rich welding fumes. Methods Twenty-six male workers were monitored by ambulatory electrocardiogram (ECG on a workday while exposed to welding fume and a non-workday (baseline. From the ECG, rMSSD (square root of the mean squared differences of successive intervals was summarized over the night (0:00–7:00. Workday, gravimetric PM2.5 samples were analyzed by x-ray fluorescence to determine metal content. We used linear mixed effects models to assess the associations between night rMSSD and PM2.5 metal exposures both with and without adjustment for total PM2.5. Matched ECG measurements from the non-workday were used to control for individual cardiac risk factors and models were also adjusted for smoking status. To address collinearity between PM2.5 and metal content, we used a two-step approach that treated the residuals from linear regression models of each metal on PM2.5 as surrogates for the differential effects of metal exposures in models for night rMSSD. Results The median PM2.5 exposure was 650 μg/m3; median metal exposures for iron, manganese, aluminum, copper, zinc, chromium, lead, and nickel ranged from 226 μg/m3 to non-detectable. We found inverse linear associations in exposure-response models with increased metal exposures associated with decreased night rMSSD. A statistically significant association for manganese was observed, with a decline of 0.130 msec (95% CI: -0.162, -0.098 in night rMSSD for every 1 μg/m3 increase in

  19. Source sector and region contributions to BC and PM2.5 in Central Asia

    NARCIS (Netherlands)

    Kulkarni, S.; Sobhani, N.; Miller-Schulze, J.P.; Shafer, M.M.; Schauer, J.J.; Solomon, P.A.; Saide, P.E.; Spak, S.N.; Cheng, Y.F.; Denier Van Der Gon, H.A.C.; Lu, Z.; Streets, D.G.; Janssens-Maenhout, G.; Wiedinmyer, C.; Lantz, J.; Artamonova, M.; Chen, B.; Imashev, S.; Sverdlik, L.; Deminter, J.T.; Adhikary, B.; D'Allura, A.; Wei, C.; Carmichael, G.R.

    2015-01-01

    Particulate matter (PM) mass concentrations, seasonal cycles, source sector, and source region contributions in Central Asia (CA) are analyzed for the period April 2008-July 2009 using the Sulfur Transport and dEposition Model (STEM) chemical transport model and modeled meteorology from the Weather

  20. Source Sector and Region Contributions to BC and PM2.5 in Central Asia

    Science.gov (United States)

    Particulate matter (PM) mass concentrations, seasonal cycles, source sector and source region contributions in Central Asia (CA) are analyzed for the period April 2008-July 2009 using the STEM chemical transport model and modeled meteorology from the WRF model. Predicted AOD valu...

  1. Characterization of PAHs and metals in indoor/outdoor PM10/PM2.5/PM1 in a retirement home and a school dormitory.

    Science.gov (United States)

    Hassanvand, Mohammad Sadegh; Naddafi, Kazem; Faridi, Sasan; Nabizadeh, Ramin; Sowlat, Mohammad Hossein; Momeniha, Fatemeh; Gholampour, Akbar; Arhami, Mohammad; Kashani, Homa; Zare, Ahad; Niazi, Sadegh; Rastkari, Noushin; Nazmara, Shahrokh; Ghani, Maryam; Yunesian, Masud

    2015-09-15

    In the present work, we investigated the characteristics of polycyclic aromatic hydrocarbons (PAHs) and metal(loid)s in indoor/outdoor PM10, PM2.5, and PM1 in a retirement home and a school dormitory in Tehran from May 2012 to May 2013. The results indicated that the annual levels of indoor and outdoor PM10 and PM2.5 were much higher than the guidelines issued by the World Health Organization (WHO). The most abundant detected metal(loid)s in PM were Si, Fe, Zn, Al, and Pb. We found higher percentages of metal(loid)s in smaller size fractions of PM. Additionally, the results showed that the total PAHs (ƩPAHs) bound to PM were predominantly (83-88%) found in PM2.5, which can penetrate deep into the alveolar regions of the lungs. In general, carcinogenic PAHs accounted for 40-47% of the total PAHs concentrations; furthermore, the smaller the particle size, the higher the percentage of carcinogenic PAHs. The percentages of trace metal(loid)s and carcinogenic PAHs in PM2.5 mass were almost twice as high as those in PM10. This can most likely be responsible for the fact that PM2.5 can cause more adverse health effects than PM10 can. The average BaP-equivalent carcinogenic (BaP-TEQ) levels both indoors and outdoors considerably exceeded the maximum permissible risk level of 1 ng/m(3) of BaP. The enrichment factors and diagnostic ratios indicated that combustion-related anthropogenic sources, such as gasoline- and diesel-fueled vehicles as well as natural gas combustion, were the major sources of PAHs and trace metal(loid)s bound to PM. Copyright © 2015 Elsevier B.V. All rights reserved.

  2. Evaluation of the Contribution of the Building Sector to PM2.5 Emissions in China

    Energy Technology Data Exchange (ETDEWEB)

    Khanna, Nina [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Zhou, Nan [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Ke, Jing [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Fridley, David [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

    2014-11-01

    In this study, we quantify the current and potential contribution of China’s building sector to direct primary and indirect PM2.5 emissions and co-benefits of key pollution reduction strategies of energy efficiency, fuel switching and pollution control technologies on PM2.5 emissions reduction. We use a bottom-up end-use accounting model to model residential and commercial buildings’ coal demand for heating and electricity demand in China’s Northern and Transition climate zones from 2010 to 2030. The model is then used to characterize the current coal-based heating (e.g., district heating, combined heat and power generation, small-scale coal-fired boilers) and power generation technologies to estimate direct and indirect PM2.5 emissions. Model scenarios are developed to evaluate and compare the potential co-benefits of efficiency improvements, fuel switching and pollution control technologies in reducing building-related direct and indirect PM2.5 emissions. An alternative pathway of development in which district heating is introduced to China’s Transition zone to meet growing demand for heat is also modeled to evaluate and quantify the potential impact on PM2.5 emissions.

  3. On the performance of the semiempirical quantum mechanical PM6 and PM7 methods for noncovalent interactions

    Science.gov (United States)

    Hostaš, Jiří; Řezáč, Jan; Hobza, Pavel

    2013-05-01

    In this Letter, we compare the recently released semiempirical method PM7 with its predecessor, PM6 with post-SCF corrections. These corrections were introduced in order to improve the description of noncovalent interactions (dispersion, hydrogen bonds and halogen bonds) and have become an integral part of PM7. A large collection of data on noncovalent interactions, covering not only interaction energies but also conformational changes and geometries, is used as a benchmark. Among the methods tested, PM6 with the latest corrections (PM6-D3H4X) yields the best results. PM7 yields only slightly worse results but brings additional improvements in the description of other molecular properties.

  4. Air pollution studies in terms of PM2.5, PM2.5-10, PM10, lead and black carbon in urban areas of Antananarivo-Madagascar

    International Nuclear Information System (INIS)

    Rasoazanany, E. O.; Andriamahenina, N. N.; Ravoson, H. N.; Raoelina Andriambololona; Randriamanivo, L. V.; Ramaherison, H.; Ahmed, H.; Harinoely, M.

    2011-01-01

    Atmospheric aerosols or particulate matters are chemically complex and dynamic mixtures of solid and liquid particles. Sources of particulate matters include both natural and anthropogenic processes. The present work consists in determining the concentrations of existing elements in the aerosols collected in Andravoahangy and in Ambodin Isotry in Antananarivo city (Madagascar). The size distribution of these elements and their main sources are also studied.The Total Reflection X-Ray Fluorescence spectrometer is used for the qualitative and quantitative analyses. The results show that the concentrations of the airborne particulate matters PM 2.5-10 are higher than those of PM 2.5 .The identified elements in the aerosol samples are Ti, Cr, Mn, Fe, Ni, Cu, Zn, Br, Sr and Pb. The average concentrations of these elements are also higher in the coarse particles than in the fine particles. The calculation of the enrichment factors by Mason's model shows that Cr, Ni, Cu, Zn, Br and Pb are of anthropogenic origins. The average concentrations of lead (2.8 ng.m -3 , 31.3 ng.m -3 and 19.6 ng.m -3 respectively in aerosols collected in Andravoahangy in 2007 and in 2008 and in Ambodin Isotry in 2008) are largely lower than the average concentration of 1.8 μg.m -3 obtained in 2000 in the Antananarivo urban areas. The concentration of black carbon is higher in the fine particles. The Air Quality Index category is variable in the two sites.

  5. Observation of the suppressed ADS modes $B^\\pm \\to [\\pi^\\pm K^\\mp \\pi^+\\pi^-]_D K^\\pm$ and $B^\\pm \\to [\\pi^\\pm K^\\mp \\pi^+\\pi^-]_D \\pi^\\pm$

    CERN Document Server

    INSPIRE-00258707; Abellan Beteta, C; Adeva, B; Adinolfi, M; Adrover, C; Affolder, A; Ajaltouni, Z; Albrecht, J; Alessio, F; Alexander, M; Ali, S; Alkhazov, G; Alvarez Cartelle, P; Alves Jr, A A; Amato, S; Amerio, S; Amhis, Y; Anderlini, L; Anderson, J; Andreassen, R; Appleby, R B; Aquines Gutierrez, O; Archilli, F; Artamonov, A; Artuso, M; Aslanides, E; Auriemma, G; Bachmann, S; Back, J J; Baesso, C; Balagura, V; Baldini, W; Barlow, R J; Barschel, C; Barsuk, S; Barter, W; Bauer, Th; Bay, A; Beddow, J; Bedeschi, F; Bediaga, I; Belogurov, S; Belous, K; Belyaev, I; Ben-Haim, E; Benayoun, M; Bencivenni, G; Benson, S; Benton, J; Berezhnoy, A; Bernet, R; Bettler, M -O; van Beuzekom, M; Bien, A; Bifani, S; Bird, T; Bizzeti, A; Bjørnstad, P M; Blake, T; Blanc, F; Blouw, J; Blusk, S; Bocci, V; Bondar, A; Bondar, N; Bonivento, W; Borghi, S; Borgia, A; Bowcock, T J V; Bowen, E; Bozzi, C; Brambach, T; van den Brand, J; Bressieux, J; Brett, D; Britsch, M; Britton, T; Brook, N H; Brown, H; Burducea, I; Bursche, A; Busetto, G; Buytaert, J; Cadeddu, S; Callot, O; Calvi, M; Calvo Gomez, M; Camboni, A; Campana, P; Carbone, A; Carboni, G; Cardinale, R; Cardini, A; Carranza-Mejia, H; Carson, L; Carvalho Akiba, K; Casse, G; Cattaneo, M; Cauet, Ch; Charles, M; Charpentier, Ph; Chen, P; Chiapolini, N; Chrzaszcz, M; Ciba, K; Cid Vidal, X; Ciezarek, G; Clarke, P E L; Clemencic, M; Cliff, H V; Closier, J; Coca, C; Coco, V; Cogan, J; Cogneras, E; Collins, P; Comerma-Montells, A; Contu, A; Cook, A; Coombes, M; Coquereau, S; Corti, G; Couturier, B; Cowan, G A; Craik, D; Cunliffe, S; Currie, R; D'Ambrosio, C; David, P; David, P N Y; De Bonis, I; De Bruyn, K; De Capua, S; De Cian, M; De Miranda, J M; De Oyanguren Campos, M; De Paula, L; De Silva, W; De Simone, P; Decamp, D; Deckenhoff, M; Del Buono, L; Derkach, D; Deschamps, O; Dettori, F; Di Canto, A; Dijkstra, H; Dogaru, M; Donleavy, S; Dordei, F; Dosil Suárez, A; Dossett, D; Dovbnya, A; Dupertuis, F; Dzhelyadin, R; Dziurda, A; Dzyuba, A; Easo, S; Egede, U; Egorychev, V; Eidelman, S; van Eijk, D; Eisenhardt, S; Eitschberger, U; Ekelhof, R; Eklund, L; El Rifai, I; Elsasser, Ch; Elsby, D; Falabella, A; Färber, C; Fardell, G; Farinelli, C; Farry, S; Fave, V; Ferguson, D; Fernandez Albor, V; Ferreira Rodrigues, F; Ferro-Luzzi, M; Filippov, S; Fitzpatrick, C; Fontana, M; Fontanelli, F; Forty, R; Francisco, O; Frank, M; Frei, C; Frosini, M; Furcas, S; Furfaro, E; Gallas Torreira, A; Galli, D; Gandelman, M; Gandini, P; Gao, Y; Garofoli, J; Garosi, P; Garra Tico, J; Garrido, L; Gaspar, C; Gauld, R; Gersabeck, E; Gersabeck, M; Gershon, T; Ghez, Ph; Gibson, V; Gligorov, V V; Göbel, C; Golubkov, D; Golutvin, A; Gomes, A; Gordon, H; Grabalosa Gándara, M; Graciani Diaz, R; Granado Cardoso, L A; Graugés, E; Graziani, G; Grecu, A; Greening, E; Gregson, S; Grünberg, O; Gui, B; Gushchin, E; Guz, Yu; Gys, T; Hadjivasiliou, C; Haefeli, G; Haen, C; Haines, S C; Hall, S; Hampson, T; Hansmann-Menzemer, S; Harnew, N; Harnew, S T; Harrison, J; Hartmann, T; He, J; Heijne, V; Hennessy, K; Henrard, P; Hernando Morata, J A; van Herwijnen, E; Hicks, E; Hill, D; Hoballah, M; Hombach, C; Hopchev, P; Hulsbergen, W; Hunt, P; Huse, T; Hussain, N; Hutchcroft, D; Hynds, D; Iakovenko, V; Idzik, M; Ilten, P; Jacobsson, R; Jaeger, A; Jans, E; Jaton, P; Jing, F; John, M; Johnson, D; Jones, C R; Jost, B; Kaballo, M; Kandybei, S; Karacson, M; Karbach, T M; Kenyon, I R; Kerzel, U; Ketel, T; Keune, A; Khanji, B; Kochebina, O; Komarov, I; Koopman, R F; Koppenburg, P; Korolev, M; Kozlinskiy, A; Kravchuk, L; Kreplin, K; Kreps, M; Krocker, G; Krokovny, P; Kruse, F; Kucharczyk, M; Kudryavtsev, V; Kvaratskheliya, T; La Thi, V N; Lacarrere, D; Lafferty, G; Lai, A; Lambert, D; Lambert, R W; Lanciotti, E; Lanfranchi, G; Langenbruch, C; Latham, T; Lazzeroni, C; Le Gac, R; van Leerdam, J; Lees, J -P; Lefèvre, R; Leflat, A; Lefrançois, J; Leo, S; Leroy, O; Leverington, B; Li, Y; Li Gioi, L; Liles, M; Lindner, R; Linn, C; Liu, B; Liu, G; von Loeben, J; Lohn, S; Lopes, J H; Lopez Asamar, E; Lopez-March, N; Lu, H; Lucchesi, D; Luisier, J; Luo, H; Machefert, F; Machikhiliyan, I V; Maciuc, F; Maev, O; Malde, S; Manca, G; Mancinelli, G; Marconi, U; Märki, R; Marks, J; Martellotti, G; Martens, A; Martin, L; Martín Sánchez, A; Martinelli, M; Martinez Santos, D; Martins Tostes, D; Massafferri, A; Matev, R; Mathe, Z; Matteuzzi, C; Maurice, E; Mazurov, A; McCarthy, J; McNulty, R; Mcnab, A; Meadows, B; Meier, F; Meissner, M; Merk, M; Milanes, D A; Minard, M -N; Molina Rodriguez, J; Monteil, S; Moran, D; Morawski, P; Morello, M J; Mountain, R; Mous, I; Muheim, F; Müller, K; Muresan, R; Muryn, B; Muster, B; Naik, P; Nakada, T; Nandakumar, R; Nasteva, I; Needham, M; Neufeld, N; Nguyen, A D; Nguyen, T D; Nguyen-Mau, C; Nicol, M; Niess, V; Niet, R; Nikitin, N; Nikodem, T; Nomerotski, A; Novoselov, A; Oblakowska-Mucha, A; Obraztsov, V; Oggero, S; Ogilvy, S; Okhrimenko, O; Oldeman, R; Orlandea, M; Otalora Goicochea, J M; Owen, P; Pal, B K; Palano, A; Palutan, M; Panman, J; Papanestis, A; Pappagallo, M; Parkes, C; Parkinson, C J; Passaleva, G; Patel, G D; Patel, M; Patrick, G N; Patrignani, C; Pavel-Nicorescu, C; Pazos Alvarez, A; Pellegrino, A; Penso, G; Pepe Altarelli, M; Perazzini, S; Perego, D L; Perez Trigo, E; Pérez-Calero Yzquierdo, A; Perret, P; Perrin-Terrin, M; Pessina, G; Petridis, K; Petrolini, A; Phan, A; Picatoste Olloqui, E; Pietrzyk, B; Pilař, T; Pinci, D; Playfer, S; Plo Casasus, M; Polci, F; Polok, G; Poluektov, A; Polycarpo, E; Popov, D; Popovici, B; Potterat, C; Powell, A; Prisciandaro, J; Pugatch, V; Puig Navarro, A; Punzi, G; Qian, W; Rademacker, J H; Rakotomiaramanana, B; Rangel, M S; Raniuk, I; Rauschmayr, N; Raven, G; Redford, S; Reid, M M; dos Reis, A C; Ricciardi, S; Richards, A; Rinnert, K; Rives Molina, V; Roa Romero, D A; Robbe, P; Rodrigues, E; Rodriguez Perez, P; Roiser, S; Romanovsky, V; Romero Vidal, A; Rouvinet, J; Ruf, T; Ruffini, F; Ruiz, H; Ruiz Valls, P; Sabatino, G; Saborido Silva, J J; Sagidova, N; Sail, P; Saitta, B; Salzmann, C; Sanmartin Sedes, B; Sannino, M; Santacesaria, R; Santamarina Rios, C; Santovetti, E; Sapunov, M; Sarti, A; Satriano, C; Satta, A; Savrie, M; Savrina, D; Schaack, P; Schiller, M; Schindler, H; Schlupp, M; Schmelling, M; Schmidt, B; Schneider, O; Schopper, A; Schune, M -H; Schwemmer, R; Sciascia, B; Sciubba, A; Seco, M; Semennikov, A; Senderowska, K; Sepp, I; Serra, N; Serrano, J; Seyfert, P; Shapkin, M; Shapoval, I; Shatalov, P; Shcheglov, Y; Shears, T; Shekhtman, L; Shevchenko, O; Shevchenko, V; Shires, A; Silva Coutinho, R; Skwarnicki, T; Smith, N A; Smith, E; Smith, M; Sokoloff, M D; Soler, F J P; Soomro, F; Souza, D; Souza De Paula, B; Spaan, B; Sparkes, A; Spradlin, P; Stagni, F; Stahl, S; Steinkamp, O; Stoica, S; Stone, S; Storaci, B; Straticiuc, M; Straumann, U; Subbiah, V K; Swientek, S; Syropoulos, V; Szczekowski, M; Szczypka, P; Szumlak, T; T'Jampens, S; Teklishyn, M; Teodorescu, E; Teubert, F; Thomas, C; Thomas, E; van Tilburg, J; Tisserand, V; Tobin, M; Tolk, S; Tonelli, D; Topp-Joergensen, S; Torr, N; Tournefier, E; Tourneur, S; Tran, M T; Tresch, M; Tsaregorodtsev, A; Tsopelas, P; Tuning, N; Ubeda Garcia, M; Ukleja, A; Urner, D; Uwer, U; Vagnoni, V; Valenti, G; Vazquez Gomez, R; Vazquez Regueiro, P; Vecchi, S; Velthuis, J J; Veltri, M; Veneziano, G; Vesterinen, M; Viaud, B; Vieira, D; Vilasis-Cardona, X; Vollhardt, A; Volyanskyy, D; Voong, D; Vorobyev, A; Vorobyev, V; Voß, C; Voss, H; Waldi, R; Wallace, R; Wandernoth, S; Wang, J; Ward, D R; Watson, N K; Webber, A D; Websdale, D; Whitehead, M; Wicht, J; Wiechczynski, J; Wiedner, D; Wiggers, L; Wilkinson, G; Williams, M P; Williams, M; Wilson, F F; Wishahi, J; Witek, M; Wotton, S A; Wright, S; Wu, S; Wyllie, K; Xie, Y; Xing, F; Xing, Z; Yang, Z; Young, R; Yuan, X; Yushchenko, O; Zangoli, M; Zavertyaev, M; Zhang, F; Zhang, L; Zhang, W C; Zhang, Y; Zhelezov, A; Zhokhov, A; Zhong, L; Zvyagin, A

    2013-01-01

    An analysis of $B^{\\pm}\\to DK^{\\pm}$ and $B^{\\pm}\\to D\\pi^{\\pm}$ decays is presented where the $D$ meson is reconstructed in the four-body final state $K^{\\pm}\\pi^{\\mp} \\pi^+ \\pi^-$. Using LHCb data corresponding to an integrated luminosity of $1.0{\\rm \\,fb}^{-1}$, first observations are made of the suppressed ADS modes $B^{\\pm}\\to [\\pi^{\\pm} K^{\\mp}\\pi^+\\pi^-]_D K^{\\pm}$ and $B^{\\pm}\\to [\\pi^{\\pm} K^{\\mp} \\pi^+\\pi^- ]_D\\pi^{\\pm}$ with a significance of $5.1\\sigma$ and greater than $10\\sigma$, respectively. Measurements of $CP$ asymmetries and $CP$-conserving ratios of partial widths from this family of decays are also performed. The magnitude of the ratio between the suppressed and favoured $B^{\\pm}\\to DK^{\\pm}$ amplitudes is determined to be $r^K_B = 0.097 \\pm{0.011}$.

  6. 20 CFR 411.230 - What is a PM?

    Science.gov (United States)

    2010-04-01

    ... is a PM? A program manager (PM) is an organization in the private or public sector that has entered into a contract to assist us in administering the Ticket to Work program. We will use a competitive...

  7. Related Rules and Programs that Help States Attain PM Standards

    Science.gov (United States)

    EPA’s national and regional rules to reduce emissions of pollutants that form particle pollution will help state and local governments meet the PM NAAQS. A number of voluntary programs also are helping areas reduce fine PM pollution.

  8. Chemical mass balance source apportionment of fine and PM10 in the Desert Southwest, USA

    Directory of Open Access Journals (Sweden)

    Andrea L. Clements

    2016-03-01

    Full Text Available The Desert Southwest Coarse Particulate Matter Study was undertaken in Pinal County, Arizona, to better understand the origin and impact of sources of fine and coarse particulate matter (PM in rural, arid regions of the U.S. southwestern desert. The desert southwest experiences some of the highest PM10 mass concentrations in the country. To augment previously reported results, 6-week aggregated organic speciation data that included ambient concentrations of n-alkanes, polycyclic aromatic hydrocarbons, organic acids, and saccharides were used in chemical mass balance modeling (CMB. A set of re-suspended soil samples were analyzed for specific marker species to provide locally-appropriate source profiles for the CMB analysis. These profiles, as well as previously collected plant and fungal spore profiles from the region, were combined with published source profiles for other relevant sources and used in the CMB analysis. The six new region-specific source profiles included both organic and inorganic species for four crustal material sources, one plant detritus source, and one fungal spore source.Results indicate that up to half of the ambient PM2.5 was apportioned to motor vehicles with the highest regional contribution observed in the small urban center of Casa Grande. Daily levels of apportioned crustal material accounted for up to 50% of PM2.5 mass with the highest contributions observed at the sites closest to active agricultural areas. Apportioned secondary PM, biomass burning, and road dust typically contributed less than 35% as a group to the apportioned PM2.5 mass. Crustal material was the primary source apportioned to PM10 and accounted for between 50–90% of the apportioned mass. Of the other sources apportioned to PM10, motor vehicles and road dust were the largest contributors at the urban and one of the rural sites, whereas road dust and meat cooking operations were the largest contributors at the other rural site.

  9. Enhancement of PM2.5 Concentrations by Aerosol-Meteorology Interactions Over China

    Science.gov (United States)

    Zhang, Xin; Zhang, Qiang; Hong, Chaopeng; Zheng, Yixuan; Geng, Guannan; Tong, Dan; Zhang, Yuxuan; Zhang, Xiaoye

    2018-01-01

    Aerosol-meteorology interactions can change surface aerosol concentrations via different mechanisms such as altering radiation budget or cloud microphysics. However, few studies investigated the impacts of different mechanisms on temporal and spatial distribution of PM2.5 concentrations over China. Here we used the fully coupled Weather Research and Forecasting model with online chemistry (WRF-Chem) to quantify the enhancement of PM2.5 concentrations by aerosol-meteorology feedback in China in 2014 for different seasons and separate the relative impacts of aerosol radiation interactions (ARIs) and aerosol-cloud interactions (ACIs). We found that ARIs and ACIs could increase population-weighted annual mean PM2.5 concentration over China by 4.0 μg/m3 and 1.6 μg/m3, respectively. We found that ARIs play a dominant role in aerosol-meteorology interactions in winter, while the enhancement of PM2.5 concentration by ARIs and ACIs is comparable in other three seasons. ARIs reduced the wintertime monthly mean wind speed and planetary boundary layer (PBL) height by up to 0.1 m/s and 160 m, respectively, but increased the relative humidity by up to 4%, leading to accumulation of pollutants within PBL. Also, ARIs reduced dry deposition velocity of aerosols by up to 20%, resulting in an increase in PM2.5 lifetime and concentrations. ARIs can increase wintertime monthly mean surface PM2.5 concentration by a maximum of 30 μg/m3 in Sichuan Basin. ACIs can also increase PM2.5 concentration with more significant impacts in wet seasons via reduced wet scavenging and enhanced in-cloud chemistry. Dominant processes in PM2.5 enhancement are also clarified in different seasons. Results show that physical process is more important than chemical processes in winter in ARIs, while chemical process of secondary inorganic aerosols production may be crucial in wet seasons via ACIs.

  10. PM2.5 mitigation in China: Socioeconomic determinants of concentrations and differential control policies.

    Science.gov (United States)

    Luo, Kui; Li, Guangdong; Fang, Chuanglin; Sun, Siao

    2018-05-01

    Elucidating the key impact factors on PM 2.5 concentrations is crucial to formulate effective mitigation policies. In this study, we employed an extended Stochastic Impacts by Regression on Population Affluence and Technology (STIRPAT) model to identify the socioeconomic determinants of PM 2.5 concentrations for 12 different regions and across China. The evaluation was based on a balanced panel dataset integrating long-term satellite-derived PM 2.5 concentrations and socio-economic data in China from 1999 to 2011. Empirical results indicate that the influencing factors can be ranked in descending order of importance as: proportion of secondary sector of the economy, GDP per capita, urbanization, population, energy intensity, and proportion of tertiary sector. Proportion of secondary sector is the greatest contribution to increasing PM 2.5 concentrations, especially for heavily polluted regions. GDP per capita is secondary in importance, and its impact is weakened by the existence of an EKC relationship between GDP per capita and PM 2.5 concentrations. Therefore, PM 2.5 pollution is an economic development mode problem, rather than a general economic development problem. The impact of urbanization varies across regions; while promoting urbanization will be conducive to decreased PM 2.5 concentrations in Northwest China and Northeast China, it will contribute to increased PM 2.5 concentrations in other regions. Population and energy intensity are significant in most regions, but neither are decisive factors because of the small absolute value of their coefficients. Finally, different combinations of mitigation policies are proposed for different regions in this study to meet the mitigation targets. Copyright © 2018 Elsevier Ltd. All rights reserved.

  11. Influence of background particulate matter (PM) on urban air quality in the Pacific Northwest.

    Science.gov (United States)

    Timonen, H; Wigder, N; Jaffe, D

    2013-11-15

    Elevated particulate matter concentrations due to Asian long-range transport (LRT) are frequently observed in the free troposphere (FT) above the Pacific Northwest, U.S. Transport of this aerosol from the FT to the boundary layer (BL) and its effect to local air quality remain poorly constrained. We used data collected at the Mount Bachelor observatory (MBO, 2.8 km a.s.l) and from ground stations in the Pacific Northwest to study transport of fine particulate matter (PM) from the FT to the BL. During Asian LRT episodes PM concentrations were clearly elevated above the corresponding monthly averages at MBO as well as at low elevation sites across Washington and Oregon. Also, a clear correlation between MBO and low elevation sites was observed, indicating that LRT episodes are seen in both the FT and BL. In addition, drum impactor measurements show that the chemical composition of PM at MBO was similar to that measured at the BL sites. Using a simple regression model, we estimate that during springtime, when the transport from Asia is most effective, the contribution of Asian sources to PM2.5 in clean background areas of the Pacific Northwest was on average 1.7 μg m(-3) (representing approximately 50-80% of PM). The influence of LRT PM was also seen in measurement stations situated in the urban and urban background areas. However, the fraction of LRT PM was less pronounced (36-50% of PM) due to larger local emissions in the urban areas. Copyright © 2013 Elsevier Ltd. All rights reserved.

  12. PM2.5 and tropospheric ozone in China: overview of situation and responses

    Science.gov (United States)

    Zhang, Hua

    This work reviewed the observational status of PM2.5 and tropospheric ozone in China. It told us the observational facts on the ratios of typical types of aerosol components to the total PM2.5/PM10, and daily and seasonal change of near surface ozone concentration at different cities of China; the global concentration distribution of tropospheric ozone observed by satellite in 2010-2013 was also given for comparison; the PM2.5 concentration distribution and their seasonal change in China region were simulated by an aerosol chemistry-global climate modeling system. Different contribution from five kinds of aerosols to the simulated PM2.5 was analyzed. Then, it linked the emissions of aerosol and greenhouse gases and their radiative forcing and thus gave their climatic effect by reducing their emissions on the basis of most recently published IPCC AR5. Finally it suggested policies on reducing emissions of short-lived climate pollutants (SLCPs) (such as PM2.5 and tropospheric ozone) in China from protecting both climate and environment.

  13. Estimating regional spatial and temporal variability of PM(2.5) concentrations using satellite data, meteorology, and land use information.

    Science.gov (United States)

    Liu, Yang; Paciorek, Christopher J; Koutrakis, Petros

    2009-06-01

    Studies of chronic health effects due to exposures to particulate matter with aerodynamic diameters meteorologic information to estimate ground-level PM(2.5) concentrations. We developed a two-stage generalized additive model (GAM) for U.S. Environmental Protection Agency PM(2.5) concentrations in a domain centered in Massachusetts. The AOD model represents conditions when AOD retrieval is successful; the non-AOD model represents conditions when AOD is missing in the domain. The AOD model has a higher predicting power judged by adjusted R(2) (0.79) than does the non-AOD model (0.48). The predicted PM(2.5) concentrations by the AOD model are, on average, 0.8-0.9 microg/m(3) higher than the non-AOD model predictions, with a more smooth spatial distribution, higher concentrations in rural areas, and the highest concentrations in areas other than major urban centers. Although AOD is a highly significant predictor of PM(2.5), meteorologic parameters are major contributors to the better performance of the AOD model. GOES aerosol/smoke product (GASP) AOD is able to summarize a set of weather and land use conditions that stratify PM(2.5) concentrations into two different spatial patterns. Even if land use regression models do not include AOD as a predictor variable, two separate models should be fitted to account for different PM(2.5) spatial patterns related to AOD availability.

  14. DATEP: 120 channel PM HV regulator

    International Nuclear Information System (INIS)

    Centro, S.; Giorgi, M. de

    1981-01-01

    DATEP (Distributore Alta Tensione Programmabile) has been designed to distribute high voltage to some 1500 PM's for the gamma detectors (part C) of EHS. Each unit has its own microprocessor controller which performs continuous checking of the 120 channels and allows operator interaction. Channel regulators are based on a specially developed thick film hybrid circuit that allows to get on overall temperature coefficient better than 50 ppm/ 0 C. (orig.)

  15. Bivariate extreme value with application to PM10 concentration analysis

    Science.gov (United States)

    Amin, Nor Azrita Mohd; Adam, Mohd Bakri; Ibrahim, Noor Akma; Aris, Ahmad Zaharin

    2015-05-01

    This study is focus on a bivariate extreme of renormalized componentwise maxima with generalized extreme value distribution as a marginal function. The limiting joint distribution of several parametric models are presented. Maximum likelihood estimation is employed for parameter estimations and the best model is selected based on the Akaike Information Criterion. The weekly and monthly componentwise maxima series are extracted from the original observations of daily maxima PM10 data for two air quality monitoring stations located in Pasir Gudang and Johor Bahru. The 10 years data are considered for both stations from year 2001 to 2010. The asymmetric negative logistic model is found as the best fit bivariate extreme model for both weekly and monthly maxima componentwise series. However the dependence parameters show that the variables for weekly maxima series is more dependence to each other compared to the monthly maxima.

  16. Monthly analysis of PM ratio characteristics and its relation to AOD.

    Science.gov (United States)

    Sorek-Hamer, Meytar; Broday, David M; Chatfield, Robert; Esswein, Robert; Stafoggia, Massimo; Lepeule, Johanna; Lyapustin, Alexei; Kloog, Itai

    2017-01-01

    MAIAC regional aerosol microphysical model assumptions used to generate look-up tables (LUTs) and conduct retrievals. Furthermore, relatively large variations in measured PM ratio shows that adding seasonality in aerosol microphysics used in LUTs, which is currently static, could also help improve accuracy of MAIAC retrievals. These results call for further scrutiny of satellite-borne AOD for better understanding of its limitations and relation to the vertical aerosol profile and particle size, shape, and composition.

  17. Quantifying Future PM2.5 and Associated Health Effects Due to Changes in US Wildfires

    Science.gov (United States)

    Pierce, J. R.; Val Martin, M.; Ford, B.; Zelasky, S.; Heald, C. L.; Li, F.; Lawrence, D. M.; Fischer, E. V.

    2017-12-01

    Fine particulate matter (PM2.5) from landscape fires has been shown to adversely affect visibility, air quality and and health across the US. Fire activity is strongly related to climate and human activities. Predictions based on climate scenarios and future land cover projections that consider socioeconomic development suggest that fire activity will rise dramatically over the next decades. As PM2.5 is associated with increased mortality and morbidity rates, increases in emissions from landscape fires may alter the health burden on the US population. Here we present an analysis of the changes in future wildfire activity and consequences for PM2.5 and health over the US from 2000 to 2100. We employ the global Community Earth System Model (CESM) with the IPCC RCP projections. Within CESM, we use a process-based global fire parameterization to project future climate-driven and human-caused fire emissions. From these simulations, we determine the current and future impact on PM2.5 concentrations and visibility for different regions of the US, and we also calculate the mortality attributable to PM2.5 and wildfire-specific PM2.5 using existing concentration-response functions. Results show that although total PM2.5 concentrations in the US are projected to be similar in 2100 as in 2000, the dominant source of PM2.5 will change. Under the RCP8.5 climate projection and SSP3 population projection, non-fire emissions (mostly anthropogenic) are projected to decrease, but PM2.5 from CONUS and non-US wildfires is projected to increase from approximately 20% of all PM2.5 in 2000 to 80% of all PM2.5 in 2100. Furthermore, although the US population is expected to decline between 2000 and 2100, the mortality attributable to wildfire smoke is expected to increase from 25,000 deaths per year in 2000 to 75,000 deaths per year in 2100.

  18. Chemical characteristics and influence of continental outflow on PM1.0, PM2.5 and PM10 measured at Tuoji island in the Bohai Sea.

    Science.gov (United States)

    Zhang, Junmei; Yang, Lingxiao; Mellouki, Abdelwahid; Wen, Liang; Yang, Yumeng; Gao, Ying; Jiang, Pan; Li, Yanyan; Wang, Wenxing

    2016-12-15

    To investigate the chemical characteristics and sources of size-segregated particles in the background region, PM 1.0 , PM 2.5 and PM 10 samples were collected in Tuoji Island (TI) during the winter of 2014. Water-soluble inorganic ions (WSIIs) including Na + , NH 4 + , K + , Mg 2+ , Ca 2+ , Cl - , NO 3 - and SO 4 2- , organic carbon (OC) and elemental carbon (EC) and water-soluble organic carbon (WSOC) were analysed. The average mass concentrations of PM 1.0 , PM 2.5 and PM 10 were 44.5μg/m 3 , 62.0μg/m 3 and 94.4μg/m 3 , respectively, and particles were importantly enriched in PM 1.0 . Secondary WSIIs (NH 4 + , NO 3 - and SO 4 2- ) were the most abundant species, and their contribution was highest in PM 1.0 . The average values of NOR and SOR were more than 0.1 in PM 1.0 , suggesting that secondary formation of SO 4 2- and NO 3 - from the gas precursors SO 2 and NO 2 occurred in PM 1.0 . Secondary organic carbon accounted for 62.3% in PM 1.0 , 61.9% in PM 1.0-2.5 and 48.9% in PM 2.5-10 of OC, formed mainly in the fine mode. The particles concentrations were mainly affected by air mass from the North China Plain, especially the air mass from the southwest of Shandong province, which had low speed and altitude. Copyright © 2016 Elsevier B.V. All rights reserved.

  19. Performance characteristics of a low-volume PM10 sampler

    Science.gov (United States)

    Four identical PM10 pre-separators, along with four identical low-volume (1m3 hr-1) total suspended particulate (TSP) samplers were tested side-by-side in a controlled laboratory particulate matter (PM) chamber. The four PM10 and four TSP samplers were also tested in an oil pipe-cleaning field to ev...

  20. Measurement of Ambient Air Particle (TSP, PM10, PM2,5) Around Candidate Location of PLTN Semenanjung Lemahabang

    International Nuclear Information System (INIS)

    AgusGindo S; Budi Hari H

    2008-01-01

    Measurement analysis of ambient air particle (TSP, PM 10 , PM 2,5 ) around location candidate of PLTN (Power Station of Nuclear Energy) Semenanjung Lemahabang has been carried out. The measurement was conducted in May 2007 with a purpose to providing information about concentration of ambient air particle (TSP, PM 10 , PM 2,5 ) and diameter distribution of its air particle. The measurement was conducted in three locations i.e. 1). Balong village 2). Bayuran 3). Bondo. Concentration of TSP, PM 10 , and PM 2,5 per 24 hours in all measured locations in area candidate of PLTN exceed quality standard of national ambient air is specified by government. All measurement locations for the TSP, PM 10 , and PM 2,5 was include category of ISPU (Standard Index of Air Pollution) moderate. (author)

  1. Particle reduction strategies - PAREST. Evaluation of emission reduction scenarios using chemical transport calculations. PM10- and PM2.5-reduction potentials by package of measures for further immission reduction in Germany. Sub-report.; Strategien zur Verminderung der Feinstaubbelastung - PAREST. Bewertung von Emissionsminderungsszenarien mit Hilfe chemischer Transportberechnungen. PM10- und PM2,5-Minderungspotenziale von Massnahmenpaketen zur weiteren Reduzierung der Immissionen in Deutschland. Teilbericht

    Energy Technology Data Exchange (ETDEWEB)

    Stern, Rainer [Freie Univ. Berlin (Germany). Inst. fuer Meteorologie, Troposphaerische Umweltforschung

    2013-06-15

    This report documents the effects of additional emission control measures the PM10 and PM2.5 air quality in Germany (PM = particulate matter). The immission effects of the planned measures were calculated with the Chemistry-Aerosol-Transport Model REM CALGRID (RCG). [German] Dieser Bericht dokumentiert die Auswirkungen zusaetzlicher emissionsmindernder Massnahmen auf die PM10 und PM2.5-Luftqualitaet in Deutschland. Die immissionsseitigen Auswirkungen der geplanten Massnahmen wurden auf der Basis von Berechnungen mit dem Chemie-Aerosol-Transportmodell REM-CALGRID (RCG) bestimmt. Grundlage der Szenarienrechnungen sind die im Rahmen des F and E-Vorhabens entwickelten Emissionsabschaetzungen, die die Aenderung der Emissionen aufgrund von technischen oder nicht-technischen Massnahmen beschreiben. Die den Berechnungen zugrunde liegende horizontale Aufloesung betraegt 0.125 Laenge und 0.0625 Breite oder circa 7 km x 8 km. Das meteorologische Referenzjahr ist 2005.

  2. The Concentrations and Reduction of Airborne Particulate Matter (PM10, PM2.5, PM1 at Shelterbelt Site in Beijing

    Directory of Open Access Journals (Sweden)

    Jungang Chen

    2015-05-01

    Full Text Available Particulate matter is a serious source of air pollution in urban areas, where it exerts adverse effects on human health. This article focuses on the study of subduction of shelterbelts for atmospheric particulates. The results suggest that (1 the PM mass concentration is higher in the morning or both morning and noon inside the shelterbelts and lower mass concentrations at other times; (2 the particle mass concentration inside shelterbelt is higher than outside; (3 the particle interception efficiency of the two forest belts over the three months in descending order was PM10 > PM1 > PM2.5; and (4 the two shelterbelts captured air pollutants at rates of 1496.285 and 909.075 kg/month and the major atmospheric pollutant in Beijing city is PM10. Future research directions are to study PM mass concentration variation of shelterbelt with different tree species and different configuration.

  3. Reduction of power consumption in motor-driven applications by using PM motors; PM = Permanent Magnet; Reduktion af elforbrug til motordrift ved anvendelse af PM motorer

    Energy Technology Data Exchange (ETDEWEB)

    Hvenegaard, C.M.; Hansen, Mads P.R.; Groenborg Nikolaisen, C. (Teknologisk Institut, Taastrup (Denmark)); Nielsen, Sandie B. (Teknologisk Institut, AArhus (Denmark)); Ritchie, E.; Leban, K. (Aalborg Univ., Aalborg (Denmark))

    2009-12-15

    The traditional asynchronous motor with aluminum rotor is today by far the most widespread and sold electric motor, but a new and more energy efficient type of engine - the permanent magnet motor (PM motor) - is expected in the coming years to win larger and larger market shares. Several engine manufacturers in Europe, USA and Asia are now beginning to market the PM motors, which can replace the traditional asynchronous motor. The project aims to uncover the pros and cons of replacing asynchronous motors including EFF1 engines with PM motors, including the price difference. Furthermore, it is identified how the efficiency of PM motors is affected by low load levels and at various forms of control. Finally, the energy savings potential is analysed, by replacing asynchronous motors with PM motors. The study includes laboratory tests of PM motors, made in a test stand at Danish Technological Institute. (ln)

  4. Kaon identification and Search for Lepton Number Violation in $K^{\\pm}$ decay-in-flight experiments at CERN

    CERN Document Server

    Massri, Karim; Goudzovski, Evgueni

    A search for the Lepton Number Violating decay $K^{\\pm} \\to \\pi^{\\pm} \\mu^{\\pm} \\mu^{\\pm}$ has been performed using the data collected by the NA48/2 experiment in 2003 and 2004. The signal event selection, the background rejection, the evaluation of the muon identifcation efficiency and the statistical methods used for the data interpretation are presented. Based on $1.8 \\times 10 ^{11}$ kaon decays in the fiducial volume and using several models for the signal, upper limits for the branching fraction B($K^{\\pm} \\to \\pi^{\\pm} \\mu^{\\pm} \\mu^{\\pm}$) of the order of $10^{-10} $ have been obtained for 90%, 95% and 99% confidence levels, improving the previous best limit by one order of magnitude. The Cherenkov differential counter used for kaon identification in the NA62 experiment, equipped with approximately 30% of the photo-detectors, was installed and tested during a Technical Run in 2012. The counter's ability of distinguishing between kaons and pions has been validated via pressure scan procedure. The da...

  5. Characterisation and quantification of the sources of PM10 during air pollution episodes in the UK

    International Nuclear Information System (INIS)

    Muir, David; Longhurst, J.W.S.; Tubb, A.

    2006-01-01

    Data for concentrations of PM 10 and gaseous pollutants from sites in the UK Automatic Urban and Rural Network have been examined during periods of elevated concentrations of PM 10 . The ratios of concentrations of PM 10 to those of the other pollutants were used to determine the most probable source of the additional particles. The hypothesis is that because the concentrations of PM 10 were divided by those of the other pollutants, the ratio should decrease when PM 10 and the other pollutants have a common source. Conversely, the ratio should increase when the sources are different. During episodes where road traffic was the most probable source of the additional particles, the ratios of concentrations of PM 10 to carbon monoxide and oxides of nitrogen did decrease, but the comparable ratios for sulphur dioxide and ozone increased. In contrast, during episodes known to have been caused by construction activity, all these ratios increased. This is taken to show that the basic hypothesis is valid. For prolonged episodes, it was possible to use data averaged over the total duration of the episode for the purposes of source identification. For sporadic construction, or other short-duration episodes, it was necessary to use time series data. The data have also been used to calculate the differences between hourly average concentrations of pollutants measured during episodes and long-term hourly average concentrations. These have been used to model the additional PM 10 during air pollution episodes associated with construction activities and road traffic emissions. This confirms the lack of relationship between PM 10 and other pollutants during construction works. During episodes arising from road traffic emissions, there was good agreement between measured and modelled additional concentrations of PM 10 when an appropriate factor, F, related to the contribution of road traffic emissions to PM 10 at different site types was applied. The values used were 0.2 (Suburban

  6. Thermal Development Test of the NEXT PM1 Ion Engine

    Science.gov (United States)

    Anderson, John R.; Snyder, John S.; VanNoord, Jonathan L.; Soulas, George C.

    2010-01-01

    NASA's Evolutionary Xenon Thruster (NEXT) is a next-generation high-power ion propulsion system under development by NASA as a part of the In-Space Propulsion Technology Program. NEXT is designed for use on robotic exploration missions of the solar system using solar electric power. Potential mission destinations that could benefit from a NEXT Solar Electric Propulsion (SEP) system include inner planets, small bodies, and outer planets and their moons. This range of robotic exploration missions generally calls for ion propulsion systems with deep throttling capability and system input power ranging from 0.6 to 25 kW, as referenced to solar array output at 1 Astronomical Unit (AU). Thermal development testing of the NEXT prototype model 1 (PM1) was conducted at JPL to assist in developing and validating a thruster thermal model and assessing the thermal design margins. NEXT PM1 performance prior to, during and subsequent to thermal testing are presented. Test results are compared to the predicted hot and cold environments expected missions and the functionality of the thruster for these missions is discussed.

  7. Monitoring of PM10 and PM2.5 around primary particulate anthropogenic emission sources

    Science.gov (United States)

    Querol, Xavier; Alastuey, Andrés; Rodriguez, Sergio; Plana, Felicià; Mantilla, Enrique; Ruiz, Carmen R.

    Investigations on the monitoring of ambient air levels of atmospheric particulates were developed around a large source of primary anthropogenic particulate emissions: the industrial ceramic area in the province of Castelló (Eastern Spain). Although these primary particulate emissions have a coarse grain-size distribution, the atmospheric transport dominated by the breeze circulation accounts for a grain-size segregation, which results in ambient air particles occurring mainly in the 2.5-10 μm range. The chemical composition of the ceramic particulate emissions is very similar to the crustal end-member but the use of high Al, Ti and Fe as tracer elements as well as a peculiar grain-size distribution in the insoluble major phases allow us to identify the ceramic input in the bulk particulate matter. PM2.5 instead of PM10 monitoring may avoid the interference of crustal particles without a major reduction in the secondary anthropogenic load, with the exception of nitrate. However, a methodology based in PM2.5 measurement alone is not adequate for monitoring the impact of primary particulate emissions (such as ceramic emissions) on air quality, since the major ambient air particles derived from these emissions are mainly in the range of 2.5-10 μm. Consequently, in areas characterised by major secondary particulate emissions, PM2.5 monitoring should detect anthropogenic particulate pollutants without crustal particulate interference, whereas PM10 measurements should be used in areas with major primary anthropogenic particulate emissions.

  8. Environmental Testing of the NEXT PM1R Ion Engine

    Science.gov (United States)

    Snyder, John S.; Anderson, John R.; VanNoord, Jonathan L.; Soulas, George C.

    2007-01-01

    The NEXT propulsion system is an advanced ion propulsion system presently under development that is oriented towards robotic exploration of the solar system using solar electric power. The subsystem includes an ion engine, power processing unit, feed system components, and thruster gimbal. The Prototype Model engine PM1 was subjected to qualification-level environmental testing in 2006 to demonstrate compatibility with environments representative of anticipated mission requirements. Although the testing was largely successful, several issues were identified including the fragmentation of potting cement on the discharge and neutralizer cathode heater terminations during vibration which led to abbreviated thermal testing, and generation of particulate contamination from manufacturing processes and engine materials. The engine was reworked to address most of these findings, renamed PM1R, and the environmental test sequence was repeated. Thruster functional testing was performed before and after the vibration and thermal-vacuum tests. Random vibration testing, conducted with the thruster mated to the breadboard gimbal, was executed at 10.0 Grms for 2 min in each of three axes. Thermal-vacuum testing included three thermal cycles from 120 to 215 C with hot engine re-starts. Thruster performance was nominal throughout the test program, with minor variations in a few engine operating parameters likely caused by facility effects. There were no significant changes in engine performance as characterized by engine operating parameters, ion optics performance measurements, and beam current density measurements, indicating no significant changes to the hardware as a result of the environmental testing. The NEXT PM1R engine and the breadboard gimbal were found to be well-designed against environmental requirements based on the results reported herein. The redesigned cathode heater terminations successfully survived the vibration environments. Based on the results of this test

  9. Characterization of PM10 sources in the central Mediterranean

    Science.gov (United States)

    Calzolai, G.; Nava, S.; Lucarelli, F.; Chiari, M.; Giannoni, M.; Becagli, S.; Traversi, R.; Marconi, M.; Frosini, D.; Severi, M.; Udisti, R.; di Sarra, A.; Pace, G.; Meloni, D.; Bommarito, C.; Monteleone, F.; Anello, F.; Sferlazzo, D. M.

    2015-12-01

    The Mediterranean Basin atmosphere is influenced by both strong natural and anthropogenic aerosol emissions and is also subject to important climatic forcings. Several programs have addressed the study of the Mediterranean basin; nevertheless important pieces of information are still missing. In this framework, PM10 samples were collected on a daily basis on the island of Lampedusa (35.5° N, 12.6° E; 45 m a.s.l.), which is far from continental pollution sources (the nearest coast, in Tunisia, is more than 100 km away). After mass gravimetric measurements, different portions of the samples were analyzed to determine the ionic content by ion chromatography (IC), the soluble metals by inductively coupled plasma atomic emission spectrometry (ICP-AES), and the total (soluble + insoluble) elemental composition by particle-induced x-ray emission (PIXE). Data from 2007 and 2008 are used in this study. The Positive Matrix Factorization (PMF) model was applied to the 2-year long data set of PM10 mass concentration and chemical composition to assess the aerosol sourc