WorldWideScience

Sample records for model predicted air

  1. A Hybrid Neural Network Prediction Model of Air Ticket Sales

    Directory of Open Access Journals (Sweden)

    Han-Chen Huang

    2013-11-01

    Full Text Available Air ticket sales revenue is an important source of revenue for travel agencies, and if future air ticket sales revenue can be accurately forecast, travel agencies will be able to advance procurement to achieve a sufficient amount of cost-effective tickets. Therefore, this study applied the Artificial Neural Network (ANN and Genetic Algorithms (GA to establish a prediction model of travel agency air ticket sales revenue. By verifying the empirical data, this study proved that the established prediction model has accurate prediction power, and MAPE (mean absolute percentage error is only 9.11%. The established model can provide business operators with reliable and efficient prediction data as a reference for operational decisions.

  2. Mathematical models for predicting indoor air quality from smoking activity.

    Science.gov (United States)

    Ott, W R

    1999-05-01

    Much progress has been made over four decades in developing, testing, and evaluating the performance of mathematical models for predicting pollutant concentrations from smoking in indoor settings. Although largely overlooked by the regulatory community, these models provide regulators and risk assessors with practical tools for quantitatively estimating the exposure level that people receive indoors for a given level of smoking activity. This article reviews the development of the mass balance model and its application to predicting indoor pollutant concentrations from cigarette smoke and derives the time-averaged version of the model from the basic laws of conservation of mass. A simple table is provided of computed respirable particulate concentrations for any indoor location for which the active smoking count, volume, and concentration decay rate (deposition rate combined with air exchange rate) are known. Using the indoor ventilatory air exchange rate causes slightly higher indoor concentrations and therefore errs on the side of protecting health, since it excludes particle deposition effects, whereas using the observed particle decay rate gives a more accurate prediction of indoor concentrations. This table permits easy comparisons of indoor concentrations with air quality guidelines and indoor standards for different combinations of active smoking counts and air exchange rates. The published literature on mathematical models of environmental tobacco smoke also is reviewed and indicates that these models generally give good agreement between predicted concentrations and actual indoor measurements.

  3. Impact of inherent meteorology uncertainty on air quality model predictions

    Science.gov (United States)

    It is well established that there are a number of different classifications and sources of uncertainties in environmental modeling systems. Air quality models rely on two key inputs, namely, meteorology and emissions. When using air quality models for decision making, it is impor...

  4. Models for the Prediction of Room Air Distribution

    DEFF Research Database (Denmark)

    Nielsen, Peter V.

    The paper describes work on simplified design methods made in connection with the International Energy Agency programme" Air Flow Pattern within Buildings" , Annex 20, subtask 1. It is shown that simplified models are able to indicate design values as the maximum velocity in the occupied zone...... and penetration depth of a non-isothermal jet in a room....

  5. Prediction of Indoor Air Exposure from Outdoor Air Quality Using an Artificial Neural Network Model for Inner City Commercial Buildings

    Directory of Open Access Journals (Sweden)

    Avril Challoner

    2015-12-01

    Full Text Available NO2 and particulate matter are the air pollutants of most concern in Ireland, with possible links to the higher respiratory and cardiovascular mortality and morbidity rates found in the country compared to the rest of Europe. Currently, air quality limits in Europe only cover outdoor environments yet the quality of indoor air is an essential determinant of a person’s well-being, especially since the average person spends more than 90% of their time indoors. The modelling conducted in this research aims to provide a framework for epidemiological studies by the use of publically available data from fixed outdoor monitoring stations to predict indoor air quality more accurately. Predictions are made using two modelling techniques, the Personal-exposure Activity Location Model (PALM, to predict outdoor air quality at a particular building, and Artificial Neural Networks, to model the indoor/outdoor relationship of the building. This joint approach has been used to predict indoor air concentrations for three inner city commercial buildings in Dublin, where parallel indoor and outdoor diurnal monitoring had been carried out on site. This modelling methodology has been shown to provide reasonable predictions of average NO2 indoor air quality compared to the monitored data, but did not perform well in the prediction of indoor PM2.5 concentrations. Hence, this approach could be used to determine NO2 exposures more rigorously of those who work and/or live in the city centre, which can then be linked to potential health impacts.

  6. Prediction of blast-induced air overpressure: a hybrid AI-based predictive model.

    Science.gov (United States)

    Jahed Armaghani, Danial; Hajihassani, Mohsen; Marto, Aminaton; Shirani Faradonbeh, Roohollah; Mohamad, Edy Tonnizam

    2015-11-01

    Blast operations in the vicinity of residential areas usually produce significant environmental problems which may cause severe damage to the nearby areas. Blast-induced air overpressure (AOp) is one of the most important environmental impacts of blast operations which needs to be predicted to minimize the potential risk of damage. This paper presents an artificial neural network (ANN) optimized by the imperialist competitive algorithm (ICA) for the prediction of AOp induced by quarry blasting. For this purpose, 95 blasting operations were precisely monitored in a granite quarry site in Malaysia and AOp values were recorded in each operation. Furthermore, the most influential parameters on AOp, including the maximum charge per delay and the distance between the blast-face and monitoring point, were measured and used to train the ICA-ANN model. Based on the generalized predictor equation and considering the measured data from the granite quarry site, a new empirical equation was developed to predict AOp. For comparison purposes, conventional ANN models were developed and compared with the ICA-ANN results. The results demonstrated that the proposed ICA-ANN model is able to predict blast-induced AOp more accurately than other presented techniques.

  7. Mathematical models for predicting indoor air quality from smoking activity.

    OpenAIRE

    Ott, W R

    1999-01-01

    Much progress has been made over four decades in developing, testing, and evaluating the performance of mathematical models for predicting pollutant concentrations from smoking in indoor settings. Although largely overlooked by the regulatory community, these models provide regulators and risk assessors with practical tools for quantitatively estimating the exposure level that people receive indoors for a given level of smoking activity. This article reviews the development of the mass balanc...

  8. Air quality measurements versus model predictions: a case study for the Sugozu power plant

    Energy Technology Data Exchange (ETDEWEB)

    A. Korur; C. Derinoz; C. Yurteri [ENVY Energy and Environmental Investments Inc., Ankara (Turkey)

    2003-07-01

    Air quality modeling is one of the tools used in Environmental Impact Assessment (EIA) studies in order to predict the potential impacts of atmospheric emissions. The main advantage of air quality modeling is the simulation of the ground-level concentrations under different conditions (i.e., meteorological variations and other pollutant sources in the vicinity). The accuracy of model predictions, on the other hand, depends mainly on the quality of the input data reflecting meteorological and topographical conditions as well as emission sources. In this regard, the model predictions should be supported with monitoring data. In the paper, the predictions of Gaussian air dispersion model (Industrial Source Complex - ISC) for SO{sub 2} and NO{sub 2} carried out in the vicinity of the Sugozu Power Plant on the coast of Turkey are compared with the air quality monitoring results of the same region. 2 refs., 3 figs., 2 tabs.

  9. Assessment and prediction of air quality using fuzzy logic and autoregressive models

    Science.gov (United States)

    Carbajal-Hernández, José Juan; Sánchez-Fernández, Luis P.; Carrasco-Ochoa, Jesús A.; Martínez-Trinidad, José Fco.

    2012-12-01

    In recent years, artificial intelligence methods have been used for the treatment of environmental problems. This work, presents two models for assessment and prediction of air quality. First, we develop a new computational model for air quality assessment in order to evaluate toxic compounds that can harm sensitive people in urban areas, affecting their normal activities. In this model we propose to use a Sigma operator to statistically asses air quality parameters using their historical data information and determining their negative impact in air quality based on toxicity limits, frequency average and deviations of toxicological tests. We also introduce a fuzzy inference system to perform parameter classification using a reasoning process and integrating them in an air quality index describing the pollution levels in five stages: excellent, good, regular, bad and danger, respectively. The second model proposed in this work predicts air quality concentrations using an autoregressive model, providing a predicted air quality index based on the fuzzy inference system previously developed. Using data from Mexico City Atmospheric Monitoring System, we perform a comparison among air quality indices developed for environmental agencies and similar models. Our results show that our models are an appropriate tool for assessing site pollution and for providing guidance to improve contingency actions in urban areas.

  10. Bending Angle Prediction Model Based on BPNN-Spline in Air Bending Springback Process

    OpenAIRE

    Zhefeng Guo; Wencheng Tang

    2017-01-01

    In order to rapidly and accurately predict the springback bending angle in V-die air bending process, a springback bending angle prediction model on the combination of error back propagation neural network and spline function (BPNN-Spline) is presented in this study. An orthogonal experimental sample set for training BPNN-Spline is obtained by finite element simulation. Through the analysis of network structure, the BPNN-Spline black box function of bending angle prediction is established, an...

  11. Predicting residential air exchange rates from questionnaires and meteorology: model evaluation in central North Carolina.

    Science.gov (United States)

    Breen, Michael S; Breen, Miyuki; Williams, Ronald W; Schultz, Bradley D

    2010-12-15

    A critical aspect of air pollution exposure models is the estimation of the air exchange rate (AER) of individual homes, where people spend most of their time. The AER, which is the airflow into and out of a building, is a primary mechanism for entry of outdoor air pollutants and removal of indoor source emissions. The mechanistic Lawrence Berkeley Laboratory (LBL) AER model was linked to a leakage area model to predict AER from questionnaires and meteorology. The LBL model was also extended to include natural ventilation (LBLX). Using literature-reported parameter values, AER predictions from LBL and LBLX models were compared to data from 642 daily AER measurements across 31 detached homes in central North Carolina, with corresponding questionnaires and meteorological observations. Data was collected on seven consecutive days during each of four consecutive seasons. For the individual model-predicted and measured AER, the median absolute difference was 43% (0.17 h(-1)) and 40% (0.17 h(-1)) for the LBL and LBLX models, respectively. Additionally, a literature-reported empirical scale factor (SF) AER model was evaluated, which showed a median absolute difference of 50% (0.25 h(-1)). The capability of the LBL, LBLX, and SF models could help reduce the AER uncertainty in air pollution exposure models used to develop exposure metrics for health studies.

  12. Model Predictive Engine Air-Ratio Control Using Online Sequential Relevance Vector Machine

    Directory of Open Access Journals (Sweden)

    Hang-cheong Wong

    2012-01-01

    Full Text Available Engine power, brake-specific fuel consumption, and emissions relate closely to air ratio (i.e., lambda among all the engine variables. An accurate and adaptive model for lambda prediction is essential to effective lambda control for long term. This paper utilizes an emerging technique, relevance vector machine (RVM, to build a reliable time-dependent lambda model which can be continually updated whenever a sample is added to, or removed from, the estimated lambda model. The paper also presents a new model predictive control (MPC algorithm for air-ratio regulation based on RVM. This study shows that the accuracy, training, and updating time of the RVM model are superior to the latest modelling methods, such as diagonal recurrent neural network (DRNN and decremental least-squares support vector machine (DLSSVM. Moreover, the control algorithm has been implemented on a real car to test. Experimental results reveal that the control performance of the proposed relevance vector machine model predictive controller (RVMMPC is also superior to DRNNMPC, support vector machine-based MPC, and conventional proportional-integral (PI controller in production cars. Therefore, the proposed RVMMPC is a promising scheme to replace conventional PI controller for engine air-ratio control.

  13. Regression-based air temperature spatial prediction models: an example from Poland

    Directory of Open Access Journals (Sweden)

    Mariusz Szymanowski

    2013-10-01

    Full Text Available A Geographically Weighted Regression ? Kriging (GWRK algorithm, based on the local Geographically Weighted Regression (GWR, is applied for spatial prediction of air temperature in Poland. Hengl's decision tree for selecting a suitable prediction model is extended for varying spatial relationships between the air temperature and environmental predictors with an assumption of existing environmental dependence of analyzed temperature variables. The procedure includes the potential choice of a local GWR instead of the global Multiple Linear Regression (MLR method for modeling the deterministic part of spatial variation, which is usual in the standard regression (residual kriging model (MLRK. The analysis encompassed: testing for environmental correlation, selecting an appropriate regression model, testing for spatial autocorrelation of the residual component, and validating the prediction accuracy. The proposed approach was performed for 69 air temperature cases, with time aggregation ranging from daily to annual average air temperatures. The results show that, irrespective of the level of data aggregation, the spatial distribution of temperature is better fitted by local models, and hence is the reason for choosing a GWR instead of the MLR for all variables analyzed. Additionally, in most cases (78% there is spatial autocorrelation in the residuals of the deterministic part, which suggests that the GWR model should be extended by ordinary kriging of residuals to the GWRK form. The decision tree used in this paper can be considered as universal as it encompasses either spatially varying relationships of modeled and explanatory variables or random process that can be modeled by a stochastic extension of the regression model (residual kriging. Moreover, for all cases analyzed, the selection of a method based on the local regression model (GWRK or GWR does not depend on the data aggregation level, showing the potential versatility of the technique.

  14. Multiyear Predictability of Surface Air Temperature in the Kiel Climate Model

    Science.gov (United States)

    Wu, Yanling; Latif, Mojib; Park, Wonsun

    2015-04-01

    The multiyear predictability of unforced surface air temperature (SAT) variability is examined in the Kiel Climate Model (KCM), a coupled ocean-atmosphere-sea ice general circulation model. A statistical method that maximizes Average Predictability Time (APT) is used to find the most predictable patterns in the model. Multiyear SAT predictability is detected in the North Atlantic and North Pacific sectors. In both regions, ocean dynamics enhances predictability, while the net heat flux is a damping factor. Enhanced predictability in the North Atlantic sector is concentrated near the sea ice margin. The multiyear predictability there is linked to the Atlantic Multidecadal Oscillation/Variability (AMO/V) and also associated with variability of the subpolar gyre. In the North Pacific, the most predictable pattern is characterized by a zonal band in the western and central mid-latitude Pacific. It is linked to the Pacific Decadal Oscillation (PDO) which produces temperature anomalies in the surface layer during winter. These are subducted into deeper layers and re-emerge during the following winters, giving rise to multiyear predictability. The results are consistent with those obtained from the CMIP5 ensemble.

  15. Multiyear predictability of Northern Hemisphere surface air temperature in the Kiel Climate Model

    Science.gov (United States)

    Wu, Y.; Latif, M.; Park, W.

    2016-08-01

    The multiyear predictability of Northern Hemisphere surface air temperature (SAT) is examined in a multi-millennial control integration of the Kiel Climate Model, a coupled ocean-atmosphere-sea ice general circulation model. A statistical method maximizing average predictability time (APT) is used to identify the most predictable SAT patterns in the model. The two leading APT modes are much localized and the physics are discussed that give rise to the enhanced predictability of SAT in these limited regions. Multiyear SAT predictability exists near the sea ice margin in the North Atlantic and mid-latitude North Pacific sector. Enhanced predictability in the North Atlantic is linked to the Atlantic Multidecadal Oscillation and to the sea ice changes. In the North Pacific, the most predictable SAT pattern is characterized by a zonal band in the western and central mid-latitude Pacific. This pattern is linked to the Pacific Decadal Oscillation, which drives sea surface temperature anomalies. The temperature anomalies subduct into deeper ocean layers and re-emerge at the sea surface during the following winters, providing multiyear memory. Results obtained from the Coupled Model Intercomparison Project Phase 5 ensemble yield similar APT modes. Overall, the results stress the importance of ocean dynamics in enhancing predictability in the atmosphere.

  16. Bending Angle Prediction Model Based on BPNN-Spline in Air Bending Springback Process

    Directory of Open Access Journals (Sweden)

    Zhefeng Guo

    2017-01-01

    Full Text Available In order to rapidly and accurately predict the springback bending angle in V-die air bending process, a springback bending angle prediction model on the combination of error back propagation neural network and spline function (BPNN-Spline is presented in this study. An orthogonal experimental sample set for training BPNN-Spline is obtained by finite element simulation. Through the analysis of network structure, the BPNN-Spline black box function of bending angle prediction is established, and the advantage of BPNN-Spline is discussed in comparison with traditional BPNN. The results show a close agreement with simulated and experimental results by application examples, which means that the BPNN-Spline model in this study has higher prediction accuracy and better applicable ability. Therefore, it could be adopted in a numerical control bending machine system.

  17. AN AIR POLLUTION PREDICTION TECHNIQUE FOR URBAN DISTRICTS BASED ON MESO-SCALE NUMERICAL MODEL

    Institute of Scientific and Technical Information of China (English)

    YAN Jing-hua; XU Jian-ping

    2005-01-01

    Taking Shenzhen city as an example, the statistical and physical relationship between the density of pollutants and various atmospheric parameters are analyzed in detail, and a space-partitioned city air pollution potential prediction scheme is established based on it. The scheme considers quantitatively more than ten factors at the surface and planetary boundary layer (PBL), especially the effects of anisotropy of geographical environment, and treats wind direction as an independent impact factor. While the scheme treats the prediction equation respectively for different pollutants according to their differences in dilute properties, it considers as well the possible differences in dilute properties at different districts of the city under the same atmospheric condition, treating predictions respectively for different districts. Finally, the temporally and spatially high resolution predictions for the atmospheric factors are made with a high resolution numerical model, and further the space-partitioned and time-variational city pollution potential predictions are made. The scheme is objective and quantitative, and with clear physical meaning, so it is suitable to use in making high resolution air pollution predictions.

  18. Modelling air pollution for epidemiologic research--part II: predicting temporal variation through land use regression.

    Science.gov (United States)

    Mölter, A; Lindley, S; de Vocht, F; Simpson, A; Agius, R

    2010-12-01

    Over recent years land use regression (LUR) has become a frequently used method in air pollution exposure studies, as it can model intra-urban variation in pollutant concentrations at a fine spatial scale. However, very few studies have used the LUR methodology to also model the temporal variation in air pollution exposure. The aim of this study is to estimate annual mean NO(2) and PM(10) concentrations from 1996 to 2008 for Greater Manchester using land use regression models. The results from these models will be used in the Manchester Asthma and Allergy Study (MAAS) birth cohort to determine health effects of air pollution exposure. The Greater Manchester LUR model for 2005 was recalibrated using interpolated and adjusted NO(2) and PM(10) concentrations as dependent variables for 1996-2008. In addition, temporally resolved variables were available for traffic intensity and PM(10) emissions. To validate the resulting LUR models, they were applied to the locations of automatic monitoring stations and the estimated concentrations were compared against measured concentrations. The 2005 LUR models were successfully recalibrated, providing individual models for each year from 1996 to 2008. When applied to the monitoring stations the mean prediction error (MPE) for NO(2) concentrations for all stations and years was -0.8μg/m³ and the root mean squared error (RMSE) was 6.7μg/m³. For PM(10) concentrations the MPE was 0.8μg/m³ and the RMSE was 3.4μg/m³. These results indicate that it is possible to model temporal variation in air pollution through LUR with relatively small prediction errors. It is likely that most previous LUR studies did not include temporal variation, because they were based on short term monitoring campaigns and did not have historic pollution data. The advantage of this study is that it uses data from an air dispersion model, which provided concentrations for 2005 and 2010, and therefore allowed extrapolation over a longer time period

  19. Impact of a new condensed toluene mechanism on air quality model predictions in the US

    Directory of Open Access Journals (Sweden)

    G. Sarwar

    2010-12-01

    Full Text Available A new condensed toluene mechanism is incorporated into the Community Multiscale Air Quality Modeling system. Model simulations are performed using the CB05 chemical mechanism containing the existing (base and the new toluene mechanism for the western and eastern US for a summer month. With current estimates of tropospheric emission burden, the new toluene mechanism increases monthly mean daily maximum 8-h ozone by 1.0–3.0 ppbv in Los Angeles, Portland, Seattle, Chicago, Cleveland, northeastern US, and Detroit compared to that with the base toluene chemistry. It reduces model mean bias for ozone at elevated observed ozone mixing ratios. While the new mechanism increases predicted ozone, it does not enhance ozone production efficiency. Sensitivity study suggests that it can further enhance ozone if elevated toluene emissions are present. While changes in total fine particulate mass are small, predictions of in-cloud SOA increase substantially.

  20. Prediction of lake surface temperature using the air2water model: guidelines, challenges, and future perspectives

    Directory of Open Access Journals (Sweden)

    Sebastiano Piccolroaz

    2016-04-01

    Full Text Available Water temperature plays a primary role in controlling a wide range of physical, geochemical and ecological processes in lakes, with considerable influences on lake water quality and ecosystem functioning. Being able to reliably predict water temperature is therefore a desired goal, which stimulated the development of models of different type and complexity, ranging from simple regression-based models to more sophisticated process-based numerical models. However, both types of models suffer of some limitations: the first are not able to address some fundamental physical processes as e.g., thermal stratification, while the latter generally require a large amount of data in input, which are not always available. In this work, lake surface temperature is simulated by means of air2water, a hybrid physically-based/statistical model, which is able to provide a robust, predictive understanding of LST dynamics knowing air temperature only. This model showed performances that are comparable with those obtained by using process based models (a root mean square error on the order of 1°C, at daily scale, while retaining the simplicity and parsimony of regression-based models, thus making it a good candidate for long-term applications.The aim of the present work is to provide the reader with useful and practical guidelines for proper use of the air2water model and for critical analysis of results. Two case studies have been selected for the analysis: Lake Superior and Lake Erie. These are clear and emblematic examples of a deep and a shallow temperate lake characterized by markedly different thermal responses to external forcing, thus are ideal for making the results of the analysis the most general and comprehensive. Particular attention is paid to assessing the influence of missing data on model performance, and to evaluating when an observed time series is sufficiently informative for proper model calibration or, conversely, data are too scarce thus

  1. Sensitivity of ozone predictions to biogenic hydrocarbon chemistry and emissions in air quality models

    Energy Technology Data Exchange (ETDEWEB)

    Jang, C.J.; Lo, S.C.Y.; Vukovich, J.; Kasibhatla, P. [MCNC-North Carolina Supercomputing Center, Research Triangle Park, NC (United States)

    1997-12-31

    Over the last decade, there is growing evidence that biogenic hydrocarbons play an important role in regional and urban ozone (O{sub 3}) formation in the United States. As a result, the regulatory guidelines issued by the USEPA require that biogenic emissions be included in photochemical modeling. Significant changes and improvement have also been made for estimating the emissions and chemical reaction rates of biogenic hydrocarbons in air quality models. In this paper the authors examine the sensitivity of ozone predictions to the changes in biogenic hydrocarbon chemistry and emissions and investigate why ozone is sensitive to these changes. They first use a Lagrangian box model, the OZIPR/EKMA model, to examine the differences of O{sub 3} predicted using two sets of chemical mechanisms, the original CB4 mechanism and the updated CB4 mechanism with new isoprene chemistry under various emission scenarios. The results show that in the selected urban case, the updated CB4 mechanism predicted lower O{sub 3} than the original CB4 mechanism because of the lower isoprene incremental reactivity in the updated CB4 mechanism. However, in the selected rural case, the updated CB4 mechanism predicted higher O{sub 3} than the original CB4, which is in contradiction to a recent OTAG study using the updated CB4 mechanism. The Eulerian grid model simulation using the MCNC`s EDSS/MAQSIP system further lends support to the box model results. The grid model simulations show that the updated CB4 mechanism predicts much lower O{sub 3} than the original CB4 mechanism over the areas where significant amount of NO{sub x} is emitted; on the contrary, over the Southeastern US region with high isoprene emission rates, the updated CB4 mechanism predicts much higher O{sub 3}.

  2. MARSpline model for lead seven-day maximum and minimum air temperature prediction in Chennai, India

    Indian Academy of Sciences (India)

    K Ramesh; R Anitha

    2014-06-01

    In this study, a Multivariate Adaptive Regression Spline (MARS) based lead seven days minimum and maximum surface air temperature prediction system is modelled for station Chennai, India. To emphasize the effectiveness of the proposed system, comparison is made with the models created using statistical learning technique Support Vector Machine Regression (SVMr). The analysis highlights that prediction accuracy of MARS models for minimum temperature forecast are promising for short-term forecast (lead days 1 to 3) with mean absolute error (MAE) less than 1°C and the prediction efficiency and skill degrades in medium term forecast (lead days 4 to 7) with slightly above 1°C. The MAE of maximum temperature is little higher than minimum temperature forecast varying from 0.87°C for day-one to 1.27°C for lag day-seven with MARS approach. The statistical error analysis emphasizes that MARS models perform well with an average 0.2°C of reduction in MAE over SVMr models for all ahead seven days and provide significant guidance for the prediction of temperature event. The study also suggests that the correlation between the atmospheric parameters used as predictors and the temperature event decreases as the lag increases with both approaches.

  3. Impact of a new condensed toluene mechanism on air quality model predictions in the US

    Directory of Open Access Journals (Sweden)

    G. Sarwar

    2011-03-01

    Full Text Available A new condensed toluene mechanism is incorporated into the Community Multiscale Air Quality Modeling system. Model simulations are performed using the CB05 chemical mechanism containing the existing (base and the new toluene mechanism for the western and eastern US for a summer month. With current estimates of tropospheric emission burden, the new toluene mechanism increases monthly mean daily maximum 8-h ozone by 1.0–3.0 ppbv in Los Angeles, Portland, Seattle, Chicago, Cleveland, northeastern US, and Detroit compared to that with the base toluene chemistry. It reduces model mean bias for ozone at elevated observed ozone concentrations. While the new mechanism increases predicted ozone, it does not enhance ozone production efficiency. A sensitivity study suggests that it can further enhance ozone if elevated toluene emissions are present. While it increases in-cloud secondary organic aerosol substantially, its impact on total fine particle mass concentration is small.

  4. Model predictive control of an air suspension system with damping multi-mode switching damper based on hybrid model

    Science.gov (United States)

    Sun, Xiaoqiang; Yuan, Chaochun; Cai, Yingfeng; Wang, Shaohua; Chen, Long

    2017-09-01

    This paper presents the hybrid modeling and the model predictive control of an air suspension system with damping multi-mode switching damper. Unlike traditional damper with continuously adjustable damping, in this study, a new damper with four discrete damping modes is applied to vehicle semi-active air suspension. The new damper can achieve different damping modes by just controlling the on-off statuses of two solenoid valves, which makes its damping adjustment more efficient and more reliable. However, since the damping mode switching induces different modes of operation, the air suspension system with the new damper poses challenging hybrid control problem. To model both the continuous/discrete dynamics and the switching between different damping modes, the framework of mixed logical dynamical (MLD) systems is used to establish the system hybrid model. Based on the resulting hybrid dynamical model, the system control problem is recast as a model predictive control (MPC) problem, which allows us to optimize the switching sequences of the damping modes by taking into account the suspension performance requirements. Numerical simulations results demonstrate the efficacy of the proposed control method finally.

  5. Fault Tolerance Automotive Air-Ratio Control Using Extreme Learning Machine Model Predictive Controller

    OpenAIRE

    Pak Kin Wong; Hang Cheong Wong; Chi Man Vong; Tong Meng Iong; Ka In Wong; Xianghui Gao

    2015-01-01

    Effective air-ratio control is desirable to maintain the best engine performance. However, traditional air-ratio control assumes the lambda sensor located at the tail pipe works properly and relies strongly on the air-ratio feedback signal measured by the lambda sensor. When the sensor is warming up during cold start or under failure, the traditional air-ratio control no longer works. To address this issue, this paper utilizes an advanced modelling technique, kernel extreme learning machine (...

  6. Spatial measurement error and correction by spatial SIMEX in linear regression models when using predicted air pollution exposures.

    Science.gov (United States)

    Alexeeff, Stacey E; Carroll, Raymond J; Coull, Brent

    2016-04-01

    Spatial modeling of air pollution exposures is widespread in air pollution epidemiology research as a way to improve exposure assessment. However, there are key sources of exposure model uncertainty when air pollution is modeled, including estimation error and model misspecification. We examine the use of predicted air pollution levels in linear health effect models under a measurement error framework. For the prediction of air pollution exposures, we consider a universal Kriging framework, which may include land-use regression terms in the mean function and a spatial covariance structure for the residuals. We derive the bias induced by estimation error and by model misspecification in the exposure model, and we find that a misspecified exposure model can induce asymptotic bias in the effect estimate of air pollution on health. We propose a new spatial simulation extrapolation (SIMEX) procedure, and we demonstrate that the procedure has good performance in correcting this asymptotic bias. We illustrate spatial SIMEX in a study of air pollution and birthweight in Massachusetts.

  7. A stochastic simulation model to predict future air quality in protected areas

    Science.gov (United States)

    Stavros, E.; McKenzie, D.; Larkin, N.; Strand, T.; Lamb, B. K.

    2010-12-01

    It is widely accepted in both scientific and political communities such as the Intergovernmental Panel on Climate Change (IPCC) and the Environmental Protection Agency (EPA), that climate is changing. Previous studies have shown that expected changes in climate will increase the severity of wild fire. It is necessary to assess the impact of global climate change on wildfire and consequent effects on air quality in order to meet existing air quality regulations such as the Regional Haze Rule, which regulates visibility in Class 1 or “pristine areas”, and the National Ambient Air Quality Standards (NAAQS). The challenge in such an assessment lies in not only integrating disciplines (climatology, fire ecology, air chemistry), but also in bridging knowledge across temporal (hourly to decadal) and spatial scales (local to global). In response to this challenge, we are integrating a stochastic model to simulate fire events, the Fire Scenario Builder (FSB), and the BlueSky Modeling Framework, which has a strong record of successfully linking wildfire emissions to air quality. FSB integrates fuel information and meteorological data to estimate regional fire season summary statistics such as total area burned and number of fire starts. The Blue Sky Modeling Framework then simulates total fuel consumption and smoke emissions both in local air sheds and downwind. Emissions are then fed into the Community Multiscale Air Quality (CMAQ) model through Sparse Matrix Operator Kernel Emissions Modeling System (SMOKE). The goal of this research is threefold: 1) to compare emission results from the FSB-Blue Sky integration for current vs. future decades; 2) to assess model uncertainty, by comparing model output to observations, analyzing parameter sensitivity, and verifying the theoretical basis of FSB model structure; and, 3) prepare data files for analysis on air quality.

  8. Deep learning architecture for air quality predictions.

    Science.gov (United States)

    Li, Xiang; Peng, Ling; Hu, Yuan; Shao, Jing; Chi, Tianhe

    2016-11-01

    With the rapid development of urbanization and industrialization, many developing countries are suffering from heavy air pollution. Governments and citizens have expressed increasing concern regarding air pollution because it affects human health and sustainable development worldwide. Current air quality prediction methods mainly use shallow models; however, these methods produce unsatisfactory results, which inspired us to investigate methods of predicting air quality based on deep architecture models. In this paper, a novel spatiotemporal deep learning (STDL)-based air quality prediction method that inherently considers spatial and temporal correlations is proposed. A stacked autoencoder (SAE) model is used to extract inherent air quality features, and it is trained in a greedy layer-wise manner. Compared with traditional time series prediction models, our model can predict the air quality of all stations simultaneously and shows the temporal stability in all seasons. Moreover, a comparison with the spatiotemporal artificial neural network (STANN), auto regression moving average (ARMA), and support vector regression (SVR) models demonstrates that the proposed method of performing air quality predictions has a superior performance.

  9. Studies on the tempo of bubble formation in recently cavitated vessels: a model to predict the pressure of air bubbles.

    Science.gov (United States)

    Wang, Yujie; Pan, Ruihua; Tyree, Melvin T

    2015-06-01

    A cavitation event in a vessel replaces water with a mixture of water vapor and air. A quantitative theory is presented to argue that the tempo of filling of vessels with air has two phases: a fast process that extracts air from stem tissue adjacent to the cavitated vessels (less than 10 s) and a slow phase that extracts air from the atmosphere outside the stem (more than 10 h). A model was designed to estimate how water tension (T) near recently cavitated vessels causes bubbles in embolized vessels to expand or contract as T increases or decreases, respectively. The model also predicts that the hydraulic conductivity of a stem will increase as bubbles collapse. The pressure of air bubbles trapped in vessels of a stem can be predicted from the model based on fitting curves of hydraulic conductivity versus T. The model was validated using data from six stem segments each of Acer mono and the clonal hybrid Populus 84 K (Populus alba × Populus glandulosa). The model was fitted to results with root mean square error less than 3%. The model provided new insight into the study of embolism formation in stem tissue and helped quantify the bubble pressure immediately after the fast process referred to above. © 2015 American Society of Plant Biologists. All Rights Reserved.

  10. Prediction of air leakage and aerosol transport through concrete cracks with a fractal based crack morphology model

    Energy Technology Data Exchange (ETDEWEB)

    Bishnoi, L.R., E-mail: lrbishnoi@aerb.gov.in [Siting and Structural Engineering Division, Atomic Energy Regulatory Board, Mumbai 400 094 (India); Vedula, R.P., E-mail: rpv@iitb.ac.in [Mechanical Engineering Department, Indian Institute of Technology, Mumbai 400 076 (India)

    2013-12-15

    Highlights: • A fractal based numerical concrete crack morphology model is presented. • Computational studies conducted for airflow and aerosol transport through cracks. • Results are compared with experimental data and other empirical relations. • Comparative studies demonstrate model effectiveness and versatility of application. - Abstract: Cracks may appear in pressurized concrete containment of a nuclear power plant during a severe accident and provide leak paths for release of radioactive aerosols dispersed in the contained air. In this paper, a fractal based crack morphology model is presented for prediction of air leakage and aerosol transport through cracks in concrete. Airflow field generated in air leakage studies is used for aerosol transport studies with the Lagrangian discrete phase model using CFD code FLUENT. Computational studies conducted with the fractal based model are compared with the experimental data as well as the predictions from empirical relations available in open literature. The comparative studies demonstrate effectiveness of the proposed fractal based model and its versatility for practical applications.

  11. The use of wind fields in a land use regression model to predict air pollution concentrations for health exposure studies

    Science.gov (United States)

    Arain, M. A.; Blair, R.; Finkelstein, N.; Brook, J. R.; Sahsuvaroglu, T.; Beckerman, B.; Zhang, L.; Jerrett, M.

    A methodology is developed to include wind flow effects in land use regression (LUR) models for predicting nitrogen dioxide (NO 2) concentrations for health exposure studies. NO 2 is widely used in health studies as an indicator of traffic-generated air pollution in urban areas. Incorporation of high-resolution interpolated observed wind direction from a network of 38 weather stations in a LUR model improved NO 2 concentration estimates in densely populated, high traffic and industrial/business areas in Toronto-Hamilton urban airshed (THUA) of Ontario, Canada. These small-area variations in air pollution concentrations that are probably more important for health exposure studies may not be detected by sparse continuous air pollution monitoring network or conventional interpolation methods. Observed wind fields were also compared with wind fields generated by Global Environmental Multiscale-High resolution Model Application Project (GEM-HiMAP) to explore the feasibility of using regional weather forecasting model simulated wind fields in LUR models when observed data are either sparse or not available. While GEM-HiMAP predicted wind fields well at large scales, it was unable to resolve wind flow patterns at smaller scales. These results suggest caution and careful evaluation of regional weather forecasting model simulated wind fields before incorporating into human exposure models for health studies. This study has demonstrated that wind fields may be integrated into the land use regression framework. Such integration has a discernable influence on both the overall model prediction and perhaps more importantly for health effects assessment on the relative spatial distribution of traffic pollution throughout the THUA. Methodology developed in this study may be applied in other large urban areas across the world.

  12. Hot air drying characteristics of mango ginger: Prediction of drying kinetics by mathematical modeling and artificial neural network.

    Science.gov (United States)

    Murthy, Thirupathihalli Pandurangappa Krishna; Manohar, Balaraman

    2014-12-01

    Mango ginger (Curcuma amada) was dried in a through-flow dryer system at different temperatures (40-70 °C) and air velocities (0.84 - 2.25 m/s) to determine the effect of drying on drying rate and effective diffusivity. As the temperature and air velocity increased, drying time significantly decreased. Among the ten different thin layer drying models considered to determine the kinetic drying parameters, semi empirical Midilli et al., model gave the best fit for all drying conditions. Effective moisture diffusivity varied from 3.7 × 10(-10) m(2)/s to 12.5 × 10(-10) m(2)/s over the temperature and air velocity range of study. Effective moisture diffusivity regressed well with Arrhenius model and activation energy of the model was found to be 32.6 kJ/mol. Artificial neural network modeling was also employed to predict the drying behaviour and found suitable to describe the drying kinetics with very high correlation coefficient of 0.998.

  13. Hardware-in-the-Loop Simulation of a Distribution System with Air Conditioners under Model Predictive Control: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Sparn, Bethany F [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Ruth, Mark F [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Krishnamurthy, Dheepak [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Pratt, Annabelle [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Lunacek, Monte S [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Jones, Wesley B [National Renewable Energy Laboratory (NREL), Golden, CO (United States)

    2017-08-01

    Many have proposed that responsive load provided by distributed energy resources (DERs) and demand response (DR) are an option to provide flexibility to the grid and especially to distribution feeders. However, because responsive load involves a complex interplay between tariffs and DER and DR technologies, it is challenging to test and evaluate options without negatively impacting customers. This paper describes a hardware-in-the-loop (HIL) simulation system that has been developed to reduce the cost of evaluating the impact of advanced controllers (e.g., model predictive controllers) and technologies (e.g., responsive appliances). The HIL simulation system combines large-scale software simulation with a small set of representative building equipment hardware. It is used to perform HIL simulation of a distribution feeder and the loads on it under various tariff structures. In the reported HIL simulation, loads include many simulated air conditioners and one physical air conditioner. Independent model predictive controllers manage operations of all air conditioners under a time-of-use tariff. Results from this HIL simulation and a discussion of future development work of the system are presented.

  14. Leaching behavior of Pb and Zn in air pollution control residues and their modeling prediction

    Institute of Scientific and Technical Information of China (English)

    ZHANG Hua; HE Pin-jing; SHAO Li-ming; FENG Jun-hui; CAO Qun-ke

    2006-01-01

    Increasing attention has been paid to air pollution control (APC) residues in China recently due to the rising proportion of waste incineration and the hazardous characteristics of the residues, among which heavy metal leaching toxicity plays an important role. Leaching behavior and potential risk of Pb and Zn in the APC residues from a Shanghai municipal solid waste (MSW) incinerator was studied, based on the leaching tests under different conditions and theoretical calculation using a geochemical thermodynamic equilibrium model MINTEQA2. Results showed that, extractant species and liquid to solid (L/S) ratio predominantly controlled the leaching toxicity of Pb and Zn, while ionic strength, vibration method and leaching time had less effect on the metals release.Leachate/final pH determined the metal leaching behavior, which changed the speciation of heavy metals in the extraction system. The equilibrium aqueous speciation, precipitation-dissolution of Pb and Zn was investigated according to the model computation, which was well in agreement with the experimental results.

  15. QSPR models for predicting generator-column-derived octanol/water and octanol/air partition coefficients of polychlorinated biphenyls.

    Science.gov (United States)

    Yuan, Jintao; Yu, Shuling; Zhang, Ting; Yuan, Xuejie; Cao, Yunyuan; Yu, Xingchen; Yang, Xuan; Yao, Wu

    2016-06-01

    Octanol/water (K(OW)) and octanol/air (K(OA)) partition coefficients are two important physicochemical properties of organic substances. In current practice, K(OW) and K(OA) values of some polychlorinated biphenyls (PCBs) are measured using generator column method. Quantitative structure-property relationship (QSPR) models can serve as a valuable alternative method of replacing or reducing experimental steps in the determination of K(OW) and K(OA). In this paper, two different methods, i.e., multiple linear regression based on dragon descriptors and hologram quantitative structure-activity relationship, were used to predict generator-column-derived log K(OW) and log K(OA) values of PCBs. The predictive ability of the developed models was validated using a test set, and the performances of all generated models were compared with those of three previously reported models. All results indicated that the proposed models were robust and satisfactory and can thus be used as alternative models for the rapid assessment of the K(OW) and K(OA) of PCBs.

  16. GNAQPMS v1.1: accelerating the Global Nested Air Quality Prediction Modeling System (GNAQPMS) on Intel Xeon Phi processors

    Science.gov (United States)

    Wang, Hui; Chen, Huansheng; Wu, Qizhong; Lin, Junmin; Chen, Xueshun; Xie, Xinwei; Wang, Rongrong; Tang, Xiao; Wang, Zifa

    2017-08-01

    The Global Nested Air Quality Prediction Modeling System (GNAQPMS) is the global version of the Nested Air Quality Prediction Modeling System (NAQPMS), which is a multi-scale chemical transport model used for air quality forecast and atmospheric environmental research. In this study, we present the porting and optimisation of GNAQPMS on a second-generation Intel Xeon Phi processor, codenamed Knights Landing (KNL). Compared with the first-generation Xeon Phi coprocessor (codenamed Knights Corner, KNC), KNL has many new hardware features such as a bootable processor, high-performance in-package memory and ISA compatibility with Intel Xeon processors. In particular, we describe the five optimisations we applied to the key modules of GNAQPMS, including the CBM-Z gas-phase chemistry, advection, convection and wet deposition modules. These optimisations work well on both the KNL 7250 processor and the Intel Xeon E5-2697 V4 processor. They include (1) updating the pure Message Passing Interface (MPI) parallel mode to the hybrid parallel mode with MPI and OpenMP in the emission, advection, convection and gas-phase chemistry modules; (2) fully employing the 512 bit wide vector processing units (VPUs) on the KNL platform; (3) reducing unnecessary memory access to improve cache efficiency; (4) reducing the thread local storage (TLS) in the CBM-Z gas-phase chemistry module to improve its OpenMP performance; and (5) changing the global communication from writing/reading interface files to MPI functions to improve the performance and the parallel scalability. These optimisations greatly improved the GNAQPMS performance. The same optimisations also work well for the Intel Xeon Broadwell processor, specifically E5-2697 v4. Compared with the baseline version of GNAQPMS, the optimised version was 3.51 × faster on KNL and 2.77 × faster on the CPU. Moreover, the optimised version ran at 26 % lower average power on KNL than on the CPU. With the combined performance and energy

  17. The statistical evaluation and comparison of ADMS-Urban model for the prediction of nitrogen dioxide with air quality monitoring network.

    Science.gov (United States)

    Dėdelė, Audrius; Miškinytė, Auksė

    2015-09-01

    In many countries, road traffic is one of the main sources of air pollution associated with adverse effects on human health and environment. Nitrogen dioxide (NO2) is considered to be a measure of traffic-related air pollution, with concentrations tending to be higher near highways, along busy roads, and in the city centers, and the exceedances are mainly observed at measurement stations located close to traffic. In order to assess the air quality in the city and the air pollution impact on public health, air quality models are used. However, firstly, before the model can be used for these purposes, it is important to evaluate the accuracy of the dispersion modelling as one of the most widely used method. The monitoring and dispersion modelling are two components of air quality monitoring system (AQMS), in which statistical comparison was made in this research. The evaluation of the Atmospheric Dispersion Modelling System (ADMS-Urban) was made by comparing monthly modelled NO2 concentrations with the data of continuous air quality monitoring stations in Kaunas city. The statistical measures of model performance were calculated for annual and monthly concentrations of NO2 for each monitoring station site. The spatial analysis was made using geographic information systems (GIS). The calculation of statistical parameters indicated a good ADMS-Urban model performance for the prediction of NO2. The results of this study showed that the agreement of modelled values and observations was better for traffic monitoring stations compared to the background and residential stations.

  18. A one-dimensional numerical model for predicting pressure and velocity oscillations of a compressed air-pocket in a vertical shaft

    Science.gov (United States)

    Choi, Y.; Leon, A.; Apte, S.

    2015-12-01

    The presence of pressurized air pockets in combined sewer systems is argued to produce geyser flows, which is an oscillating jetting of a mixture of gas-liquid flows through vertical shafts. A 1D numerical model is developed for predicting pressure and velocity oscillations of a compressed air-pocket in a vertical shaft which in turn attempts to simulate geyser like flows. The vertical shaft is closed at the bottom and open to ambient pressure at the top. Initially, the lower section of the vertical shaft is filled with compressed air and the upper section with water. The interaction between the pressurized air pocket and the water column in the vertical shaft exhibits an oscillatory motion of the water column that decays over time. The model accounts for steady and unsteady friction to estimate the energy dissipation. The model also includes the falling flow of water around the external perimeter of the pressurized air pocket by assuming that any expansion in the pressurized air pocket would result in the falling volume of water. The acceleration of air-water interface is predicted through a force balance between the pressurized air pocket and the water column combined with the Method of Characteristics that resolves pressure and velocity within the water column. The expansion and compression of the pressurized air pocket is assumed to follow either isothermal process or adiabatic process. Results for both assumptions; isothermal and adiabatic processes, are presented. The performance of the developed 1D numerical model is compared with that of a commercial 3D CFD model. Overall, a good agreement between both models is obtained for pressure and velocity oscillations. The paper will also present a sensitivity analysis of the 3D CFD model.

  19. Predictions of U.K. regulated power station contributions to regional air pollution and deposition: a model comparison exercise.

    Science.gov (United States)

    Chemel, Charles; Sokhi, Ranjeet S; Dore, Anthony J; Sutton, Paul; Vincent, Keith J; Griffiths, Stephen J; Hayman, Garry D; Wright, Raymond D; Baggaley, Matthew; Hallsworth, Stephen; Prain, H Douglas; Fisher, Bernard E A

    2011-11-01

    Contributions of the emissions from a U.K. regulated fossil-fuel power station to regional air pollution and deposition are estimated using four air quality modeling systems for the year 2003. The modeling systems vary in complexity and emphasis in the way they treat atmospheric and chemical processes, and include the Community Multiscale Air Quality (CMAQ) modeling system in its versions 4.6 and 4.7, a nested modeling system that combines long- and short-range impacts (referred to as TRACK-ADMS [Trajectory Model with Atmospheric Chemical Kinetics-Atmospheric Dispersion Modelling System]), and the Fine Resolution Atmospheric Multi-pollutant Exchange (FRAME) model. An evaluation of the baseline calculations against U.K. monitoring network data is performed. The CMAQ modeling system version 4.6 data set is selected as the reference data set for the model footprint comparison. The annual mean air concentration and total deposition footprints are summarized for each modeling system. The footprints of the power station emissions can account for a significant fraction of the local impacts for some species (e.g., more than 50% for SO2 air concentration and non-sea-salt sulfur deposition close to the source) for 2003. The spatial correlation and the coefficient of variation of the root mean square error (CVRMSE) are calculated between each model footprint and that calculated by the CMAQ modeling system version 4.6. The correlation coefficient quantifies model agreement in terms of spatial patterns, and the CVRMSE measures the magnitude of the difference between model footprints. Possible reasons for the differences between model results are discussed. Finally, implications and recommendations for the regulatory assessment of the impact of major industrial sources using regional air quality modeling systems are discussed in the light of results from this case study.

  20. Studies on the Tempo of Bubble Formation in Recently Cavitated Vessels: A Model to Predict the Pressure of Air Bubbles1

    Science.gov (United States)

    Wang, Yujie; Pan, Ruihua; Tyree, Melvin T.

    2015-01-01

    A cavitation event in a vessel replaces water with a mixture of water vapor and air. A quantitative theory is presented to argue that the tempo of filling of vessels with air has two phases: a fast process that extracts air from stem tissue adjacent to the cavitated vessels (less than 10 s) and a slow phase that extracts air from the atmosphere outside the stem (more than 10 h). A model was designed to estimate how water tension (T) near recently cavitated vessels causes bubbles in embolized vessels to expand or contract as T increases or decreases, respectively. The model also predicts that the hydraulic conductivity of a stem will increase as bubbles collapse. The pressure of air bubbles trapped in vessels of a stem can be predicted from the model based on fitting curves of hydraulic conductivity versus T. The model was validated using data from six stem segments each of Acer mono and the clonal hybrid Populus 84K (Populus alba × Populus glandulosa). The model was fitted to results with root mean square error less than 3%. The model provided new insight into the study of embolism formation in stem tissue and helped quantify the bubble pressure immediately after the fast process referred to above. PMID:25907963

  1. AQA - Air Quality model for Austria: comparison of ALADIN and ALARO forecasts with observed meteorological profiles and PM10 predictions with CAMx

    Science.gov (United States)

    Hirtl, M.; Krüger, B. C.; Kaiser, A.

    2009-09-01

    In AQA, Air Quality model for Austria, the regional weather forecast model ALADIN-Austria of the Central Institute for Meteorology and Geodynamics (ZAMG) is used in combination with the chemical transport model CAMx (www.camx.com) to conduct forecasts of gaseous and particulate air pollutants over Austria. The forecasts which are done in cooperation with the University of Natural Resources and Applied Life Sciences in Vienna (BOKU) are supported by the regional governments since 2005. In the current model version AQA uses the operational meteorological forecasts conducted with ALADIN which has a horizontal resolution of 9.7 km. Since 2008 the higher resolved ALARO is also available at the ZAMG. It has a horizontal resolution of 4.9 km and models the PBL with more vertical layers than ALADIN. ALARO also uses more complex algorithms to calculate precipitation, radiation and TKE. Another advantage of ALARO concerning the chemical modelling with CAMx is that additionally to the higher resolved meteorological forecasts it is possible to use finer emission inventories which are available for Austria. From 2006 to 2007 a SODAR-RASS of the ZAMG was operated in the north-eastern Austrian flat lands (Kittsee). In this study the measured vertical profiles of wind and temperature are compared with the model predictions. The evaluation is conducted for an episode in January 2007 when high PM10 concentrations were measured at the air quality station Kittsee. Analysis of the RASS-temperature-profiles show that during this episode a strong nocturnal inversion developed at the investigated area. The ability of the models ALADIN and ALARO to predict this complex meteorological condition is investigated. Both models are also used as meteorological driver for the chemical dispersion model CAMx and the results of predicted PM10 concentrations are compared to air quality measurements.

  2. Air Pollution Modelling to Predict Maximum Ground Level Concentration for Dust from a Palm Oil Mill Stack

    Directory of Open Access Journals (Sweden)

    Regina A. A.

    2010-12-01

    Full Text Available The study is to model emission from a stack to estimate ground level concentration from a palm oil mill. The case study is a mill located in Kuala Langat, Selangor. Emission source is from boilers stacks. The exercise determines the estimate the ground level concentrations for dust to the surrounding areas through the utilization of modelling software. The surround area is relatively flat, an industrial area surrounded by factories and with palm oil plantations in the outskirts. The model utilized in the study was to gauge the worst-case scenario. Ambient air concentrations were garnered calculate the increase to localized conditions. Keywords: emission, modelling, palm oil mill, particulate, POME

  3. Verification of a prognostic meteorological and air pollution model for year-long predictions in the Kwinana industrial region of Western Australia

    Energy Technology Data Exchange (ETDEWEB)

    Hurley, P.J. [CSIRO Atmospheric Research, Aspendale, Vic (Australia); Blockley, A.; Rayner, K. [Department of Environmental Protection, Perth, WA (Australia)

    2001-04-01

    A prognostic air pollution model (TAPM) has been used to predict meteorology and sulphur dioxide concentration in the Kwinana industrial region of Western Australia for 1997, with a view to verifying TAPM for use in environmental impact assessments and associated air pollution studies. The regulatory plume model, DISPMOD, developed for the Kwinana region has also been run using both an observationally based meteorological file (denoted DISPMOD-O) and using a TAPM-based meteorological file (denoted DISPMOD-T). TAPM predictions of the meteorology for 1997 compare well with the observed values at each of the five monitoring sites. Root mean square error and index of agreement values for temperature and winds indicate that TAPM performs well at predicting the meteorology, compared to the performance of similar models from other studies. The yearly average, 99.9 percentile, maximum and mean of the top 10 ground-level sulphur dioxide concentrations for 1997 were predicted well by all of the model runs, although DISPMOD-O and DISPMOD-T tended to overpredict extreme statistics at sites furthest from the sources. Overall, TAPM performed better than DISPMOD-O, which in turn performed better than DISPMOD-T, for all statistics considered, but we consider that all three sets of results are sufficiently accurate for regulatory applications. The mean of the top ten concentrations is generally considered to be a robust performance statistic for air pollution applications, and we show that compared to the site-averaged observed value of 95{mu}gm{sup -3}, TAPM predicted 94{mu}gm{sup -3}, DISPMOD-O predicted 111{mu}gm{sup -3} and DISPMOD-T predicted 125{mu}gm{sup -3}. The results indicate that the prognostic meteorological and air pollution approach to regulatory modelling used by TAPM, gives comparable or better results than the current regulatory approach used in the Kwinana region (DISPMOD), and also indicates that the approach of using a currently accepted regulatory model with a

  4. Development of temporally refined land-use regression models predicting daily household-level air pollution in a panel study of lung function among asthmatic children.

    Science.gov (United States)

    Johnson, Markey; Macneill, Morgan; Grgicak-Mannion, Alice; Nethery, Elizabeth; Xu, Xiaohong; Dales, Robert; Rasmussen, Pat; Wheeler, Amanda

    2013-01-01

    Regulatory monitoring data and land-use regression (LUR) models have been widely used for estimating individual exposure to ambient air pollution in epidemiologic studies. However, LUR models lack fine-scale temporal resolution for predicting acute exposure and regulatory monitoring provides daily concentrations, but fails to capture spatial variability within urban areas. This study coupled LUR models with continuous regulatory monitoring to predict daily ambient nitrogen dioxide (NO(2)) and particulate matter (PM(2.5)) at 50 homes in Windsor, Ontario. We compared predicted versus measured daily outdoor concentrations for 5 days in winter and 5 days in summer at each home. We also examined the implications of using modeled versus measured daily pollutant concentrations to predict daily lung function among asthmatic children living in those homes. Mixed effect analysis suggested that temporally refined LUR models explained a greater proportion of the spatial and temporal variance in daily household-level outdoor NO(2) measurements compared with daily concentrations based on regulatory monitoring. Temporally refined LUR models captured 40% (summer) and 10% (winter) more of the spatial variance compared with regulatory monitoring data. Ambient PM(2.5) showed little spatial variation; therefore, daily PM(2.5) models were similar to regulatory monitoring data in the proportion of variance explained. Furthermore, effect estimates for forced expiratory volume in 1 s (FEV(1)) and peak expiratory flow (PEF) based on modeled pollutant concentrations were consistent with effects based on household-level measurements for NO(2) and PM(2.5). These results suggest that LUR modeling can be combined with continuous regulatory monitoring data to predict daily household-level exposure to ambient air pollution. Temporally refined LUR models provided a modest improvement in estimating daily household-level NO(2) compared with regulatory monitoring data alone, suggesting that this

  5. Apply Woods Model in the Predictions of Ambient Air Particles and Metallic Elements (Mn, Fe, Zn, Cr, and Cu at Industrial, Suburban/Coastal, and Residential Sampling Sites

    Directory of Open Access Journals (Sweden)

    Guor-Cheng Fang

    2012-01-01

    Full Text Available The main purpose for this study was to monitor ambient air particles and metallic elements (Mn, Fe, Zn, Cr, and Cu in total suspended particulates (TSPs concentration, dry deposition at three characteristic sampling sites of central Taiwan. Additionally, the calculated/measured dry deposition flux ratios of ambient air particles and metallic elements were calculated with Woods models at these three characteristic sampling sites during years of 2009-2010. As for ambient air particles, the results indicated that the Woods model generated the most accurate dry deposition prediction results when particle size was 18 μm in this study. The results also indicated that the Woods model exhibited better dry deposition prediction performance when the particle size was greater than 10 μm for the ambient air metallic elements in this study. Finally, as for Quan-xing sampling site, the main sources were many industrial factories under process around these regions and were severely polluted areas. In addition, the highest average dry deposition for Mn, Fe, Zn, and Cu species occurred at Bei-shi sampling site, and the main sources were the nearby science park, fossil fuel combustion, and Taichung thermal power plant (TTPP. Additionally, as for He-mei sampling site, the main sources were subjected to traffic mobile emissions.

  6. Application of Air Prediction Model and Online Monitoring in Environmental Air Quality Prediction%论大气预测模型与在线监测在环境空气质量预报中的应用

    Institute of Scientific and Technical Information of China (English)

    刘鲁新

    2013-01-01

    With the development of society and the development of industry, the worsening ecological environment poses a serious threat to human survival and development. Therefore, we must monitor the environment by certain means. The traditional environmental detection has not been able to meet the requirements for modernization and informatization. Therefore, the current monitoring system uses modern technology. This paper studies the present situation of research and development status and the problems in on-line environmental air quality monitoring at home and abroad. In the end, it presents some common atmospheric environment quality monitoring model. It puts forward the design scheme of atmospheric environmental quality prediction system by the third generation air forecasting model, combined with geographic information system software.%  随着社会以及工业的发展,日益恶化的生态环境严重威胁人类的生存和发展。因此,必须采用一定的手段对环境进行监测。传统的环境检测已经不能够满足现代化以及信息化的要求,因此,目前的监测系统都应用了现代化技术。本文研究了大气环境质量在线监测预报的国外发展现状、国内的研究现状及存在的问题,对一些常用的大气环境质量预测模型进行了介绍分析,最后选用第三代大气预测模型结合地理信息系统软件,提出了大气环境质量预报系统设计方案。

  7. On-line Chemistry within WRF: Description and Evaluation of a State-of-the-Art Multiscale Air Quality and Weather Prediction Model

    Energy Technology Data Exchange (ETDEWEB)

    Grell, Georg; Fast, Jerome D.; Gustafson, William I.; Peckham, Steven E.; McKeen, Stuart A.; Salzmann, Marc; Freitas, Saulo

    2010-01-01

    This is a conference proceeding that is now being put together as a book. This is chapter 2 of the book: "INTEGRATED SYSTEMS OF MESO-METEOROLOGICAL AND CHEMICAL TRANSPORT MODELS" published by Springer. The chapter title is "On-line Chemistry within WRF: Description and Evaluation of a State-of-the-Art Multiscale Air Quality and Weather Prediction Model." The original conference was the COST-728/NetFAM workshop on Integrated systems of meso-meteorological and chemical transport models, Danish Meteorological Institute, Copenhagen, May 21-23, 2007.

  8. Modelling and prediction of air pollutant transport during the 2014 biomass burning and forest fires in peninsular Southeast Asia.

    Science.gov (United States)

    Duc, Hiep Nguyen; Bang, Ho Quoc; Quang, Ngo Xuan

    2016-02-01

    During the dry season, from November to April, agricultural biomass burning and forest fires especially from March to late April in mainland Southeast Asian countries of Myanmar, Thailand, Laos and Vietnam frequently cause severe particulate pollution not only in the local areas but also across the whole region and beyond due to the prevailing meteorological conditions. Recently, the BASE-ASIA (Biomass-burning Aerosols in South East Asia: Smoke Impact Assessment) and 7-SEAS (7-South-East Asian Studies) studies have provided detailed analysis and important understandings of the transport of pollutants, in particular, the aerosols and their characteristics across the region due to biomass burning in Southeast Asia (SEA). Following these studies, in this paper, we study the transport of particulate air pollution across the peninsular region of SEA and beyond during the March 2014 burning period using meteorological modelling approach and available ground-based and satellite measurements to ascertain the extent of the aerosol pollution and transport in the region of this particular event. The results show that the air pollutants from SEA biomass burning in March 2014 were transported at high altitude to southern China, Hong Kong, Taiwan and beyond as has been highlighted in the BASE-ASIA and 7-SEAS studies. There are strong evidences that the biomass burning in SEA especially in mid-March 2014 has not only caused widespread high particle pollution in Thailand (especially the northern region where most of the fires occurred) but also impacted on the air quality in Hong Kong as measured at the ground-based stations and in LulinC (Taiwan) where a remote background monitoring station is located.

  9. Comparison of regression models with land-use and emissions data to predict the spatial distribution of traffic-related air pollution in Rome.

    Science.gov (United States)

    Rosenlund, Mats; Forastiere, Francesco; Stafoggia, Massimo; Porta, Daniela; Perucci, Mara; Ranzi, Andrea; Nussio, Fabio; Perucci, Carlo A

    2008-03-01

    Spatial modeling of traffic-related air pollution typically involves either regression modeling of land-use and traffic data or dispersion modeling of emissions data, but little is known to what extent land-use regression models might be improved by incorporating emissions data. The aim of this study was to develop a land-use regression model to predict nitrogen dioxide (NO2) concentrations and compare its performance with a model including emissions data. The association between each land-use variable and NO2 concentrations at 68 locations in Rome in 1995 and 1996 was assessed by univariate linear regression and a multiple linear regression model that was constructed based on the importance of each variable. Traffic emissions (particulate matter, carbon monoxide, nitrogen oxides, and benzene) were estimated for 164 areas of the city based on vehicle type, traffic counts and driving patterns. Mean NO2 concentration across the 68 sites was 46.8 microg/m3 (SD 9.8 microg/m3; inter-quartile range 11.5 microg/m3; min 24 microg/m3; max 73 microg/m3). The most important predicting variables were the circular traffic zones (main ring road, green strip, inner ring road, traffic-limited zone), distance from busy streets, size of the census block, the inverse population density, and altitude. A multiple regression model including these variables resulted in an R2 of 0.686. The best-fitting model adding an emission term of benzene resulted in an R2 of 0.690, but was not significantly different from the model without emissions (P=0.147). In conclusion, these results suggest that a land-use regression model explains the traffic-related air pollution levels with reasonable accuracy and that emissions data do not significantly improve the model.

  10. A new air quality monitoring and early warning system: Air quality assessment and air pollutant concentration prediction.

    Science.gov (United States)

    Yang, Zhongshan; Wang, Jian

    2017-10-01

    Air pollution in many countries is worsening with industrialization and urbanization, resulting in climate change and affecting people's health, thus, making the work of policymakers more difficult. It is therefore both urgent and necessary to establish amore scientific air quality monitoring and early warning system to evaluate the degree of air pollution objectively, and predict pollutant concentrations accurately. However, the integration of air quality assessment and air pollutant concentration prediction to establish an air quality system is not common. In this paper, we propose a new air quality monitoring and early warning system, including an assessment module and forecasting module. In the air quality assessment module, fuzzy comprehensive evaluation is used to determine the main pollutants and evaluate the degree of air pollution more scientifically. In the air pollutant concentration prediction module, a novel hybridization model combining complementary ensemble empirical mode decomposition, a modified cuckoo search and differential evolution algorithm, and an Elman neural network, is proposed to improve the forecasting accuracy of six main air pollutant concentrations. To verify the effectiveness of this system, pollutant data for two cities in China are used. The result of the fuzzy comprehensive evaluation shows that the major air pollutants in Xi'an and Jinan are PM10 and PM2.5 respectively, and that the air quality of Xi'an is better than that of Jinan. The forecasting results indicate that the proposed hybrid model is remarkably superior to all benchmark models on account of its higher prediction accuracy and stability. Copyright © 2017 Elsevier Inc. All rights reserved.

  11. Influence of lateral and top boundary conditions on regional air quality prediction: A multiscale study coupling regional and global chemical transport models

    Science.gov (United States)

    Tang, Youhua; Carmichael, Gregory R.; Thongboonchoo, Narisara; Chai, Tianfeng; Horowitz, Larry W.; Pierce, Robert B.; Al-Saadi, Jassim A.; Pfister, Gabriele; Vukovich, Jeffrey M.; Avery, Melody A.; Sachse, Glen W.; Ryerson, Thomas B.; Holloway, John S.; Atlas, Elliot L.; Flocke, Frank M.; Weber, Rodney J.; Huey, L. Gregory; Dibb, Jack E.; Streets, David G.; Brune, William H.

    2007-05-01

    The sensitivity of regional air quality model to various lateral and top boundary conditions is studied at 2 scales: a 60 km domain covering the whole USA and a 12 km domain over northeastern USA. Three global models (MOZART-NCAR, MOZART-GFDL and RAQMS) are used to drive the STEM-2K3 regional model with time-varied lateral and top boundary conditions (BCs). The regional simulations with different global BCs are examined using ICARTT aircraft measurements performed in the summer of 2004, and the simulations are shown to be sensitive to the boundary conditions from the global models, especially for relatively long-lived species, like CO and O3. Differences in the mean CO concentrations from three different global-model boundary conditions are as large as 40 ppbv, and the effects of the BCs on CO are shown to be important throughout the troposphere, even near surface. Top boundary conditions show strong effect on O3 predictions above 4 km. Over certain model grids, the model's sensitivity to BCs is found to depend not only on the distance from the domain's top and lateral boundaries, downwind/upwind situation, but also on regional emissions and species properties. The near-surface prediction over polluted area is usually not as sensitive to the variation of BCs, but to the magnitude of their background concentrations. We also test the sensitivity of model to temporal and spatial variations of the BCs by comparing the simulations with time-varied BCs to the corresponding simulations with time-mean and profile BCs. Removing the time variation of BCs leads to a significant bias on the variation prediction and sometime causes the bias in predicted mean values. The effect of model resolution on the BC sensitivity is also studied.

  12. Assessing chemistry schemes and constraints in air quality models used to predict ozone in London against the detailed Master Chemical Mechanism.

    Science.gov (United States)

    Malkin, Tamsin L; Heard, Dwayne E; Hood, Christina; Stocker, Jenny; Carruthers, David; MacKenzie, Ian A; Doherty, Ruth M; Vieno, Massimo; Lee, James; Kleffmann, Jörg; Laufs, Sebastian; Whalley, Lisa K

    2016-07-18

    Air pollution is the environmental factor with the greatest impact on human health in Europe. Understanding the key processes driving air quality across the relevant spatial scales, especially during pollution exceedances and episodes, is essential to provide effective predictions for both policymakers and the public. It is particularly important for policy regulators to understand the drivers of local air quality that can be regulated by national policies versus the contribution from regional pollution transported from mainland Europe or elsewhere. One of the main objectives of the Coupled Urban and Regional processes: Effects on AIR quality (CUREAIR) project is to determine local and regional contributions to ozone events. A detailed zero-dimensional (0-D) box model run with the Master Chemical Mechanism (MCMv3.2) is used as the benchmark model against which the less explicit chemistry mechanisms of the Generic Reaction Set (GRS) and the Common Representative Intermediates (CRIv2-R5) schemes are evaluated. GRS and CRI are used by the Atmospheric Dispersion Modelling System (ADMS-Urban) and the regional chemistry transport model EMEP4UK, respectively. The MCM model uses a near-explicit chemical scheme for the oxidation of volatile organic compounds (VOCs) and is constrained to observations of VOCs, NOx, CO, HONO (nitrous acid), photolysis frequencies and meteorological parameters measured during the ClearfLo (Clean Air for London) campaign. The sensitivity of the less explicit chemistry schemes to different model inputs has been investigated: Constraining GRS to the total VOC observed during ClearfLo as opposed to VOC derived from ADMS-Urban dispersion calculations, including emissions and background concentrations, led to a significant increase (674% during winter) in modelled ozone. The inclusion of HONO chemistry in this mechanism, particularly during wintertime when other radical sources are limited, led to substantial increases in the ozone levels predicted

  13. Uncertainty in Air Quality Modeling.

    Science.gov (United States)

    Fox, Douglas G.

    1984-01-01

    Under the direction of the AMS Steering Committee for the EPA Cooperative Agreement on Air Quality Modeling, a small group of scientists convened to consider the question of uncertainty in air quality modeling. Because the group was particularly concerned with the regulatory use of models, its discussion focused on modeling tall stack, point source emissions.The group agreed that air quality model results should be viewed as containing both reducible error and inherent uncertainty. Reducible error results from improper or inadequate meteorological and air quality data inputs, and from inadequacies in the models. Inherent uncertainty results from the basic stochastic nature of the turbulent atmospheric motions that are responsible for transport and diffusion of released materials. Modelers should acknowledge that all their predictions to date contain some associated uncertainty and strive also to quantify uncertainty.How can the uncertainty be quantified? There was no consensus from the group as to precisely how uncertainty should be calculated. One subgroup, which addressed statistical procedures, suggested that uncertainty information could be obtained from comparisons of observations and predictions. Following recommendations from a previous AMS workshop on performance evaluation (Fox. 1981), the subgroup suggested construction of probability distribution functions from the differences between observations and predictions. Further, they recommended that relatively new computer-intensive statistical procedures be considered to improve the quality of uncertainty estimates for the extreme value statistics of interest in regulatory applications.A second subgroup, which addressed the basic nature of uncertainty in a stochastic system, also recommended that uncertainty be quantified by consideration of the differences between observations and predictions. They suggested that the average of the difference squared was appropriate to isolate the inherent uncertainty that

  14. Micrometeorological measurement of hexachlorobenzene and polychlorinated biphenyl compound air-water gas exchange in Lake Superior and comparison to model predictions

    Directory of Open Access Journals (Sweden)

    M. D. Rowe

    2012-05-01

    Full Text Available Air-water exchange fluxes of persistent, bioaccumulative and toxic (PBT substances are frequently estimated using the Whitman two-film (W2F method, but micrometeorological flux measurements of these compounds over water are rarely attempted. We measured air-water exchange fluxes of hexachlorobenzene (HCB and polychlorinated biphenyls (PCBs on 14 July 2006 in Lake Superior using the modified Bowen ratio (MBR method. Measured fluxes were compared to estimates using the W2F method, and to estimates from an Internal Boundary Layer Transport and Exchange (IBLTE model that implements the NOAA COARE bulk flux algorithm and gas transfer model. We reveal an inaccuracy in the estimate of water vapor transfer velocity that is commonly used with the W2F method for PBT flux estimation, and demonstrate the effect of use of an improved estimation method. Flux measurements were conducted at three stations with increasing fetch in offshore flow (15, 30, and 60 km in southeastern Lake Superior. This sampling strategy enabled comparison of measured and predicted flux, as well as modification in near-surface atmospheric concentration with fetch, using the IBLTE model. Fluxes estimated using the W2F model were compared to fluxes measured by MBR. In five of seven cases in which the MBR flux was significantly greater than zero, concentration increased with fetch at 1-m height, which is qualitatively consistent with the measured volatilization flux. As far as we are aware, these are the first reported ship-based micrometeorological air-water exchange flux measurements of PCBs.

  15. Micrometeorological measurement of hexachlorobenzene and polychlorinated biphenyl compound air-water gas exchange in Lake Superior and comparison to model predictions

    Directory of Open Access Journals (Sweden)

    M. D. Rowe

    2012-01-01

    Full Text Available Air-water exchange fluxes of persistent, bioaccumulative and toxic (PBT substances are frequently estimated using the Whitman two-film (W2F method, but micrometeorological flux measurements of these compounds over water are rarely attempted. We measured air-water exchange fluxes of hexachlorobenzene (HCB and polychlorinated biphenyls (PCBs on 14 July 2006 in Lake Superior using the modified Bowen ratio (MBR method. Measured fluxes were compared to estimates using the W2F method, and to estimates from an Internal Boundary Layer Transport and Exchange (IBLTE model that implements the NOAA COARE bulk flux algorithm and gas transfer model. We reveal an inaccuracy in the estimate of water vapor transfer velocity that is commonly used with the W2F method for PBT flux estimation, and demonstrate the effect of use of an improved estimation method. Flux measurements were conducted at three stations with increasing fetch in offshore flow (15, 30, and 60 km in southeastern Lake Superior. This sampling strategy enabled comparison of measured and predicted flux, as well as modification in near-surface atmospheric concentration with fetch, using the IBLTE model. Fluxes estimated using the W2F model were compared to fluxes measured by MBR. In five of seven cases in which the MBR flux was significantly greater than zero, concentration increased with fetch at 1-m height, which is qualitatively consistent with the measured volatilization flux. As far as we are aware, these are the first reported micrometeorological air-water exchange flux measurements of PCBs.

  16. Evaluation of chemical transport model predictions of primary organic aerosol for air masses classified by particle component-based factor analysis

    Directory of Open Access Journals (Sweden)

    C. A. Stroud

    2012-09-01

    Full Text Available Observations from the 2007 Border Air Quality and Meteorology Study (BAQS-Met 2007 in Southern Ontario, Canada, were used to evaluate predictions of primary organic aerosol (POA and two other carbonaceous species, black carbon (BC and carbon monoxide (CO, made for this summertime period by Environment Canada's AURAMS regional chemical transport model. Particle component-based factor analysis was applied to aerosol mass spectrometer measurements made at one urban site (Windsor, ON and two rural sites (Harrow and Bear Creek, ON to derive hydrocarbon-like organic aerosol (HOA factors. A novel diagnostic model evaluation was performed by investigating model POA bias as a function of HOA mass concentration and indicator ratios (e.g. BC/HOA. Eight case studies were selected based on factor analysis and back trajectories to help classify model bias for certain POA source types. By considering model POA bias in relation to co-located BC and CO biases, a plausible story is developed that explains the model biases for all three species.

    At the rural sites, daytime mean PM1 POA mass concentrations were under-predicted compared to observed HOA concentrations. POA under-predictions were accentuated when the transport arriving at the rural sites was from the Detroit/Windsor urban complex and for short-term periods of biomass burning influence. Interestingly, the daytime CO concentrations were only slightly under-predicted at both rural sites, whereas CO was over-predicted at the urban Windsor site with a normalized mean bias of 134%, while good agreement was observed at Windsor for the comparison of daytime PM1 POA and HOA mean values, 1.1 μg m−3 and 1.2 μg m−3, respectively. Biases in model POA predictions also trended from positive to negative with increasing HOA values. Periods of POA over-prediction were most evident at the urban site on calm nights due to an overly-stable model surface layer

  17. Tritium in the food chain. Intercomparison of model predictions of contamination in soil, crops, milk and beef after a short exposure to tritiated water vapour in air

    Energy Technology Data Exchange (ETDEWEB)

    Barry, P. [PJS Barry (Canada)] [and others

    1996-09-01

    Future fusion reactors using tritium as fuel will contain large inventories of the gas. The possibility that a significant fraction of an inventory may accidentally escape into the atmosphere from this and other potential sources such as tritium handling facilities and some fission reactors e g, PWRs has to be recognized and its potential impact on local human populations and biota assessed. Tritium gas is relatively inert chemically and of low radiotoxicity but it is readily oxidized by soil organisms to the mixed oxide, HTO or tritiated water. In this form it is highly mobile, strongly reactive biologically and much more toxic. Models of how tritiated water vapour is transported through the biosphere to foodstuffs important to man are essential components of such an assessment and it is important to test the models for their suitability when used for this purpose. To evaluate such models, access to experimental measurements made after actual releases are needed. There have however, been very few accidental releases of tritiated water to the atmosphere and the experimental findings of those that have occurred have been used to develop the models under test. Models must nevertheless be evaluated before their predictions can be used to decide the acceptability or otherwise of designing and operating major nuclear facilities. To fulfil this need a model intercomparison study was carried out for a hypothetical release scenario. The study described in this report is a contribution to the development of model evaluation procedures in general as well as a description of the results of applying these procedures to the particular case of models of HTO transport in the biosphere which are currently in use or being developed. The study involved eight modelers using seven models in as many countries. In the scenario farmland was exposed to 1E10 Bq d/m{sup 3} of HTO in air during 1 hour starting at midnight in one case and at 10.00 a.m. in the other, 30 days before harvest of

  18. Prediction of harmful water quality parameters combining weather, air quality and ecosystem models with in situ measurement

    Science.gov (United States)

    The ability to predict water quality in lakes is important since lakes are sources of water for agriculture, drinking, and recreational uses. Lakes are also home to a dynamic ecosystem of lacustrine wetlands and deep waters. They are sensitive to pH changes and are dependent on d...

  19. Air Pollution Modelling to Predict Maximum Ground Level Concentration for Dust from a Palm Oil Mill Stack

    OpenAIRE

    Regina A. A.; I. Mohammad Halim Shah

    2010-01-01

    The study is to model emission from a stack to estimate ground level concentration from a palm oil mill. The case study is a mill located in Kuala Langat, Selangor. Emission source is from boilers stacks. The exercise determines the estimate the ground level concentrations for dust to the surrounding areas through the utilization of modelling software. The surround area is relatively flat, an industrial area surrounded by factories and with palm oil plantations in the outskirts. The model uti...

  20. Heating, ventilation and air conditioning system modelling

    Energy Technology Data Exchange (ETDEWEB)

    Whalley, R.; Abdul-Ameer, A. [British University in Dubai (United Arab Emirates)

    2011-03-15

    Heating, ventilation and air conditioning modelling methods, for large scale, spatially dispersed systems are considered. Existing techniques are discussed and proposals for the application of novel analysis approaches are outlined. The use of distributed-lumped parameter procedures enabling the incorporation of the relatively concentrated and significantly dispersed, system element characteristics, is advocated. A dynamic model for a heating, ventilation and air conditioning system comprising inlet and exhaust fans, with air recirculation, heating/cooling and filtration units is presented. Pressure, airflow and temperature predictions within the system are computed following input, disturbance changes and purging operations. The generalised modelling advancements adopted and the applicability of the model for heating, ventilation and air conditioning system simulation, re-configuration and diagnostics is emphasised. The employment of the model for automatic, multivariable controller design purposes is commented upon. (author)

  1. Prediction of air-fuel and oxy-fuel combustion through a generic gas radiation property model

    DEFF Research Database (Denmark)

    Yin, Chungen

    2017-01-01

    Thermal radiation plays an important role in heat transfer in combustion furnaces. The weighted-sum-of-gray-gases model (WSGGM), representing a good compromise between computational efficiency and accuracy, is commonly used in computational fluid dynamics (CFD) modeling of combustion processes...... for evaluating gaseous radiative properties. However, the WSGGMs still have some limitations in practical use, e.g., unable to naturally accommodate different combustion environments, difficult to accurately address the variations in species concentrations in a flame, and inconvenient to account for the impacts...... of participating species other than H2O and CO2. As a result, WSGGMs with different coefficients have been published for specific applications. In this paper, a reliable generic model for gaseous radiation property calculation, which is a computationally efficient exponential wide band model (E-EWBM) applicable...

  2. Predicting indoor pollutant concentrations, and applications to air quality management

    Energy Technology Data Exchange (ETDEWEB)

    Lorenzetti, David M.

    2002-10-01

    Because most people spend more than 90% of their time indoors, predicting exposure to airborne pollutants requires models that incorporate the effect of buildings. Buildings affect the exposure of their occupants in a number of ways, both by design (for example, filters in ventilation systems remove particles) and incidentally (for example, sorption on walls can reduce peak concentrations, but prolong exposure to semivolatile organic compounds). Furthermore, building materials and occupant activities can generate pollutants. Indoor air quality depends not only on outdoor air quality, but also on the design, maintenance, and use of the building. For example, ''sick building'' symptoms such as respiratory problems and headaches have been related to the presence of air-conditioning systems, to carpeting, to low ventilation rates, and to high occupant density (1). The physical processes of interest apply even in simple structures such as homes. Indoor air quality models simulate the processes, such as ventilation and filtration, that control pollutant concentrations in a building. Section 2 describes the modeling approach, and the important transport processes in buildings. Because advection usually dominates among the transport processes, Sections 3 and 4 describe methods for predicting airflows. The concluding section summarizes the application of these models.

  3. Wind power prediction models

    Science.gov (United States)

    Levy, R.; Mcginness, H.

    1976-01-01

    Investigations were performed to predict the power available from the wind at the Goldstone, California, antenna site complex. The background for power prediction was derived from a statistical evaluation of available wind speed data records at this location and at nearby locations similarly situated within the Mojave desert. In addition to a model for power prediction over relatively long periods of time, an interim simulation model that produces sample wind speeds is described. The interim model furnishes uncorrelated sample speeds at hourly intervals that reproduce the statistical wind distribution at Goldstone. A stochastic simulation model to provide speed samples representative of both the statistical speed distributions and correlations is also discussed.

  4. A Course in Hazardous Chemical Spills: Use of the CAMEO Air Dispersion Model to Predict Evacuation Distances.

    Science.gov (United States)

    Kumar, Ashok; And Others

    1989-01-01

    Provides an overview of the Computer-Aided Management of Emergency Operations (CAMEO) model and its use in the classroom as a training tool in the "Hazardous Chemical Spills" course. Presents six problems illustrating classroom use of CAMEO. Lists 16 references. (YP)

  5. The Predictive Control Method of VAV Air Conditioning System

    Directory of Open Access Journals (Sweden)

    Jiejia LI

    2013-09-01

    Full Text Available Aiming at the characteristics which variable air volume air conditioning system is multi-variable, nonlinear and uncertain system, normal fuzzy neural network is hard to meet the requirements which dynamic control of multi-variable. In this paper, we put forward a recursive neural network predictive control strategy based on wavelet neural network model. Through recursive wavelet neural network predictor on line established controlled object’s mathematical model, and using Elman neural network controller on line corrected information we get, thus to improve control effect. The simulation results show that recursive wavelet neural network predictive control has stronger robustness and adaptive ability, high control precision, better and reliable control effect and other advantages.

  6. Evaluation of chemical transport model predictions of primary organic aerosol for air masses classified by particle-component-based factor analysis

    Directory of Open Access Journals (Sweden)

    C. A. Stroud

    2012-02-01

    Full Text Available Observations from the 2007 Border Air Quality and Meteorology Study (BAQS-Met 2007 in southern Ontario (ON, Canada, were used to evaluate Environment Canada's regional chemical transport model predictions of primary organic aerosol (POA. Environment Canada's operational numerical weather prediction model and the 2006 Canadian and 2005 US national emissions inventories were used as input to the chemical transport model (named AURAMS. Particle-component-based factor analysis was applied to aerosol mass spectrometer measurements made at one urban site (Windsor, ON and two rural sites (Harrow and Bear Creek, ON to derive hydrocarbon-like organic aerosol (HOA factors. Co-located carbon monoxide (CO, PM2.5 black carbon (BC, and PM1 SO4 measurements were also used for evaluation and interpretation, permitting a detailed diagnostic model evaluation.

    At the urban site, good agreement was observed for the comparison of daytime campaign PM1 POA and HOA mean values: 1.1 μg m−3 vs. 1.2 μg m−3, respectively. However, a POA overprediction was evident on calm nights due to an overly-stable model surface layer. Biases in model POA predictions trended from positive to negative with increasing HOA values. This trend has several possible explanations, including (1 underweighting of urban locations in particulate matter (PM spatial surrogate fields, (2 overly-coarse model grid spacing for resolving urban-scale sources, and (3 lack of a model particle POA evaporation process during dilution of vehicular POA tail-pipe emissions to urban scales. Furthermore, a trend in POA bias was observed at the urban site as a function of the BC/HOA ratio, suggesting a possible association of POA underprediction for diesel combustion sources. For several time periods, POA overprediction was also observed for sulphate-rich plumes, suggesting that our model POA fractions for the PM2.5 chemical

  7. Measurements and prediction of inhaled air quality with personalized ventilation

    DEFF Research Database (Denmark)

    Cermak, Radim; Majer, M.; Melikov, Arsen Krikor

    2002-01-01

    This paper examines the performance of five different air terminal devices for personalized ventilation in relation to the quality of air inhaled by a breathing thermal manikin in a climate chamber. The personalized air was supplied either isothermally or non-isothermally (6 deg.C cooler than...... the room air) at flow rates ranging from less than 5 L/s up to 23 L/s. The air quality assessment was based on temperature measurements of the inhaled air and on the portion of the personalized air inhaled. The percentage of dissatisfied with the air quality was predicted. The results suggest...

  8. INEEL AIR MODELING PROTOCOL ext

    Energy Technology Data Exchange (ETDEWEB)

    C. S. Staley; M. L. Abbott; P. D. Ritter

    2004-12-01

    Various laws stemming from the Clean Air Act of 1970 and the Clean Air Act amendments of 1990 require air emissions modeling. Modeling is used to ensure that air emissions from new projects and from modifications to existing facilities do not exceed certain standards. For radionuclides, any new airborne release must be modeled to show that downwind receptors do not receive exposures exceeding the dose limits and to determine the requirements for emissions monitoring. For criteria and toxic pollutants, emissions usually must first exceed threshold values before modeling of downwind concentrations is required. This document was prepared to provide guidance for performing environmental compliance-driven air modeling of emissions from Idaho National Engineering and Environmental Laboratory facilities. This document assumes that the user has experience in air modeling and dose and risk assessment. It is not intended to be a "cookbook," nor should all recommendations herein be construed as requirements. However, there are certain procedures that are required by law, and these are pointed out. It is also important to understand that air emissions modeling is a constantly evolving process. This document should, therefore, be reviewed periodically and revised as needed. The document is divided into two parts. Part A is the protocol for radiological assessments, and Part B is for nonradiological assessments. This document is an update of and supersedes document INEEL/INT-98-00236, Rev. 0, INEEL Air Modeling Protocol. This updated document incorporates changes in some of the rules, procedures, and air modeling codes that have occurred since the protocol was first published in 1998.

  9. Modeling indoor air pollution

    National Research Council Canada - National Science Library

    Pepper, D. W; Carrington, David B

    2009-01-01

    ... and ventilation from the more popular textbooks and monographs. We wish to especially acknowledge Dr. Xiuling Wang, who diligently converted many of our old FORTRAN codes into MATLAB files, and also developed the COMSOL example files. Also we thank Ms. Kathryn Nelson who developed the website for the book and indoor air quality computer codes. We are grateful to ...

  10. Evaluation of high-resolution forecasts with the non-hydrostaticnumerical weather prediction model Lokalmodell for urban air pollutionepisodes in Helsinki, Oslo and Valencia

    Directory of Open Access Journals (Sweden)

    B. Fay

    2006-01-01

    Full Text Available The operational numerical weather prediction model Lokalmodell LM with 7,km horizontal resolution was evaluated for forecasting meteorological conditions during observed urban air pollution episodes. The resolution was increased to experimental 2.8 km and 1.1 km resolution by one-way interactive nesting without introducing urbanisation of physiographic parameters or parameterisations. The episodes examined are two severe winter inversion-induced episodes in Helsinki in December 1995 and Oslo in January 2003, three suspended dust episodes in spring and autumn in Helsinki and Oslo, and a late-summer photochemical episode in the Valencia area. The evaluation was basically performed against observations and radiosoundings and focused on the LM skill at forecasting the key meteorological parameters characteristic for the specific episodes. These included temperature inversions, atmospheric stability and low wind speeds for the Scandinavian episodes and the development of mesoscale recirculations in the Valencia area. LM forecasts often improved due to higher model resolution especially in mountainous areas like Oslo and Valencia where features depending on topography like temperature, wind fields and mesoscale valley circulations were better described. At coastal stations especially in Helsinki, forecast gains were due to the improved physiographic parameters (land fraction, soil type, or roughness length. The Helsinki and Oslo winter inversions with extreme nocturnal inversion strengths of 18°C were not sufficiently predicted with all LM resolutions. In Helsinki, overprediction of surface temperatures and low-level wind speeds basically led to underpredicted inversion strength. In the Oslo episode, the situation was more complex involving erroneous temperature advection and mountain-induced effects for the higher resolutions. Possible explanations include the influence of the LM treatment of snow cover, sea ice and stability-dependence of transfer

  11. AirPEx: Air Pollution Exposure Model

    OpenAIRE

    Freijer JI; Bloemen HJTh; de Loos S; Marra M; Rombout PJA; Steentjes GM; Veen MP van; LBO

    1997-01-01

    Analysis of inhalatory exposure to air pollution is an important area of investigation when assessing the risks of air pollution for human health. Inhalatory exposure research focuses on the exposure of humans to air pollutants and the entry of these pollutants into the human respiratory tract. The principal grounds for studying the inhalatory exposure of humans to air pollutants are formed by the need for realistic exposure/dose estimates to evaluate the health effects of these pollutants. T...

  12. AirPEx: Air Pollution Exposure Model

    NARCIS (Netherlands)

    Freijer JI; Bloemen HJTh; Loos S de; Marra M; Rombout PJA; Steentjes GM; Veen MP van; LBO

    1997-01-01

    Analysis of inhalatory exposure to air pollution is an important area of investigation when assessing the risks of air pollution for human health. Inhalatory exposure research focuses on the exposure of humans to air pollutants and the entry of these pollutants into the human respiratory tract. The

  13. Predictive models in urology.

    Science.gov (United States)

    Cestari, Andrea

    2013-01-01

    Predictive modeling is emerging as an important knowledge-based technology in healthcare. The interest in the use of predictive modeling reflects advances on different fronts such as the availability of health information from increasingly complex databases and electronic health records, a better understanding of causal or statistical predictors of health, disease processes and multifactorial models of ill-health and developments in nonlinear computer models using artificial intelligence or neural networks. These new computer-based forms of modeling are increasingly able to establish technical credibility in clinical contexts. The current state of knowledge is still quite young in understanding the likely future direction of how this so-called 'machine intelligence' will evolve and therefore how current relatively sophisticated predictive models will evolve in response to improvements in technology, which is advancing along a wide front. Predictive models in urology are gaining progressive popularity not only for academic and scientific purposes but also into the clinical practice with the introduction of several nomograms dealing with the main fields of onco-urology.

  14. Modeling of global surface air temperature

    Science.gov (United States)

    Gusakova, M. A.; Karlin, L. N.

    2012-04-01

    A model to assess a number of factors, such as total solar irradiance, albedo, greenhouse gases and water vapor, affecting climate change has been developed on the basis of Earth's radiation balance principle. To develop the model solar energy transformation in the atmosphere was investigated. It's a common knowledge, that part of the incoming radiation is reflected into space from the atmosphere, land and water surfaces, and another part is absorbed by the Earth's surface. Some part of outdoing terrestrial radiation is retained in the atmosphere by greenhouse gases (carbon dioxide, methane, nitrous oxide) and water vapor. Making use of the regression analysis a correlation between concentration of greenhouse gases, water vapor and global surface air temperature was obtained which, it is turn, made it possible to develop the proposed model. The model showed that even smallest fluctuations of total solar irradiance intensify both positive and negative feedback which give rise to considerable changes in global surface air temperature. The model was used both to reconstruct the global surface air temperature for the 1981-2005 period and to predict global surface air temperature until 2030. The reconstructions of global surface air temperature for 1981-2005 showed the models validity. The model makes it possible to assess contribution of the factors listed above in climate change.

  15. Experimental technique of calibration of symmetrical air pollution models

    Indian Academy of Sciences (India)

    P Kumar

    2005-10-01

    Based on the inherent property of symmetry of air pollution models,a Symmetrical Air Pollution Model Index (SAPMI)has been developed to calibrate the accuracy of predictions made by such models,where the initial quantity of release at the source is not known.For exact prediction the value of SAPMI should be equal to 1.If the predicted values are overestimating then SAPMI is > 1and if it is underestimating then SAPMI is < 1.Specific design for the layout of receptors has been suggested as a requirement for the calibration experiments.SAPMI is applicable for all variations of symmetrical air pollution dispersion models.

  16. Numerical Prediction of Buoyant Air Flow in Livestock Buildings

    DEFF Research Database (Denmark)

    Svidt, Kjeld

    not include the effect of room geometry, obstacles or heat sources. This paper describes the use of Computational Fluid Dynamics to predict air flow patterns and temperature distribution in a ventilated space. Good agreement is found when results of numerical predictions are compared with experimental data.......In modern livestock buildings air distribution and air quality are important parameters to animal welfare and to the health of full-tithe employees in animal production. Traditional methods for calculating air distribution in farm buildings are mainly based on formulas for air jets which do...

  17. MODEL PREDIKSI PENGARUH LIMBAH CAIR HOTEL TERHADAP KUALITAS AIR LAUT DI PESISIR TELUK KUPANG (A Prediction Model of Liquid Waste Hotel Impact on The Sea Water along The Coast of Kupang Bay

    Directory of Open Access Journals (Sweden)

    Inty Megarini

    2015-11-01

    Full Text Available ABSTRAK Hotel-hotel di pesisir Teluk Kupang sebagian besar membuang efluen limbah cairnya ke laut. Kondisi ini akan berpengaruh terhadap kualitas air laut dan berdampak pada kelangsungan hidup biota dan mikroorganisme laut. Penelitian ini bertujuan untuk membuat prediksi pengaruh efluen limbah cair hotel yang dibuang terhadap kualitas air laut di hadapannya. Parameter yang diteliti adalah minyak dan lemak dan ortofosfat efluen limbah cair hotel. Parameter kualitas air laut yang diteliti adalah kekeruhan, minyak dan lemak dan klorofil. Metode pengambilan sampel dan pengujian menggunakan SNI dan USEPA. Analisis data menggunakan uji korelasi dan regresi. Hasil penelitian menunjukkan bahwa kekeruhan air laut pada jarak 0 meter dan 25 meter dapat diprediksi dari kadar minyak dan lemak efluen limbah cair hotel melalui model regresi y = 0,0051 x + 4,8456 dan y = 0,0015 x + 4,5440. Kadar klorofil air laut pada jarak 25 meter dan 75 meter dapat diprediksi dari kadar ortofosfat efluen limbah cair hotel melalui persamaan regresi y = 0,0430 x + 0,0004 dan y = 0,0075 x + 0,0001. ABSTRACT Most of the hotels located along the coast of Kupang Bay dump their effluent liquid waste to the sea. This action will definitely affect the sea water quality and in turn, will unavoidably give deep impact on the life of both microorganism and all the living things in the sea. This research intends to make an impact prediction on the sea water quality over the dumping hotels’ affluent liquid waste to the sea. The parameters which are observed are oil and fat and orthophosphate of the hotels’ affluent liquid waste. While the observed parameters of the sea water quality are turbidity, oil and fat, and chlorophyll. The methods used to take and test the sample are SNI and USEPA. And to analyze the data, testing on both correlation and regression are applied. The result of the study reveals that the turbidity of the sea water within the range of 0 to 25 meters can be

  18. MODEL PREDICTIVE CONTROL FUNDAMENTALS

    African Journals Online (AJOL)

    2012-07-02

    Jul 2, 2012 ... paper, we will present an introduction to the theory and application of MPC with Matlab codes written to ... model predictive control, linear systems, discrete-time systems, ... and then compute very rapidly for this open-loop con-.

  19. Computer Prediction of Air Quality in Livestock Buildings

    DEFF Research Database (Denmark)

    Svidt, Kjeld; Bjerg, Bjarne

    In modem livestock buildings the design of ventilation systems is important in order to obtain good air quality. The use of Computational Fluid Dynamics for predicting the air distribution makes it possible to include the effect of room geometry and heat sources in the design process. This paper...... presents numerical prediction of air flow in a livestock building compared with laboratory measurements. An example of the calculation of contaminant distribution is given, and the future possibilities of the method are discussed....

  20. Uncertainty in Regional Air Quality Modeling

    Science.gov (United States)

    Digar, Antara

    Effective pollution mitigation is the key to successful air quality management. Although states invest millions of dollars to predict future air quality, the regulatory modeling and analysis process to inform pollution control strategy remains uncertain. Traditionally deterministic ‘bright-line’ tests are applied to evaluate the sufficiency of a control strategy to attain an air quality standard. A critical part of regulatory attainment demonstration is the prediction of future pollutant levels using photochemical air quality models. However, because models are uncertain, they yield a false sense of precision that pollutant response to emission controls is perfectly known and may eventually mislead the selection of control policies. These uncertainties in turn affect the health impact assessment of air pollution control strategies. This thesis explores beyond the conventional practice of deterministic attainment demonstration and presents novel approaches to yield probabilistic representations of pollutant response to emission controls by accounting for uncertainties in regional air quality planning. Computationally-efficient methods are developed and validated to characterize uncertainty in the prediction of secondary pollutant (ozone and particulate matter) sensitivities to precursor emissions in the presence of uncertainties in model assumptions and input parameters. We also introduce impact factors that enable identification of model inputs and scenarios that strongly influence pollutant concentrations and sensitivity to precursor emissions. We demonstrate how these probabilistic approaches could be applied to determine the likelihood that any control measure will yield regulatory attainment, or could be extended to evaluate probabilistic health benefits of emission controls, considering uncertainties in both air quality models and epidemiological concentration-response relationships. Finally, ground-level observations for pollutant (ozone) and precursor

  1. Application of cellular neural network (CNN) to the prediction of missing air pollutant data

    Science.gov (United States)

    Şahin, Ülkü Alver; Bayat, Cuma; Uçan, Osman N.

    2011-07-01

    For air-quality assessments in most major urban centers, air pollutants are monitored using continuous samplers. Sometimes data are not collected due to equipment failure or during equipment calibration. In this paper, we predict daily air pollutant concentrations (PM 10 and SO 2) from the Yenibosna and Umraniye air pollution measurement stations in Istanbul for times at which pollution data was not recorded. We predicted these pollutant concentrations using the CNN model with meteorological parameters, estimating missing daily pollutant concentrations for two data sets from 2002 to 2003. These data sets had 50 and 20% of data missing. The results of the CNN model predictions are compared with the results of a multivariate linear regression (LR). Results show that the correlation between predicted and observed data was higher for all pollutants using the CNN model (0.54-0.87). The CNN model predicted SO 2 concentrations better than PM 10 concentrations. Another interesting result is that winter concentrations of all pollutants were predicted better than summer concentrations. Experiments showed that accurate predictions of missing air pollutant concentrations are possible using the new approach contained in the CNN model. We therefore proposed a new approach to model air-pollution monitoring problem using CNN.

  2. Predicting partitioning of volatile organic compounds from air into plant cuticular matrix by quantum chemical descriptors

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    Based on theoretical linear solvation energy relationship and quantum chemical descriptors computed by AM1 Hamiltonian, a new model is developed to predict the partitioning of some volatile organic compounds between the plant cuticular matrix and air.

  3. Nominal model predictive control

    OpenAIRE

    Grüne, Lars

    2013-01-01

    5 p., to appear in Encyclopedia of Systems and Control, Tariq Samad, John Baillieul (eds.); International audience; Model Predictive Control is a controller design method which synthesizes a sampled data feedback controller from the iterative solution of open loop optimal control problems.We describe the basic functionality of MPC controllers, their properties regarding feasibility, stability and performance and the assumptions needed in order to rigorously ensure these properties in a nomina...

  4. Nominal Model Predictive Control

    OpenAIRE

    Grüne, Lars

    2014-01-01

    5 p., to appear in Encyclopedia of Systems and Control, Tariq Samad, John Baillieul (eds.); International audience; Model Predictive Control is a controller design method which synthesizes a sampled data feedback controller from the iterative solution of open loop optimal control problems.We describe the basic functionality of MPC controllers, their properties regarding feasibility, stability and performance and the assumptions needed in order to rigorously ensure these properties in a nomina...

  5. Candidate Prediction Models and Methods

    DEFF Research Database (Denmark)

    Nielsen, Henrik Aalborg; Nielsen, Torben Skov; Madsen, Henrik

    2005-01-01

    This document lists candidate prediction models for Work Package 3 (WP3) of the PSO-project called ``Intelligent wind power prediction systems'' (FU4101). The main focus is on the models transforming numerical weather predictions into predictions of power production. The document also outlines...... the possibilities w.r.t. different numerical weather predictions actually available to the project....

  6. Predictive Surface Complexation Modeling

    Energy Technology Data Exchange (ETDEWEB)

    Sverjensky, Dimitri A. [Johns Hopkins Univ., Baltimore, MD (United States). Dept. of Earth and Planetary Sciences

    2016-11-29

    Surface complexation plays an important role in the equilibria and kinetics of processes controlling the compositions of soilwaters and groundwaters, the fate of contaminants in groundwaters, and the subsurface storage of CO2 and nuclear waste. Over the last several decades, many dozens of individual experimental studies have addressed aspects of surface complexation that have contributed to an increased understanding of its role in natural systems. However, there has been no previous attempt to develop a model of surface complexation that can be used to link all the experimental studies in order to place them on a predictive basis. Overall, my research has successfully integrated the results of the work of many experimentalists published over several decades. For the first time in studies of the geochemistry of the mineral-water interface, a practical predictive capability for modeling has become available. The predictive correlations developed in my research now enable extrapolations of experimental studies to provide estimates of surface chemistry for systems not yet studied experimentally and for natural and anthropogenically perturbed systems.

  7. NBC Hazard Prediction Model Capability Analysis

    Science.gov (United States)

    1999-09-01

    Puff( SCIPUFF ) Model Verification and Evaluation Study, Air Resources Laboratory, NOAA, May 1998. Based on the NOAA review, the VLSTRACK developers...TO SUBSTANTIAL DIFFERENCES IN PREDICTIONS HPAC uses a transport and dispersion (T&D) model called SCIPUFF and an associated mean wind field model... SCIPUFF is a model for atmospheric dispersion that uses the Gaussian puff method - an arbitrary time-dependent concentration field is represented

  8. Air Conditioner Compressor Performance Model

    Energy Technology Data Exchange (ETDEWEB)

    Lu, Ning; Xie, YuLong; Huang, Zhenyu

    2008-09-05

    During the past three years, the Western Electricity Coordinating Council (WECC) Load Modeling Task Force (LMTF) has led the effort to develop the new modeling approach. As part of this effort, the Bonneville Power Administration (BPA), Southern California Edison (SCE), and Electric Power Research Institute (EPRI) Solutions tested 27 residential air-conditioning units to assess their response to delayed voltage recovery transients. After completing these tests, different modeling approaches were proposed, among them a performance modeling approach that proved to be one of the three favored for its simplicity and ability to recreate different SVR events satisfactorily. Funded by the California Energy Commission (CEC) under its load modeling project, researchers at Pacific Northwest National Laboratory (PNNL) led the follow-on task to analyze the motor testing data to derive the parameters needed to develop a performance models for the single-phase air-conditioning (SPAC) unit. To derive the performance model, PNNL researchers first used the motor voltage and frequency ramping test data to obtain the real (P) and reactive (Q) power versus voltage (V) and frequency (f) curves. Then, curve fitting was used to develop the P-V, Q-V, P-f, and Q-f relationships for motor running and stalling states. The resulting performance model ignores the dynamic response of the air-conditioning motor. Because the inertia of the air-conditioning motor is very small (H<0.05), the motor reaches from one steady state to another in a few cycles. So, the performance model is a fair representation of the motor behaviors in both running and stalling states.

  9. Candidate Prediction Models and Methods

    DEFF Research Database (Denmark)

    Nielsen, Henrik Aalborg; Nielsen, Torben Skov; Madsen, Henrik

    2005-01-01

    This document lists candidate prediction models for Work Package 3 (WP3) of the PSO-project called ``Intelligent wind power prediction systems'' (FU4101). The main focus is on the models transforming numerical weather predictions into predictions of power production. The document also outlines...

  10. Monitoring and prediction of air polution from traffic in the urban environment

    OpenAIRE

    Reynolds, Shirley Anne

    1996-01-01

    Traffic-related air pollution is now a major concern. The Rio Earth Summit and the Government's commitment to Agenda 21 has led to Local Authorities taking responsibility to manage the growing number of vehicles and to reduce the impact of traffic on the environment. There is an urgent need to effectively monitor urban air quality at reasonable cost and to develop long and short term air pollution prediction models. The aim of the research described was to investigate relationships betw...

  11. Monitoring ambient air pollutants and apply Woods' model in the prediction seasonal dry deposition at Chang-Hua (urban) and Kao-Mei (wetland) county, Taiwan.

    Science.gov (United States)

    Fang, Guor-Cheng; Chang, Chia-Ying

    2014-09-01

    The main purpose for this study was to monitor ambient air particles and metallic elements (Mn, Fe, Zn, Cr, Cu and Pb) in total suspended particulate (TSP) concentration and dry deposition. In addition, the calculated/measured dry deposition flux ratios of ambient air particles and metallic elements (Mn, Fe, Zn, Cr, Cu and Pb) were evaluated using Woods' model at urban and wetland areas for the 2009-2010 period. The results indicated that the mean highest concentrations of metallic elements Mn, Fe, Zn, Cr, Cu and Pb in TSP were found in Chang-Hua (urban) sampling site. And as for the two characteristic sampling sites, the Woods' model exhibits better dry deposition of particulates of 18 µm particle size than the rest of the other particle sizes at any sampling site in this study. The average calculated/measured flux ratios for two seasons (summer and fall) by using Woods model at 2.5, 10 and 18 µm particles sizes were also studied. The results indicated that the average calculated/measured flux ratios orders for two seasons of various particles sizes were all displayed as Fe > Mn > Zn > Cu > Cr > Pb > particle. And these calculated/measured flux ratios orders were Fe > Mn > Cu > Zn > Cr > Pb > particle and were Fe > Mn > Zn > Cu > Cr > particle > Pb, during spring and winter seasons, respectively. Finally, in the spring and summer seasons of Gao-Mei (wetland) sampling site, the average calculated/measured flux ratios using Woods' model was found to be 2.5, 10 and 18 µm, showing the order of the calculated/measured flux ratios to be Fe > Cu > Zn > Mn > Cr > Pb > particle. And the calculated/measured flux ratio orders were Fe > Zn > Mn > Cu > Cr > particle > Pb and were Fe > Cu > Zn > Mn > Cr > particle > Pb for fall and winter season, respectively.

  12. Modeling personal exposure to traffic related air pollutants

    NARCIS (Netherlands)

    Montagne, D.R.

    2015-01-01

    The first part of this thesis is about the VE3SPA project. Land use regression (LUR) models are often used to predict the outdoor air pollution at the home address of study participants, to study long-term effects of air pollution. While several studies have documented that PM2.5 mass measured at a

  13. Melanoma risk prediction models

    Directory of Open Access Journals (Sweden)

    Nikolić Jelena

    2014-01-01

    Full Text Available Background/Aim. The lack of effective therapy for advanced stages of melanoma emphasizes the importance of preventive measures and screenings of population at risk. Identifying individuals at high risk should allow targeted screenings and follow-up involving those who would benefit most. The aim of this study was to identify most significant factors for melanoma prediction in our population and to create prognostic models for identification and differentiation of individuals at risk. Methods. This case-control study included 697 participants (341 patients and 356 controls that underwent extensive interview and skin examination in order to check risk factors for melanoma. Pairwise univariate statistical comparison was used for the coarse selection of the most significant risk factors. These factors were fed into logistic regression (LR and alternating decision trees (ADT prognostic models that were assessed for their usefulness in identification of patients at risk to develop melanoma. Validation of the LR model was done by Hosmer and Lemeshow test, whereas the ADT was validated by 10-fold cross-validation. The achieved sensitivity, specificity, accuracy and AUC for both models were calculated. The melanoma risk score (MRS based on the outcome of the LR model was presented. Results. The LR model showed that the following risk factors were associated with melanoma: sunbeds (OR = 4.018; 95% CI 1.724- 9.366 for those that sometimes used sunbeds, solar damage of the skin (OR = 8.274; 95% CI 2.661-25.730 for those with severe solar damage, hair color (OR = 3.222; 95% CI 1.984-5.231 for light brown/blond hair, the number of common naevi (over 100 naevi had OR = 3.57; 95% CI 1.427-8.931, the number of dysplastic naevi (from 1 to 10 dysplastic naevi OR was 2.672; 95% CI 1.572-4.540; for more than 10 naevi OR was 6.487; 95%; CI 1.993-21.119, Fitzpatricks phototype and the presence of congenital naevi. Red hair, phototype I and large congenital naevi were

  14. The cold air drainage model KLAM_21

    Science.gov (United States)

    Kossmann, M.

    2010-09-01

    A brief description of the physics and numerical techniques of the cold air drainage model KLAM_21 is presented. The model has been developed by the Deutscher Wetterdienst (Sievers, 2005) for simulations of nocturnal airflow in hilly and mountainous terrain under dry fair weather conditions. The model has been widely used as an environmental consultancy tool. Typical model applications include frost protection (cold air ponding) and air quality (nocturnal ventilation). The single-layer model calculates the depth and the mean wind of a surface based stable layer that evolves from a neutrally stratified atmosphere during nighttime. The prediction of the velocity and direction of the cold air drainage is based on vertically averaged momentum tendency equations. Temporal changes in the total heat deficit in the cold air layer are calculated from a prescribed local heat loss rate (describing turbulent and radiative cooling) and advection (donor-cell algorithm). The depth of the cold air layer (depth of the surface based temperature inversion) is calculated diagnostically from the total heat loss deficit. The model is initialised with neutral stratification at sunset (onset time of nocturnal cooling). Optionally, effects of an ambient (regional) wind and/or the dispersion of a passive tracer can be simulated. Integration over time is carried out on a regular Arakawa C grid using dynamically calculated time steps. Spatial gradients are discretised using centred differential quotients. The standard size of the computational domains can reach up to 1500 x 1500 grid cells. Grid resolutions usually range between 10 m and 500 m. High resolution simulation can be limited to a nested inner grid domain, while the courser outer domain is covering the entire airshed of interest. A friendly user interface allows easy setup, control, and evaluation of model simulations. Some selected examples of KLAM_21 applications are shown to illustrate the features and capabilities of the model

  15. Long short-term memory neural network for air pollutant concentration predictions: Method development and evaluation.

    Science.gov (United States)

    Li, Xiang; Peng, Ling; Yao, Xiaojing; Cui, Shaolong; Hu, Yuan; You, Chengzeng; Chi, Tianhe

    2017-09-08

    Air pollutant concentration forecasting is an effective method of protecting public health by providing an early warning against harmful air pollutants. However, existing methods of air pollutant concentration prediction fail to effectively model long-term dependencies, and most neglect spatial correlations. In this paper, a novel long short-term memory neural network extended (LSTME) model that inherently considers spatiotemporal correlations is proposed for air pollutant concentration prediction. Long short-term memory (LSTM) layers were used to automatically extract inherent useful features from historical air pollutant data, and auxiliary data, including meteorological data and time stamp data, were merged into the proposed model to enhance the performance. Hourly PM2.5 (particulate matter with an aerodynamic diameter less than or equal to 2.5 μm) concentration data collected at 12 air quality monitoring stations in Beijing City from Jan/01/2014 to May/28/2016 were used to validate the effectiveness of the proposed LSTME model. Experiments were performed using the spatiotemporal deep learning (STDL) model, the time delay neural network (TDNN) model, the autoregressive moving average (ARMA) model, the support vector regression (SVR) model, and the traditional LSTM NN model, and a comparison of the results demonstrated that the LSTME model is superior to the other statistics-based models. Additionally, the use of auxiliary data improved model performance. For the one-hour prediction tasks, the proposed model performed well and exhibited a mean absolute percentage error (MAPE) of 11.93%. In addition, we conducted multiscale predictions over different time spans and achieved satisfactory performance, even for 13-24 h prediction tasks (MAPE = 31.47%). Copyright © 2017 Elsevier Ltd. All rights reserved.

  16. Prediction and mitigation of air preheater fouling due to ammonium bisulfate

    Energy Technology Data Exchange (ETDEWEB)

    Afonso, R.; Tavoulareas, S.; Stallings, J. [Energy and Environmental Strategies, MA (United States)

    2001-07-01

    This paper provides a brief review of the fundamentals of ammonium bisulfate (ABS) formation, deposition and fouling in the air preheater. It presents a software-based predictive model for assessing the potential for air preheater fouling as a result of proposed SNCR or SCR retrofits and considering site-specific conditions and introduces a software-based cost-benefit model for assessing the economic trade-offs of various ABS fouling mitigation options. 7 refs., 13 figs.

  17. HADOOP-BASED DISTRIBUTED SYSTEM FOR ONLINE PREDICTION OF AIR POLLUTION BASED ON SUPPORT VECTOR MACHINE

    Directory of Open Access Journals (Sweden)

    Z. Ghaemi

    2015-12-01

    Full Text Available The critical impact of air pollution on human health and environment in one hand and the complexity of pollutant concentration behavior in the other hand lead the scientists to look for advance techniques for monitoring and predicting the urban air quality. Additionally, recent developments in data measurement techniques have led to collection of various types of data about air quality. Such data is extremely voluminous and to be useful it must be processed at high velocity. Due to the complexity of big data analysis especially for dynamic applications, online forecasting of pollutant concentration trends within a reasonable processing time is still an open problem. The purpose of this paper is to present an online forecasting approach based on Support Vector Machine (SVM to predict the air quality one day in advance. In order to overcome the computational requirements for large-scale data analysis, distributed computing based on the Hadoop platform has been employed to leverage the processing power of multiple processing units. The MapReduce programming model is adopted for massive parallel processing in this study. Based on the online algorithm and Hadoop framework, an online forecasting system is designed to predict the air pollution of Tehran for the next 24 hours. The results have been assessed on the basis of Processing Time and Efficiency. Quite accurate predictions of air pollutant indicator levels within an acceptable processing time prove that the presented approach is very suitable to tackle large scale air pollution prediction problems.

  18. Hadoop-Based Distributed System for Online Prediction of Air Pollution Based on Support Vector Machine

    Science.gov (United States)

    Ghaemi, Z.; Farnaghi, M.; Alimohammadi, A.

    2015-12-01

    The critical impact of air pollution on human health and environment in one hand and the complexity of pollutant concentration behavior in the other hand lead the scientists to look for advance techniques for monitoring and predicting the urban air quality. Additionally, recent developments in data measurement techniques have led to collection of various types of data about air quality. Such data is extremely voluminous and to be useful it must be processed at high velocity. Due to the complexity of big data analysis especially for dynamic applications, online forecasting of pollutant concentration trends within a reasonable processing time is still an open problem. The purpose of this paper is to present an online forecasting approach based on Support Vector Machine (SVM) to predict the air quality one day in advance. In order to overcome the computational requirements for large-scale data analysis, distributed computing based on the Hadoop platform has been employed to leverage the processing power of multiple processing units. The MapReduce programming model is adopted for massive parallel processing in this study. Based on the online algorithm and Hadoop framework, an online forecasting system is designed to predict the air pollution of Tehran for the next 24 hours. The results have been assessed on the basis of Processing Time and Efficiency. Quite accurate predictions of air pollutant indicator levels within an acceptable processing time prove that the presented approach is very suitable to tackle large scale air pollution prediction problems.

  19. Predictive Models for Music

    OpenAIRE

    Paiement, Jean-François; Grandvalet, Yves; Bengio, Samy

    2008-01-01

    Modeling long-term dependencies in time series has proved very difficult to achieve with traditional machine learning methods. This problem occurs when considering music data. In this paper, we introduce generative models for melodies. We decompose melodic modeling into two subtasks. We first propose a rhythm model based on the distributions of distances between subsequences. Then, we define a generative model for melodies given chords and rhythms based on modeling sequences of Narmour featur...

  20. Mathematical Models for Room Air Distribution - Addendum

    DEFF Research Database (Denmark)

    Nielsen, Peter V.

    1982-01-01

    A number of different models on the air distribution in rooms are introduced. This includes the throw model, a model on penetration length of a cold wall jet and a model for maximum velocity in the dimensioning of an air distribution system in highly loaded rooms and shows that the amount of heat...

  1. Mathematical Models for Room Air Distribution

    DEFF Research Database (Denmark)

    Nielsen, Peter V.

    1982-01-01

    A number of different models on the air distribution in rooms are introduced. This includes the throw model, a model on penetration length of a cold wall jet and a model for maximum velocity in the dimensioning of an air distribution system in highly loaded rooms and shows that the amount of heat...

  2. Stochastic Modeling of Traffic Air Pollution

    DEFF Research Database (Denmark)

    Thoft-Christensen, Palle

    2014-01-01

    In this paper, modeling of traffic air pollution is discussed with special reference to infrastructures. A number of subjects related to health effects of air pollution and the different types of pollutants are briefly presented. A simple model for estimating the social cost of traffic related air...... and using simple Monte Carlo techniques to obtain a stochastic estimate of the costs of traffic air pollution for infrastructures....... pollution is derived. Several authors have published papers on this very complicated subject, but no stochastic modelling procedure have obtained general acceptance. The subject is discussed basis of a deterministic model. However, it is straightforward to modify this model to include uncertain parameters...

  3. Stochastic Modeling of Traffic Air Pollution

    DEFF Research Database (Denmark)

    Thoft-Christensen, Palle

    2014-01-01

    In this paper, modeling of traffic air pollution is discussed with special reference to infrastructures. A number of subjects related to health effects of air pollution and the different types of pollutants are briefly presented. A simple model for estimating the social cost of traffic related air...... and using simple Monte Carlo techniques to obtain a stochastic estimate of the costs of traffic air pollution for infrastructures....... pollution is derived. Several authors have published papers on this very complicated subject, but no stochastic modelling procedure have obtained general acceptance. The subject is discussed basis of a deterministic model. However, it is straightforward to modify this model to include uncertain parameters...

  4. Projection pursuit regression model for prediction of air permeability of woven fabrics%机织物透气性预测的投影寻踪回归模型

    Institute of Scientific and Technical Information of China (English)

    王健; 张晓丽; 刘陶

    2011-01-01

    In view of the difficulty in nonlinear modeling for prediction of woven fabric permeability, a projection pursuit regression ( PPR) model for prediction of air permeability of woven fabrics was established using the structural parameters such as the total tightness, thickness, and weight per square meter and average float as factors affecting the prediction of woven fabric permeability. The fitted values of tested samples and the predicted values of trained samples were analyzed with the means and standard deviations of relative error as the indicators and were compared with the results of BP neural network and multiple linear regression model. The results showed that the PPR model fitting and prediction accuracy was better than those of BP neural network and multiple linear regression model. In the case of less trained samples, the PPR model still had relatively high prediction accuracy and good generalization ability, providing a novel approach to the prediction of woven fabric permeability.%针对机织物透气性预测中存在非线性建模困难的问题,选择机织物总紧度、厚度、面密度及平均浮长等结构参数作为机织物透气性预测的影响因素,建立机织物透气性预测的投影寻踪回归模型.对模型训练样本的拟合值及检验样本的预测值以相对误差的均值及标准差为指标进行分析,并与BP神经网络及多元线性回归模型进行对比.结果表明,投影寻踪回归模型的拟合及预测精度均优于BP神经网络及多元线性回归模型,且在训练样本较少的情况下,投影寻踪回归模型仍有较高的预测精度和较强的泛化能力,可为机织物透气性预测提供一种新的方法.

  5. Zephyr - the prediction models

    DEFF Research Database (Denmark)

    Nielsen, Torben Skov; Madsen, Henrik; Nielsen, Henrik Aalborg

    2001-01-01

    This paper briefly describes new models and methods for predicationg the wind power output from wind farms. The system is being developed in a project which has the research organization Risø and the department of Informatics and Mathematical Modelling (IMM) as the modelling team and all the Dani...

  6. Computer simulation tool for predicting sound propagation in air-filled tubes with acoustic impedance discontinuities.

    Science.gov (United States)

    Albors, Gabriel O; Kyle, Aaron M; Wodicka, George R; Juan, Eduardo J

    2007-01-01

    A computer tool, based on an acoustic transmission line model, was developed for modeling and predicting sound propagation and reflections in cascaded tube segments. This subroutine considered the number of interconnected tubes, their dimensions and wall properties, as well as medium properties to create a network of cascaded transmission line model segments, from which the impulse response of the network was estimated. Acoustic propagation was examined in air-filled cascaded tube networks and model predictions were compared to measured acoustic pulse responses. The model was able to accurately predict the location and morphology of reflections. The developed code proved to be a useful design tool for applications such as the guidance of catheters through compliant air-filled biological conduits.

  7. Soil temperature prediction from air temperature for alluvial soils in lower Indo-Gangetic plain

    Science.gov (United States)

    Barman, D.; Kundu, D. K.; Pal, Soumen; Pal, Susanto; Chakraborty, A. K.; Jha, A. K.; Mazumdar, S. P.; Saha, R.; Bhattacharyya, P.

    2017-01-01

    Soil temperature is an important factor in biogeochemical processes. On-site monitoring of soil temperature is limited in spatiotemporal scale as compared to air temperature data inventories due to various management difficulties. Therefore, empirical models were developed by taking 30-year long-term (1985-2014) air and soil temperature data for prediction of soil temperatures at three depths (5, 15, 30 cm) in morning (0636 Indian standard time) and afternoon (1336 Indian standard time) for alluvial soils in lower Indo-Gangetic plain. At 5 cm depth, power and exponential regression models were best fitted for daily data in morning and afternoon, respectively, but it was reverse at 15 cm. However, at 30 cm, exponential models were best fitted for both the times. Regression analysis revealed that in morning for all three depths and in afternoon for 30 cm depth, soil temperatures (daily, weekly, and monthly) could be predicted more efficiently with the help of corresponding mean air temperature than that of maximum and minimum. However, in afternoon, prediction of soil temperature at 5 and 15 cm depths were more precised for all the time intervals when maximum air temperature was used, except for weekly soil temperature at 15 cm, where the use of mean air temperature gave better prediction.

  8. Prediction of Perceived Air Quality for Personalized Ventilation Systems

    Institute of Scientific and Technical Information of China (English)

    ZENG Qingfan; ZHAO Rongyi

    2005-01-01

    The characteristics of the air jet from the outlet of a personalized ventilation system were related to the perceived air quality and ventilation rate. The perceived air quality was expressed as percentage of dissatisfied people for a system supplying isothermal fresh air. The relationship was verified using a thermal manikin with a breathing function in a climate chamber sitting by a desk equipped with a personalized ventilation system. A trace gas was introduced into the climate chamber and fully mixed. The personal exposure effectiveness (εp) is based on concentrations of trace gas in the chamber and in the manikin nose which is affected more by the distance between the movable outlet and the occupant's breathing zone than by the personalized air flowrate and does not change much for the personalized air flowrate higher than 10 L/s when the distance is fixed. Some predicted dissatisfied values for a personalized ventilation system compared with those acquired in human subject experiments have an absolute difference of less than 3%.

  9. Development and initial application of a sub-grid scale plume treatment in a state-of-the-art online Multi-scale Air Quality and Weather Prediction Model

    Science.gov (United States)

    Karamchandani, Prakash; Zhang, Yang; Chen, Shu-Yun

    2012-12-01

    from the model application show that overall model performance is only slightly affected when the PinG treatment is used, although there are some generally small improvements, with the PinG treatment showing a 5% lower bias in predicting ozone concentrations, and 3% lower bias in sulfate predictions. However, the predicted spatial patterns of ozone and PM2.5 concentrations from the two simulations show both large decreases of up to 40 ppb ozone and 14 μg m-3 PM2.5, and increases of up to 80 ppb ozone and 33 μg m-3 PM2.5 as a result of using the PinG treatment. These differences are attributed to both direct effects of the PinG treatment (i.e., differences in dispersion, transport and chemistry of point source emissions) and indirect effects (i.e., impacts of air quality changes on meteorology).

  10. The cost of simplifying air travel when modeling disease spread.

    Directory of Open Access Journals (Sweden)

    Justin Lessler

    Full Text Available BACKGROUND: Air travel plays a key role in the spread of many pathogens. Modeling the long distance spread of infectious disease in these cases requires an air travel model. Highly detailed air transportation models can be over determined and computationally problematic. We compared the predictions of a simplified air transport model with those of a model of all routes and assessed the impact of differences on models of infectious disease. METHODOLOGY/PRINCIPAL FINDINGS: Using U.S. ticket data from 2007, we compared a simplified "pipe" model, in which individuals flow in and out of the air transport system based on the number of arrivals and departures from a given airport, to a fully saturated model where all routes are modeled individually. We also compared the pipe model to a "gravity" model where the probability of travel is scaled by physical distance; the gravity model did not differ significantly from the pipe model. The pipe model roughly approximated actual air travel, but tended to overestimate the number of trips between small airports and underestimate travel between major east and west coast airports. For most routes, the maximum number of false (or missed introductions of disease is small (<1 per day but for a few routes this rate is greatly underestimated by the pipe model. CONCLUSIONS/SIGNIFICANCE: If our interest is in large scale regional and national effects of disease, the simplified pipe model may be adequate. If we are interested in specific effects of interventions on particular air routes or the time for the disease to reach a particular location, a more complex point-to-point model will be more accurate. For many problems a hybrid model that independently models some frequently traveled routes may be the best choice. Regardless of the model used, the effect of simplifications and sensitivity to errors in parameter estimation should be analyzed.

  11. Measurements and predictions of the air distribution systems in high compute density (Internet) data centers

    Energy Technology Data Exchange (ETDEWEB)

    Cho, Jinkyun [HIMEC (Hanil Mechanical Electrical Consultants) Ltd., Seoul 150-103 (Korea); Department of Architectural Engineering, Yonsei University, Seoul 120-749 (Korea); Lim, Taesub; Kim, Byungseon Sean [Department of Architectural Engineering, Yonsei University, Seoul 120-749 (Korea)

    2009-10-15

    When equipment power density increases, a critical goal of a data center cooling system is to separate the equipment exhaust air from the equipment intake air in order to prevent the IT server from overheating. Cooling systems for data centers are primarily differentiated according to the way they distribute air. The six combinations of flooded and locally ducted air distribution make up the vast majority of all installations, except fully ducted air distribution methods. Once the air distribution system (ADS) is selected, there are other elements that must be integrated into the system design. In this research, the design parameters and IT environmental aspects of the cooling system were studied with a high heat density data center. CFD simulation analysis was carried out in order to compare the heat removal efficiencies of various air distribution systems. The IT environment of an actual operating data center is measured to validate a model for predicting the effect of different air distribution systems. A method for planning and design of the appropriate air distribution system is described. IT professionals versed in precision air distribution mechanisms, components, and configurations can work more effectively with mechanical engineers to ensure the specification and design of optimized cooling solutions. (author)

  12. Mathematical Modeling of Photochemical Air Pollution.

    Science.gov (United States)

    McRae, Gregory John

    Air pollution is an environmental problem that is both pervasive and difficult to control. An important element of any rational control approach is a reliable means for evaluating the air quality impact of alternative abatement measures. This work presents such a capability, in the form of a mathematical description of the production and transport of photochemical oxidants within an urban airshed. The combined influences of advection, turbulent diffusion, chemical reaction, emissions and surface removal processes are all incorporated into a series of models that are based on the species continuity equations. A delineation of the essential assumptions underlying the formulation of a three-dimensional, a Lagrangian trajectory, a vertically integrated and single cell air quality model is presented. Since each model employs common components and input data the simpler forms can be used for rapid screening calculations and the more complex ones for detailed evaluations. The flow fields, needed for species transport, are constructed using inverse distance weighted polynomial interpolation techniques that map routine monitoring data onto a regular computational mesh. Variational analysis procedures are then employed to adjust the field so that mass is conserved. Initial concentration and mixing height distributions can be established with the same interpolation algorithms. Subgrid scale turbulent transport is characterized by a gradient diffusion hypothesis. Similarity solutions are used to model the surface layer fluxes. Above this layer different treatments of turbulent diffusivity are required to account for variations in atmospheric stability. Convective velocity scaling is utilized to develop eddy diffusivities for unstable conditions. The predicted mixing times are in accord with results obtained during sulfur hexafluoride (SF(,6)) tracer experiments. Conventional models are employed for neutral and stable conditions. A new formulation for gaseous deposition fluxes

  13. Mixed deterministic statistical modelling of regional ozone air pollution

    KAUST Repository

    Kalenderski, Stoitchko Dimitrov

    2011-03-17

    We develop a physically motivated statistical model for regional ozone air pollution by separating the ground-level pollutant concentration field into three components, namely: transport, local production and large-scale mean trend mostly dominated by emission rates. The model is novel in the field of environmental spatial statistics in that it is a combined deterministic-statistical model, which gives a new perspective to the modelling of air pollution. The model is presented in a Bayesian hierarchical formalism, and explicitly accounts for advection of pollutants, using the advection equation. We apply the model to a specific case of regional ozone pollution-the Lower Fraser valley of British Columbia, Canada. As a predictive tool, we demonstrate that the model vastly outperforms existing, simpler modelling approaches. Our study highlights the importance of simultaneously considering different aspects of an air pollution problem as well as taking into account the physical bases that govern the processes of interest. © 2011 John Wiley & Sons, Ltd..

  14. Confidence scores for prediction models

    DEFF Research Database (Denmark)

    Gerds, Thomas Alexander; van de Wiel, MA

    2011-01-01

    modelling strategy is applied to different training sets. For each modelling strategy we estimate a confidence score based on the same repeated bootstraps. A new decomposition of the expected Brier score is obtained, as well as the estimates of population average confidence scores. The latter can be used...... to distinguish rival prediction models with similar prediction performances. Furthermore, on the subject level a confidence score may provide useful supplementary information for new patients who want to base a medical decision on predicted risk. The ideas are illustrated and discussed using data from cancer...

  15. Prediction of air temperature in the aircraft cabin under different operational conditions

    Directory of Open Access Journals (Sweden)

    Fišer J.

    2013-04-01

    Full Text Available This paper deals with the prediction of the air temperature in the aircraft cabin by means of Computational Fluid Dynamics. The simulations are performed on the CFD model which is based on geometry and cabin interior arrangement of the Flight Test Facility (FTF located at Fraunhofer IBP, Germany. The experimental test flights under three different cabin temperatures were done in FTF and the various data were gathered during these flights. Air temperature in the cabin was measured on probes located near feet, torso and head of each passenger and also surface temperature and air temperature distributed from inlets were measured. The data were firstly analysed in order to obtain boundary conditions for cabin surfaces and inlets. Then the results of air temperature from the simulations were compared with measured data. The suitability and accuracy of the CFD approach for temperature prediction is discussed.

  16. Model pelayanan air bersih perdesaan

    Directory of Open Access Journals (Sweden)

    Rini Dorojati

    2016-09-01

    Full Text Available Low coverage of clean water in Indonesia leads to minimum consumption of clean water with proper health requirement. Increasement of clean water coverage is undergoing an effort from independent community in society. This research aims to find a service model of clean water for group based rural communities. Type of this research is descriptive qualitative, with research object is clean water independent provider group, Oyo Wening Santosa community, in a village called Bunder, district of Patuk, Gunung Kidul. Data was gathered by document utilization, parsitipatory observation, in-depth interview, and focus group discussion. Data was analyzed with qualitative method. This research shows that clean water coverage organized by communiy Oyo Wening is a model of sinergy for organization that was established by concern from society and government support, emerge in a program called “Sistem Penyediaan Air Minum Ibu Kota Kecamatan” (SPAM IKK. There are 1170 households channel subscribers spread across four villages. The service procedures are applied based on local conditions. This service has some drawbacks, namely the limited knowledge of the officer, the legality of which is not owned by the organization, facilities and infrastructure, and the relatively low tarrif, Rp 3,500 per m3. In conclusion, rural water services with the model applied in Oyo Wening Sentosa showed a changing trend in people's access to clean water and the local economy has increased. The legality of the business management of water services should become a priority for the stakeholders to ensure the realization of excellent service in providing clean water.

  17. Modelling, controlling, predicting blackouts

    CERN Document Server

    Wang, Chengwei; Baptista, Murilo S

    2016-01-01

    The electric power system is one of the cornerstones of modern society. One of its most serious malfunctions is the blackout, a catastrophic event that may disrupt a substantial portion of the system, playing havoc to human life and causing great economic losses. Thus, understanding the mechanisms leading to blackouts and creating a reliable and resilient power grid has been a major issue, attracting the attention of scientists, engineers and stakeholders. In this paper, we study the blackout problem in power grids by considering a practical phase-oscillator model. This model allows one to simultaneously consider different types of power sources (e.g., traditional AC power plants and renewable power sources connected by DC/AC inverters) and different types of loads (e.g., consumers connected to distribution networks and consumers directly connected to power plants). We propose two new control strategies based on our model, one for traditional power grids, and another one for smart grids. The control strategie...

  18. Melanoma Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing melanoma cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  19. Development of Indoor Air Pollution Concentration Prediction by Geospatial Analysis

    Directory of Open Access Journals (Sweden)

    Adyati Pradini Yudison

    2015-07-01

    Full Text Available People living near busy roads are potentially exposed to traffic-induced air pollutants. The pollutants may intrude into the indoor environment, causing health risks to the occupants. Prediction of pollutant exposure therefore is of great importance for impact assessment and policy making related to environmentally sustainable transport. This study involved the selection of spatial interpolation methods that can be used for prediction of indoor air quality based on outdoor pollutant mapping without indoor measurement data. The research was undertaken in the densely populated area of Karees, Bandung, Indonesia. The air pollutant NO2 was monitored in this area as a preliminary study. Nitrogen dioxide concentrations were measured by passive diffusion tube. Outdoor NO2 concentrations were measured at 94 locations, consisting of 30 roadside and 64 outdoor locations. Residential indoor NO2 concentrations were measured at 64 locations. To obtain a spatially continuous air quality map, the spatial interpolation methods of inverse distance weighting (IDW and Kriging were applied. Selection of interpolation method was done based on the smallest root mean square error (RMSE and standard deviation (SD. The most appropriate interpolation method for outdoor NO2 concentration mapping was Kriging with an SD value of 5.45 µg/m3 and an RMSE value of 5.45 µg/m3, while for indoor NO2 concentration mapping the IDW was best fitted with an RMSE value of 5.92 µg/m3 and an SD value of 5.92 µg/m3.

  20. Large Scale Computations in Air Pollution Modelling

    DEFF Research Database (Denmark)

    Zlatev, Z.; Brandt, J.; Builtjes, P. J. H.

    Proceedings of the NATO Advanced Research Workshop on Large Scale Computations in Air Pollution Modelling, Sofia, Bulgaria, 6-10 July 1998......Proceedings of the NATO Advanced Research Workshop on Large Scale Computations in Air Pollution Modelling, Sofia, Bulgaria, 6-10 July 1998...

  1. Large Scale Computations in Air Pollution Modelling

    DEFF Research Database (Denmark)

    Zlatev, Z.; Brandt, J.; Builtjes, P. J. H.

    Proceedings of the NATO Advanced Research Workshop on Large Scale Computations in Air Pollution Modelling, Sofia, Bulgaria, 6-10 July 1998......Proceedings of the NATO Advanced Research Workshop on Large Scale Computations in Air Pollution Modelling, Sofia, Bulgaria, 6-10 July 1998...

  2. Prediction models in complex terrain

    DEFF Research Database (Denmark)

    Marti, I.; Nielsen, Torben Skov; Madsen, Henrik

    2001-01-01

    are calculated using on-line measurements of power production as well as HIRLAM predictions as input thus taking advantage of the auto-correlation, which is present in the power production for shorter pediction horizons. Statistical models are used to discribe the relationship between observed energy production......The objective of the work is to investigatethe performance of HIRLAM in complex terrain when used as input to energy production forecasting models, and to develop a statistical model to adapt HIRLAM prediction to the wind farm. The features of the terrain, specially the topography, influence...... and HIRLAM predictions. The statistical models belong to the class of conditional parametric models. The models are estimated using local polynomial regression, but the estimation method is here extended to be adaptive in order to allow for slow changes in the system e.g. caused by the annual variations...

  3. Community Multi-scale Air Quality (CMAQ) Modeling System for Air Quality Management

    Science.gov (United States)

    CMAQ simultaneously models multiple air pollutants including ozone, particulate matter and a variety of air toxics to help air quality managers determine the best air quality management scenarios for their communities, regions and states.

  4. Modeling of membrane processes for air revitalization and water recovery

    Science.gov (United States)

    Lange, Kevin E.; Foerg, Sandra L.; Dall-Bauman, Liese A.

    1992-01-01

    Gas-separation and reverse-osmosis membrane models are being developed in conjunction with membrane testing at NASA JSC. The completed gas-separation membrane model extracts effective component permeabilities from multicomponent test data, and predicts the effects of flow configuration, operating conditions, and membrane dimensions on module performance. Variable feed- and permeate-side pressures are considered. The model has been applied to test data for hollow-fiber membrane modules with simulated cabin-air feeds. Results are presented for a membrane designed for air drying applications. Extracted permeabilities are used to predict the effect of operating conditions on water enrichment in the permeate. A first-order reverse-osmosis model has been applied to test data for spiral wound membrane modules with a simulated hygiene water feed. The model estimates an effective local component rejection coefficient under pseudosteady-state conditions. Results are used to define requirements for a detailed reverse-osmosis model.

  5. Prediction models in complex terrain

    DEFF Research Database (Denmark)

    Marti, I.; Nielsen, Torben Skov; Madsen, Henrik

    2001-01-01

    The objective of the work is to investigatethe performance of HIRLAM in complex terrain when used as input to energy production forecasting models, and to develop a statistical model to adapt HIRLAM prediction to the wind farm. The features of the terrain, specially the topography, influence...

  6. Predicting blood:air partition coefficients using basic physicochemical properties

    NARCIS (Netherlands)

    Buist, H.E.; Wit-Bos, L. de; Bouwman, T.; Vaes, W.H.J.

    2012-01-01

    Quantitative Property Property Relationships (QPPRs) for human and rat blood:air partition coefficients (PBAs) have been derived, based on vapour pressure (Log(VP)), the octanol:water partition coefficient (Log(K_OW)) and molecular weight (MW), using partial least squares multilinear modelling. Thes

  7. An Overview of Atmospheric Chemistry and Air Quality Modeling

    Science.gov (United States)

    Johnson, Matthew S.

    2017-01-01

    This presentation will include my personal research experience and an overview of atmospheric chemistry and air quality modeling to the participants of the NASA Student Airborne Research Program (SARP 2017). The presentation will also provide examples on ways to apply airborne observations for chemical transport (CTM) and air quality (AQ) model evaluation. CTM and AQ models are important tools in understanding tropospheric-stratospheric composition, atmospheric chemistry processes, meteorology, and air quality. This presentation will focus on how NASA scientist currently apply CTM and AQ models to better understand these topics. Finally, the importance of airborne observation in evaluating these topics and how in situ and remote sensing observations can be used to evaluate and improve CTM and AQ model predictions will be highlighted.

  8. Megacities, air quality and climate: Seamless prediction approach

    Science.gov (United States)

    Baklanov, Alexander; Molina, Luisa T.; Gauss, Michael

    2016-04-01

    The rapid urbanization and growing number of megacities and urban complexes requires new types of research and services that make best use of science and available technology. With an increasing number of humans now living in urban sprawls, there are urgent needs of examining what the rising number of megacities means for air pollution, local climate and the effects these changes have on global climate. Such integrated studies and services should assist cities in facing hazards such as storm surge, flooding, heat waves, and air pollution episodes, especially in changing climates. While important advances have been made, new interdisciplinary research studies are needed to increase our understanding of the interactions between emissions, air quality, and regional and global climates. Studies need to address both basic and applied research and bridge the spatial and temporal scales connecting local emissions and air pollution and local weather, global atmospheric chemistry and climate. This paper reviews the current status of studies of the complex interactions between climate, air quality and megacities, and identifies the main gaps in our current knowledge as well as further research needs in this important field of research. Highlights • Climate, air quality and megacities interactions: gaps in knowledge, research needs. • Urban hazards: pollution episodes, storm surge, flooding, heat waves, public health. • Global climate change affects megacities' climate, environment and comfort. • Growing urbanization requires integrated weather, environment and climate monitoring systems. • New generation of multi-scale models and seamless integrated urban services are needed. Reference Baklanov, A., L.T. Molina, M. Gauss (2016) Megacities, air quality and climate. Atmospheric Environment, 126: 235-249. doi:10.1016/j.atmosenv.2015.11.059

  9. InMAP: a new model for air pollution interventions

    Science.gov (United States)

    Tessum, C. W.; Hill, J. D.; Marshall, J. D.

    2015-10-01

    Mechanistic air pollution models are essential tools in air quality management. Widespread use of such models is hindered, however, by the extensive expertise or computational resources needed to run most models. Here, we present InMAP (Intervention Model for Air Pollution), which offers an alternative to comprehensive air quality models for estimating the air pollution health impacts of emission reductions and other potential interventions. InMAP estimates annual-average changes in primary and secondary fine particle (PM2.5) concentrations - the air pollution outcome generally causing the largest monetized health damages - attributable to annual changes in precursor emissions. InMAP leverages pre-processed physical and chemical information from the output of a state-of-the-science chemical transport model (WRF-Chem) within an Eulerian modeling framework, to perform simulations that are several orders of magnitude less computationally intensive than comprehensive model simulations. InMAP uses a variable resolution grid that focuses on human exposures by employing higher spatial resolution in urban areas and lower spatial resolution in rural and remote locations and in the upper atmosphere; and by directly calculating steady-state, annual average concentrations. In comparisons run here, InMAP recreates WRF-Chem predictions of changes in total PM2.5 concentrations with population-weighted mean fractional error (MFE) and bias (MFB) planned for future model releases include a larger spatial domain, more temporal information, and the ability to predict ground-level ozone (O3) concentrations. The InMAP model source code and input data are freely available online.

  10. Detection and estimation trends linked to air quality and mortality on French Riviera over the 1990-2005 period to develop a prediction model of an aggregate risk index

    Science.gov (United States)

    Sicard, P.; Mangin, A.; Hebel, P.; Lesne, O.; Malléa, P.

    2009-04-01

    There is a profound relation between human health and well being from the one side and air pollution levels from the other. Air quality in South of France and more specifically in Nice, is known to be bad, especially in summer. The main objectives are to establish correlations between air pollution, exposure of people and reactivity of these people to this aggression, to validate a risk index built from air quality and pollen data in the area of Nice and to construct a prediction model of this sanitary index. The spatial extent of the experiment will be mainly the territory of "Alpes Maritimes". All the tasks are performed in collaboration with the "Heath-Environment Network" of the "Centre Hospitalier Universitaire" of Nice. The development of an adequate tool for observation (health index and/or indices per pathology) to understand impacts of pollution levels in an area is of utmost importance. These indexes should take into account the possible adverse effects associated with the coexistence of all the pollutants and environmental parameters. This tool must be able to inform the citizens about the levels of pollution in an adequate and understandable way but also to be used by relevant authorities to take a series of predetermined measures to protect the health of the population. This paper describes the first step to construct a prediction model of this sanitary index with a confidence interval 99% (and 95%): detection and estimation trends observed in concentrations of pollutants, emissions and mortality over the 1990-2005 period in the "Alpes Maritimes" area. The non-parametric Mann-Kendall test has been developed for detecting and estimating monotonic trends in the time series and applied in our study at annual values of pollutants air concentrations. An important objective of many environmental monitoring programs is to detect changes or trends in pollution levels over time. Over the period 1990-2005, concerning the emissions of the main pollutants, we

  11. Mississippi State University Center for Air Sea Technology. FY93 and FY 94 Research Program in Navy Ocean Modeling and Prediction

    Science.gov (United States)

    1994-09-30

    model to the Navy. The Co-Principal Investigators are CAST researchers Dr. David E. Dietrich and Mr. Alberto Mestas - Nunez. With the Navy’s new...Applications Mr. Alberto Mestas -Nunez An Evaluation of ECMWF-Based 20 May 93 College of Oceanic Sci. Climatological Wind Stress Fields Oregon State...to assist in data browser applications. Oregon State UniversiLy. CAST employed graduate student Mr. Alberto Mestas - Nunez to assist in ocean modeling

  12. Predictive models of forest dynamics.

    Science.gov (United States)

    Purves, Drew; Pacala, Stephen

    2008-06-13

    Dynamic global vegetation models (DGVMs) have shown that forest dynamics could dramatically alter the response of the global climate system to increased atmospheric carbon dioxide over the next century. But there is little agreement between different DGVMs, making forest dynamics one of the greatest sources of uncertainty in predicting future climate. DGVM predictions could be strengthened by integrating the ecological realities of biodiversity and height-structured competition for light, facilitated by recent advances in the mathematics of forest modeling, ecological understanding of diverse forest communities, and the availability of forest inventory data.

  13. The prediction of transducer element performance from in air measurements

    Science.gov (United States)

    Schafer, M. E.

    1982-01-01

    A technique has been developed which accurately predicts the performance of underwater acoustic arrays prior to array construction. The technique is based upon the measurement of lumped-parameter equivalent circuit values for each element in the array, and is accurate in predicting the array transmit, receive and beam pattern response. The measurement procedure determines the shunt electrical and motional circuit elements from electrical imittance measurements. The electromechanical transformation ratio is derived from in-air measurements of the radiating face velocity and the input current to the transducer at resonance. The equivalent circuit values of a group of Tonpilz-type transducers were measured, and the self and mutual interaction acoustic loadings for a specific array geometry were calculated. The response of the elements was then predicted for water-loaded array conditions. Based on the predictions, a selection scheme was developed which minimized the effects of inter-element variability on array performance. The measured transmitting, receiving and beam pattern characteristics of a test array, built using the selected elements, were compared to predictions made before the array was built. The results indicated that the technique is accurate over a wide frequency range.

  14. Data mining methods for prediction of air pollution

    Directory of Open Access Journals (Sweden)

    Siwek Krzysztof

    2016-06-01

    Full Text Available The paper discusses methods of data mining for prediction of air pollution. Two tasks in such a problem are important: generation and selection of the prognostic features, and the final prognostic system of the pollution for the next day. An advanced set of features, created on the basis of the atmospheric parameters, is proposed. This set is subject to analysis and selection of the most important features from the prediction point of view. Two methods of feature selection are compared. One applies a genetic algorithm (a global approach, and the other-a linear method of stepwise fit (a locally optimized approach. On the basis of such analysis, two sets of the most predictive features are selected. These sets take part in prediction of the atmospheric pollutants PM10, SO2, NO2 and O3. Two approaches to prediction are compared. In the first one, the features selected are directly applied to the random forest (RF, which forms an ensemble of decision trees. In the second case, intermediate predictors built on the basis of neural networks (the multilayer perceptron, the radial basis function and the support vector machine are used. They create an ensemble integrated into the final prognosis. The paper shows that preselection of the most important features, cooperating with an ensemble of predictors, allows increasing the forecasting accuracy of atmospheric pollution in a significant way.

  15. Prediction of thermal sensation in non-air-conditioned buildings in warm climates

    DEFF Research Database (Denmark)

    Fanger, Povl Ole; Toftum, Jørn

    2002-01-01

    The PMV model agrees well with high-quality field studies in buildings with HVAC systems, situated in cold, temperate and warm climates, studied during both summer and winter. In non-air-conditioned buildings in warm climates, occupants may sense the warmth as being less severe than the PMV...... predicts. The main reason is low expectations, but a metabolic rate that is estimated too high can also contribute to explaining the difference. An extension of the PMV model that includes an expectancy factor is introduced for use in non-air-conditioned buildings in warm climates. The extended PMV model...... agrees well with quality field studies in non-air-conditioned buildings of three continents....

  16. Air Pollution Exposure Modeling for Health Studies | Science ...

    Science.gov (United States)

    Dr. Michael Breen is leading the development of air pollution exposure models, integrated with novel personal sensor technologies, to improve exposure and risk assessments for individuals in health studies. He is co-investigator for multiple health studies assessing the exposure and effects of air pollutants. These health studies include participants with asthma, diabetes, and coronary artery disease living in various U.S. cities. He has developed, evaluated, and applied novel exposure modeling and time-activity tools, which includes the Exposure Model for Individuals (EMI), GPS-based Microenvironment Tracker (MicroTrac) and Exposure Tracker models. At this seminar, Dr. Breen will present the development and application of these models to predict individual-level personal exposures to particulate matter (PM) for two health studies in central North Carolina. These health studies examine the association between PM and adverse health outcomes for susceptible individuals. During Dr. Breen’s visit, he will also have the opportunity to establish additional collaborations with researchers at Harvard University that may benefit from the use of exposure models for cohort health studies. These research projects that link air pollution exposure with adverse health outcomes benefit EPA by developing model-predicted exposure-dose metrics for individuals in health studies to improve the understanding of exposure-response behavior of air pollutants, and to reduce participant

  17. Meteorological and air pollution modeling for an urban airport

    Science.gov (United States)

    Swan, P. R.; Lee, I. Y.

    1980-01-01

    Results are presented of numerical experiments modeling meteorology, multiple pollutant sources, and nonlinear photochemical reactions for the case of an airport in a large urban area with complex terrain. A planetary boundary-layer model which predicts the mixing depth and generates wind, moisture, and temperature fields was used; it utilizes only surface and synoptic boundary conditions as input data. A version of the Hecht-Seinfeld-Dodge chemical kinetics model is integrated with a new, rapid numerical technique; both the San Francisco Bay Area Air Quality Management District source inventory and the San Jose Airport aircraft inventory are utilized. The air quality model results are presented in contour plots; the combined results illustrate that the highly nonlinear interactions which are present require that the chemistry and meteorology be considered simultaneously to make a valid assessment of the effects of individual sources on regional air quality.

  18. STEMS-Air: a simple GIS-based air pollution dispersion model for city-wide exposure assessment.

    Science.gov (United States)

    Gulliver, John; Briggs, David

    2011-05-15

    Current methods of air pollution modelling do not readily meet the needs of air pollution mapping for short-term (i.e. daily) exposure studies. The main limiting factor is that for those few models that couple with a GIS there are insufficient tools for directly mapping air pollution both at high spatial resolution and over large areas (e.g. city wide). A simple GIS-based air pollution model (STEMS-Air) has been developed for PM(10) to meet these needs with the option to choose different exposure averaging periods (e.g. daily and annual). STEMS-Air uses the grid-based FOCALSUM function in ArcGIS in conjunction with a fine grid of emission sources and basic information on meteorology to implement a simple Gaussian plume model of air pollution dispersion. STEMS-Air was developed and validated in London, UK, using data on concentrations of PM(10) from routinely available monitoring data. Results from the validation study show that STEMS-Air performs well in predicting both daily (at four sites) and annual (at 30 sites) concentrations of PM(10). For daily modelling, STEMS-Air achieved r(2) values in the range 0.19-0.43 (pmaps either as a screening process in urban air quality planning and management, or as the basis for health risk assessment and epidemiological studies.

  19. Non-linear model predictive supervisory controller for building, air handling unit with recuperator and refrigeration system with heat waste recovery

    DEFF Research Database (Denmark)

    Minko, Tomasz; Wisniewski, Rafal; Bendtsen, Jan Dimon

    2016-01-01

    In this paper we examine a supermarket system. In order to grasp the most important dynamics we present a model that includes the single zone building thermal envelope with its heating, cooling and ventilation. Moreover we include heat waste recovery from the refrigeration high pressure side. The...

  20. Parameter sets for upper and lower bounds on soil-to-indoor-air contaminant attenuation predicted by the Johnson and Ettinger vapor intrusion model

    Science.gov (United States)

    Tillman, Fred D.; Weaver, James W.

    Migration of volatile chemicals from the subsurface into overlying buildings is known as vapor intrusion (VI). Under certain circumstances, people living in homes above contaminated soil or ground water may be exposed to harmful levels of these vapors. A popular VI screening-level algorithm widely used in the United States, Canada and the UK to assess this potential risk is the "Johnson and Ettinger" (J&E) model. Concern exists over using the J&E model for deciding whether or not further action is necessary at sites, as many parameters are not routinely measured (or are un-measurable). Using EPA-recommended ranges of parameter values for nine soil-type/source depth combinations, input parameter sets were identified that correspond to bounding results of the J&E model. The results established the existence of generic upper and lower bound parameter sets for maximum and minimum exposure for all soil types and depths investigated. Using the generic upper and lower bound parameter sets, an analysis can be performed that, given the limitations of the input ranges and the model, bounds the attenuation factor in a VI investigation.

  1. Heat Transfer Model for Hot Air Balloons

    Science.gov (United States)

    Llado-Gambin, Adriana

    A heat transfer model and analysis for hot air balloons is presented in this work, backed with a flow simulation using SolidWorks. The objective is to understand the major heat losses in the balloon and to identify the parameters that affect most its flight performance. Results show that more than 70% of the heat losses are due to the emitted radiation from the balloon envelope and that convection losses represent around 20% of the total. A simulated heating source is also included in the modeling based on typical thermal input from a balloon propane burner. The burner duty cycle to keep a constant altitude can vary from 10% to 28% depending on the atmospheric conditions, and the ambient temperature is the parameter that most affects the total thermal input needed. The simulation and analysis also predict that the gas temperature inside the balloon decreases at a rate of -0.25 K/s when there is no burner activity, and it increases at a rate of +1 K/s when the balloon pilot operates the burner. The results were compared to actual flight data and they show very good agreement indicating that the major physical processes responsible for balloon performance aloft are accurately captured in the simulation.

  2. Wind Prediction Accuracy for Air Traffic Management Decision Support Tools

    Science.gov (United States)

    Cole, Rod; Green, Steve; Jardin, Matt; Schwartz, Barry; Benjamin, Stan

    2000-01-01

    The performance of Air Traffic Management and flight deck decision support tools depends in large part on the accuracy of the supporting 4D trajectory predictions. This is particularly relevant to conflict prediction and active advisories for the resolution of conflicts and the conformance with of traffic-flow management flow-rate constraints (e.g., arrival metering / required time of arrival). Flight test results have indicated that wind prediction errors may represent the largest source of trajectory prediction error. The tests also discovered relatively large errors (e.g., greater than 20 knots), existing in pockets of space and time critical to ATM DST performance (one or more sectors, greater than 20 minutes), are inadequately represented by the classic RMS aggregate prediction-accuracy studies of the past. To facilitate the identification and reduction of DST-critical wind-prediction errors, NASA has lead a collaborative research and development activity with MIT Lincoln Laboratories and the Forecast Systems Lab of the National Oceanographic and Atmospheric Administration (NOAA). This activity, begun in 1996, has focussed on the development of key metrics for ATM DST performance, assessment of wind-prediction skill for state of the art systems, and development/validation of system enhancements to improve skill. A 13 month study was conducted for the Denver Center airspace in 1997. Two complementary wind-prediction systems were analyzed and compared to the forecast performance of the then standard 60 km Rapid Update Cycle - version 1 (RUC-1). One system, developed by NOAA, was the prototype 40-km RUC-2 that became operational at NCEP in 1999. RUC-2 introduced a faster cycle (1 hr vs. 3 hr) and improved mesoscale physics. The second system, Augmented Winds (AW), is a prototype en route wind application developed by MITLL based on the Integrated Terminal Wind System (ITWS). AW is run at a local facility (Center) level, and updates RUC predictions based on an

  3. Wind Prediction Accuracy for Air Traffic Management Decision Support Tools

    Science.gov (United States)

    Cole, Rod; Green, Steve; Jardin, Matt; Schwartz, Barry; Benjamin, Stan

    2000-01-01

    The performance of Air Traffic Management and flight deck decision support tools depends in large part on the accuracy of the supporting 4D trajectory predictions. This is particularly relevant to conflict prediction and active advisories for the resolution of conflicts and the conformance with of traffic-flow management flow-rate constraints (e.g., arrival metering / required time of arrival). Flight test results have indicated that wind prediction errors may represent the largest source of trajectory prediction error. The tests also discovered relatively large errors (e.g., greater than 20 knots), existing in pockets of space and time critical to ATM DST performance (one or more sectors, greater than 20 minutes), are inadequately represented by the classic RMS aggregate prediction-accuracy studies of the past. To facilitate the identification and reduction of DST-critical wind-prediction errors, NASA has lead a collaborative research and development activity with MIT Lincoln Laboratories and the Forecast Systems Lab of the National Oceanographic and Atmospheric Administration (NOAA). This activity, begun in 1996, has focussed on the development of key metrics for ATM DST performance, assessment of wind-prediction skill for state of the art systems, and development/validation of system enhancements to improve skill. A 13 month study was conducted for the Denver Center airspace in 1997. Two complementary wind-prediction systems were analyzed and compared to the forecast performance of the then standard 60 km Rapid Update Cycle - version 1 (RUC-1). One system, developed by NOAA, was the prototype 40-km RUC-2 that became operational at NCEP in 1999. RUC-2 introduced a faster cycle (1 hr vs. 3 hr) and improved mesoscale physics. The second system, Augmented Winds (AW), is a prototype en route wind application developed by MITLL based on the Integrated Terminal Wind System (ITWS). AW is run at a local facility (Center) level, and updates RUC predictions based on an

  4. Validation of the Air Force Weather Agency Ensemble Prediction Systems

    Science.gov (United States)

    2014-03-27

    to deterministic models. Results from ensemble weather input into operational risk management ( ORM ) destruction of enemy air defense simulations...growth during the analysis period (Toth and Kalnay, 1993; Toth and Kalnay, 1997). From this framework the ensemble transform bred vector, ensemble...features. Each of its 10 members is run independently using different configurations in the framework of the Weather Research and Forecasting (WRF

  5. The Impact of Trajectory Prediction Uncertainty on Air Traffic Controller Performance and Acceptability

    Science.gov (United States)

    Mercer, Joey S.; Bienert, Nancy; Gomez, Ashley; Hunt, Sarah; Kraut, Joshua; Martin, Lynne; Morey, Susan; Green, Steven M.; Prevot, Thomas; Wu, Minghong G.

    2013-01-01

    A Human-In-The-Loop air traffic control simulation investigated the impact of uncertainties in trajectory predictions on NextGen Trajectory-Based Operations concepts, seeking to understand when the automation would become unacceptable to controllers or when performance targets could no longer be met. Retired air traffic controllers staffed two en route transition sectors, delivering arrival traffic to the northwest corner-post of Atlanta approach control under time-based metering operations. Using trajectory-based decision-support tools, the participants worked the traffic under varying levels of wind forecast error and aircraft performance model error, impacting the ground automations ability to make accurate predictions. Results suggest that the controllers were able to maintain high levels of performance, despite even the highest levels of trajectory prediction errors.

  6. Surface Flux Modeling for Air Quality Applications

    Directory of Open Access Journals (Sweden)

    Limei Ran

    2011-08-01

    Full Text Available For many gasses and aerosols, dry deposition is an important sink of atmospheric mass. Dry deposition fluxes are also important sources of pollutants to terrestrial and aquatic ecosystems. The surface fluxes of some gases, such as ammonia, mercury, and certain volatile organic compounds, can be upward into the air as well as downward to the surface and therefore should be modeled as bi-directional fluxes. Model parameterizations of dry deposition in air quality models have been represented by simple electrical resistance analogs for almost 30 years. Uncertainties in surface flux modeling in global to mesoscale models are being slowly reduced as more field measurements provide constraints on parameterizations. However, at the same time, more chemical species are being added to surface flux models as air quality models are expanded to include more complex chemistry and are being applied to a wider array of environmental issues. Since surface flux measurements of many of these chemicals are still lacking, resistances are usually parameterized using simple scaling by water or lipid solubility and reactivity. Advances in recent years have included bi-directional flux algorithms that require a shift from pre-computation of deposition velocities to fully integrated surface flux calculations within air quality models. Improved modeling of the stomatal component of chemical surface fluxes has resulted from improved evapotranspiration modeling in land surface models and closer integration between meteorology and air quality models. Satellite-derived land use characterization and vegetation products and indices are improving model representation of spatial and temporal variations in surface flux processes. This review describes the current state of chemical dry deposition modeling, recent progress in bi-directional flux modeling, synergistic model development research with field measurements, and coupling with meteorological land surface models.

  7. Off-site air monitoring following methyl bromide chamber and building fumigations and evaluation of the ISCST air dispersion model

    Energy Technology Data Exchange (ETDEWEB)

    Barry, T.; Swgawa, R.; Wofford, P. [Cal EPA, Sacramento, CA (United States)] [and others

    1995-12-31

    The Department of Pesticide Regulation`s preliminary risk characterization of methyl bromide indicated an inadequate margin of safety for several exposure scenarios. Characterization of the air concentrations associated with common methyl bromide use patterns was necessary to determine specific scenarios that result in an unacceptable margin of safety. Field monitoring data were used in conjunction with the Industrial Source Complex, Short Tenn (ISCST) air dispersion model to characterize air concentrations associated with various types of methyl bromide applications. Chamber and building fumigations were monitored and modelled. For each fumigation the emission rates, chamber or building specifications and on-site meteorological data were input into the ISCST model. The model predicted concentrations were compared to measured air concentrations. The concentrations predicted by the ISCST model reflect both the pattern and magnitude of the measured concentrations. Required buffer zones were calculated using the ISCST output.

  8. Prediction of Monthly Mean Surface Air Temperature in a Region of China

    Institute of Scientific and Technical Information of China (English)

    Jeong-Hyeong LEE; Keon-Tae SOHN

    2007-01-01

    In conventional time series analysis, a process is often modeled as three additive components: linear trend, seasonal effect, and random noise. In this paper, we perform an analysis of surface air temperature in a region of China using a decomposition method in time series analysis. Applications to the National Centers for Environmental Prediction/the National Center for Atmospheric Research (NCEP/NCAR) Collaborative Reanalysis data in this region of China are discussed. The main finding was that the surface air temperature trend estimated for January 1948 to February 2006 was not statistically significant at 0.5904℃ (100 yr)-1.Forecasting aspects are also considered.

  9. Modelling heat and mass transfer in a membrane-based air-to-air enthalpy exchanger

    Science.gov (United States)

    Dugaria, S.; Moro, L.; Del, D., Col

    2015-11-01

    The diffusion of total energy recovery systems could lead to a significant reduction in the energy demand for building air-conditioning. With these devices, sensible heat and humidity can be recovered in winter from the exhaust airstream, while, in summer, the incoming air stream can be cooled and dehumidified by transferring the excess heat and moisture to the exhaust air stream. Membrane based enthalpy exchangers are composed by different channels separated by semi-permeable membranes. The membrane allows moisture transfer under vapour pressure difference, or water concentration difference, between the two sides and, at the same time, it is ideally impermeable to air and other contaminants present in exhaust air. Heat transfer between the airstreams occurs through the membrane due to the temperature gradient. The aim of this work is to develop a detailed model of the coupled heat and mass transfer mechanisms through the membrane between the two airstreams. After a review of the most relevant models published in the scientific literature, the governing equations are presented and some simplifying assumptions are analysed and discussed. As a result, a steady-state, two-dimensional finite difference numerical model is setup. The developed model is able to predict temperature and humidity evolution inside the channels. Sensible and latent heat transfer rate, as well as moisture transfer rate, are determined. A sensitive analysis is conducted in order to determine the more influential parameters on the thermal and vapour transfer.

  10. The ASAC Air Carrier Investment Model (Second Generation)

    Science.gov (United States)

    Wingrove, Earl R., III; Johnson, Jesse P.; Sickles, Robin C.; Good, David H.

    1997-01-01

    To meet its objective of assisting the U.S. aviation industry with the technological challenges of the future, NASA must identify research areas that have the greatest potential for improving the operation of the air transportation system. To accomplish this, NASA is building an Aviation System Analysis Capability (ASAC). The ASAC differs from previous NASA modeling efforts in that the economic behavior of buyers and sellers in the air transportation and aviation industries is central to its conception. To link the economics of flight with the technology of flight, ASAC requires a parametrically based mode with extensions that link airline operations and investments in aircraft with aircraft characteristics. This model also must provide a mechanism for incorporating air travel demand and profitability factors into the airlines' investment decisions. Finally, the model must be flexible and capable of being incorporated into a wide-ranging suite of economic and technical models that are envisioned for ASAC. We describe a second-generation Air Carrier Investment Model that meets these requirements. The enhanced model incorporates econometric results from the supply and demand curves faced by U.S.-scheduled passenger air carriers. It uses detailed information about their fleets in 1995 to make predictions about future aircraft purchases. It enables analysts with the ability to project revenue passenger-miles flown, airline industry employment, airline operating profit margins, numbers and types of aircraft in the fleet, and changes in aircraft manufacturing employment under various user-defined scenarios.

  11. Simplified Atmospheric Dispersion Model andModel Based Real Field Estimation System ofAir Pollution

    Institute of Scientific and Technical Information of China (English)

    2015-01-01

    The atmospheric dispersion model has been well developed and applied in pollution emergency and prediction. Based on thesophisticated air diffusion model, this paper proposes a simplified model and some optimization about meteorological andgeological conditions. The model is suitable for what is proposed as Real Field Monitor and Estimation system. The principle ofsimplified diffusion model and its optimization is studied. The design of Real Field Monitor system based on this model and itsfundamental implementations are introduced.

  12. Predicting SVOC Emissions into Air and Foods in Support of ...

    Science.gov (United States)

    The release of semi-volatile organic compounds (SVOCs) from consumer articles may be a critical human exposure pathway. In addition, the migration of SVOCs from food packaging materials into foods may also be a dominant source of exposure for some chemicals. Here we describe recent efforts to characterize emission-related parameters for these exposure pathways to support prediction of aggregate exposures for thousands of chemicals For chemicals in consumer articles, Little et al. (2012) developed a screening-level indoor exposure prediction model which, for a given SVOC, principally depends on steady-state gas-phase concentrations (y0). We have developed a model that predicts y0 for SVOCs in consumer articles, allowing exposure predictions for 274 ToxCast chemicals. Published emissions data for 31 SVOCs found in flooring materials, provided a training set where both chemical-specific physicochemical properties, article specific formulation properties, and experimental design aspects were available as modeling descriptors. A linear regression yielded R2- and p- values of approximately 0.62 and 3.9E-05, respectively. A similar model was developed based upon physicochemical properties alone, since article information is often not available for a given SVOC or product. This latter model yielded R2 - and p- values of approximately 0.47 and 1.2E-10, respectively. Many SVOCs are also used as additives (e.g. plasticizers, antioxidants, lubricants) in plastic food pac

  13. Modeling Trends in Air Pollutant Concentrations over the ...

    Science.gov (United States)

    Regional model calculations over annual cycles have pointed to the need for accurately representing impacts of long-range transport. Linking regional and global scale models have met with mixed success as biases in the global model can propagate and influence regional calculations and often confound interpretation of model results. Since transport is efficient in the free-troposphere and since simulations over Continental scales and annual cycles provide sufficient opportunity for “atmospheric turn-over”, i.e., exchange between the free-troposphere and the boundary-layer, a conceptual framework is needed wherein interactions between processes occurring at various spatial and temporal scales can be consistently examined. The coupled WRF-CMAQ model is expanded to hemispheric scales and model simulations over period spanning 1990-current are analyzed to examine changes in hemispheric air pollution resulting from changes in emissions over this period. The National Exposure Research Laboratory (NERL) Atmospheric Modeling and Analysis Division (AMAD) conducts research in support of EPA mission to protect human health and the environment. AMAD research program is engaged in developing and evaluating predictive atmospheric models on all spatial and temporal scales for forecasting the air quality and for assessing changes in air quality and air pollutant exposures, as affected by changes in ecosystem management and regulatory decisions. AMAD is responsible for pr

  14. A branching model for hadronic air showers

    CERN Document Server

    Novotny, Vladimir; Ebr, Jan

    2015-01-01

    We introduce a simple branching model for the development of hadronic showers in the Earth's atmosphere. Based on this model, we show how the size of the pionic component followed by muons can be estimated. Several aspects of the subsequent muonic component are also discussed. We focus on the energy evolution of the muon production depth. We also estimate the impact of the primary particle mass on the size of the hadronic component. Even though a precise calculation of the development of air showers must be left to complex Monte Carlo simulations, the proposed model can reveal qualitative insight into the air shower physics.

  15. The regional prediction model of PM10 concentrations for Turkey

    Science.gov (United States)

    Güler, Nevin; Güneri İşçi, Öznur

    2016-11-01

    This study is aimed to predict a regional model for weekly PM10 concentrations measured air pollution monitoring stations in Turkey. There are seven geographical regions in Turkey and numerous monitoring stations at each region. Predicting a model conventionally for each monitoring station requires a lot of labor and time and it may lead to degradation in quality of prediction when the number of measurements obtained from any õmonitoring station is small. Besides, prediction models obtained by this way only reflect the air pollutant behavior of a small area. This study uses Fuzzy C-Auto Regressive Model (FCARM) in order to find a prediction model to be reflected the regional behavior of weekly PM10 concentrations. The superiority of FCARM is to have the ability of considering simultaneously PM10 concentrations measured monitoring stations in the specified region. Besides, it also works even if the number of measurements obtained from the monitoring stations is different or small. In order to evaluate the performance of FCARM, FCARM is executed for all regions in Turkey and prediction results are compared to statistical Autoregressive (AR) Models predicted for each station separately. According to Mean Absolute Percentage Error (MAPE) criteria, it is observed that FCARM provides the better predictions with a less number of models.

  16. Prediction of Air Flow and Temperature Distribution Inside a Yogurt Cooling Room Using Computational Fluid Dynamics

    Directory of Open Access Journals (Sweden)

    A Surendhar

    2015-01-01

    Full Text Available Air flow and heat transfer inside a yogurt cooling room were analysed using Computational Fluid Dynamics. Air flow and heat transfer models were based on 3D, unsteady state, incompressible, Reynolds-averaged Navier-Stokes equations and energy equations. Yogurt cooling room was modelled with the measured geometry using 3D design tool AutoCAD. Yogurt cooling room model was exported into the flow simulation software by specifying properties of inlet air, yogurt, pallet and walls of the room. Packing material was not considered in this study because of less thickness (cup-0.5mm, carton box-1.5mm and negligible resistance created in the conduction of heat. 3D Computational domain was meshed with hexahedral cells and governing equations were solved using explicit finite volume method. Air flow pattern inside the room and the temperature distribution in the bulk of palletized yogurt were predicted. Through validation, the variation in the temperature distribution and velocity vector from the measured value was found to be 2.0oC (maximum and 30% respectively. From the simulation and the measured value of the temperature distribution, it was observed that the temperature was non-uniform over the bulk of yogurt. This might be due to refrigeration capacity, air flow pattern, stacking of yogurt or geometry of the room. Required results were achieved by changing the location of the cooling fan.

  17. Predictive factors of psychosomatic reactions during air raids

    Directory of Open Access Journals (Sweden)

    Samardžić Radomir

    2005-01-01

    Full Text Available Background. Civilian population of Yugoslavia was exposed to massive stressors of air raids by NATO in 1999. The aim of this study was to investigate somatic complaints and their predictive factors during stresses of air raids. Methods. Random sample of 434 subjects, consisting of 139 inhabitants of several Belgrade municipalities and 295 employees of Military Medical Academy, were assessed in the cross-sectional study. The basic factors of interest were stress severity, variables of personality and habits and behavior relevant for somatic complaints. Self-report of stress severity and the most common somatic complaints were performed by Questionnaire specially designed for this purpose and personality evaluation by EPQ-38. Multiple regression analysis was used to determine the influence of predictors on dependent variable (psychosomatic symptoms. Results. Personality was the most important predictor that explained 29% of variance in somatic symptoms (with the strongest impact of neuroticism; 11% was explained by habits and behavior and only 1% of variance by stress. Conclusion. The finding that personality had higher impact on stress reaction outcome could be important for preventive and therapeutic aspects of stress reactions.

  18. Air quality modeling in support of the Near-Road Exposures and Effects of Urban Air Pollutants Study (NEXUS).

    Science.gov (United States)

    Isakov, Vlad; Arunachalam, Saravanan; Batterman, Stuart; Bereznicki, Sarah; Burke, Janet; Dionisio, Kathie; Garcia, Val; Heist, David; Perry, Steve; Snyder, Michelle; Vette, Alan

    2014-08-27

    A major challenge in traffic-related air pollution exposure studies is the lack of information regarding pollutant exposure characterization. Air quality modeling can provide spatially and temporally varying exposure estimates for examining relationships between traffic-related air pollutants and adverse health outcomes. A hybrid air quality modeling approach was used to estimate exposure to traffic-related air pollutants in support of the Near-Road Exposures and Effects of Urban Air Pollutants Study (NEXUS) conducted in Detroit (Michigan, USA). Model-based exposure metrics, associated with local variations of emissions and meteorology, were estimated using a combination of the American Meteorological Society/Environmental Protection Agency Regulatory Model (AERMOD) and Research LINE-source dispersion model for near-surface releases (RLINE) dispersion models, local emission source information from the National Emissions Inventory, detailed road network locations and traffic activity, and meteorological data from the Detroit City Airport. The regional background contribution was estimated using a combination of the Community Multi-scale Air Quality (CMAQ) and the Space-Time Ordinary Kriging (STOK) models. To capture the near-road pollutant gradients, refined "mini-grids" of model receptors were placed around participant homes. Exposure metrics for CO, NOx, PM2.5 and its components (elemental and organic carbon) were predicted at each home location for multiple time periods including daily and rush hours. The exposure metrics were evaluated for their ability to characterize the spatial and temporal variations of multiple ambient air pollutants compared to measurements across the study area.

  19. An air quality model for Central Mexico

    Energy Technology Data Exchange (ETDEWEB)

    Jazcilevich, D. Aron; Garcia, R. Agustin; Suarez, Gerardo Ruiz; Magana, R. Victor; Perez, L. Jose Luis [Universidad Nacional Autonoma de Mexico, Centro de Ciencias de la Atmosfera, Mexico City (Mexico); Fuentes-Gea, Vicente [Universidad Nacional Autonoma de Mexico, Div. de Estudios del Posgrado, Mexico City (Mexico)

    1999-07-01

    A computational air quality model for Central Mexico that includes the Basin of the Valley of Mexico, the Valleys of Toluca, Puebla and Cuernavaca already in experimental operation, is presented. The meteorology of the region is obtained combining two non-hydrostatic models: a model designed for synoptic scales called MM5 provides initial and boundary data to a model specially designed for urban environments and scales called MEMO. The transport model used numerical techniques developed by the authors that eliminate numerical diffusion and dispersion. For the photochemical model several ODE's integrators were tested. The emissions model developed uses the latest inventory data gathered in the region. (Author)

  20. Models of Inflammation: Carrageenan Air Pouch.

    Science.gov (United States)

    Duarte, Djane B; Vasko, Michael R; Fehrenbacher, Jill C

    2016-03-18

    The subcutaneous air pouch is an in vivo model that can be used to study the components of acute and chronic inflammation, the resolution of the inflammatory response, the oxidative stress response, and potential therapeutic targets for treating inflammation. Injection of irritants into an air pouch in rats or mice induces an inflammatory response that can be quantified by the volume of exudate produced, the infiltration of cells, and the release of inflammatory mediators. The model presented in this unit has been extensively used to identify potential anti-inflammatory drugs.

  1. AQA - Air Quality model for Austria - Evaluation and Developments

    Science.gov (United States)

    Hirtl, M.; Krüger, B. C.; Baumann-Stanzer, K.; Skomorowski, P.

    2009-04-01

    The regional weather forecast model ALADIN of the Central Institute for Meteorology and Geodynamics (ZAMG) is used in combination with the chemical transport model CAMx (www.camx.com) to conduct forecasts of gaseous and particulate air pollution over Europe. The forecasts which are done in cooperation with the University of Natural Resources and Applied Life Sciences in Vienna (BOKU) are supported by the regional governments since 2005 with the main interest on the prediction of tropospheric ozone. The daily ozone forecasts are evaluated for the summer 2008 with the observations of about 150 air quality stations in Austria. In 2008 the emission-model SMOKE was integrated into the modelling system to calculate the biogenic emissions. The anthropogenic emissions are based on the newest EMEP data set as well as on regional inventories for the core domain. The performance of SMOKE is shown for a summer period in 2007. In the frame of the COST-action 728 „Enhancing mesoscale meteorological modelling capabilities for air pollution and dispersion applications", multi-model ensembles are used to conduct an international model evaluation. The model calculations of meteorological- and concentration fields are compared to measurements on the ensemble platform at the Joint Research Centre (JRC) in Ispra. The results for 2 episodes in 2006 show the performance of the different models as well as of the model ensemble.

  2. Modeling the air-soil transport pathway of perfluorooctanoic acid in the mid-Ohio Valley using linked air dispersion and vadose zone models

    Science.gov (United States)

    Shin, Hyeong-Moo; Ryan, P. Barry; Vieira, Verónica M.; Bartell, Scott M.

    2012-05-01

    As part of an extensive modeling effort on the air-soil-groundwater transport pathway of perfluorooctanoic acid (PFOA), this study was designed to compare the performance of different air dispersion modeling systems (AERMOD vs. ISCST3), and different approaches to handling incomplete meteorological data using a data set with substantial soil measurements and a well characterized point source for air emissions. Two of the most commonly used EPA air dispersion models, AERMOD and ISCST3, were linked with the EPA vadose zone model PRZM-3. Predicted deposition rates from the air dispersion model were used as input values for the vadose zone model to estimate soil concentrations of PFOA at different depths. We applied 34 years of meteorological data including hourly surface measurements from Parkersburg Airport and 5 years of onsite wind direction and speed to the air dispersion models. We compared offsite measured soil concentrations to predictions made for the corresponding sampling depths, focusing on soil rather than air measurements because the offsite soil samples were less likely to be influenced by short-term variability in emission rates and meteorological conditions. PFOA concentrations in surface soil (0-30 cm depth) were under-predicted and those in subsurface soil (>30 cm depth) were over-predicted compared to observed concentrations by both linked air and vadose zone model. Overall, the simulated values from the linked modeling system were positively correlated with those observed in surface soil (Spearman's rho, Rsp = 0.59-0.70) and subsurface soil (Rsp = 0.46-0.48). This approach provides a useful modeling scheme for similar exposure and risk analyses where the air-soil-groundwater transport is a primary contamination pathway.

  3. Modelling of radio emission from cosmic ray air showers

    Science.gov (United States)

    Ludwig, Marianne

    2011-06-01

    Cosmic rays entering the Earth's atmosphere induce extensive air showers consisting of up to billions of secondary particles. Among them, a multitude of electrons and positrons are generated. These get deflected in the Earth's magnetic field, creating time-varying transverse currents. Thereby, the air shower emits coherent radiation in the MHz frequency range measured by radio antenna arrays on the ground such as LOPES at the KIT. This detection method provides a possibility to study cosmic rays with energies above 1017 eV. At this time, the radio technique undergoes the change from prototype experiments to large scale application. Thus, a detailed understanding of the radio emission process is needed more than ever. Before starting this work, different models made conflicting predictions on the pulse shape and the amplitude of the radio signal. It turned out that a radiation component caused by the variation of the number of charged particles within the air shower was missed in several models. The Monte Carlo code REAS2 superposing the radiation of the individual air shower electrons and positrons was one of those. At this time, it was not known how to take the missing component into account. For REAS3, we developed and implemented the endpoint formalism, a universal approach, to calculate the radiation from each single particle. For the first time, we achieve a good agreement between REAS3 and MGMR, an independent and completely different simulation approach. In contrast to REAS3, MGMR is based on a macroscopic approach and on parametrisations of the air shower. We studied the differences in the underlying air shower models to explain the remaining deviations. For comparisons with LOPES data, we developed a new method which allows "top-down" simulations of air showers. From this, we developed an air shower selection criterion based on the number of muons measured with KASCADE to take shower-to-shower fluctuations for a single event analysis into account. With

  4. Joint space-time geostatistical model for air quality surveillance

    Science.gov (United States)

    Russo, A.; Soares, A.; Pereira, M. J.

    2009-04-01

    Air pollution and peoples' generalized concern about air quality are, nowadays, considered to be a global problem. Although the introduction of rigid air pollution regulations has reduced pollution from industry and power stations, the growing number of cars on the road poses a new pollution problem. Considering the characteristics of the atmospheric circulation and also the residence times of certain pollutants in the atmosphere, a generalized and growing interest on air quality issues led to research intensification and publication of several articles with quite different levels of scientific depth. As most natural phenomena, air quality can be seen as a space-time process, where space-time relationships have usually quite different characteristics and levels of uncertainty. As a result, the simultaneous integration of space and time is not an easy task to perform. This problem is overcome by a variety of methodologies. The use of stochastic models and neural networks to characterize space-time dispersion of air quality is becoming a common practice. The main objective of this work is to produce an air quality model which allows forecasting critical concentration episodes of a certain pollutant by means of a hybrid approach, based on the combined use of neural network models and stochastic simulations. A stochastic simulation of the spatial component with a space-time trend model is proposed to characterize critical situations, taking into account data from the past and a space-time trend from the recent past. To identify near future critical episodes, predicted values from neural networks are used at each monitoring station. In this paper, we describe the design of a hybrid forecasting tool for ambient NO2 concentrations in Lisbon, Portugal.

  5. Differential neural network approach in information process for prediction of roadside air pollution by peat fire

    Science.gov (United States)

    Lozhkin, V.; Tarkhov, D.; Timofeev, V.; Lozhkina, O.; Vasilyev, A.

    2016-11-01

    The paper presents a novel differential neural network model estimating the dispersion of CO emissions from a peat fire near a highway. We have developed approaches for the optimization of the model on the base of simulated and experimental measurements of CO concentrations in the area of dispersion of the smoke cloud. The numerical solutions of the problem are presented in the form of neural network approximations by the Gaussian model and in the form of neural network approximate solutions of partial differential equations. The trained neural network model can be used for the prediction of emergency when wind speed and direction and other fire parameters are changing. The method is also recommended for the development of air quality monitoring and predicting information systems.

  6. Modeling exposure to air pollution from the WTC disaster based on reports of perceived air pollution.

    Science.gov (United States)

    Lederman, Sally Ann; Becker, Mark; Sheets, Stephen; Stein, Janet; Tang, Deliang; Weiss, Lisa; Perera, Frederica P

    2008-04-01

    We examined the utility of a newly developed perceived air pollution (PAP) scale and of a modeled air pollution (MAP) scale derived from it for predicting previously observed birth outcomes of pregnant women enrolled following September 11, 2001. Women reported their home and work locations in the four weeks after September 11, 2001 and the PAP at each site on a four-point scale designed for this purpose. Locations were geocoded and their distance from the World Trade Center (WTC) site determined. PAP values were used to develop a model of air pollution for a 20-mile radius from the WTC site. MAP values were assigned to each geocoded location. We examined the relationship of PAP and MAP values to maternal characteristics and to distance of home and work sites from the WTC site. Both PAP and MAP values were highly correlated with distance from the WTC. Maternal characteristics that were associated with PAP values reported for home or work sites (race, demoralization, material hardship, first trimester on September 11) were not associated with modeled MAP values. Relationships of several birth outcomes to proximity to the WTC, which we previously reported using this data set, were also seen when MAP values were used as the measure of exposure, instead of proximity. MAP developed from reports of PAP may be useful to identify high-risk areas and predict health outcomes when there are multiple sources of pollution and a "distance from source" analysis is impossible.

  7. Using soft computing techniques to predict corrected air permeability using Thomeer parameters, air porosity and grain density

    Science.gov (United States)

    Nooruddin, Hasan A.; Anifowose, Fatai; Abdulraheem, Abdulazeez

    2014-03-01

    Soft computing techniques are recently becoming very popular in the oil industry. A number of computational intelligence-based predictive methods have been widely applied in the industry with high prediction capabilities. Some of the popular methods include feed-forward neural networks, radial basis function network, generalized regression neural network, functional networks, support vector regression and adaptive network fuzzy inference system. A comparative study among most popular soft computing techniques is presented using a large dataset published in literature describing multimodal pore systems in the Arab D formation. The inputs to the models are air porosity, grain density, and Thomeer parameters obtained using mercury injection capillary pressure profiles. Corrected air permeability is the target variable. Applying developed permeability models in recent reservoir characterization workflow ensures consistency between micro and macro scale information represented mainly by Thomeer parameters and absolute permeability. The dataset was divided into two parts with 80% of data used for training and 20% for testing. The target permeability variable was transformed to the logarithmic scale as a pre-processing step and to show better correlations with the input variables. Statistical and graphical analysis of the results including permeability cross-plots and detailed error measures were created. In general, the comparative study showed very close results among the developed models. The feed-forward neural network permeability model showed the lowest average relative error, average absolute relative error, standard deviations of error and root means squares making it the best model for such problems. Adaptive network fuzzy inference system also showed very good results.

  8. Modeling urban air pollution with optimized hierarchical fuzzy inference system.

    Science.gov (United States)

    Tashayo, Behnam; Alimohammadi, Abbas

    2016-10-01

    Environmental exposure assessments (EEA) and epidemiological studies require urban air pollution models with appropriate spatial and temporal resolutions. Uncertain available data and inflexible models can limit air pollution modeling techniques, particularly in under developing countries. This paper develops a hierarchical fuzzy inference system (HFIS) to model air pollution under different land use, transportation, and meteorological conditions. To improve performance, the system treats the issue as a large-scale and high-dimensional problem and develops the proposed model using a three-step approach. In the first step, a geospatial information system (GIS) and probabilistic methods are used to preprocess the data. In the second step, a hierarchical structure is generated based on the problem. In the third step, the accuracy and complexity of the model are simultaneously optimized with a multiple objective particle swarm optimization (MOPSO) algorithm. We examine the capabilities of the proposed model for predicting daily and annual mean PM2.5 and NO2 and compare the accuracy of the results with representative models from existing literature. The benefits provided by the model features, including probabilistic preprocessing, multi-objective optimization, and hierarchical structure, are precisely evaluated by comparing five different consecutive models in terms of accuracy and complexity criteria. Fivefold cross validation is used to assess the performance of the generated models. The respective average RMSEs and coefficients of determination (R (2)) for the test datasets using proposed model are as follows: daily PM2.5 = (8.13, 0.78), annual mean PM2.5 = (4.96, 0.80), daily NO2 = (5.63, 0.79), and annual mean NO2 = (2.89, 0.83). The obtained results demonstrate that the developed hierarchical fuzzy inference system can be utilized for modeling air pollution in EEA and epidemiological studies.

  9. Air quality modeling in Warsaw Metropolitan Area

    Directory of Open Access Journals (Sweden)

    Piotr Holnicki

    2013-04-01

    Full Text Available Decision support of air quality management needs to connect several categories of the input data with the analytical process of air pollution dispersion. The aim of the respective model of air pollution is to provide a quantitative assessment of environmental impact of emission sources in a form of spatial/temporal maps of pollutants’ concentration or deposition in the domain. These results are in turn used in assessment of environmental risk and supporting respective planning actions. However, due to the complexity of the forecasting system and the required input data, such environmental prognosis and related decisions contain many potential sources of imprecision and uncertainty. The main sources of uncertainty are commonly considered meteorological and emission input data. This paper addresses the problem of emission uncertainty, and impact of this uncertainty on the forecasted air pollution concentrations and adverse health effects. The computational experiment implemented for Warsaw Metropolitan Area, Poland, encompasses one-year forecast with the year 2005 meteorological dataset. The annual mean concentrations of the main urban pollutants are computed. The impact of uncertainty in emission field inventory is also considered. Uncertainty assessment is based on the Monte Carlo technique where the regional scale CALPUFF model is the main forecasting tool used in air quality analysis.

  10. Engineering Model of High Pressure Moist Air

    OpenAIRE

    Hyhlík Tomáš

    2017-01-01

    The article deals with the moist air equation of state. There are equations of state discussed in the article, i.e. the model of an ideal mixture of ideal gases, the model of an ideal mixture of real gases and the model based on the virial equation of state. The evaluation of sound speed based on the ideal mixture concept is mentioned. The sound speed calculated by the model of an ideal mixture of ideal gases is compared with the sound speed calculated by using the model based on the concept ...

  11. New smoke predictions for Alaska in NOAA’s National Air Quality Forecast Capability

    Science.gov (United States)

    Davidson, P. M.; Ruminski, M.; Draxler, R.; Kondragunta, S.; Zeng, J.; Rolph, G.; Stajner, I.; Manikin, G.

    2009-12-01

    Smoke from wildfire is an important component of fine particle pollution, which is responsible for tens of thousands of premature deaths each year in the US. In Alaska, wildfire smoke is the leading cause of poor air quality in summer. Smoke forecast guidance helps air quality forecasters and the public take steps to limit exposure to airborne particulate matter. A new smoke forecast guidance tool, built by a cross-NOAA team, leverages efforts of NOAA’s partners at the USFS on wildfire emissions information, and with EPA, in coordinating with state/local air quality forecasters. Required operational deployment criteria, in categories of objective verification, subjective feedback, and production readiness, have been demonstrated in experimental testing during 2008-2009, for addition to the operational products in NOAA's National Air Quality Forecast Capability. The Alaska smoke forecast tool is an adaptation of NOAA’s smoke predictions implemented operationally for the lower 48 states (CONUS) in 2007. The tool integrates satellite information on location of wildfires with weather (North American mesoscale model) and smoke dispersion (HYSPLIT) models to produce daily predictions of smoke transport for Alaska, in binary and graphical formats. Hour-by hour predictions at 12km grid resolution of smoke at the surface and in the column are provided each day by 13 UTC, extending through midnight next day. Forecast accuracy and reliability are monitored against benchmark criteria for accuracy and reliability. While wildfire activity in the CONUS is year-round, the intense wildfire activity in AK is limited to the summer. Initial experimental testing during summer 2008 was hindered by unusually limited wildfire activity and very cloudy conditions. In contrast, heavier than average wildfire activity during summer 2009 provided a representative basis (more than 60 days of wildfire smoke) for demonstrating required prediction accuracy. A new satellite observation product

  12. A process model for air bending

    NARCIS (Netherlands)

    de Vin, L.J.; de Vin, L.J.; Streppel, A.H.; Singh, U.P.; Kals, H.J.J.

    1996-01-01

    A so called `three-section¿ model for air bending is presented. It is assumed that a state of plane strain exists and that Bernoulli's law is valid. The material behaviour is described with Swift's equation, and the change of Young's modulus under deformation is addressed. As compared with other

  13. Tracks FAQs: What is Modeled Air Data?

    Centers for Disease Control (CDC) Podcasts

    2011-04-25

    In this podcast, CDC Tracking experts discuss modeled air data. Do you have a question for our Tracking experts? Please e-mail questions to trackingsupport@cdc.gov.  Created: 4/25/2011 by National Center for Environmental Health, Division of Environmental Hazards and Health Effects, Environmental Health Tracking Branch.   Date Released: 4/25/2011.

  14. Analysis and prediction of the influence of energy utilization on air quality in Beijing

    Institute of Scientific and Technical Information of China (English)

    LI Lin; HAO Jiming; HU Jingnan

    2007-01-01

    This work evaluates the influence of energy consumption on the future air quality in Beijing,using 2000 as the base year and 2008 as the target year.It establishes the emission inventory of primary PM10,SO2 and NOx related to energy utilization in eight areas of Beijing.The air quality model was adopted to simulate the temporal and spatial distribution of each pollutant concentration in the eight urban areas.Their emission,concentration distribution,and sectoral share responsibility rate were analyzed,and air quality in 2008 was predicted.The industrial sector contributed above 40% of primary PM10 and SO2 resulting from energy consumption,while vehicles accounted for about 65% of NOx.According to the current policy and development trend,air quality in the eight urban areas could become better in 2008 when the average concentrations of primary PM10,SO2 and NO2 related to energy utilization at each monitored site are predicted to be about 25,50 and 51 μg/m3,respectively.

  15. Air Quality Dispersion Modeling - Alternative Models

    Science.gov (United States)

    Models, not listed in Appendix W, that can be used in regulatory applications with case-by-case justification to the Reviewing Authority as noted in Section 3.2, Use of Alternative Models, in Appendix W.

  16. Prediction of free air space in initial composting mixtures by a statistical design approach.

    Science.gov (United States)

    Soares, Micaela A R; Quina, Margarida J; Quinta-Ferreira, Rosa

    2013-10-15

    Free air space (FAS) is a physical parameter that can play an important role in composting processes to maintain favourable aerobic conditions. Aiming to predict the FAS of initial composting mixtures, specific materials proportions ranged from 0 to 1 were tested for a case study comprising industrial potato peel, which is characterized by low air void volume, thus requiring additional components for its composting. The characterization and prediction of FAS for initial mixtures involving potato peel, grass clippings and rice husks (set A) or sawdust (set B) was accomplished by means of an augmented simplex-centroid mixture design approach. The experimental data were fitted to second order Scheffé polynomials. Synergistic or antagonistic effects of mixture proportions in the FAS response were identified from the surface and response trace plots in the FAS response. Moreover, a good agreement was achieved between the model predictions and supplementary experimental data. Moreover, theoretical and empirical approaches for estimating FAS available in literature were compared with the predictions generated by the mixture design approach. This study demonstrated that the mixture design methodology can be a valuable tool to predict the initial FAS of composting mixtures, specifically in making adjustments to improve composting processes containing primarily potato peel.

  17. PREDICT : model for prediction of survival in localized prostate cancer

    NARCIS (Netherlands)

    Kerkmeijer, Linda G W; Monninkhof, Evelyn M.; van Oort, Inge M.; van der Poel, Henk G.; de Meerleer, Gert; van Vulpen, Marco

    2016-01-01

    Purpose: Current models for prediction of prostate cancer-specific survival do not incorporate all present-day interventions. In the present study, a pre-treatment prediction model for patients with localized prostate cancer was developed.Methods: From 1989 to 2008, 3383 patients were treated with I

  18. Modeling Air Pollution Exposure Metrics for the Diabetes and Environment Panel Study (DEPS)

    Science.gov (United States)

    Air pollution health studies of fine particulate matter (PM) often use outdoor concentrations as exposure surrogates. To improve exposure assessments, we developed and evaluated an exposure model for individuals (EMI), which predicts five tiers of individual-level exposure metric...

  19. Evaluation of air pollution control policies in Mexico City using finite Markov chain observation model

    OpenAIRE

    Luis Hoyos; Pedro Lara; Elba Ortiz; Rafael López; Jesús González

    2009-01-01

    This paper proposes a Markov observation based model, where the transition matrix is formulated using air quality monitoring data for specific pollutant emissions, with the primary objective to analyze the corresponding stationary distributions and evaluate sceneries for the air quality impact of pollution control policies. The model is non predictive and could be applied to every source of pollutant emissions included in air monitoring data. Two cases of study are presented, ozone and sulfur...

  20. Bioaccumulation Potential Of Air Contaminants: Combining Biological Allometry, Chemical Equilibrium And Mass-Balances To Predict Accumulation Of Air Pollutants In Various Mammals

    Energy Technology Data Exchange (ETDEWEB)

    Veltman, Karin; McKone, Thomas E.; Huijbregts, Mark A.J.; Hendriks, A. Jan

    2009-03-01

    In the present study we develop and test a uniform model intended for single compartment analysis in the context of human and environmental risk assessment of airborne contaminants. The new aspects of the model are the integration of biological allometry with fugacity-based mass-balance theory to describe exchange of contaminants with air. The developed model is applicable to various mammalian species and a range of chemicals, while requiring few and typically well-known input parameters, such as the adult mass and composition of the species, and the octanol-water and air-water partition coefficient of the chemical. Accumulation of organic chemicals is typically considered to be a function of the chemical affinity forlipid components in tissues. Here, we use a generic description of chemical affinity for neutral and polar lipids and proteins to estimate blood-air partition coefficients (Kba) and tissue-air partition coefficients (Kta) for various mammals. This provides a more accurate prediction of blood-air partition coefficients, as proteins make up a large fraction of total blood components. The results show that 75percent of the modeled inhalation and exhalation rate constants are within a factor of 2 from independent empirical values for humans, rats and mice, and 87percent of the predicted blood-air partition coefficients are within a factor of 5 from empirical data. At steady-state, the bioaccumulation potential of air pollutants is shown to be mainly a function of the tissue-air partition coefficient and the biotransformation capacity of the species and depends weakly on the ventilation rate and the cardiac output of mammals.

  1. Predictive Modeling of Cardiac Ischemia

    Science.gov (United States)

    Anderson, Gary T.

    1996-01-01

    The goal of the Contextual Alarms Management System (CALMS) project is to develop sophisticated models to predict the onset of clinical cardiac ischemia before it occurs. The system will continuously monitor cardiac patients and set off an alarm when they appear about to suffer an ischemic episode. The models take as inputs information from patient history and combine it with continuously updated information extracted from blood pressure, oxygen saturation and ECG lines. Expert system, statistical, neural network and rough set methodologies are then used to forecast the onset of clinical ischemia before it transpires, thus allowing early intervention aimed at preventing morbid complications from occurring. The models will differ from previous attempts by including combinations of continuous and discrete inputs. A commercial medical instrumentation and software company has invested funds in the project with a goal of commercialization of the technology. The end product will be a system that analyzes physiologic parameters and produces an alarm when myocardial ischemia is present. If proven feasible, a CALMS-based system will be added to existing heart monitoring hardware.

  2. Climate predictability and prediction skill on seasonal time scales over South America from CHFP models

    Science.gov (United States)

    Osman, Marisol; Vera, C. S.

    2016-11-01

    This work presents an assessment of the predictability and skill of climate anomalies over South America. The study was made considering a multi-model ensemble of seasonal forecasts for surface air temperature, precipitation and regional circulation, from coupled global circulation models included in the Climate Historical Forecast Project. Predictability was evaluated through the estimation of the signal-to-total variance ratio while prediction skill was assessed computing anomaly correlation coefficients. Both indicators present over the continent higher values at the tropics than at the extratropics for both, surface air temperature and precipitation. Moreover, predictability and prediction skill for temperature are slightly higher in DJF than in JJA while for precipitation they exhibit similar levels in both seasons. The largest values of predictability and skill for both variables and seasons are found over northwestern South America while modest but still significant values for extratropical precipitation at southeastern South America and the extratropical Andes. The predictability levels in ENSO years of both variables are slightly higher, although with the same spatial distribution, than that obtained considering all years. Nevertheless, predictability at the tropics for both variables and seasons diminishes in both warm and cold ENSO years respect to that in all years. The latter can be attributed to changes in signal rather than in the noise. Predictability and prediction skill for low-level winds and upper-level zonal winds over South America was also assessed. Maximum levels of predictability for low-level winds were found were maximum mean values are observed, i.e. the regions associated with the equatorial trade winds, the midlatitudes westerlies and the South American Low-Level Jet. Predictability maxima for upper-level zonal winds locate where the subtropical jet peaks. Seasonal changes in wind predictability are observed that seem to be related to

  3. Actual measurement, hygrothermal response experiment and growth prediction analysis of microbial contamination of central air conditioning system in Dalian, China.

    Science.gov (United States)

    Lv, Yang; Hu, Guangyao; Wang, Chunyang; Yuan, Wenjie; Wei, Shanshan; Gao, Jiaoqi; Wang, Boyuan; Song, Fangchao

    2017-04-03

    The microbial contamination of central air conditioning system is one of the important factors that affect the indoor air quality. Actual measurement and analysis were carried out on microbial contamination in central air conditioning system at a venue in Dalian, China. Illumina miseq method was used and three fungal samples of two units were analysed by high throughput sequencing. Results showed that the predominant fungus in air conditioning unit A and B were Candida spp. and Cladosporium spp., and two fungus were further used in the hygrothermal response experiment. Based on the data of Cladosporium in hygrothermal response experiment, this paper used the logistic equation and the Gompertz equation to fit the growth predictive model of Cladosporium genera in different temperature and relative humidity conditions, and the square root model was fitted based on the two environmental factors. In addition, the models were carried on the analysis to verify the accuracy and feasibility of the established model equation.

  4. Air Quality Modelling and the National Emission Database

    DEFF Research Database (Denmark)

    Jensen, S. S.

    The project focuses on development of institutional strengthening to be able to carry out national air emission inventories based on the CORINAIR methodology. The present report describes the link between emission inventories and air quality modelling to ensure that the new national air emission...... inventory is able to take into account the data requirements of air quality models...

  5. Numerical weather prediction model tuning via ensemble prediction system

    Science.gov (United States)

    Jarvinen, H.; Laine, M.; Ollinaho, P.; Solonen, A.; Haario, H.

    2011-12-01

    This paper discusses a novel approach to tune predictive skill of numerical weather prediction (NWP) models. NWP models contain tunable parameters which appear in parameterizations schemes of sub-grid scale physical processes. Currently, numerical values of these parameters are specified manually. In a recent dual manuscript (QJRMS, revised) we developed a new concept and method for on-line estimation of the NWP model parameters. The EPPES ("Ensemble prediction and parameter estimation system") method requires only minimal changes to the existing operational ensemble prediction infra-structure and it seems very cost-effective because practically no new computations are introduced. The approach provides an algorithmic decision making tool for model parameter optimization in operational NWP. In EPPES, statistical inference about the NWP model tunable parameters is made by (i) generating each member of the ensemble of predictions using different model parameter values, drawn from a proposal distribution, and (ii) feeding-back the relative merits of the parameter values to the proposal distribution, based on evaluation of a suitable likelihood function against verifying observations. In the presentation, the method is first illustrated in low-order numerical tests using a stochastic version of the Lorenz-95 model which effectively emulates the principal features of ensemble prediction systems. The EPPES method correctly detects the unknown and wrongly specified parameters values, and leads to an improved forecast skill. Second, results with an atmospheric general circulation model based ensemble prediction system show that the NWP model tuning capacity of EPPES scales up to realistic models and ensemble prediction systems. Finally, a global top-end NWP model tuning exercise with preliminary results is published.

  6. Model Identification of a Micro Air Vehicle

    Institute of Scientific and Technical Information of China (English)

    Jorge Ni(n)o; Flavius Mitrache; Peter Cosyn; Robin De Keyser

    2007-01-01

    This paper is focused on the model identification of a Micro Air Vehicle (MAV) in straight steady flight condition. The identification is based on input-output data collected from flight tests using both frequency and time dontain techniques. The vehicle is an in-house 40 cm wingspan airplane. Because of the complex coupled, multivariable and nonlinear dynamics of the aircraft, linear SISO structures for both the lateral and longitudinal models around a reference state were derived. The aim of the identification is to provide models that can be used in future development of control techniques for the MAV.

  7. Generating scenarios to predict air quality impact in public health

    Energy Technology Data Exchange (ETDEWEB)

    Garcia, J.M.; Coelho, L.M.R.; Gouveia, C.; Cerdeira, R. [Escola Superior de Tecnologia de Setubal (EST-IPS), Setubal (Portugal); Ferreira, T.; Baptista, M.N. [Hospital Na. Sa. do Rosario, Servico de Pediatria, Barreiro (Portugal)

    2004-07-01

    This study intends to associate air quality with public health by generating air quality scenarios, under different future perspectives in Barreiro. This city is located in middle south of Portugal nearby Lisbon and it has a large resident population, an important industrial area and intense traffic. In this study ADMS-urban was used to simulate the possible scenarios of future air quality in this city, taking into consideration the probable city development and future activities. Special attention was given to the future evolutions of traffic, industrial activities, demographical and geographical expansion. The new EU directives about air quality and the CAFE program were also considered. To correlate the impact of the future air quality of the city and public health, a children population sample was used. This study team is also composed by paediatric doctors from Hospital N{sup a}. S{sup a}. do Rosario that contribute with public health information and helped to identify air quality related diseases. (orig.)

  8. Mathematical modeling of a primary zinc/air battery

    Science.gov (United States)

    Mao, Z.; White, R. E.

    1992-01-01

    The mathematical model developed by Sunu and Bennion has been extended to include the separator, precipitation of both solid ZnO and K2Zn(OH)4, and the air electrode, and has been used to investigate the behavior of a primary Zn-Air battery with respect to battery design features. Predictions obtained from the model indicate that anode material utilization is predominantly limited by depletion of the concentration of hydroxide ions. The effect of electrode thickness on anode material utilization is insignificant, whereas material loading per unit volume has a great effect on anode material utilization; a higher loading lowers both the anode material utilization and delivered capacity. Use of a thick separator will increase the anode material utilization, but may reduce the cell voltage.

  9. Measurement and prediction of indoor air quality using a breathing thermal manikin

    DEFF Research Database (Denmark)

    Melikov, Arsen Krikor; Kaczmarczyk, J.

    2007-01-01

    temperature is sensitive enough to perform reliable measurement of characteristics of air as inhaled by occupants. The temperature, humidity, and pollution concentration in the inhaled air can be measured accurately with a thermal manikin without breathing simulation if they are measured at the upper lip...... at a distance of inhaled air parameters. Proper simulation of breathing, especially of exhalation, is needed for studying the transport of exhaled air between occupants. A method...... for predicting air acceptability based on inhaled air parameters and known exposure–response relationships established in experiments with human subjects is suggested....

  10. 基于CFD的温室气温时空变化预测模型及通风调控措施%Prediction model on temporal and spatial variation of air temperature in greenhouse and ventilation control measures based on CFD

    Institute of Scientific and Technical Information of China (English)

    任守纲; 杨薇; 王浩云; 薛卫; 徐焕良; 熊迎军

    2015-01-01

    夏季温室高温湿热,对作物生长产生重大危害,制定合理的夏季温室气温调控方案,是提高温室生产效益,降低温室气温调控能耗的关键问题。该文基于计算流体力学(computational fluid dynamics,CFD)方法,结合气象预报信息,针对苏南地区大型连栋温室,建立了夏季温室气温时空变化预测模型,通过设置边界参数,对不同通风条件下温室气温的时空变化进行了预测,并通过试验验证了模型的有效性。试验结果表明,预测值与实测值吻合良好,均方根误差在1.2℃以内,最大相对误差在6%以内,平均相对误差在4%以内。不同通风降温条件下的试验结果显示,温室气温空间分布存在明显差异,湿帘-风机系统较自然通风降温效果显著,降温幅度在5℃左右,持续的湿帘-风机降温措施可将温室高温控制在较低水平。基于该文模型的预测结果和温室调控目标,选取合适的时间点、时间长度和不同类型的通风降温措施,可有效提高温室气温调控效率和效益。同时,该研究还可为优化传感器布局提供依据。%Hot and humid environment inside greenhouse in summer is a major threat to crop growth. How to make reasonable control schemes of greenhouse air temperature in summer is the key to improve the greenhouse production efficiency and reduce the energy consumption. In this study, we established a prediction model on temporal and spatial variation of air temperature in greenhouse based on the computational fluid dynamics (CFD) method and meteorology forecast information. The prediction model was adapted to the large multi-span plastic greenhouse in the southern regions of Jiangsu under the natural ventilation and fan-pad cooling system. It can predict the spatial and temporal distribution of greenhouse air temperature under different ventilation cooling conditions. The numerical computation of this

  11. Computerized Simulation of Automotive Air-Conditioning System: Development of Mathematical Model and Its Validation

    Directory of Open Access Journals (Sweden)

    Haslinda Mohamed Kamar

    2012-03-01

    Full Text Available A semi-empirical model for simulating thermal and energy performance of an automotive air-conditioning (AAC system in passenger vehicles has been developed. The model consists of two sections, namely empirical evaporator correlations and dynamic load simulation. The correlations used consider sensible and latent heat transfer performance of the evaporator coil. The correlations were obtained from the experimental data of actual air conditioning system for a compact size passenger car. The sensible heat transfer correlation relates the evaporator air off dry-bulb temperature to inlet air dry-bulb temperature, humidity ratio, evaporator air velocity, condenser inlet air dry-bulb temperature, condenser air velocity and compressor speed. The latent heat transfer correlation relates the coil air-off humidity ratio to the same six independent variables. The dynamic load simulation model was developed based on the z-transfer function method with a one-minute time step. The cooling load calculations were performed using heat gain weighting factors. Heat extraction rate and cabin air dry-bulb temperature calculations were carried out using air temperature weighting factors. The empirical evaporator sensible and latent heat transfer correlations were embedded in the loads calculation program to enable the determination of evaporator inlet and outlet air conditions, the cabin air temperature and relative humidity. Comparisons with road test data indicated that the program was capable of predicting the performance of the automotive air-conditioning system with reasonable accuracy.

  12. Return Predictability, Model Uncertainty, and Robust Investment

    DEFF Research Database (Denmark)

    Lukas, Manuel

    Stock return predictability is subject to great uncertainty. In this paper we use the model confidence set approach to quantify uncertainty about expected utility from investment, accounting for potential return predictability. For monthly US data and six representative return prediction models, we...

  13. Predictive Model Assessment for Count Data

    Science.gov (United States)

    2007-09-05

    critique count regression models for patent data, and assess the predictive performance of Bayesian age-period-cohort models for larynx cancer counts...the predictive performance of Bayesian age-period-cohort models for larynx cancer counts in Germany. We consider a recent suggestion by Baker and...Figure 5. Boxplots for various scores for patent data count regressions. 11 Table 1 Four predictive models for larynx cancer counts in Germany, 1998–2002

  14. Development of a forecast model for global air traffic emissions

    Energy Technology Data Exchange (ETDEWEB)

    Schaefer, Martin

    2012-07-01

    The thesis describes the methodology and results of a simulation model that quantifies fuel consumption and emissions of civil air traffic. Besides covering historical emissions, the model aims at forecasting emissions in the medium-term future. For this purpose, simulation models of aircraft and engine types are used in combination with a database of global flight movements and assumptions about traffic growth, fleet rollover and operational aspects. Results from an application of the model include emissions of scheduled air traffic for the years 2000 to 2010 as well as forecasted emissions until the year 2030. In a baseline scenario of the forecast, input assumptions (e.g. traffic growth rates) are in line with predictions by the aircraft industry. Considering the effects of advanced technologies of the short-term and medium-term future, the forecast focusses on fuel consumption and emissions of nitric oxides. Calculations for historical air traffic additionally cover emissions of carbon monoxide, unburned hydrocarbons and soot. Results are validated against reference data including studies by the International Civil Aviation Organization (ICAO) and simulation results from international research projects. (orig.)

  15. Predicted thermal superluminescence in low-pressure air

    CERN Document Server

    Aramyan, A R; Galechyan, G A; Mangasaryan, N R; Nersisyan, H B

    2009-01-01

    It is shown that due to the dissociation of the molecular oxygen it is possible to obtain inverted population in low pressure air by heating. As a result of the quenching of the corresponding levels of the atomic oxygen the thermal superluminescent radiation is generated. It has been found that the threshold of the overpopulation is exceeded at the air temperature 2300-3000 K. Using this effect a possible mechanism for the generation of the flashes of the radiation in air observed on the airframe of the space shuttle during its descent and reentry in the atmosphere is suggested.

  16. Air quality modeling`s brave new world

    Energy Technology Data Exchange (ETDEWEB)

    Appleton, E.L.

    1996-05-01

    Since 1992, EPA has been creating a new generation of software - Models-3 - that is widely regarded as the next-generation air quality modeling system. The system has a modular framework that allows users to integrate a broad variety of air quality models. In the future, users will also be able to plug in economic decision support tools. A prototype version of Models-3 already exists in the Atmospheric Modeling Division of EPA`s National Exposure Research Laboratory in Research Triangle Park. EDSS was developed as a raid prototype of Models-3 under a three-year, $7.8 million cooperative agreement with EPA. An operational version of Models-3 may be in the hands of scientists and state air quality regulators by late 1997. Developers hope the new, more user-friendly system will make it easier to run models and present information to policy makers in graphical ways that are easy to understand. In addition, Models-3 will ultimately become a so-called `comprehensive modeling system` that enables users to simulate pollutants in other media, such as water. EPA also plans to include models that simulate health effects and other pollution consequences. 6 refs.

  17. Air Quality Modeling in Support of the Near-Road Exposures and Effects of Urban Air Pollutants Study (NEXUS

    Directory of Open Access Journals (Sweden)

    Vlad Isakov

    2014-08-01

    Full Text Available A major challenge in traffic-related air pollution exposure studies is the lack of information regarding pollutant exposure characterization. Air quality modeling can provide spatially and temporally varying exposure estimates for examining relationships between traffic-related air pollutants and adverse health outcomes. A hybrid air quality modeling approach was used to estimate exposure to traffic-related air pollutants in support of the Near-Road Exposures and Effects of Urban Air Pollutants Study (NEXUS conducted in Detroit (Michigan, USA. Model-based exposure metrics, associated with local variations of emissions and meteorology, were estimated using a combination of the American Meteorological Society/Environmental Protection Agency Regulatory Model (AERMOD and Research LINE-source dispersion model for near-surface releases (RLINE dispersion models, local emission source information from the National Emissions Inventory, detailed road network locations and traffic activity, and meteorological data from the Detroit City Airport. The regional background contribution was estimated using a combination of the Community Multi-scale Air Quality (CMAQ and the Space-Time Ordinary Kriging (STOK models. To capture the near-road pollutant gradients, refined “mini-grids” of model receptors were placed around participant homes. Exposure metrics for CO, NOx, PM2.5 and its components (elemental and organic carbon were predicted at each home location for multiple time periods including daily and rush hours. The exposure metrics were evaluated for their ability to characterize the spatial and temporal variations of multiple ambient air pollutants compared to measurements across the study area.

  18. Nonlinear chaotic model for predicting storm surges

    Directory of Open Access Journals (Sweden)

    M. Siek

    2010-09-01

    Full Text Available This paper addresses the use of the methods of nonlinear dynamics and chaos theory for building a predictive chaotic model from time series. The chaotic model predictions are made by the adaptive local models based on the dynamical neighbors found in the reconstructed phase space of the observables. We implemented the univariate and multivariate chaotic models with direct and multi-steps prediction techniques and optimized these models using an exhaustive search method. The built models were tested for predicting storm surge dynamics for different stormy conditions in the North Sea, and are compared to neural network models. The results show that the chaotic models can generally provide reliable and accurate short-term storm surge predictions.

  19. Nonlinear chaotic model for predicting storm surges

    NARCIS (Netherlands)

    Siek, M.; Solomatine, D.P.

    This paper addresses the use of the methods of nonlinear dynamics and chaos theory for building a predictive chaotic model from time series. The chaotic model predictions are made by the adaptive local models based on the dynamical neighbors found in the reconstructed phase space of the observables.

  20. Engineering Model of High Pressure Moist Air

    Directory of Open Access Journals (Sweden)

    Hyhlík Tomáš

    2017-01-01

    Full Text Available The article deals with the moist air equation of state. There are equations of state discussed in the article, i.e. the model of an ideal mixture of ideal gases, the model of an ideal mixture of real gases and the model based on the virial equation of state. The evaluation of sound speed based on the ideal mixture concept is mentioned. The sound speed calculated by the model of an ideal mixture of ideal gases is compared with the sound speed calculated by using the model based on the concept of an ideal mixture of real gases. The comparison of enthalpy end entropy based on the model of an ideal mixture of ideal gases and the model of an ideal mixture of real gases is performed. It is shown that the model of an ideal mixture of real gases deviates from the model of an ideal mixture of ideal gases only in the case of high pressure. An impossibility of the definition of partial pressure in the mixture of real gases is discussed, where the virial equation of state is used.

  1. EFFICIENT PREDICTIVE MODELLING FOR ARCHAEOLOGICAL RESEARCH

    OpenAIRE

    Balla, A.; Pavlogeorgatos, G.; Tsiafakis, D.; Pavlidis, G.

    2014-01-01

    The study presents a general methodology for designing, developing and implementing predictive modelling for identifying areas of archaeological interest. The methodology is based on documented archaeological data and geographical factors, geospatial analysis and predictive modelling, and has been applied to the identification of possible Macedonian tombs’ locations in Northern Greece. The model was tested extensively and the results were validated using a commonly used predictive gain,...

  2. Penentuan Umur Simpan Lengkuas dengan Model Arrhenius Berdasarkan Kadar Air dan Kadar Sari Larut dalam Air

    Directory of Open Access Journals (Sweden)

    Rita Khathir

    2014-04-01

    Full Text Available Abstrak. Lengkuas (Alpinia galanga adalah salah satu tanaman penting bagi masyarakat Indonesia. Tanaman ini dapat digunakan untuk bumbu masakan dan obat herbal. Tujuan kajian ini adalah untuk menduga umur simpan lengkuas segar dengan menggunakan model Arrhenius. Lengkuas segar yang baru dipanen dibersihkan dan dipotong-potong dengan ukuran 2cm, kemudian disimpan pada suhu 5, 10 dan 28°C. Evaluasi dilakukan oleh 25 orang panelis dengan menggunakan skala hedonic dari sangat suka sampai sangat tidak suka terhadap warna, kesegaran, aroma dan tekstur. Parameter yang diamati adalah kadar air dan kadar sari larut dalam air. Parameter tersebut diamati dalam interval 3 hari selama 21 hari atau sampai sampel dinyatakan tidak disukai oleh panelis pada salah satu kriteria hedoniknya. Hasil penelitian menunjukkan bahwa pad asuhu 28°C, lengkuas dapat disimpan selama 3 hari, sedangkan pada suhu 10 dan 5°C, lengkuas dapat disimpan selama 12 dan 21 hari. Energi aktivasi (EA dan tingkat perubahan mutu (Q10 karena kadar sari larut dalam air lebih besar dari energi aktivasi (EA dan tingkat perubahan mutu (Q10 karena kadar air lengkuas. Namun demikian, kedua parameter tersebut tidak tepat digunakan untuk menduga umur simpan lengkuas.   Shelf-Life Prediction of Galanga by Using Arrhenius Model Based on Its Moisture and Water Soluble Extract Content Abstract. Galanga (Alpinia galanga is one of important plants for Indonesian people. It can be used as spices and also as herbal medicine. The aim of this study is to predict the shelf-life of fresh galanga by using Arrhenius model. Fresh harvested galanga, which was cleaned and chopped at width about 2 cm, was stored at temperatures 5, 10, and 28°C. The evaluation was done by 25 respondents by using hedonic scale from the range of like very much until dislike very much. This hedonic evaluation was assessed, based on colour, freshness, aroma, and texture. Parameters observed were moisture and water soluble extract

  3. Experimental and predicted approaches for biomass gasification with enriched air-steam in a fluidised bed.

    Science.gov (United States)

    Fu, Qirang; Huang, Yaji; Niu, Miaomiao; Yang, Gaoqiang; Shao, Zhiwei

    2014-10-01

    Thermo-chemical gasification of sawdust refuse-derived fuel was performed on a bench-scale fluidised bed gasifier with enriched air and steam as fluidising and oxidising agents. Dolomite as a natural mineral catalyst was used as bed material to reform tars and hydrocarbons. A series of experiments were carried out under typical operating conditions for gasification, as reported in the article. A modified equilibrium model, based on equilibrium constants, was developed to predict the gasification process. The sensitivity analysis of operating parameters, such as the fluidisation velocity, oxygen percentage of the enriched air and steam to biomass ratios on the produced gas composition, lower heating value, carbon conversion and cold gas efficiency was investigated. The results showed that the predicted syngas composition was in better agreement with the experimental data compared with the original equilibrium model. The higher fluidisation velocity enhanced gas-solid mixing, heat and mass transfers, and carbon fines elutriation, simultaneously. With the increase of oxygen percentage from 21% to 45%, the lower heating value of syngas increased from 5.52 MJ m(-3) to 7.75 MJ m(-3) and cold gas efficiency from 49.09% to 61.39%. The introduction of steam improved gas quality, but a higher steam to biomass ratio could decrease carbon conversion and gasification efficiency owing to a low steam temperature. The optimal value of steam to biomass ratio in this work was 1.0.

  4. How to Establish Clinical Prediction Models

    Directory of Open Access Journals (Sweden)

    Yong-ho Lee

    2016-03-01

    Full Text Available A clinical prediction model can be applied to several challenging clinical scenarios: screening high-risk individuals for asymptomatic disease, predicting future events such as disease or death, and assisting medical decision-making and health education. Despite the impact of clinical prediction models on practice, prediction modeling is a complex process requiring careful statistical analyses and sound clinical judgement. Although there is no definite consensus on the best methodology for model development and validation, a few recommendations and checklists have been proposed. In this review, we summarize five steps for developing and validating a clinical prediction model: preparation for establishing clinical prediction models; dataset selection; handling variables; model generation; and model evaluation and validation. We also review several studies that detail methods for developing clinical prediction models with comparable examples from real practice. After model development and vigorous validation in relevant settings, possibly with evaluation of utility/usability and fine-tuning, good models can be ready for the use in practice. We anticipate that this framework will revitalize the use of predictive or prognostic research in endocrinology, leading to active applications in real clinical practice.

  5. Evaluation of observation-fused regional air quality model results for population air pollution exposure estimation.

    Science.gov (United States)

    Chen, Gang; Li, Jingyi; Ying, Qi; Sherman, Seth; Perkins, Neil; Rajeshwari, Sundaram; Mendola, Pauline

    2014-07-01

    In this study, Community Multiscale Air Quality (CMAQ) model was applied to predict ambient gaseous and particulate concentrations during 2001 to 2010 in 15 hospital referral regions (HRRs) using a 36-km horizontal resolution domain. An inverse distance weighting based method was applied to produce exposure estimates based on observation-fused regional pollutant concentration fields using the differences between observations and predictions at grid cells where air quality monitors were located. Although the raw CMAQ model is capable of producing satisfying results for O3 and PM2.5 based on EPA guidelines, using the observation data fusing technique to correct CMAQ predictions leads to significant improvement of model performance for all gaseous and particulate pollutants. Regional average concentrations were calculated using five different methods: 1) inverse distance weighting of observation data alone, 2) raw CMAQ results, 3) observation-fused CMAQ results, 4) population-averaged raw CMAQ results and 5) population-averaged fused CMAQ results. It shows that while O3 (as well as NOx) monitoring networks in the HRRs are dense enough to provide consistent regional average exposure estimation based on monitoring data alone, PM2.5 observation sites (as well as monitors for CO, SO2, PM10 and PM2.5 components) are usually sparse and the difference between the average concentrations estimated by the inverse distance interpolated observations, raw CMAQ and fused CMAQ results can be significantly different. Population-weighted average should be used to account for spatial variation in pollutant concentration and population density. Using raw CMAQ results or observations alone might lead to significant biases in health outcome analyses.

  6. Evaluation of Observation-Fused Regional Air Quality Model Results for Population Air Pollution Exposure Estimation

    Science.gov (United States)

    Chen, Gang; Li, Jingyi; Ying, Qi; Sherman, Seth; Perkins, Neil; Rajeshwari, Sundaram; Mendola, Pauline

    2014-01-01

    In this study, Community Multiscale Air Quality (CMAQ) model was applied to predict ambient gaseous and particulate concentrations during 2001 to 2010 in 15 hospital referral regions (HRRs) using a 36-km horizontal resolution domain. An inverse distance weighting based method was applied to produce exposure estimates based on observation-fused regional pollutant concentration fields using the differences between observations and predictions at grid cells where air quality monitors were located. Although the raw CMAQ model is capable of producing satisfying results for O3 and PM2.5 based on EPA guidelines, using the observation data fusing technique to correct CMAQ predictions leads to significant improvement of model performance for all gaseous and particulate pollutants. Regional average concentrations were calculated using five different methods: 1) inverse distance weighting of observation data alone, 2) raw CMAQ results, 3) observation-fused CMAQ results, 4) population-averaged raw CMAQ results and 5) population-averaged fused CMAQ results. It shows that while O3 (as well as NOx) monitoring networks in the HRR regions are dense enough to provide consistent regional average exposure estimation based on monitoring data alone, PM2.5 observation sites (as well as monitors for CO, SO2, PM10 and PM2.5 components) are usually sparse and the difference between the average concentrations estimated by the inverse distance interpolated observations, raw CMAQ and fused CMAQ results can be significantly different. Population-weighted average should be used to account spatial variation in pollutant concentration and population density. Using raw CMAQ results or observations alone might lead to significant biases in health outcome analyses. PMID:24747248

  7. Application of data mining to the analysis of meteorological data for air quality prediction: A case study in Shenyang

    Science.gov (United States)

    Zhao, Chang; Song, Guojun

    2017-08-01

    Air pollution is one of the important reasons for restricting the current economic development. PM2.5 which is a vital factor in the measurement of air pollution is defined as a kind of suspended particulate matter with its equivalent diameter less than 25μm, which may enter the alveoli and therefore make a great impact on the human body. Meteorological factors are also one of the main factors affecting the production of PM2.5, therefore, it is essential to establish the model between meteorological factors and PM2.5 for the prediction. Data mining is a promising approach to model PM2.5 change, Shenyang which is one of the most important industrial city in Northeast China with severe air pollutions is set as the case city. Meteorological data (wind direction, wind speed, temperature, humidity, rainfall, etc.) from 2013 to 2015 and PM2.5 concentration data are used for this prediction. As to the requirements of the World Health Organization (WHO), three data mining models, whereby the predictions of PM2.5 are directly generated by the meteorological data. After assessment, the random forest model is appeared to offer better prediction performance than the other two. At last, the accuracy of the generated models are analysed.

  8. Test of the hadronic interaction model EPOS with KASCADE air shower data

    Energy Technology Data Exchange (ETDEWEB)

    Hoerandel, J.R., E-mail: j.horandel@astro.ru.n [Institut fuer Experimentelle Kernphysik, Universitaet Karlsruhe, D-76021 Karlsruhe (Germany); Apel, W.D. [Institut fuer Kernphysik, Forschungszentrum Karlsruhe, D-76021 Karlsruhe (Germany); Arteaga, J.C. [Institut fuer Experimentelle Kernphysik, Universitaet Karlsruhe, D-76021 Karlsruhe (Germany); Badea, F.; Bekk, K. [Institut fuer Kernphysik, Forschungszentrum Karlsruhe, D-76021 Karlsruhe (Germany); Bertaina, M. [Dipartimento di Fisica Generale dell' Universita, 10125 Torino (Italy); Bluemer, J. [Institut fuer Experimentelle Kernphysik, Universitaet Karlsruhe, D-76021 Karlsruhe (Germany); Institut fuer Kernphysik, Forschungszentrum Karlsruhe, D-76021 Karlsruhe (Germany); Bozdog, H. [Institut fuer Kernphysik, Forschungszentrum Karlsruhe, D-76021 Karlsruhe (Germany); Brancus, I.M. [National Institute of Physics and Nuclear Engineering, P.O. Box Mg-6, RO-7690 Bucharest (Romania); Brueggemann, M.; Buchholz, P. [Fachbereich Physik, Universitaet Siegen, 57068 Siegen (Germany); Cantoni, E. [Dipartimento di Fisica Generale dell' Universita, 10125 Torino (Italy); Istituto di Fisica dello Spazio Interplanetario, INAF, 10133 Torino (Italy); Chiavassa, A. [Dipartimento di Fisica Generale dell' Universita, 10125 Torino (Italy); Cossavella, F. [Institut fuer Experimentelle Kernphysik, Universitaet Karlsruhe, D-76021 Karlsruhe (Germany); Daumiller, K. [Institut fuer Kernphysik, Forschungszentrum Karlsruhe, D-76021 Karlsruhe (Germany); Souza, V. de [Institut fuer Experimentelle Kernphysik, Universitaet Karlsruhe, D-76021 Karlsruhe (Germany); Di Pierro, F. [Dipartimento di Fisica Generale dell' Universita, 10125 Torino (Italy); Doll, P.; Engel, R.; Engler, J. [Institut fuer Kernphysik, Forschungszentrum Karlsruhe, D-76021 Karlsruhe (Germany)

    2009-12-15

    Predictions of the hadronic interaction model EPOS 1.61 as implemented in the air shower simulation program CORSIKA are compared to observations with the KASCADE experiment. The investigations reveal that the predictions of EPOS are not compatible with KASCADE measurements. The discrepancies seen are most likely due to use of a set of inelastic hadronic cross sections that are too high.

  9. Test of the hadronic interaction model EPOS with air shower data

    CERN Document Server

    Apel, W D

    2009-01-01

    Predictions of the hadronic interaction model EPOS~1.61 as implemented in the air shower simulation program CORSIKA are compared to observations with the KASCADE experiment. The investigations reveal that the predictions of EPOS are not compatible with KASCADE measurements. The discrepancies seen are most likely due to use of a set of inelastic hadronic cross sections that are too high.

  10. Comparison of Prediction-Error-Modelling Criteria

    DEFF Research Database (Denmark)

    Jørgensen, John Bagterp; Jørgensen, Sten Bay

    2007-01-01

    is a realization of a continuous-discrete multivariate stochastic transfer function model. The proposed prediction error-methods are demonstrated for a SISO system parameterized by the transfer functions with time delays of a continuous-discrete-time linear stochastic system. The simulations for this case suggest......Single and multi-step prediction-error-methods based on the maximum likelihood and least squares criteria are compared. The prediction-error methods studied are based on predictions using the Kalman filter and Kalman predictors for a linear discrete-time stochastic state space model, which...... computational resources. The identification method is suitable for predictive control....

  11. Forest fire forecasting tool for air quality modelling systems

    Energy Technology Data Exchange (ETDEWEB)

    San Jose, R.; Perez, J. L.; Perez, L.; Gonzalez, R. M.; Pecci, J.; Palacios, M.

    2015-07-01

    Adverse effects of smoke on air quality are of great concern; however, even today the estimates of atmospheric fire emissions are a key issue. It is necessary to implement systems for predicting smoke into an air quality modelling system, and in this work a first attempt towards creating a system of this type is presented. Wild land fire spread and behavior are complex phenomena due to both the number of involved physic-chemical factors, and the nonlinear relationship between variables. WRF-Fire was employed to simulate spread and behavior of some real fires occurred in South-East of Spain and North of Portugal. The use of fire behavior models requires the availability of high resolution environmental and fuel data. A new custom fuel moisture content model has been developed. The new module allows each time step to calculate the fuel moisture content of the dead fuels and live fuels. The results confirm that the use of accurate meteorological data and a custom fuel moisture content model is crucial to obtain precise simulations of fire behavior. To simulate air pollution over Europe, we use the regional meteorological-chemistry transport model WRF-Chem. In this contribution, we show the impact of using two different fire emissions inventories (FINN and IS4FIRES) and how the coupled WRF-Fire- Chem model improves the results of the forest fire emissions and smoke concentrations. The impact of the forest fire emissions on concentrations is evident, and it is quite clear from these simulations that the choice of emission inventory is very important. We conclude that using the WRF-fire behavior model produces better results than using forest fire emission inventories although the requested computational power is much higher. (Author)

  12. Forest fire forecasting tool for air quality modelling systems

    Energy Technology Data Exchange (ETDEWEB)

    San Jose, R.; Perez, J.L.; Perez, L.; Gonzalez, R.M.; Pecci, J.; Palacios, M.

    2015-07-01

    Adverse effects of smoke on air quality are of great concern; however, even today the estimates of atmospheric fire emissions are a key issue. It is necessary to implement systems for predicting smoke into an air quality modelling system, and in this work a first attempt towards creating a system of this type is presented. Wildland fire spread and behavior are complex Phenomena due to both the number of involved physic-chemical factors, and the nonlinear relationship between variables. WRF-Fire was employed to simulate spread and behavior of some real fires occurred in South-East of Spain and North of Portugal. The use of fire behavior models requires the availability of high resolution environmental and fuel data. A new custom fuel moisture content model has been developed. The new module allows each time step to calculate the fuel moisture content of the dead fuels and live fuels. The results confirm that the use of accurate meteorological data and a custom fuel moisture content model is crucial to obtain precise simulations of fire behavior. To simulate air pollution over Europe, we use the regional meteorological-chemistry transport model WRF-Chem. In this contribution, we show the impact of using two different fire emissions inventories (FINN and IS4FIRES) and how the coupled WRF-FireChem model improves the results of the forest fire emissions and smoke concentrations. The impact of the forest fire emissions on concentrations is evident, and it is quite clear from these simulations that the choice of emission inventory is very important. We conclude that using the WRF-fire behavior model produces better results than using forest fire emission inventories although the requested computational power is much higher. (Author)

  13. Model for predicting mountain wave field uncertainties

    Science.gov (United States)

    Damiens, Florentin; Lott, François; Millet, Christophe; Plougonven, Riwal

    2017-04-01

    Studying the propagation of acoustic waves throughout troposphere requires knowledge of wind speed and temperature gradients from the ground up to about 10-20 km. Typical planetary boundary layers flows are known to present vertical low level shears that can interact with mountain waves, thereby triggering small-scale disturbances. Resolving these fluctuations for long-range propagation problems is, however, not feasible because of computer memory/time restrictions and thus, they need to be parameterized. When the disturbances are small enough, these fluctuations can be described by linear equations. Previous works by co-authors have shown that the critical layer dynamics that occur near the ground produces large horizontal flows and buoyancy disturbances that result in intense downslope winds and gravity wave breaking. While these phenomena manifest almost systematically for high Richardson numbers and when the boundary layer depth is relatively small compare to the mountain height, the process by which static stability affects downslope winds remains unclear. In the present work, new linear mountain gravity wave solutions are tested against numerical predictions obtained with the Weather Research and Forecasting (WRF) model. For Richardson numbers typically larger than unity, the mesoscale model is used to quantify the effect of neglected nonlinear terms on downslope winds and mountain wave patterns. At these regimes, the large downslope winds transport warm air, a so called "Foehn" effect than can impact sound propagation properties. The sensitivity of small-scale disturbances to Richardson number is quantified using two-dimensional spectral analysis. It is shown through a pilot study of subgrid scale fluctuations of boundary layer flows over realistic mountains that the cross-spectrum of mountain wave field is made up of the same components found in WRF simulations. The impact of each individual component on acoustic wave propagation is discussed in terms of

  14. Air dispersion of starch-protein mixtures : a predictive tool for air classification performance

    NARCIS (Netherlands)

    Dijkink, B.H.; Speranza, L.; Paltsidis, D.; Vereijken, J.M.

    2007-01-01

    Milling and air classification is a well-known procedure to obtain protein and starch enriched fractions from cereals and grain legumes. Adhesion of small protein particles to larger starch granules adversely affects the separation efficiency during air classification. To gain insight into this phen

  15. Air dispersion of starch-protein mixtures : a predictive tool for air classification performance

    NARCIS (Netherlands)

    Dijkink, B.H.; Speranza, L.; Paltsidis, D.; Vereijken, J.M.

    2007-01-01

    Milling and air classification is a well-known procedure to obtain protein and starch enriched fractions from cereals and grain legumes. Adhesion of small protein particles to larger starch granules adversely affects the separation efficiency during air classification. To gain insight into this

  16. Linkage between an advanced air quality model and a mechanistic watershed model

    Directory of Open Access Journals (Sweden)

    K. Vijayaraghavan

    2010-09-01

    Full Text Available An offline linkage between two advanced multi-pollutant air quality and watershed models is presented. The models linked are (1 the Advanced Modeling System for Transport, Emissions, Reactions and Deposition of Atmospheric Matter (AMSTERDAM (a three-dimensional Eulerian plume-in-grid model derived from the Community Multiscale Air Quality (CMAQ model and (2 the Watershed Analysis Risk Management Framework (WARMF. The pollutants linked include gaseous and particulate nitrogen, sulfur and mercury compounds. The linkage may also be used to obtain meteorological fields such as precipitation and air temperature required by WARMF from the outputs of the meteorology chemistry interface processor (MCIP that processes meteorology simulated by the fifth generation Mesoscale Model (MM5 or the Weather Research and Forecast (WRF model for input to AMSTERDAM. The linkage is tested in the Catawba River basin of North and South Carolina for ammonium, nitrate and sulfate. Modeled air quality and meteorological fields transferred by the linkage can supplement the conventional measurements used to drive WARMF and may be used to help predict the impact of changes in atmospheric emissions on water quality.

  17. Linkage between an advanced air quality model and a mechanistic watershed model

    Science.gov (United States)

    Vijayaraghavan, K.; Herr, J.; Chen, S.-Y.; Knipping, E.

    2010-09-01

    An offline linkage between two advanced multi-pollutant air quality and watershed models is presented. The models linked are (1) the Advanced Modeling System for Transport, Emissions, Reactions and Deposition of Atmospheric Matter (AMSTERDAM) (a three-dimensional Eulerian plume-in-grid model derived from the Community Multiscale Air Quality (CMAQ) model) and (2) the Watershed Analysis Risk Management Framework (WARMF). The pollutants linked include gaseous and particulate nitrogen, sulfur and mercury compounds. The linkage may also be used to obtain meteorological fields such as precipitation and air temperature required by WARMF from the outputs of the meteorology chemistry interface processor (MCIP) that processes meteorology simulated by the fifth generation Mesoscale Model (MM5) or the Weather Research and Forecast (WRF) model for input to AMSTERDAM. The linkage is tested in the Catawba River basin of North and South Carolina for ammonium, nitrate and sulfate. Modeled air quality and meteorological fields transferred by the linkage can supplement the conventional measurements used to drive WARMF and may be used to help predict the impact of changes in atmospheric emissions on water quality.

  18. Indoor Air Quality Building Education and Assessment Model Forms

    Science.gov (United States)

    The Indoor Air Quality Building Education and Assessment Model (I-BEAM) is a guidance tool designed for use by building professionals and others interested in indoor air quality in commercial buildings.

  19. Indoor Air Quality Building Education and Assessment Model

    Science.gov (United States)

    The Indoor Air Quality Building Education and Assessment Model (I-BEAM), released in 2002, is a guidance tool designed for use by building professionals and others interested in indoor air quality in commercial buildings.

  20. Exposure Modeling of Residential Air Exchange Rates for NEXUS Participants.

    Science.gov (United States)

    Due to cost and participant burden of personal measurements, air pollution health studies often estimate exposures using local ambient air monitors. Since outdoor levels do not necessarily reflect personal exposures, we developed the Exposure Model for Individuals (EMI) to improv...

  1. Climate Prediction Center (CPC) Global Land Surface Air Temperature Analysis

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — A station observation-based global land monthly mean surface air temperature dataset at 0.5 x 0.5 latitude-longitude resolution for the period from 1948 to the...

  2. Climate Prediction Center (CPC) Global Land Surface Air Temperature Analysis

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — A station observation-based global land monthly mean surface air temperature dataset at 0.5 0.5 latitude-longitude resolution for the period from 1948 to the present...

  3. Approach to predict partitioning of organic compounds from air into airborne particulate

    Institute of Scientific and Technical Information of China (English)

    SUN Cong; FENG Liu

    2005-01-01

    Based on the theoretical linear solvation energy relationship and quantum chemical descriptors computed by AM1 Hamiltonian, a new approach was developed to predict the partitioning of some organic compounds between the airborne particulate and air. It could be successfully used to study the partitioning of organic compounds from air into airborne particulate, and evaluate the potential risk of organic compounds.

  4. Methodology for modeling the microbial contamination of air filters.

    Directory of Open Access Journals (Sweden)

    Yun Haeng Joe

    Full Text Available In this paper, we propose a theoretical model to simulate microbial growth on contaminated air filters and entrainment of bioaerosols from the filters to an indoor environment. Air filter filtration and antimicrobial efficiencies, and effects of dust particles on these efficiencies, were evaluated. The number of bioaerosols downstream of the filter could be characterized according to three phases: initial, transitional, and stationary. In the initial phase, the number was determined by filtration efficiency, the concentration of dust particles entering the filter, and the flow rate. During the transitional phase, the number of bioaerosols gradually increased up to the stationary phase, at which point no further increase was observed. The antimicrobial efficiency and flow rate were the dominant parameters affecting the number of bioaerosols downstream of the filter in the transitional and stationary phase, respectively. It was found that the nutrient fraction of dust particles entering the filter caused a significant change in the number of bioaerosols in both the transitional and stationary phases. The proposed model would be a solution for predicting the air filter life cycle in terms of microbiological activity by simulating the microbial contamination of the filter.

  5. Case studies in archaeological predictive modelling

    NARCIS (Netherlands)

    Verhagen, Jacobus Wilhelmus Hermanus Philippus

    2007-01-01

    In this thesis, a collection of papers is put together dealing with various quantitative aspects of predictive modelling and archaeological prospection. Among the issues covered are the effects of survey bias on the archaeological data used for predictive modelling, and the complexities of testing p

  6. Childhood asthma prediction models: a systematic review.

    Science.gov (United States)

    Smit, Henriette A; Pinart, Mariona; Antó, Josep M; Keil, Thomas; Bousquet, Jean; Carlsen, Kai H; Moons, Karel G M; Hooft, Lotty; Carlsen, Karin C Lødrup

    2015-12-01

    Early identification of children at risk of developing asthma at school age is crucial, but the usefulness of childhood asthma prediction models in clinical practice is still unclear. We systematically reviewed all existing prediction models to identify preschool children with asthma-like symptoms at risk of developing asthma at school age. Studies were included if they developed a new prediction model or updated an existing model in children aged 4 years or younger with asthma-like symptoms, with assessment of asthma done between 6 and 12 years of age. 12 prediction models were identified in four types of cohorts of preschool children: those with health-care visits, those with parent-reported symptoms, those at high risk of asthma, or children in the general population. Four basic models included non-invasive, easy-to-obtain predictors only, notably family history, allergic disease comorbidities or precursors of asthma, and severity of early symptoms. Eight extended models included additional clinical tests, mostly specific IgE determination. Some models could better predict asthma development and other models could better rule out asthma development, but the predictive performance of no single model stood out in both aspects simultaneously. This finding suggests that there is a large proportion of preschool children with wheeze for which prediction of asthma development is difficult.

  7. Dispersion model computations of urban air pollution in Espoo, Finland

    Energy Technology Data Exchange (ETDEWEB)

    Valkonen, E.; Haerkoenen, J.; Kukkonen, J.; Rantakrans, E.; Jalkanen, L.

    1997-12-31

    This report presents the numerical results of air quality studies of the city of Espoo in southern Finland. This city is one of the four cities in the Helsinki metropolitan area, having a total population of 850 000. A thorough emission inventory was made of both mobile and stationary sources in the Helsinki metropolitan area. The atmospheric dispersion was evaluated using an urban dispersion modelling system, including a Gaussian multiple-source plume model and a meteorological pre-processing model. The hourly time series of CO, NO{sub 2} and SO{sub 2} concentrations were predicted, using the emissions and meteorological data for the year 1990. The predicted results show a clear decrease in the yearly mean concentrations from southeast to northwest. This is due in part to the denser traffic in the southern parts of Espoo, and in part to pollution from the neighbouring cities of Helsinki and Vantaa, located east of Espoo. The statistical concentration parameters found for Espoo were lower than the old national air quality guidelines (1984); however, some occurrences of above-threshold values were found for NO{sub 2} in terms of the new guidelines (1996). The contribution of traffic to the total concentrations varies spatially from 30 to 90 % for NO{sub 2} from 1 to 65 % for SO{sub 2} while for CO it is nearly 100 %. The concentrations database will be further utilised to analyse the influence of urban air pollution on the health of children attending selected day nurseries in Espoo. The results of this study can also be applied in traffic and city planning. In future work the results will also be compared with data from the urban measurement network of the Helsinki Metropolitan Area Council. (orig.) 19 refs.

  8. Low GWP Refrigerants Modelling Study for a Room Air Conditioner Having Microchannel Heat Exchangers

    Energy Technology Data Exchange (ETDEWEB)

    Shen, Bo [ORNL; Bhandari, Mahabir S [ORNL

    2016-01-01

    Microchannel heat exchangers (MHX) have found great successes in residential and commercial air conditioning applications, being compact heat exchangers, to reduce refrigerant charge and material cost. This investigation aims to extend the application of MHXs in split, room air conditioners (RAC), per fundamental heat exchanger and system modelling. For this paper, microchannel condenser and evaporator models were developed, using a segment-to-segment modelling approach. The microchannel heat exchanger models were integrated to a system design model. The system model is able to predict the performance indices, such as cooling capacity, efficiency, sensible heat ratio, etc. Using the calibrated system and heat exchanger models, we evaluated numerous low GWP (global warming potential) refrigerants. The predicted system performance indices, e.g. cooling efficiency, compressor discharge temperature, and required compressor displacement volume etc., are compared. Suitable replacements for R22 and R-410A for the room air conditioner application are recommended.

  9. Modeling and Simulation of Air Pollutant Dispartion a Case Study of an Industrial Area in Nigeria

    Directory of Open Access Journals (Sweden)

    AbdulFatai JIMOH

    2006-07-01

    Full Text Available This work was carried out to develop a model equation for predicting air pollutant dispersion. Major air pollutant were identified, their source, how they cause air pollution, effects and control measures were analysed. Chemiluminecent analyser, non dispersive infrared analyzer (NDN, flame ionization detector, charcoal column absorber, and titration techniques were used for the analysis. Great emphasis was laid on the pollutants resulting from united African textile in Lagos State. A predictive model for air pollutant dispersion was developed and simulated using data collected from the industry for the year 2001, 2002 and 2003. Both the model and simulated result shows that pollutants such as NO, CO, and CO2 are dispersed in accordance with the law of the dispersion (which state that there is a trend in the reduction of pollutant concentration with increasing distance, The quantities of air pollutants emitted from the industries were compared with that of FEPA regulated emission limit for each pollutant and it was discover that UNTL Lagos at a certain point in time exceeded the regulated limits. Hence the model could be used in predicting air pollutant dispersion in air pollution control and the safe distance for human habitation from the industrial area.

  10. Independent air dehumidification with membrane-based total heat recovery: Modeling and experimental validation

    Energy Technology Data Exchange (ETDEWEB)

    Liang, C.H.; Zhang, L.Z.; Pei, L.X. [Key Laboratory of Enhanced Heat Transfer and Energy Conservation of Education Ministry, School of Chemistry and Chemical Engineering, South China University of Technology, Guangzhou 510640 (China)

    2010-03-15

    Fresh air ventilation is helpful for the control of epidemic respiratory disease like Swine flu (H1N1). Fresh air dehumidification systems with energy recovery measures are the key equipments to realize this goal. As a solution, an independent air dehumidification system with membrane-based total heat recovery is proposed. A prototype is built in laboratory. A detailed model is proposed and a cell-by-cell simulation technique is used in simulation to evaluate performances. The results indicate that the model can predict the system accurately. The effects of varying operating conditions like air-flow rates, temperature, and air relative humidity on the air dehumidification rates, cooling powers, electric power consumption, and thermal coefficient of performance are evaluated. The prototype has a COP of 6.8 under nominal operating conditions with total heat recovery. The performance is rather robust to outside weather conditions with a membrane-based total heat exchanger. (author)

  11. A PROBABILISTIC MODELING FRAMEWORK FOR PREDICTING POPULATION EXPOSURES TO BENZENE

    Science.gov (United States)

    The US Environmental Protection Agency (EPA) is modifying their probabilistic Stochastic Human Exposure Dose Simulation (SHEDS) model to assess aggregate exposures to air toxics. Air toxics include urban Hazardous Air Pollutants (HAPS) such as benzene from mobile sources, part...

  12. Prediction model for spring dust weather frequency in North China

    Institute of Scientific and Technical Information of China (English)

    LANG XianMei

    2008-01-01

    It is of great social and scientific importance and also very difficult to make reliable prediction for dust weather frequency (DWF) in North China. In this paper, the correlation between spring DWF in Beijing and Tianjin observation stations, taken as examples in North China, and seasonally averaged surface air temperature, precipitation, Arctic Oscillation, Antarctic Oscillation, South Oscillation, near surface meridional wind and Eurasian westerly index is respectively calculated so as to construct a prediction model for spring DWF in North China by using these climatic factors. Two prediction models, I.e. Model-Ⅰ and model-Ⅱ, are then set up respectively based on observed climate data and the 32-year (1970--2001) extra-seasonal hindcast experiment data as reproduced by the nine-level Atmospheric General Circulation Model developed at the Institute of Atmospheric Physics (IAP9L-AGCM). It is indicated that the correlation coefficient between the observed and predicted DWF reaches 0.933 in the model-Ⅰ, suggesting a high prediction skill one season ahead. The corresponding value is high up to 0.948 for the subsequent model-Ⅱ, which involves synchronous spring climate data reproduced by the IAP9L-AGCM relative to the model-Ⅰ. The model-Ⅱ can not only make more precise prediction but also can bring forward the lead time of real-time prediction from the model-Ⅰ's one season to half year. At last, the real-time predictability of the two models is evaluated. It follows that both the models display high prediction skill for both the interannual variation and linear trend of spring DWF in North China, and each is also featured by different advantages. As for the model-Ⅱ, the prediction skill is much higher than that of original approach by use of the IAP9L-AGCM alone. Therefore, the prediction idea put forward here should be popularized in other regions in China where dust weather occurs frequently.

  13. Prediction model for spring dust weather frequency in North China

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    It is of great social and scientific importance and also very difficult to make reliable prediction for dust weather frequency (DWF) in North China. In this paper, the correlation between spring DWF in Beijing and Tianjin observation stations, taken as examples in North China, and seasonally averaged surface air temperature, precipitation, Arctic Oscillation, Antarctic Oscillation, South Oscillation, near surface meridional wind and Eurasian westerly index is respectively calculated so as to construct a prediction model for spring DWF in North China by using these climatic factors. Two prediction models, i.e. model-I and model-II, are then set up respectively based on observed climate data and the 32-year (1970 -2001) extra-seasonal hindcast experiment data as reproduced by the nine-level Atmospheric General Circulation Model developed at the Institute of Atmospheric Physics (IAP9L-AGCM). It is indicated that the correlation coefficient between the observed and predicted DWF reaches 0.933 in the model-I, suggesting a high prediction skill one season ahead. The corresponding value is high up to 0.948 for the subsequent model-II, which involves synchronous spring climate data reproduced by the IAP9L-AGCM relative to the model-I. The model-II can not only make more precise prediction but also can bring forward the lead time of real-time prediction from the model-I’s one season to half year. At last, the real-time predictability of the two models is evaluated. It follows that both the models display high prediction skill for both the interannual variation and linear trend of spring DWF in North China, and each is also featured by different advantages. As for the model-II, the prediction skill is much higher than that of original approach by use of the IAP9L-AGCM alone. Therefore, the prediction idea put forward here should be popularized in other regions in China where dust weather occurs frequently.

  14. Model predictive control classical, robust and stochastic

    CERN Document Server

    Kouvaritakis, Basil

    2016-01-01

    For the first time, a textbook that brings together classical predictive control with treatment of up-to-date robust and stochastic techniques. Model Predictive Control describes the development of tractable algorithms for uncertain, stochastic, constrained systems. The starting point is classical predictive control and the appropriate formulation of performance objectives and constraints to provide guarantees of closed-loop stability and performance. Moving on to robust predictive control, the text explains how similar guarantees may be obtained for cases in which the model describing the system dynamics is subject to additive disturbances and parametric uncertainties. Open- and closed-loop optimization are considered and the state of the art in computationally tractable methods based on uncertainty tubes presented for systems with additive model uncertainty. Finally, the tube framework is also applied to model predictive control problems involving hard or probabilistic constraints for the cases of multiplic...

  15. Multiscale model for pedestrian and infection dynamics during air travel

    Science.gov (United States)

    Namilae, Sirish; Derjany, Pierrot; Mubayi, Anuj; Scotch, Mathew; Srinivasan, Ashok

    2017-05-01

    In this paper we develop a multiscale model combining social-force-based pedestrian movement with a population level stochastic infection transmission dynamics framework. The model is then applied to study the infection transmission within airplanes and the transmission of the Ebola virus through casual contacts. Drastic limitations on air-travel during epidemics, such as during the 2014 Ebola outbreak in West Africa, carry considerable economic and human costs. We use the computational model to evaluate the effects of passenger movement within airplanes and air-travel policies on the geospatial spread of infectious diseases. We find that boarding policy by an airline is more critical for infection propagation compared to deplaning policy. Enplaning in two sections resulted in fewer infections than the currently followed strategy with multiple zones. In addition, we found that small commercial airplanes are better than larger ones at reducing the number of new infections in a flight. Aggregated results indicate that passenger movement strategies and airplane size predicted through these network models can have significant impact on an event like the 2014 Ebola epidemic. The methodology developed here is generic and can be readily modified to incorporate the impact from the outbreak of other directly transmitted infectious diseases.

  16. Estimation of whole lemon mass transfer parameters during hot air drying using different modelling methods

    Science.gov (United States)

    Torki-Harchegani, Mehdi; Ghanbarian, Davoud; Sadeghi, Morteza

    2015-08-01

    To design new dryers or improve existing drying equipments, accurate values of mass transfer parameters is of great importance. In this study, an experimental and theoretical investigation of drying whole lemons was carried out. The whole lemons were dried in a convective hot air dryer at different air temperatures (50, 60 and 75 °C) and a constant air velocity (1 m s-1). In theoretical consideration, three moisture transfer models including Dincer and Dost model, Bi- G correlation approach and conventional solution of Fick's second law of diffusion were used to determine moisture transfer parameters and predict dimensionless moisture content curves. The predicted results were then compared with the experimental data and the higher degree of prediction accuracy was achieved by the Dincer and Dost model.

  17. A Multiple Agent Model of Human Performance in Automated Air Traffic Control and Flight Management Operations

    Science.gov (United States)

    Corker, Kevin; Pisanich, Gregory; Condon, Gregory W. (Technical Monitor)

    1995-01-01

    A predictive model of human operator performance (flight crew and air traffic control (ATC)) has been developed and applied in order to evaluate the impact of automation developments in flight management and air traffic control. The model is used to predict the performance of a two person flight crew and the ATC operators generating and responding to clearances aided by the Center TRACON Automation System (CTAS). The purpose of the modeling is to support evaluation and design of automated aids for flight management and airspace management and to predict required changes in procedure both air and ground in response to advancing automation in both domains. Additional information is contained in the original extended abstract.

  18. Energy based prediction models for building acoustics

    DEFF Research Database (Denmark)

    Brunskog, Jonas

    2012-01-01

    In order to reach robust and simplified yet accurate prediction models, energy based principle are commonly used in many fields of acoustics, especially in building acoustics. This includes simple energy flow models, the framework of statistical energy analysis (SEA) as well as more elaborated...... principles as, e.g., wave intensity analysis (WIA). The European standards for building acoustic predictions, the EN 12354 series, are based on energy flow and SEA principles. In the present paper, different energy based prediction models are discussed and critically reviewed. Special attention is placed...

  19. Prediction of Air Flow and Temperature Profiles Inside Convective Solar Dryer

    Directory of Open Access Journals (Sweden)

    Marian Vintilă

    2014-11-01

    Full Text Available Solar tray drying is an effective alternative for post-harvest processing of fruits and vegetables. Product quality and uniformity of the desired final moisture content are affected by the uneven air flow and temperature distribution inside the drying chamber. The purpose of this study is to numerically evaluate the operation parameters of a new indirect solar dryer having an appropriate design based on thermal uniformity inside the drying chamber, low construction costs and easy accessibility to resources needed for manufacture. The research was focused on both the investigation of different operation conditions and analysis of the influence of the damper position, which is incorporated into the chimney, on the internal cabinet temperature and air flow distribution. Numerical simulation was carried out with Comsol Multiphysics CFD commercial code using a reduced 2D domain model by neglecting any end effects from the side walls. The analysis of the coupled thermal-fluid model provided the velocity field, pressure distribution and temperature distribution in the solar collector and in the drying chamber when the damper was totally closed, half open and fully open and for different operation conditions. The predicted results were compared with measurements taken in-situ. With progressing computing power, it is conceivable that CFD will continue to provide explanations for more fluid flow, heat and mass transfer phenomena, leading to better equipment design and process control for the food industry.

  20. Economic Modeling of Compressed Air Energy Storage

    Directory of Open Access Journals (Sweden)

    Rui Bo

    2013-04-01

    Full Text Available Due to the variable nature of wind resources, the increasing penetration level of wind power will have a significant impact on the operation and planning of the electric power system. Energy storage systems are considered an effective way to compensate for the variability of wind generation. This paper presents a detailed production cost simulation model to evaluate the economic value of compressed air energy storage (CAES in systems with large-scale wind power generation. The co-optimization of energy and ancillary services markets is implemented in order to analyze the impacts of CAES, not only on energy supply, but also on system operating reserves. Both hourly and 5-minute simulations are considered to capture the economic performance of CAES in the day-ahead (DA and real-time (RT markets. The generalized network flow formulation is used to model the characteristics of CAES in detail. The proposed model is applied on a modified IEEE 24-bus reliability test system. The numerical example shows that besides the economic benefits gained through energy arbitrage in the DA market, CAES can also generate significant profits by providing reserves, compensating for wind forecast errors and intra-hour fluctuation, and participating in the RT market.

  1. Navier-Stokes predictions of dynamic stability derivatives for air-breathing hypersonic vehicle

    Science.gov (United States)

    Liu, Xu; Liu, Wei; Zhao, Yunfei

    2016-01-01

    Dynamic derivatives are important parameters for designing vehicle trajectory and attitude control system that directly decide the divergence behavior of vibration of the aircraft open-loop system under interference. After calibration model validation, the dynamic behavior of air-breathing hypersonic vehicle WR-A is characterized. The unsteady flow field of aircraft forced simple harmonic vibration (SHV) is simulated using N-S equation. The direct damping derivatives, cross derivatives, acceleration derivatives and rotary derivatives of WR-A under different frequencies, amplitudes and positions of centroid are obtained. Research demonstrates that the proportion of acceleration derivatives, which represents the flow time lag effect, in the direct damping derivatives can be as high as 40% but is opposite to the damping derivative value symbols in some cases, contributing to dynamic instability. Numerical simulation on large-amplitude forced vibration of WR-A indicates that the aerodynamic behavior predicted by the dynamic derivative model agrees well with unsteady calculations. The inlet performance parameter derivatives are solved using the Etkin theory. The inlet performance parameters under large-amplitude vibration are successfully predicted using the dynamic derivative model. This offers a guideline for characterizing the dynamic internal flow field and unsteady inlet performance.

  2. Massive Predictive Modeling using Oracle R Enterprise

    CERN Document Server

    CERN. Geneva

    2014-01-01

    R is fast becoming the lingua franca for analyzing data via statistics, visualization, and predictive analytics. For enterprise-scale data, R users have three main concerns: scalability, performance, and production deployment. Oracle's R-based technologies - Oracle R Distribution, Oracle R Enterprise, Oracle R Connector for Hadoop, and the R package ROracle - address these concerns. In this talk, we introduce Oracle's R technologies, highlighting how each enables R users to achieve scalability and performance while making production deployment of R results a natural outcome of the data analyst/scientist efforts. The focus then turns to Oracle R Enterprise with code examples using the transparency layer and embedded R execution, targeting massive predictive modeling. One goal behind massive predictive modeling is to build models per entity, such as customers, zip codes, simulations, in an effort to understand behavior and tailor predictions at the entity level. Predictions...

  3. Evaluating NOx emission inventories for regulatory air quality modeling using satellite and air quality model data

    Science.gov (United States)

    Kemball-Cook, Susan; Yarwood, Greg; Johnson, Jeremiah; Dornblaser, Bright; Estes, Mark

    2015-09-01

    The purpose of this study was to assess the accuracy of NOx emissions in the Texas Commission on Environmental Quality's (TCEQ) State Implementation Plan (SIP) modeling inventories of the southeastern U.S. We used retrieved satellite tropospheric NO2 columns from the Ozone Monitoring Instrument (OMI) together with NO2 columns from the Comprehensive Air Quality Model with Extensions (CAMx) to make top-down NOx emissions estimates using the mass balance method. Two different top-down NOx emissions estimates were developed using the KNMI DOMINO v2.0 and NASA SP2 retrievals of OMI NO2 columns. Differences in the top-down NOx emissions estimates made with these two operational products derived from the same OMI radiance data were sufficiently large that they could not be used to constrain the TCEQ NOx emissions in the southeast. The fact that the two available operational NO2 column retrievals give such different top-down NOx emissions results is important because these retrievals are increasingly being used to diagnose air quality problems and to inform efforts to solve them. These results reflect the fact that NO2 column retrievals are a blend of measurements and modeled data and should be used with caution in analyses that will inform policy development. This study illustrates both benefits and challenges of using satellite NO2 data for air quality management applications. Comparison with OMI NO2 columns pointed the way toward improvements in the CAMx simulation of the upper troposphere, but further refinement of both regional air quality models and the NO2 column retrievals is needed before the mass balance and other emission inversion methods can be used to successfully constrain NOx emission inventories used in U.S. regulatory modeling.

  4. Effects of various representations of temporally and spatially variable agricultural processes in air quality dispersion modeling

    Science.gov (United States)

    Agricultural activities that are both temporally and spatially variable, such as tillage and harvesting, can be challenging to represent as sources in air quality dispersion modeling. Existing models were mainly developed to predict concentrations resulting from a stationary and continuous source wi...

  5. MCCM-WEPS: Coupling of Meteorological, Air Quality and Erosion Models for Mexico City

    Science.gov (United States)

    Díaz, E. N.; Tatarko, J.; Jazcilevich, A. D.; García, A. R.; Caetano, E.

    2007-05-01

    Since natural dust emissions are an important factor in the air quality of Mexico City, a modeling effort to quantify their sources and evaluate their impact on the population is presented. The meteorological and air quality model Multiscale Climate and Chemistry Model (MCCM) provides the meteorological inputs to the erosion model Wind Erosion Prediction System (WEPS) that then provides the natural PM10 emissions to be transported. The system was developed to study the particles dispersion from natural sources (unprotected soils) as agricultural lands and Lake of Texcoco. These sources are located around the Valley of Mexico City. As a result of this research we developed a system with the capability of modeling the phenomenon of air pollution by natural particles emitted by wind erosion and to generate case study scenarios useful to propose control policies. Some of them are presented here. Also an effort to predict with anticipation this phenomenon is under way.

  6. Liver Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing liver cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  7. Colorectal Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing colorectal cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  8. Cervical Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing cervical cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  9. Prostate Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing prostate cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  10. Pancreatic Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing pancreatic cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  11. Colorectal Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing colorectal cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  12. Bladder Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing bladder cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  13. Esophageal Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing esophageal cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  14. Lung Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing lung cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  15. Breast Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing breast cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  16. Ovarian Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing ovarian cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  17. Testicular Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of testicular cervical cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  18. Dynamic evaluation of air quality models over European regions

    NARCIS (Netherlands)

    Thunis, P.; Pisoni, E.; Degraeuwe, B.; Kranenburg, R.; Schaap, M.; Clappier, A.

    2015-01-01

    Chemistry-transport models are increasingly used in Europe for estimating air quality or forecasting changes in pollution levels. But with this increased use of modeling arises the need of harmonizing the methodologies to determine the quality of air quality model applications. This is complex for p

  19. APPLICATION OF ARTIFICIAL NEURAL NETWORKS FOR PREDICTION OF AIR POLLUTION LEVELS IN ENVIRONMENTAL MONITORING

    Directory of Open Access Journals (Sweden)

    Małgorzata Pawul

    2016-09-01

    Full Text Available Recently, a lot of attention was paid to the improvement of methods which are used to air quality forecasting. Artificial neural networks can be applied to model these problems. Their advantage is that they can solve the problem in the conditions of incomplete information, without the knowledge of the analytical relationship between the input and output data. In this paper we applied artificial neural networks to predict the PM 10 concentrations as factors determining the occurrence of smog phenomena. To create these networks we used meteorological data and concentrations of PM 10. The data were recorded in 2014 and 2015 at three measuring stations operating in Krakow under the State Environmental Monitoring. The best results were obtained by three-layer perceptron with back-propagation algorithm. The neural networks received a good fit in all cases.

  20. Implementation of a WRF-CMAQ Air Quality Modeling System in Bogotá, Colombia

    Science.gov (United States)

    Nedbor-Gross, R.; Henderson, B. H.; Pachon, J. E.; Davis, J. R.; Baublitz, C. B.; Rincón, A.

    2014-12-01

    Due to a continuous economic growth Bogotá, Colombia has experienced air pollution issues in recent years. The local environmental authority has implemented several strategies to curb air pollution that have resulted in the decrease of PM10 concentrations since 2010. However, more activities are necessary in order to meet international air quality standards in the city. The University of Florida Air Quality and Climate group is collaborating with the Universidad de La Salle to prioritize regulatory strategies for Bogotá using air pollution simulations. To simulate pollution, we developed a modeling platform that combines the Weather Research and Forecasting Model (WRF), local emissions, and the Community Multi-scale Air Quality model (CMAQ). This platform is the first of its kind to be implemented in the megacity of Bogota, Colombia. The presentation will discuss development and evaluation of the air quality modeling system, highlight initial results characterizing photochemical conditions in Bogotá, and characterize air pollution under proposed regulatory strategies. The WRF model has been configured and applied to Bogotá, which resides in a tropical climate with complex mountainous topography. Developing the configuration included incorporation of local topography and land-use data, a physics sensitivity analysis, review, and systematic evaluation. The threshold, however, was set based on synthesis of model performance under less mountainous conditions. We will evaluate the impact that differences in autocorrelation contribute to the non-ideal performance. Air pollution predictions are currently under way. CMAQ has been configured with WRF meteorology, global boundary conditions from GEOS-Chem, and a locally produced emission inventory. Preliminary results from simulations show promising performance of CMAQ in Bogota. Anticipated results include a systematic performance evaluation of ozone and PM10, characterization of photochemical sensitivity, and air

  1. A survey of air flow models for multizone structures

    Energy Technology Data Exchange (ETDEWEB)

    Feustel, H.E.; Dieris, J.

    1991-03-01

    Air flow models are used to simulate the rates of incoming and outgoing air flows for a building with known leakage under given weather and shielding conditions. Additional information about the flow paths and air-mass flows inside the building can only by using multizone air flow models. In order to obtain more information on multizone air flow models, a literature review was performed in 1984. A second literature review and a questionnaire survey performed in 1989, revealed the existence of 50 multizone air flow models, all developed since 1966, two of which are still under development. All these programs use similar flow equations for crack flow but differ in the versatility to describe the full range of flow phenomena and the algorithm provided for solving the set of nonlinear equations. This literature review was found that newer models are able to describe and simulate the ventilation systems and interrelation of mechanical and natural ventilation. 27 refs., 2 figs., 1 tab.

  2. Posterior Predictive Model Checking in Bayesian Networks

    Science.gov (United States)

    Crawford, Aaron

    2014-01-01

    This simulation study compared the utility of various discrepancy measures within a posterior predictive model checking (PPMC) framework for detecting different types of data-model misfit in multidimensional Bayesian network (BN) models. The investigated conditions were motivated by an applied research program utilizing an operational complex…

  3. Forecasts using Box-Jenkins models for the ambient air quality data of Delhi City.

    Science.gov (United States)

    Sharma, Pragati; Chandra, Avinash; Kaushik, S C

    2009-10-01

    The monthly maximum of the 24-h average time-series data of ambient air quality-sulphur dioxide (SO(2)), nitrogen dioxide (NO(2)) and suspended particulate matter (SPM) concentration monitored at the six National Ambient Air Quality Monitoring (NAAQM) stations in Delhi, was analysed using Box-Jenkins modelling approach (Box et al. 1994). Univariate linear stochastic models were developed to examine the degree of prediction possible for situations where only the past record of pollutant data are available. In all, 18 models were developed, three for each station for each of the respective pollutant. The model evaluation statistics suggest that considerably satisfactory real-time forecasts of pollution concentrations can be generated using the Box-Jenkins approach. The developed models can be used to provide short-term, real-time forecasts of extreme air pollution concentrations for the Air Quality Control Region (AQCR) of Delhi City, India.

  4. A Course in... Model Predictive Control.

    Science.gov (United States)

    Arkun, Yaman; And Others

    1988-01-01

    Describes a graduate engineering course which specializes in model predictive control. Lists course outline and scope. Discusses some specific topics and teaching methods. Suggests final projects for the students. (MVL)

  5. Prediction of Air Pollutants Concentration Based on an Extreme Learning Machine: The Case of Hong Kong.

    Science.gov (United States)

    Zhang, Jiangshe; Ding, Weifu

    2017-01-24

    With the development of the economy and society all over the world, most metropolitan cities are experiencing elevated concentrations of ground-level air pollutants. It is urgent to predict and evaluate the concentration of air pollutants for some local environmental or health agencies. Feed-forward artificial neural networks have been widely used in the prediction of air pollutants concentration. However, there are some drawbacks, such as the low convergence rate and the local minimum. The extreme learning machine for single hidden layer feed-forward neural networks tends to provide good generalization performance at an extremely fast learning speed. The major sources of air pollutants in Hong Kong are mobile, stationary, and from trans-boundary sources. We propose predicting the concentration of air pollutants by the use of trained extreme learning machines based on the data obtained from eight air quality parameters in two monitoring stations, including Sham Shui Po and Tap Mun in Hong Kong for six years. The experimental results show that our proposed algorithm performs better on the Hong Kong data both quantitatively and qualitatively. Particularly, our algorithm shows better predictive ability, with R 2 increased and root mean square error values decreased respectively.

  6. Prediction of Air Pollutants Concentration Based on an Extreme Learning Machine: The Case of Hong Kong

    Directory of Open Access Journals (Sweden)

    Jiangshe Zhang

    2017-01-01

    Full Text Available With the development of the economy and society all over the world, most metropolitan cities are experiencing elevated concentrations of ground-level air pollutants. It is urgent to predict and evaluate the concentration of air pollutants for some local environmental or health agencies. Feed-forward artificial neural networks have been widely used in the prediction of air pollutants concentration. However, there are some drawbacks, such as the low convergence rate and the local minimum. The extreme learning machine for single hidden layer feed-forward neural networks tends to provide good generalization performance at an extremely fast learning speed. The major sources of air pollutants in Hong Kong are mobile, stationary, and from trans-boundary sources. We propose predicting the concentration of air pollutants by the use of trained extreme learning machines based on the data obtained from eight air quality parameters in two monitoring stations, including Sham Shui Po and Tap Mun in Hong Kong for six years. The experimental results show that our proposed algorithm performs better on the Hong Kong data both quantitatively and qualitatively. Particularly, our algorithm shows better predictive ability, with R 2 increased and root mean square error values decreased respectively.

  7. Good manufacturing practice for modelling air pollution: Quality criteria for computer models to calculate air pollution

    Science.gov (United States)

    Dekker, C. M.; Sliggers, C. J.

    To spur on quality assurance for models that calculate air pollution, quality criteria for such models have been formulated. By satisfying these criteria the developers of these models and producers of the software packages in this field can assure and account for the quality of their products. In this way critics and users of such (computer) models can gain a clear understanding of the quality of the model. Quality criteria have been formulated for the development of mathematical models, for their programming—including user-friendliness, and for the after-sales service, which is part of the distribution of such software packages. The criteria have been introduced into national and international frameworks to obtain standardization.

  8. Equivalency and unbiasedness of grey prediction models

    Institute of Scientific and Technical Information of China (English)

    Bo Zeng; Chuan Li; Guo Chen; Xianjun Long

    2015-01-01

    In order to deeply research the structure discrepancy and modeling mechanism among different grey prediction mo-dels, the equivalence and unbiasedness of grey prediction mo-dels are analyzed and verified. The results show that al the grey prediction models that are strictly derived from x(0)(k) +az(1)(k) = b have the identical model structure and simulation precision. Moreover, the unbiased simulation for the homoge-neous exponential sequence can be accomplished. However, the models derived from dx(1)/dt+ax(1) =b are only close to those derived from x(0)(k)+az(1)(k)=b provided that|a|has to satisfy|a| < 0.1; neither could the unbiased simulation for the homoge-neous exponential sequence be achieved. The above conclusions are proved and verified through some theorems and examples.

  9. Predictability of extreme values in geophysical models

    Directory of Open Access Journals (Sweden)

    A. E. Sterk

    2012-09-01

    Full Text Available Extreme value theory in deterministic systems is concerned with unlikely large (or small values of an observable evaluated along evolutions of the system. In this paper we study the finite-time predictability of extreme values, such as convection, energy, and wind speeds, in three geophysical models. We study whether finite-time Lyapunov exponents are larger or smaller for initial conditions leading to extremes. General statements on whether extreme values are better or less predictable are not possible: the predictability of extreme values depends on the observable, the attractor of the system, and the prediction lead time.

  10. Implementation of Models for Building Envelope Air Flow Fields in a Whole Building Hygrothermal Simulation Tool

    DEFF Research Database (Denmark)

    Rode, Carsten; Grau, Karl

    2009-01-01

    Simulation tools are becoming available which predict the heat and moisture conditions in the indoor environment as well as in the envelope of buildings, and thus it has become possible to consider the important interaction between the different components of buildings and the different physical...... phenomena which occur. However, there is still room for further development of such tools. This paper will present an attempt to integrate modelling of air flows in building envelopes into a whole building hygrothermal simulation tool. Two kinds of air flows have been considered: 1. Air flow in ventilated...... cavity such as in the exterior cladding of building envelopes, i.e. a flow which is parallel to the construction plane. 2. Infiltration/exfiltration of air through the building envelope, i.e. a flow which is perpendicular to the construction plane. The new models make it possible to predict the thermal...

  11. Predicting dermal absorption of gas-phase chemicals: transient model development, evaluation, and application

    DEFF Research Database (Denmark)

    Gong, M.; Zhang, Y.; Weschler, Charles J.

    2014-01-01

    A transient model is developed to predict dermal absorption of gas-phase chemicals via direct air-to-skin-to-blood transport under non-steady-state conditions. It differs from published models in that it considers convective mass-transfer resistance in the boundary layer of air adjacent to the skin....... Results calculated with this transient model are in good agreement with the limited experimental results that are available for comparison. The sensitivity of the modeled estimates to key parameters is examined. The model is then used to estimate air-to-skin-to-blood absorption of six phthalate esters...

  12. Effects of air-sea interaction on extended-range prediction of geopotential height at 500 hPa over the northern extratropical region

    Science.gov (United States)

    Wang, Xujia; Zheng, Zhihai; Feng, Guolin

    2017-02-01

    The contribution of air-sea interaction on the extended-range prediction of geopotential height at 500 hPa in the northern extratropical region has been analyzed with a coupled model form Beijing Climate Center and its atmospheric components. Under the assumption of the perfect model, the extended-range prediction skill was evaluated by anomaly correlation coefficient (ACC), root mean square error (RMSE), and signal-to-noise ratio (SNR). The coupled model has a better prediction skill than its atmospheric model, especially, the air-sea interaction in July made a greater contribution for the improvement of prediction skill than other months. The prediction skill of the extratropical region in the coupled model reaches 16-18 days in all months, while the atmospheric model reaches 10-11 days in January, April, and July and only 7-8 days in October, indicating that the air-sea interaction can extend the prediction skill of the atmospheric model by about 1 week. The errors of both the coupled model and the atmospheric model reach saturation in about 20 days, suggesting that the predictable range is less than 3 weeks.

  13. Quasi-steady-state model of a counter flow air-to-air heat exchanger with phase change

    DEFF Research Database (Denmark)

    Rose, Jørgen; Nielsen, Toke Rammer; Kragh, Jesper;

    2008-01-01

    into account the effects of condensation and frost formation. The model is developed as an Excel spreadsheet, and specific results are compared with laboratory measurements. As an example, the model is used to determine the most energy-efficient control strategy for a specific heat-exchanger under northern......Using mechanical ventilation with highly efficient heat-recovery in northern European or arctic climates is a very efficient way of reducing the energy use for heating in buildings. However, it also presents a series of problems concerning condensation and frost formation in the heat......-exchanger. Developing highly efficient heat-exchangers and strategies to avoid/remove frost formation implies the use of detailed models to predict and evaluate different heat-exchanger designs and strategies. This paper presents a quasi-steady-state model of a counter-flow air-to-air heat-exchanger that takes...

  14. Hybrid modeling and prediction of dynamical systems

    Science.gov (United States)

    Lloyd, Alun L.; Flores, Kevin B.

    2017-01-01

    Scientific analysis often relies on the ability to make accurate predictions of a system’s dynamics. Mechanistic models, parameterized by a number of unknown parameters, are often used for this purpose. Accurate estimation of the model state and parameters prior to prediction is necessary, but may be complicated by issues such as noisy data and uncertainty in parameters and initial conditions. At the other end of the spectrum exist nonparametric methods, which rely solely on data to build their predictions. While these nonparametric methods do not require a model of the system, their performance is strongly influenced by the amount and noisiness of the data. In this article, we consider a hybrid approach to modeling and prediction which merges recent advancements in nonparametric analysis with standard parametric methods. The general idea is to replace a subset of a mechanistic model’s equations with their corresponding nonparametric representations, resulting in a hybrid modeling and prediction scheme. Overall, we find that this hybrid approach allows for more robust parameter estimation and improved short-term prediction in situations where there is a large uncertainty in model parameters. We demonstrate these advantages in the classical Lorenz-63 chaotic system and in networks of Hindmarsh-Rose neurons before application to experimentally collected structured population data. PMID:28692642

  15. Risk terrain modeling predicts child maltreatment.

    Science.gov (United States)

    Daley, Dyann; Bachmann, Michael; Bachmann, Brittany A; Pedigo, Christian; Bui, Minh-Thuy; Coffman, Jamye

    2016-12-01

    As indicated by research on the long-term effects of adverse childhood experiences (ACEs), maltreatment has far-reaching consequences for affected children. Effective prevention measures have been elusive, partly due to difficulty in identifying vulnerable children before they are harmed. This study employs Risk Terrain Modeling (RTM), an analysis of the cumulative effect of environmental factors thought to be conducive for child maltreatment, to create a highly accurate prediction model for future substantiated child maltreatment cases in the City of Fort Worth, Texas. The model is superior to commonly used hotspot predictions and more beneficial in aiding prevention efforts in a number of ways: 1) it identifies the highest risk areas for future instances of child maltreatment with improved precision and accuracy; 2) it aids the prioritization of risk-mitigating efforts by informing about the relative importance of the most significant contributing risk factors; 3) since predictions are modeled as a function of easily obtainable data, practitioners do not have to undergo the difficult process of obtaining official child maltreatment data to apply it; 4) the inclusion of a multitude of environmental risk factors creates a more robust model with higher predictive validity; and, 5) the model does not rely on a retrospective examination of past instances of child maltreatment, but adapts predictions to changing environmental conditions. The present study introduces and examines the predictive power of this new tool to aid prevention efforts seeking to improve the safety, health, and wellbeing of vulnerable children.

  16. Property predictions using microstructural modeling

    Energy Technology Data Exchange (ETDEWEB)

    Wang, K.G. [Department of Materials Science and Engineering, Rensselaer Polytechnic Institute, CII 9219, 110 8th Street, Troy, NY 12180-3590 (United States)]. E-mail: wangk2@rpi.edu; Guo, Z. [Sente Software Ltd., Surrey Technology Centre, 40 Occam Road, Guildford GU2 7YG (United Kingdom); Sha, W. [Metals Research Group, School of Civil Engineering, Architecture and Planning, The Queen' s University of Belfast, Belfast BT7 1NN (United Kingdom); Glicksman, M.E. [Department of Materials Science and Engineering, Rensselaer Polytechnic Institute, CII 9219, 110 8th Street, Troy, NY 12180-3590 (United States); Rajan, K. [Department of Materials Science and Engineering, Rensselaer Polytechnic Institute, CII 9219, 110 8th Street, Troy, NY 12180-3590 (United States)

    2005-07-15

    Precipitation hardening in an Fe-12Ni-6Mn maraging steel during overaging is quantified. First, applying our recent kinetic model of coarsening [Phys. Rev. E, 69 (2004) 061507], and incorporating the Ashby-Orowan relationship, we link quantifiable aspects of the microstructures of these steels to their mechanical properties, including especially the hardness. Specifically, hardness measurements allow calculation of the precipitate size as a function of time and temperature through the Ashby-Orowan relationship. Second, calculated precipitate sizes and thermodynamic data determined with Thermo-Calc[copyright] are used with our recent kinetic coarsening model to extract diffusion coefficients during overaging from hardness measurements. Finally, employing more accurate diffusion parameters, we determined the hardness of these alloys independently from theory, and found agreement with experimental hardness data. Diffusion coefficients determined during overaging of these steels are notably higher than those found during the aging - an observation suggesting that precipitate growth during aging and precipitate coarsening during overaging are not controlled by the same diffusion mechanism.

  17. Spatial Economics Model Predicting Transport Volume

    Directory of Open Access Journals (Sweden)

    Lu Bo

    2016-10-01

    Full Text Available It is extremely important to predict the logistics requirements in a scientific and rational way. However, in recent years, the improvement effect on the prediction method is not very significant and the traditional statistical prediction method has the defects of low precision and poor interpretation of the prediction model, which cannot only guarantee the generalization ability of the prediction model theoretically, but also cannot explain the models effectively. Therefore, in combination with the theories of the spatial economics, industrial economics, and neo-classical economics, taking city of Zhuanghe as the research object, the study identifies the leading industry that can produce a large number of cargoes, and further predicts the static logistics generation of the Zhuanghe and hinterlands. By integrating various factors that can affect the regional logistics requirements, this study established a logistics requirements potential model from the aspect of spatial economic principles, and expanded the way of logistics requirements prediction from the single statistical principles to an new area of special and regional economics.

  18. Air Target Fuzzy Pattern Recognition Threat-Judgment Model

    Institute of Scientific and Technical Information of China (English)

    童幼堂; 王建明

    2003-01-01

    Threat-judgment is a complicated fuzzy inference problem. Up to now no relevant unified theory and measuring standard have been developed. It is very difficult to establish a threat-judgment model with high reliability in the air defense system for the naval warships. Air target threat level judgment is an important component in naval warship combat command decision-making systems. According to the threat level judgment of air targets during the air defense of single naval warship, a fuzzy pattern recognition model for judging the threat from air targets is established. Then an algorithm for identifying the parameters in the model is presented. The model has an adaptive feature and can dynamically update its parameters according to the state change of the attacking targets and the environment. The method presented here can be used for the air defense system threat judgment in the naval warships.

  19. Density-corrected models for gas diffusivity and air permeability in unsaturated soil

    DEFF Research Database (Denmark)

    Deepagoda Thuduwe Kankanamge Kelum, Chamindu; Møldrup, Per; Schjønning, Per

    2011-01-01

    . Also, a power-law ka model with exponent 1.5 (derived from analogy with a previous gas diffusivity model) used in combination with the D-C approach for ka,100 (reference point) seemed promising for ka(e) predictions, with good accuracy and minimum parameter requirements. Finally, the new D-C model......Accurate prediction of gas diffusivity (Dp/Do) and air permeability (ka) and their variations with air-filled porosity (e) in soil is critical for simulating subsurface migration and emission of climate gases and organic vapors. Gas diffusivity and air permeability measurements from Danish soil...... profile data (total of 150 undisturbed soil samples) were used to investigate soil type and density effects on the gas transport parameters and for model development. The measurements were within a given range of matric potentials (-10 to -500 cm H2O) typically representing natural field conditions...

  20. Modeling of a split type air conditioner with integrated water heater

    Energy Technology Data Exchange (ETDEWEB)

    Techarungpaisan, P.; Theerakulpisut, S.; Priprem, S. [Mechanical Engineering Department, Faculty of Engineering, Khon Kaen University, 123 Mittrapab Rd., Muang, Khon Kaen 40002 (Thailand)

    2007-04-15

    This paper presents a steady state simulation model to predict the performance of a small split type air conditioner with integrated water heater. The mathematical model consists of submodels of system components such as evaporator, condenser, compressor, capillary tube, receiver and water heater. These submodels were built based on fundamental principles of heat transfer, thermodynamics, fluid mechanics, empirical relationships and manufacturer's data as necessary. The model was coded into a simulation program and used to predict system parameters of interest such as hot water temperature, condenser exit air temperature, evaporator exit air temperature, mass flow rate of refrigerant, heat rejection in the condenser and cooling capacity of the system. The simulation results were compared with experimental data obtained from an experimental rig built for validating the mathematical model. It was found that the experimental and simulation results are in good agreement. (author)

  1. Modeling and Prediction Using Stochastic Differential Equations

    DEFF Research Database (Denmark)

    Juhl, Rune; Møller, Jan Kloppenborg; Jørgensen, John Bagterp

    2016-01-01

    Pharmacokinetic/pharmakodynamic (PK/PD) modeling for a single subject is most often performed using nonlinear models based on deterministic ordinary differential equations (ODEs), and the variation between subjects in a population of subjects is described using a population (mixed effects) setup...... that describes the variation between subjects. The ODE setup implies that the variation for a single subject is described by a single parameter (or vector), namely the variance (covariance) of the residuals. Furthermore the prediction of the states is given as the solution to the ODEs and hence assumed...... deterministic and can predict the future perfectly. A more realistic approach would be to allow for randomness in the model due to e.g., the model be too simple or errors in input. We describe a modeling and prediction setup which better reflects reality and suggests stochastic differential equations (SDEs...

  2. Precision Plate Plan View Pattern Predictive Model

    Institute of Scientific and Technical Information of China (English)

    ZHAO Yang; YANG Quan; HE An-rui; WANG Xiao-chen; ZHANG Yun

    2011-01-01

    According to the rolling features of plate mill, a 3D elastic-plastic FEM (finite element model) based on full restart method of ANSYS/LS-DYNA was established to study the inhomogeneous plastic deformation of multipass plate rolling. By analyzing the simulation results, the difference of head and tail ends predictive models was found and modified. According to the numerical simulation results of 120 different kinds of conditions, precision plate plan view pattern predictive model was established. Based on these models, the sizing MAS (mizushima automatic plan view pattern control system) method was designed and used on a 2 800 mm plate mill. Comparing the rolled plates with and without PVPP (plan view pattern predictive) model, the reduced width deviation indicates that the olate !olan view Dattern predictive model is preeise.

  3. Hydrodynamic modeling of semi-planing hulls with air cavities

    Directory of Open Access Journals (Sweden)

    Konstantin I. Matveev

    2015-05-01

    Full Text Available High-speed heavy loaded monohull ships can benefit from application of drag-reducing air cavities under stepped hull bottoms. The subject of this paper is the steady hydrodynamic modeling of semi-planing air-cavity hulls. The current method is based on a linearized potential-flow theory for surface flows. The mathematical model description and parametric calculation results for a selected configuration with pressurized and open air cavities are presented.

  4. Fractal Derivative Model for Air Permeability in Hierarchic Porous Media

    Directory of Open Access Journals (Sweden)

    Jie Fan

    2012-01-01

    Full Text Available Air permeability in hierarchic porous media does not obey Fick's equation or its modification because fractal objects have well-defined geometric properties, which are discrete and discontinuous. We propose a theoretical model dealing with, for the first time, a seemingly complex air permeability process using fractal derivative method. The fractal derivative model has been successfully applied to explain the novel air permeability phenomenon of cocoon. The theoretical analysis was in agreement with experimental results.

  5. AIR TOXICS MODELING RESEARCH PROGRAM: AN OVERVIEW

    Science.gov (United States)

    This product is a Microsoft Powerpoint slide presentation which was given at the joint EPA Region 3 - Mid-Atlantic Regional Air Management Association (MARAMA) Air Toxic Summit in Philadelphia, Pennsylvania held from October 18, 2005 through October 20, 2005. The slide presentat...

  6. Density-Corrected Models for Gas Diffusivity and Air Permeability in Unsaturated Soil

    DEFF Research Database (Denmark)

    Chamindu, Deepagoda; Møldrup, Per; Schjønning, Per

    2011-01-01

    Accurate prediction of gas diffusivity (Dp/Do) and air permeability (ka) and their variations with air-filled porosity (e) in soil is critical for simulating subsurface migration and emission of climate gases and organic vapors. Gas diffusivity and air permeability measurements from Danish soil...... in subsurface soil. The data were regrouped into four categories based on compaction (total porosity F 0.4 m3 m-3) and soil texture (volume-based content of clay, silt, and organic matter 15%). The results suggested that soil compaction more than soil type was the major control on gas...... diffusivity and to some extent also on air permeability. We developed a density-corrected (D-C) Dp(e)/Do model as a generalized form of a previous model for Dp/ Do at -100 cm H2O of matric potential (Dp,100/Do). The D-C model performed well across soil types and density levels compared with existing models...

  7. Air pollution dispersion model in Prague - land development plan modelling of the year 2010

    Energy Technology Data Exchange (ETDEWEB)

    Hornicek, K. [Road and Motorway Directorate (Czech Republic)

    2000-07-01

    Prague municipality operates an unique air pollution model, which reflects changing of air quality situation in the town. The model was generated at the beginning of last decade and solves gradual changes of air quality in a period of 90's. The next logical step aims to upgrade this model as a tool for visualisation of all predictable air quality changes, which can affect environmental condition for inhabitants in Prague region. The object of model simulation is to estimate these changes in term of 2010. The model simulation includes following problems: Share of heating media in near future variants of heating systems used in the different residential parts of the town planned system of new roads used at that time traffic volume in network of existing and new roads traffic condition, emission parameters of air pollutants coming from power plants, heating systems, cars and trucks All the above described problems content emission and concentration parts of air pollutants. The road network of the city has been simulated by using about 3000 pieces of road segment and 350 pieces of crossing with original track values, speed, grade and traffic conditions. One of the aims of the final project is to visualize the main changes between present concentrations of different pollutants and the target concentrations in 2010. The key pollutant, which is very significant for the road network of the city, is NOx. Present situation in the field of transport is: 90 % of the area - air pollution is getting better remaining 10 % of the area - concentration of pollutants are increasing (mainly in the neighbourhood of main motorways and newly designed roads). The main reason for improvement of global air pollution situation in Prague is rapid progress of changes in heating systems of residential areas in the town. In comparison with that total emissions coming from transport are generally in the same level like in 1998. The highest values of concentration are present along new large

  8. Neural Network Modeling to Predict Shelf Life of Greenhouse Lettuce

    Directory of Open Access Journals (Sweden)

    Wei-Chin Lin

    2009-04-01

    Full Text Available Greenhouse-grown butter lettuce (Lactuca sativa L. can potentially be stored for 21 days at constant 0°C. When storage temperature was increased to 5°C or 10°C, shelf life was shortened to 14 or 10 days, respectively, in our previous observations. Also, commercial shelf life of 7 to 10 days is common, due to postharvest temperature fluctuations. The objective of this study was to establish neural network (NN models to predict the remaining shelf life (RSL under fluctuating postharvest temperatures. A box of 12 - 24 lettuce heads constituted a sample unit. The end of the shelf life of each head was determined when it showed initial signs of decay or yellowing. Air temperatures inside a shipping box were recorded. Daily average temperatures in storage and averaged shelf life of each box were used as inputs, and the RSL was modeled as an output. An R2 of 0.57 could be observed when a simple NN structure was employed. Since the "future" (or remaining storage temperatures were unavailable at the time of making a prediction, a second NN model was introduced to accommodate a range of future temperatures and associated shelf lives. Using such 2-stage NN models, an R2 of 0.61 could be achieved for predicting RSL. This study indicated that NN modeling has potential for cold chain quality control and shelf life prediction.

  9. Econometric Forecasting Models for Air Traffic Passenger of Indonesia

    Directory of Open Access Journals (Sweden)

    Viktor Suryan

    2017-01-01

    Full Text Available One of the major benefits of the air transport services operating in bigger countries is the fact that they provide a vital social economic linkage. This study is an attempt to establish the determinants of the passenger air traffic in Indonesia. The main objective of the study is to determine the economic variables that affect the number of airline passengers using the econometrics model of projection with an emphasis on the use of panel data and to determine the economic variables that affect the number of airline passengers using the econometrics model of projection with an emphasis on the use of time series data. This research also predicts the upcoming number of air traffic passenger until 2030. Air transportation and the economic activity in a country are interdependent. This work first uses the data at the country level and then at the selected airport level for review. The methodology used in this study has adopted the study for both normal regression and panel data regression techniques. Once all these steps are performed, the final equation is taken up for the forecast of the passenger inflow data in the Indonesian airports. To forecast the same, the forecasted numbers of the GDP (Gross Domestic Product and population (independent variables were chosen as a part of the literature review exercise are used. The result of this study shows the GDP per capita have significant related to a number of passengers which the elasticity 2.23 (time-series data and 1.889 for panel data. The exchange rate variable is unrelated to a number of passengers as shown in the value of elasticity. In addition, the total of population gives small value for the elasticity. Moreover, the number of passengers is also affected by the dummy variable (deregulation. With three scenarios: low, medium and high for GDP per capita, the percentage of growth for total number of air traffic passenger from the year 2015 to 2030 is 199.3%, 205.7%, and 320.9% respectively.

  10. Prediction of hydrodynamics and chemistry of confined turbulent methane-air flames with attention to formation of oxides of nitrogen

    Science.gov (United States)

    Elghobashi, S.; Spalding, D. B.; Srivatsa, S. K.

    1977-01-01

    A formulation of the governing partial differential equations for fluid flow and reacting chemical species in a tubular combustor is presented. A numerical procedure for the solution of the governing differential equations is described, and models for chemical equilibrium and chemical kinetics calculations are presented. The chemical equilibrium model is used to characterize the hydrocarbon reactions. The chemical kinetics model is used to predict the concentrations of the oxides of nitrogen. The combustor consists of a cylindrical duct of varying cross sections with concentric streams of gaseous fuel and air entering the duct at one end. Four sample cases with specified inlet and boundary conditions are considered, and the results are discussed

  11. Running Large-Scale Air Pollution Models on Parallel Computers

    DEFF Research Database (Denmark)

    Georgiev, K.; Zlatev, Z.

    2000-01-01

    Proceedings of the 23rd NATO/CCMS International Technical Meeting on Air Pollution Modeling and Its Application, held 28 September - 2 October 1998, in Varna, Bulgaria.......Proceedings of the 23rd NATO/CCMS International Technical Meeting on Air Pollution Modeling and Its Application, held 28 September - 2 October 1998, in Varna, Bulgaria....

  12. Application of Parallel Algorithms in an Air Pollution Model

    DEFF Research Database (Denmark)

    Georgiev, K.; Zlatev, Z.

    1999-01-01

    Proceedings of the NATO Advanced Research Workshop on Large Scale Computations in Air Pollution Modelling, Sofia, Bulgaria, 6-10 July 1998......Proceedings of the NATO Advanced Research Workshop on Large Scale Computations in Air Pollution Modelling, Sofia, Bulgaria, 6-10 July 1998...

  13. Long-Term Calculations with Large Air Pollution Models

    DEFF Research Database (Denmark)

    1999-01-01

    Proceedings of the NATO Advanced Research Workshop on Large Scale Computations in Air Pollution Modelling, Sofia, Bulgaria, 6-10 July 1998......Proceedings of the NATO Advanced Research Workshop on Large Scale Computations in Air Pollution Modelling, Sofia, Bulgaria, 6-10 July 1998...

  14. A physically based analytical spatial air temperature and humidity model

    Science.gov (United States)

    Yang Yang; Theodore A. Endreny; David J. Nowak

    2013-01-01

    Spatial variation of urban surface air temperature and humidity influences human thermal comfort, the settling rate of atmospheric pollutants, and plant physiology and growth. Given the lack of observations, we developed a Physically based Analytical Spatial Air Temperature and Humidity (PASATH) model. The PASATH model calculates spatial solar radiation and heat...

  15. Long-Term Calculations with Large Air Pollution Models

    DEFF Research Database (Denmark)

    1999-01-01

    Proceedings of the NATO Advanced Research Workshop on Large Scale Computations in Air Pollution Modelling, Sofia, Bulgaria, 6-10 July 1998......Proceedings of the NATO Advanced Research Workshop on Large Scale Computations in Air Pollution Modelling, Sofia, Bulgaria, 6-10 July 1998...

  16. Study on Model of Indoor Air Pollution Forecast for Decoration Under Natural Ventilation Condition

    Institute of Scientific and Technical Information of China (English)

    YAN-FENG HONG; XUN CHEN; NING XU

    2005-01-01

    Objective To establish the model of indoor air pollution forecast for decoration. Methods The model was based on the balance model for diffusing mass. Results The data between testing concentration and estimating concentration were compared. The maximal error was less than 30% and average error was 14.6%. Conclusion The model can easily predict whether the pollution for decoration exceeds the standard and how long the room is decorated.

  17. Air Quality Modeling in Support of the Near-Road Exposures and Effects of Urban Air Pollutants Study (NEXUS)

    OpenAIRE

    Vlad Isakov; Saravanan Arunachalam; Stuart Batterman; Sarah Bereznicki; Janet Burke; Kathie Dionisio; Val Garcia; David Heist; Steve Perry; Michelle Snyder; Alan Vette

    2014-01-01

    A major challenge in traffic-related air pollution exposure studies is the lack of information regarding pollutant exposure characterization. Air quality modeling can provide spatially and temporally varying exposure estimates for examining relationships between traffic-related air pollutants and adverse health outcomes. A hybrid air quality modeling approach was used to estimate exposure to traffic-related air pollutants in support of the Near-Road Exposures and Effects of Urban Air Pollutan...

  18. A coupled surface/subsurface flow model accounting for air entrapment and air pressure counterflow

    DEFF Research Database (Denmark)

    Delfs, Jens Olaf; Wang, Wenqing; Kalbacher, Thomas

    2013-01-01

    This work introduces the soil air system into integrated hydrology by simulating the flow processes and interactions of surface runoff, soil moisture and air in the shallow subsurface. The numerical model is formulated as a coupled system of partial differential equations for hydrostatic (diffusive...

  19. Predicting Air Quality at First Ingress into Vehicles Visiting the International Space Station.

    Science.gov (United States)

    Romoser, Amelia A; Scully, Robert R; Limero, Thomas F; De Vera, Vanessa; Cheng, Patti F; Hand, Jennifer J; James, John T; Ryder, Valerie E

    2017-02-01

    NASA regularly performs ground-based offgas tests (OGTs), which allow prediction of accumulated volatile pollutant concentrations at first entry on orbit, on whole modules and vehicles scheduled to connect to the International Space Station (ISS). These data guide crew safety operations and allow for estimation of ISS air revitalization systems impact from additional pollutant load. Since volatiles released from vehicle, module, and payload materials can affect crew health and performance, prediction of first ingress air quality is important. To assess whether toxicological risk is typically over or underpredicted, OGT and first ingress samples from 10 vehicles and modules were compared. Samples were analyzed by gas chromatography and gas chromatography-mass spectrometry. The rate of pollutant accumulation was extrapolated over time. Ratios of analytical values and Spacecraft Maximum Allowable Concentrations were used to predict total toxicity values (T-values) at first entry. Results were also compared by compound. Frequently overpredicted was 2-butanone (9/10), whereas propanal (6/10) and ethanol (8/10) were typically underpredicted, but T-values were not substantially affected. Ingress sample collection delay (estimated by octafluoropropane introduced from ISS atmosphere) and T-value prediction accuracy correlated well (R2 = 0.9008), highlighting the importance of immediate air sample collection and accounting for ISS air dilution. Importantly, T-value predictions were conservative 70% of the time. Results also suggest that T-values can be normalized to octafluoropropane levels to adjust for ISS air dilution at first ingress. Finally, OGT and ingress sampling has allowed small leaks in vehicle fluid systems to be recognized and addressed.Romoser AA, Scully RR, Limero TF, De Vera V, Cheng PF, Hand JJ, James JT, Ryder VE. Predicting air quality at first ingress into vehicles visiting the International Space Station. Aerosp Med Hum Perform. 2017; 88(2):104-113.

  20. Corporate prediction models, ratios or regression analysis?

    NARCIS (Netherlands)

    Bijnen, E.J.; Wijn, M.F.C.M.

    1994-01-01

    The models developed in the literature with respect to the prediction of a company s failure are based on ratios. It has been shown before that these models should be rejected on theoretical grounds. Our study of industrial companies in the Netherlands shows that the ratios which are used in

  1. A review of air exchange rate models for air pollution exposure assessments.

    Science.gov (United States)

    Breen, Michael S; Schultz, Bradley D; Sohn, Michael D; Long, Thomas; Langstaff, John; Williams, Ronald; Isaacs, Kristin; Meng, Qing Yu; Stallings, Casson; Smith, Luther

    2014-11-01

    A critical aspect of air pollution exposure assessments is estimation of the air exchange rate (AER) for various buildings where people spend their time. The AER, which is the rate of exchange of indoor air with outdoor air, is an important determinant for entry of outdoor air pollutants and for removal of indoor-emitted air pollutants. This paper presents an overview and critical analysis of the scientific literature on empirical and physically based AER models for residential and commercial buildings; the models highlighted here are feasible for exposure assessments as extensive inputs are not required. Models are included for the three types of airflows that can occur across building envelopes: leakage, natural ventilation, and mechanical ventilation. Guidance is provided to select the preferable AER model based on available data, desired temporal resolution, types of airflows, and types of buildings included in the exposure assessment. For exposure assessments with some limited building leakage or AER measurements, strategies are described to reduce AER model uncertainty. This review will facilitate the selection of AER models in support of air pollution exposure assessments.

  2. Modeling near-road air quality using a computational fluid dynamics model, CFD-VIT-RIT.

    Science.gov (United States)

    Wang, Y Jason; Zhang, K Max

    2009-10-15

    It is well recognized that dilution is an important mechanism governing the near-road air pollutant concentrations. In this paper, we aim to advance our understanding of turbulent mixing mechanisms on and near roadways using computation fluid dynamics. Turbulent mixing mechanisms can be classified into three categories according to their origins: vehicle-induced turbulence (VIT), road-induced turbulence (RIT), and atmospheric boundary layer turbulence. RIT includes the turbulence generated by road embankment, road surface thermal effects, and roadside structures. Both VIT and RIT are affected by the roadway designs. We incorporate the detailed treatment of VIT and RIT into the CFD (namely CFD-VIT-RIT) and apply the model in simulating the spatial gradients of carbon monoxide near two major highways with different traffic mix and roadway configurations. The modeling results are compared to the field measurements and those from CALINE4 and CFD without considering VIT and RIT. We demonstrate that the incorporation of VIT and RIT considerably improves the modeling predictions, especially on vertical gradients and seasonal variations of carbon monoxide. Our study implies that roadway design can significantly influence the near-road air pollution. Thus we recommend that mitigating near-road air pollution through roadway designs be considered in the air quality and transportation management In addition, thanks to the rigorous representation of turbulent mixing mechanisms, CFD-VIT-RIT can become valuable tools in the roadway designs process.

  3. Evaluation of AirGIS: a GIS-based air pollution and human exposure modelling system

    DEFF Research Database (Denmark)

    Ketzel, Matthias; Berkowicz, Ruwim; Hvidberg, Martin

    2011-01-01

    This study describes in brief the latest extensions of the Danish Geographic Information System (GIS)-based air pollution and human exposure modelling system (AirGIS), which has been developed in Denmark since 2001 and gives results of an evaluation with measured air pollution data. The system...... shows, in general, a good performance for both long-term averages (annual and monthly averages), short-term averages (hourly and daily) as well as when reproducing spatial variation in air pollution concentrations. Some shortcomings and future perspectives of the system are discussed too....

  4. Modelling Chemical Reasoning to Predict Reactions

    CERN Document Server

    Segler, Marwin H S

    2016-01-01

    The ability to reason beyond established knowledge allows Organic Chemists to solve synthetic problems and to invent novel transformations. Here, we propose a model which mimics chemical reasoning and formalises reaction prediction as finding missing links in a knowledge graph. We have constructed a knowledge graph containing 14.4 million molecules and 8.2 million binary reactions, which represents the bulk of all chemical reactions ever published in the scientific literature. Our model outperforms a rule-based expert system in the reaction prediction task for 180,000 randomly selected binary reactions. We show that our data-driven model generalises even beyond known reaction types, and is thus capable of effectively (re-) discovering novel transformations (even including transition-metal catalysed reactions). Our model enables computers to infer hypotheses about reactivity and reactions by only considering the intrinsic local structure of the graph, and because each single reaction prediction is typically ac...

  5. A novel air quality analysis and prediction system for São Paulo, Brazil to support decision-making

    Science.gov (United States)

    Hoshyaripour, Gholam Ali; Brasseur, Guy; Andrade, Maria Fatima; Gavidia-Calderón, Mario; Bouarar, Idir

    2016-04-01

    The extensive economic development and urbanization in southeastern Brazil (SEB) in recent decades have notably degraded the air quality with adverse impacts on human health. Since the Metropolitan Area of São Paulo (MASP) accommodates the majority of the economic growth in SEB, it overwhelmingly suffers from the air pollution. Consequently, there is a strong demand for developing ever-better assessment mechanisms to monitor the air quality and to assist the decision makers to mitigate the air pollution in MASP. Here we present the results of an air quality modeling system designed for SEB with focuses on MASP. The Weather Research and Forecast model with Chemistry (WRF-Chem) is used considering the anthropogenic, biomass-burning and biogenic emissions within a 1000×1500 km domain with resolution of 10 km. FINN and MEGAN are used for the biomass-burning and biogenic emissions, respectively. For the anthropogenic emissions we use a local bottom-up inventory for the transport sector and the HTAPv2 global inventory for all other sectors. The bottom-up inventory accounts for the traffic patterns, vehicle types and their emission factors in the area and thus could be used to evaluate the effect of changes in these parameters on air quality in MASP. The model outputs are compered to the satellite and ground-based observations for O3 and NOx. The results show that using the bottom-up or top-down inventories individually can result in a huge deviation between the predictions and observations. On the other hand, combining the inventories significantly enhances the forecast accuracy. It also provides a powerful tool to quantify the effects of traffic and vehicle emission policies on air quality in MASP.

  6. INTERCOMPARISON OF ALTERNATIVE VEGETATION DATABASES FOR REGIONAL AIR QUALITY MODELING

    Science.gov (United States)

    Vegetation cover data are used to characterize several regional air quality modeling processes, including the calculation of heat, moisture, and momentum fluxes with the Mesoscale Meteorological Model (MM5) and the estimate of biogenic volatile organic compound and nitric oxide...

  7. Regional air quality modeling: North American and European perspectives

    NARCIS (Netherlands)

    Steyn, D.; Builtjes, P.; Schaap, M.; Yarwood, G.

    2013-01-01

    An overview of regional-scale quality modeling practices and perspectives in North America and Europe, highlighting the differences and commonalities in how regional-scale air quality modeling systems are being used and evaluated across both continents

  8. Evaluation of CASP8 model quality predictions

    KAUST Repository

    Cozzetto, Domenico

    2009-01-01

    The model quality assessment problem consists in the a priori estimation of the overall and per-residue accuracy of protein structure predictions. Over the past years, a number of methods have been developed to address this issue and CASP established a prediction category to evaluate their performance in 2006. In 2008 the experiment was repeated and its results are reported here. Participants were invited to infer the correctness of the protein models submitted by the registered automatic servers. Estimates could apply to both whole models and individual amino acids. Groups involved in the tertiary structure prediction categories were also asked to assign local error estimates to each predicted residue in their own models and their results are also discussed here. The correlation between the predicted and observed correctness measures was the basis of the assessment of the results. We observe that consensus-based methods still perform significantly better than those accepting single models, similarly to what was concluded in the previous edition of the experiment. © 2009 WILEY-LISS, INC.

  9. Fired Models of Air-gun Source and Its Application

    Institute of Scientific and Technical Information of China (English)

    Luo Guichun; Ge Hongkui; Wang Baoshan; Hu Ping; Mu Hongwang; Chen Yong

    2008-01-01

    Air-gun is an important active seismic source. With the development of the theory about air-gun array, the technique for air-gun array design becomes mature and is widely used in petroleum exploration and geophysics. In order to adapt it to different research domains,different combination and fired models are needed. At the present time, there are two firedmodels of air-gun source, namely, reinforced initial pulse and reinforced first bubble pulse.The fired time, space between single guns, frequency and resolution of the two models are different. This comparison can supply the basis for its extensive application.

  10. Genetic models of homosexuality: generating testable predictions

    OpenAIRE

    Gavrilets, Sergey; Rice, William R.

    2006-01-01

    Homosexuality is a common occurrence in humans and other species, yet its genetic and evolutionary basis is poorly understood. Here, we formulate and study a series of simple mathematical models for the purpose of predicting empirical patterns that can be used to determine the form of selection that leads to polymorphism of genes influencing homosexuality. Specifically, we develop theory to make contrasting predictions about the genetic characteristics of genes influencing homosexuality inclu...

  11. Wind farm production prediction - The Zephyr model

    Energy Technology Data Exchange (ETDEWEB)

    Landberg, L. [Risoe National Lab., Wind Energy Dept., Roskilde (Denmark); Giebel, G. [Risoe National Lab., Wind Energy Dept., Roskilde (Denmark); Madsen, H. [IMM (DTU), Kgs. Lyngby (Denmark); Nielsen, T.S. [IMM (DTU), Kgs. Lyngby (Denmark); Joergensen, J.U. [Danish Meteorologisk Inst., Copenhagen (Denmark); Lauersen, L. [Danish Meteorologisk Inst., Copenhagen (Denmark); Toefting, J. [Elsam, Fredericia (DK); Christensen, H.S. [Eltra, Fredericia (Denmark); Bjerge, C. [SEAS, Haslev (Denmark)

    2002-06-01

    This report describes a project - funded by the Danish Ministry of Energy and the Environment - which developed a next generation prediction system called Zephyr. The Zephyr system is a merging between two state-of-the-art prediction systems: Prediktor of Risoe National Laboratory and WPPT of IMM at the Danish Technical University. The numerical weather predictions were generated by DMI's HIRLAM model. Due to technical difficulties programming the system, only the computational core and a very simple version of the originally very complex system were developed. The project partners were: Risoe, DMU, DMI, Elsam, Eltra, Elkraft System, SEAS and E2. (au)

  12. Prediction of gas/particle partition quotients of Polybrominated Diphenyl Ethers (PBDEs) in north temperate zone air: an empirical approach.

    Science.gov (United States)

    Li, Yi-Fan; Jia, Hong-Liang

    2014-10-01

    Gas/particle (G/P) partitioning process is an important factor governing the transport and fate of chemicals in the atmosphere. Based on a large dataset of more than 700 pairs of air samples in gaseous and particulate phases with a wide ambient temperature range of 60°C from -22°C to +38°C obtained from our Chinese POPs Soil and Air Monitoring Program, Phase 2 (China-SAMP-II), we investigated G/P partitioning behavior of polybrominated diphenyl ether (PBDEs) in Chinese air. We derived for the first time empirical equations to predict the values of slopes and intercepts for both subcooled-liquid-vapor-pressure (PL)-based and octanol-air-partition-coefficient (KOA)-based models as functions of temperature, and thus predicted partition quotient (KP) without assuming an equilibrium status and free of artifacts. These equations have been successfully applied to predict the values of KP for PBDEs in air of China and other countries in the north temperate zone (NTZ) and also at an Arctic site in East Greenland, and our results matched the monitoring data well at background, rural, urban, and suburban sites, but not at e-waste sites due to the unpredictable PBDE emissions at these sites. Our equations predicted that the ranges of slopes were 0.02-0.82 for logKP-logKOA plots and -0.82 to -0.02 for logKP-logPL plots at temperatures ranged of 60°C from -22°C to +38°C. Our new KOA-based equation was compared with the Harner-Bidleman equation that was derived at a condition of equilibrium, and the results indicated that our new equation has a better performance than the Harner-Bidleman equation in describing G/P partitioning behavior of PBDEs in air as functions of logKOA. We also found for the first time that the G/P partitioning of PBDE congeners would become saturated in the particulate phase respect to the gas phase if the ambient temperature is low enough. A criterion to classify the equilibrium and nonequilibrium status for PBDEs was also established using log

  13. Regression analysis in modeling of air surface temperature and factors affecting its value in Peninsular Malaysia

    Science.gov (United States)

    Rajab, Jasim Mohammed; Jafri, Mohd. Zubir Mat; Lim, Hwee San; Abdullah, Khiruddin

    2012-10-01

    This study encompasses air surface temperature (AST) modeling in the lower atmosphere. Data of four atmosphere pollutant gases (CO, O3, CH4, and H2O) dataset, retrieved from the National Aeronautics and Space Administration Atmospheric Infrared Sounder (AIRS), from 2003 to 2008 was employed to develop a model to predict AST value in the Malaysian peninsula using the multiple regression method. For the entire period, the pollutants were highly correlated (R=0.821) with predicted AST. Comparisons among five stations in 2009 showed close agreement between the predicted AST and the observed AST from AIRS, especially in the southwest monsoon (SWM) season, within 1.3 K, and for in situ data, within 1 to 2 K. The validation results of AST with AST from AIRS showed high correlation coefficient (R=0.845 to 0.918), indicating the model's efficiency and accuracy. Statistical analysis in terms of β showed that H2O (0.565 to 1.746) tended to contribute significantly to high AST values during the northeast monsoon season. Generally, these results clearly indicate the advantage of using the satellite AIRS data and a correlation analysis study to investigate the impact of atmospheric greenhouse gases on AST over the Malaysian peninsula. A model was developed that is capable of retrieving the Malaysian peninsulan AST in all weather conditions, with total uncertainties ranging between 1 and 2 K.

  14. Developing a methodology to predict PM10 concentrations in urban areas using generalized linear models.

    Science.gov (United States)

    Garcia, J M; Teodoro, F; Cerdeira, R; Coelho, L M R; Kumar, Prashant; Carvalho, M G

    2016-09-01

    A methodology to predict PM10 concentrations in urban outdoor environments is developed based on the generalized linear models (GLMs). The methodology is based on the relationship developed between atmospheric concentrations of air pollutants (i.e. CO, NO2, NOx, VOCs, SO2) and meteorological variables (i.e. ambient temperature, relative humidity (RH) and wind speed) for a city (Barreiro) of Portugal. The model uses air pollution and meteorological data from the Portuguese monitoring air quality station networks. The developed GLM considers PM10 concentrations as a dependent variable, and both the gaseous pollutants and meteorological variables as explanatory independent variables. A logarithmic link function was considered with a Poisson probability distribution. Particular attention was given to cases with air temperatures both below and above 25°C. The best performance for modelled results against the measured data was achieved for the model with values of air temperature above 25°C compared with the model considering all ranges of air temperatures and with the model considering only temperature below 25°C. The model was also tested with similar data from another Portuguese city, Oporto, and results found to behave similarly. It is concluded that this model and the methodology could be adopted for other cities to predict PM10 concentrations when these data are not available by measurements from air quality monitoring stations or other acquisition means.

  15. The contribution of activity-based transport models to air quality modelling: a validation of the ALBATROSS-AURORA model chain.

    Science.gov (United States)

    Beckx, Carolien; Int Panis, Luc; Van De Vel, Karen; Arentze, Theo; Lefebvre, Wouter; Janssens, Davy; Wets, Geert

    2009-06-01

    The potential advantages of using activity-based transport models for air quality purposes have been recognized for a long time but models that have been developed along these lines are still scarce. In this paper we demonstrate that an activity-based model provides useful information for predicting hourly ambient pollutant concentrations. For this purpose, the traffic emissions obtained in a previous application of the activity-based model ALBATROSS were used as input for the AURORA air quality model to predict hourly concentrations of NO(2), PM(10) and O(3) in the Netherlands. Predicted concentrations were compared with measured concentrations at 37 monitoring stations from the Dutch air quality monitoring network. A statistical analysis was performed to evaluate model performance for different pollutants, locations and time periods. Results confirm that modelled and measured concentrations present the same geographical and temporal variation. The overall index of agreement for the prediction of hourly pollutant concentrations amounted to 0.64, 0.75 and 0.57 for NO(2), O(3) and PM(10) respectively. Concerning the predictions for NO2, a major traffic pollutant, a more thorough analysis revealed that the ALBATROSS-AURORA model chain yielded better predictions near traffic locations than near background stations. Further, the model performed better in urban areas, on weekdays and during the day, consistent with the emission results obtained in a previous study. The results in this paper demonstrate the ability of the activity-based model to predict the contribution of traffic sources to local air pollution with sufficient accuracy and confirms the usefulness of activity-based transport models for air quality purposes. The fact that the ALBATROSS-AURORA chain provides reliable pollutant concentrations on hourly basis for the whole Netherlands instead of using only daily averages near traffic stations is a plus for future exposure studies aiming at more realistic

  16. Modelling of air pollution on a military airfield

    Science.gov (United States)

    Brzozowski, Krzysztof; Kotlarz, Wojciech

    The paper presents a numerical study of exhaust emission and pollutant dispersion of carbon monoxide on a military airfield. Investigations have been carried out for typical conditions of aircraft usage in the Polish Air Force Academy in Dęblin. Two different types of aircraft have been taken into account. One of them is an MI-2 helicopter, the second is a TS-11 plane. Both are used in military pilot education in Poland. Exhaust emission of CO from those aircrafts has been obtained in an experiment carried out on an engine test stand. CO concentrations have been calculated for different meteorological conditions (averaged from 5 years observations) and selected conditions of aircraft use. The finite volume method has been used to discretise the equation describing the process of pollutant dispersion. In addition, the two-cycle decomposition method has been employed to solve the set of ordinary differential equations of the first order obtained after discretisation of the advection-diffusion equation. A meteorological pre-processor, based on relationships resulting from the Monin-Obukhov theory, is used to define eddy diffusivity and the profile of air speed in the lower layer of the atmosphere. In the paper, the computer model and calculated average concentration of CO in the Dęblin airfield during typical flights are presented. The goal of the computational analysis is to predict CO pollution level in the workplace of aircraft service personnel.

  17. Predictive model for segmented poly(urea

    Directory of Open Access Journals (Sweden)

    Frankl P.

    2012-08-01

    Full Text Available Segmented poly(urea has been shown to be of significant benefit in protecting vehicles from blast and impact and there have been several experimental studies to determine the mechanisms by which this protective function might occur. One suggested route is by mechanical activation of the glass transition. In order to enable design of protective structures using this material a constitutive model and equation of state are needed for numerical simulation hydrocodes. Determination of such a predictive model may also help elucidate the beneficial mechanisms that occur in polyurea during high rate loading. The tool deployed to do this has been Group Interaction Modelling (GIM – a mean field technique that has been shown to predict the mechanical and physical properties of polymers from their structure alone. The structure of polyurea has been used to characterise the parameters in the GIM scheme without recourse to experimental data and the equation of state and constitutive model predicts response over a wide range of temperatures and strain rates. The shock Hugoniot has been predicted and validated against existing data. Mechanical response in tensile tests has also been predicted and validated.

  18. PEEX Modelling Platform for Seamless Environmental Prediction

    Science.gov (United States)

    Baklanov, Alexander; Mahura, Alexander; Arnold, Stephen; Makkonen, Risto; Petäjä, Tuukka; Kerminen, Veli-Matti; Lappalainen, Hanna K.; Ezau, Igor; Nuterman, Roman; Zhang, Wen; Penenko, Alexey; Gordov, Evgeny; Zilitinkevich, Sergej; Kulmala, Markku

    2017-04-01

    The Pan-Eurasian EXperiment (PEEX) is a multidisciplinary, multi-scale research programme stared in 2012 and aimed at resolving the major uncertainties in Earth System Science and global sustainability issues concerning the Arctic and boreal Northern Eurasian regions and in China. Such challenges include climate change, air quality, biodiversity loss, chemicalization, food supply, and the use of natural resources by mining, industry, energy production and transport. The research infrastructure introduces the current state of the art modeling platform and observation systems in the Pan-Eurasian region and presents the future baselines for the coherent and coordinated research infrastructures in the PEEX domain. The PEEX modeling Platform is characterized by a complex seamless integrated Earth System Modeling (ESM) approach, in combination with specific models of different processes and elements of the system, acting on different temporal and spatial scales. The ensemble approach is taken to the integration of modeling results from different models, participants and countries. PEEX utilizes the full potential of a hierarchy of models: scenario analysis, inverse modeling, and modeling based on measurement needs and processes. The models are validated and constrained by available in-situ and remote sensing data of various spatial and temporal scales using data assimilation and top-down modeling. The analyses of the anticipated large volumes of data produced by available models and sensors will be supported by a dedicated virtual research environment developed for these purposes.

  19. Eight Year Climatologies from Observational (AIRS) and Model (MERRA) Data

    Science.gov (United States)

    Hearty, Thomas; Savtchenko, Andrey; Won, Young-In; Theobalk, Mike; Vollmer, Bruce; Manning, Evan; Smith, Peter; Ostrenga, Dana; Leptoukh, Greg

    2010-01-01

    We examine climatologies derived from eight years of temperature, water vapor, cloud, and trace gas observations made by the Atmospheric Infrared Sounder (AIRS) instrument flying on the Aqua satellite and compare them to similar climatologies constructed with data from a global assimilation model, the Modern Era Retrospective-Analysis for Research and Applications (MERRA). We use the AIRS climatologies to examine anomalies and trends in the AIRS data record. Since sampling can be an issue for infrared satellites in low earth orbit, we also use the MERRA data to examine the AIRS sampling biases. By sampling the MERRA data at the AIRS space-time locations both with and without the AIRS quality control we estimate the sampling bias of the AIRS climatology and the atmospheric conditions where AIRS has a lower sampling rate. While the AIRS temperature and water vapor sampling biases are small at low latitudes, they can be more than a few degrees in temperature or 10 percent in water vapor at higher latitudes. The largest sampling biases are over desert. The AIRS and MERRA data are available from the Goddard Earth Sciences Data and Information Services Center (GES DISC). The AIRS climatologies we used are available for analysis with the GIOVANNI data exploration tool. (see, http://disc.gsfc.nasa.gov).

  20. PREDICTIVE CAPACITY OF ARCH FAMILY MODELS

    Directory of Open Access Journals (Sweden)

    Raphael Silveira Amaro

    2016-03-01

    Full Text Available In the last decades, a remarkable number of models, variants from the Autoregressive Conditional Heteroscedastic family, have been developed and empirically tested, making extremely complex the process of choosing a particular model. This research aim to compare the predictive capacity, using the Model Confidence Set procedure, than five conditional heteroskedasticity models, considering eight different statistical probability distributions. The financial series which were used refers to the log-return series of the Bovespa index and the Dow Jones Industrial Index in the period between 27 October 2008 and 30 December 2014. The empirical evidences showed that, in general, competing models have a great homogeneity to make predictions, either for a stock market of a developed country or for a stock market of a developing country. An equivalent result can be inferred for the statistical probability distributions that were used.

  1. Predictive QSAR modeling of phosphodiesterase 4 inhibitors.

    Science.gov (United States)

    Kovalishyn, Vasyl; Tanchuk, Vsevolod; Charochkina, Larisa; Semenuta, Ivan; Prokopenko, Volodymyr

    2012-02-01

    A series of diverse organic compounds, phosphodiesterase type 4 (PDE-4) inhibitors, have been modeled using a QSAR-based approach. 48 QSAR models were compared by following the same procedure with different combinations of descriptors and machine learning methods. QSAR methodologies used random forests and associative neural networks. The predictive ability of the models was tested through leave-one-out cross-validation, giving a Q² = 0.66-0.78 for regression models and total accuracies Ac=0.85-0.91 for classification models. Predictions for the external evaluation sets obtained accuracies in the range of 0.82-0.88 (for active/inactive classifications) and Q² = 0.62-0.76 for regressions. The method showed itself to be a potential tool for estimation of IC₅₀ of new drug-like candidates at early stages of drug development. Copyright © 2011 Elsevier Inc. All rights reserved.

  2. Developing a fuzzy model for Tehran's air quality

    Directory of Open Access Journals (Sweden)

    Nafiseh Tokhmehchi

    2015-01-01

    Full Text Available This research aims to offer a fuzzy approach for calculating Tehran's air pollution index. The method is based on fuzzy analysis model, and uses the information about air quality index (AQI, included on the website of Tehran’s Air Quality Monitoring And Supervision Bureau. The contrived fuzzy logic is considered a powerful tool for demonstrating the information associated with uncertainty. In the end, several graphs visualize this inferential system in various levels of pollution.

  3. The effect of improved nowcasting of precipitation on air quality modeling

    NARCIS (Netherlands)

    Geertsema, G.T.; Wichers Schreur, B.G.J.

    2009-01-01

    The predictive potential of air quality models and thus their value in emergency management and public health support are critically dependent on the quality of their meteorological inputs. The atmospheric flow is the primary cause of the dispersion of airborne substances. The scavenging of

  4. Air gap membrane distillation. 2. Model validation and hollow fibre module performance analysis

    NARCIS (Netherlands)

    Guijt, C.M.; Meindersma, G.W.; Reith, T.; de Haan, A.B.

    2005-01-01

    In this paper the experimental results of counter current flow air gap membrane distillation experiments are presented and compared with predictive model calculations. Measurements were carried out with a cylindrical test module containing a single hollow fibre membrane in the centre and a

  5. RAQ-A Random Forest Approach for Predicting Air Quality in Urban Sensing Systems.

    Science.gov (United States)

    Yu, Ruiyun; Yang, Yu; Yang, Leyou; Han, Guangjie; Move, Oguti Ann

    2016-01-09

    Air quality information such as the concentration of PM2.5 is of great significance for human health and city management. It affects the way of traveling, urban planning, government policies and so on. However, in major cities there is typically only a limited number of air quality monitoring stations. In the meantime, air quality varies in the urban areas and there can be large differences, even between closely neighboring regions. In this paper, a random forest approach for predicting air quality (RAQ) is proposed for urban sensing systems. The data generated by urban sensing includes meteorology data, road information, real-time traffic status and point of interest (POI) distribution. The random forest algorithm is exploited for data training and prediction. The performance of RAQ is evaluated with real city data. Compared with three other algorithms, this approach achieves better prediction precision. Exciting results are observed from the experiments that the air quality can be inferred with amazingly high accuracy from the data which are obtained from urban sensing.

  6. RAQ–A Random Forest Approach for Predicting Air Quality in Urban Sensing Systems

    Directory of Open Access Journals (Sweden)

    Ruiyun Yu

    2016-01-01

    Full Text Available Air quality information such as the concentration of PM2.5 is of great significance for human health and city management. It affects the way of traveling, urban planning, government policies and so on. However, in major cities there is typically only a limited number of air quality monitoring stations. In the meantime, air quality varies in the urban areas and there can be large differences, even between closely neighboring regions. In this paper, a random forest approach for predicting air quality (RAQ is proposed for urban sensing systems. The data generated by urban sensing includes meteorology data, road information, real-time traffic status and point of interest (POI distribution. The random forest algorithm is exploited for data training and prediction. The performance of RAQ is evaluated with real city data. Compared with three other algorithms, this approach achieves better prediction precision. Exciting results are observed from the experiments that the air quality can be inferred with amazingly high accuracy from the data which are obtained from urban sensing.

  7. Sequential box models for indoor air quality: Application to airliner cabin air quality

    Science.gov (United States)

    Ryan, P. Barry; Spengler, John D.; Halfpenny, Paul F.

    In this paper we present the development and application of a model for indoor air quality. The model represents a departure from the standard box models typically used for indoor environments which has applicability in residences and office buildings. The model has been developed for a physical system consisting of sequential compartments which communicate only with adjacent compartments. Each compartment may contain various source and sink terms for a pollutant as well as leakage, and air transfer from adjacent compartments. The mathematical derivation affords rapid calculation of equilibrium concentrations in an essentially unlimited number of compartments. The model has been applied to air quality in the passenger cabin of three commercial aircraft. Simulations have been performed for environmental tobacco smoke (ETS) under two scenarios, CO 2 and water vapor. Additionally, concentrations in one aircraft have been simulated under conditions different from the standard configuration. Results of the simulations suggest the potential for elevated concentrations of ETS in smoking sections of non-air-recirculating aircraft and throughout the aircraft when air is recirculated. Concentrations of CO 2 and water vapor are consistent with expected results. We conclude that this model may be a useful tool in understanding indoor air quality in general and on aircraft in particular.

  8. Prediction of cold start hydrocarbon emissions of air cooled two wheeler spark ignition engines by simple fuzzy logic simulation

    Directory of Open Access Journals (Sweden)

    Samuel Raja Ayyanan

    2014-01-01

    Full Text Available The cold start hydrocarbon emission from the increasing population of two wheelers in countries like India is one of the research issues to be addressed. This work describes the prediction of cold start hydrocarbon emissions from air cooled spark ignition engines through fuzzy logic technique. Hydrocarbon emissions were experimentally measured from test engines of different cubic capacity, at different lubricating oil temperature and at different idling speeds with and without secondary air supply in exhaust. The experimental data were used as input for modeling average hydrocarbon emissions for 180 seconds counted from cold start and warm start of gasoline bike engines. In fuzzy logic simulation, member functions were assigned for input variables (cubic capacity and idling rpm and output variables (average hydrocarbon emission for first 180 seconds at cold start and warm start. The knowledge based rules were adopted from the analyzed experimental data and separate simulations were carried out for predicting hydrocarbon emissions from engines equipped with and without secondary air supply. The simulation yielded the average hydrocarbon emissions of air cooled gasoline engine for a set of given input data with accuracy over 90%.

  9. Comparative analysis of modified PMV models and SET models to predict human thermal sensation in naturally ventilated buildings

    DEFF Research Database (Denmark)

    Gao, Jie; Wang, Yi; Wargocki, Pawel

    2015-01-01

    In this paper, a comparative analysis was performed on the human thermal sensation estimated by modified predicted mean vote (PMV) models and modified standard effective temperature (SET) models in naturally ventilated buildings; the data were collected in field study. These prediction models were...... between the measured and predicted values using the modified PMV models exceeded 25%, while the difference between the measured thermal sensation and the predicted thermal sensation using modified SET models was approximately less than 25%. It is concluded that the modified SET models can predict human...... developed on the basis of the original PMV/SET models and consider the influence of occupants' expectations and human adaptive functions, including the extended PMV/SET models and the adaptive PMV/SET models. The results showed that when the indoor air velocity ranged from 0 to 0.2m/s and from 0.2 to 0.8m...

  10. Measurement error in epidemiologic studies of air pollution based on land-use regression models.

    Science.gov (United States)

    Basagaña, Xavier; Aguilera, Inmaculada; Rivera, Marcela; Agis, David; Foraster, Maria; Marrugat, Jaume; Elosua, Roberto; Künzli, Nino

    2013-10-15

    Land-use regression (LUR) models are increasingly used to estimate air pollution exposure in epidemiologic studies. These models use air pollution measurements taken at a small set of locations and modeling based on geographical covariates for which data are available at all study participant locations. The process of LUR model development commonly includes a variable selection procedure. When LUR model predictions are used as explanatory variables in a model for a health outcome, measurement error can lead to bias of the regression coefficients and to inflation of their variance. In previous studies dealing with spatial predictions of air pollution, bias was shown to be small while most of the effect of measurement error was on the variance. In this study, we show that in realistic cases where LUR models are applied to health data, bias in health-effect estimates can be substantial. This bias depends on the number of air pollution measurement sites, the number of available predictors for model selection, and the amount of explainable variability in the true exposure. These results should be taken into account when interpreting health effects from studies that used LUR models.

  11. Atmospheric Modelling for Air Quality Study over the complex Himalayas

    Science.gov (United States)

    Surapipith, Vanisa; Panday, Arnico; Mukherji, Aditi; Banmali Pradhan, Bidya; Blumer, Sandro

    2014-05-01

    An Atmospheric Modelling System has been set up at International Centre for Integrated Mountain Development (ICIMOD) for the assessment of Air Quality across the Himalaya mountain ranges. The Weather Research and Forecasting (WRF) model version 3.5 has been implemented over the regional domain, stretching across 4995 x 4455 km2 centred at Ichhyakamana , the ICIMOD newly setting-up mountain-peak station (1860 m) in central Nepal, and covering terrains from sea-level to the Everest (8848 m). Simulation is carried out for the winter time period, i.e. December 2012 to February 2013, when there was an intensive field campaign SusKat, where at least 7 super stations were collecting meteorology and chemical parameters on various sites. The very complex terrain requires a high horizontal resolution (1 × 1 km2), which is achieved by nesting the domain of interest, e.g. Kathmandu Valley, into 3 coarser ones (27, 9, 3 km resolution). Model validation is performed against the field data as well as satellite data, and the challenge of capturing the necessary atmospheric processes is discussed, before moving forward with the fully coupled chemistry module (WRF-Chem), having local and regional emission databases as input. The effort aims at finding a better understanding of the atmospheric processes and air quality impact on the mountain population, as well as the impact of the long-range transport, particularly of Black Carbon aerosol deposition, to the radiative budget over the Himalayan glaciers. The higher rate of snowcap melting, and shrinkage of permafrost as noticed by glaciologists is a concern. Better prediction will supply crucial information to form the proper mitigation and adaptation strategies for saving people lives across the Himalayas in the changing climate.

  12. Modelling the predictive performance of credit scoring

    Directory of Open Access Journals (Sweden)

    Shi-Wei Shen

    2013-02-01

    Full Text Available Orientation: The article discussed the importance of rigour in credit risk assessment.Research purpose: The purpose of this empirical paper was to examine the predictive performance of credit scoring systems in Taiwan.Motivation for the study: Corporate lending remains a major business line for financial institutions. However, in light of the recent global financial crises, it has become extremely important for financial institutions to implement rigorous means of assessing clients seeking access to credit facilities.Research design, approach and method: Using a data sample of 10 349 observations drawn between 1992 and 2010, logistic regression models were utilised to examine the predictive performance of credit scoring systems.Main findings: A test of Goodness of fit demonstrated that credit scoring models that incorporated the Taiwan Corporate Credit Risk Index (TCRI, micro- and also macroeconomic variables possessed greater predictive power. This suggests that macroeconomic variables do have explanatory power for default credit risk.Practical/managerial implications: The originality in the study was that three models were developed to predict corporate firms’ defaults based on different microeconomic and macroeconomic factors such as the TCRI, asset growth rates, stock index and gross domestic product.Contribution/value-add: The study utilises different goodness of fits and receiver operator characteristics during the examination of the robustness of the predictive power of these factors.

  13. Calibrated predictions for multivariate competing risks models.

    Science.gov (United States)

    Gorfine, Malka; Hsu, Li; Zucker, David M; Parmigiani, Giovanni

    2014-04-01

    Prediction models for time-to-event data play a prominent role in assessing the individual risk of a disease, such as cancer. Accurate disease prediction models provide an efficient tool for identifying individuals at high risk, and provide the groundwork for estimating the population burden and cost of disease and for developing patient care guidelines. We focus on risk prediction of a disease in which family history is an important risk factor that reflects inherited genetic susceptibility, shared environment, and common behavior patterns. In this work family history is accommodated using frailty models, with the main novel feature being allowing for competing risks, such as other diseases or mortality. We show through a simulation study that naively treating competing risks as independent right censoring events results in non-calibrated predictions, with the expected number of events overestimated. Discrimination performance is not affected by ignoring competing risks. Our proposed prediction methodologies correctly account for competing events, are very well calibrated, and easy to implement.

  14. Improving Air-Conditioner and Heat Pump Modeling (Presentation)

    Energy Technology Data Exchange (ETDEWEB)

    Winkler, J.

    2012-03-01

    A new approach to modeling residential air conditioners and heat pumps allows users to model systems by specifying only the more readily-available SEER/EER/HSPF-type metrics. Manufacturer data was used to generate full sets of model inputs for over 450 heat pumps and air conditioners. A sensitivity analysis identified which inputs can be safely defaulted 'behind-the-scenes' without negatively impacting the reliability of energy simulations.

  15. Improving Air-Conditioner and Heat Pump Modeling

    Energy Technology Data Exchange (ETDEWEB)

    Winkler, Jon [National Renewable Energy Lab. (NREL), Golden, CO (United States)

    2012-03-02

    This presentation describes a new approach to modeling residential air conditioners and heat pumps, which allows users to model systems by specifying only the more readily-available SEER/EER/HSPF-type metrics. Manufacturer data was used to generate full sets of model inputs for over 450 heat pumps and air conditioners. A sensitivity analysis identified which inputs can be safely defaulted “behind-the-scenes” without negatively impacting the reliability of energy simulations.

  16. Physical and JIT Model Based Hybrid Modeling Approach for Building Thermal Load Prediction

    Science.gov (United States)

    Iino, Yutaka; Murai, Masahiko; Murayama, Dai; Motoyama, Ichiro

    Energy conservation in building fields is one of the key issues in environmental point of view as well as that of industrial, transportation and residential fields. The half of the total energy consumption in a building is occupied by HVAC (Heating, Ventilating and Air Conditioning) systems. In order to realize energy conservation of HVAC system, a thermal load prediction model for building is required. This paper propose a hybrid modeling approach with physical and Just-in-Time (JIT) model for building thermal load prediction. The proposed method has features and benefits such as, (1) it is applicable to the case in which past operation data for load prediction model learning is poor, (2) it has a self checking function, which always supervises if the data driven load prediction and the physical based one are consistent or not, so it can find if something is wrong in load prediction procedure, (3) it has ability to adjust load prediction in real-time against sudden change of model parameters and environmental conditions. The proposed method is evaluated with real operation data of an existing building, and the improvement of load prediction performance is illustrated.

  17. ARAMIS a regional air quality model for air pollution management: evaluation and validation

    Energy Technology Data Exchange (ETDEWEB)

    Solar, M. R.; Gamez, P.; Olid, M.

    2015-07-01

    The aim of this research was to better understand the dynamics of air pollutants and to forecast the air quality over regional areas in order to develop emission abatement strategies for air pollution and adverse health effects. To accomplish this objective, we developed and applied a high resolution Eulerian system named ARAMIS (A Regional Air Quality Modelling Integrated System) over the north-east of Spain (Catalonia), where several pollutants exceed threshold values for the protection of human health. The results indicate that the model reproduced reasonably well observed concentrations, as statistical values fell within Environmental Protection Agency (EPA) recommendations and European (EU) regulations. Nevertheless, some hourly O{sub 3} exceedances in summer and hourly peaks of NO{sub 2} in winter were underestimated. Concerning PM10 concentrations less accurate model levels were obtained with a moderate trend towards underestimation during the day. (Author)

  18. ARAMIS a regional air quality model for air pollution management: evaluation and validation

    Energy Technology Data Exchange (ETDEWEB)

    Soler, M.R.; Gamez, P.; Olid, M.

    2015-07-01

    The aim of this research was to better understand the dynamics of air pollutants and to forecast the air quality over regional areas in order to develop emission abatement strategies for air pollution and adverse health effects. To accomplish this objective, we developed and applied a high resolution Eulerian system named ARAMIS (A Regional Air Quality Modelling Integrated System) over the north-east of Spain (Catalonia), where several pollutants exceed threshold values for the protection of human health. The results indicate that the model reproduced reasonably well observed concentrations, as statistical values fell within Environmental Protection Agency (EPA) recommendations and European (EU) regulations. Nevertheless, some hourly O3 exceedances in summer and hourly peaks of NO2 in winter were underestimated. Concerning PM10 concentrations less accurate model levels were obtained with a moderate trend towards underestimation during the day. (Author)

  19. Modelling language evolution: Examples and predictions.

    Science.gov (United States)

    Gong, Tao; Shuai, Lan; Zhang, Menghan

    2014-06-01

    We survey recent computer modelling research of language evolution, focusing on a rule-based model simulating the lexicon-syntax coevolution and an equation-based model quantifying the language competition dynamics. We discuss four predictions of these models: (a) correlation between domain-general abilities (e.g. sequential learning) and language-specific mechanisms (e.g. word order processing); (b) coevolution of language and relevant competences (e.g. joint attention); (c) effects of cultural transmission and social structure on linguistic understandability; and (d) commonalities between linguistic, biological, and physical phenomena. All these contribute significantly to our understanding of the evolutions of language structures, individual learning mechanisms, and relevant biological and socio-cultural factors. We conclude the survey by highlighting three future directions of modelling studies of language evolution: (a) adopting experimental approaches for model evaluation; (b) consolidating empirical foundations of models; and (c) multi-disciplinary collaboration among modelling, linguistics, and other relevant disciplines.

  20. Modelling language evolution: Examples and predictions

    Science.gov (United States)

    Gong, Tao; Shuai, Lan; Zhang, Menghan

    2014-06-01

    We survey recent computer modelling research of language evolution, focusing on a rule-based model simulating the lexicon-syntax coevolution and an equation-based model quantifying the language competition dynamics. We discuss four predictions of these models: (a) correlation between domain-general abilities (e.g. sequential learning) and language-specific mechanisms (e.g. word order processing); (b) coevolution of language and relevant competences (e.g. joint attention); (c) effects of cultural transmission and social structure on linguistic understandability; and (d) commonalities between linguistic, biological, and physical phenomena. All these contribute significantly to our understanding of the evolutions of language structures, individual learning mechanisms, and relevant biological and socio-cultural factors. We conclude the survey by highlighting three future directions of modelling studies of language evolution: (a) adopting experimental approaches for model evaluation; (b) consolidating empirical foundations of models; and (c) multi-disciplinary collaboration among modelling, linguistics, and other relevant disciplines.

  1. 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

  2. Global Solar Dynamo Models: Simulations and Predictions

    Indian Academy of Sciences (India)

    Mausumi Dikpati; Peter A. Gilman

    2008-03-01

    Flux-transport type solar dynamos have achieved considerable success in correctly simulating many solar cycle features, and are now being used for prediction of solar cycle timing and amplitude.We first define flux-transport dynamos and demonstrate how they work. The essential added ingredient in this class of models is meridional circulation, which governs the dynamo period and also plays a crucial role in determining the Sun’s memory about its past magnetic fields.We show that flux-transport dynamo models can explain many key features of solar cycles. Then we show that a predictive tool can be built from this class of dynamo that can be used to predict mean solar cycle features by assimilating magnetic field data from previous cycles.

  3. A new multimodel ensemble method using nonlinear genetic algorithm: An application to boreal winter surface air temperature and precipitation prediction

    Science.gov (United States)

    Ahn, Joong-Bae; Lee, Joonlee

    2016-08-01

    A new multimodel ensemble (MME) method that uses a genetic algorithm (GA) is developed and applied to the prediction of winter surface air temperature (SAT) and precipitation. The GA based on the biological process of natural evolution is a nonlinear method which solves nonlinear optimization problems. Hindcast data of winter SAT and precipitation from the six coupled general circulation models participating in the seasonal MME prediction system of the Asia-Pacific Economic Conference Climate Center are used. Three MME methods using GA (MME/GAs) are examined in comparison with a simple composite MME strategy (MS0): MS1 which applies GA to single-model ensembles (SMEs), MS2 which applies GA to each ensemble member and then performs a simple composite method for MME, and MS3 which applies GA to both MME and SME. MS3 shows the highest predictability compared to MS0, MS1, and MS2 for both winter SAT and precipitation. These results indicate that biases of ensemble members of each model and model ensemble are more reduced with MS3 than with other MME/GAs and MS0. The predictability of the MME/GAs shows a greater improvement than that of MS0, particularly in higher-latitude land areas. The reason for the more improved increase of predictability over the land area, particularly in MS3, seems to be the fact that GA is more efficient in finding an optimum solution in a complex region where nonlinear physical properties are evident.

  4. Model Predictive Control of Sewer Networks

    Science.gov (United States)

    Pedersen, Einar B.; Herbertsson, Hannes R.; Niemann, Henrik; Poulsen, Niels K.; Falk, Anne K. V.

    2017-01-01

    The developments in solutions for management of urban drainage are of vital importance, as the amount of sewer water from urban areas continues to increase due to the increase of the world’s population and the change in the climate conditions. How a sewer network is structured, monitored and controlled have thus become essential factors for effcient performance of waste water treatment plants. This paper examines methods for simplified modelling and controlling a sewer network. A practical approach to the problem is used by analysing simplified design model, which is based on the Barcelona benchmark model. Due to the inherent constraints the applied approach is based on Model Predictive Control.

  5. The use of MODIS data and aerosol products for air quality prediction

    Science.gov (United States)

    Hutchison, Keith D.; Smith, Solar; Faruqui, Shazia

    2004-09-01

    The Center for Space Research (CSR) is exploring new approaches to integrate data collected by the MODerate resolution Imaging Spectroradiometer (MODIS) sensor, flown on NASA's Earth Observing System (EOS) satellites, into a real-time prediction methodology to support operational air quality forecasts issued by the Monitoring Operations Division (MOD) of the Texas Commission on Environmental Quality (TCEQ). Air pollution is a widespread problem in the United States, with over 130 million individuals exposed to levels of air pollution that exceed one or more health-based standards. Texas air quality is under assault by a variety of anthropogenic sources associated with a rapidly growing population along with increases in emissions from the diesel engines that drive international trade between the US and Central America. The challenges of meeting air quality standards established by the Environmental Protection Agency are further impacted by the transport of pollution into Texas that originates from outside its borders and are cumulative with those generated by local sources. In an earlier study, CSR demonstrated the value of MODIS imagery and aerosol products for monitoring ozone-laden pollution that originated in the central US before migrating into Texas and causing TCEQ to issue a health alert for 150 counties. Now, data from this same event are re-analyzed in an attempt to predict air quality from MODIS aerosol optical thickness (AOT) observations. The results demonstrate a method to forecast air quality from remotely sensed satellite observations when the transient pollution can be isolated from local sources. These pollution sources can be separated using TCEQ's network of ground-based Continuous Air quality Monitoring (CAM) stations.

  6. DKIST Polarization Modeling and Performance Predictions

    Science.gov (United States)

    Harrington, David

    2016-05-01

    Calibrating the Mueller matrices of large aperture telescopes and associated coude instrumentation requires astronomical sources and several modeling assumptions to predict the behavior of the system polarization with field of view, altitude, azimuth and wavelength. The Daniel K Inouye Solar Telescope (DKIST) polarimetric instrumentation requires very high accuracy calibration of a complex coude path with an off-axis f/2 primary mirror, time dependent optical configurations and substantial field of view. Polarization predictions across a diversity of optical configurations, tracking scenarios, slit geometries and vendor coating formulations are critical to both construction and contined operations efforts. Recent daytime sky based polarization calibrations of the 4m AEOS telescope and HiVIS spectropolarimeter on Haleakala have provided system Mueller matrices over full telescope articulation for a 15-reflection coude system. AEOS and HiVIS are a DKIST analog with a many-fold coude optical feed and similar mirror coatings creating 100% polarization cross-talk with altitude, azimuth and wavelength. Polarization modeling predictions using Zemax have successfully matched the altitude-azimuth-wavelength dependence on HiVIS with the few percent amplitude limitations of several instrument artifacts. Polarization predictions for coude beam paths depend greatly on modeling the angle-of-incidence dependences in powered optics and the mirror coating formulations. A 6 month HiVIS daytime sky calibration plan has been analyzed for accuracy under a wide range of sky conditions and data analysis algorithms. Predictions of polarimetric performance for the DKIST first-light instrumentation suite have been created under a range of configurations. These new modeling tools and polarization predictions have substantial impact for the design, fabrication and calibration process in the presence of manufacturing issues, science use-case requirements and ultimate system calibration

  7. A coupled surface/subsurface flow model accounting for air entrapment and air pressure counterflow

    DEFF Research Database (Denmark)

    Delfs, Jens Olaf; Wang, Wenqing; Kalbacher, Thomas

    2013-01-01

    This work introduces the soil air system into integrated hydrology by simulating the flow processes and interactions of surface runoff, soil moisture and air in the shallow subsurface. The numerical model is formulated as a coupled system of partial differential equations for hydrostatic (diffusive...... algorithm, leakances operate as a valve for gas pressure in a liquid-covered porous medium facilitating the simulation of air out-break events through the land surface. General criteria are stated to guarantee stability in a sequential iterative coupling algorithm and, in addition, for leakances to control...... the mass exchange between compartments. A benchmark test, which is based on a classic experimental data set on infiltration excess (Horton) overland flow, identified a feedback mechanism between surface runoff and soil air pressures. Our study suggests that air compression in soils amplifies surface runoff...

  8. Modelling Chemical Reasoning to Predict Reactions

    OpenAIRE

    Segler, Marwin H. S.; Waller, Mark P.

    2016-01-01

    The ability to reason beyond established knowledge allows Organic Chemists to solve synthetic problems and to invent novel transformations. Here, we propose a model which mimics chemical reasoning and formalises reaction prediction as finding missing links in a knowledge graph. We have constructed a knowledge graph containing 14.4 million molecules and 8.2 million binary reactions, which represents the bulk of all chemical reactions ever published in the scientific literature. Our model outpe...

  9. Predictive Modeling of the CDRA 4BMS

    Science.gov (United States)

    Coker, Robert; Knox, James

    2016-01-01

    Fully predictive models of the Four Bed Molecular Sieve of the Carbon Dioxide Removal Assembly on the International Space Station are being developed. This virtual laboratory will be used to help reduce mass, power, and volume requirements for future missions. In this paper we describe current and planned modeling developments in the area of carbon dioxide removal to support future crewed Mars missions as well as the resolution of anomalies observed in the ISS CDRA.

  10. Raman Model Predicting Hardness of Covalent Crystals

    OpenAIRE

    Zhou, Xiang-Feng; Qian, Quang-Rui; Sun, Jian; Tian, Yongjun; Wang, Hui-Tian

    2009-01-01

    Based on the fact that both hardness and vibrational Raman spectrum depend on the intrinsic property of chemical bonds, we propose a new theoretical model for predicting hardness of a covalent crystal. The quantitative relationship between hardness and vibrational Raman frequencies deduced from the typical zincblende covalent crystals is validated to be also applicable for the complex multicomponent crystals. This model enables us to nondestructively and indirectly characterize the hardness o...

  11. Predictive Modelling of Mycotoxins in Cereals

    NARCIS (Netherlands)

    Fels, van der H.J.; Liu, C.

    2015-01-01

    In dit artikel worden de samenvattingen van de presentaties tijdens de 30e bijeenkomst van de Werkgroep Fusarium weergegeven. De onderwerpen zijn: Predictive Modelling of Mycotoxins in Cereals.; Microbial degradation of DON.; Exposure to green leaf volatiles primes wheat against FHB but boosts

  12. Unreachable Setpoints in Model Predictive Control

    DEFF Research Database (Denmark)

    Rawlings, James B.; Bonné, Dennis; Jørgensen, John Bagterp

    2008-01-01

    steady state is established for terminal constraint model predictive control (MPC). The region of attraction is the steerable set. Existing analysis methods for closed-loop properties of MPC are not applicable to this new formulation, and a new analysis method is developed. It is shown how to extend...

  13. Predictive Modelling of Mycotoxins in Cereals

    NARCIS (Netherlands)

    Fels, van der H.J.; Liu, C.

    2015-01-01

    In dit artikel worden de samenvattingen van de presentaties tijdens de 30e bijeenkomst van de Werkgroep Fusarium weergegeven. De onderwerpen zijn: Predictive Modelling of Mycotoxins in Cereals.; Microbial degradation of DON.; Exposure to green leaf volatiles primes wheat against FHB but boosts produ

  14. Prediction modelling for population conviction data

    NARCIS (Netherlands)

    Tollenaar, N.

    2017-01-01

    In this thesis, the possibilities of using prediction models for judicial penal case data are investigated. The development and refinement of a risk taxation scale based on these data is discussed. When false positives are weighted equally severe as false negatives, 70% can be classified correctly.

  15. A Predictive Model for MSSW Student Success

    Science.gov (United States)

    Napier, Angela Michele

    2011-01-01

    This study tested a hypothetical model for predicting both graduate GPA and graduation of University of Louisville Kent School of Social Work Master of Science in Social Work (MSSW) students entering the program during the 2001-2005 school years. The preexisting characteristics of demographics, academic preparedness and culture shock along with…

  16. Predictability of extreme values in geophysical models

    NARCIS (Netherlands)

    Sterk, A.E.; Holland, M.P.; Rabassa, P.; Broer, H.W.; Vitolo, R.

    2012-01-01

    Extreme value theory in deterministic systems is concerned with unlikely large (or small) values of an observable evaluated along evolutions of the system. In this paper we study the finite-time predictability of extreme values, such as convection, energy, and wind speeds, in three geophysical model

  17. A revised prediction model for natural conception

    NARCIS (Netherlands)

    Bensdorp, A.J.; Steeg, J.W. van der; Steures, P.; Habbema, J.D.; Hompes, P.G.; Bossuyt, P.M.; Veen, F. van der; Mol, B.W.; Eijkemans, M.J.; Kremer, J.A.M.; et al.,

    2017-01-01

    One of the aims in reproductive medicine is to differentiate between couples that have favourable chances of conceiving naturally and those that do not. Since the development of the prediction model of Hunault, characteristics of the subfertile population have changed. The objective of this analysis

  18. Distributed Model Predictive Control via Dual Decomposition

    DEFF Research Database (Denmark)

    Biegel, Benjamin; Stoustrup, Jakob; Andersen, Palle

    2014-01-01

    This chapter presents dual decomposition as a means to coordinate a number of subsystems coupled by state and input constraints. Each subsystem is equipped with a local model predictive controller while a centralized entity manages the subsystems via prices associated with the coupling constraints...

  19. Predictive Modelling of Mycotoxins in Cereals

    NARCIS (Netherlands)

    Fels, van der H.J.; Liu, C.

    2015-01-01

    In dit artikel worden de samenvattingen van de presentaties tijdens de 30e bijeenkomst van de Werkgroep Fusarium weergegeven. De onderwerpen zijn: Predictive Modelling of Mycotoxins in Cereals.; Microbial degradation of DON.; Exposure to green leaf volatiles primes wheat against FHB but boosts produ

  20. Leptogenesis in minimal predictive seesaw models

    CERN Document Server

    Björkeroth, Fredrik; Varzielas, Ivo de Medeiros; King, Stephen F

    2015-01-01

    We estimate the Baryon Asymmetry of the Universe (BAU) arising from leptogenesis within a class of minimal predictive seesaw models involving two right-handed neutrinos and simple Yukawa structures with one texture zero. The two right-handed neutrinos are dominantly responsible for the "atmospheric" and "solar" neutrino masses with Yukawa couplings to $(\

  1. Evaluation of the United States National Air Quality Forecast Capability experimental real-time predictions in 2010 using Air Quality System ozone and NO2 measurements

    Directory of Open Access Journals (Sweden)

    T. Chai

    2013-10-01

    Full Text Available The National Air Quality Forecast Capability (NAQFC project provides the US with operational and experimental real-time ozone predictions using two different versions of the three-dimensional Community Multi-scale Air Quality (CMAQ modeling system. Routine evaluation using near-real-time AIRNow ozone measurements through 2011 showed better performance of the operational ozone predictions. In this work, quality-controlled and -assured Air Quality System (AQS ozone and nitrogen dioxide (NO2 observations are used to evaluate the experimental predictions in 2010. It is found that both ozone and NO2 are overestimated over the contiguous US (CONUS, with annual biases of +5.6 and +5.1 ppbv, respectively. The annual root mean square errors (RMSEs are 15.4 ppbv for ozone and 13.4 ppbv for NO2. For both species the overpredictions are most pronounced in the summer. The locations of the AQS monitoring sites are also utilized to stratify comparisons by the degree of urbanization. Comparisons for six predefined US regions show the highest annual biases for ozone predictions in Southeast (+10.5 ppbv and for NO2 in the Lower Middle (+8.1 ppbv and Pacific Coast (+7.1 ppbv regions. The spatial distributions of the NO2 biases in August show distinctively high values in the Los Angeles, Houston, and New Orleans areas. In addition to the standard statistics metrics, daily maximum eight-hour ozone categorical statistics are calculated using the current US ambient air quality standard (75 ppbv and another lower threshold (70 ppbv. Using the 75 ppbv standard, the hit rate and proportion of correct over CONUS for the entire year are 0.64 and 0.96, respectively. Summertime biases show distinctive weekly patterns for ozone and NO2. Diurnal comparisons show that ozone overestimation is most severe in the morning, from 07:00 to 10:00 local time. For NO2, the morning predictions agree with the AQS observations reasonably well, but nighttime concentrations are overpredicted

  2. Modeling indoor air pollution of outdoor origin in homes of SAPALDIA subjects in Switzerland.

    Science.gov (United States)

    Meier, Reto; Schindler, Christian; Eeftens, Marloes; Aguilera, Inmaculada; Ducret-Stich, Regina E; Ineichen, Alex; Davey, Mark; Phuleria, Harish C; Probst-Hensch, Nicole; Tsai, Ming-Yi; Künzli, Nino

    2015-09-01

    Given the shrinking spatial contrasts in outdoor air pollution in Switzerland and the trends toward tightly insulated buildings, the Swiss Cohort Study on Air Pollution and Lung and Heart Diseases in Adults (SAPALDIA) needs to understand to what extent outdoor air pollution remains a determinant for residential indoor exposure. The objectives of this paper are to identify determining factors for indoor air pollution concentrations of particulate matter (PM), ultrafine particles in the size range from 15 to 300nm, black smoke measured as light absorbance of PM (PMabsorbance) and nitrogen dioxide (NO2) and to develop predictive indoor models for SAPALDIA. Multivariable regression models were developed based on indoor and outdoor measurements among homes of selected SAPALDIA participants in three urban (Basel, Geneva, Lugano) and one rural region (Wald ZH) in Switzerland, various home characteristics and reported indoor sources such as cooking. Outdoor levels of air pollutants were important predictors for indoor air pollutants, except for the coarse particle fraction. The fractions of outdoor concentrations infiltrating indoors were between 30% and 66%, the highest one was observed for PMabsorbance. A modifying effect of open windows was found for NO2 and the ultrafine particle number concentration. Cooking was associated with increased particle and NO2 levels. This study shows that outdoor air pollution remains an important determinant of residential indoor air pollution in Switzerland.

  3. Data assimilation for air quality models

    DEFF Research Database (Denmark)

    Silver, Jeremy David

    2014-01-01

    -dimensional optimal interpolation procedure (OI), an Ensemble Kalman Filter (EnKF), and a three-dimensional variational scheme (3D-var). The three assimilation procedures are described and tested. A multi-faceted approach is taken for the verification, using independent measurements from surface air-quality...

  4. Stratospheric age-of-air trends: Reanalysis v. climate models

    Science.gov (United States)

    Monge-Sanz, Beatriz; Dee, Dick; Hersbach, Hans; Simmons, Adrian; Parodi, Jose A.; Haenel, Florian; Stiller, Gabriele; Chipperfield, Martyn; Feng, Wuhu

    2017-04-01

    Knowing how the Brewer-Dobson circulation (BDC) has evolved in the recent past and will continue to evolve is crucial for atmospheric composition in the UTLS and stratosphere, as well as for feedbacks with climate. Most climate models have predicted an intensification of the stratospheric circulation with the increase in greenhouse gases concentrations, which translates into younger age-of-air (AoA) values modelled in the stratosphere. Nevertheless, balloon and satellite observations do not agree with the widespread modelled trend towards younger age-of-air for the recent past (Engel et al., 2009; Stiller et al., 2012; Haenel et al. 2015). Furthermore, a few recent studies with chemistry transport models (CTMs) driven by ERA-Interim reanalysis (Dee et al., 2011) have also shown agreement with the observed trends and not with those from climate models (e.g. Monge-Sanz et al., 2012; Diallo et al., 2012; Ploeger et al., 2015). To increase our confidence in climate-chemistry projections, the causes for the apparent disagreement in trends of age-of-air between observations and most climate models need to be identified. In this study we have carried out simulations with a CTM to assess the stratospheric circulation with the ERA-Interim dataset produced by the European Centre for Medium-Range Weather Forecasts (ECMWF), as well as with data produced from an equivalent climate system. AoA trends from our model results with ERA-Interim fields are in good agreement with the recent age-of-air studies based on observations and differ from the results we obtain with the corresponding climate data. We will show that biases in the mean AoA values are also different for these datasets compared to observations. In addition we have used recent experimental datasets from the ECMWF system to identify potential causes for the differences in AoA distribution and trends. The validation of our model results has been performed against the new revised AoA dataset based on MIPAS SF6

  5. Comparisons of Air Radiation Model with Shock Tube Measurements

    Science.gov (United States)

    Bose, Deepak; McCorkle, Evan; Bogdanoff, David W.; Allen, Gary A., Jr.

    2009-01-01

    This paper presents an assessment of the predictive capability of shock layer radiation model appropriate for NASA s Orion Crew Exploration Vehicle lunar return entry. A detailed set of spectrally resolved radiation intensity comparisons are made with recently conducted tests in the Electric Arc Shock Tube (EAST) facility at NASA Ames Research Center. The spectral range spanned from vacuum ultraviolet wavelength of 115 nm to infrared wavelength of 1400 nm. The analysis is done for 9.5-10.5 km/s shock passing through room temperature synthetic air at 0.2, 0.3 and 0.7 Torr. The comparisons between model and measurements show discrepancies in the level of background continuum radiation and intensities of atomic lines. Impurities in the EAST facility in the form of carbon bearing species are also modeled to estimate the level of contaminants and their impact on the comparisons. The discrepancies, although large is some cases, exhibit order and consistency. A set of tests and analyses improvements are proposed as forward work plan in order to confirm or reject various proposed reasons for the observed discrepancies.

  6. Dispersion modeling of selected PAHs in urban air: A new approach combining dispersion model with GIS and passive air sampling

    Science.gov (United States)

    Sáňka, Ondřej; Melymuk, Lisa; Čupr, Pavel; Dvorská, Alice; Klánová, Jana

    2014-10-01

    This study introduces a new combined air concentration measurement and modeling approach that we propose can be useful in medium and long term air quality assessment. A dispersion study was carried out for four high molecular weight polycyclic aromatic hydrocarbons (PAHs) in an urban area with industrial, traffic and domestic heating sources. A geographic information system (GIS) was used both for processing of input data as well as visualization of the modeling results. The outcomes of the dispersion model were compared to the results of passive air sampling (PAS). Despite discrepancies between measured and modeled concentrations, an approach combining the two techniques is promising for future air quality assessment. Differences between measured and modeled concentrations, in particular when measured values exceed the modeled concentrations, are indicative of undocumented, sporadic pollutant sources. Thus, these differences can also be useful for assessing and refining emission inventories.

  7. Specialized Language Models using Dialogue Predictions

    CERN Document Server

    Popovici, C; Popovici, Cosmin; Baggia, Paolo

    1996-01-01

    This paper analyses language modeling in spoken dialogue systems for accessing a database. The use of several language models obtained by exploiting dialogue predictions gives better results than the use of a single model for the whole dialogue interaction. For this reason several models have been created, each one for a specific system question, such as the request or the confirmation of a parameter. The use of dialogue-dependent language models increases the performance both at the recognition and at the understanding level, especially on answers to system requests. Moreover other methods to increase performance, like automatic clustering of vocabulary words or the use of better acoustic models during recognition, does not affect the improvements given by dialogue-dependent language models. The system used in our experiments is Dialogos, the Italian spoken dialogue system used for accessing railway timetable information over the telephone. The experiments were carried out on a large corpus of dialogues coll...

  8. Assessment and prediction of urban air pollution caused by motor transport exhaust gases using computer simulation methods

    Science.gov (United States)

    Boyarshinov, Michael G.; Vaismana, Yakov I.

    2016-10-01

    The following methods were used in order to identify the pollution fields of urban air caused by the motor transport exhaust gases: the mathematical model, which enables to consider the influence of the main factors that determine pollution fields formation in the complex spatial domain; the authoring software designed for computational modeling of the gas flow, generated by numerous mobile point sources; the results of computing experiments on pollutant spread analysis and evolution of their concentration fields. The computational model of exhaust gas distribution and dispersion in a spatial domain, which includes urban buildings, structures and main traffic arteries, takes into account a stochastic character of cars apparition on the borders of the examined territory and uses a Poisson process. The model also considers the traffic lights switching and permits to define the fields of velocity, pressure and temperature of the discharge gases in urban air. The verification of mathematical model and software used confirmed their satisfactory fit to the in-situ measurements data and the possibility to use the obtained computing results for assessment and prediction of urban air pollution caused by motor transport exhaust gases.

  9. The Oak Ridge Heat Pump Models: I. A Steady-State Computer Design Model of Air-to-Air Heat Pumps

    Energy Technology Data Exchange (ETDEWEB)

    Fischer, S.K. Rice, C.K.

    1999-12-10

    The ORNL Heat Pump Design Model is a FORTRAN-IV computer program to predict the steady-state performance of conventional, vapor compression, electrically-driven, air-to-air heat pumps in both heating and cooling modes. This model is intended to serve as an analytical design tool for use by heat pump manufacturers, consulting engineers, research institutions, and universities in studies directed toward the improvement of heat pump performance. The Heat Pump Design Model allows the user to specify: system operating conditions, compressor characteristics, refrigerant flow control devices, fin-and-tube heat exchanger parameters, fan and indoor duct characteristics, and any of ten refrigerants. The model will compute: system capacity and COP (or EER), compressor and fan motor power consumptions, coil outlet air dry- and wet-bulb temperatures, air- and refrigerant-side pressure drops, a summary of the refrigerant-side states throughout the cycle, and overall compressor efficiencies and heat exchanger effectiveness. This report provides thorough documentation of how to use and/or modify the model. This is a revision of an earlier report containing miscellaneous corrections and information on availability and distribution of the model--including an interactive version.

  10. Caries risk assessment models in caries prediction

    Directory of Open Access Journals (Sweden)

    Amila Zukanović

    2013-11-01

    Full Text Available Objective. The aim of this research was to assess the efficiency of different multifactor models in caries prediction. Material and methods. Data from the questionnaire and objective examination of 109 examinees was entered into the Cariogram, Previser and Caries-Risk Assessment Tool (CAT multifactor risk assessment models. Caries risk was assessed with the help of all three models for each patient, classifying them as low, medium or high-risk patients. The development of new caries lesions over a period of three years [Decay Missing Filled Tooth (DMFT increment = difference between Decay Missing Filled Tooth Surface (DMFTS index at baseline and follow up], provided for examination of the predictive capacity concerning different multifactor models. Results. The data gathered showed that different multifactor risk assessment models give significantly different results (Friedman test: Chi square = 100.073, p=0.000. Cariogram is the model which identified the majority of examinees as medium risk patients (70%. The other two models were more radical in risk assessment, giving more unfavorable risk –profiles for patients. In only 12% of the patients did the three multifactor models assess the risk in the same way. Previser and CAT gave the same results in 63% of cases – the Wilcoxon test showed that there is no statistically significant difference in caries risk assessment between these two models (Z = -1.805, p=0.071. Conclusions. Evaluation of three different multifactor caries risk assessment models (Cariogram, PreViser and CAT showed that only the Cariogram can successfully predict new caries development in 12-year-old Bosnian children.

  11. Disease prediction models and operational readiness.

    Directory of Open Access Journals (Sweden)

    Courtney D Corley

    Full Text Available The objective of this manuscript is to present a systematic review of biosurveillance models that operate on select agents and can forecast the occurrence of a disease event. We define a disease event to be a biological event with focus on the One Health paradigm. These events are characterized by evidence of infection and or disease condition. We reviewed models that attempted to predict a disease event, not merely its transmission dynamics and we considered models involving pathogens of concern as determined by the US National Select Agent Registry (as of June 2011. We searched commercial and government databases and harvested Google search results for eligible models, using terms and phrases provided by public health analysts relating to biosurveillance, remote sensing, risk assessments, spatial epidemiology, and ecological niche modeling. After removal of duplications and extraneous material, a core collection of 6,524 items was established, and these publications along with their abstracts are presented in a semantic wiki at http://BioCat.pnnl.gov. As a result, we systematically reviewed 44 papers, and the results are presented in this analysis. We identified 44 models, classified as one or more of the following: event prediction (4, spatial (26, ecological niche (28, diagnostic or clinical (6, spread or response (9, and reviews (3. The model parameters (e.g., etiology, climatic, spatial, cultural and data sources (e.g., remote sensing, non-governmental organizations, expert opinion, epidemiological were recorded and reviewed. A component of this review is the identification of verification and validation (V&V methods applied to each model, if any V&V method was reported. All models were classified as either having undergone Some Verification or Validation method, or No Verification or Validation. We close by outlining an initial set of operational readiness level guidelines for disease prediction models based upon established Technology

  12. Model Predictive Control based on Finite Impulse Response Models

    DEFF Research Database (Denmark)

    Prasath, Guru; Jørgensen, John Bagterp

    2008-01-01

    We develop a regularized l2 finite impulse response (FIR) predictive controller with input and input-rate constraints. Feedback is based on a simple constant output disturbance filter. The performance of the predictive controller in the face of plant-model mismatch is investigated by simulations...

  13. Modeling air quality over China: Results from the Panda project

    Science.gov (United States)

    Katinka Petersen, Anna; Bouarar, Idir; Brasseur, Guy; Granier, Claire; Xie, Ying; Wang, Lili; Wang, Xuemei

    2015-04-01

    China faces strong air pollution problems related to rapid economic development in the past decade and increasing demand for energy. Air quality monitoring stations often report high levels of particle matter and ozone all over the country. Knowing its long-term health impacts, air pollution became then a pressing problem not only in China but also in other Asian countries. The PANDA project is a result of cooperation between scientists from Europe and China who joined their efforts for a better understanding of the processes controlling air pollution in China, improve methods for monitoring air quality and elaborate indicators in support of European and Chinese policies. A modeling system of air pollution is being setup within the PANDA project and include advanced global (MACC, EMEP) and regional (WRF-Chem, EMEP) meteorological and chemical models to analyze and monitor air quality in China. The poster describes the accomplishments obtained within the first year of the project. Model simulations for January and July 2010 are evaluated with satellite measurements (SCIAMACHY NO2 and MOPITT CO) and in-situ data (O3, CO, NOx, PM10 and PM2.5) observed at several surface stations in China. Using the WRF-Chem model, we investigate the sensitivity of the model performance to emissions (MACCity, HTAPv2), horizontal resolution (60km, 20km) and choice of initial and boundary conditions.

  14. Air Gun Launch Simulation Modeling and Finite Element Model Sensitivity Analysis

    Science.gov (United States)

    2006-01-01

    Air Gun Launch Simulation Modeling and Finite Element Model Sensitivity Analysis by Mostafiz R. Chowdhury and Ala Tabiei ARL-TR-3703...Adelphi, MD 20783-1145 ARL-TR-3703 January 2006 Air Gun Launch Simulation Modeling and Finite Element Model Sensitivity Analysis...GRANT NUMBER 4. TITLE AND SUBTITLE Air Gun Launch Simulation Modeling and Finite Element Model Sensitivity Analysis 5c. PROGRAM

  15. Comparison of mixed layer models predictions with experimental data

    Energy Technology Data Exchange (ETDEWEB)

    Faggian, P.; Riva, G.M. [CISE Spa, Divisione Ambiente, Segrate (Italy); Brusasca, G. [ENEL Spa, CRAM, Milano (Italy)

    1997-10-01

    The temporal evolution of the PBL vertical structure for a North Italian rural site, situated within relatively large agricultural fields and almost flat terrain, has been investigated during the period 22-28 June 1993 by experimental and modellistic point of view. In particular, the results about a sunny day (June 22) and a cloudy day (June 25) are presented in this paper. Three schemes to estimate mixing layer depth have been compared, i.e. Holzworth (1967), Carson (1973) and Gryning-Batchvarova models (1990), which use standard meteorological observations. To estimate their degree of accuracy, model outputs were analyzed considering radio-sounding meteorological profiles and stability atmospheric classification criteria. Besides, the mixed layer depths prediction were compared with the estimated values obtained by a simple box model, whose input requires hourly measures of air concentrations and ground flux of {sup 222}Rn. (LN)

  16. A Coupled Probabilistic Wake Vortex and Aircraft Response Prediction Model

    Science.gov (United States)

    Gloudemans, Thijs; Van Lochem, Sander; Ras, Eelco; Malissa, Joel; Ahmad, Nashat N.; Lewis, Timothy A.

    2016-01-01

    Wake vortex spacing standards along with weather and runway occupancy time, restrict terminal area throughput and impose major constraints on the overall capacity and efficiency of the National Airspace System (NAS). For more than two decades, the National Aeronautics and Space Administration (NASA) has been conducting research on characterizing wake vortex behavior in order to develop fast-time wake transport and decay prediction models. It is expected that the models can be used in the systems level design of advanced air traffic management (ATM) concepts that safely increase the capacity of the NAS. It is also envisioned that at a later stage of maturity, these models could potentially be used operationally, in groundbased spacing and scheduling systems as well as on the flight deck.

  17. ENSO Prediction using Vector Autoregressive Models

    Science.gov (United States)

    Chapman, D. R.; Cane, M. A.; Henderson, N.; Lee, D.; Chen, C.

    2013-12-01

    A recent comparison (Barnston et al, 2012 BAMS) shows the ENSO forecasting skill of dynamical models now exceeds that of statistical models, but the best statistical models are comparable to all but the very best dynamical models. In this comparison the leading statistical model is the one based on the Empirical Model Reduction (EMR) method. Here we report on experiments with multilevel Vector Autoregressive models using only sea surface temperatures (SSTs) as predictors. VAR(L) models generalizes Linear Inverse Models (LIM), which are a VAR(1) method, as well as multilevel univariate autoregressive models. Optimal forecast skill is achieved using 12 to 14 months of prior state information (i.e 12-14 levels), which allows SSTs alone to capture the effects of other variables such as heat content as well as seasonality. The use of multiple levels allows the model advancing one month at a time to perform at least as well for a 6 month forecast as a model constructed to explicitly forecast 6 months ahead. We infer that the multilevel model has fully captured the linear dynamics (cf. Penland and Magorian, 1993 J. Climate). Finally, while VAR(L) is equivalent to L-level EMR, we show in a 150 year cross validated assessment that we can increase forecast skill by improving on the EMR initialization procedure. The greatest benefit of this change is in allowing the prediction to make effective use of information over many more months.

  18. Electrostatic ion thrusters - towards predictive modeling

    Energy Technology Data Exchange (ETDEWEB)

    Kalentev, O.; Matyash, K.; Duras, J.; Lueskow, K.F.; Schneider, R. [Ernst-Moritz-Arndt Universitaet Greifswald, D-17489 (Germany); Koch, N. [Technische Hochschule Nuernberg Georg Simon Ohm, Kesslerplatz 12, D-90489 Nuernberg (Germany); Schirra, M. [Thales Electronic Systems GmbH, Soeflinger Strasse 100, D-89077 Ulm (Germany)

    2014-02-15

    The development of electrostatic ion thrusters so far has mainly been based on empirical and qualitative know-how, and on evolutionary iteration steps. This resulted in considerable effort regarding prototype design, construction and testing and therefore in significant development and qualification costs and high time demands. For future developments it is anticipated to implement simulation tools which allow for quantitative prediction of ion thruster performance, long-term behavior and space craft interaction prior to hardware design and construction. Based on integrated numerical models combining self-consistent kinetic plasma models with plasma-wall interaction modules a new quality in the description of electrostatic thrusters can be reached. These open the perspective for predictive modeling in this field. This paper reviews the application of a set of predictive numerical modeling tools on an ion thruster model of the HEMP-T (High Efficiency Multi-stage Plasma Thruster) type patented by Thales Electron Devices GmbH. (copyright 2014 WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim) (orig.)

  19. Gas explosion prediction using CFD models

    Energy Technology Data Exchange (ETDEWEB)

    Niemann-Delius, C.; Okafor, E. [RWTH Aachen Univ. (Germany); Buhrow, C. [TU Bergakademie Freiberg Univ. (Germany)

    2006-07-15

    A number of CFD models are currently available to model gaseous explosions in complex geometries. Some of these tools allow the representation of complex environments within hydrocarbon production plants. In certain explosion scenarios, a correction is usually made for the presence of buildings and other complexities by using crude approximations to obtain realistic estimates of explosion behaviour as can be found when predicting the strength of blast waves resulting from initial explosions. With the advance of computational technology, and greater availability of computing power, computational fluid dynamics (CFD) tools are becoming increasingly available for solving such a wide range of explosion problems. A CFD-based explosion code - FLACS can, for instance, be confidently used to understand the impact of blast overpressures in a plant environment consisting of obstacles such as buildings, structures, and pipes. With its porosity concept representing geometry details smaller than the grid, FLACS can represent geometry well, even when using coarse grid resolutions. The performance of FLACS has been evaluated using a wide range of field data. In the present paper, the concept of computational fluid dynamics (CFD) and its application to gas explosion prediction is presented. Furthermore, the predictive capabilities of CFD-based gaseous explosion simulators are demonstrated using FLACS. Details about the FLACS-code, some extensions made to FLACS, model validation exercises, application, and some results from blast load prediction within an industrial facility are presented. (orig.)

  20. Genetic models of homosexuality: generating testable predictions.

    Science.gov (United States)

    Gavrilets, Sergey; Rice, William R

    2006-12-22

    Homosexuality is a common occurrence in humans and other species, yet its genetic and evolutionary basis is poorly understood. Here, we formulate and study a series of simple mathematical models for the purpose of predicting empirical patterns that can be used to determine the form of selection that leads to polymorphism of genes influencing homosexuality. Specifically, we develop theory to make contrasting predictions about the genetic characteristics of genes influencing homosexuality including: (i) chromosomal location, (ii) dominance among segregating alleles and (iii) effect sizes that distinguish between the two major models for their polymorphism: the overdominance and sexual antagonism models. We conclude that the measurement of the genetic characteristics of quantitative trait loci (QTLs) found in genomic screens for genes influencing homosexuality can be highly informative in resolving the form of natural selection maintaining their polymorphism.

  1. Characterizing Attention with Predictive Network Models.

    Science.gov (United States)

    Rosenberg, M D; Finn, E S; Scheinost, D; Constable, R T; Chun, M M

    2017-04-01

    Recent work shows that models based on functional connectivity in large-scale brain networks can predict individuals' attentional abilities. While being some of the first generalizable neuromarkers of cognitive function, these models also inform our basic understanding of attention, providing empirical evidence that: (i) attention is a network property of brain computation; (ii) the functional architecture that underlies attention can be measured while people are not engaged in any explicit task; and (iii) this architecture supports a general attentional ability that is common to several laboratory-based tasks and is impaired in attention deficit hyperactivity disorder (ADHD). Looking ahead, connectivity-based predictive models of attention and other cognitive abilities and behaviors may potentially improve the assessment, diagnosis, and treatment of clinical dysfunction. Copyright © 2017 Elsevier Ltd. All rights reserved.

  2. A Study On Distributed Model Predictive Consensus

    CERN Document Server

    Keviczky, Tamas

    2008-01-01

    We investigate convergence properties of a proposed distributed model predictive control (DMPC) scheme, where agents negotiate to compute an optimal consensus point using an incremental subgradient method based on primal decomposition as described in Johansson et al. [2006, 2007]. The objective of the distributed control strategy is to agree upon and achieve an optimal common output value for a group of agents in the presence of constraints on the agent dynamics using local predictive controllers. Stability analysis using a receding horizon implementation of the distributed optimal consensus scheme is performed. Conditions are given under which convergence can be obtained even if the negotiations do not reach full consensus.

  3. A FEDERATED PARTNERSHIP FOR URBAN METEOROLOGICAL AND AIR QUALITY MODELING

    Science.gov (United States)

    Recently, applications of urban meteorological and air quality models have been performed at resolutions on the order of km grid sizes. This necessitated development and incorporation of high resolution landcover data and additional boundary layer parameters that serve to descri...

  4. Prediction of Excess Air Factor in Automatic Feed Coal Burners by Processing of Flame Images

    Science.gov (United States)

    Talu, Muhammed Fatih; Onat, Cem; Daskin, Mahmut

    2017-05-01

    In this study, the relationship between the visual information gathered from the flame images and the excess air factor λ in coal burners is investigated. In conventional coal burners the excess air factor λ. can be obtained using very expensive air measurement instruments. The proposed method to predict λ for a specific time in the coal burners consists of three distinct and consecutive stages; a) online flame images acquisition using a CCD camera, b) extraction meaningful information (flame intensity and brightness)from flame images, and c) learning these information (image features) with ANNs and estimate λ. Six different feature extraction methods have been used: CDF of Blue Channel, Co-Occurrence Matrix, L ∞-Frobenius Norms, Radiant Energy Signal (RES), PCA and Wavelet. When compared prediction results, it has seen that the use of co-occurrence matrix with ANNs has the best performance (RMSE = 0.07) in terms of accuracy. The results show that the proposed predicting system using flame images can be preferred instead of using expensive devices to measure excess air factor in during combustion.

  5. Predicting Air-Water Geysers and Their Implications on Reducing Combined Sewer Overflows

    Science.gov (United States)

    Choi, Y.; Leon, A.; Apte, S.

    2014-12-01

    An air-water geyser in a closed conduit system is characterized by an explosive jetting of a mixture of air and water through drop-shafts. In this study, three scenarios of geysers are numerically simulated using a 3D computational fluid dynamics (CFD) model. The three tested scenarios are comprised of a drop shaft that is closed at its bottom and partially or fully open at the top. Initially, the lower section of the drop shaft is filled with pressurized air, the middle section with stagnant water and the upper section with air at atmospheric pressure. The pressure and volume of the pressurized air, and hence the stored energy, is different for all three test cases. The volume of the stagnant water and the air at atmospheric pressure are kept constant in the tests. The numerical simulations aim to identify the correlation between dimensionless energy stored in the pressurized air pocket and dimensionless maximum pressure reached at the outlet. This dimensionless correlation could be used to determine the energy threshold that does not produce air-water geyser, which in turn could be used in the design of combined sewer systems for minimizing geysers.

  6. NONLINEAR MODEL PREDICTIVE CONTROL OF CHEMICAL PROCESSES

    Directory of Open Access Journals (Sweden)

    R. G. SILVA

    1999-03-01

    Full Text Available A new algorithm for model predictive control is presented. The algorithm utilizes a simultaneous solution and optimization strategy to solve the model's differential equations. The equations are discretized by equidistant collocation, and along with the algebraic model equations are included as constraints in a nonlinear programming (NLP problem. This algorithm is compared with the algorithm that uses orthogonal collocation on finite elements. The equidistant collocation algorithm results in simpler equations, providing a decrease in computation time for the control moves. Simulation results are presented and show a satisfactory performance of this algorithm.

  7. Boreal summer intraseasonal oscillations and seasonal Indian monsoon prediction in DEMETER coupled models

    Energy Technology Data Exchange (ETDEWEB)

    Joseph, Susmitha; Sahai, A.K.; Goswami, B.N. [Indian Institute of Tropical Meteorology, Climate and Global Modeling Division, Pune (India)

    2010-09-15

    Even though multi-model prediction systems may have better skill in predicting the interannual variability (IAV) of Indian summer monsoon (ISM), the overall performance of the system is limited by the skill of individual models (single model ensembles). The DEMETER project aimed at seasonal-to-interannual prediction is not an exception to this case. The reasons for the poor skill of the DEMETER individual models in predicting the IAV of monsoon is examined in the context of the influence of external and internal components and the interaction between intraseasonal variability (ISV) and IAV. Recently it has been shown that the ISV influences the IAV through very long breaks (VLBs; breaks with duration of more than 10 days) by generating droughts. Further, all VLBs are associated with an eastward propagating Madden-Julian Oscillation (MJO) in the equatorial region, facilitated by air-sea interaction on intraseasonal timescales. This VLB-drought-MJO relationship is analyzed here in detail in the DEMETER models. Analyses indicate that the VLB-drought relationship is poorly captured by almost all the models. VLBs in observations are generated through air-sea interaction on intraseasonal time scale and the models' inability to simulate VLB-drought relationship is shown to be linked to the models' inability to represent the air-sea interaction on intraseasonal time scale. Identification of this particular deficiency of the models provides a direction for improvement of the model for monsoon prediction. (orig.)

  8. Knock prediction for dual fuel engines by using a simplified combustion model

    Institute of Scientific and Technical Information of China (English)

    费少梅; 刘震涛; 严兆大

    2003-01-01

    The present work used a methane-air mixture chemical kinetics scheme consisting of 119 elementary reaction steps and 41 chemical species to develop a simplified combustion model for prediction of the knock in dual fuel engines. Calculated values by the model for natural gas operation showed good agreement with corresponding experimental values over a broad range of operating conditions.

  9. Performance model to predict overall defect density

    Directory of Open Access Journals (Sweden)

    J Venkatesh

    2012-08-01

    Full Text Available Management by metrics is the expectation from the IT service providers to stay as a differentiator. Given a project, the associated parameters and dynamics, the behaviour and outcome need to be predicted. There is lot of focus on the end state and in minimizing defect leakage as much as possible. In most of the cases, the actions taken are re-active. It is too late in the life cycle. Root cause analysis and corrective actions can be implemented only to the benefit of the next project. The focus has to shift left, towards the execution phase than waiting for lessons to be learnt post the implementation. How do we pro-actively predict defect metrics and have a preventive action plan in place. This paper illustrates the process performance model to predict overall defect density based on data from projects in an organization.

  10. Neuro-fuzzy modeling in bankruptcy prediction

    Directory of Open Access Journals (Sweden)

    Vlachos D.

    2003-01-01

    Full Text Available For the past 30 years the problem of bankruptcy prediction had been thoroughly studied. From the paper of Altman in 1968 to the recent papers in the '90s, the progress of prediction accuracy was not satisfactory. This paper investigates an alternative modeling of the system (firm, combining neural networks and fuzzy controllers, i.e. using neuro-fuzzy models. Classical modeling is based on mathematical models that describe the behavior of the firm under consideration. The main idea of fuzzy control, on the other hand, is to build a model of a human control expert who is capable of controlling the process without thinking in a mathematical model. This control expert specifies his control action in the form of linguistic rules. These control rules are translated into the framework of fuzzy set theory providing a calculus, which can stimulate the behavior of the control expert and enhance its performance. The accuracy of the model is studied using datasets from previous research papers.

  11. Pressure prediction model for compression garment design.

    Science.gov (United States)

    Leung, W Y; Yuen, D W; Ng, Sun Pui; Shi, S Q

    2010-01-01

    Based on the application of Laplace's law to compression garments, an equation for predicting garment pressure, incorporating the body circumference, the cross-sectional area of fabric, applied strain (as a function of reduction factor), and its corresponding Young's modulus, is developed. Design procedures are presented to predict garment pressure using the aforementioned parameters for clinical applications. Compression garments have been widely used in treating burning scars. Fabricating a compression garment with a required pressure is important in the healing process. A systematic and scientific design method can enable the occupational therapist and compression garments' manufacturer to custom-make a compression garment with a specific pressure. The objectives of this study are 1) to develop a pressure prediction model incorporating different design factors to estimate the pressure exerted by the compression garments before fabrication; and 2) to propose more design procedures in clinical applications. Three kinds of fabrics cut at different bias angles were tested under uniaxial tension, as were samples made in a double-layered structure. Sets of nonlinear force-extension data were obtained for calculating the predicted pressure. Using the value at 0° bias angle as reference, the Young's modulus can vary by as much as 29% for fabric type P11117, 43% for fabric type PN2170, and even 360% for fabric type AP85120 at a reduction factor of 20%. When comparing the predicted pressure calculated from the single-layered and double-layered fabrics, the double-layered construction provides a larger range of target pressure at a particular strain. The anisotropic and nonlinear behaviors of the fabrics have thus been determined. Compression garments can be methodically designed by the proposed analytical pressure prediction model.

  12. Statistical assessment of predictive modeling uncertainty

    Science.gov (United States)

    Barzaghi, Riccardo; Marotta, Anna Maria

    2017-04-01

    When the results of geophysical models are compared with data, the uncertainties of the model are typically disregarded. We propose a method for defining the uncertainty of a geophysical model based on a numerical procedure that estimates the empirical auto and cross-covariances of model-estimated quantities. These empirical values are then fitted by proper covariance functions and used to compute the covariance matrix associated with the model predictions. The method is tested using a geophysical finite element model in the Mediterranean region. Using a novel χ2 analysis in which both data and model uncertainties are taken into account, the model's estimated tectonic strain pattern due to the Africa-Eurasia convergence in the area that extends from the Calabrian Arc to the Alpine domain is compared with that estimated from GPS velocities while taking into account the model uncertainty through its covariance structure and the covariance of the GPS estimates. The results indicate that including the estimated model covariance in the testing procedure leads to lower observed χ2 values that have better statistical significance and might help a sharper identification of the best-fitting geophysical models.

  13. Seasonal Predictability in a Model Atmosphere.

    Science.gov (United States)

    Lin, Hai

    2001-07-01

    The predictability of atmospheric mean-seasonal conditions in the absence of externally varying forcing is examined. A perfect-model approach is adopted, in which a global T21 three-level quasigeostrophic atmospheric model is integrated over 21 000 days to obtain a reference atmospheric orbit. The model is driven by a time-independent forcing, so that the only source of time variability is the internal dynamics. The forcing is set to perpetual winter conditions in the Northern Hemisphere (NH) and perpetual summer in the Southern Hemisphere.A significant temporal variability in the NH 90-day mean states is observed. The component of that variability associated with the higher-frequency motions, or climate noise, is estimated using a method developed by Madden. In the polar region, and to a lesser extent in the midlatitudes, the temporal variance of the winter means is significantly greater than the climate noise, suggesting some potential predictability in those regions.Forecast experiments are performed to see whether the presence of variance in the 90-day mean states that is in excess of the climate noise leads to some skill in the prediction of these states. Ensemble forecast experiments with nine members starting from slightly different initial conditions are performed for 200 different 90-day means along the reference atmospheric orbit. The serial correlation between the ensemble means and the reference orbit shows that there is skill in the 90-day mean predictions. The skill is concentrated in those regions of the NH that have the largest variance in excess of the climate noise. An EOF analysis shows that nearly all the predictive skill in the seasonal means is associated with one mode of variability with a strong axisymmetric component.

  14. Modelling and analysis of ozone concentration by artificial intelligent techniques for estimating air quality

    Science.gov (United States)

    Taylan, Osman

    2017-02-01

    High ozone concentration is an important cause of air pollution mainly due to its role in the greenhouse gas emission. Ozone is produced by photochemical processes which contain nitrogen oxides and volatile organic compounds in the lower atmospheric level. Therefore, monitoring and controlling the quality of air in the urban environment is very important due to the public health care. However, air quality prediction is a highly complex and non-linear process; usually several attributes have to be considered. Artificial intelligent (AI) techniques can be employed to monitor and evaluate the ozone concentration level. The aim of this study is to develop an Adaptive Neuro-Fuzzy inference approach (ANFIS) to determine the influence of peripheral factors on air quality and pollution which is an arising problem due to ozone level in Jeddah city. The concentration of ozone level was considered as a factor to predict the Air Quality (AQ) under the atmospheric conditions. Using Air Quality Standards of Saudi Arabia, ozone concentration level was modelled by employing certain factors such as; nitrogen oxide (NOx), atmospheric pressure, temperature, and relative humidity. Hence, an ANFIS model was developed to observe the ozone concentration level and the model performance was assessed by testing data obtained from the monitoring stations established by the General Authority of Meteorology and Environment Protection of Kingdom of Saudi Arabia. The outcomes of ANFIS model were re-assessed by fuzzy quality charts using quality specification and control limits based on US-EPA air quality standards. The results of present study show that the ANFIS model is a comprehensive approach for the estimation and assessment of ozone level and is a reliable approach to produce more genuine outcomes.

  15. Important meteorological variables for statistical long-term air quality prediction in eastern China

    Science.gov (United States)

    Zhang, Libo; Liu, Yongqiang; Zhao, Fengjun

    2017-09-01

    Weather is an important factor for air quality. While there have been increasing attentions to long-term (monthly and seasonal) air pollution such as regional hazes from land-clearing fires during El Niño, the weather-air quality relationships are much less understood at long-term than short-term (daily and weekly) scales. This study is aimed to fill this gap through analyzing correlations between meteorological variables and air quality at various timescales. A regional correlation scale was defined to measure the longest time with significant correlations at a substantial large number of sites. The air quality index (API) and five meteorological variables during 2001-2012 at 40 eastern China sites were used. The results indicate that the API is correlated to precipitation negatively and air temperature positively across eastern China, and to wind, relative humidity and air pressure with spatially varied signs. The major areas with significant correlations vary with meteorological variables. The correlations are significant not only at short-term but also at long-term scales, and the important variables are different between the two types of scales. The concurrent regional correlation scales reach seasonal at p correlations are much smaller in magnitude than the concurrent correlations and their regional correction scales are at long term only for wind speed and relative humidity. It is concluded that wind speed should be considered as a primary predictor for statistical prediction of long-term air quality in a large region over eastern China. Relative humidity and temperature are also useful predictors but at less significant levels.

  16. Spatial distribution of emissions to air – the SPREAD model

    DEFF Research Database (Denmark)

    Plejdrup, Marlene Schmidt; Gyldenkærne, Steen

    The National Environmental Research Institute (NERI), Aarhus University, completes the annual national emission inventories for greenhouse gases and air pollutants according to Denmark’s obligations under international conventions, e.g. the climate convention, UNFCCC and the convention on long......-range transboundary air pollution, CLRTAP. NERI has developed a model to distribute emissions from the national emission inventories on a 1x1 km grid covering the Danish land and sea territory. The new spatial high resolution distribution model for emissions to air (SPREAD) has been developed according...... to the requirements for reporting of gridded emissions to CLRTAP. Spatial emission data is e.g. used as input for air quality modelling, which again serves as input for assessment and evaluation of health effects. For these purposes distributions with higher spatial resolution have been requested. Previously...

  17. A kinetic model for predicting biodegradation.

    Science.gov (United States)

    Dimitrov, S; Pavlov, T; Nedelcheva, D; Reuschenbach, P; Silvani, M; Bias, R; Comber, M; Low, L; Lee, C; Parkerton, T; Mekenyan, O

    2007-01-01

    Biodegradation plays a key role in the environmental risk assessment of organic chemicals. The need to assess biodegradability of a chemical for regulatory purposes supports the development of a model for predicting the extent of biodegradation at different time frames, in particular the extent of ultimate biodegradation within a '10 day window' criterion as well as estimating biodegradation half-lives. Conceptually this implies expressing the rate of catabolic transformations as a function of time. An attempt to correlate the kinetics of biodegradation with molecular structure of chemicals is presented. A simplified biodegradation kinetic model was formulated by combining the probabilistic approach of the original formulation of the CATABOL model with the assumption of first order kinetics of catabolic transformations. Nonlinear regression analysis was used to fit the model parameters to OECD 301F biodegradation kinetic data for a set of 208 chemicals. The new model allows the prediction of biodegradation multi-pathways, primary and ultimate half-lives and simulation of related kinetic biodegradation parameters such as biological oxygen demand (BOD), carbon dioxide production, and the nature and amount of metabolites as a function of time. The model may also be used for evaluating the OECD ready biodegradability potential of a chemical within the '10-day window' criterion.

  18. Daily air quality forecast (gases and aerosols) over Switzerland. Modeling tool description and first results analysis.

    Science.gov (United States)

    Couach, O.; Kirchner, F.; Porchet, P.; Balin, I.; Parlange, M.; Balin, D.

    2009-04-01

    Map3D, the acronym for "Mesoscale Air Pollution 3D modelling", was developed at the EFLUM laboratory (EPFL) and received an INNOGRANTS awards in Summer 2007 in order to move from a research phase to a professional product giving daily air quality forecast. It is intended to give an objective base for political decisions addressing the improvement of regional air quality. This tool is a permanent modelling system which provides daily forecast of the local meteorology and the air pollutant (gases and particles) concentrations. Map3D has been successfully developed and calculates each day at the EPFL site a three days air quality forecast over Europe and the Alps with 50 km and 15 km resolution, respectively (see http://map3d.epfl.ch). The Map3D user interface is a web-based application with a PostgreSQL database. It is written in object-oriented PHP5 on a MVC (Model-View-Controller) architecture. Our prediction system is operational since August 2008. A first validation of the calculations for Switzerland is performed for the period of August 2008 - January 2009 comparing the model results for O3, NO2 and particulates with the results of the Nabel measurements stations. The subject of air pollution regimes (NOX/VOC) and specific indicators application with the forecast will be also addressed.

  19. Disease Prediction Models and Operational Readiness

    Energy Technology Data Exchange (ETDEWEB)

    Corley, Courtney D.; Pullum, Laura L.; Hartley, David M.; Benedum, Corey M.; Noonan, Christine F.; Rabinowitz, Peter M.; Lancaster, Mary J.

    2014-03-19

    INTRODUCTION: The objective of this manuscript is to present a systematic review of biosurveillance models that operate on select agents and can forecast the occurrence of a disease event. One of the primary goals of this research was to characterize the viability of biosurveillance models to provide operationally relevant information for decision makers to identify areas for future research. Two critical characteristics differentiate this work from other infectious disease modeling reviews. First, we reviewed models that attempted to predict the disease event, not merely its transmission dynamics. Second, we considered models involving pathogens of concern as determined by the US National Select Agent Registry (as of June 2011). Methods: We searched dozens of commercial and government databases and harvested Google search results for eligible models utilizing terms and phrases provided by public health analysts relating to biosurveillance, remote sensing, risk assessments, spatial epidemiology, and ecological niche-modeling, The publication date of search results returned are bound by the dates of coverage of each database and the date in which the search was performed, however all searching was completed by December 31, 2010. This returned 13,767 webpages and 12,152 citations. After de-duplication and removal of extraneous material, a core collection of 6,503 items was established and these publications along with their abstracts are presented in a semantic wiki at http://BioCat.pnnl.gov. Next, PNNL’s IN-SPIRE visual analytics software was used to cross-correlate these publications with the definition for a biosurveillance model resulting in the selection of 54 documents that matched the criteria resulting Ten of these documents, However, dealt purely with disease spread models, inactivation of bacteria, or the modeling of human immune system responses to pathogens rather than predicting disease events. As a result, we systematically reviewed 44 papers and the

  20. Nonlinear model predictive control theory and algorithms

    CERN Document Server

    Grüne, Lars

    2017-01-01

    This book offers readers a thorough and rigorous introduction to nonlinear model predictive control (NMPC) for discrete-time and sampled-data systems. NMPC schemes with and without stabilizing terminal constraints are detailed, and intuitive examples illustrate the performance of different NMPC variants. NMPC is interpreted as an approximation of infinite-horizon optimal control so that important properties like closed-loop stability, inverse optimality and suboptimality can be derived in a uniform manner. These results are complemented by discussions of feasibility and robustness. An introduction to nonlinear optimal control algorithms yields essential insights into how the nonlinear optimization routine—the core of any nonlinear model predictive controller—works. Accompanying software in MATLAB® and C++ (downloadable from extras.springer.com/), together with an explanatory appendix in the book itself, enables readers to perform computer experiments exploring the possibilities and limitations of NMPC. T...

  1. EnergyPlus Air Source Integrated Heat Pump Model

    Energy Technology Data Exchange (ETDEWEB)

    Shen, Bo [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Energy and Transportation Science Division; Adams, Mark B. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Energy and Transportation Science Division; New, Joshua Ryan [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Energy and Transportation Science Division

    2016-03-30

    This report summarizes the development of the EnergyPlus air-source integrated heat pump model. It introduces its physics, sub-models, working modes, and control logic. In addition, inputs and outputs of the new model are described, and input data file (IDF) examples are given.

  2. Predictive models of procedural human supervisory control behavior

    Science.gov (United States)

    Boussemart, Yves

    Human supervisory control systems are characterized by the computer-mediated nature of the interactions between one or more operators and a given task. Nuclear power plants, air traffic management and unmanned vehicles operations are examples of such systems. In this context, the role of the operators is typically highly proceduralized due to the time and mission-critical nature of the tasks. Therefore, the ability to continuously monitor operator behavior so as to detect and predict anomalous situations is a critical safeguard for proper system operation. In particular, such models can help support the decision J]l8king process of a supervisor of a team of operators by providing alerts when likely anomalous behaviors are detected By exploiting the operator behavioral patterns which are typically reinforced through standard operating procedures, this thesis proposes a methodology that uses statistical learning techniques in order to detect and predict anomalous operator conditions. More specifically, the proposed methodology relies on hidden Markov models (HMMs) and hidden semi-Markov models (HSMMs) to generate predictive models of unmanned vehicle systems operators. Through the exploration of the resulting HMMs in two distinct single operator scenarios, the methodology presented in this thesis is validated and shown to provide models capable of reliably predicting operator behavior. In addition, the use of HSMMs on the same data scenarios provides the temporal component of the predictions missing from the HMMs. The final step of this work is to examine how the proposed methodology scales to more complex scenarios involving teams of operators. Adopting a holistic team modeling approach, both HMMs and HSMMs are learned based on two team-based data sets. The results show that the HSMMs can provide valuable timing information in the single operator case, whereas HMMs tend to be more robust to increased team complexity. In addition, this thesis discusses the

  3. Predictive Modeling in Actinide Chemistry and Catalysis

    Energy Technology Data Exchange (ETDEWEB)

    Yang, Ping [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2016-05-16

    These are slides from a presentation on predictive modeling in actinide chemistry and catalysis. The following topics are covered in these slides: Structures, bonding, and reactivity (bonding can be quantified by optical probes and theory, and electronic structures and reaction mechanisms of actinide complexes); Magnetic resonance properties (transition metal catalysts with multi-nuclear centers, and NMR/EPR parameters); Moving to more complex systems (surface chemistry of nanomaterials, and interactions of ligands with nanoparticles); Path forward and conclusions.

  4. A Model for Air Flow in Ventilated Cavities Implemented in a Tool for Whole-Building Hygrothermal Analysis

    DEFF Research Database (Denmark)

    Grau, Karl; Rode, Carsten

    2006-01-01

    A model for calculating air flows in ventilated cavities has been implemented in the whole-building hygrothermal simulation tool BSim. The tool is able to predict indoor humidity conditions using a transient model for the moisture conditions in the building envelope.......A model for calculating air flows in ventilated cavities has been implemented in the whole-building hygrothermal simulation tool BSim. The tool is able to predict indoor humidity conditions using a transient model for the moisture conditions in the building envelope....

  5. Rocket exhaust effluent modeling for tropospheric air quality and environmental assessments

    Science.gov (United States)

    Stephens, J. B.; Stewart, R. B.

    1977-01-01

    The various techniques for diffusion predictions to support air quality predictions and environmental assessments for aerospace applications are discussed in terms of limitations imposed by atmospheric data. This affords an introduction to the rationale behind the selection of the National Aeronautics and Space Administration (NASA)/Marshall Space Flight Center (MSFC) Rocket Exhaust Effluent Diffusion (REED) program. The models utilized in the NASA/MSFC REED program are explained. This program is then evaluated in terms of some results from a joint MSFC/Langley Research Center/Kennedy Space Center Titan Exhaust Effluent Prediction and Monitoring Program.

  6. Modeling of Air Temperature for Heat Exchange due to Vertical Turbulence and Horizontal Air Flow

    Institute of Scientific and Technical Information of China (English)

    ZHANG Lei; MENG Qing-lin

    2009-01-01

    In order to calculate the air temperature of the near surface layer in urban environment,the Sur-face layer air was divided into several layers in the vertical direction,and some energy bakmce equations were de-veloped for each air layer,in which the heat exchange due to vertical turbulence and horizontal air flow was tak-en into account.Then,the vertical temperature distribution of the surface layer air was obtained through the coupled calculation using the energy balance equations of underlying surfaces and building walls.Moreover,the measured air temperatures in a small area (with a horizontal scale of less than 500 m) and a large area (with ahorizontal scale of more than 1000 m) in Guangzhou in summer were used to validate the proposed model.The calculated results agree well with the measured ones,with a maximum relative error of 4.18%.It is thus con-cluded that the proposed model is a high-accuracy method to theoretically analyze the urban heat island and the thermal environment.

  7. Prediction of acoustic absorption performance of a perforated plate with air jets

    Science.gov (United States)

    Hamakawa, Hiromitsu; Miyazaki, Masanori; Asai, Yuta; Kurihara, Eru; Nishida, Eiichi; Hayashi, Hidechito

    2017-08-01

    The present study focuses on the prediction of acoustic absorption performance of a perforated plate with air jets by theoretical calculations. In addition, we experimentally measured the flow rate, internal pressure, acoustic pressure, and transfer function using an acoustic impedance tube. The normal incidence absorption coefficient was calculated from the measured transfer function using transfer function methods. We investigated the influences of background air space, flow velocity, thickness, aperture rate, and aperture diameter of a perforated plate on the acoustic absorption characteristics. The frequency characteristics of the acoustic absorption coefficient showed a maximum value at a local frequency. As the background air space increased, the peak frequency of acoustic absorption characteristics decreased. As the flow velocity passing through the apertures increased, the peak level of the acoustic absorption coefficient also increased. The theoretical results agreed well with the experimental ones qualitatively.

  8. Probabilistic prediction models for aggregate quarry siting

    Science.gov (United States)

    Robinson, G.R.; Larkins, P.M.

    2007-01-01

    Weights-of-evidence (WofE) and logistic regression techniques were used in a GIS framework to predict the spatial likelihood (prospectivity) of crushed-stone aggregate quarry development. The joint conditional probability models, based on geology, transportation network, and population density variables, were defined using quarry location and time of development data for the New England States, North Carolina, and South Carolina, USA. The Quarry Operation models describe the distribution of active aggregate quarries, independent of the date of opening. The New Quarry models describe the distribution of aggregate quarries when they open. Because of the small number of new quarries developed in the study areas during the last decade, independent New Quarry models have low parameter estimate reliability. The performance of parameter estimates derived for Quarry Operation models, defined by a larger number of active quarries in the study areas, were tested and evaluated to predict the spatial likelihood of new quarry development. Population density conditions at the time of new quarry development were used to modify the population density variable in the Quarry Operation models to apply to new quarry development sites. The Quarry Operation parameters derived for the New England study area, Carolina study area, and the combined New England and Carolina study areas were all similar in magnitude and relative strength. The Quarry Operation model parameters, using the modified population density variables, were found to be a good predictor of new quarry locations. Both the aggregate industry and the land management community can use the model approach to target areas for more detailed site evaluation for quarry location. The models can be revised easily to reflect actual or anticipated changes in transportation and population features. ?? International Association for Mathematical Geology 2007.

  9. Modelling air pollution for epidemiologic research--Part I: A novel approach combining land use regression and air dispersion.

    Science.gov (United States)

    Mölter, A; Lindley, S; de Vocht, F; Simpson, A; Agius, R

    2010-11-01

    A common limitation of epidemiological studies on health effects of air pollution is the quality of exposure data available for study participants. Exposure data derived from urban monitoring networks is usually not adequately representative of the spatial variation of pollutants, while personal monitoring campaigns are often not feasible, due to time and cost restrictions. Therefore, many studies now rely on empirical modelling techniques, such as land use regression (LUR), to estimate pollution exposure. However, LUR still requires a quantity of specifically measured data to develop a model, which is usually derived from a dedicated monitoring campaign. A dedicated air dispersion modelling exercise is also possible but is similarly resource and data intensive. This study adopted a novel approach to LUR, which utilised existing data from an air dispersion model rather than monitored data. There are several advantages to such an approach such as a larger number of sites to develop the LUR model compared to monitored data. Furthermore, through this approach the LUR model can be adapted to predict temporal variation as well as spatial variation. The aim of this study was to develop two LUR models for an epidemiologic study based in Greater Manchester by using modelled NO(2) and PM(10) concentrations as dependent variables, and traffic intensity, emissions, land use and physical geography as potential predictor variables. The LUR models were validated through a set aside "validation" dataset and data from monitoring stations. The final models for PM(10) and NO(2) comprised nine and eight predictor variables respectively and had determination coefficients (R²) of 0.71 (PM(10): Adj. R²=0.70, F=54.89, p<0.001, NO(2): Adj. R²=0.70, F=62.04, p<0.001). Validation of the models using the validation data and measured data showed that the R² decreases compared to the final models, except for NO(2) validation in the measured data (validation data: PM(10): R²=0.33, NO(2

  10. Predicting Footbridge Response using Stochastic Load Models

    DEFF Research Database (Denmark)

    Pedersen, Lars; Frier, Christian

    2013-01-01

    Walking parameters such as step frequency, pedestrian mass, dynamic load factor, etc. are basically stochastic, although it is quite common to adapt deterministic models for these parameters. The present paper considers a stochastic approach to modeling the action of pedestrians, but when doing s...... as it pinpoints which decisions to be concerned about when the goal is to predict footbridge response. The studies involve estimating footbridge responses using Monte-Carlo simulations and focus is on estimating vertical structural response to single person loading....

  11. Nonconvex Model Predictive Control for Commercial Refrigeration

    DEFF Research Database (Denmark)

    Hovgaard, Tobias Gybel; Larsen, Lars F.S.; Jørgensen, John Bagterp

    2013-01-01

    is to minimize the total energy cost, using real-time electricity prices, while obeying temperature constraints on the zones. We propose a variation on model predictive control to achieve this goal. When the right variables are used, the dynamics of the system are linear, and the constraints are convex. The cost...... the iterations, which is more than fast enough to run in real-time. We demonstrate our method on a realistic model, with a full year simulation and 15 minute time periods, using historical electricity prices and weather data, as well as random variations in thermal load. These simulations show substantial cost...

  12. A study on the sound quality evaluation model of mechanical air-cleaners

    DEFF Research Database (Denmark)

    Ih, Jeong-Guon; Jang, Su-Won; Jeong, Cheol-Ho;

    2009-01-01

    of an immediate cleaning of pollutants. In this context, it is important to evaluate and design the air-cleaner noise to satisfy such contradictory expectations from the customers. In this study, a model for evaluating the sound quality of air-cleaners of mechanical type was developed based on objective...... perceptive descriptor. Annoyance and performance indices of air-cleaners were modeled from the subjective responses of the juries and the measured sound quality metrics: loudness, sharpness, roughness, and fluctuation strength. The multiple regression method was employed to generate sound quality evaluation...... models. Using the developed indices, sound quality of the measured data was evaluated and compared with the subjective data. The difference between predicted and tested scores was less than 0.5 points. © 2009 by ASME....

  13. Far-field dispersal modeling for fuel-air-explosive devices

    Energy Technology Data Exchange (ETDEWEB)

    Glass, M.W.

    1990-05-01

    A computer model for simulating the explosive dispersal of a fuel agent in the far-field regime is described and is applied to a wide variety of initial conditions to judge their effect upon the resulting fuel/air cloud. This work was directed toward modeling the dispersal process associated with Fuel-Air-Explosives devices. The far-field dispersal regime is taken to be that time after the initial burster charge detonation in which the shock forces no longer dominate the flow field and initial canister and fuel mass breakup has occurred. The model was applied to a low vapor pressure fuel, a high vapor pressure fuel and a solid fuel. A strong dependence of the final cloud characteristics upon the initial droplet size distribution was demonstrated. The predicted fuel-air clouds were highly non-uniform in concentration. 18 refs., 86 figs., 4 tabs.

  14. Extension of the PMV model to non-air-conditioned building in warm climates

    DEFF Research Database (Denmark)

    Fanger, Povl Ole; Toftum, Jørn

    2002-01-01

    The PMV model agrees well with high-quality field studies in buildings with HVAC systems, situated in cold, temperate and warm climates, studied during both summer and winter. In non-air-conditioned buildings in warm climates, occupants may sense the warmth as being less severe than the PMV...... predicts. The main reason is low expectations, but a metabolic rate that is estimated too high can also contribute to explaining the difference. An extension of the PMV model that includes an expectancy factor is introduced for use in non-air-conditioned buildings in warm climates. The extended PMV model...... agrees well with quality field studies in non-air-conditioned buildings of three continents....

  15. Predictive In Vivo Models for Oncology.

    Science.gov (United States)

    Behrens, Diana; Rolff, Jana; Hoffmann, Jens

    2016-01-01

    Experimental oncology research and preclinical drug development both substantially require specific, clinically relevant in vitro and in vivo tumor models. The increasing knowledge about the heterogeneity of cancer requested a substantial restructuring of the test systems for the different stages of development. To be able to cope with the complexity of the disease, larger panels of patient-derived tumor models have to be implemented and extensively characterized. Together with individual genetically engineered tumor models and supported by core functions for expression profiling and data analysis, an integrated discovery process has been generated for predictive and personalized drug development.Improved “humanized” mouse models should help to overcome current limitations given by xenogeneic barrier between humans and mice. Establishment of a functional human immune system and a corresponding human microenvironment in laboratory animals will strongly support further research.Drug discovery, systems biology, and translational research are moving closer together to address all the new hallmarks of cancer, increase the success rate of drug development, and increase the predictive value of preclinical models.

  16. Constructing predictive models of human running.

    Science.gov (United States)

    Maus, Horst-Moritz; Revzen, Shai; Guckenheimer, John; Ludwig, Christian; Reger, Johann; Seyfarth, Andre

    2015-02-06

    Running is an essential mode of human locomotion, during which ballistic aerial phases alternate with phases when a single foot contacts the ground. The spring-loaded inverted pendulum (SLIP) provides a starting point for modelling running, and generates ground reaction forces that resemble those of the centre of mass (CoM) of a human runner. Here, we show that while SLIP reproduces within-step kinematics of the CoM in three dimensions, it fails to reproduce stability and predict future motions. We construct SLIP control models using data-driven Floquet analysis, and show how these models may be used to obtain predictive models of human running with six additional states comprising the position and velocity of the swing-leg ankle. Our methods are general, and may be applied to any rhythmic physical system. We provide an approach for identifying an event-driven linear controller that approximates an observed stabilization strategy, and for producing a reduced-state model which closely recovers the observed dynamics. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

  17. Statistical Seasonal Sea Surface based Prediction Model

    Science.gov (United States)

    Suarez, Roberto; Rodriguez-Fonseca, Belen; Diouf, Ibrahima

    2014-05-01

    The interannual variability of the sea surface temperature (SST) plays a key role in the strongly seasonal rainfall regime on the West African region. The predictability of the seasonal cycle of rainfall is a field widely discussed by the scientific community, with results that fail to be satisfactory due to the difficulty of dynamical models to reproduce the behavior of the Inter Tropical Convergence Zone (ITCZ). To tackle this problem, a statistical model based on oceanic predictors has been developed at the Universidad Complutense of Madrid (UCM) with the aim to complement and enhance the predictability of the West African Monsoon (WAM) as an alternative to the coupled models. The model, called S4CAST (SST-based Statistical Seasonal Forecast) is based on discriminant analysis techniques, specifically the Maximum Covariance Analysis (MCA) and Canonical Correlation Analysis (CCA). Beyond the application of the model to the prediciton of rainfall in West Africa, its use extends to a range of different oceanic, atmospheric and helth related parameters influenced by the temperature of the sea surface as a defining factor of variability.

  18. Predictive functional control based on fuzzy T-S model for HVAC systems temperature control

    Institute of Scientific and Technical Information of China (English)

    Hongli L(U); Lei JIA; Shulan KONG; Zhaosheng ZHANG

    2007-01-01

    In heating,ventilating and air-conditioning(HVAC)systems,there exist severe nonlinearity,time-varying nature,disturbances and uncertainties.A new predictive functional control based on Takagi-Sugeno(T-S)fuzzy model was proposed to control HVAC systems.The T-S fuzzy model of stabilized controlled process was obtained using the least squares method,then on the basis of global linear predictive model from T-S fuzzy model,the process was controlled by the predictive functional controller.Especially the feedback regulation part was developed to compensate uncertainties of fuzzy predictive model.Finally simulation test results in HVAC systems control applications showed that the proposed fuzzy model predictive functional control improves tracking effect and robustness.Compared with the conventional PID controller,this control strategy has the advantages of less overshoot and shorter setting time,etc.

  19. Prediction of air to liver partition coefficient for volatile organic compounds using QSAR approaches.

    Science.gov (United States)

    Dashtbozorgi, Zahra; Golmohammadi, Hassan

    2010-06-01

    In this work a quantitative structure-activity relationship (QSAR) technique was developed to investigate the air to liver partition coefficient (log Kliver) for volatile organic compounds (VOCs). Suitable set of molecular descriptors was calculated and the important descriptors were selected by GA-PLS methods. These variables were served as inputs to generate neural networks. After optimization and training of the networks, they were used for the calculation of log Kliver for the validation set. The root mean square errors for the neural network calculated log Kliver of training, test, and validation sets are 0.100, 0.091, and 0.112, respectively. Results obtained reveal the reliability and good predictivity of neural network for the prediction of air to liver partition coefficient for volatile organic compounds.

  20. Improvement of springback prediction of wide sheet metal air bending process

    Institute of Scientific and Technical Information of China (English)

    李建; 赵军; 孙红磊; 马瑞

    2008-01-01

    Accurate springback prediction of wide sheet metal air bending process is important to improve product quality and ensure the precision in dimension. The definition of elastic limit bend angle was proposed. Based on cantilever beam elastic deforming theory, the geometrical parameters of forming tools, sheet thickness and the material yielding strain were derived and validated by the finite element method (FEM). Employing the degree of elastic limit bend angle, the equation for springback prediction was constructed, the results calculated fit well with experimental data. Especially for the small bend angle, the predicted results by equation were applied to conduct the springback prediction and compensation in industries and give closer correlation to the experimental data than those calculated by engineering theory of plastic bending.

  1. Validation of ice loads predicted from meteorological models

    Energy Technology Data Exchange (ETDEWEB)

    Veal, A.; Skea, A. [UK Met Office, Exeter, England (United Kingdom); Wareing, B. [Brian Wareing Tech Ltd., England (United Kingdom)

    2005-07-01

    Results of a field trial conducted on 2 Gerber PVM-100 instruments at Deadwater Fell test site in the United Kingdom were presented. The trials were conducted to assess whether the instruments were capable of measuring the liquid water content of the air, as well as to validate an ice model in terms of accretion rates on different sized conductors. Ambient air temperature, wind speed and direction were monitored at the Deadwater Fell weather station along with load cell values. Time lapse video recorders and a web camera system were used to view the performance of the conductors in varying weather conditions. All data was collected and stored at the site. It was anticipated that output from the instruments could be related to the conditions under which overhead line conductors suffer from ice loads, and help to revise weather maps which have proved to be incompatible with utility experience and the lifetimes achieved by overhead line designs. The data provided from the Deadwater work included logged data from the Gerbers, weather data and load data from a 10 mm diameter aluminium alloy conductor. When the combination of temperature, wind direction and Gerber output indicated icing conditions, they were confirmed by the conductor's load cell data. The tests confirmed the validity of the Gerber instruments to predict the occurrence of icing conditions, when combined with other meteorological data. It was concluded that the instruments may aid in optimized prediction methods for ice loads and icing events. 2 refs., 4 figs.

  2. Design and dynamic characteristic prediction of air-powered twin-rotor piston engine

    Institute of Scientific and Technical Information of China (English)

    徐海军; 张雷; 潘存云; 张湘

    2015-01-01

    A novel air-powered twin-rotor piston engine (ATPE) utilizing a differential velocity driving mechanism to achieve a high output torque was proposed. The ATPE had eight separated rotary cylinders which can dynamically enlarge the engine displacement as a result of the special driving mechanism, which was named dynamic volume expansion. The mathematical model of ATPE comprising a dynamic model and a thermodynamic model was established under the assumption of no mechanical friction. The model was numerically simulated in Matlab. The results show that shortage of low output torque confusing traditional air-powered engines can be overcome. The average output torque sharply increases to 100 N·m, which is about three times that of traditional air-powered engines with equal cylinder displacement under the pressure of 0.6 MPa at 480 r/min. ATPE can be used to drive vehicles directly without transmission box, therefore the energy transfer efficiency of ATPE can be increased. Furthermore, benefitting from the novel gas distribution system, the engine shows an ability in self-adjusting under different loads. The arrangements of air ports automatically adjust the open interval of air ports according to the load, which may simplify the speed control system.

  3. Predictive modeling by the cerebellum improves proprioception.

    Science.gov (United States)

    Bhanpuri, Nasir H; Okamura, Allison M; Bastian, Amy J

    2013-09-04

    Because sensation is delayed, real-time movement control requires not just sensing, but also predicting limb position, a function hypothesized for the cerebellum. Such cerebellar predictions could contribute to perception of limb position (i.e., proprioception), particularly when a person actively moves the limb. Here we show that human cerebellar patients have proprioceptive deficits compared with controls during active movement, but not when the arm is moved passively. Furthermore, when healthy subjects move in a force field with unpredictable dynamics, they have active proprioceptive deficits similar to cerebellar patients. Therefore, muscle activity alone is likely insufficient to enhance proprioception and predictability (i.e., an internal model of the body and environment) is important for active movement to benefit proprioception. We conclude that cerebellar patients have an active proprioceptive deficit consistent with disrupted movement prediction rather than an inability to generally enhance peripheral proprioceptive signals during action and suggest that active proprioceptive deficits should be considered a fundamental cerebellar impairment of clinical importance.

  4. A prediction model for Clostridium difficile recurrence

    Directory of Open Access Journals (Sweden)

    Francis D. LaBarbera

    2015-02-01

    Full Text Available Background: Clostridium difficile infection (CDI is a growing problem in the community and hospital setting. Its incidence has been on the rise over the past two decades, and it is quickly becoming a major concern for the health care system. High rate of recurrence is one of the major hurdles in the successful treatment of C. difficile infection. There have been few studies that have looked at patterns of recurrence. The studies currently available have shown a number of risk factors associated with C. difficile recurrence (CDR; however, there is little consensus on the impact of most of the identified risk factors. Methods: Our study was a retrospective chart review of 198 patients diagnosed with CDI via Polymerase Chain Reaction (PCR from February 2009 to Jun 2013. In our study, we decided to use a machine learning algorithm called the Random Forest (RF to analyze all of the factors proposed to be associated with CDR. This model is capable of making predictions based on a large number of variables, and has outperformed numerous other models and statistical methods. Results: We came up with a model that was able to accurately predict the CDR with a sensitivity of 83.3%, specificity of 63.1%, and area under curve of 82.6%. Like other similar studies that have used the RF model, we also had very impressive results. Conclusions: We hope that in the future, machine learning algorithms, such as the RF, will see a wider application.

  5. Gamma-Ray Pulsars Models and Predictions

    CERN Document Server

    Harding, A K

    2001-01-01

    Pulsed emission from gamma-ray pulsars originates inside the magnetosphere, from radiation by charged particles accelerated near the magnetic poles or in the outer gaps. In polar cap models, the high energy spectrum is cut off by magnetic pair production above an energy that is dependent on the local magnetic field strength. While most young pulsars with surface fields in the range B = 10^{12} - 10^{13} G are expected to have high energy cutoffs around several GeV, the gamma-ray spectra of old pulsars having lower surface fields may extend to 50 GeV. Although the gamma-ray emission of older pulsars is weaker, detecting pulsed emission at high energies from nearby sources would be an important confirmation of polar cap models. Outer gap models predict more gradual high-energy turnovers at around 10 GeV, but also predict an inverse Compton component extending to TeV energies. Detection of pulsed TeV emission, which would not survive attenuation at the polar caps, is thus an important test of outer gap models. N...

  6. Artificial Neural Network Model for Predicting Compressive

    Directory of Open Access Journals (Sweden)

    Salim T. Yousif

    2013-05-01

    Full Text Available   Compressive strength of concrete is a commonly used criterion in evaluating concrete. Although testing of the compressive strength of concrete specimens is done routinely, it is performed on the 28th day after concrete placement. Therefore, strength estimation of concrete at early time is highly desirable. This study presents the effort in applying neural network-based system identification techniques to predict the compressive strength of concrete based on concrete mix proportions, maximum aggregate size (MAS, and slump of fresh concrete. Back-propagation neural networks model is successively developed, trained, and tested using actual data sets of concrete mix proportions gathered from literature.    The test of the model by un-used data within the range of input parameters shows that the maximum absolute error for model is about 20% and 88% of the output results has absolute errors less than 10%. The parametric study shows that water/cement ratio (w/c is the most significant factor  affecting the output of the model.     The results showed that neural networks has strong potential as a feasible tool for predicting compressive strength of concrete.

  7. Ground Motion Prediction Models for Caucasus Region

    Science.gov (United States)

    Jorjiashvili, Nato; Godoladze, Tea; Tvaradze, Nino; Tumanova, Nino

    2016-04-01

    Ground motion prediction models (GMPMs) relate ground motion intensity measures to variables describing earthquake source, path, and site effects. Estimation of expected ground motion is a fundamental earthquake hazard assessment. The most commonly used parameter for attenuation relation is peak ground acceleration or spectral acceleration because this parameter gives useful information for Seismic Hazard Assessment. Since 2003 development of Georgian Digital Seismic Network has started. In this study new GMP models are obtained based on new data from Georgian seismic network and also from neighboring countries. Estimation of models is obtained by classical, statistical way, regression analysis. In this study site ground conditions are additionally considered because the same earthquake recorded at the same distance may cause different damage according to ground conditions. Empirical ground-motion prediction models (GMPMs) require adjustment to make them appropriate for site-specific scenarios. However, the process of making such adjustments remains a challenge. This work presents a holistic framework for the development of a peak ground acceleration (PGA) or spectral acceleration (SA) GMPE that is easily adjustable to different seismological conditions and does not suffer from the practical problems associated with adjustments in the response spectral domain.

  8. Modeling and Prediction of Krueger Device Noise

    Science.gov (United States)

    Guo, Yueping; Burley, Casey L.; Thomas, Russell H.

    2016-01-01

    This paper presents the development of a noise prediction model for aircraft Krueger flap devices that are considered as alternatives to leading edge slotted slats. The prediction model decomposes the total Krueger noise into four components, generated by the unsteady flows, respectively, in the cove under the pressure side surface of the Krueger, in the gap between the Krueger trailing edge and the main wing, around the brackets supporting the Krueger device, and around the cavity on the lower side of the main wing. For each noise component, the modeling follows a physics-based approach that aims at capturing the dominant noise-generating features in the flow and developing correlations between the noise and the flow parameters that control the noise generation processes. The far field noise is modeled using each of the four noise component's respective spectral functions, far field directivities, Mach number dependencies, component amplitudes, and other parametric trends. Preliminary validations are carried out by using small scale experimental data, and two applications are discussed; one for conventional aircraft and the other for advanced configurations. The former focuses on the parametric trends of Krueger noise on design parameters, while the latter reveals its importance in relation to other airframe noise components.

  9. A generative model for predicting terrorist incidents

    Science.gov (United States)

    Verma, Dinesh C.; Verma, Archit; Felmlee, Diane; Pearson, Gavin; Whitaker, Roger

    2017-05-01

    A major concern in coalition peace-support operations is the incidence of terrorist activity. In this paper, we propose a generative model for the occurrence of the terrorist incidents, and illustrate that an increase in diversity, as measured by the number of different social groups to which that an individual belongs, is inversely correlated with the likelihood of a terrorist incident in the society. A generative model is one that can predict the likelihood of events in new contexts, as opposed to statistical models which are used to predict the future incidents based on the history of the incidents in an existing context. Generative models can be useful in planning for persistent Information Surveillance and Reconnaissance (ISR) since they allow an estimation of regions in the theater of operation where terrorist incidents may arise, and thus can be used to better allocate the assignment and deployment of ISR assets. In this paper, we present a taxonomy of terrorist incidents, identify factors related to occurrence of terrorist incidents, and provide a mathematical analysis calculating the likelihood of occurrence of terrorist incidents in three common real-life scenarios arising in peace-keeping operations

  10. Application of MM5/CMAQ for modelling urban air pollution a case study for London, UK

    Science.gov (United States)

    Kitwiroon, N.; Fragkou, E.; Sokhi, R. S.; San Jose, R.; Pérez Camaño, J. L.; Middleton, D.

    2003-04-01

    Urban air pollution has been particularly studied for the last few decades because of its recognised environmental dangers and health implications. The complexity of the urban surface characteristics and turbulence patterns has dictated the use of numerical models by environmental research agencies and regulators in order to predict and manage urban air pollution. However, most of these models are not specifically adapted to urban applications and normally do not include detailed urban parameterisation, such as for surface roughness or urban heat fluxes. Flow structure and dispersion of air pollutants within cities, however, are influenced by urban features such as increased surface roughness. This paper presents a study using MM5 and CMAQ to assess the effect of urban boundary layer features on meteorological parameters, and hence London's air quality. MM5 is a non-hydrostatic (version 3), terrain-following sigma-coordinate model designed to simulate mesoscale and regional-scale atmospheric circulation. This paper employs an improved surface roughness treatment on meteorological profiles and pollution dispersion. A surface roughness scale has been developed for London and the surrounding region. The land cover data was derived from the Centre for Ecology and Hydrology (CEH) data, with a spatial resolution of 25 × 25 m. These z_o values are employed with MM5 for modelling meteorological parameters over London, covering an inner domain area of 49 × 49 km. The outputs of MM5 have been coupled to CMAQ photochemical model to predict concentrations of particles, NO_2 and O_3 for London and the surrounding regions at a spatial resolution of 1 × 1 km. The predicted concentrations have been compared with monitored data obtained from a range of national air quality monitoring sites including Central London (Bloomsbury, Brent), East London (Bexley) and West London (Hillingdon). Comparison of hourly model predictions with measured data is made for pollution levels for

  11. COP Prediction of an ejector refrigeration cycle combined with a vapour compression cycle for automotive air conditioning

    Directory of Open Access Journals (Sweden)

    Nat Suvarnakuta

    2014-03-01

    Full Text Available This paper presents the COP prediction of an ejector refrigeration cycle combined with a vapour compression cycle for automotive air conditioning. Using computational fluid dynamics (CFD technique, the performance of an ejector was analyzed in term of the entrainment ratio (Rm and critical back pressure (CBP. The results from this study were compared with a previous study of combined ejector refrigeration system for automotive air conditioning application [1] which the entrainment ratio (Rm were predicted from one-dimensional (1-D equation. The performance of an ejector (Rm and CBP from CFD and onedimensional method were analyzed and used as database for a mathematical modeling. In order to predict the COP of the combined system, a set of mathematical equations was developed using EES. The operating conditions are chosen accordingly as, intercooler temperature between 15 ๐ C and 25 ๐ C, condenser temperature equal to 35 ๐ C and evaporator temperature equal to 5 ๐ C. However, when generator temperatures are 80 ๐ C, 85 ๐ C and 90 ๐ C, the results showed average relative errors of the COP of an ejector refrigeration cycle (COPej, between CFD and 1-D are 44.64%, 50.47% and 59.68% respectively, and between CFD and 1-D NEW are 1.54%, 0.08% and 6.49% respectively.

  12. Optimal feedback scheduling of model predictive controllers

    Institute of Scientific and Technical Information of China (English)

    Pingfang ZHOU; Jianying XIE; Xiaolong DENG

    2006-01-01

    Model predictive control (MPC) could not be reliably applied to real-time control systems because its computation time is not well defined. Implemented as anytime algorithm, MPC task allows computation time to be traded for control performance, thus obtaining the predictability in time. Optimal feedback scheduling (FS-CBS) of a set of MPC tasks is presented to maximize the global control performance subject to limited processor time. Each MPC task is assigned with a constant bandwidth server (CBS), whose reserved processor time is adjusted dynamically. The constraints in the FSCBS guarantee scheduler of the total task set and stability of each component. The FS-CBS is shown robust against the variation of execution time of MPC tasks at runtime. Simulation results illustrate its effectiveness.

  13. Objective calibration of numerical weather prediction models

    Science.gov (United States)

    Voudouri, A.; Khain, P.; Carmona, I.; Bellprat, O.; Grazzini, F.; Avgoustoglou, E.; Bettems, J. M.; Kaufmann, P.

    2017-07-01

    Numerical weather prediction (NWP) and climate models use parameterization schemes for physical processes, which often include free or poorly confined parameters. Model developers normally calibrate the values of these parameters subjectively to improve the agreement of forecasts with available observations, a procedure referred as expert tuning. A practicable objective multi-variate calibration method build on a quadratic meta-model (MM), that has been applied for a regional climate model (RCM) has shown to be at least as good as expert tuning. Based on these results, an approach to implement the methodology to an NWP model is presented in this study. Challenges in transferring the methodology from RCM to NWP are not only restricted to the use of higher resolution and different time scales. The sensitivity of the NWP model quality with respect to the model parameter space has to be clarified, as well as optimize the overall procedure, in terms of required amount of computing resources for the calibration of an NWP model. Three free model parameters affecting mainly turbulence parameterization schemes were originally selected with respect to their influence on the variables associated to daily forecasts such as daily minimum and maximum 2 m temperature as well as 24 h accumulated precipitation. Preliminary results indicate that it is both affordable in terms of computer resources and meaningful in terms of improved forecast quality. In addition, the proposed methodology has the advantage of being a replicable procedure that can be applied when an updated model version is launched and/or customize the same model implementation over different climatological areas.

  14. Modeling air entrainment in plunging jet using 3DYNAFS

    CERN Document Server

    Hsiao, Chao-Tsung; Wu, Xiongjun; Chahine, Georges L

    2011-01-01

    As the liquid jet plunges into a free surface, significant air is entrained into the water and forms air pockets. These air pockets eventually break up into small bubbles, which travel downstream to form a bubbly wake. To better understand the underlying flow physics involved in the bubble entrainment, in the linked videos, air entrainment due to a water jet plunging onto a pool of stationary water was numerically studied by using the 3DYNAFS software suit. The flow field is simulated by directly solving the Navier-Stokes equations through the viscous module, 3DYNAFS-VIS, using a level set method for capturing the free surface. The breakup of entrained air pockets and the resulting bubbly flow were modeled by coupling 3DYNAFS-VIS with a Lagrangian multi-bubble tracking model, 3DYNAFS-DSM (Hsiao & Chahine, 2003), which emits bubbles into the liquid according to local liquid/gas interface flow conditions based on the sub-grid air entrainment modeling proposed by Ma et al. (2011), and tracks all bubbles in t...

  15. Modeling green infrastructure land use changes on future air ...

    Science.gov (United States)

    Green infrastructure can be a cost-effective approach for reducing stormwater runoff and improving water quality as a result, but it could also bring co-benefits for air quality: less impervious surfaces and more vegetation can decrease the urban heat island effect, and also result in more removal of air pollutants via dry deposition with increased vegetative surfaces. Cooler surface temperatures can also decrease ozone formation through the increases of NOx titration; however, cooler surface temperatures also lower the height of the boundary layer resulting in more concentrated pollutants within the same volume of air, especially for primary emitted pollutants (e.g. NOx, CO, primary particulate matter). To better understand how green infrastructure impacts air quality, the interactions between all of these processes must be considered collectively. In this study, we use a comprehensive coupled meteorology-air quality model (WRF-CMAQ) to simulate the influence of planned land use changes that include green infrastructure in Kansas City (KC) on regional meteorology and air quality. Current and future land use data was provided by the Mid-America Regional Council for 2012 and 2040 (projected land use due to population growth, city planning and green infrastructure implementation). These land use datasets were incorporated into the WRF-CMAQ modeling system allowing the modeling system to propagate the changes in vegetation and impervious surface coverage on meteoro

  16. Prediction models from CAD models of 3D objects

    Science.gov (United States)

    Camps, Octavia I.

    1992-11-01

    In this paper we present a probabilistic prediction based approach for CAD-based object recognition. Given a CAD model of an object, the PREMIO system combines techniques of analytic graphics and physical models of lights and sensors to predict how features of the object will appear in images. In nearly 4,000 experiments on analytically-generated and real images, we show that in a semi-controlled environment, predicting the detectability of features of the image can successfully guide a search procedure to make informed choices of model and image features in its search for correspondences that can be used to hypothesize the pose of the object. Furthermore, we provide a rigorous experimental protocol that can be used to determine the optimal number of correspondences to seek so that the probability of failing to find a pose and of finding an inaccurate pose are minimized.

  17. Model predictive control of MSMPR crystallizers

    Science.gov (United States)

    Moldoványi, Nóra; Lakatos, Béla G.; Szeifert, Ferenc

    2005-02-01

    A multi-input-multi-output (MIMO) control problem of isothermal continuous crystallizers is addressed in order to create an adequate model-based control system. The moment equation model of mixed suspension, mixed product removal (MSMPR) crystallizers that forms a dynamical system is used, the state of which is represented by the vector of six variables: the first four leading moments of the crystal size, solute concentration and solvent concentration. Hence, the time evolution of the system occurs in a bounded region of the six-dimensional phase space. The controlled variables are the mean size of the grain; the crystal size-distribution and the manipulated variables are the input concentration of the solute and the flow rate. The controllability and observability as well as the coupling between the inputs and the outputs was analyzed by simulation using the linearized model. It is shown that the crystallizer is a nonlinear MIMO system with strong coupling between the state variables. Considering the possibilities of the model reduction, a third-order model was found quite adequate for the model estimation in model predictive control (MPC). The mean crystal size and the variance of the size distribution can be nearly separately controlled by the residence time and the inlet solute concentration, respectively. By seeding, the controllability of the crystallizer increases significantly, and the overshoots and the oscillations become smaller. The results of the controlling study have shown that the linear MPC is an adaptable and feasible controller of continuous crystallizers.

  18. An Anisotropic Hardening Model for Springback Prediction

    Science.gov (United States)

    Zeng, Danielle; Xia, Z. Cedric

    2005-08-01

    As more Advanced High-Strength Steels (AHSS) are heavily used for automotive body structures and closures panels, accurate springback prediction for these components becomes more challenging because of their rapid hardening characteristics and ability to sustain even higher stresses. In this paper, a modified Mroz hardening model is proposed to capture realistic Bauschinger effect at reverse loading, such as when material passes through die radii or drawbead during sheet metal forming process. This model accounts for material anisotropic yield surface and nonlinear isotropic/kinematic hardening behavior. Material tension/compression test data are used to accurately represent Bauschinger effect. The effectiveness of the model is demonstrated by comparison of numerical and experimental springback results for a DP600 straight U-channel test.

  19. Comparison between the Chinese EIA Guidelines for Air Dispersion Modelling and the Advanced Air Dispersion Model ADMS

    Institute of Scientific and Technical Information of China (English)

    David Carruthers; Sheng Xiangyu; Christine McHugh

    2005-01-01

    This paper makes comparisons between Chinese Environmental Impact Assessment (EIA)Guidelines for Air dispersion modelling and the advanced air dispersion model ADMS. Since 2001 the ADMS model has been the first and only foreign model that has been approved by the Appraisal Center for Environment and Engineering (ACEE) to be used in EIA projects in China (http://www. china-eia.com/inden_content/rjrz/rjrz_ADMS/htm). In the paper the following sections provide brief descriptions of the main features of the Chinese Guidelines for Air Dispersion (Section 2) and ADMS (Section 3);Section 4 provides a comparison of the two modelling methods for some simple cases and conclusions and discussion are given in Section 5.

  20. Artificial intelligence modeling to evaluate field performance of photocatalytic asphalt pavement for ambient air purification.

    Science.gov (United States)

    Asadi, Somayeh; Hassan, Marwa; Nadiri, Ataallah; Dylla, Heather

    2014-01-01

    In recent years, the application of titanium dioxide (TiO₂) as a photocatalyst in asphalt pavement has received considerable attention for purifying ambient air from traffic-emitted pollutants via photocatalytic processes. In order to control the increasing deterioration of ambient air quality, urgent and proper risk assessment tools are deemed necessary. However, in practice, monitoring all process parameters for various operating conditions is difficult due to the complex and non-linear nature of air pollution-based problems. Therefore, the development of models to predict air pollutant concentrations is very useful because it can provide early warnings to the population and also reduce the number of measuring sites. This study used artificial neural network (ANN) and neuro-fuzzy (NF) models to predict NOx concentration in the air as a function of traffic count (Tr) and climatic conditions including humidity (H), temperature (T), solar radiation (S), and wind speed (W) before and after the application of TiO₂ on the pavement surface. These models are useful for modeling because of their ability to be trained using historical data and because of their capability for modeling highly non-linear relationships. To build these models, data were collected from a field study where an aqueous nano TiO₂ solution was sprayed on a 0.2-mile of asphalt pavement in Baton Rouge, LA. Results of this study showed that the NF model provided a better fitting to NOx measurements than the ANN model in the training, validation, and test steps. Results of a parametric study indicated that traffic level, relative humidity, and solar radiation had the most influence on photocatalytic efficiency.

  1. Towards a forecasting system of air quality for Asia using the WRF-Chem model

    Science.gov (United States)

    Katinka Petersen, Anna; Kumar, Rajesh; Brasseur, Guy; Granier, Claire

    2013-04-01

    The degradation of air quality in Asia resulting from the intensification of human activities, and the related impacts on the health of billions of people have become an urgent matter of concern. The World Health Organization states that each year nearly 3.3 million people die worldwide prematurely because of air pollution. The situation is particularly acute in Asia. Improving air quality over the Asian continent has become a major challenge for national, regional and local authorities. A prerequisite for air quality improvement is the development of a reliable monitoring system with surface instrumentation and space platforms as well as an analysis and prediction system based on an advanced chemical-meteorological model. The aim is to use the WRF-Chem model for the prediction of daily air quality for the Asian continent with spatial resolution that will be increased in densely populated areas by grid nesting. The modeling system covers a nearly the entire Asian continent so that transport processes of chemical compounds within the continent are simulated and analyzed. To additionally account for the long-range effects and assess their relative importance against regional emissions, the regional chemical transport modeling system uses information from a global modeling system as boundary conditions. The first steps towards a forecasting system over Asia are to test the model performance over this large model domain and the different emissions inventories available for Asia. In this study, the WRF-Chem model was run for a domain covering 60°E to 150°E, 5°S to 50°N at a resolution of 60 km x 60 km for January 2006 with three alternative emission inventories available for Asia (MACCITY, INTEX-B and REAS). We present an intercomparison of the three different simulations and evaluate the simulations with satellite and in situ observations, with focus on ozone, particulate matter, nitrogen oxides and carbon monoxide. The differences between the simulations using

  2. new model for solar radiation estimation from measured air ...

    African Journals Online (AJOL)

    HOD

    Nigerian Meteorological Agency (NIMET) were used as inputs to the ANFIS model and monthly mean global solar radiation was ... models were used to predict solar radiation in Nigeria by. [12-15]. .... calculate them as total output [32] and [34].

  3. Performance of Air Pollution Models on Massively Parallel Computers

    DEFF Research Database (Denmark)

    Brown, John; Hansen, Per Christian; Wasniewski, Jerzy

    1996-01-01

    To compare the performance and use of three massively parallel SIMD computers, we implemented a large air pollution model on the computers. Using a realistic large-scale model, we gain detailed insight about the performance of the three computers when used to solve large-scale scientific problems...

  4. A hybrid model for predicting carbon monoxide from vehicular exhausts in urban environments

    Science.gov (United States)

    Gokhale, Sharad; Khare, Mukesh

    Several deterministic-based air quality models evaluate and predict the frequently occurring pollutant concentration well but, in general, are incapable of predicting the 'extreme' concentrations. In contrast, the statistical distribution models overcome the above limitation of the deterministic models and predict the 'extreme' concentrations. However, the environmental damages are caused by both extremes as well as by the sustained average concentration of pollutants. Hence, the model should predict not only 'extreme' ranges but also the 'middle' ranges of pollutant concentrations, i.e. the entire range. Hybrid modelling is one of the techniques that estimates/predicts the 'entire range' of the distribution of pollutant concentrations by combining the deterministic based models with suitable statistical distribution models ( Jakeman, et al., 1988). In the present paper, a hybrid model has been developed to predict the carbon monoxide (CO) concentration distributions at one of the traffic intersections, Income Tax Office (ITO), in the Delhi city, where the traffic is heterogeneous in nature and meteorology is 'tropical'. The model combines the general finite line source model (GFLSM) as its deterministic, and log logistic distribution (LLD) model, as its statistical components. The hybrid (GFLSM-LLD) model is then applied at the ITO intersection. The results show that the hybrid model predictions match with that of the observed CO concentration data within the 5-99 percentiles range. The model is further validated at different street location, i.e. Sirifort roadway. The validation results show that the model predicts CO concentrations fairly well ( d=0.91) in 10-95 percentiles range. The regulatory compliance is also developed to estimate the probability of exceedance of hourly CO concentration beyond the National Ambient Air Quality Standards (NAAQS) of India. It consists of light vehicles, heavy vehicles, three- wheelers (auto rickshaws) and two

  5. Modelling the impact of room temperature on concentrations of polychlorinated biphenyls (PCBs) in indoor air

    DEFF Research Database (Denmark)

    Lyng, Nadja; Clausen, Per Axel; Lundsgaard, Claus

    2016-01-01

    Buildings contaminated with polychlorinated biphenyls (PCBs) are a health concern for the building occupants. Inhalation exposure is linked to indoor air concentrations of PCBs, which are known to be affected by indoor temperatures. In this study, a highly PCB contaminated room was heated to six....... The results showed that one easured concentration of PCB at a known steady-state temperature can be used to predict the steady-state concentrations at other temperatures under circumstances where e.g. direct sunlight does not influence temperatures and the air exchange rate is constant. The model was also...

  6. Yule-Nielsen based recto-verso color halftone transmittance prediction model.

    Science.gov (United States)

    Hébert, Mathieu; Hersch, Roger D

    2011-02-01

    The transmittance spectrum of halftone prints on paper is predicted thanks to a model inspired by the Yule-Nielsen modified spectral Neugebauer model used for reflectance predictions. This model is well adapted for strongly scattering printing supports and applicable to recto-verso prints. Model parameters are obtained by a few transmittance measurements of calibration patches printed on one side of the paper. The model was verified with recto-verso specimens printed by inkjet with classical and custom inks, at different halftone frequencies and on various types of paper. Predictions are as accurate as those obtained with a previously developed reflectance and transmittance prediction model relying on the multiple reflections of light between the paper and the print-air interfaces. Optimal n values are smaller in transmission mode compared with the reflection model. This indicates a smaller amount of lateral light propagation in the transmission mode.

  7. The results of air treatment process modeling at the location of the air curtain in the air suppliers and ventilation shafts

    Directory of Open Access Journals (Sweden)

    Nikolaev Aleksandr

    2017-01-01

    Full Text Available In the existing shaft air heater installations (AHI, that heat the air for air suppliers in cold seasons, a heater channel is used. Some parts of the air from the heater go to the channel, other parts are sucked through a pithead by the general shaft pressure drawdown formed by the main ventilation installation (MVI. When this happens, a mix of two air flows leads to a shaft heat regime violation that can break pressurization of intertubular sealers. The problem of energy saving while airing underground mining enterprises is also very important. The proposed solution of both tasks due to the application of an air curtain is described in the article. In cold seasons the air treatment process should be used and it is offered to place an air curtain in the air suppliers shaft above the place of interface of the calorifer channel to a trunk in order to avoid an infiltration (suction of air through the pithead. It’s recommended to use an air curtain in a ventilation shaft because it reduces external air leaks thereby improving energy efficiency of the MVI work. During the mathematical modeling of ventilation and air preparation process (in SolidWorks Flowsimulation software package it was found out that the use of the air curtain in the air supply shaft can increase the efficiency of the AHI, and reduce the electricity consumption for ventilation in the ventilation shaft.

  8. Predictive modelling of ferroelectric tunnel junctions

    Science.gov (United States)

    Velev, Julian P.; Burton, John D.; Zhuravlev, Mikhail Ye; Tsymbal, Evgeny Y.

    2016-05-01

    Ferroelectric tunnel junctions combine the phenomena of quantum-mechanical tunnelling and switchable spontaneous polarisation of a nanometre-thick ferroelectric film into novel device functionality. Switching the ferroelectric barrier polarisation direction produces a sizable change in resistance of the junction—a phenomenon known as the tunnelling electroresistance effect. From a fundamental perspective, ferroelectric tunnel junctions and their version with ferromagnetic electrodes, i.e., multiferroic tunnel junctions, are testbeds for studying the underlying mechanisms of tunnelling electroresistance as well as the interplay between electric and magnetic degrees of freedom and their effect on transport. From a practical perspective, ferroelectric tunnel junctions hold promise for disruptive device applications. In a very short time, they have traversed the path from basic model predictions to prototypes for novel non-volatile ferroelectric random access memories with non-destructive readout. This remarkable progress is to a large extent driven by a productive cycle of predictive modelling and innovative experimental effort. In this review article, we outline the development of the ferroelectric tunnel junction concept and the role of theoretical modelling in guiding experimental work. We discuss a wide range of physical phenomena that control the functional properties of ferroelectric tunnel junctions and summarise the state-of-the-art achievements in the field.

  9. Simple predictions from multifield inflationary models.

    Science.gov (United States)

    Easther, Richard; Frazer, Jonathan; Peiris, Hiranya V; Price, Layne C

    2014-04-25

    We explore whether multifield inflationary models make unambiguous predictions for fundamental cosmological observables. Focusing on N-quadratic inflation, we numerically evaluate the full perturbation equations for models with 2, 3, and O(100) fields, using several distinct methods for specifying the initial values of the background fields. All scenarios are highly predictive, with the probability distribution functions of the cosmological observables becoming more sharply peaked as N increases. For N=100 fields, 95% of our Monte Carlo samples fall in the ranges ns∈(0.9455,0.9534), α∈(-9.741,-7.047)×10-4, r∈(0.1445,0.1449), and riso∈(0.02137,3.510)×10-3 for the spectral index, running, tensor-to-scalar ratio, and isocurvature-to-adiabatic ratio, respectively. The expected amplitude of isocurvature perturbations grows with N, raising the possibility that many-field models may be sensitive to postinflationary physics and suggesting new avenues for testing these scenarios.

  10. A model of airflow in the nasal cavities: Implications for nasal air conditioning and epistaxis.

    Science.gov (United States)

    Bailie, Neil; Hanna, Brendan; Watterson, John; Gallagher, Geraldine

    2009-01-01

    A friction force is generated when moving air contacts the nasal walls, referred to as wall shear stress. This interaction facilitates heat and mass transfer between the mucosa and air, i.e., air-conditioning. The objective of this research was to study the distribution of wall shear stress within the nasal cavity to identify areas that contribute significantly to air-conditioning within the nasal cavity. Three-dimensional computational models of the nasal airways of five healthy subjects (three male and two female subjects) were constructed from nasal CT scans. Numerical simulations of nasal airflow were conducted using the commercial computational fluid dynamics code Fluent 6 (Ansys, Inc., Canonsburg, PA). Wall shear stress was derived from the numerical simulation. Air-conditioning was simulated to confirm the relationship with wall shear stress. Nasal airflow simulations predicted high wall shear stress along the anterior aspect of the inferior turbinate, the anteroinferior aspect of the middle turbinate, and within Little's area. The airflow simulations indicate that the inferior and middle turbinates and Little's area on the anterior nasal septum contribute significantly to nasal air-conditioning. The concentration of wall shear stress within Little's area indicates a desiccating and potentially traumatic effect of inhaled air that may explain the predilection for spontaneous epistaxis at this site.

  11. Monthly prediction of air temperature in Australia and New Zealand with machine learning algorithms

    Science.gov (United States)

    Salcedo-Sanz, S.; Deo, R. C.; Carro-Calvo, L.; Saavedra-Moreno, B.

    2016-07-01

    Long-term air temperature prediction is of major importance in a large number of applications, including climate-related studies, energy, agricultural, or medical. This paper examines the performance of two Machine Learning algorithms (Support Vector Regression (SVR) and Multi-layer Perceptron (MLP)) in a problem of monthly mean air temperature prediction, from the previous measured values in observational stations of Australia and New Zealand, and climate indices of importance in the region. The performance of the two considered algorithms is discussed in the paper and compared to alternative approaches. The results indicate that the SVR algorithm is able to obtain the best prediction performance among all the algorithms compared in the paper. Moreover, the results obtained have shown that the mean absolute error made by the two algorithms considered is significantly larger for the last 20 years than in the previous decades, in what can be interpreted as a change in the relationship among the prediction variables involved in the training of the algorithms.

  12. Predictions of models for environmental radiological assessment

    Energy Technology Data Exchange (ETDEWEB)

    Peres, Sueli da Silva; Lauria, Dejanira da Costa, E-mail: suelip@ird.gov.br, E-mail: dejanira@irg.gov.br [Instituto de Radioprotecao e Dosimetria (IRD/CNEN-RJ), Servico de Avaliacao de Impacto Ambiental, Rio de Janeiro, RJ (Brazil); Mahler, Claudio Fernando [Coppe. Instituto Alberto Luiz Coimbra de Pos-Graduacao e Pesquisa de Engenharia, Universidade Federal do Rio de Janeiro (UFRJ) - Programa de Engenharia Civil, RJ (Brazil)

    2011-07-01

    In the field of environmental impact assessment, models are used for estimating source term, environmental dispersion and transfer of radionuclides, exposure pathway, radiation dose and the risk for human beings Although it is recognized that the specific information of local data are important to improve the quality of the dose assessment results, in fact obtaining it can be very difficult and expensive. Sources of uncertainties are numerous, among which we can cite: the subjectivity of modelers, exposure scenarios and pathways, used codes and general parameters. The various models available utilize different mathematical approaches with different complexities that can result in different predictions. Thus, for the same inputs different models can produce very different outputs. This paper presents briefly the main advances in the field of environmental radiological assessment that aim to improve the reliability of the models used in the assessment of environmental radiological impact. The intercomparison exercise of model supplied incompatible results for {sup 137}Cs and {sup 60}Co, enhancing the need for developing reference methodologies for environmental radiological assessment that allow to confront dose estimations in a common comparison base. The results of the intercomparison exercise are present briefly. (author)

  13. Predicting Protein Secondary Structure with Markov Models

    DEFF Research Database (Denmark)

    Fischer, Paul; Larsen, Simon; Thomsen, Claus

    2004-01-01

    we are considering here, is to predict the secondary structure from the primary one. To this end we train a Markov model on training data and then use it to classify parts of unknown protein sequences as sheets, helices or coils. We show how to exploit the directional information contained......The primary structure of a protein is the sequence of its amino acids. The secondary structure describes structural properties of the molecule such as which parts of it form sheets, helices or coils. Spacial and other properties are described by the higher order structures. The classification task...

  14. A Modified Model Predictive Control Scheme

    Institute of Scientific and Technical Information of China (English)

    Xiao-Bing Hu; Wen-Hua Chen

    2005-01-01

    In implementations of MPC (Model Predictive Control) schemes, two issues need to be addressed. One is how to enlarge the stability region as much as possible. The other is how to guarantee stability when a computational time limitation exists. In this paper, a modified MPC scheme for constrained linear systems is described. An offline LMI-based iteration process is introduced to expand the stability region. At the same time, a database of feasible control sequences is generated offline so that stability can still be guaranteed in the case of computational time limitations. Simulation results illustrate the effectiveness of this new approach.

  15. Hierarchical Model Predictive Control for Resource Distribution

    DEFF Research Database (Denmark)

    Bendtsen, Jan Dimon; Trangbæk, K; Stoustrup, Jakob

    2010-01-01

    This paper deals with hierarchichal model predictive control (MPC) of distributed systems. A three level hierachical approach is proposed, consisting of a high level MPC controller, a second level of so-called aggregators, controlled by an online MPC-like algorithm, and a lower level of autonomous...... facilitates plug-and-play addition of subsystems without redesign of any controllers. The method is supported by a number of simulations featuring a three-level smart-grid power control system for a small isolated power grid....

  16. Explicit model predictive control accuracy analysis

    OpenAIRE

    Knyazev, Andrew; Zhu, Peizhen; Di Cairano, Stefano

    2015-01-01

    Model Predictive Control (MPC) can efficiently control constrained systems in real-time applications. MPC feedback law for a linear system with linear inequality constraints can be explicitly computed off-line, which results in an off-line partition of the state space into non-overlapped convex regions, with affine control laws associated to each region of the partition. An actual implementation of this explicit MPC in low cost micro-controllers requires the data to be "quantized", i.e. repre...

  17. Prediction of hydrodynamics and chemistry of confined turbulent methane-air frames in a two concentric tube combustor

    Science.gov (United States)

    Markatos, N. C.; Spalding, D. B.; Srivatsa, S. K.

    1978-01-01

    A formulation of the governing partial differential equations for fluid flow and reacting chemical species in a two-concentric-tube combustor is presented. A numerical procedure for the solution of the governing differential equations is described and models for chemical-equilibrium and chemical-kinetics calculations are presented. The chemical-equilibrium model is used to characterize the hydrocarbon reactions. The chemical-kinetics model is used to predict the concentrations of the oxides of nitrogen. The combustor considered consists of two coaxial ducts. Concentric streams of gaseous fuel and air enter the inlet duct at one end; the flow then reverses and flows out through the outer duct. Two sample cases with specified inlet and boundary conditions are considered and the results are discussed.

  18. Critical conceptualism in environmental modeling and prediction.

    Science.gov (United States)

    Christakos, G

    2003-10-15

    Many important problems in environmental science and engineering are of a conceptual nature. Research and development, however, often becomes so preoccupied with technical issues, which are themselves fascinating, that it neglects essential methodological elements of conceptual reasoning and theoretical inquiry. This work suggests that valuable insight into environmental modeling can be gained by means of critical conceptualism which focuses on the software of human reason and, in practical terms, leads to a powerful methodological framework of space-time modeling and prediction. A knowledge synthesis system develops the rational means for the epistemic integration of various physical knowledge bases relevant to the natural system of interest in order to obtain a realistic representation of the system, provide a rigorous assessment of the uncertainty sources, generate meaningful predictions of environmental processes in space-time, and produce science-based decisions. No restriction is imposed on the shape of the distribution model or the form of the predictor (non-Gaussian distributions, multiple-point statistics, and nonlinear models are automatically incorporated). The scientific reasoning structure underlying knowledge synthesis involves teleologic criteria and stochastic logic principles which have important advantages over the reasoning method of conventional space-time techniques. Insight is gained in terms of real world applications, including the following: the study of global ozone patterns in the atmosphere using data sets generated by instruments on board the Nimbus 7 satellite and secondary information in terms of total ozone-tropopause pressure models; the mapping of arsenic concentrations in the Bangladesh drinking water by assimilating hard and soft data from an extensive network of monitoring wells; and the dynamic imaging of probability distributions of pollutants across the Kalamazoo river.

  19. Modeling and imaging land-cover influences on air temperature in and near Baltimore, MD

    Science.gov (United States)

    Heisler, Gordon M.; Ellis, Alexis; Nowak, David J.; Yesilonis, Ian

    2016-04-01

    Over the course of 1681 hours between May 5 and September 30, 2006, air temperatures measured at the 1.5-m height at seven sites in and near the city of Baltimore, MD were used to empirically model Δ widehat{T} R-p , the difference in air temperature between a site in downtown Baltimore and the six other sites. Variables in the prediction equation included difference between the downtown reference and each of the other sites in upwind tree cover and impervious cover as obtained from 10-m resolution geographic information system (GIS) data. Other predictor variables included an index of atmospheric stability, topographic indices, wind speed, vapor pressure deficit, and antecedent precipitation. The model was used to map predicted hourly Δ widehat{T} R-p across the Baltimore region based on hourly weather data from the airport. Despite the numerous sources of variability in the regression modeling, the method produced reasonable map patterns of Δ widehat{T} R-p that, except for some areas evidently affected by sea breeze from the Chesapeake, closely matched results of mesoscale modeling. Potential applications include predictions of the effect of changing tree cover on air temperature in the area.

  20. [Applying temporally-adjusted land use regression models to estimate ambient air pollution exposure during pregnancy].

    Science.gov (United States)

    Zhang, Y J; Xue, F X; Bai, Z P

    2017-03-06

    The impact of maternal air pollution exposure on offspring health has received much attention. Precise and feasible exposure estimation is particularly important for clarifying exposure-response relationships and reducing heterogeneity among studies. Temporally-adjusted land use regression (LUR) models are exposure assessment methods developed in recent years that have the advantage of having high spatial-temporal resolution. Studies on the health effects of outdoor air pollution exposure during pregnancy have been increasingly carried out using this model. In China, research applying LUR models was done mostly at the model construction stage, and findings from related epidemiological studies were rarely reported. In this paper, the sources of heterogeneity and research progress of meta-analysis research on the associations between air pollution and adverse pregnancy outcomes were analyzed. The methods of the characteristics of temporally-adjusted LUR models were introduced. The current epidemiological studies on adverse pregnancy outcomes that applied this model were systematically summarized. Recommendations for the development and application of LUR models in China are presented. This will encourage the implementation of more valid exposure predictions during pregnancy in large-scale epidemiological studies on the health effects of air pollution in China.

  1. Model Reference Adaptive Control of the Air Flow Rate of Centrifugal Compressor Using State Space Method

    Energy Technology Data Exchange (ETDEWEB)

    Han, Jaeyoung; Jung, Mooncheong; Yu, Sangseok [Chungnam Nat’l Univ., Daejeon (Korea, Republic of); Yi, Sun [North Carolina A and T State Univ., Raleigh (United States)

    2016-08-15

    In this study, a model reference adaptive controller is developed to regulate the outlet air flow rate of centrifugal compressor for automotive supercharger. The centrifugal compressor is developed using the analytical based method to predict the transient behavior of operating and the designed model is validated with experimental data to confirm the system accuracy. The model reference adaptive control structure consists of a compressor model and a MRAC(model reference adaptive control) mechanism. The feedback control do not robust with variation of system parameter but the applied adaptive control is robust even if the system parameter is changed. As a result, the MRAC was regulated to reference air flow rate. Also MRAC was found to be more robust control compared with the feedback control even if the system parameter is changed.

  2. Spatial air pollution modelling for a West-African town

    Directory of Open Access Journals (Sweden)

    Sirak Zenebe Gebreab

    2015-11-01

    Full Text Available Land use regression (LUR modelling is a common approach used in European and Northern American epidemiological studies to assess urban and traffic related air pollution exposures. Studies applying LUR in Africa are lacking. A need exists to understand if this approach holds for an African setting, where urban features, pollutant exposures and data availability differ considerably from other continents. We developed a parsimonious regression model based on 48-hour nitrogen dioxide (NO2 concentrations measured at 40 sites in Kaédi, a medium sized West-African town, and variables generated in a geographic information system (GIS. Road variables and settlement land use characteristics were found to be important predictors of 48-hour NO2 concentration in the model. About 68% of concentration variability in the town was explained by the model. The model was internally validated by leave-one-out cross-validation and it was found to perform moderately well. Furthermore, its parameters were robust to sampling variation. We applied the model at 100 m pixels to create a map describing the broad spatial pattern of NO2 across Kaédi. In this research, we demonstrated the potential for LUR as a valid, cost-effective approach for air pollution modelling and mapping in an African town. If the methodology were to be adopted by environmental and public health authorities in these regions, it could provide a quick assessment of the local air pollution burden and potentially support air pollution policies and guidelines.

  3. Air pollution modeling at road sides using the operational street pollution model--a case study in Hanoi, Vietnam.

    Science.gov (United States)

    Hung, Ngo Tho; Ketzel, Matthias; Jensen, Steen Solvang; Oanh, Nguyen Thi Kim

    2010-11-01

    In many metropolitan areas, traffic is the main source of air pollution. The high concentrations of pollutants in streets have the potential to affect human health. Therefore, estimation of air pollution at the street level is required for health impact assessment. This task has been carried out in many developed countries by a combination of air quality measurements and modeling. This study focuses on how to apply a dispersion model to cities in the developing world, where model input data and data from air quality monitoring stations are limited or of varying quality. This research uses the operational street pollution model (OSPM) developed by the National Environmental Research Institute in Denmark for a case study in Hanoi, the capital of Vietnam. OSPM predictions from five streets were evaluated against air pollution measurements of nitrogen oxides (NO(x)), sulfur dioxide (SO2), carbon monoxide (CO), and benzene (BNZ) that were available from previous studies. Hourly measurements and passive sample measurements collected over 3-week periods were compared with model outputs, applying emission factors from previous studies. In addition, so-called "backward calculations" were performed to adapt the emission factors for Hanoi conditions. The average fleet emission factors estimated can be used for emission calculations at other streets in Hanoi and in other locations in Southeast Asia with similar vehicle types. This study also emphasizes the need to further eliminate uncertainties in input data for the street-scale air pollution modeling in Vietnam, namely by providing reliable emission factors and hourly air pollution measurements of high quality.

  4. Mathematical model of an air-filled alpha stirling refrigerator

    Science.gov (United States)

    McFarlane, Patrick; Semperlotti, Fabio; Sen, Mihir

    2013-10-01

    This work develops a mathematical model for an alpha Stirling refrigerator with air as the working fluid and will be useful in optimizing the mechanical design of these machines. Two pistons cyclically compress and expand air while moving sinusoidally in separate chambers connected by a regenerator, thus creating a temperature difference across the system. A complete non-linear mathematical model of the machine, including air thermodynamics, and heat transfer from the walls, as well as heat transfer and fluid resistance in the regenerator, is developed. Non-dimensional groups are derived, and the mathematical model is numerically solved. The heat transfer and work are found for both chambers, and the coefficient of performance of each chamber is calculated. Important design parameters are varied and their effect on refrigerator performance determined. This sensitivity analysis, which shows what the significant parameters are, is a useful tool for the design of practical Stirling refrigeration systems.

  5. Priliminary Modeling of Air Breakdown with the ICEPIC code

    CERN Document Server

    Schulz, A E; Cartwright, K L; Mardahl, P J; Peterkin, R E; Bruner, N; Genoni, T; Hughes, T P; Welch, D

    2004-01-01

    Interest in air breakdown phenomena has recently been re-kindled with the advent of advanced virtual prototyping of radio frequency (RF) sources for use in high power microwave (HPM) weapons technology. Air breakdown phenomena are of interest because the formation of a plasma layer at the aperture of an RF source decreases the transmitted power to the target, and in some cases can cause significant reflection of RF radiation. Understanding the mechanisms behind the formation of such plasma layers will aid in the development of maximally effective sources. This paper begins with some of the basic theory behind air breakdown, and describes two independent approaches to modeling the formation of plasmas, the dielectric fluid model and the Particle in Cell (PIC) approach. Finally we present the results of preliminary studies in numerical modeling and simulation of breakdown.

  6. Predictive Capability Maturity Model for computational modeling and simulation.

    Energy Technology Data Exchange (ETDEWEB)

    Oberkampf, William Louis; Trucano, Timothy Guy; Pilch, Martin M.

    2007-10-01

    The Predictive Capability Maturity Model (PCMM) is a new model that can be used to assess the level of maturity of computational modeling and simulation (M&S) efforts. The development of the model is based on both the authors experience and their analysis of similar investigations in the past. The perspective taken in this report is one of judging the usefulness of a predictive capability that relies on the numerical solution to partial differential equations to better inform and improve decision making. The review of past investigations, such as the Software Engineering Institute's Capability Maturity Model Integration and the National Aeronautics and Space Administration and Department of Defense Technology Readiness Levels, indicates that a more restricted, more interpretable method is needed to assess the maturity of an M&S effort. The PCMM addresses six contributing elements to M&S: (1) representation and geometric fidelity, (2) physics and material model fidelity, (3) code verification, (4) solution verification, (5) model validation, and (6) uncertainty quantification and sensitivity analysis. For each of these elements, attributes are identified that characterize four increasing levels of maturity. Importantly, the PCMM is a structured method for assessing the maturity of an M&S effort that is directed toward an engineering application of interest. The PCMM does not assess whether the M&S effort, the accuracy of the predictions, or the performance of the engineering system satisfies or does not satisfy specified application requirements.

  7. Transient modeling of an air conditioner with a rapid cycling compressor and multi-indoor units

    Energy Technology Data Exchange (ETDEWEB)

    Zhang, Wei-Jiang [Institute of Refrigeration and Cryogenics, Shanghai Jiaotong University, Shanghai 200240 (China); Zhang, Chun-Lu [College of Mechanical Engineering, Tongji University, 4800 Cao An Highway, Shanghai 201804 (China)

    2011-01-15

    Rapid cycling the compressor is an alternative of the variable speed compressor to modulate the capacity of refrigeration systems for the purpose of energy saving at part-load conditions. The multi-evaporator air conditioner combined with the rapid cycling compressor brings difficulties in control design because of the sophisticated system physics and dynamics. In this paper the transient model of a multi-split air conditioner with a digital scroll compressor is developed for predicting the system transients under performance modulations. The predicted cycling dynamics are in good agreement with the experimental data. Based on the validated model, the impact of compressor idle power and cycle period to the part load performance is discussed. (author)

  8. Transient modeling of an air conditioner with a rapid cycling compressor and multi-indoor units

    Energy Technology Data Exchange (ETDEWEB)

    Zhang Weijiang [Institute of Refrigeration and Cryogenics, Shanghai Jiaotong University, Shanghai 200240 (China); Zhang Chunlu, E-mail: chunlu.zhang@carrier.utc.co [College of Mechanical Engineering, Tongji University, 4800 Cao An Highway, Shanghai 201804 (China)

    2011-01-15

    Rapid cycling the compressor is an alternative of the variable speed compressor to modulate the capacity of refrigeration systems for the purpose of energy saving at part-load conditions. The multi-evaporator air conditioner combined with the rapid cycling compressor brings difficulties in control design because of the sophisticated system physics and dynamics. In this paper the transient model of a multi-split air conditioner with a digital scroll compressor is developed for predicting the system transients under performance modulations. The predicted cycling dynamics are in good agreement with the experimental data. Based on the validated model, the impact of compressor idle power and cycle period to the part load performance is discussed.

  9. Artificial Neural Networks: A New Approach for Predicting Application Behavior. AIR 2001 Annual Forum Paper.

    Science.gov (United States)

    Gonzalez, Julie M. Byers; DesJardins, Stephen L.

    This paper examines how predictive modeling can be used to study application behavior. A relatively new technique, artificial neural networks (ANNs), was applied to help predict which students were likely to get into a large Research I university. Data were obtained from a university in Iowa. Two cohorts were used, each containing approximately…

  10. A Predictive Maintenance Model for Railway Tracks

    DEFF Research Database (Denmark)

    Li, Rui; Wen, Min; Salling, Kim Bang

    2015-01-01

    For the modern railways, maintenance is critical for ensuring safety, train punctuality and overall capacity utilization. The cost of railway maintenance in Europe is high, on average between 30,000 – 100,000 Euro per km per year [1]. Aiming to reduce such maintenance expenditure, this paper...... presents a mathematical model based on Mixed Integer Programming (MIP) which is designed to optimize the predictive railway tamping activities for ballasted track for the time horizon up to four years. The objective function is setup to minimize the actual costs for the tamping machine (measured by time...... recovery on the track quality after tamping operation and (5) Tamping machine operation factors. A Danish railway track between Odense and Fredericia with 57.2 km of length is applied for a time period of two to four years in the proposed maintenance model. The total cost can be reduced with up to 50...

  11. A predictive fitness model for influenza

    Science.gov (United States)

    Łuksza, Marta; Lässig, Michael

    2014-03-01

    The seasonal human influenza A/H3N2 virus undergoes rapid evolution, which produces significant year-to-year sequence turnover in the population of circulating strains. Adaptive mutations respond to human immune challenge and occur primarily in antigenic epitopes, the antibody-binding domains of the viral surface protein haemagglutinin. Here we develop a fitness model for haemagglutinin that predicts the evolution of the viral population from one year to the next. Two factors are shown to determine the fitness of a strain: adaptive epitope changes and deleterious mutations outside the epitopes. We infer both fitness components for the strains circulating in a given year, using population-genetic data of all previous strains. From fitness and frequency of each strain, we predict the frequency of its descendent strains in the following year. This fitness model maps the adaptive history of influenza A and suggests a principled method for vaccine selection. Our results call for a more comprehensive epidemiology of influenza and other fast-evolving pathogens that integrates antigenic phenotypes with other viral functions coupled by genetic linkage.

  12. Predictive Model of Radiative Neutrino Masses

    CERN Document Server

    Babu, K S

    2013-01-01

    We present a simple and predictive model of radiative neutrino masses. It is a special case of the Zee model which introduces two Higgs doublets and a charged singlet. We impose a family-dependent Z_4 symmetry acting on the leptons, which reduces the number of parameters describing neutrino oscillations to four. A variety of predictions follow: The hierarchy of neutrino masses must be inverted; the lightest neutrino mass is extremely small and calculable; one of the neutrino mixing angles is determined in terms of the other two; the phase parameters take CP-conserving values with \\delta_{CP} = \\pi; and the effective mass in neutrinoless double beta decay lies in a narrow range, m_{\\beta \\beta} = (17.6 - 18.5) meV. The ratio of vacuum expectation values of the two Higgs doublets, tan\\beta, is determined to be either 1.9 or 0.19 from neutrino oscillation data. Flavor-conserving and flavor-changing couplings of the Higgs doublets are also determined from neutrino data. The non-standard neutral Higgs bosons, if t...

  13. Mapping real-time air pollution health risk for environmental management: Combining mobile and stationary air pollution monitoring with neural network models.

    Science.gov (United States)

    Adams, Matthew D; Kanaroglou, Pavlos S

    2016-03-01

    Air pollution poses health concerns at the global scale. The challenge of managing air pollution is significant because of the many air pollutants, insufficient funds for monitoring and abatement programs, and political and social challenges in defining policy to limit emissions. Some governments provide citizens with air pollution health risk information to allow them to limit their exposure. However, many regions still have insufficient air pollution monitoring networks to provide real-time mapping. Where available, these risk mapping systems either provide absolute concentration data or the concentrations are used to derive an Air Quality Index, which provides the air pollution risk for a mix of air pollutants with a single value. When risk information is presented as a single value for an entire region it does not inform on the spatial variation within the region. Without an understanding of the local variation residents can only make a partially informed decision when choosing daily activities. The single value is typically provided because of a limited number of active monitoring units in the area. In our work, we overcome this issue by leveraging mobile air pollution monitoring techniques, meteorological information and land use information to map real-time air pollution health risks. We propose an approach that can provide improved health risk information to the public by applying neural network models within a framework that is inspired by land use regression. Mobile air pollution monitoring campaigns were conducted across Hamilton from 2005 to 2013. These mobile air pollution data were modelled with a number of predictor variables that included information on the surrounding land use characteristics, the meteorological conditions, air pollution concentrations from fixed location monitors, and traffic information during the time of collection. Fine particulate matter and nitrogen dioxide were both modelled. During the model fitting process we reserved

  14. A predictive model for dimensional errors in fused deposition modeling

    DEFF Research Database (Denmark)

    Stolfi, A.

    2015-01-01

    This work concerns the effect of deposition angle (a) and layer thickness (L) on the dimensional performance of FDM parts using a predictive model based on the geometrical description of the FDM filament profile. An experimental validation over the whole a range from 0° to 177° at 3° steps and two...

  15. Effect on Prediction when Modeling Covariates in Bayesian Nonparametric Models.

    Science.gov (United States)

    Cruz-Marcelo, Alejandro; Rosner, Gary L; Müller, Peter; Stewart, Clinton F

    2013-04-01

    In biomedical research, it is often of interest to characterize biologic processes giving rise to observations and to make predictions of future observations. Bayesian nonparametric methods provide a means for carrying out Bayesian inference making as few assumptions about restrictive parametric models as possible. There are several proposals in the literature for extending Bayesian nonparametric models to include dependence on covariates. Limited attention, however, has been directed to the following two aspects. In this article, we examine the effect on fitting and predictive performance of incorporating covariates in a class of Bayesian nonparametric models by one of two primary ways: either in the weights or in the locations of a discrete random probability measure. We show that different strategies for incorporating continuous covariates in Bayesian nonparametric models can result in big differences when used for prediction, even though they lead to otherwise similar posterior inferences. When one needs the predictive density, as in optimal design, and this density is a mixture, it is better to make the weights depend on the covariates. We demonstrate these points via a simulated data example and in an application in which one wants to determine the optimal dose of an anticancer drug used in pediatric oncology.

  16. Continuous-Discrete Time Prediction-Error Identification Relevant for Linear Model Predictive Control

    DEFF Research Database (Denmark)

    Jørgensen, John Bagterp; Jørgensen, Sten Bay

    2007-01-01

    model is realized from a continuous-discrete-time linear stochastic system specified using transfer functions with time-delays. It is argued that the prediction-error criterion should be selected such that it is compatible with the objective function of the predictive controller in which the model......A Prediction-error-method tailored for model based predictive control is presented. The prediction-error method studied are based on predictions using the Kalman filter and Kalman predictors for a linear discrete-time stochastic state space model. The linear discrete-time stochastic state space...

  17. Air Quality Model System For The Vienna/bratislava Region

    Science.gov (United States)

    Krüger, B. C.; Schmittner, W.; Kromp-Kolb, H.

    A model system has been build up, consisting of the mesoscale meteorological fore- cast model MM5 and the chemical air-quality model CAMx. The coarse grid covers central Europe. By nesting, a spatial resolution of 3 km is reached for the core area, which includes the cities of Vienna (Austria) and Bratislava (Slovakia). In a first approach, the model system has been applied to a 6-day period in Febru- ary 1997, which was characterized by stagnant meteorological conditions. During this episode, primary pollutants like CO and NO2 have been compared with ambient mea- surements for the validation of the new model system. In the future it is foreseen to improve the spatial resolution, to apply the model system also for ozone and particulates, and to utilize it for a short-time forecast of air-quality parameters.

  18. Modeling the Environmental Impact of Air Traffic Operations

    Science.gov (United States)

    Chen, Neil

    2011-01-01

    There is increased interest to understand and mitigate the impacts of air traffic on the climate, since greenhouse gases, nitrogen oxides, and contrails generated by air traffic can have adverse impacts on the climate. The models described in this presentation are useful for quantifying these impacts and for studying alternative environmentally aware operational concepts. These models have been developed by leveraging and building upon existing simulation and optimization techniques developed for the design of efficient traffic flow management strategies. Specific enhancements to the existing simulation and optimization techniques include new models that simulate aircraft fuel flow, emissions and contrails. To ensure that these new models are beneficial to the larger climate research community, the outputs of these new models are compatible with existing global climate modeling tools like the FAA's Aviation Environmental Design Tool.

  19. Air injection test on a Kaplan turbine: prototype - model comparison

    Science.gov (United States)

    Angulo, M.; Rivetti, A.; Díaz, L.; Liscia, S.

    2016-11-01

    Air injection is a very well-known resource to reduce pressure pulsation magnitude in turbines, especially on Francis type. In the case of large Kaplan designs, even when not so usual, it could be a solution to mitigate vibrations arising when tip vortex cavitation phenomenon becomes erosive and induces structural vibrations. In order to study this alternative, aeration tests were performed on a Kaplan turbine at model and prototype scales. The research was focused on efficiency of different air flow rates injected in reducing vibrations, especially at the draft tube and the discharge ring and also in the efficiency drop magnitude. It was found that results on both scales presents the same trend in particular for vibration levels at the discharge ring. The efficiency drop was overestimated on model tests while on prototype were less than 0.2 % for all power output. On prototype, air has a beneficial effect in reducing pressure fluctuations up to 0.2 ‰ of air flow rate. On model high speed image computing helped to quantify the volume of tip vortex cavitation that is strongly correlated with the vibration level. The hydrophone measurements did not capture the cavitation intensity when air is injected, however on prototype, it was detected by a sonometer installed at the draft tube access gallery.

  20. Modeling of air-gap membrane distillation process: A theoretical and experimental study

    KAUST Repository

    Alsaadi, Ahmad Salem

    2013-06-03

    A one dimensional (1-D) air gap membrane distillation (AGMD) model for flat sheet type modules has been developed. This model is based on mathematical equations that describe the heat and mass transfer mechanisms of a single-stage AGMD process. It can simulate AGMD modules in both co-current and counter-current flow regimes. The theoretical model was validated using AGMD experimental data obtained under different operating conditions and parameters. The predicted water vapor flux was compared to the flux measured at five different feed water temperatures, two different feed water salinities, three different air gap widths and two MD membranes with different average pore sizes. This comparison showed that the model flux predictions are strongly correlated with the experimental data, with model predictions being within +10% of the experimentally determined values. The model was then used to study and analyze the parameters that have significant effect on scaling-up the AGMD process such as the effect of increasing the membrane length, and feed and coolant flow rates. The model was also used to analyze the maximum thermal efficiency of the AGMD process by tracing changes in water production rate and the heat input to the process along the membrane length. This was used to understand the gain in both process production and thermal efficiency for different membrane surface areas and the resultant increases in process capital and water unit cost. © 2013 Elsevier B.V.

  1. DEVELOPMENT AND VALIDATION OF AN AIR-TO-BEEF FOOD CHAIN MODEL FOR DIOXIN-LIKE COMPOUNDS

    Science.gov (United States)

    A model for predicting concentrations of dioxin-like compounds in beef is developed and tested. The key premise of the model is that concentrations of these compounds in air are the source term, or starting point, for estimating beef concentrations. Vapor-phase concentrations t...

  2. DEVELOPMENT AND VALIDATION OF AN AIR-TO-BEEF FOOD CHAIN MODEL FOR DIOXIN-LIKE COMPOUNDS

    Science.gov (United States)

    A model for predicting concentrations of dioxin-like compounds in beef is developed and tested. The key premise of the model is that concentrations of these compounds in air are the source term, or starting point, for estimating beef concentrations. Vapor-phase concentrations t...

  3. Data assimilation for air quality models

    DEFF Research Database (Denmark)

    Silver, Jeremy David

    2014-01-01

    The chemical composition of the Earth’s atmosphere has major ramifications for not only human health, but also biodiversity and the climate; hence there are scientific, environmental and societal interests in accurate estimates of atmospheric chemical composition and in understanding the governing......-transport models (CTMs). Each of these methods has their limitations: direct measurements provide only data at point locations and may not be representative of a wider area, remotely-sensed data from polar-orbiting satellites cannot investigate diurnal variation, and CTM simulations are often associated...... with higher uncertainties. It is possible, however, to combine information from measurements and models to more accurately estimate the state of the atmosphere using a statistically consistent framework known as “data assimilation”. In this study, three data assimilation schemes are implemented and evaluated...

  4. Validation of Air Traffic Controller Workload Models

    Science.gov (United States)

    1979-09-01

    SAR) tapes dtirinq the data reduc- tion phase of the project. Kentron International Limited provided the software support for the oroject. This included... ETABS ) or to revised traffic control procedures. The models also can be used to verify productivity benefits after new configurations have been...col- lected and processed manually. A preliminary compari- son has been made between standard NAS Stage A and ETABS operations at Miami. 1.2

  5. Two criteria for evaluating risk prediction models.

    Science.gov (United States)

    Pfeiffer, R M; Gail, M H

    2011-09-01

    We propose and study two criteria to assess the usefulness of models that predict risk of disease incidence for screening and prevention, or the usefulness of prognostic models for management following disease diagnosis. The first criterion, the proportion of cases followed PCF (q), is the proportion of individuals who will develop disease who are included in the proportion q of individuals in the population at highest risk. The second criterion is the proportion needed to follow-up, PNF (p), namely the proportion of the general population at highest risk that one needs to follow in order that a proportion p of those destined to become cases will be followed. PCF (q) assesses the effectiveness of a program that follows 100q% of the population at highest risk. PNF (p) assess the feasibility of covering 100p% of cases by indicating how much of the population at highest risk must be followed. We show the relationship of those two criteria to the Lorenz curve and its inverse, and present distribution theory for estimates of PCF and PNF. We develop new methods, based on influence functions, for inference for a single risk model, and also for comparing the PCFs and PNFs of two risk models, both of which were evaluated in the same validation data.

  6. Methods for Handling Missing Variables in Risk Prediction Models

    NARCIS (Netherlands)

    Held, Ulrike; Kessels, Alfons; Aymerich, Judith Garcia; Basagana, Xavier; ter Riet, Gerben; Moons, Karel G. M.; Puhan, Milo A.

    2016-01-01

    Prediction models should be externally validated before being used in clinical practice. Many published prediction models have never been validated. Uncollected predictor variables in otherwise suitable validation cohorts are the main factor precluding external validation.We used individual patient

  7. Predicting chick body mass by artificial intelligence-based models

    Directory of Open Access Journals (Sweden)

    Patricia Ferreira Ponciano Ferraz

    2014-07-01

    Full Text Available The objective of this work was to develop, validate, and compare 190 artificial intelligence-based models for predicting the body mass of chicks from 2 to 21 days of age subjected to different duration and intensities of thermal challenge. The experiment was conducted inside four climate-controlled wind tunnels using 210 chicks. A database containing 840 datasets (from 2 to 21-day-old chicks - with the variables dry-bulb air temperature, duration of thermal stress (days, chick age (days, and the daily body mass of chicks - was used for network training, validation, and tests of models based on artificial neural networks (ANNs and neuro-fuzzy networks (NFNs. The ANNs were most accurate in predicting the body mass of chicks from 2 to 21 days of age after they were subjected to the input variables, and they showed an R² of 0.9993 and a standard error of 4.62 g. The ANNs enable the simulation of different scenarios, which can assist in managerial decision-making, and they can be embedded in the heating control systems.

  8. A model of particle removal in a dissolved air flotation tank: importance of stratified flow and bubble size.

    Science.gov (United States)

    Lakghomi, B; Lawryshyn, Y; Hofmann, R

    2015-01-01

    An analytical model and a computational fluid dynamic model of particle removal in dissolved air flotation were developed that included the effects of stratified flow and bubble-particle clustering. The models were applied to study the effect of operating conditions and formation of stratified flow on particle removal. Both modeling approaches demonstrated that the presence of stratified flow enhanced particle removal in the tank. A higher air fraction was shown to be needed at higher loading rates to achieve the same removal efficiency. The model predictions showed that an optimum bubble size was present that increased with an increase in particle size.

  9. Demand modelling of passenger air travel: An analysis and extension. Volume 1: Background and summary

    Science.gov (United States)

    Jacobson, I. D.

    1978-01-01

    The framework for a model of travel demand which will be useful in predicting the total market for air travel between two cities is discussed. Variables to be used in determining the need for air transportation where none currently exists and the effect of changes in system characteristics on attracting latent demand are identified. Existing models are examined in order to provide insight into their strong points and shortcomings. Much of the existing behavioral research in travel demand is incorporated to allow the inclusion of non-economic factors, such as convenience. The model developed is characterized as a market segmentation model. This is a consequence of the strengths of disaggregation and its natural evolution to a usable aggregate formulation. The need for this approach both pedagogically and mathematically is discussed.

  10. The Aviation System Analysis Capability Air Carrier Cost-Benefit Model

    Science.gov (United States)

    Gaier, Eric M.; Edlich, Alexander; Santmire, Tara S.; Wingrove, Earl R.., III

    1999-01-01

    wide-ranging suite of economic and technical models that comprise ASAC. This report describes an Air Carrier Cost-Benefit Model (CBM) that meets these requirements. The ASAC CBM is distinguished from many of the aviation cost-benefit models by its exclusive focus on commercial air carriers. The model considers such benefit categories as time and fuel savings, utilization opportunities, reliability and capacity enhancements, and safety and security improvements. The model distinguishes between benefits that are predictable and those that occur randomly. By making such a distinction, the model captures the ability of air carriers to reoptimize scheduling and crew assignments for predictable benefits. In addition, the model incorporates a life-cycle cost module for new technology, which applies the costs of nonrecurring acquisitions, recurring maintenance and operation, and training to each aircraft equipment type independently.

  11. Prediction of air-to-blood partition coefficients of volatile organic compounds using genetic algorithm and artificial neural network

    Energy Technology Data Exchange (ETDEWEB)

    Konoz, Elahe [Department of Chemistry, Central Tehran Branch, Islamic Azad University, Tehran (Iran, Islamic Republic of); Golmohammadi, Hassan [Department of Chemistry, Mazandaran University, Babolsar (Iran, Islamic Republic of)], E-mail: hassan.gol@gmail.com

    2008-07-07

    An artificial neural network (ANN) was constructed and trained for the prediction of air-to-blood partition coefficients of volatile organic compounds. The inputs of this neural network are theoretically derived descriptors that were chosen by genetic algorithm (GA) and multiple linear regression (MLR) features selection techniques. These descriptors are: R maximal autocorrelation of lag 1 weighted by atomic Sanderson electronegativities (R1E+), electron density on the most negative atom in molecule (EDNA), maximum partial charge for C atom (MXPCC), surface weighted charge partial surface area (WNSA1), fractional charge partial surface area (FNSA2) and atomic charge weighted partial positive surface area (PPSA3). The standard errors of training, test and validation sets for the ANN model are 0.095, 0.148 and 0.120, respectively. Result obtained showed that nonlinear model can simulate the relationship between structural descriptors and the partition coefficients of the molecules in data set accurately.

  12. Estimating the magnitude of prediction uncertainties for the APLE model

    Science.gov (United States)

    Models are often used to predict phosphorus (P) loss from agricultural fields. While it is commonly recognized that model predictions are inherently uncertain, few studies have addressed prediction uncertainties using P loss models. In this study, we conduct an uncertainty analysis for the Annual P ...

  13. Toward an integrated quasi-operational air quality analysis and prediction system for South America

    Science.gov (United States)

    Hoshyaripour, Gholam Ali; Brasseur, Guy; Petersen, Katinka; Bouarar, Idiir; Andrade, Maria de Fatima

    2015-04-01

    Recent industrialization and urbanization in South America (SA) have notably exacerbated the air pollution with adverse impacts on human health and socio-economic systems. Consequently, there is a strong demand for developing ever-better assessment mechanisms to monitor the air quality at different temporal and spatial scales and minimize its damages. Based on previous achievements (e.g., MACC project in Europe and PANDA project in East Asia) we aim to design and implement an integrated system to monitor, analyze and forecast the air quality in SA along with its impacts upon public health and agriculture. An initiative will be established to combine observations (both satellite and in-situ) with advanced numerical models in order to provide a robust scientific basis for short- and long-term decision-making concerning air quality issues in SA countries. The main objectives of the project are defined as 3E: Enhancement of the air quality monitoring system through coupling models and observations, Elaboration of comprehensive indicators and assessment tools to support policy-making, Establishment of efficient information-exchange platforms to facilitate communication among scientists, authorities, stockholders and the public. Here we present the results of the initial stage, where a coarse resolution (50×50 km) set up of Weather Research and Forecast model with Chemistry (WRF-Chem) is used to simulate the air quality in SA considering anthropogenic, biomass-burning (based on MACCity, FINN inventories, respectively) and biogenic emissions (using MEGAN model). According to the availability of the observation data for Metropolitan Area of São Paulo, August 2012 is selected as the simulation period. Nested domains with higher resolution (15×15 km) are also embedded within the parent domain over the megacities (Sao Paolo and Rio de Janeiro in Brazil and Buenos Aires in Argentina), which account for the major anthropogenic emission sources located along coastal regions

  14. Modeling, Monitoring and Fault Diagnosis of Spacecraft Air Contaminants

    Science.gov (United States)

    Ramirez, W. Fred; Skliar, Mikhail; Narayan, Anand; Morgenthaler, George W.; Smith, Gerald J.

    1998-01-01

    Control of air contaminants is a crucial factor in the safety considerations of crewed space flight. Indoor air quality needs to be closely monitored during long range missions such as a Mars mission, and also on large complex space structures such as the International Space Station. This work mainly pertains to the detection and simulation of air contaminants in the space station, though much of the work is easily extended to buildings, and issues of ventilation systems. Here we propose a method with which to track the presence of contaminants using an accurate physical model, and also develop a robust procedure that would raise alarms when certain tolerance levels are exceeded. A part of this research concerns the modeling of air flow inside a spacecraft, and the consequent dispersal pattern of contaminants. Our objective is to also monitor the contaminants on-line, so we develop a state estimation procedure that makes use of the measurements from a sensor system and determines an optimal estimate of the contamination in the system as a function of time and space. The real-time optimal estimates in turn are used to detect faults in the system and also offer diagnoses as to their sources. This work is concerned with the monitoring of air contaminants aboard future generation spacecraft and seeks to satisfy NASA's requirements as outlined in their Strategic Plan document (Technology Development Requirements, 1996).

  15. Air, contaminant and heat transport models. Integration and application

    Energy Technology Data Exchange (ETDEWEB)

    Dorer, V.; Weber, A. [Swiss Federal Laboratories for Materials Testing and Research (EMPA), Section 175 Building Equipment, CH-8600 Duebendorf (Switzerland)

    1999-07-01

    Comfort evaluations cover air quality, thermal, visual and acoustic comfort. Today, only few computer programs allow for the integrated evaluation of several or all relevant parameters. Heat transport, ventilation as well as lighting in a room are influenced by each other. Therefore they should be integrally modelled. As a part of the IEA-ECBCS Annex 23 'Multizone Air Flow Modelling' (IEA, International Energy Agency; ECBCS, Energy Conservation in Buildings and Community Systems, an IEA research programme), such a coupling has been realised by integrating the air flow and contaminant transport simulation code of COMIS into the building and systems simulation code TRNSYS. This paper gives a short description of the concept used for the coupling. Then, two application examples typical for a building design study situation are presented, the first being a multi-storey school building which was passively cooled at night due to natural stack airflow. In the second example the facade of the same building was retrofitted with a glazed outer facade. Ventilation was provided by naturally driven shaft ventilation through the facade spaces. For such cases as described in the examples, it may be necessary due to the complex interactions, to study many configurations to find optimum control strategies for the openings and the blinds with respect to overheating risk as well as to air quality. For the upper floors, the risk of overheating and low air quality may be difficult to minimize without extending the shaft above roof level. (author)

  16. Prediction and Control of Air Flow in Acid-Generating Waste Rock Dumps

    Science.gov (United States)

    Wels, C.; Lefebvre, R.; Robertson, A. M.

    2004-05-01

    Air movement and associated oxygen transport through waste rock dumps has the potential to significantly enhance the rate of oxidation of pyrite-bearing material. While this is a desired outcome for most heap leach operations, airflow in waste rock storage facilities can result in significant increases in generation and acceleration of acid rock drainage. Hence, a good understanding of internal airflow through waste rock dumps is required to control ARD and minimize any associated liability. The principal mechanisms contributing to airflow and oxygen transport in a waste rock pile include (i) diffusion, (ii) advection due to a thermal gradient (chimney effect) and/or wind pressure gradients and (iii) advection due to barometric pumping. While diffusion is typically limited to a near-surface zone of a few meters depth, advection and barometric pumping have the potential to move air (and oxygen) to much greater depths into the pile. In general, the more permeable the waste rock material, and the greater the height-to-width ratio of the waste rock pile, the greater is the potential for advective air movement. The reactivity of the waste rock material as well as the coarseness (hence air permeability), and the spatial variability of these properties within a pile, have a strong influence on the magnitude of thermally induced advection. In contrast, air movement due to barometric pumping is controlled by the waste rock porosity, changes in ambient air pressure and the heterogeneity of air permeability of the waste rock dump. Results of field monitoring and numerical modeling using TOUGH AMD are presented to illustrate the concepts on air movement in waste rock piles. During the design and construction phase, airflow can be controlled by judicious placement of reactive waste rock and use of selective placement techniques to control the internal structure of the waste rock facility (e.g. introduction of horizontal layering, prevention of inclined, high

  17. Air-Sea interactions:Observations and models

    Science.gov (United States)

    Clayson, C. A.

    2014-12-01

    The interaction between the atmosphere and ocean consists of intense and episodic exchanges of heat, momentum, and moisture. However, the way in which these episodic events, and higher-scale frequency events in general, influence the coupled atmosphere/ocean system on longer time scales is not well understood. Models have predicted anthropogenic changes in the mean state and variability of various aspects of the climate which are closely tied to surface turbulent fluxes, but disagree on the nature of the changes. The competing influences of increased humidity, which should reduce fluxes, and increased wind speed and variability, which should increase fluxes, in determining the overall flux under a warming climate is not known. One motivating factor for this research is that there are enormous difficulties in understanding the coupled atmosphere-ocean system, given the complexity of two fluids with large-scale circulations that differ in time/space scales and are coupled through relatively small-scale surface fluxes and cloud processes. In order to make a tractable problem, such statistical techniques as joint distribution analysis, clustering, and variabilities of extremes in time and space must be used to simplify these complex relationships. In order to understand how the climate responds to variations in forcing, one necessary component is to understand the full distribution of variability of exchanges of heat and moisture between the atmosphere and ocean. A number of studies recognize the important role of surface heat and moisture fluxes in the generation and decay of important coupled air-sea phenomena. These mechanisms operate across a number of scales and contain significant contributions from interactions between the anomalous (i.e. non-mean), often extreme-valued, flux components. It is important to have a characterization and understanding of these processes for the development of accurate modeling efforts. In this talk I will make comparisons of some of

  18. Global Air Quality Predictions of Particulate Matter in the Middle East and Sensitivity to Future Emissions Scenarios

    Science.gov (United States)

    Couzo, E. A.; Holmes, C. D.; Paltsev, S.; Alawad, A.; Selin, N. E.

    2014-12-01

    We examine the influence of natural and anthropogenic drivers of future PM in the Middle East region using two future emissions scenarios to drive the GEOS-Chem atmospheric chemistry model. The Arabian Peninsula is a major source of windblown dust as well as anthropogenic aerosols. Future emissions - driven jointly and individually by climate change and anthropogenic emissions from this rapidly growing region - will play an important role in both climate forcing and human health impacts from particulate matter. We use two scenarios to compare their climate and air quality implications. First, we use the Intergovernmental Panel on Climate Change Representative Concentration Pathways (RCPs) for four radiative forcing cases. Second, we develop a consistent future greenhouse gas and conventional pollutant emission inventory using the MIT Emissions Prediction and Policy Analysis (EPPA) model, which is a general equilibrium model of the global economy that calculates how economic growth and anthropogenic emissions change as a result of policies and other stressors. With EPPA, we examine three emissions cases, a business-as-usual case and two stabilization cases leading to anthropogenic radiative forcings of 3.7 W/m2 and 4.5 W/m2. We use these scenarios to drive GEOS-Chem for present and future climate, assessing changes in chemical composition of aerosol and drivers, both natural and anthropogenic, out to 2050. We find that projected anthropogenic emissions are strong determinants of future particulate matter air quality in the Middle East region.

  19. Prediction of Catastrophes: an experimental model

    CERN Document Server

    Peters, Randall D; Pomeau, Yves

    2012-01-01

    Catastrophes of all kinds can be roughly defined as short duration-large amplitude events following and followed by long periods of "ripening". Major earthquakes surely belong to the class of 'catastrophic' events. Because of the space-time scales involved, an experimental approach is often difficult, not to say impossible, however desirable it could be. Described in this article is a "laboratory" setup that yields data of a type that is amenable to theoretical methods of prediction. Observations are made of a critical slowing down in the noisy signal of a solder wire creeping under constant stress. This effect is shown to be a fair signal of the forthcoming catastrophe in both of two dynamical models. The first is an "abstract" model in which a time dependent quantity drifts slowly but makes quick jumps from time to time. The second is a realistic physical model for the collective motion of dislocations (the Ananthakrishna set of equations for creep). Hope thus exists that similar changes in the response to ...

  20. Innovations in projecting emissions for air quality modeling ...

    Science.gov (United States)

    Air quality modeling is used in setting air quality standards and in evaluating their costs and benefits. Historically, modeling applications have projected emissions and the resulting air quality only 5 to 10 years into the future. Recognition that the choice of air quality management strategy has climate change implications is encouraging longer modeling time horizons. However, for multi-decadal time horizons, many questions about future conditions arise. For example, will current population, economic, and land use trends continue, or will we see shifts that may alter the spatial and temporal pattern of emissions? Similarly, will technologies such as building-integrated solar photovoltaics, battery storage, electric vehicles, and CO2 capture emerge as disruptive technologies - shifting how we produce and use energy - or will these technologies achieve only niche markets and have little impact? These are some of the questions that are being evaluated by researchers within the U.S. EPA’s Office of Research and Development. In this presentation, Dr. Loughlin will describe a range of analytical approaches that are being explored. These include: (i) the development of alternative scenarios of the future that can be used to evaluate candidate management strategies over wide-ranging conditions, (ii) the application of energy system models to project emissions decades into the future and to assess the environmental implications of new technologies, (iii) and methodo