WorldWideScience

Sample records for model predicted air

  1. Review of Model Predictions for Extensive Air Showers

    Science.gov (United States)

    Pierog, Tanguy

    In detailed air shower simulations, the uncertainty in the prediction of shower observable for different primary particles and energies is currently dominated by differences between hadronic interaction models. With the results of the first run of the LHC, the difference between post-LHC model predictions has been reduced at the same level as experimental uncertainties of cosmic ray experiments. At the same time new types of air shower observables, like the muon production depth, have been measured, adding new constraints on hadronic models. Currently no model is able to reproduce consistently all mass composition measurements possible with the Pierre Auger Observatory for instance. We review the current model predictions for various particle production observables and their link with air shower observables and discuss the future possible improvements.

  2. METEOROLOGICAL MODELLING INFLUENCE ON REGIONAL AND URBAN AIR POLLUTION PREDICTABILITY

    OpenAIRE

    Bande, Stefano; D'Allura, Alessio; Finardi, Sandro; Giorcelli, Matteo; Muraro, Massimo

    2008-01-01

    Abstract: ARPA Piemonte performs yearly air quality assessment running a modelling system based on a chemical transport model. The model is capable to simulate air pollutant emission, transport, diffusion and chemical transformation, to provide concentration fields of the main atmospheric pollutants (CO, NOX, SO2, PM10, PM2.5, O3, and benzene) on a hourly basis and to compute all the indicators required by EU legislation. Meteorological fields to drive air quality simulations are rec...

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

    Science.gov (United States)

    Gilliam, Robert C.; Hogrefe, Christian; Godowitch, James M.; Napelenok, Sergey; Mathur, Rohit; Rao, S. Trivikrama

    2015-12-01

    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 important to understand how uncertainties in these inputs affect the simulated concentrations. Ensembles are one method to explore how uncertainty in meteorology affects air pollution concentrations. Most studies explore this uncertainty by running different meteorological models or the same model with different physics options and in some cases combinations of different meteorological and air quality models. While these have been shown to be useful techniques in some cases, we present a technique that leverages the initial condition perturbations of a weather forecast ensemble, namely, the Short-Range Ensemble Forecast system to drive the four-dimensional data assimilation in the Weather Research and Forecasting (WRF)-Community Multiscale Air Quality (CMAQ) model with a key focus being the response of ozone chemistry and transport. Results confirm that a sizable spread in WRF solutions, including common weather variables of temperature, wind, boundary layer depth, clouds, and radiation, can cause a relatively large range of ozone-mixing ratios. Pollutant transport can be altered by hundreds of kilometers over several days. Ozone-mixing ratios of the ensemble can vary as much as 10-20 ppb or 20-30% in areas that typically have higher pollution levels.

  4. 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.

  5. A model to predict the removal of oxygen from air using a zirconia solid electrolyte membrane

    Science.gov (United States)

    Marner, W. J.; Suitor, J. W.; Glazer, C. R.

    1988-01-01

    A finite difference mathematical model has been developed to predict the removal of oxygen from air using a zirconia separation cell. The model predicts the electrical and mass transfer processes in circular disk cells with either axial or radial current flow in the electrodes and in tubular cells with axial current flow in the electrodes. Representative results are presented and discussed.

  6. A Machine Learning Approach for Air Quality Prediction: Model Regularization and Optimization

    Directory of Open Access Journals (Sweden)

    Dixian Zhu

    2018-02-01

    Full Text Available In this paper, we tackle air quality forecasting by using machine learning approaches to predict the hourly concentration of air pollutants (e.g., ozone, particle matter ( PM 2.5 and sulfur dioxide. Machine learning, as one of the most popular techniques, is able to efficiently train a model on big data by using large-scale optimization algorithms. Although there exist some works applying machine learning to air quality prediction, most of the prior studies are restricted to several-year data and simply train standard regression models (linear or nonlinear to predict the hourly air pollution concentration. In this work, we propose refined models to predict the hourly air pollution concentration on the basis of meteorological data of previous days by formulating the prediction over 24 h as a multi-task learning (MTL problem. This enables us to select a good model with different regularization techniques. We propose a useful regularization by enforcing the prediction models of consecutive hours to be close to each other and compare it with several typical regularizations for MTL, including standard Frobenius norm regularization, nuclear norm regularization, and ℓ 2 , 1 -norm regularization. Our experiments have showed that the proposed parameter-reducing formulations and consecutive-hour-related regularizations achieve better performance than existing standard regression models and existing regularizations.

  7. A predictive model of flight crew performance in automated air traffic control and flight management operations

    Science.gov (United States)

    1995-01-01

    Prepared ca. 1995. This paper describes Air-MIDAS, a model of pilot performance in interaction with varied levels of automation in flight management operations. The model was used to predict the performance of a two person flight crew responding to c...

  8. 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.

  9. Ion current prediction model considering columnar recombination in alpha radioactivity measurement using ionized air transportation

    International Nuclear Information System (INIS)

    Naito, Susumu; Hirata, Yosuke; Izumi, Mikio; Sano, Akira; Miyamoto, Yasuaki; Aoyama, Yoshio; Yamaguchi, Hiromi

    2007-01-01

    We present a reinforced ion current prediction model in alpha radioactivity measurement using ionized air transportation. Although our previous model explained the qualitative trend of the measured ion current values, the absolute values of the theoretical curves were about two times as large as the measured values. In order to accurately predict the measured values, we reinforced our model by considering columnar recombination and turbulent diffusion, which affects columnar recombination. Our new model explained the considerable ion loss in the early stage of ion diffusion and narrowed the gap between the theoretical and measured values. The model also predicted suppression of ion loss due to columnar recombination by spraying a high-speed air flow near a contaminated surface. This suppression was experimentally investigated and confirmed. In conclusion, we quantitatively clarified the theoretical relation between alpha radioactivity and ion current in laminar flow and turbulent pipe flow. (author)

  10. 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.

  11. Developing a Predictive Model for Facility Repair Costs on United States Air Force Installations

    Science.gov (United States)

    2011-06-01

    what impact making the decision to defer maintenance will have for future facility repairs. Therefore, one of the independent variables for this...DoD budgetary resources. This predictive model is relevant to making sound financial decisions at the corporate Air Force level. Making resource...July/August). Budgeting for facility maintenance and repair II: Multicriteria process for model selection. Journal of Management in Engineering , 71

  12. Preparing the Model for Prediction Across Scales (MPAS) for global retrospective air quality modeling

    Science.gov (United States)

    The US EPA has a plan to leverage recent advances in meteorological modeling to develop a "Next-Generation" air quality modeling system that will allow consistent modeling of problems from global to local scale. The meteorological model of choice is the Model for Predic...

  13. Computation of geographic variables for air pollution prediction models in South Korea.

    Science.gov (United States)

    Eum, Youngseob; Song, Insang; Kim, Hwan-Cheol; Leem, Jong-Han; Kim, Sun-Young

    2015-01-01

    Recent cohort studies have relied on exposure prediction models to estimate individuallevel air pollution concentrations because individual air pollution measurements are not available for cohort locations. For such prediction models, geographic variables related to pollution sources are important inputs. We demonstrated the computation process of geographic variables mostly recorded in 2010 at regulatory air pollution monitoring sites in South Korea. On the basis of previous studies, we finalized a list of 313 geographic variables related to air pollution sources in eight categories including traffic, demographic characteristics, land use, transportation facilities, physical geography, emissions, vegetation, and altitude. We then obtained data from different sources such as the Statistics Geographic Information Service and Korean Transport Database. After integrating all available data to a single database by matching coordinate systems and converting non-spatial data to spatial data, we computed geographic variables at 294 regulatory monitoring sites in South Korea. The data integration and variable computation were performed by using ArcGIS version 10.2 (ESRI Inc., Redlands, CA, USA). For traffic, we computed the distances to the nearest roads and the sums of road lengths within different sizes of circular buffers. In addition, we calculated the numbers of residents, households, housing buildings, companies, and employees within the buffers. The percentages of areas for different types of land use compared to total areas were calculated within the buffers. For transportation facilities and physical geography, we computed the distances to the closest public transportation depots and the boundary lines. The vegetation index and altitude were estimated at a given location by using satellite data. The summary statistics of geographic variables in Seoul across monitoring sites showed different patterns between urban background and urban roadside sites. This study

  14. Computation of geographic variables for air pollution prediction models in South Korea

    Directory of Open Access Journals (Sweden)

    Youngseob Eum

    2015-10-01

    Full Text Available Recent cohort studies have relied on exposure prediction models to estimate individuallevel air pollution concentrations because individual air pollution measurements are not available for cohort locations. For such prediction models, geographic variables related to pollution sources are important inputs. We demonstrated the computation process of geographic variables mostly recorded in 2010 at regulatory air pollution monitoring sites in South Korea. On the basis of previous studies, we finalized a list of 313 geographic variables related to air pollution sources in eight categories including traffic, demographic characteristics, land use, transportation facilities, physical geography, emissions, vegetation, and altitude. We then obtained data from different sources such as the Statistics Geographic Information Service and Korean Transport Database. After integrating all available data to a single database by matching coordinate systems and converting non-spatial data to spatial data, we computed geographic variables at 294 regulatory monitoring sites in South Korea. The data integration and variable computation were performed by using ArcGIS version 10.2 (ESRI Inc., Redlands, CA, USA. For traffic, we computed the distances to the nearest roads and the sums of road lengths within different sizes of circular buffers. In addition, we calculated the numbers of residents, households, housing buildings, companies, and employees within the buffers. The percentages of areas for different types of land use compared to total areas were calculated within the buffers. For transportation facilities and physical geography, we computed the distances to the closest public transportation depots and the boundary lines. The vegetation index and altitude were estimated at a given location by using satellite data. The summary statistics of geographic variables in Seoul across monitoring sites showed different patterns between urban background and urban roadside

  15. Optimizing Blasting’s Air Overpressure Prediction Model using Swarm Intelligence

    Science.gov (United States)

    Nur Asmawisham Alel, Mohd; Ruben Anak Upom, Mark; Asnida Abdullah, Rini; Hazreek Zainal Abidin, Mohd

    2018-04-01

    Air overpressure (AOp) resulting from blasting can cause damage and nuisance to nearby civilians. Thus, it is important to be able to predict AOp accurately. In this study, 8 different Artificial Neural Network (ANN) were developed for the purpose of prediction of AOp. The ANN models were trained using different variants of Particle Swarm Optimization (PSO) algorithm. AOp predictions were also made using an empirical equation, as suggested by United States Bureau of Mines (USBM), to serve as a benchmark. In order to develop the models, 76 blasting operations in Hulu Langat were investigated. All the ANN models were found to outperform the USBM equation in three performance metrics; root mean square error (RMSE), mean absolute percentage error (MAPE) and coefficient of determination (R2). Using a performance ranking method, MSO-Rand-Mut was determined to be the best prediction model for AOp with a performance metric of RMSE=2.18, MAPE=1.73% and R2=0.97. The result shows that ANN models trained using PSO are capable of predicting AOp with great accuracy.

  16. An improved mathematical model for prediction of air quantity to minimise radiation levels in underground uranium mines.

    Science.gov (United States)

    Panigrahi, Durga Charan; Sahu, Patitapaban; Mishra, Devi Prasad

    2015-02-01

    Ventilation is the primary means of controlling radon and its daughter concentrations in an underground uranium mine environment. Therefore, prediction of air quantity is the vital component for planning and designing of ventilation systems to minimise the radiation exposure of miners in underground uranium mines. This paper comprehensively describes the derivation and verification of an improved mathematical model for prediction of air quantity, based on the growth of radon daughters in terms of potential alpha energy concentration (PAEC), to reduce the radiation levels in uranium mines. The model also explains the prediction of air quantity depending upon the quality of intake air to the stopes. This model can be used to evaluate the contribution of different sources to radon concentration in mine atmosphere based on the measurements of radon emanation and exhalation. Moreover, a mathematical relationship has been established for quick prediction of air quantity to achieve the desired radon daughter concentration in the mines. Copyright © 2014 Elsevier Ltd. All rights reserved.

  17. 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

  18. 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

  19. [Establishment of A Clinical Prediction Model of Prolonged Air Leak 
after Anatomic Lung Resection].

    Science.gov (United States)

    Wu, Xianning; Xu, Shibin; Ke, Li; Fan, Jun; Wang, Jun; Xie, Mingran; Jiang, Xianliang; Xu, Meiqing

    2017-12-20

    Prolonged air leak (PAL) after anatomic lung resection is a common and challenging complication in thoracic surgery. No available clinical prediction model of PAL has been established in China. The aim of this study was to construct a model to identify patients at increased risk of PAL by using preoperative factors exclusively. We retrospectively reviewed clinical data and PAL occurrence of patients after anatomic lung resection, in department of thoracic surgery, Anhui Provincial Hospital Affiliated to Anhui Medical University, from January 2016 to October 2016. 359 patients were in group A, clinical data including age, body mass index (BMI), gender, smoking history, surgical methods, pulmonary function index, pleural adhesion, pathologic diagnosis, side and site of resected lung were analyzed. By using univariate and multivariate analysis, we found the independent predictors of PAL after anatomic lung resection and subsequently established a clinical prediction model. Then, another 112 patients (group B), who underwent anatomic lung resection in different time by different team, were chosen to verify the accuracy of the prediction model. Receiver-operating characteristic (ROC) curve was constructed using the prediction model. Multivariate Logistic regression analysis was used to identify six clinical characteristics [BMI, gender, smoking history, forced expiratory volume in one second to forced vital capacity ratio (FEV1%), pleural adhesion, site of resection] as independent predictors of PAL after anatomic lung resection. The area under the ROC curve for our model was 0.886 (95%CI: 0.835-0.937). The best predictive P value was 0.299 with sensitivity of 78.5% and specificity of 93.2%. Our prediction model could accurately identify occurrence risk of PAL in patients after anatomic lung resection, which might allow for more effective use of intraoperative prophylactic strategies.
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  20. 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.

  1. A Unified Air-Sea Interface in Fully Coupled Atmosphere-Wave-Ocean Models for Data Assimilation and Ensemble Prediction

    Science.gov (United States)

    Chen, Shuyi; Curcic, Milan; Donelan, Mark; Campbell, Tim; Smith, Travis; Chen, Sue; Allard, Rick; Michalakes, John

    2014-05-01

    The goals of this study are to 1) better understand the physical processes controlling air-sea interaction and their impact on coastal marine and storm predictions, 2) explore the use of coupled atmosphere-ocean observations in model verification and data assimilation, and 3) develop a physically based and computationally efficient coupling at the air-sea interface that is flexible for use in a multi-model system and portable for transition to the next generation research and operational coupled atmosphere-wave-ocean-land models. We have developed a unified air-sea interface module that couples multiple atmosphere, wave, and ocean models using the Earth System Modeling Framework (ESMF). This standardized coupling framework allows researchers to develop and test air-sea coupling parameterizations and coupled data assimilation, and to better facilitate research-to-operation activities. It also allows for future ensemble forecasts using coupled models that can be used for coupled data assimilation and assessment of uncertainties in coupled model predictions. The current component models include two atmospheric models (WRF and COAMPS), two ocean models (HYCOM and NCOM), and two wave models (UMWM and SWAN). The coupled modeling systems have been tested and evaluated using the coupled air-sea observations (e.g., GPS dropsondes and AXBTs, drifters and floats) collected in recent field campaigns in the Gulf of Mexico and tropical cyclones in the Atlantic and Pacific basins. This talk will provide an overview of the unified air-sea interface model and fully coupled atmosphere-wave-ocean model predictions over various coastal regions and tropical cyclones in the Pacific and Atlantic basins including an example from coupled ensemble prediction of Superstorm Sandy (2012).

  2. Development of a Data-Driven Predictive Model of Supply Air Temperature in an Air-Handling Unit for Conserving Energy

    Directory of Open Access Journals (Sweden)

    Goopyo Hong

    2018-02-01

    Full Text Available The purpose of this study was to develop a data-driven predictive model that can predict the supply air temperature (SAT in an air-handling unit (AHU by using a neural network. A case study was selected, and AHU operational data from December 2015 to November 2016 was collected. A data-driven predictive model was generated through an evolving process that consisted of an initial model, an optimal model, and an adaptive model. In order to develop the optimal model, input variables, the number of neurons and hidden layers, and the period of the training data set were considered. Since AHU data changes over time, an adaptive model, which has the ability to actively cope with constantly changing data, was developed. This adaptive model determined the model with the lowest mean square error (MSE of the 91 models, which had two hidden layers and sets up a 12-hour test set at every prediction. The adaptive model used recently collected data as training data and utilized the sliding window technique rather than the accumulative data method. Furthermore, additional testing was performed to validate the adaptive model using AHU data from another building. The final adaptive model predicts SAT to a root mean square error (RMSE of less than 0.6 °C.

  3. A state of the art regarding urban air quality prediction models

    Science.gov (United States)

    Croitoru, Cristiana; Nastase, Ilinca

    2018-02-01

    Urban pollution represents an increasing risk to residents of urban regions, particularly in large, over-industrialized cities knowing that the traffic is responsible for more than 25% of air gaseous pollutants and dust particles. Air quality modelling plays an important role in addressing air pollution control and management approaches by providing guidelines for better and more efficient air quality forecasting, along with smart monitoring sensor networks. The advances in technology regarding simulations, forecasting and monitoring are part of the new smart cities which offers a healthy environment for their occupants.

  4. A state of the art regarding urban air quality prediction models

    Directory of Open Access Journals (Sweden)

    Croitoru Cristiana

    2018-01-01

    Full Text Available Urban pollution represents an increasing risk to residents of urban regions, particularly in large, over-industrialized cities knowing that the traffic is responsible for more than 25% of air gaseous pollutants and dust particles. Air quality modelling plays an important role in addressing air pollution control and management approaches by providing guidelines for better and more efficient air quality forecasting, along with smart monitoring sensor networks. The advances in technology regarding simulations, forecasting and monitoring are part of the new smart cities which offers a healthy environment for their occupants.

  5. Ensemble prediction of air quality using the WRF/CMAQ model system for health effect studies in China

    Science.gov (United States)

    Hu, Jianlin; Li, Xun; Huang, Lin; Ying, Qi; Zhang, Qiang; Zhao, Bin; Wang, Shuxiao; Zhang, Hongliang

    2017-11-01

    Accurate exposure estimates are required for health effect analyses of severe air pollution in China. Chemical transport models (CTMs) are widely used to provide spatial distribution, chemical composition, particle size fractions, and source origins of air pollutants. The accuracy of air quality predictions in China is greatly affected by the uncertainties of emission inventories. The Community Multiscale Air Quality (CMAQ) model with meteorological inputs from the Weather Research and Forecasting (WRF) model were used in this study to simulate air pollutants in China in 2013. Four simulations were conducted with four different anthropogenic emission inventories, including the Multi-resolution Emission Inventory for China (MEIC), the Emission Inventory for China by School of Environment at Tsinghua University (SOE), the Emissions Database for Global Atmospheric Research (EDGAR), and the Regional Emission inventory in Asia version 2 (REAS2). Model performance of each simulation was evaluated against available observation data from 422 sites in 60 cities across China. Model predictions of O3 and PM2.5 generally meet the model performance criteria, but performance differences exist in different regions, for different pollutants, and among inventories. Ensemble predictions were calculated by linearly combining the results from different inventories to minimize the sum of the squared errors between the ensemble results and the observations in all cities. The ensemble concentrations show improved agreement with observations in most cities. The mean fractional bias (MFB) and mean fractional errors (MFEs) of the ensemble annual PM2.5 in the 60 cities are -0.11 and 0.24, respectively, which are better than the MFB (-0.25 to -0.16) and MFE (0.26-0.31) of individual simulations. The ensemble annual daily maximum 1 h O3 (O3-1h) concentrations are also improved, with mean normalized bias (MNB) of 0.03 and mean normalized errors (MNE) of 0.14, compared to MNB of 0.06-0.19 and

  6. Developing a Predictive Model for Unscheduled Maintenance Requirements on United States Air Force Installations

    National Research Council Canada - National Science Library

    Kovich, Matthew D; Norton, J. D

    2008-01-01

    .... This paper develops one such method by using linear regression and time series analysis to develop a predictive model to forecast future year man-hour and funding requirements for unscheduled maintenance...

  7. Improving Air Quality (and Weather) Predictions using Advanced Data Assimilation Techniques Applied to Coupled Models during KORUS-AQ

    Science.gov (United States)

    Carmichael, G. R.; Saide, P. E.; Gao, M.; Streets, D. G.; Kim, J.; Woo, J. H.

    2017-12-01

    Ambient aerosols are important air pollutants with direct impacts on human health and on the Earth's weather and climate systems through their interactions with radiation and clouds. Their role is dependent on their distributions of size, number, phase and composition, which vary significantly in space and time. There remain large uncertainties in simulated aerosol distributions due to uncertainties in emission estimates and in chemical and physical processes associated with their formation and removal. These uncertainties lead to large uncertainties in weather and air quality predictions and in estimates of health and climate change impacts. Despite these uncertainties and challenges, regional-scale coupled chemistry-meteorological models such as WRF-Chem have significant capabilities in predicting aerosol distributions and explaining aerosol-weather interactions. We explore the hypothesis that new advances in on-line, coupled atmospheric chemistry/meteorological models, and new emission inversion and data assimilation techniques applicable to such coupled models, can be applied in innovative ways using current and evolving observation systems to improve predictions of aerosol distributions at regional scales. We investigate the impacts of assimilating AOD from geostationary satellite (GOCI) and surface PM2.5 measurements on predictions of AOD and PM in Korea during KORUS-AQ through a series of experiments. The results suggest assimilating datasets from multiple platforms can improve the predictions of aerosol temporal and spatial distributions.

  8. Validation of an individualised model of human thermoregulation for predicting responses to cold air

    Science.gov (United States)

    van Marken Lichtenbelt, Wouter D.; Frijns, Arjan J. H.; van Ooijen, Marieke J.; Fiala, Dusan; Kester, Arnold M.; van Steenhoven, Anton A.

    2007-01-01

    Most computer models of human thermoregulation are population based. Here, we individualised the Fiala model [Fiala et al. (2001) Int J Biometeorol 45:143 159] with respect to anthropometrics, body fat, and metabolic rate. The predictions of the adapted multisegmental thermoregulatory model were compared with measured skin temperatures of individuals. Data from two experiments, in which reclining subjects were suddenly exposed to mild to moderate cold environmental conditions, were used to study the effect on dynamic skin temperature responses. Body fat was measured by the three-compartment method combining underwater weighing and deuterium dilution. Metabolic rate was determined by indirect calorimetry. In experiment 1, the bias (mean difference) between predicted and measured mean skin temperature decreased from 1.8°C to -0.15°C during cold exposure. The standard deviation of the mean difference remained of the same magnitude (from 0.7°C to 0.9°C). In experiment 2 the bias of the skin temperature changed from 2.0±1.09°C using the standard model to 1.3±0.93°C using individual characteristics in the model. The inclusion of individual characteristics thus improved the predictions for an individual and led to a significantly smaller systematic error. However, a large part of the discrepancies in individual response to cold remained unexplained. Possible further improvements to the model accomplished by inclusion of more subject characteristics (i.e. body fat distribution, body shape) and model refinements on the level of (skin) blood perfusion, and control functions, are discussed.

  9. 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); Wu, Hongyu [Kansas State University; Mittal, Saurabh [Mitre Corporation; Marks, Jesse [University of Missouri

    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.

  10. Influence of Temperature, Relative Humidity, and Soil Properties on the Soil-Air Partitioning of Semivolatile Pesticides: Laboratory Measurements and Predictive Models.

    Science.gov (United States)

    Davie-Martin, Cleo L; Hageman, Kimberly J; Chin, Yu-Ping; Rougé, Valentin; Fujita, Yuki

    2015-09-01

    Soil-air partition coefficient (Ksoil-air) values are often employed to investigate the fate of organic contaminants in soils; however, these values have not been measured for many compounds of interest, including semivolatile current-use pesticides. Moreover, predictive equations for estimating Ksoil-air values for pesticides (other than the organochlorine pesticides) have not been robustly developed, due to a lack of measured data. In this work, a solid-phase fugacity meter was used to measure the Ksoil-air values of 22 semivolatile current- and historic-use pesticides and their degradation products. Ksoil-air values were determined for two soils (semiarid and volcanic) under a range of environmentally relevant temperature (10-30 °C) and relative humidity (30-100%) conditions, such that 943 Ksoil-air measurements were made. Measured values were used to derive a predictive equation for pesticide Ksoil-air values based on temperature, relative humidity, soil organic carbon content, and pesticide-specific octanol-air partition coefficients. Pesticide volatilization losses from soil, calculated with the newly derived Ksoil-air predictive equation and a previously described pesticide volatilization model, were compared to previous results and showed that the choice of Ksoil-air predictive equation mainly affected the more-volatile pesticides and that the way in which relative humidity was accounted for was the most critical difference.

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

    Directory of Open Access Journals (Sweden)

    H. Wang

    2017-08-01

    Full Text Available 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

  12. 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

  13. Prediction of octanol-air partition coefficients for polychlorinated biphenyls (PCBs) using 3D-QSAR models.

    Science.gov (United States)

    Chen, Ying; Cai, Xiaoyu; Jiang, Long; Li, Yu

    2016-02-01

    Based on the experimental data of octanol-air partition coefficients (KOA) for 19 polychlorinated biphenyl (PCB) congeners, two types of QSAR methods, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA), are used to establish 3D-QSAR models using the structural parameters as independent variables and using logKOA values as the dependent variable with the Sybyl software to predict the KOA values of the remaining 190 PCB congeners. The whole data set (19 compounds) was divided into a training set (15 compounds) for model generation and a test set (4 compounds) for model validation. As a result, the cross-validation correlation coefficient (q(2)) obtained by the CoMFA and CoMSIA models (shuffled 12 times) was in the range of 0.825-0.969 (>0.5), the correlation coefficient (r(2)) obtained was in the range of 0.957-1.000 (>0.9), and the SEP (standard error of prediction) of test set was within the range of 0.070-0.617, indicating that the models were robust and predictive. Randomly selected from a set of models, CoMFA analysis revealed that the corresponding percentages of the variance explained by steric and electrostatic fields were 23.9% and 76.1%, respectively, while CoMSIA analysis by steric, electrostatic and hydrophobic fields were 0.6%, 92.6%, and 6.8%, respectively. The electrostatic field was determined as a primary factor governing the logKOA. The correlation analysis of the relationship between the number of Cl atoms and the average logKOA values of PCBs indicated that logKOA values gradually increased as the number of Cl atoms increased. Simultaneously, related studies on PCB detection in the Arctic and Antarctic areas revealed that higher logKOA values indicate a stronger PCB migration ability. From CoMFA and CoMSIA contour maps, logKOA decreased when substituents possessed electropositive groups at the 2-, 3-, 3'-, 5- and 6- positions, which could reduce the PCB migration ability. These results are

  14. 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.

  15. A Novel Distributed Economic Model Predictive Control Approach for Building Air-Conditioning Systems in Microgrids

    Directory of Open Access Journals (Sweden)

    Xinan Zhang

    2018-04-01

    Full Text Available With the penetration of grid-connected renewable energy generation, microgrids are facing stability and power quality problems caused by renewable intermittency. To alleviate such problems, demand side management (DSM of responsive loads, such as building air-conditioning system (BACS, has been proposed and studied. In recent years, numerous control approaches have been published for proper management of single BACS. The majority of these approaches focus on either the control of BACS for attenuating power fluctuations in the grid or the operating cost minimization on behalf of the residents. These two control objectives are paramount for BACS control in microgrids and can be conflicting. As such, they should be considered together in control design. As individual buildings may have different owners/residents, it is natural to control different BACSs in an autonomous and self-interested manner to minimize the operational costs for the owners/residents. Unfortunately, such “selfish” operation can result in abrupt and large power fluctuations at the point of common coupling (PCC of the microgrid due to lack of coordination. Consequently, the original objective of mitigating power fluctuations generated by renewable intermittency cannot be achieved. To minimize the operating costs of individual BACSs and simultaneously ensure desirable overall power flow at PCC, this paper proposes a novel distributed control framework based on the dissipativity theory. The proposed method achieves the objective of renewable intermittency mitigation through proper coordination of distributed BACS controllers and is scalable and computationally efficient. Simulation studies are carried out to illustrate the efficacy of the proposed control framework.

  16. Atmospheric Model Evaluation Tool for meteorological and air quality simulations

    Science.gov (United States)

    The Atmospheric Model Evaluation Tool compares model predictions to observed data from various meteorological and air quality observation networks to help evaluate meteorological and air quality simulations.

  17. Development of visibility forecasting modelling framework for the lower fraser valley of British Columbia using Canada's regional air quality deterministic prediction system.

    Science.gov (United States)

    So, Rita; Teakles, Andrew; Baik, Jonathan; Vingarzan, Roxanne; Jones, Keith

    2018-01-17

    Visibility degradation, one of the most noticeable indicators of poor air quality, can occur despite relatively low levels of particulate matter when the risk to human health is low. The availability of timely and reliable visibility forecasts can provide a more comprehensive understanding of the anticipated air quality conditions to better inform local jurisdictions and the public. This paper describes the development of a visibility forecasting modelling framework, which leverages the existing air quality and meteorological forecasts from Canada's operational Regional Air Quality Deterministic Prediction System (RAQDPS) for the Lower Fraser Valley of British Columbia. A baseline model (GM-IMPROVE) was constructed using the revised IMPROVE algorithm based on unprocessed forecasts from the RAQDPS. Three additional prototypes (UMOS-HYB, GM-MLR, GM-RF) were also developed and assessed for forecast performance of up to 48 hour lead time during various air quality and meteorological conditions. Forecast performance was assessed by examining their ability to provide both numerical and categorical forecasts in the form of 1-hr total extinction and Visual Air Quality Ratings (VAQR), respectively. While GM-IMPROVE generally overestimated extinction over twofold, it had skill in forecasting the relative species contribution to visibility impairment, including ammonium sulphate and ammonium nitrate. Both statistical prototypes, GM-MLR and GM-RF, performed well in forecasting 1-hr extinction during daylight hours, with correlation coefficients (R) ranging from 0.59 to 0.77. UMOS-HYB, a prototype based on post-processed air quality forecasts without additional statistical modelling, provided reasonable forecasts during most daylight hours. In terms of categorical forecasts, the best prototype was approximately 75 to 87% correct, when forecasting for a condensed three-category VAQR. A case study, focusing on a poor visual air quality yet low Air Quality Health Index episode

  18. Implementing subgrid-scale cloudiness into the Model for Prediction Across Scales-Atmosphere (MPAS-A) for next generation global air quality modeling

    Science.gov (United States)

    A next generation air quality modeling system is being developed at the U.S. EPA to enable seamless modeling of air quality from global to regional to (eventually) local scales. State of the science chemistry and aerosol modules from the Community Multiscale Air Quality (CMAQ) mo...

  19. Impact of chemical lateral boundary conditions in a regional air quality forecast model on surface ozone predictions during stratospheric intrusions

    Science.gov (United States)

    Pendlebury, Diane; Gravel, Sylvie; Moran, Michael D.; Lupu, Alexandru

    2018-02-01

    A regional air quality forecast model, GEM-MACH, is used to examine the conditions under which a limited-area air quality model can accurately forecast near-surface ozone concentrations during stratospheric intrusions. Periods in 2010 and 2014 with known stratospheric intrusions over North America were modelled using four different ozone lateral boundary conditions obtained from a seasonal climatology, a dynamically-interpolated monthly climatology, global air quality forecasts, and global air quality reanalyses. It is shown that the mean bias and correlation in surface ozone over the course of a season can be improved by using time-varying ozone lateral boundary conditions, particularly through the correct assignment of stratospheric vs. tropospheric ozone along the western lateral boundary (for North America). Part of the improvement in surface ozone forecasts results from improvements in the characterization of near-surface ozone along the lateral boundaries that then directly impact surface locations near the boundaries. However, there is an additional benefit from the correct characterization of the location of the tropopause along the western lateral boundary such that the model can correctly simulate stratospheric intrusions and their associated exchange of ozone from stratosphere to troposphere. Over a three-month period in spring 2010, the mean bias was seen to improve by as much as 5 ppbv and the correlation by 0.1 depending on location, and on the form of the chemical lateral boundary condition.

  20. Seasonal Prediction of Surface Air Temperature across Vietnam Using the Regional Climate Model Version 4.2 (RegCM4.2)

    OpenAIRE

    Phan Van, Tan; Van Nguyen, Hiep; Trinh Tuan, Long; Nguyen Quang, Trung; Ngo-Duc, Thanh; Laux, Patrick; Nguyen Xuan, Thanh

    2014-01-01

    To investigate the ability of dynamical seasonal climate predictions for Vietnam, the RegCM4.2 is employed to perform seasonal prediction of 2 m mean (T2m), maximum (Tx), and minimum (Tn) air temperature for the period from January 2012 to November 2013 by downscaling the NCEP Climate Forecast System (CFS) data. For model bias correction, the model and observed climatology is constructed using the CFS reanalysis and observed temperatures over Vietnam for the period 1980–2010, respectively. Th...

  1. 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

  2. Satellite data driven modeling system for predicting air quality and visibility during wildfire and prescribed burn events

    Science.gov (United States)

    Nair, U. S.; Keiser, K.; Wu, Y.; Maskey, M.; Berendes, D.; Glass, P.; Dhakal, A.; Christopher, S. A.

    2012-12-01

    The Alabama Forestry Commission (AFC) is responsible for wildfire control and also prescribed burn management in the state of Alabama. Visibility and air quality degradation resulting from smoke are two pieces of information that are crucial for this activity. Currently the tools available to AFC are the dispersion index available from the National Weather Service and also surface smoke concentrations. The former provides broad guidance for prescribed burning activities but does not provide specific information regarding smoke transport, areas affected and quantification of air quality and visibility degradation. While the NOAA operational air quality guidance includes surface smoke concentrations from existing fire events, it does not account for contributions from background aerosols, which are important for the southeastern region including Alabama. Also lacking is the quantification of visibility. The University of Alabama in Huntsville has developed a state-of-the-art integrated modeling system to address these concerns. This system based on the Community Air Quality Modeling System (CMAQ) that ingests satellite derived smoke emissions and also assimilates NASA MODIS derived aerosol optical thickness. In addition, this operational modeling system also simulates the impact of potential prescribed burn events based on location information derived from the AFC prescribed burn permit database. A lagrangian model is used to simulate smoke plumes for the prescribed burns requests. The combined air quality and visibility degradation resulting from these smoke plumes and background aerosols is computed and the information is made available through a web based decision support system utilizing open source GIS components. This system provides information regarding intersections between highways and other critical facilities such as old age homes, hospitals and schools. The system also includes satellite detected fire locations and other satellite derived datasets

  3. 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

  4. 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.

  5. Predicting soil, water and air concentrations of environmental contaminants locally and regionally; multimedia transport and transformation models

    International Nuclear Information System (INIS)

    McKone, T.E.; Daniels, J.I.

    1991-01-01

    Environmental scientists recognize that the environment functions as a complex, interconnected system. A realistic risk-management strategy for many contaminants requires a comprehensive and integrated assessment of local and regional transport and transformation processes. In response to this need, we have developed multimedia models that simulate the movement and transformation of chemicals as they spread through air, water, biota, soils, sediments, surface water and ground water. Each component of the environment is treated as a homogeneous subsystem that can exchange water, nutrients, and chemical contaminants with other adjacent compartments. In this paper, we illustrate the use of multimedia models and measurements as tools for screening the potential risks of contaminants released to air and deposited onto soil and plants. The contaminant list includes the volatile organic compounds (VOCs) tetrachloroethylene (PCE) and trichloroethylene (TCE), the semi-volatile organic compound benzo(a)pyrene, and the radionuclides tritium and uranium-238. We examine how chemical properties effect both the ultimate route and quantity of human and ecosystem contact and identify sensitivities and uncertainties in the model results. We consider the advantages of multimedia models relative to environmental monitoring data. (au)

  6. Predicting soil, water, and air concentrations of environmental contaminants locally and regionally: Multimedia transport and transformation models

    International Nuclear Information System (INIS)

    McKone, T.E.; Daniels, J.I.

    1991-10-01

    Environmental scientists recognize that the environment functions as a complex, interconnected system. A realistic risk-management strategy for many contaminants requires a comprehensive and integrated assessment of local and regional transport and transformation processes. In response to this need, we have developed multimedia models that simulate the movement and transformation of chemicals as they spread through air, water, biota, soils, sediments, surface water, and ground water. Each component of the environment is treated as a homogeneous subsystem that can exchange water, nutrients, and chemical contaminants with other adjacent compartments. In this paper, we illustrate the use of multimedia models and measurements as tools for screening the potential risks of contaminants released to air and deposited onto soil and plants. The contaminant list includes the volatile organic compounds (VOCs) tetrachloroethylene (PCE) and trichloroethylene (TCE), the semi-volatile organic compound benzo(a)pyrene, and the radionuclides tritium and uranium-238. We examine how chemical properties effect both the ultimate route and quantity of human and ecosystem contact and identify sensitivities and uncertainties in the model results

  7. 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.

  8. A model to predict radon exhalation from walls to indoor air based on the exhalation from building material samples.

    Science.gov (United States)

    Sahoo, B K; Sapra, B K; Gaware, J J; Kanse, S D; Mayya, Y S

    2011-06-01

    In recognition of the fact that building materials are an important source of indoor radon, second only to soil, surface radon exhalation fluxes have been extensively measured from the samples of these materials. Based on this flux data, several researchers have attempted to predict the inhalation dose attributable to radon emitted from walls and ceilings made up of these materials. However, an important aspect not considered in this methodology is the enhancement of the radon flux from the wall or the ceiling constructed using the same building material. This enhancement occurs mainly because of the change in the radon diffusion process from the former to the latter configuration. To predict the true radon flux from the wall based on the flux data of building material samples, we now propose a semi-empirical model involving radon diffusion length and the physical dimensions of the samples as well as wall thickness as other input parameters. This model has been established by statistically fitting the ratio of the solution to radon diffusion equations for the cases of three-dimensional cuboidal shaped building materials (such as brick, concrete block) and one dimensional wall system to a simple mathematical function. The model predictions have been validated against the measurements made at a new construction site. This model provides an alternative tool (substitute to conventional 1-D model) to estimate radon flux from a wall without relying on ²²⁶Ra content, radon emanation factor and bulk density of the samples. Moreover, it may be very useful in the context of developing building codes for radon regulation in new buildings. Copyright © 2011 Elsevier B.V. All rights reserved.

  9. 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 PM 10 and PM 2.5 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.

  10. 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

  11. 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

  12. Cultural Resource Predictive Modeling

    Science.gov (United States)

    2017-10-01

    refining formal, inductive predictive models is the quality of the archaeological and environmental data. To build models efficiently, relevant...geomorphology, and historic information . Lessons Learned: The original model was focused on the identification of prehistoric resources. This...system but uses predictive modeling informally . For example, there is no probability for buried archaeological deposits on the Burton Mesa, but there is

  13. 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.

  14. Predictive modeling of complications.

    Science.gov (United States)

    Osorio, Joseph A; Scheer, Justin K; Ames, Christopher P

    2016-09-01

    Predictive analytic algorithms are designed to identify patterns in the data that allow for accurate predictions without the need for a hypothesis. Therefore, predictive modeling can provide detailed and patient-specific information that can be readily applied when discussing the risks of surgery with a patient. There are few studies using predictive modeling techniques in the adult spine surgery literature. These types of studies represent the beginning of the use of predictive analytics in spine surgery outcomes. We will discuss the advancements in the field of spine surgery with respect to predictive analytics, the controversies surrounding the technique, and the future directions.

  15. A Predictive Model for the Determination of the Economic Feasibility of Construction and Demolition Waste Recycling in the Air Force

    Science.gov (United States)

    1993-09-01

    Force Base. Research - 4.5 cf/sf floor area of Laboratory, Battelle wooden structures - Study offered strong proof of Labs , Columbus, correlation...Department, School of Engineering, Air Force Institute of Technology. Permanent Address: 4286 Fowler Dr. Bellbrook , OH 45305 89 Fo:rm •’r~c REPORT

  16. 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

  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

    International Nuclear Information System (INIS)

    Barry, P.

    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 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 crops

  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. Archaeological predictive model set.

    Science.gov (United States)

    2015-03-01

    This report is the documentation for Task 7 of the Statewide Archaeological Predictive Model Set. The goal of this project is to : develop a set of statewide predictive models to assist the planning of transportation projects. PennDOT is developing t...

  20. 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.

  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 fo...

  2. Zephyr - the prediction models

    DEFF Research Database (Denmark)

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

    2001-01-01

    utilities as partners and users. The new models are evaluated for five wind farms in Denmark as well as one wind farm in Spain. It is shown that the predictions based on conditional parametric models are superior to the predictions obatined by state-of-the-art parametric models.......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 Danish...

  3. Inverse and Predictive Modeling

    Energy Technology Data Exchange (ETDEWEB)

    Syracuse, Ellen Marie [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2017-09-27

    The LANL Seismo-Acoustic team has a strong capability in developing data-driven models that accurately predict a variety of observations. These models range from the simple – one-dimensional models that are constrained by a single dataset and can be used for quick and efficient predictions – to the complex – multidimensional models that are constrained by several types of data and result in more accurate predictions. Team members typically build models of geophysical characteristics of Earth and source distributions at scales of 1 to 1000s of km, the techniques used are applicable for other types of physical characteristics at an even greater range of scales. The following cases provide a snapshot of some of the modeling work done by the Seismo- Acoustic team at LANL.

  4. 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.

  5. 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 ...

  6. Assessing the Impact of Surface and Upper-Air Observations on the Forecast Skill of the ACCESS Numerical Weather Prediction Model over Australia

    Directory of Open Access Journals (Sweden)

    Sergei Soldatenko

    2018-01-01

    Full Text Available The impact of the Australian Bureau of Meteorology’s in situ observations (land and sea surface observations, upper air observations by radiosondes, pilot balloons, wind profilers, and aircraft observations on the short-term forecast skill provided by the ACCESS (Australian Community Climate and Earth-System Simulator global numerical weather prediction (NWP system is evaluated using an adjoint-based method. This technique makes use of the adjoint perturbation forecast model utilized within the 4D-Var assimilation system, and is able to calculate the individual impact of each assimilated observation in a cycling NWP system. The results obtained show that synoptic observations account for about 60% of the 24-h forecast error reduction, with the remainder accounted for by aircraft (12.8%, radiosondes (10.5%, wind profilers (3.9%, pilot balloons (2.8%, buoys (1.7% and ships (1.2%. In contrast, the largest impact per observation is from buoys and aircraft. Overall, all observation types have a positive impact on the 24-h forecast skill. Such results help to support the decision-making process regarding the evolution of the observing network, particularly at the national level. Consequently, this 4D-Var-based approach has great potential as a tool to assist the design and running of an efficient and effective observing network.

  7. 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

  8. Community Multiscale Air Quality Modeling System (CMAQ)

    Science.gov (United States)

    CMAQ is a computational tool used for air quality management. It models air pollutants including ozone, particulate matter and other air toxics to help determine optimum air quality management scenarios.

  9. Uncertainty estimation and risk prediction in air quality

    International Nuclear Information System (INIS)

    Garaud, Damien

    2011-01-01

    This work is about uncertainty estimation and risk prediction in air quality. Firstly, we build a multi-model ensemble of air quality simulations which can take into account all uncertainty sources related to air quality modeling. Ensembles of photochemical simulations at continental and regional scales are automatically generated. Then, these ensemble are calibrated with a combinatorial optimization method. It selects a sub-ensemble which is representative of uncertainty or shows good resolution and reliability for probabilistic forecasting. This work shows that it is possible to estimate and forecast uncertainty fields related to ozone and nitrogen dioxide concentrations or to improve the reliability of threshold exceedance predictions. The approach is compared with Monte Carlo simulations, calibrated or not. The Monte Carlo approach appears to be less representative of the uncertainties than the multi-model approach. Finally, we quantify the observational error, the representativeness error and the modeling errors. The work is applied to the impact of thermal power plants, in order to quantify the uncertainty on the impact estimates. (author) [fr

  10. Modeling of air flow through a narrow crack

    International Nuclear Information System (INIS)

    Trojek, T.; Cechak, T.; Moucka, L.; Fronka, A.

    2004-01-01

    Radon transport in dwellings is governed to a significant extent by pressure differences and properties of transport pathways. A model of air flow through narrow cracks was created in order to facilitate prediction of air velocity and air flow. Theoretical calculations, based on numerical solution of a system of differential equations, were compared with measurements carried out on a window crack. (P.A.)

  11. 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

  12. Thermo-Electrical Mathematical Model for Prediction of Ni-Cr Hot-Wire Temperature in Free Air and Inside Small Circular Cavities

    DEFF Research Database (Denmark)

    Petkov, Kiril; Hattel, Jesper Henri

    2017-01-01

    A one-dimensional thermo-electrical mathematical model describing the heating and cooling of thin Ni-Cr20% wires is presented. The model is applied for wires in a free air environment and to wires placed in small circular cavities formed by expanded polystyrene material. The basis of the model...... to select an appropriate heat transfer coefficient for the time-dependent heating and cooling of a wire. The model is tested against experimental data and is found to be in a good agreement with previous investigations. Based on the findings, expressions for the heat transfer coefficient of a hot wire...

  13. Assessing the influence of land use land cover pattern, socio economic factors and air quality status to predict morbidity on the basis of logistic based regression model

    Science.gov (United States)

    Dixit, A.; Singh, V. K.

    2017-12-01

    Recent studies conducted by World Health Organisation (WHO) estimated that 92 % of the total world population are living in places where the air quality level has exceeded the WHO standard limit for air quality. This is due to the change in Land Use Land Cover (LULC) pattern, socio economic drivers and anthropogenic heat emission caused by manmade activity. Thereby, many prevalent human respiratory diseases such as lung cancer, chronic obstructive pulmonary disease and emphysema have increased in recent times. In this study, a quantitative relationship is developed between land use (built-up land, water bodies, and vegetation), socio economic drivers and air quality parameters using logistic based regression model over 7 different cities of India for the winter season of 2012 to 2016. Different LULC, socio economic, industrial emission sources, meteorological condition and air quality level from the monitoring stations are taken to estimate the influence on morbidity of each city. Results of correlation are analyzed between land use variables and monthly concentration of pollutants. These values range from 0.63 to 0.76. Similarly, the correlation value between land use variable with socio economic and morbidity ranges from 0.57 to 0.73. The performance of model is improved from 67 % to 79 % in estimating morbidity for the year 2015 and 2016 due to the better availability of observed data.The study highlights the growing importance of incorporating socio-economic drivers with air quality data for evaluating morbidity rate for each city in comparison to just change in quantitative analysis of air quality.

  14. 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...

  15. Predicting submicron air pollution indicators: a machine learning approach.

    Science.gov (United States)

    Pandey, Gaurav; Zhang, Bin; Jian, Le

    2013-05-01

    The regulation of air pollutant levels is rapidly becoming one of the most important tasks for the governments of developing countries, especially China. Submicron particles, such as ultrafine particles (UFP, aerodynamic diameter ≤ 100 nm) and particulate matter ≤ 1.0 micrometers (PM1.0), are an unregulated emerging health threat to humans, but the relationships between the concentration of these particles and meteorological and traffic factors are poorly understood. To shed some light on these connections, we employed a range of machine learning techniques to predict UFP and PM1.0 levels based on a dataset consisting of observations of weather and traffic variables recorded at a busy roadside in Hangzhou, China. Based upon the thorough examination of over twenty five classifiers used for this task, we find that it is possible to predict PM1.0 and UFP levels reasonably accurately and that tree-based classification models (Alternating Decision Tree and Random Forests) perform the best for both these particles. In addition, weather variables show a stronger relationship with PM1.0 and UFP levels, and thus cannot be ignored for predicting submicron particle levels. Overall, this study has demonstrated the potential application value of systematically collecting and analysing datasets using machine learning techniques for the prediction of submicron sized ambient air pollutants.

  16. 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...... removed from the room at constant penetration length is proportional to the cube of the velocities in the occupied zone. It is also shown that a large number of diffusers increases the amount of heat which may be removed without affecting the thermal conditions. Control strategies for dual duct and single...... duct systems are given and the paper is concluded by mentioning a computer-based prediction method which gives the velocity and temperature distribution in the whole room....

  17. 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...... removed from the room at constant penetration length is proportional to the cube of the velocities in the occupied zone. It is also shown that a large number of diffusers increases the amount of heat which may be removed without affecting the thermal conditions. Control strategies for dual duct and single...... duct systems are given and the paper is concluded by mentioning a computer-based prediction method which gives the velocity and temperature distribution in the whole room....

  18. 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.

  19. 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.

  20. 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...... that regardless of the temperature combinations, personalized ventilation may decrease significantly the number of occupants dissatisfied with the air quality. Under non-isothermal conditions the percentage of dissatisfied may decrease up to 4 times....

  1. 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...

  2. AIR POLLUITON INDEX PREDICTION USING MULTIPLE NEURAL NETWORKS

    OpenAIRE

    Zainal Ahmad; Nazira Anisa Rahim; Alireza Bahadori; Jie Zhang

    2017-01-01

    Air quality monitoring and forecasting tools are necessary for the purpose of taking precautionary measures against air pollution, such as reducing the effect of a predicted air pollution peak on the surrounding population and ecosystem. In this study a single Feed-forward Artificial Neural Network (FANN) is shown to be able to predict the Air Pollution Index (API) with a Mean Squared Error (MSE) and coefficient determination, R2, of 0.1856 and 0.7950 respectively. However, due to the non-rob...

  3. Predicting success in the tactical air combat party training pipeline.

    Science.gov (United States)

    Kalns, John; Baskin, Jonathan; Reinert, Andrew; Michael, Darren; Santos, Adrienne; Daugherty, Sheena; Wright, James K

    2011-04-01

    To develop a statistical model that predicts the likelihood of success or failure of military training candidates using tests administered before initial skill training as inputs. Data were acquired from candidates before the start of U.S. Air Force Tactical Air Control Party training, including (1) demographic, (2) psychological composition evaluated using Emotional Quotient Inventory, (3) physical performance capability, (4) a physical activity questionnaire, and (5) salivary fatigue biomarker index. A total of 126 candidates were tracked until they either passed or failed the training, and a total of 55 variables were used as inputs for creation of the model. Clustering analysis of the data revealed that only 4 of 55 variables were useful for predicting success or failure. The variables in the order of their importance are as follows: run time, number of miles run per week in the last year, level of salivary fatigue biomarker, and height. The results suggest that simple testing methods can identify candidates at high risk of failure.

  4. 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

  5. Identifying pollution sources and predicting urban air quality using ensemble learning methods

    Science.gov (United States)

    Singh, Kunwar P.; Gupta, Shikha; Rai, Premanjali

    2013-12-01

    In this study, principal components analysis (PCA) was performed to identify air pollution sources and tree based ensemble learning models were constructed to predict the urban air quality of Lucknow (India) using the air quality and meteorological databases pertaining to a period of five years. PCA identified vehicular emissions and fuel combustion as major air pollution sources. The air quality indices revealed the air quality unhealthy during the summer and winter. Ensemble models were constructed to discriminate between the seasonal air qualities, factors responsible for discrimination, and to predict the air quality indices. Accordingly, single decision tree (SDT), decision tree forest (DTF), and decision treeboost (DTB) were constructed and their generalization and predictive performance was evaluated in terms of several statistical parameters and compared with conventional machine learning benchmark, support vector machines (SVM). The DT and SVM models discriminated the seasonal air quality rendering misclassification rate (MR) of 8.32% (SDT); 4.12% (DTF); 5.62% (DTB), and 6.18% (SVM), respectively in complete data. The AQI and CAQI regression models yielded a correlation between measured and predicted values and root mean squared error of 0.901, 6.67 and 0.825, 9.45 (SDT); 0.951, 4.85 and 0.922, 6.56 (DTF); 0.959, 4.38 and 0.929, 6.30 (DTB); 0.890, 7.00 and 0.836, 9.16 (SVR) in complete data. The DTF and DTB models outperformed the SVM both in classification and regression which could be attributed to the incorporation of the bagging and boosting algorithms in these models. The proposed ensemble models successfully predicted the urban ambient air quality and can be used as effective tools for its management.

  6. 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.......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...

  7. 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

  8. 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

    . The retrieved heat excess can be stored in the water tank. For this purpose the charging and the discharging water loops has been designed. We present the non-linear model of the above described system and a non-linear model predictive supervisory controller that according to the received price signal......, occupancy information and ambient temperature minimizes the operation cost of the whole system and distributes set points to local controllers of supermarkets subsystems. We find that when reliable information about the high price period is available, it is profitable to use the refrigeration system...

  9. Confidence scores for prediction models

    DEFF Research Database (Denmark)

    Gerds, Thomas Alexander; van de Wiel, MA

    2011-01-01

    In medical statistics, many alternative strategies are available for building a prediction model based on training data. Prediction models are routinely compared by means of their prediction performance in independent validation data. If only one data set is available for training and validation......, then rival strategies can still be compared based on repeated bootstraps of the same data. Often, however, the overall performance of rival strategies is similar and it is thus difficult to decide for one model. Here, we investigate the variability of the prediction models that results when the same...... 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...

  10. Prediction of air temperature for thermal comfort of people using sleeping bags: a review.

    Science.gov (United States)

    Huang, Jianhua

    2008-11-01

    Six models for determining air temperatures for thermal comfort of people using sleeping bags were reviewed. These models were based on distinctive metabolic rates and mean skin temperatures. All model predictions of air temperatures are low when the insulation values of the sleeping bag are high. Nevertheless, prediction variations are greatest for the sleeping bags with high insulation values, and there is a high risk of hypothermia if an inappropriate sleeping bag is chosen for the intended conditions of use. There is, therefore, a pressing need to validate the models by wear trial and determine which one best reflects ordinary consumer needs.

  11. Using neural networks for prediction of air pollution index in industrial city

    Science.gov (United States)

    Rahman, P. A.; Panchenko, A. A.; Safarov, A. M.

    2017-10-01

    This scientific paper is dedicated to the use of artificial neural networks for the ecological prediction of state of the atmospheric air of an industrial city for capability of the operative environmental decisions. In the paper, there is also the described development of two types of prediction models for determining of the air pollution index on the basis of neural networks: a temporal (short-term forecast of the pollutants content in the air for the nearest days) and a spatial (forecast of atmospheric pollution index in any point of city). The stages of development of the neural network models are briefly overviewed and description of their parameters is also given. The assessment of the adequacy of the prediction models, based on the calculation of the correlation coefficient between the output and reference data, is also provided. Moreover, due to the complexity of perception of the «neural network code» of the offered models by the ordinary users, the software implementations allowing practical usage of neural network models are also offered. It is established that the obtained neural network models provide sufficient reliable forecast, which means that they are an effective tool for analyzing and predicting the behavior of dynamics of the air pollution in an industrial city. Thus, this scientific work successfully develops the urgent matter of forecasting of the atmospheric air pollution index in industrial cities based on the use of neural network models.

  12. Improved Impact of Atmospheric Infrared Sounder (AIRS) Radiance Assimilation in Numerical Weather Prediction

    Science.gov (United States)

    Zavodsky, Bradley; Chou, Shih-Hung; Jedlovec, Gary

    2012-01-01

    Improvements to global and regional numerical weather prediction (NWP) have been demonstrated through assimilation of data from NASA s Atmospheric Infrared Sounder (AIRS). Current operational data assimilation systems use AIRS radiances, but impact on regional forecasts has been much smaller than for global forecasts. Retrieved profiles from AIRS contain much of the information that is contained in the radiances and may be able to reveal reasons for this reduced impact. Assimilating AIRS retrieved profiles in an identical analysis configuration to the radiances, tracking the quantity and quality of the assimilated data in each technique, and examining analysis increments and forecast impact from each data type can yield clues as to the reasons for the reduced impact. By doing this with regional scale models individual synoptic features (and the impact of AIRS on these features) can be more easily tracked. This project examines the assimilation of hyperspectral sounder data used in operational numerical weather prediction by comparing operational techniques used for AIRS radiances and research techniques used for AIRS retrieved profiles. Parallel versions of a configuration of the Weather Research and Forecasting (WRF) model with Gridpoint Statistical Interpolation (GSI) that mimics the analysis methodology, domain, and observational datasets for the regional North American Mesoscale (NAM) model run at the National Centers for Environmental Prediction (NCEP)/Environmental Modeling Center (EMC) are run to examine the impact of each type of AIRS data set. The first configuration will assimilate the AIRS radiance data along with other conventional and satellite data using techniques implemented within the operational system; the second configuration will assimilate AIRS retrieved profiles instead of AIRS radiances in the same manner. Preliminary results of this study will be presented and focus on the analysis impact of the radiances and profiles for selected cases.

  13. Quantitative computed tomography versus spirometry in predicting air leak duration after major lung resection for cancer.

    Science.gov (United States)

    Ueda, Kazuhiro; Kaneda, Yoshikazu; Sudo, Manabu; Mitsutaka, Jinbo; Li, Tao-Sheng; Suga, Kazuyoshi; Tanaka, Nobuyuki; Hamano, Kimikazu

    2005-11-01

    Emphysema is a well-known risk factor for developing air leak or persistent air leak after pulmonary resection. Although quantitative computed tomography (CT) and spirometry are used to diagnose emphysema, it remains controversial whether these tests are predictive of the duration of postoperative air leak. Sixty-two consecutive patients who were scheduled to undergo major lung resection for cancer were enrolled in this prospective study to define the best predictor of postoperative air leak duration. Preoperative factors analyzed included spirometric variables and area of emphysema (proportion of the low-attenuation area) that was quantified in a three-dimensional CT lung model. Chest tubes were removed the day after disappearance of the air leak, regardless of pleural drainage. Univariate and multivariate proportional hazards analyses were used to determine the influence of preoperative factors on chest tube time (air leak duration). By univariate analysis, site of resection (upper, lower), forced expiratory volume in 1 second, predicted postoperative forced expiratory volume in 1 second, and area of emphysema ( 10%) were significant predictors of air leak duration. By multivariate analysis, site of resection and area of emphysema were the best independent determinants of air leak duration. The results were similar for patients with a smoking history (n = 40), but neither forced expiratory volume in 1 second nor predicted postoperative forced expiratory volume in 1 second were predictive of air leak duration. Quantitative CT is superior to spirometry in predicting air leak duration after major lung resection for cancer. Quantitative CT may aid in the identification of patients, particularly among those with a smoking history, requiring additional preventive procedures against air leak.

  14. Polluted Morality: Air Pollution Predicts Criminal Activity and Unethical Behavior.

    Science.gov (United States)

    Lu, Jackson G; Lee, Julia J; Gino, Francesca; Galinsky, Adam D

    2018-02-01

    Air pollution is a serious problem that affects billions of people globally. Although the environmental and health costs of air pollution are well known, the present research investigates its ethical costs. We propose that air pollution can increase criminal and unethical behavior by increasing anxiety. Analyses of a 9-year panel of 9,360 U.S. cities found that air pollution predicted six major categories of crime; these analyses accounted for a comprehensive set of control variables (e.g., city and year fixed effects, population, law enforcement) and survived various robustness checks (e.g., balanced panel, nonparametric bootstrapped standard errors). Three subsequent experiments involving American and Indian participants established the causal effect of psychologically experiencing a polluted (vs. clean) environment on unethical behavior. Consistent with our theoretical perspective, results revealed that anxiety mediated this effect. Air pollution not only corrupts people's health, but also can contaminate their morality.

  15. A Multi-Model Assessment for the 2006 and 2010 Simulations under the Air Quality Model Evaluation International Initiative (AQMEII) Phase 2 over North America: Part II. Evaluation of Column Variable Predictions Using Satellite Data

    Science.gov (United States)

    Within the context of the Air Quality Model Evaluation International Initiative phase 2 (AQMEII2) project, this part II paper performs a multi-model assessment of major column abundances of gases, radiation, aerosol, and cloud variables for 2006 and 2010 simulations with three on...

  16. TEHRAN AIR POLLUTANTS PREDICTION BASED ON RANDOM FOREST FEATURE SELECTION METHOD

    Directory of Open Access Journals (Sweden)

    A. Shamsoddini

    2017-09-01

    Full Text Available Air pollution as one of the most serious forms of environmental pollutions poses huge threat to human life. Air pollution leads to environmental instability, and has harmful and undesirable effects on the environment. Modern prediction methods of the pollutant concentration are able to improve decision making and provide appropriate solutions. This study examines the performance of the Random Forest feature selection in combination with multiple-linear regression and Multilayer Perceptron Artificial Neural Networks methods, in order to achieve an efficient model to estimate carbon monoxide and nitrogen dioxide, sulfur dioxide and PM2.5 contents in the air. The results indicated that Artificial Neural Networks fed by the attributes selected by Random Forest feature selection method performed more accurate than other models for the modeling of all pollutants. The estimation accuracy of sulfur dioxide emissions was lower than the other air contaminants whereas the nitrogen dioxide was predicted more accurate than the other pollutants.

  17. Tehran Air Pollutants Prediction Based on Random Forest Feature Selection Method

    Science.gov (United States)

    Shamsoddini, A.; Aboodi, M. R.; Karami, J.

    2017-09-01

    Air pollution as one of the most serious forms of environmental pollutions poses huge threat to human life. Air pollution leads to environmental instability, and has harmful and undesirable effects on the environment. Modern prediction methods of the pollutant concentration are able to improve decision making and provide appropriate solutions. This study examines the performance of the Random Forest feature selection in combination with multiple-linear regression and Multilayer Perceptron Artificial Neural Networks methods, in order to achieve an efficient model to estimate carbon monoxide and nitrogen dioxide, sulfur dioxide and PM2.5 contents in the air. The results indicated that Artificial Neural Networks fed by the attributes selected by Random Forest feature selection method performed more accurate than other models for the modeling of all pollutants. The estimation accuracy of sulfur dioxide emissions was lower than the other air contaminants whereas the nitrogen dioxide was predicted more accurate than the other pollutants.

  18. Assessment of air quality benefits from national air pollution control policies in China. Part II: Evaluation of air quality predictions and air quality benefits assessment

    Science.gov (United States)

    Wang, Litao; Jang, Carey; Zhang, Yang; Wang, Kai; Zhang, Qiang; Streets, David; Fu, Joshua; Lei, Yu; Schreifels, Jeremy; He, Kebin; Hao, Jiming; Lam, Yun-Fat; Lin, Jerry; Meskhidze, Nicholas; Voorhees, Scott; Evarts, Dale; Phillips, Sharon

    2010-09-01

    Following the meteorological evaluation in Part I, this Part II paper presents the statistical evaluation of air quality predictions by the U.S. Environmental Protection Agency (U.S. EPA)'s Community Multi-Scale Air Quality (Models-3/CMAQ) model for the four simulated months in the base year 2005. The surface predictions were evaluated using the Air Pollution Index (API) data published by the China Ministry of Environmental Protection (MEP) for 31 capital cities and daily fine particulate matter (PM 2.5, particles with aerodiameter less than or equal to 2.5 μm) observations of an individual site in Tsinghua University (THU). To overcome the shortage in surface observations, satellite data are used to assess the column predictions including tropospheric nitrogen dioxide (NO 2) column abundance and aerosol optical depth (AOD). The result shows that CMAQ gives reasonably good predictions for the air quality. The air quality improvement that would result from the targeted sulfur dioxide (SO 2) and nitrogen oxides (NO x) emission controls in China were assessed for the objective year 2010. The results show that the emission controls can lead to significant air quality benefits. SO 2 concentrations in highly polluted areas of East China in 2010 are estimated to be decreased by 30-60% compared to the levels in the 2010 Business-As-Usual (BAU) case. The annual PM 2.5 can also decline by 3-15 μg m -3 (4-25%) due to the lower SO 2 and sulfate concentrations. If similar controls are implemented for NO x emissions, NO x concentrations are estimated to decrease by 30-60% as compared with the 2010 BAU scenario. The annual mean PM 2.5 concentrations will also decline by 2-14 μg m -3 (3-12%). In addition, the number of ozone (O 3) non-attainment areas in the northern China is projected to be much lower, with the maximum 1-h average O 3 concentrations in the summer reduced by 8-30 ppb.

  19. 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...

  20. Bootstrap prediction and Bayesian prediction under misspecified models

    OpenAIRE

    Fushiki, Tadayoshi

    2005-01-01

    We consider a statistical prediction problem under misspecified models. In a sense, Bayesian prediction is an optimal prediction method when an assumed model is true. Bootstrap prediction is obtained by applying Breiman's `bagging' method to a plug-in prediction. Bootstrap prediction can be considered to be an approximation to the Bayesian prediction under the assumption that the model is true. However, in applications, there are frequently deviations from the assumed model. In this paper, bo...

  1. 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...... the performance of HIRLAM in particular with respect to wind predictions. To estimate the performance of the model two spatial resolutions (0,5 Deg. and 0.2 Deg.) and different sets of HIRLAM variables were used to predict wind speed and energy production. The predictions of energy production for the wind farms...... 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...

  2. VALMET: a valley air pollution model. Final report. Revision 1

    Energy Technology Data Exchange (ETDEWEB)

    Whiteman, C.D.; Allwine, K.J.

    1985-04-01

    An air quality model is described for predicting air pollution concentrations in deep mountain valleys arising from nocturnal down-valley transport and diffusion of an elevated pollutant plume, and the fumigation of the plume on the valley floor and sidewalls after sunrise. Included is a technical description of the model, a discussion of the model's applications, the required model inputs, sample calculations and model outputs, and a full listing of the FORTRAN computer program. 55 refs., 27 figs., 6 tabs.

  3. NOAA's National Air Quality Predictions and Development of Aerosol and Atmospheric Composition Prediction Components for the Next Generation Global Prediction System

    Science.gov (United States)

    Stajner, I.; Hou, Y. T.; McQueen, J.; Lee, P.; Stein, A. F.; Tong, D.; Pan, L.; Huang, J.; Huang, H. C.; Upadhayay, S.

    2016-12-01

    NOAA provides operational air quality predictions using the National Air Quality Forecast Capability (NAQFC): ozone and wildfire smoke for the United States and airborne dust for the contiguous 48 states at http://airquality.weather.gov. NOAA's predictions of fine particulate matter (PM2.5) became publicly available in February 2016. Ozone and PM2.5 predictions are produced using a system that operationally links the Community Multiscale Air Quality (CMAQ) model with meteorological inputs from the North American mesoscale forecast Model (NAM). Smoke and dust predictions are provided using the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model. Current NAQFC focus is on updating CMAQ to version 5.0.2, improving PM2.5 predictions, and updating emissions estimates, especially for NOx using recently observed trends. Wildfire smoke emissions from a newer version of the USFS BlueSky system are being included in a new configuration of the NAQFC NAM-CMAQ system, which is re-run for the previous 24 hours when the wildfires were observed from satellites, to better represent wildfire emissions prior to initiating predictions for the next 48 hours. In addition, NOAA is developing the Next Generation Global Prediction System (NGGPS) to represent the earth system for extended weather prediction. NGGPS will include a representation of atmospheric dynamics, physics, aerosols and atmospheric composition as well as coupling with ocean, wave, ice and land components. NGGPS is being developed with a broad community involvement, including community developed components and academic research to develop and test potential improvements for potentially inclusion in NGGPS. Several investigators at NOAA's research laboratories and in academia are working to improve the aerosol and gaseous chemistry representation for NGGPS, to develop and evaluate the representation of atmospheric composition, and to establish and improve the coupling with radiation and microphysics

  4. RCA: A route city attraction model for air passengers

    Science.gov (United States)

    Huang, Feihu; Xiong, Xi; Peng, Jian; Guo, Bing; Tong, Bo

    2018-02-01

    Human movement pattern is a research hotspot of social computing and has practical values in various fields, such as traffic planning. Previous studies mainly focus on the travel activities of human beings on the ground rather than those in the air. In this paper, we use the reservation records of air passengers to explore air passengers' movement characteristics. After analyzing the effect of the route-trip length on the throughput, we find that most passengers eventually return to their original departure city and that the mobility of air passengers is not related to the route length. Based on these characteristics, we present a route city attraction (RCA) model, in which GDP or population is considered for the calculation of the attraction. The sub models of our RCA model show the better prediction performance of throughput than the radiation model and the gravity model.

  5. MODEL PREDICTIVE CONTROL FUNDAMENTALS

    African Journals Online (AJOL)

    2012-07-02

    Jul 2, 2012 ... Linear MPC. 1. Uses linear model: ˙x = Ax + Bu. 2. Quadratic cost function: F = xT Qx + uT Ru. 3. Linear constraints: Hx + Gu < 0. 4. Quadratic program. Nonlinear MPC. 1. Nonlinear model: ˙x = f(x, u). 2. Cost function can be nonquadratic: F = (x, u). 3. Nonlinear constraints: h(x, u) < 0. 4. Nonlinear program.

  6. 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.

  7. Modelling bankruptcy prediction models in Slovak companies

    Directory of Open Access Journals (Sweden)

    Kovacova Maria

    2017-01-01

    Full Text Available An intensive research from academics and practitioners has been provided regarding models for bankruptcy prediction and credit risk management. In spite of numerous researches focusing on forecasting bankruptcy using traditional statistics techniques (e.g. discriminant analysis and logistic regression and early artificial intelligence models (e.g. artificial neural networks, there is a trend for transition to machine learning models (support vector machines, bagging, boosting, and random forest to predict bankruptcy one year prior to the event. Comparing the performance of this with unconventional approach with results obtained by discriminant analysis, logistic regression, and neural networks application, it has been found that bagging, boosting, and random forest models outperform the others techniques, and that all prediction accuracy in the testing sample improves when the additional variables are included. On the other side the prediction accuracy of old and well known bankruptcy prediction models is quiet high. Therefore, we aim to analyse these in some way old models on the dataset of Slovak companies to validate their prediction ability in specific conditions. Furthermore, these models will be modelled according to new trends by calculating the influence of elimination of selected variables on the overall prediction ability of these models.

  8. Mixed deterministic statistical modelling of regional ozone air pollution

    KAUST Repository

    Kalenderski, Stoitchko

    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..

  9. 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.

  10. 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.

  11. A Physically Based Model for Air-Lift Pumping

    Science.gov (United States)

    FrançOis, Odile; Gilmore, Tyler; Pinto, Michael J.; Gorelick, Steven M.

    1996-08-01

    A predictive, physically based model for pumping water from a well using air injection (air-lift pumping) was developed for the range of flow rates that we explored in a series of laboratory experiments. The goal was to determine the air flow rate required to pump a specific flow rate of water in a given well, designed for in-well air stripping of volatile organic compounds from an aquifer. The model was validated against original laboratory data as well as data from the literature. A laboratory air-lift system was constructed that consisted of a 70-foot-long (21-m-long) pipe, 5.5 inches (14 cm) inside diameter, in which an air line of 1.3 inches (3.3 cm) outside diameter was placed with its bottom at different elevations above the base of the long pipe. Experiments were conducted for different levels of submergence, with water-pumping rates ranging from 5 to 70 gallons/min (0.32-4.4 L/s), and air flow ranging from 7 to 38 standard cubic feet/min (0.2-1.1 m3 STP/min). The theoretical approach adopted in the model was based on an analysis of the system as a one-dimensional two-phase flow problem. The expression for the pressure gradient includes inertial energy terms, friction, and gas expansion versus elevation. Data analysis revealed that application of the usual drift-flux model to estimate the air void fraction is not adequate for the observed flow patterns: either slug or churn flow. We propose a modified drift-flux model that accurately predicts air-lift pumping requirements for a range of conditions representative of in-well air-stripping operations.

  12. Predictive models of moth development

    Science.gov (United States)

    Degree-day models link ambient temperature to insect life-stages, making such models valuable tools in integrated pest management. These models increase management efficacy by predicting pest phenology. In Wisconsin, the top insect pest of cranberry production is the cranberry fruitworm, Acrobasis v...

  13. Predictive Models and Computational Embryology

    Science.gov (United States)

    EPA’s ‘virtual embryo’ project is building an integrative systems biology framework for predictive models of developmental toxicity. One schema involves a knowledge-driven adverse outcome pathway (AOP) framework utilizing information from public databases, standardized ontologies...

  14. 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...

  15. Predictions models with neural nets

    Directory of Open Access Journals (Sweden)

    Vladimír Konečný

    2008-01-01

    Full Text Available The contribution is oriented to basic problem trends solution of economic pointers, using neural networks. Problems include choice of the suitable model and consequently configuration of neural nets, choice computational function of neurons and the way prediction learning. The contribution contains two basic models that use structure of multilayer neural nets and way of determination their configuration. It is postulate a simple rule for teaching period of neural net, to get most credible prediction.Experiments are executed with really data evolution of exchange rate Kč/Euro. The main reason of choice this time series is their availability for sufficient long period. In carry out of experiments the both given basic kind of prediction models with most frequent use functions of neurons are verified. Achieve prediction results are presented as in numerical and so in graphical forms.

  16. Air quality modelling using chemometric techniques | Azid | Journal ...

    African Journals Online (AJOL)

    DA shows all seven parameters (CO, O3, PM10, SO2, NOx, NO and NO2) gave the most significant variables after stepwise backward mode. PCA identifies the major source of air pollution is due to combustion of fossil fuels in motor vehicles and industrial activities. The ANN model shows a better prediction compared to the ...

  17. 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.

  18. A novel laser air puff and shape profile method for predicting tenderness of broiler breast meat.

    Science.gov (United States)

    Lee, Y S; Owens, C M; Meullenet, J F

    2008-07-01

    The potential application of a new laser air puff system to assess poultry meat tenderness was investigated. Ninety broilers were deboned at either 1.25, 4, or 24 h postmortem. The raw breast fillets were scanned on a conveyor belt longitudinally by a laser distance sensor to obtain overall shape profiles and scanned again with a pressurized source of air (206.8 kPa). The 2 resulting profiles were superimposed to quantify the amount of deformation caused by the application of pressurized air. Five parameters including a height and length of each fillet were calculated and used to establish a model to predict tenderness. Tenderness of cooked fillets was determined instrumentally with the Meullenet-Owens razor shear, Blunt-Meullenet-Owens razor shear, and with sensory analysis. Hardness, Meullenet-Owens razor shear energy, and Blunt-Meullenet-Owens razor shear energy were modeled with the parameters extracted from the air puff system. Predicted values obtained from the models and observed values of individual fillets were subjected to logistic regression to classify fillets into tenderness levels. Tender fillets in the air puff predicted tender group represented 82, 81, and 88% based on hardness, Meullenet-Owens razor shear energy, and Blunt-Meullenet-Owens razor shear energy, respectively. The use of this tool resulted in more than a 20% improvement in the number of tender fillets after classification. The results suggested that this new system could potentially be implemented as an online tool for sorting poultry breast fillets by tenderness levels.

  19. Spatial Prediction of Air Temperature in East Central Anatolia of Turkey

    Science.gov (United States)

    Bilgili, B. C.; Erşahin, S.; Özyavuz, M.

    2017-11-01

    Air temperature is an essential component of the factors used in landscape planning. At similar topographic conditions, vegetation may show considerable differences depending on air temperature and precipitation. In large areas, measuring temperature is a cost and time-consuming work. Therefore, prediction of climate variables at unmeasured sites at an acceptable accuracy is very important in regional resource planning. In addition, use a more proper prediction method is crucial since many different prediction techniques yield different performance in different landscape and geographical conditions. We compared inverse distance weighted (IDW), ordinary kriging (OK), and ordinary cokriging (OCK) to predict air temperature at unmeasured sites in Malatya region (East Central Anatolia) of Turkey. Malatya region is the most important apricot production area of Turkey and air temperature is the most important factor determining the apricot growing zones in this region. We used mean monthly temperatures from 1975 to 2010 measured at 28 sites in the study area and predicted temperature with IDW, OC, and OCK techniques, mapped temperature in the region, and tested the reliability of these maps. The OCK with elevation as an auxiliary variable occurred the best procedure to predict temperature against the criteria of model efficiency and relative root mean squared error.

  20. SPATIAL PREDICTION OF AIR TEMPERATURE IN EAST CENTRAL ANATOLIA OF TURKEY

    Directory of Open Access Journals (Sweden)

    B. C. Bilgili

    2017-11-01

    Full Text Available Air temperature is an essential component of the factors used in landscape planning. At similar topographic conditions, vegetation may show considerable differences depending on air temperature and precipitation. In large areas, measuring temperature is a cost and time-consuming work. Therefore, prediction of climate variables at unmeasured sites at an acceptable accuracy is very important in regional resource planning. In addition, use a more proper prediction method is crucial since many different prediction techniques yield different performance in different landscape and geographical conditions. We compared inverse distance weighted (IDW, ordinary kriging (OK, and ordinary cokriging (OCK to predict air temperature at unmeasured sites in Malatya region (East Central Anatolia of Turkey. Malatya region is the most important apricot production area of Turkey and air temperature is the most important factor determining the apricot growing zones in this region. We used mean monthly temperatures from 1975 to 2010 measured at 28 sites in the study area and predicted temperature with IDW, OC, and OCK techniques, mapped temperature in the region, and tested the reliability of these maps. The OCK with elevation as an auxiliary variable occurred the best procedure to predict temperature against the criteria of model efficiency and relative root mean squared error.

  1. 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.

  2. What do saliency models predict?

    Science.gov (United States)

    Koehler, Kathryn; Guo, Fei; Zhang, Sheng; Eckstein, Miguel P.

    2014-01-01

    Saliency models have been frequently used to predict eye movements made during image viewing without a specified task (free viewing). Use of a single image set to systematically compare free viewing to other tasks has never been performed. We investigated the effect of task differences on the ability of three models of saliency to predict the performance of humans viewing a novel database of 800 natural images. We introduced a novel task where 100 observers made explicit perceptual judgments about the most salient image region. Other groups of observers performed a free viewing task, saliency search task, or cued object search task. Behavior on the popular free viewing task was not best predicted by standard saliency models. Instead, the models most accurately predicted the explicit saliency selections and eye movements made while performing saliency judgments. Observers' fixations varied similarly across images for the saliency and free viewing tasks, suggesting that these two tasks are related. The variability of observers' eye movements was modulated by the task (lowest for the object search task and greatest for the free viewing and saliency search tasks) as well as the clutter content of the images. Eye movement variability in saliency search and free viewing might be also limited by inherent variation of what observers consider salient. Our results contribute to understanding the tasks and behavioral measures for which saliency models are best suited as predictors of human behavior, the relationship across various perceptual tasks, and the factors contributing to observer variability in fixational eye movements. PMID:24618107

  3. Letter to the Editor: Applications Air Q Model on Estimate Health Effects Exposure to Air Pollutants

    Directory of Open Access Journals (Sweden)

    Gholamreza Goudarzi

    2016-02-01

    Full Text Available Epidemiologic studies in worldwide have measured increases in mortality and morbidity associated with air pollution (1-3. Quantifying the effects of air pollution on the human health in urban area causes an increasingly critical component in policy discussion (4-6. Air Q model was proved to be a valid and reliable tool to predicts health effects related to criteria  pollutants (particulate matter (PM, ozone (O3, nitrogen dioxide (NO2, sulfur dioxide (SO2, and carbon monoxide (CO, determinate  the  potential short term effects of air pollution  and allows the examination of various scenarios in which emission rates of pollutants are varied (7,8. Air Q software provided by the WHO European Centre for Environment and Health (ECEH (9. Air Q model is based on cohort studies and used to estimates of both attributable average reductions in life-span and numbers of mortality and morbidity associated with exposure to air pollution (10,11. Applications

  4. Spatial Allocator for air quality modeling

    Science.gov (United States)

    The Spatial Allocator is a set of tools that helps users manipulate and generate data files related to emissions and air quality modeling without requiring the use of a commercial Geographic Information System.

  5. 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

  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.

  7. 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

  8. 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.

  9. 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.

  10. 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

  11. Multicomponent gas mixture air bearing modeling via lattice Boltzmann method

    Science.gov (United States)

    Tae Kim, Woo; Kim, Dehee; Hari Vemuri, Sesha; Kang, Soo-Choon; Seung Chung, Pil; Jhon, Myung S.

    2011-04-01

    As the demand for ultrahigh recording density increases, development of an integrated head disk interface (HDI) modeling tool, which considers the air bearing and lubricant film morphology simultaneously is of paramount importance. To overcome the shortcomings of the existing models based on the modified Reynolds equation (MRE), the lattice Boltzmann method (LBM) is a natural choice in modeling high Knudsen number (Kn) flows owing to its advantages over conventional methods. The transient and parallel nature makes this LBM an attractive tool for the next generation air bearing design. Although LBM has been successfully applied to single component systems, a multicomponent system analysis has been thwarted because of the complexity in coupling the terms for each component. Previous studies have shown good results in modeling immiscible component mixtures by use of an interparticle potential. In this paper, we extend our LBM model to predict the flow rate of high Kn pressure-driven flows in multicomponent gas mixture air bearings, such as the air-helium system. For accurate modeling of slip conditions near the wall, we adopt our LBM scheme with spatially dependent relaxation times for air bearings in HDIs. To verify the accuracy of our code, we tested our scheme via simple two-dimensional benchmark flows. In the pressure-driven flow of an air-helium mixture, we found that the simple linear combination of pure helium and pure air flow rates, based on helium and air mole fraction, gives considerable error when compared to our LBM calculation. Hybridization with the existing MRE database can be adopted with the procedure reported here to develop the state-of-the-art slider design software.

  12. Prediction of air pollutant concentration based on sparse response back-propagation training feedforward neural networks.

    Science.gov (United States)

    Ding, Weifu; Zhang, Jiangshe; Leung, Yee

    2016-10-01

    In this paper, we predict air pollutant concentration using a feedforward artificial neural network inspired by the mechanism of the human brain as a useful alternative to traditional statistical modeling techniques. The neural network is trained based on sparse response back-propagation in which only a small number of neurons respond to the specified stimulus simultaneously and provide a high convergence rate for the trained network, in addition to low energy consumption and greater generalization. Our method is evaluated on Hong Kong air monitoring station data and corresponding meteorological variables for which five air quality parameters were gathered at four monitoring stations in Hong Kong over 4 years (2012-2015). Our results show that our training method has more advantages in terms of the precision of the prediction, effectiveness, and generalization of traditional linear regression algorithms when compared with a feedforward artificial neural network trained using traditional back-propagation.

  13. 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....

  14. 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.

  15. 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 (pplanning and management, or as the basis for health risk assessment and epidemiological studies. Crown Copyright © 2011. Published by Elsevier B.V. All rights reserved.

  16. 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.

  17. Prediction of the thermohydraulic performance of porous-media reservoirs for compressed-air energy storage

    Energy Technology Data Exchange (ETDEWEB)

    Wiles, L.E.; McCann, R.A.

    1981-09-01

    The numerical modeling capability that has been developed at the Pacific Northwest Laboratory (PNL) for the prediction of the thermohydraulic performance of porous media reservoirs for compressed air energy storage (CAES) is described. The capability of the numerical models was demonstrated by application to a variety of parametric analyses and the support analyses for the CAES porous media field demonstration program. The demonstration site analyses include calculations for the displacement of aquifer water to develop the air storage zone, the potential for water coning, thermal development in the reservoir, and the dehydration of the near-wellbore region. Unique features of the demonstration site reservoir that affect the thermohydraulic performance are identified and contrasted against the predicted performance for conditions that would be considered more typical of a commercial CAES site.

  18. EMMA model: an advanced operational mesoscale air quality model for urban and regional environments

    International Nuclear Information System (INIS)

    Jose, R.S.; Rodriguez, M.A.; Cortes, E.; Gonzalez, R.M.

    1999-01-01

    Mesoscale air quality models are an important tool to forecast and analyse the air quality in regional and urban areas. In recent years an increased interest has been shown by decision makers in these types of software tools. The complexity of such a model has grown exponentially with the increase of computer power. Nowadays, medium workstations can run operational versions of these modelling systems successfully. Presents a complex mesoscale air quality model which has been installed in the Environmental Office of the Madrid community (Spain) in order to forecast accurately the ozone, nitrogen dioxide and sulphur dioxide air concentrations in a 3D domain centred on Madrid city. Describes the challenging scientific matters to be solved in order to develop an operational version of the atmospheric mesoscale numerical pollution model for urban and regional areas (ANA). Some encouraging results have been achieved in the attempts to improve the accuracy of the predictions made by the version already installed. (Author)

  19. Modelling the risk of airborne infectious disease using exhaled air.

    Science.gov (United States)

    Issarow, Chacha M; Mulder, Nicola; Wood, Robin

    2015-05-07

    In this paper we develop and demonstrate a flexible mathematical model that predicts the risk of airborne infectious diseases, such as tuberculosis under steady state and non-steady state conditions by monitoring exhaled air by infectors in a confined space. In the development of this model, we used the rebreathed air accumulation rate concept to directly determine the average volume fraction of exhaled air in a given space. From a biological point of view, exhaled air by infectors contains airborne infectious particles that cause airborne infectious diseases such as tuberculosis in confined spaces. Since not all infectious particles can reach the target infection site, we took into account that the infectious particles that commence the infection are determined by respiratory deposition fraction, which is the probability of each infectious particle reaching the target infection site of the respiratory tracts and causing infection. Furthermore, we compute the quantity of carbon dioxide as a marker of exhaled air, which can be inhaled in the room with high likelihood of causing airborne infectious disease given the presence of infectors. We demonstrated mathematically and schematically the correlation between TB transmission probability and airborne infectious particle generation rate, ventilation rate, average volume fraction of exhaled air, TB prevalence and duration of exposure to infectors in a confined space. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

  20. 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

  1. 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

  2. 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.

  3. Examination of the uncertainty in air concentration predictions using Hanford field data

    International Nuclear Information System (INIS)

    Miller, C.W.; Fields, D.E.; Cotter, S.J.

    1986-10-01

    The accuracy of an environmental transport model is best determined by comparing model predictions with environmental measurements made under conditions similar to those assumed by the model, a process commonly referred to as model validation. Over the past several years, we have done a variety of validation studies with the popular Gaussian plume atmospheric dispersion model using data from tests conducted on the Hanford reservation. Data for short-term releases of small particles for release heights of 2 m, 56 m, and 111 m have been used. Up to six different sets of atmospheric dispersion parameters and three different atmospheric stability class specification schemes have been examined. Overall, dispersion parameters based on measurements made near Juelich, West Germany, give the best comparisons between observed and predicted air concentrations. The commonly-used vertical temperature gradient method for determining atmospheric stability class consistently gives poor results. The accuracy of air concentration predictions improves when dry deposition processes are included in the model. Further validation studies using various Hanford data sets are planned

  4. 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.

  5. 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.

  6. Modeling aluminum-air battery systems

    Science.gov (United States)

    Savinell, R. F.; Willis, M. S.

    The performance of a complete aluminum-air battery system was studied with a flowsheet model built from unit models of each battery system component. A plug flow model for heat transfer was used to estimate the amount of heat transferred from the electrolyte to the air stream. The effect of shunt currents on battery performance was found to be insignificant. Using the flowsheet simulator to analyze a 100 cell battery system now under development demonstrated that load current, aluminate concentration, and electrolyte temperature are dominant variables controlling system performance. System efficiency was found to decrease as both load current and aluminate concentration increases. The flowsheet model illustrates the interdependence of separate units on overall system performance.

  7. A simplified building airflow model for agent concentration prediction.

    Science.gov (United States)

    Jacques, David R; Smith, David A

    2010-11-01

    A simplified building airflow model is presented that can be used to predict the spread of a contaminant agent from a chemical or biological attack. If the dominant means of agent transport throughout the building is an air-handling system operating at steady-state, a linear time-invariant (LTI) model can be constructed to predict the concentration in any room of the building as a result of either an internal or external release. While the model does not capture weather-driven and other temperature-driven effects, it is suitable for concentration predictions under average daily conditions. The model is easily constructed using information that should be accessible to a building manager, supplemented with assumptions based on building codes and standard air-handling system design practices. The results of the model are compared with a popular multi-zone model for a simple building and are demonstrated for building examples containing one or more air-handling systems. The model can be used for rapid concentration prediction to support low-cost placement strategies for chemical and biological detection sensors.

  8. Mathematical models for indoor radon prediction

    International Nuclear Information System (INIS)

    Malanca, A.; Pessina, V.; Dallara, G.

    1995-01-01

    It is known that the indoor radon (Rn) concentration can be predicted by means of mathematical models. The simplest model relies on two variables only: the Rn source strength and the air exchange rate. In the Lawrence Berkeley Laboratory (LBL) model several environmental parameters are combined into a complex equation; besides, a correlation between the ventilation rate and the Rn entry rate from the soil is admitted. The measurements were carried out using activated carbon canisters. Seventy-five measurements of Rn concentrations were made inside two rooms placed on the second floor of a building block. One of the rooms had a single-glazed window whereas the other room had a double pane window. During three different experimental protocols, the mean Rn concentration was always higher into the room with a double-glazed window. That behavior can be accounted for by the simplest model. A further set of 450 Rn measurements was collected inside a ground-floor room with a grounding well in it. This trend maybe accounted for by the LBL model

  9. Models of Weather Impact on Air Traffic

    Science.gov (United States)

    Kulkarni, Deepak; Wang, Yao

    2017-01-01

    Flight delays have been a serious problem in the national airspace system costing about $30B per year. About 70 of the delays are attributed to weather and upto two thirds of these are avoidable. Better decision support tools would reduce these delays and improve air traffic management tools. Such tools would benefit from models of weather impacts on the airspace operations. This presentation discusses use of machine learning methods to mine various types of weather and traffic data to develop such models.

  10. Air Quality – monitoring and modelling

    Directory of Open Access Journals (Sweden)

    Marius DEACONU

    2012-12-01

    Full Text Available Air pollution is a major concern for all nations, regardless of their development. The rapid growth of the industrial sector and urban development have lead to significant quantities of substances and toxic materials, mostly discharged into the atmosphere and having adverse effects both on human health and environment in general. Human society has to recognize that environment has only a limited capacity to process all of its waste without major changes. Each of us is a pollutant but also a victim of pollution. If monitoring of air pollutants is particularly important for assessing the air quality at any moment, by modelling the monitoring data spectacular results are obtained both through the factor analysis and identification of potential pollution mitigation measures. Latest equipment and techniques come and support these problems giving medium and long term solutions.

  11. Comparison of Predictive Modeling Methods of Aircraft Landing Speed

    Science.gov (United States)

    Diallo, Ousmane H.

    2012-01-01

    Expected increases in air traffic demand have stimulated the development of air traffic control tools intended to assist the air traffic controller in accurately and precisely spacing aircraft landing at congested airports. Such tools will require an accurate landing-speed prediction to increase throughput while decreasing necessary controller interventions for avoiding separation violations. There are many practical challenges to developing an accurate landing-speed model that has acceptable prediction errors. This paper discusses the development of a near-term implementation, using readily available information, to estimate/model final approach speed from the top of the descent phase of flight to the landing runway. As a first approach, all variables found to contribute directly to the landing-speed prediction model are used to build a multi-regression technique of the response surface equation (RSE). Data obtained from operations of a major airlines for a passenger transport aircraft type to the Dallas/Fort Worth International Airport are used to predict the landing speed. The approach was promising because it decreased the standard deviation of the landing-speed error prediction by at least 18% from the standard deviation of the baseline error, depending on the gust condition at the airport. However, when the number of variables is reduced to the most likely obtainable at other major airports, the RSE model shows little improvement over the existing methods. Consequently, a neural network that relies on a nonlinear regression technique is utilized as an alternative modeling approach. For the reduced number of variables cases, the standard deviation of the neural network models errors represent over 5% reduction compared to the RSE model errors, and at least 10% reduction over the baseline predicted landing-speed error standard deviation. Overall, the constructed models predict the landing-speed more accurately and precisely than the current state-of-the-art.

  12. North Atlantic climate model bias influence on multiyear predictability

    Science.gov (United States)

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

    2018-01-01

    The influences of North Atlantic biases on multiyear predictability of unforced surface air temperature (SAT) variability are examined in the Kiel Climate Model (KCM). By employing a freshwater flux correction over the North Atlantic to the model, which strongly alleviates both North Atlantic sea surface salinity (SSS) and sea surface temperature (SST) biases, the freshwater flux-corrected integration depicts significantly enhanced multiyear SAT predictability in the North Atlantic sector in comparison to the uncorrected one. The enhanced SAT predictability in the corrected integration is due to a stronger and more variable Atlantic Meridional Overturning Circulation (AMOC) and its enhanced influence on North Atlantic SST. Results obtained from preindustrial control integrations of models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) support the findings obtained from the KCM: models with large North Atlantic biases tend to have a weak AMOC influence on SAT and exhibit a smaller SAT predictability over the North Atlantic sector.

  13. 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.

  14. Detonation cell size measurements and predictions in hydrogen-air-steam mixtures at elevated temperatures

    Energy Technology Data Exchange (ETDEWEB)

    Ciccarelli, G.; Ginsberg, T.; Boccio, J.; Economos, C.

    1994-01-01

    The present research reports on the effect of initial mixture temperature on the experimentally measured detonation cell size for hydrogen-air-steam mixtures. Experimental and theoretical research related to combustion phenomena in hydrogen-air-steam mixtures has been ongoing for many years. However, detonation cell size data currently exists or hydrogen-air-steam mixtures up to a temperature of only 400K. Sever accident scenarios have been identified for light water reactors (LWRs) where hydrogen-air mixture temperatures in excess of 400K could be generated within containment. The experiments in this report focus on extending the cell size data base for initial mixture temperatures in excess of 400K. The experiments were carried out in a 10-cm inner-diameter, 6.1-m long heated detonation tube with a maximum operating temperature of 700K and spatial temperature uniformity of {plus_minus}14K. Detonation cell size measurements provide clear evidence that the effect of hydrogen-air initial gas mixture temperature, in the range 300K--650K, is to decrease cell size and, hence, to increase the sensitivity of the mixture to undergo detonations. The effect of steam content, at any given temperature, is to increase the cell size and, thereby, to decrease the sensitivity of stoichiometric hydrogen-air mixtures. The hydrogen-air detonability limits for the 10-cm inside-diameter test vessel, based upon the onset of single-head spin, decreased from 15 percent by hydrogen at 300K down to about 9 percent hydrogen at 650K. The one-dimensional ZND model does a very good job at predicting the overall trends in the cell size data over the range of hydrogen-air-steam mixture compositions and temperature studied in the experiments.

  15. Detonation cell size measurements and predictions in hydrogen-air-steam mixtures at elevated temperatures

    International Nuclear Information System (INIS)

    Ciccarelli, G.; Ginsberg, T.; Boccio, J.; Economos, C.

    1994-01-01

    The present research reports on the effect of initial mixture temperature on the experimentally measured detonation cell size for hydrogen-air-steam mixtures. Experimental and theoretical research related to combustion phenomena in hydrogen-air-steam mixtures has been ongoing for many years. However, detonation cell size data currently exists or hydrogen-air-steam mixtures up to a temperature of only 400K. Sever accident scenarios have been identified for light water reactors (LWRs) where hydrogen-air mixture temperatures in excess of 400K could be generated within containment. The experiments in this report focus on extending the cell size data base for initial mixture temperatures in excess of 400K. The experiments were carried out in a 10-cm inner-diameter, 6.1-m long heated detonation tube with a maximum operating temperature of 700K and spatial temperature uniformity of ±14K. Detonation cell size measurements provide clear evidence that the effect of hydrogen-air initial gas mixture temperature, in the range 300K--650K, is to decrease cell size and, hence, to increase the sensitivity of the mixture to undergo detonations. The effect of steam content, at any given temperature, is to increase the cell size and, thereby, to decrease the sensitivity of stoichiometric hydrogen-air mixtures. The hydrogen-air detonability limits for the 10-cm inside-diameter test vessel, based upon the onset of single-head spin, decreased from 15 percent by hydrogen at 300K down to about 9 percent hydrogen at 650K. The one-dimensional ZND model does a very good job at predicting the overall trends in the cell size data over the range of hydrogen-air-steam mixture compositions and temperature studied in the experiments

  16. Predictability, Work-Family Conflict, and Intent to Stay: An Air Force Case Study

    National Research Council Canada - National Science Library

    Obruba, Patrick

    2001-01-01

    A survey was completed by 362 active duty Air Force members in December 2000 regarding their perceptions of schedule predictability, work-family conflict, job satisfaction, organizational commitment...

  17. 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

  18. Predictions and Verification of an Isotope Marine Boundary Layer Model

    Science.gov (United States)

    Feng, X.; Posmentier, E. S.; Sonder, L. J.; Fan, N.

    2017-12-01

    A one-dimensional (1D), steady state isotope marine boundary layer (IMBL) model is constructed. The model includes meteorologically important features absent in Craig and Gordon type models, namely height-dependent diffusion/mixing and convergence of subsiding external air. Kinetic isotopic fractionation results from this height-dependent diffusion which starts as pure molecular diffusion at the air-water interface and increases linearly with height due to turbulent mixing. The convergence permits dry, isotopically depleted air subsiding adjacent to the model column to mix into ambient air. In δD-δ18O space, the model results fill a quadrilateral, of which three sides represent 1) vapor in equilibrium with various sea surface temperatures (SSTs) (high d18O boundary of quadrilateral); 2) mixture of vapor in equilibrium with seawater and vapor in the subsiding air (lower boundary depleted in both D and 18O); and 3) vapor that has experienced the maximum possible kinetic fractionation (high δD upper boundary). The results can be plotted in d-excess vs. δ18O space, indicating that these processes all cause variations in d-excess of MBL vapor. In particular, due to relatively high d-excess in the descending air, mixing of this air into the MBL causes an increase in d-excess, even without kinetic isotope fractionation. The model is tested by comparison with seven datasets of marine vapor isotopic ratios, with excellent correspondence; >95% of observational data fall within the quadrilateral area predicted by the model. The distribution of observations also highlights the significant influence of vapor from the nearby converging descending air on isotopic variations in the MBL. At least three factors may explain the affect the isotopic composition of precipitation. The model can be applied to modern as well as paleo- climate conditions.

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

    Science.gov (United States)

    Osman, Marisol; Vera, C. S.

    2017-10-01

    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

  20. 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...

  1. 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.

  2. Predicting Weight Support Based on Wake Measurements of a Flying Bird in Still Air

    Science.gov (United States)

    Gutierrez, Eric; Lentink, David

    2014-11-01

    The wake development of a freely flying Pacific Parrotlet (Forpus coelestis) was examined in still air. The bird was trained to fly from perch to perch through the laser sheet while wearing custom-made laser safety goggles. This enabled a detailed study of the evolution of the vortices shed in its wake using high-speed particle image velocimetry at 1000 Hz in the plane transverse to the flight path. The measurement started when the bird was approximately 0.25 wingbeats in front of the laser sheet and stopped after it traveled 3.5 wingbeats beyond the laser sheet. The instantaneous lift force that supports body weight was calculated based on the velocity field, using both the Kuttta-Joukowski and the actuator disk quasi-steady model. During the first few flaps, both models predict an instantaneous lift that is reasonably close to the weight of the bird. Several flaps away from the laser sheet, however, the models predict that the lift steadily declines to about 50% of the weight of the bird. In contrast to earlier reports for bat wakes in wind tunnels, these findings for bird wakes in still air suggest that the predictive strength of quasi-steady force calculations depends on the distance between the animal and the laser sheet.

  3. Validation of annual average air concentration predictions from the AIRDOS-EPA computer code

    International Nuclear Information System (INIS)

    Miller, C.W.; Fields, D.E.; Cotter, S.J.

    1981-01-01

    The AIRDOS-EPA computer code is used to assess the annual doses to the general public resulting from releases of radionuclides to the atmosphere by Oak Ridge National Laboratory (ORNL) facilities. This code uses a modified Gaussian plume equation to estimate air concentrations resulting from the release of a maximum of 36 radionuclides. Radionuclide concentrations in food products are estimated from the output of the atmospheric transport model using the terrestrial transport model described in US Nuclear Regulatory Commission Regulatory Guide 1.109. Doses to man at each distance and direction specified are estimated for up to eleven organs and five exposure modes. To properly use any environmental transport model, some estimate of the model's predictive accuracy must be obtained. Because of a lack of sufficient data for the ORNL site, one year of weekly average 85 Kr concentrations observed at 13 stations located 30 to 150 km distant from an assumed-continuous point source at the Savannah River Plant, Aiken, South Carolina, have been used in a validation study of the atmospheric transport portion of AIRDOS-EPA. The predicted annual average concentration at each station exceeded the observed value in every case. The overprediction factor ranged from 1.4 to 3.4 with an average value of 2.4. Pearson's correlation between pairs of logarithms of observed and predicted values was r = 0.93. Based on a one-tailed students's test, we can be 98% confident that for this site under similar meteorological, release, and monitoring conditions no annual average air concentrations will be observed at the sampling stations in excess of those predicted by the code. As the averaging time of the prdiction decreases, however, the uncertainty in the prediction increases

  4. 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.

  5. Bioaccumulation potential of air contaminants: Combining biological allometry, chemical equilibrium and mass-balances to predict accumulation of air pollutants in various mammals

    International Nuclear Information System (INIS)

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

    2009-01-01

    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 for lipid 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 (K ba ) and tissue-air partition coefficients (K ta ) 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 68% of the modeled inhalation and exhalation rate constants are within a factor of 2.1 from independent empirical values for humans, rats and mice, and 87% 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.

  6. Clearing the air. Air quality modelling for policy support

    NARCIS (Netherlands)

    Hendriks, C.

    2017-01-01

    The studies presented in this thesis were performed to provide policy makers with more accurate information about the sources of air pollution and the possible consequences of future developments on air quality. This enables policy makers to make better informed decisions when formulating policies

  7. Multi-dimensional modelling of spray, in-cylinder air motion and fuel ...

    Indian Academy of Sciences (India)

    stoichiometric fuel–air ratio region is observed at different locations depending on the load. The model developed serves to predict the fuel–air mixing spatially and temporally, and hence is a useful tool in design and optimization of direct injection ...

  8. Analytical prediction of the piezoelectric d33response of fluoropolymer arrays with tubular air channels.

    Science.gov (United States)

    Zhukov, Sergey; Eder-Goy, Dagmar; Fedosov, Sergey; Xu, Bai-Xiang; von Seggern, Heinz

    2018-03-15

    The present study is focused on tubular multi-channel arrays composed of commercial fluoropolymer (FEP) tubes with different wall thickness. After proper charging in a high electric field, such tubular structures exhibit a large piezoelectric [Formula: see text] coefficient significantly exceeding the values of classical polymer ferroelectrics and being even comparable to conventional lead-free piezoceramics. The quasistatic piezoelectric [Formula: see text] coefficient was theoretically derived and its upper limits were evaluated considering charging and mechanical properties of the arrays. In order to optimize the [Formula: see text] coefficient the remanent polarization and the mechanical properties were taken into account, both being strongly dependent on the air channel geometry as well as on the wall thickness of the FEP tubes. The model predictions are compared with experimental d 33 coefficients for two particular arrays with equal air gaps of 250 μm, but with different wall thickness of utilized FEP tubes of 50 μm and 120 μm, respectively. Analytical modeling allows for the prediction that arrays made of FEP tubes with a wall thickness of 10 μm are foreseen to exhibit a superb piezoelectric response of up to 600 pC/N if the height of stadium-like shaped air channels is reduced down to 50 μm, making them potentially interesting for application as highly sensitive sensors and energy harvesting.

  9. Iowa calibration of MEPDG performance prediction models.

    Science.gov (United States)

    2013-06-01

    This study aims to improve the accuracy of AASHTO Mechanistic-Empirical Pavement Design Guide (MEPDG) pavement : performance predictions for Iowa pavement systems through local calibration of MEPDG prediction models. A total of 130 : representative p...

  10. The Air Quality Model Evaluation International Initiative ...

    Science.gov (United States)

    This presentation provides an overview of the Air Quality Model Evaluation International Initiative (AQMEII). It contains a synopsis of the three phases of AQMEII, including objectives, logistics, and timelines. It also provides a number of examples of analyses conducted through AQMEII with a particular focus on past and future analyses of deposition. The National Exposure Research Laboratory (NERL) Computational Exposure Division (CED) develops and evaluates data, decision-support tools, and models to be applied to media-specific or receptor-specific problem areas. CED uses modeling-based approaches to characterize exposures, evaluate fate and transport, and support environmental diagnostics/forensics with input from multiple data sources. It also develops media- and receptor-specific models, process models, and decision support tools for use both within and outside of EPA.

  11. VALMET-A valley air pollution model

    Energy Technology Data Exchange (ETDEWEB)

    Whiteman, C.D.; Allwine, K.J.

    1983-09-01

    Following a thorough analysis of meteorological data obtained from deep valleys of western Colorado, a modular air-pollution model has been developed to simulate the transport and diffusion of pollutants released from an elevated point source in a well-defined mountain valley during the nighttime and morning transition periods. This initial version of the model, named VALMET, operates on a valley cross section at an arbitrary distance down-valley from a continuous point source. The model has been constructed to include parameterizations of the major physical processes that act to disperse pollution during these time periods. The model has not been fully evaluated. Further testing, evaluations, and development of the model are needed. Priorities for further development and testing are provided.

  12. Model complexity control for hydrologic prediction

    NARCIS (Netherlands)

    Schoups, G.; Van de Giesen, N.C.; Savenije, H.H.G.

    2008-01-01

    A common concern in hydrologic modeling is overparameterization of complex models given limited and noisy data. This leads to problems of parameter nonuniqueness and equifinality, which may negatively affect prediction uncertainties. A systematic way of controlling model complexity is therefore

  13. Bayesian analysis of a reduced-form air quality model.

    Science.gov (United States)

    Foley, Kristen M; Reich, Brian J; Napelenok, Sergey L

    2012-07-17

    Numerical air quality models are being used for assessing emission control strategies for improving ambient pollution levels across the globe. This paper applies probabilistic modeling to evaluate the effectiveness of emission reduction scenarios aimed at lowering ground-level ozone concentrations. A Bayesian hierarchical model is used to combine air quality model output and monitoring data in order to characterize the impact of emissions reductions while accounting for different degrees of uncertainty in the modeled emissions inputs. The probabilistic model predictions are weighted based on population density in order to better quantify the societal benefits/disbenefits of four hypothetical emission reduction scenarios in which domain-wide NO(x) emissions from various sectors are reduced individually and then simultaneously. Cross validation analysis shows the statistical model performs well compared to observed ozone levels. Accounting for the variability and uncertainty in the emissions and atmospheric systems being modeled is shown to impact how emission reduction scenarios would be ranked, compared to standard methodology.

  14. Urban Landscape Characterization Using Remote Sensing Data For Input into Air Quality Modeling

    Science.gov (United States)

    Quattrochi, Dale A.; Estes, Maurice G., Jr.; Crosson, William; Khan, Maudood

    2005-01-01

    The urban landscape is inherently complex and this complexity is not adequately captured in air quality models that are used to assess whether urban areas are in attainment of EPA air quality standards, particularly for ground level ozone. This inadequacy of air quality models to sufficiently respond to the heterogeneous nature of the urban landscape can impact how well these models predict ozone pollutant levels over metropolitan areas and ultimately, whether cities exceed EPA ozone air quality standards. We are exploring the utility of high-resolution remote sensing data and urban growth projections as improved inputs to meteorological and air quality models focusing on the Atlanta, Georgia metropolitan area as a case study. The National Land Cover Dataset at 30m resolution is being used as the land use/land cover input and aggregated to the 4km scale for the MM5 mesoscale meteorological model and the Community Multiscale Air Quality (CMAQ) modeling schemes. Use of these data have been found to better characterize low density/suburban development as compared with USGS 1 km land use/land cover data that have traditionally been used in modeling. Air quality prediction for future scenarios to 2030 is being facilitated by land use projections using a spatial growth model. Land use projections were developed using the 2030 Regional Transportation Plan developed by the Atlanta Regional Commission. This allows the State Environmental Protection agency to evaluate how these transportation plans will affect future air quality.

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

    OpenAIRE

    Zhang, Jiangshe; Ding, Weifu

    2017-01-01

    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 e...

  16. 77 FR 4808 - Conference on Air Quality Modeling

    Science.gov (United States)

    2012-01-31

    ... preferred air quality models and to provide a forum for public review and comment on how the agency determines and applies air quality models in the future. DATES: Comments: Comments on how the agency determines and applies air quality models must be received on or before April 16, 2012. Conference: The...

  17. Staying Power of Churn Prediction Models

    NARCIS (Netherlands)

    Risselada, Hans; Verhoef, Peter C.; Bijmolt, Tammo H. A.

    In this paper, we study the staying power of various churn prediction models. Staying power is defined as the predictive performance of a model in a number of periods after the estimation period. We examine two methods, logit models and classification trees, both with and without applying a bagging

  18. 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.)

  19. QUANTIFYING SUBGRID POLLUTANT VARIABILITY IN EULERIAN AIR QUALITY MODELS

    Science.gov (United States)

    In order to properly assess human risk due to exposure to hazardous air pollutants or air toxics, detailed information is needed on the location and magnitude of ambient air toxic concentrations. Regional scale Eulerian air quality models are typically limited to relatively coar...

  20. Numerical Predictions of Air Distribution in Rooms - Status and Potentials

    DEFF Research Database (Denmark)

    Nielsen, Peter V.

    The purpose of an air distribution system in a ventilated room is to supply fresh air, remove heat load or supply heating, and create a pleasant and uniform climate in the occupied zone. A pleasant climate is in this context defined as a fairly low air velocity, small velocity and temperature gra...... gradients through the room and a low concentration of polluting substances, if any....

  1. Estimating air emissions from a remediation of a petroleum sump using direct measurement and modeling

    International Nuclear Information System (INIS)

    Schmidt, C.E.

    1991-01-01

    A technical approach was developed for the remediation of a petroleum sump near a residential neighborhood. The approach evolved around sludge handling/in-situ solidification and on-site disposal. As part of the development of the engineering approach, a field investigation and modeling program was conducted to predict air emissions from the proposed remediation. Field measurements using the EPA recommended surface isolation flux chamber were conducted to represent each major activity or air exposure involving waste at the site. Air emissions from freshly disturbed petroleum waste, along with engineering estimates were used to predict emissions from each phase of the engineering approach. This paper presents the remedial approach and the measurement/modeling technologies used to predict air toxic emissions from the remediation. Emphasis will be placed on the measurement approaches used in obtaining the emission rate data and the assumptions used in the modeling to estimate emissions from engineering scenarios

  2. Short-Term Prediction of Air Pollution in Macau Using Support Vector Machines

    Directory of Open Access Journals (Sweden)

    Chi-Man Vong

    2012-01-01

    Full Text Available Forecasting of air pollution is a popular and important topic in recent years due to the health impact caused by air pollution. It is necessary to build an early warning system, which provides forecast and also alerts health alarm to local inhabitants by medical practitioners and the local government. Meteorological and pollutions data collected daily at monitoring stations of Macau can be used in this study to build a forecasting system. Support vector machines (SVMs, a novel type of machine learning technique based on statistical learning theory, can be used for regression and time series prediction. SVM is capable of good generalization while the performance of the SVM model is often hinged on the appropriate choice of the kernel.

  3. NASA Air Force Cost Model (NAFCOM): Capabilities and Results

    Science.gov (United States)

    McAfee, Julie; Culver, George; Naderi, Mahmoud

    2011-01-01

    NAFCOM is a parametric estimating tool for space hardware. Uses cost estimating relationships (CERs) which correlate historical costs to mission characteristics to predict new project costs. It is based on historical NASA and Air Force space projects. It is intended to be used in the very early phases of a development project. NAFCOM can be used at the subsystem or component levels and estimates development and production costs. NAFCOM is applicable to various types of missions (crewed spacecraft, uncrewed spacecraft, and launch vehicles). There are two versions of the model: a government version that is restricted and a contractor releasable version.

  4. 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.

  5. Comparison of Prediction-Error-Modelling Criteria

    DEFF Research Database (Denmark)

    Jørgensen, John Bagterp; Jørgensen, Sten Bay

    2007-01-01

    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 is a r...

  6. CMAQ Involvement in Air Quality Model Evaluation International Initiative

    Science.gov (United States)

    Description of Air Quality Model Evaluation International Initiative (AQMEII). Different chemical transport models are applied by different groups over North America and Europe and evaluated against observations.

  7. 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

  8. Calibration of PMIS pavement performance prediction models.

    Science.gov (United States)

    2012-02-01

    Improve the accuracy of TxDOTs existing pavement performance prediction models through calibrating these models using actual field data obtained from the Pavement Management Information System (PMIS). : Ensure logical performance superiority patte...

  9. Predictive Model Assessment for Count Data

    National Research Council Canada - National Science Library

    Czado, Claudia; Gneiting, Tilmann; Held, Leonhard

    2007-01-01

    .... In case studies, we critique count regression models for patent data, and assess the predictive performance of Bayesian age-period-cohort models for larynx cancer counts in Germany. Key words: Calibration...

  10. Modeling and Prediction Using Stochastic Differential Equations

    DEFF Research Database (Denmark)

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

    2016-01-01

    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......) for modeling and forecasting. It is argued that this gives models and predictions which better reflect reality. The SDE approach also offers a more adequate framework for modeling and a number of efficient tools for model building. A software package (CTSM-R) for SDE-based modeling is briefly described....... 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...

  11. 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

  12. Improving short-term air quality predictions over the U.S. using chemical data assimilation

    Science.gov (United States)

    Kumar, R.; Delle Monache, L.; Alessandrini, S.; Saide, P.; Lin, H. C.; Liu, Z.; Pfister, G.; Edwards, D. P.; Baker, B.; Tang, Y.; Lee, P.; Djalalova, I.; Wilczak, J. M.

    2017-12-01

    State and local air quality forecasters across the United States use air quality forecasts from the National Air Quality Forecasting Capability (NAQFC) at the National Oceanic and Atmospheric Administration (NOAA) as one of the key tools to protect the public from adverse air pollution related health effects by dispensing timely information about air pollution episodes. This project funded by the National Aeronautics and Space Administration (NASA) aims to enhance the decision-making process by improving the accuracy of NAQFC short-term predictions of ground-level particulate matter of less than 2.5 µm in diameter (PM2.5) by exploiting NASA Earth Science Data with chemical data assimilation. The NAQFC is based on the Community Multiscale Air Quality (CMAQ) model. To improve the initialization of PM2.5 in CMAQ, we developed a new capability in the community Gridpoint Statistical Interpolation (GSI) system to assimilate Terra/Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) retrievals in CMAQ. Specifically, we developed new capabilities within GSI to read/write CMAQ data, a forward operator that calculates AOD at 550 nm from CMAQ aerosol chemical composition and an adjoint of the forward operator that translates the changes in AOD to aerosol chemical composition. A generalized background error covariance program called "GEN_BE" has been extended to calculate background error covariance using CMAQ output. The background error variances are generated using a combination of both emissions and meteorological perturbations to better capture sources of uncertainties in PM2.5 simulations. The newly developed CMAQ-GSI system is used to perform daily 24-h PM2.5 forecasts with and without data assimilation from 15 July to 14 August 2014, and the resulting forecasts are compared against AirNOW PM2.5 measurements at 550 stations across the U. S. We find that the assimilation of MODIS AOD retrievals improves initialization of the CMAQ model

  13. Predicted thermal superluminescence in low-pressure air

    OpenAIRE

    Aramyan, A. R.; Haroyan, K. P.; 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 airf...

  14. 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)

  15. Forest fire forecasting tool for air quality modelling systems

    International Nuclear Information System (INIS)

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

    2015-01-01

    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)

  16. Forest fire forecasting tool for air quality modelling systems

    International Nuclear Information System (INIS)

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

    2015-01-01

    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)

  17. 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)

  18. Predictive models for arteriovenous fistula maturation.

    Science.gov (United States)

    Al Shakarchi, Julien; McGrogan, Damian; Van der Veer, Sabine; Sperrin, Matthew; Inston, Nicholas

    2016-05-07

    Haemodialysis (HD) is a lifeline therapy for patients with end-stage renal disease (ESRD). A critical factor in the survival of renal dialysis patients is the surgical creation of vascular access, and international guidelines recommend arteriovenous fistulas (AVF) as the gold standard of vascular access for haemodialysis. Despite this, AVFs have been associated with high failure rates. Although risk factors for AVF failure have been identified, their utility for predicting AVF failure through predictive models remains unclear. The objectives of this review are to systematically and critically assess the methodology and reporting of studies developing prognostic predictive models for AVF outcomes and assess them for suitability in clinical practice. Electronic databases were searched for studies reporting prognostic predictive models for AVF outcomes. Dual review was conducted to identify studies that reported on the development or validation of a model constructed to predict AVF outcome following creation. Data were extracted on study characteristics, risk predictors, statistical methodology, model type, as well as validation process. We included four different studies reporting five different predictive models. Parameters identified that were common to all scoring system were age and cardiovascular disease. This review has found a small number of predictive models in vascular access. The disparity between each study limits the development of a unified predictive model.

  19. Model Predictive Control Fundamentals | Orukpe | Nigerian Journal ...

    African Journals Online (AJOL)

    Model Predictive Control (MPC) has developed considerably over the last two decades, both within the research control community and in industries. MPC strategy involves the optimization of a performance index with respect to some future control sequence, using predictions of the output signal based on a process model, ...

  20. Unreachable Setpoints in Model Predictive Control

    DEFF Research Database (Denmark)

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

    2008-01-01

    In this work, a new model predictive controller is developed that handles unreachable setpoints better than traditional model predictive control methods. The new controller induces an interesting fast/slow asymmetry in the tracking response of the system. Nominal asymptotic stability of the optim...

  1. A linear regression model for predicting PNW estuarine temperatures in a changing climate

    Science.gov (United States)

    Pacific Northwest coastal regions, estuaries, and associated ecosystems are vulnerable to the potential effects of climate change, especially to changes in nearshore water temperature. While predictive climate models simulate future air temperatures, no such projections exist for...

  2. Predictive methods for estimating pesticide flux to air

    Energy Technology Data Exchange (ETDEWEB)

    Woodrow, J.E.; Seiber, J.N. [Univ. of Nevada, Reno, NV (United States)

    1996-10-01

    Published evaporative flux values for pesticides volatilizing from soil, plants, and water were correlated with compound vapor pressures (VP), modified by compound properties appropriate to the treated matrix (e.g., soil adsorption coefficient [K{sub oc}], water solubility [S{sub w}]). These correlations were formulated as Ln-Ln plots with correlation (r{sup 2}) coefficients in the range 0.93-0.99: (1) Soil surface - Ln flux vs Ln (VP/[K{sub oc} x S{sub w}]); (2) soil incorporation - Ln flux vs Ln [(VP x AR)/(K{sub oc} x S{sub w} x d)] (AR = application rate, d = incorporation depth); (3) plants - Ln flux vs Ln VP; and (4) water - Ln (flux/water conc) vs Ln (VP/Sw). Using estimated flux values from the plant correlation as source terms in the EPA`s SCREEN-2 dispersion model gave downwind concentrations that agreed to within 65-114% with measured concentrations. Further validation using other treated matrices is in progress. These predictive methods for estimating flux, when coupled with downwind dispersion modeling, provide tools for limiting downwind exposures.

  3. Clinical Prediction Models for Cardiovascular Disease: Tufts Predictive Analytics and Comparative Effectiveness Clinical Prediction Model Database.

    Science.gov (United States)

    Wessler, Benjamin S; Lai Yh, Lana; Kramer, Whitney; Cangelosi, Michael; Raman, Gowri; Lutz, Jennifer S; Kent, David M

    2015-07-01

    Clinical prediction models (CPMs) estimate the probability of clinical outcomes and hold the potential to improve decision making and individualize care. For patients with cardiovascular disease, there are numerous CPMs available although the extent of this literature is not well described. We conducted a systematic review for articles containing CPMs for cardiovascular disease published between January 1990 and May 2012. Cardiovascular disease includes coronary heart disease, heart failure, arrhythmias, stroke, venous thromboembolism, and peripheral vascular disease. We created a novel database and characterized CPMs based on the stage of development, population under study, performance, covariates, and predicted outcomes. There are 796 models included in this database. The number of CPMs published each year is increasing steadily over time. Seven hundred seventeen (90%) are de novo CPMs, 21 (3%) are CPM recalibrations, and 58 (7%) are CPM adaptations. This database contains CPMs for 31 index conditions, including 215 CPMs for patients with coronary artery disease, 168 CPMs for population samples, and 79 models for patients with heart failure. There are 77 distinct index/outcome pairings. Of the de novo models in this database, 450 (63%) report a c-statistic and 259 (36%) report some information on calibration. There is an abundance of CPMs available for a wide assortment of cardiovascular disease conditions, with substantial redundancy in the literature. The comparative performance of these models, the consistency of effects and risk estimates across models and the actual and potential clinical impact of this body of literature is poorly understood. © 2015 American Heart Association, Inc.

  4. Human-model hybrid Korean air quality forecasting system.

    Science.gov (United States)

    Chang, Lim-Seok; Cho, Ara; Park, Hyunju; Nam, Kipyo; Kim, Deokrae; Hong, Ji-Hyoung; Song, Chang-Keun

    2016-09-01

    The Korean national air quality forecasting system, consisting of the Weather Research and Forecasting, the Sparse Matrix Operator Kernel Emissions, and the Community Modeling and Analysis (CMAQ), commenced from August 31, 2013 with target pollutants of particulate matters (PM) and ozone. Factors contributing to PM forecasting accuracy include CMAQ inputs of meteorological field and emissions, forecasters' capacity, and inherent CMAQ limit. Four numerical experiments were conducted including two global meteorological inputs from the Global Forecast System (GFS) and the Unified Model (UM), two emissions from the Model Intercomparison Study Asia (MICS-Asia) and the Intercontinental Chemical Transport Experiment (INTEX-B) for the Northeast Asia with Clear Air Policy Support System (CAPSS) for South Korea, and data assimilation of the Monitoring Atmospheric Composition and Climate (MACC). Significant PM underpredictions by using both emissions were found for PM mass and major components (sulfate and organic carbon). CMAQ predicts PM2.5 much better than PM10 (NMB of PM2.5: -20~-25%, PM10: -43~-47%). Forecasters' error usually occurred at the next day of high PM event. Once CMAQ fails to predict high PM event the day before, forecasters are likely to dismiss the model predictions on the next day which turns out to be true. The best combination of CMAQ inputs is the set of UM global meteorological field, MICS-Asia and CAPSS 2010 emissions with the NMB of -12.3%, the RMSE of 16.6μ/m(3) and the R(2) of 0.68. By using MACC data as an initial and boundary condition, the performance skill of CMAQ would be improved, especially in the case of undefined coarse emission. A variety of methods such as ensemble and data assimilation are considered to improve further the accuracy of air quality forecasting, especially for high PM events to be comparable to for all cases. The growing utilization of the air quality forecast induced the public strongly to demand that the accuracy of the

  5. 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.

  6. 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.

  7. Hybrid approaches to physiologic modeling and prediction

    Science.gov (United States)

    Olengü, Nicholas O.; Reifman, Jaques

    2005-05-01

    This paper explores how the accuracy of a first-principles physiological model can be enhanced by integrating data-driven, "black-box" models with the original model to form a "hybrid" model system. Both linear (autoregressive) and nonlinear (neural network) data-driven techniques are separately combined with a first-principles model to predict human body core temperature. Rectal core temperature data from nine volunteers, subject to four 30/10-minute cycles of moderate exercise/rest regimen in both CONTROL and HUMID environmental conditions, are used to develop and test the approach. The results show significant improvements in prediction accuracy, with average improvements of up to 30% for prediction horizons of 20 minutes. The models developed from one subject's data are also used in the prediction of another subject's core temperature. Initial results for this approach for a 20-minute horizon show no significant improvement over the first-principles model by itself.

  8. 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.

  9. 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. © The Author(s) 2014.

  10. 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.

  11. Numerical modeling of aerosol particles scavenging by drops as a process of air depollution

    OpenAIRE

    Cherrier , Gaël

    2017-01-01

    This PhD-Thesis is dedicated to the numerical modeling of aerosol particles scavenging by drops. Investigated situations are about aerosol particles of aerodynamic diameter ranging from 1 nm to 100 µm captured in the air by water drops of diameter varying between 80 µm and 600 µm, with corresponding droplet Reynolds number ranging between 1 and 100. This air depollution modeling is achieved in two steps. The first step consists in obtaining a scavenging kernel predicting the flow rate of aero...

  12. 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.

  13. Evaluating the Predictive Value of Growth Prediction Models

    Science.gov (United States)

    Murphy, Daniel L.; Gaertner, Matthew N.

    2014-01-01

    This study evaluates four growth prediction models--projection, student growth percentile, trajectory, and transition table--commonly used to forecast (and give schools credit for) middle school students' future proficiency. Analyses focused on vertically scaled summative mathematics assessments, and two performance standards conditions (high…

  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. Modeling quiescent phase transport of air bubbles induced by breaking waves

    Science.gov (United States)

    Shi, Fengyan; Kirby, James T.; Ma, Gangfeng

    Simultaneous modeling of both the acoustic phase and quiescent phase of breaking wave-induced air bubbles involves a large range of length scales from microns to meters and time scales from milliseconds to seconds, and thus is computational unaffordable in a surfzone-scale computational domain. In this study, we use an air bubble entrainment formula in a two-fluid model to predict air bubble evolution in the quiescent phase in a breaking wave event. The breaking wave-induced air bubble entrainment is formulated by connecting the shear production at the air-water interface and the bubble number intensity with a certain bubble size spectra observed in laboratory experiments. A two-fluid model is developed based on the partial differential equations of the gas-liquid mixture phase and the continuum bubble phase, which has multiple size bubble groups representing a polydisperse bubble population. An enhanced 2-DV VOF (Volume of Fluid) model with a k - ɛ turbulence closure is used to model the mixture phase. The bubble phase is governed by the advection-diffusion equations of the gas molar concentration and bubble intensity for groups of bubbles with different sizes. The model is used to simulate air bubble plumes measured in laboratory experiments. Numerical results indicate that, with an appropriate parameter in the air entrainment formula, the model is able to predict the main features of bubbly flows as evidenced by reasonable agreement with measured void fraction. Bubbles larger than an intermediate radius of O(1 mm) make a major contribution to void fraction in the near-crest region. Smaller bubbles tend to penetrate deeper and stay longer in the water column, resulting in significant contribution to the cross-sectional area of the bubble cloud. An underprediction of void fraction is found at the beginning of wave breaking when large air pockets take place. The core region of high void fraction predicted by the model is dislocated due to use of the shear

  16. A statistical model for characterizing common air pollutants in air-conditioned offices

    Science.gov (United States)

    Wong, L. T.; Mui, K. W.; Hui, P. S.

    Maintaining acceptable indoor air quality (IAQ) for a healthy environment is of primary concern, policymakers have developed different strategies to address the performance of it based on proper assessment methodologies and monitoring plans. It could be cost prohibitive to sample all toxic pollutants in a building. In search of a more manageable number of parameters for cost-effective IAQ assessment, this study investigated the probable correlations among the 12 indoor environmental parameters listed in the IAQ certification scheme of the Hong Kong Environment Protection Department (HKEPD) in 422 Hong Kong offices. These 12 parameters consists of nine indoor air pollutants: carbon dioxide (CO 2), carbon monoxide (CO), respirable suspended particulates (RSP), nitrogen dioxide (NO 2), ozone (O 3), formaldehyde (HCHO), total volatile organic compounds (TVOC), radon (Rn), airborne bacteria count (ABC); and three thermal comfort parameters: temperature ( T), relative humidity (RH) and air velocity ( V). The relative importance of the correlations derived, from largest to smallest loadings, was ABC, Rn, CO, RH, RSP, CO 2, TVOC, O 3, T, V, NO 2 and HCHO. Together with the mathematical expressions derived, an alternative sampling protocol for IAQ assessment with the three 'most representative and independent' parameters namely RSP, CO 2 and TVOC measured in an office environment was proposed. The model validity was verified with on site measurements from 43 other offices in Hong Kong. The measured CO 2, RSP and TVOC concentrations were used to predict the probable levels of the other nine parameters and good agreement was found between the predictions and measurements. This simplified protocol provides an easy tool for performing IAQ monitoring in workplaces and will be useful for determining appropriate mitigation measures to finally honor the certification scheme in a cost-effective way.

  17. 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.

  18. A predictive model for indoor radon occurrences - A first approximation

    International Nuclear Information System (INIS)

    LeGrand, H.E.

    1987-01-01

    Knowledge of how radon gas is transmitted in the shallow ground environment and how it emanates into buildings is grossly incomplete. Admittedly, some excellent research studies have been made and some general associations between certain aspects of the environment and radon occurrences in buildings are recognized. Yet, a technique for precisely predicting the radon concentrations indoors is not likely to be developed soon. As knowledge increases, successive approximations toward a final predictive model may be required. An early approximation of a predictive model for indoor radon is presented in this paper. It applies specifically to the crystalline rock region of the eastern United States, but it should have some application on a broader basis. The predictive model described focuses on understanding the wide-ranging permeability characteristics in the soil and rock fracture system. Radon is thought to accrete in confined subsurface air and moves under ground to low-pressure places, such as house niched in hill sloped. Driving forces for the air-laden and entrapped radon gas are considered to be a rising water table and infiltrating moisture from the land surface

  19. A Global Model for Bankruptcy Prediction.

    Science.gov (United States)

    Alaminos, David; Del Castillo, Agustín; Fernández, Manuel Ángel

    2016-01-01

    The recent world financial crisis has increased the number of bankruptcies in numerous countries and has resulted in a new area of research which responds to the need to predict this phenomenon, not only at the level of individual countries, but also at a global level, offering explanations of the common characteristics shared by the affected companies. Nevertheless, few studies focus on the prediction of bankruptcies globally. In order to compensate for this lack of empirical literature, this study has used a methodological framework of logistic regression to construct predictive bankruptcy models for Asia, Europe and America, and other global models for the whole world. The objective is to construct a global model with a high capacity for predicting bankruptcy in any region of the world. The results obtained have allowed us to confirm the superiority of the global model in comparison to regional models over periods of up to three years prior to bankruptcy.

  20. 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

  1. Fingerprint verification prediction model in hand dermatitis.

    Science.gov (United States)

    Lee, Chew K; Chang, Choong C; Johor, Asmah; Othman, Puwira; Baba, Roshidah

    2015-07-01

    Hand dermatitis associated fingerprint changes is a significant problem and affects fingerprint verification processes. This study was done to develop a clinically useful prediction model for fingerprint verification in patients with hand dermatitis. A case-control study involving 100 patients with hand dermatitis. All patients verified their thumbprints against their identity card. Registered fingerprints were randomized into a model derivation and model validation group. Predictive model was derived using multiple logistic regression. Validation was done using the goodness-of-fit test. The fingerprint verification prediction model consists of a major criterion (fingerprint dystrophy area of ≥ 25%) and two minor criteria (long horizontal lines and long vertical lines). The presence of the major criterion predicts it will almost always fail verification, while presence of both minor criteria and presence of one minor criterion predict high and low risk of fingerprint verification failure, respectively. When none of the criteria are met, the fingerprint almost always passes the verification. The area under the receiver operating characteristic curve was 0.937, and the goodness-of-fit test showed agreement between the observed and expected number (P = 0.26). The derived fingerprint verification failure prediction model is validated and highly discriminatory in predicting risk of fingerprint verification in patients with hand dermatitis. © 2014 The International Society of Dermatology.

  2. Massive Predictive Modeling using Oracle R Enterprise

    CERN Multimedia

    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. 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...

  4. 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...

  5. Sensor Data Air Pollution Prediction by Machine Learning Methods

    Czech Academy of Sciences Publication Activity Database

    Vidnerová, Petra; Neruda, Roman

    submitted 25. 1. (2018) ISSN 1530-437X R&D Projects: GA ČR GA15-18108S Grant - others:GA MŠk(CZ) LM2015042 Institutional support: RVO:67985807 Keywords : machine learning * sensors * air pollution * deep neural networks * regularization networks Subject RIV: IN - Informatics, Computer Science Impact factor: 2.512, year: 2016

  6. 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.

  7. On Regional Modeling to Support Air Quality Policies (book chapter)

    Science.gov (United States)

    We examine the use of the Community Multiscale Air Quality (CMAQ) model in simulating the changes in the extreme values of air quality that are of interest to the regulatory agencies. Year-to-year changes in ozone air quality are attributable to variations in the prevailing meteo...

  8. 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.

  9. Predictive Model of Systemic Toxicity (SOT)

    Science.gov (United States)

    In an effort to ensure chemical safety in light of regulatory advances away from reliance on animal testing, USEPA and L’Oréal have collaborated to develop a quantitative systemic toxicity prediction model. Prediction of human systemic toxicity has proved difficult and remains a ...

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. 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.

  15. 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.

  16. 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.

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  1. 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…

  2. 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.

  3. A review on air pollution and various dust models for open cast mines in India

    International Nuclear Information System (INIS)

    Sangeeth, M.G.; Ahmed, Siraj; Bhagoria, J.L.; Pandit, G.G.

    2010-01-01

    Open cast coal mining continues to create significant environmental problems in India. In particular, this type of mining creates high rates of air pollution SPM, RPM, SO 2 and NO x . In these particulate matter i.e. SPM and RPM is major pollution in the open cast mines. It creates several heath hazards to mine workers and surrounding peoples and high environmental deterioration occurs. Several studies are carried out in the field of air pollution and air quality modeling of open cast projects and many researchers suggested several control measures for the air pollution control in mines. Different dust models FDM, ISC3 are available for prediction and transport of the pollutants. In this paper a review has been studied about air pollution in the open cast mines and dust dispersion models for open cast mines in India. (author)

  4. Modeling urban air pollution in Budapest using WRF-Chem model

    Science.gov (United States)

    Kovács, Attila; Leelőssy, Ádám; Lagzi, István; Mészáros, Róbert

    2017-04-01

    Air pollution is a major problem for urban areas since the industrial revolution, including Budapest, the capital and largest city of Hungary. The main anthropogenic sources of air pollutants are industry, traffic and residential heating. In this study, we investigated the contribution of major industrial point sources to the urban air pollution in Budapest. We used the WRF (Weather Research and Forecasting) nonhydrostatic mesoscale numerical weather prediction system online coupled with chemistry (WRF-Chem, version 3.6).The model was configured with three nested domains with grid spacings of 15, 5 and 1 km, representing Central Europe, the Carpathian Basin and Budapest with its surrounding area. Emission data was obtained from the National Environmental Information System. The point source emissions were summed in their respective cells in the second nested domain according to latitude-longitude coordinates. The main examined air pollutants were carbon monoxide (CO) and nitrogen oxides (NOx), from which the secondary compound, ozone (O3) forms through chemical reactions. Simulations were performed under different weather conditions and compared to observations from the automatic monitoring site of the Hungarian Air Quality Network. Our results show that the industrial emissions have a relatively weak role in the urban background air pollution, confirming the effect of industrial developments and regulations in the recent decades. However, a few significant industrial sources and their impact area has been demonstrated.

  5. MODELING OF GENERIC AIR POLLUTION DISPERSION ANALYSIS FROM CEMENT FACTORY

    Directory of Open Access Journals (Sweden)

    Moses E EMETERE

    2013-06-01

    Full Text Available Air pollution from cement factory is classified as one of the sources of air pollution. The control of the air pollution by addressing the wind field dynamics was the main objective of the paper. The dynamics of dispersion showed a three way flow which was calculated and explained accordingly. The 3D model showed good level of accuracy by determining field values of air deposited pollutants. Mean concentration of diffusing pollutants was shown to be directly proportional to the plume angular displacement. The 2D model explained the details of the wind field dynamics and proffers a solution which may be relevant in controlling air pollution from anthropogenic sources.

  6. Predicting the Air Quality, Thermal Comfort and Draught Risk for a Virtual Classroom with Desk-Type Personalized Ventilation Systems

    Directory of Open Access Journals (Sweden)

    Eusébio Z. E. Conceição

    2018-02-01

    Full Text Available This paper concerns the prediction of indoor air quality (IAQ, thermal comfort (TC and draught risk (DR for a virtual classroom with desk-type personalized ventilation system (PVS. This numerical study considers a coupling of the computational fluid dynamics (CFD, human thermal comfort (HTC and building thermal behavior (BTB numerical models. The following indexes are used: the predicted percentage of dissatisfied people (PPD index is used for the evaluation of the TC level; the carbon dioxide (CO2 concentration in the breathing zone is used for the calculation of IAQ; and the DR level around the occupants is used for the evaluation of the discomfort due to draught. The air distribution index (ADI, based in the TC level, the IAQ level, the effectiveness for heat removal and the effectiveness for contaminant removal, is used for evaluating the performance of the personalized air distribution system. The numerical simulation is made for a virtual classroom with six desks. Each desk is equipped with one PVS with two air terminal devices located overhead and two air terminal devices located below the desktop. In one numerical simulation six occupants are used, while in another simulation twelve occupants are considered. For each numerical simulation an air supply temperature of 20 °C and 24 °C is applied. The results obtained show that the ADI value is higher for twelve persons than for six persons in the classroom and it is higher for an inlet air temperature of 20 °C than for an inlet air temperature of 24 °C. In future works, more combinations of upper and lower air terminal devices located around the body area and more combinations of occupants located in the desks will be analyzed.

  7. Predicting and Modeling RNA Architecture

    Science.gov (United States)

    Westhof, Eric; Masquida, Benoît; Jossinet, Fabrice

    2011-01-01

    SUMMARY A general approach for modeling the architecture of large and structured RNA molecules is described. The method exploits the modularity and the hierarchical folding of RNA architecture that is viewed as the assembly of preformed double-stranded helices defined by Watson-Crick base pairs and RNA modules maintained by non-Watson-Crick base pairs. Despite the extensive molecular neutrality observed in RNA structures, specificity in RNA folding is achieved through global constraints like lengths of helices, coaxiality of helical stacks, and structures adopted at the junctions of helices. The Assemble integrated suite of computer tools allows for sequence and structure analysis as well as interactive modeling by homology or ab initio assembly with possibilities for fitting within electronic density maps. The local key role of non-Watson-Crick pairs guides RNA architecture formation and offers metrics for assessing the accuracy of three-dimensional models in a more useful way than usual root mean square deviation (RMSD) values. PMID:20504963

  8. Multiple Steps Prediction with Nonlinear ARX Models

    OpenAIRE

    Zhang, Qinghua; Ljung, Lennart

    2007-01-01

    NLARX (NonLinear AutoRegressive with eXogenous inputs) models are frequently used in black-box nonlinear system identication. Though it is easy to make one step ahead prediction with such models, multiple steps prediction is far from trivial. The main difficulty is that in general there is no easy way to compute the mathematical expectation of an output conditioned by past measurements. An optimal solution would require intensive numerical computations related to nonlinear filltering. The pur...

  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. 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.

  11. Model complexity control for hydrologic prediction

    Science.gov (United States)

    Schoups, G.; van de Giesen, N. C.; Savenije, H. H. G.

    2008-12-01

    A common concern in hydrologic modeling is overparameterization of complex models given limited and noisy data. This leads to problems of parameter nonuniqueness and equifinality, which may negatively affect prediction uncertainties. A systematic way of controlling model complexity is therefore needed. We compare three model complexity control methods for hydrologic prediction, namely, cross validation (CV), Akaike's information criterion (AIC), and structural risk minimization (SRM). Results show that simulation of water flow using non-physically-based models (polynomials in this case) leads to increasingly better calibration fits as the model complexity (polynomial order) increases. However, prediction uncertainty worsens for complex non-physically-based models because of overfitting of noisy data. Incorporation of physically based constraints into the model (e.g., storage-discharge relationship) effectively bounds prediction uncertainty, even as the number of parameters increases. The conclusion is that overparameterization and equifinality do not lead to a continued increase in prediction uncertainty, as long as models are constrained by such physical principles. Complexity control of hydrologic models reduces parameter equifinality and identifies the simplest model that adequately explains the data, thereby providing a means of hydrologic generalization and classification. SRM is a promising technique for this purpose, as it (1) provides analytic upper bounds on prediction uncertainty, hence avoiding the computational burden of CV, and (2) extends the applicability of classic methods such as AIC to finite data. The main hurdle in applying SRM is the need for an a priori estimation of the complexity of the hydrologic model, as measured by its Vapnik-Chernovenkis (VC) dimension. Further research is needed in this area.

  12. 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

  13. The Atlanta Urban Heat Island Mitigation and Air Quality Modeling Project: How High-Resoution Remote Sensing Data Can Improve Air Quality Models

    Science.gov (United States)

    Quattrochi, Dale A.; Estes, Maurice G., Jr.; Crosson, William L.; Khan, Maudood N.

    2006-01-01

    The Atlanta Urban Heat Island and Air Quality Project had its genesis in Project ATLANTA (ATlanta Land use Analysis: Temperature and Air quality) that began in 1996. Project ATLANTA examined how high-spatial resolution thermal remote sensing data could be used to derive better measurements of the Urban Heat Island effect over Atlanta. We have explored how these thermal remote sensing, as well as other imaged datasets, can be used to better characterize the urban landscape for improved air quality modeling over the Atlanta area. For the air quality modeling project, the National Land Cover Dataset and the local scale Landpro99 dataset at 30m spatial resolutions have been used to derive land use/land cover characteristics for input into the MM5 mesoscale meteorological model that is one of the foundations for the Community Multiscale Air Quality (CMAQ) model to assess how these data can improve output from CMAQ. Additionally, land use changes to 2030 have been predicted using a Spatial Growth Model (SGM). SGM simulates growth around a region using population, employment and travel demand forecasts. Air quality modeling simulations were conducted using both current and future land cover. Meteorological modeling simulations indicate a 0.5 C increase in daily maximum air temperatures by 2030. Air quality modeling simulations show substantial differences in relative contributions of individual atmospheric pollutant constituents as a result of land cover change. Enhanced boundary layer mixing over the city tends to offset the increase in ozone concentration expected due to higher surface temperatures as a result of urbanization.

  14. Predicting Human Error in Air Traffic Control Decision Support Tools and Free Flight Concepts

    Science.gov (United States)

    Mogford, Richard; Kopardekar, Parimal

    2001-01-01

    The document is a set of briefing slides summarizing the work the Advanced Air Transportation Technologies (AATT) Project is doing on predicting air traffic controller and airline pilot human error when using new decision support software tools and when involved in testing new air traffic control concepts. Previous work in this area is reviewed as well as research being done jointly with the FAA. Plans for error prediction work in the AATT Project are discussed. The audience is human factors researchers and aviation psychologists from government and industry.

  15. An Internet of Things System for Underground Mine Air Quality Pollutant Prediction Based on Azure Machine Learning

    Directory of Open Access Journals (Sweden)

    ByungWan Jo

    2018-03-01

    Full Text Available The implementation of wireless sensor networks (WSNs for monitoring the complex, dynamic, and harsh environment of underground coal mines (UCMs is sought around the world to enhance safety. However, previously developed smart systems are limited to monitoring or, in a few cases, can report events. Therefore, this study introduces a reliable, efficient, and cost-effective internet of things (IoT system for air quality monitoring with newly added features of assessment and pollutant prediction. This system is comprised of sensor modules, communication protocols, and a base station, running Azure Machine Learning (AML Studio over it. Arduino-based sensor modules with eight different parameters were installed at separate locations of an operational UCM. Based on the sensed data, the proposed system assesses mine air quality in terms of the mine environment index (MEI. Principal component analysis (PCA identified CH4, CO, SO2, and H2S as the most influencing gases significantly affecting mine air quality. The results of PCA were fed into the ANN model in AML studio, which enabled the prediction of MEI. An optimum number of neurons were determined for both actual input and PCA-based input parameters. The results showed a better performance of the PCA-based ANN for MEI prediction, with R2 and RMSE values of 0.6654 and 0.2104, respectively. Therefore, the proposed Arduino and AML-based system enhances mine environmental safety by quickly assessing and predicting mine air quality.

  16. An Internet of Things System for Underground Mine Air Quality Pollutant Prediction Based on Azure Machine Learning.

    Science.gov (United States)

    Jo, ByungWan; Khan, Rana Muhammad Asad

    2018-03-21

    The implementation of wireless sensor networks (WSNs) for monitoring the complex, dynamic, and harsh environment of underground coal mines (UCMs) is sought around the world to enhance safety. However, previously developed smart systems are limited to monitoring or, in a few cases, can report events. Therefore, this study introduces a reliable, efficient, and cost-effective internet of things (IoT) system for air quality monitoring with newly added features of assessment and pollutant prediction. This system is comprised of sensor modules, communication protocols, and a base station, running Azure Machine Learning (AML) Studio over it. Arduino-based sensor modules with eight different parameters were installed at separate locations of an operational UCM. Based on the sensed data, the proposed system assesses mine air quality in terms of the mine environment index (MEI). Principal component analysis (PCA) identified CH₄, CO, SO₂, and H₂S as the most influencing gases significantly affecting mine air quality. The results of PCA were fed into the ANN model in AML studio, which enabled the prediction of MEI. An optimum number of neurons were determined for both actual input and PCA-based input parameters. The results showed a better performance of the PCA-based ANN for MEI prediction, with R ² and RMSE values of 0.6654 and 0.2104, respectively. Therefore, the proposed Arduino and AML-based system enhances mine environmental safety by quickly assessing and predicting mine air quality.

  17. Quantifying predictive accuracy in survival models.

    Science.gov (United States)

    Lirette, Seth T; Aban, Inmaculada

    2017-12-01

    For time-to-event outcomes in medical research, survival models are the most appropriate to use. Unlike logistic regression models, quantifying the predictive accuracy of these models is not a trivial task. We present the classes of concordance (C) statistics and R 2 statistics often used to assess the predictive ability of these models. The discussion focuses on Harrell's C, Kent and O'Quigley's R 2 , and Royston and Sauerbrei's R 2 . We present similarities and differences between the statistics, discuss the software options from the most widely used statistical analysis packages, and give a practical example using the Worcester Heart Attack Study dataset.

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

    Czech Academy of Sciences Publication Activity Database

    Sáňka, O.; Melymuk, L.; Čupr, P.; Dvorská, Alice; Klánová, J.

    2014-01-01

    Roč. 90, oct (2014), s. 88-95 ISSN 1352-2310 Institutional support: RVO:67179843 Keywords : passive air sampling * air dispersion modeling * GIS * polycyclic aromatic hydrocarbons * emission inventories Subject RIV: DI - Air Pollution ; Quality Impact factor: 3.281, year: 2014

  19. Predictive power of nuclear-mass models

    Directory of Open Access Journals (Sweden)

    Yu. A. Litvinov

    2013-12-01

    Full Text Available Ten different theoretical models are tested for their predictive power in the description of nuclear masses. Two sets of experimental masses are used for the test: the older set of 2003 and the newer one of 2011. The predictive power is studied in two regions of nuclei: the global region (Z, N ≥ 8 and the heavy-nuclei region (Z ≥ 82, N ≥ 126. No clear correlation is found between the predictive power of a model and the accuracy of its description of the masses.

  20. 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...... find that confidence sets are very wide, change significantly with the predictor variables, and frequently include expected utilities for which the investor prefers not to invest. The latter motivates a robust investment strategy maximizing the minimal element of the confidence set. The robust investor...... allocates a much lower share of wealth to stocks compared to a standard investor....

  1. Problems of air traffic management. II., Prediction of success in air traffic controller school.

    Science.gov (United States)

    1962-02-01

    An analysis of scores for an extensive battery of psychological tests administered to a large number of air traffic controller (ATC) trainees indicated that such tests can make a useful contribution in the selection of personnel for ATC training. Fiv...

  2. 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...

  3. 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.

  4. Clearing the air : Air quality modelling for policy support

    NARCIS (Netherlands)

    Hendriks, C.

    2018-01-01

    The origin of particulate matter (PM) concentrations in the Netherlands is established using the LOTOS-EUROS model with a source attribution module. Emissions from the ten main economic sectors (SNAP1) were tracked, separating Dutch and foreign sources. Of the modelled PM10 in the Netherlands, about

  5. Accuracy assessment of landslide prediction models

    International Nuclear Information System (INIS)

    Othman, A N; Mohd, W M N W; Noraini, S

    2014-01-01

    The increasing population and expansion of settlements over hilly areas has greatly increased the impact of natural disasters such as landslide. Therefore, it is important to developed models which could accurately predict landslide hazard zones. Over the years, various techniques and models have been developed to predict landslide hazard zones. The aim of this paper is to access the accuracy of landslide prediction models developed by the authors. The methodology involved the selection of study area, data acquisition, data processing and model development and also data analysis. The development of these models are based on nine different landslide inducing parameters i.e. slope, land use, lithology, soil properties, geomorphology, flow accumulation, aspect, proximity to river and proximity to road. Rank sum, rating, pairwise comparison and AHP techniques are used to determine the weights for each of the parameters used. Four (4) different models which consider different parameter combinations are developed by the authors. Results obtained are compared to landslide history and accuracies for Model 1, Model 2, Model 3 and Model 4 are 66.7, 66.7%, 60% and 22.9% respectively. From the results, rank sum, rating and pairwise comparison can be useful techniques to predict landslide hazard zones

  6. 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

    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......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...

  7. Modeling activities in air traffic control systems: antecedents and consequences of a mid-air collision.

    Science.gov (United States)

    de Carvalho, Paulo Victor R; Ferreira, Bemildo

    2012-01-01

    In this article we present a model of some functions and activities of the Brazilian Air traffic Control System (ATS) in the period in which occurred a mid-air collision between flight GLO1907, a commercial aircraft Boeing 737-800, and flight N600XL, an executive jet EMBRAER E-145, to investigate key resilience characteristics of the ATM. Modeling in some detail activities during the collision and related them to overall behavior and antecedents that stress the organization uncover some drift into failure mechanisms that erode safety defenses provided by the Air Navigation Service Provider (ANSP), enabling a mid-air collision to be happen.

  8. An open-access modeled passenger flow matrix for the global air network in 2010.

    Directory of Open Access Journals (Sweden)

    Zhuojie Huang

    Full Text Available The expanding global air network provides rapid and wide-reaching connections accelerating both domestic and international travel. To understand human movement patterns on the network and their socioeconomic, environmental and epidemiological implications, information on passenger flow is required. However, comprehensive data on global passenger flow remain difficult and expensive to obtain, prompting researchers to rely on scheduled flight seat capacity data or simple models of flow. This study describes the construction of an open-access modeled passenger flow matrix for all airports with a host city-population of more than 100,000 and within two transfers of air travel from various publicly available air travel datasets. Data on network characteristics, city population, and local area GDP amongst others are utilized as covariates in a spatial interaction framework to predict the air transportation flows between airports. Training datasets based on information from various transportation organizations in the United States, Canada and the European Union were assembled. A log-linear model controlling the random effects on origin, destination and the airport hierarchy was then built to predict passenger flows on the network, and compared to the results produced using previously published models. Validation analyses showed that the model presented here produced improved predictive power and accuracy compared to previously published models, yielding the highest successful prediction rate at the global scale. Based on this model, passenger flows between 1,491 airports on 644,406 unique routes were estimated in the prediction dataset. The airport node characteristics and estimated passenger flows are freely available as part of the Vector-Borne Disease Airline Importation Risk (VBD-Air project at: www.vbd-air.com/data.

  9. Regional Air Toxics Modeling in California's San Francisco Bay Area

    Science.gov (United States)

    Martien, P. T.; Tanrikulu, S.; Tran, C.; Fairley, D.; Jia, Y.; Fanai, A.; Reid, S.; Yarwood, G.; Emery, C.

    2011-12-01

    Regional toxics modeling conducted for California's San Francisco Bay Area (SFBA) estimated potential cancer risk from diesel particulate matter (DPM) and four key reactive toxic gaseous pollutants (1,3-butadiene, benzene, formaldehyde, and acetaldehyde). Concentrations of other non-cancerous gaseous toxic air contaminants, including acrolein, were also generated. In this study, meteorological fields generated from July and December periods in 2000 and emissions from 2005 provided inputs to a three-dimensional air quality model at high spatial resolution (1x1 km^2 grid), from which a baseline set of annual risk values was estimated. Simulated risk maps show highest annual average DPM concentrations and cancer risks were located near and downwind of major freeways and near the Port of Oakland, a major container port in the area. Population weighted risks, using 2000 census data, were found to be highest in highly urbanized areas adjacent to significant DPM sources. For summer, the ratio of mean measured elemental carbon to mean modeled DPM was 0.78, conforming roughly to expectations. But for winter the ratio is 1.13, suggesting other sources of elemental carbon, such as wood smoke, are important. Simulated annual estimates for benzene and 1-3, butadiene compared well to measured annual estimates. Simulated acrolein and formaldehyde significantly under-predicted observed values. Simulations repeated using projected 2015 toxic emissions predicted that potential cancer risk dropped significantly in all areas throughout the SFBA. Emissions estimates for 2015 included the State of California's recently adopted on-road truck rule. Emission estimates of DPM are projected to drop about 70% between 2005 and 2015 in the SFBA, with a commensurate reduction in potential cancer risks. However, due to projected shifts in population during this period, with urban densification close to DPM sources outpacing emission reductions, there are some areas where population-weighted risks

  10. 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

    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...... 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...

  11. Predictive validation of an influenza spread model.

    Directory of Open Access Journals (Sweden)

    Ayaz Hyder

    Full Text Available BACKGROUND: Modeling plays a critical role in mitigating impacts of seasonal influenza epidemics. Complex simulation models are currently at the forefront of evaluating optimal mitigation strategies at multiple scales and levels of organization. Given their evaluative role, these models remain limited in their ability to predict and forecast future epidemics leading some researchers and public-health practitioners to question their usefulness. The objective of this study is to evaluate the predictive ability of an existing complex simulation model of influenza spread. METHODS AND FINDINGS: We used extensive data on past epidemics to demonstrate the process of predictive validation. This involved generalizing an individual-based model for influenza spread and fitting it to laboratory-confirmed influenza infection data from a single observed epidemic (1998-1999. Next, we used the fitted model and modified two of its parameters based on data on real-world perturbations (vaccination coverage by age group and strain type. Simulating epidemics under these changes allowed us to estimate the deviation/error between the expected epidemic curve under perturbation and observed epidemics taking place from 1999 to 2006. Our model was able to forecast absolute intensity and epidemic peak week several weeks earlier with reasonable reliability and depended on the method of forecasting-static or dynamic. CONCLUSIONS: Good predictive ability of influenza epidemics is critical for implementing mitigation strategies in an effective and timely manner. Through the process of predictive validation applied to a current complex simulation model of influenza spread, we provided users of the model (e.g. public-health officials and policy-makers with quantitative metrics and practical recommendations on mitigating impacts of seasonal influenza epidemics. This methodology may be applied to other models of communicable infectious diseases to test and potentially improve

  12. Predictive Validation of an Influenza Spread Model

    Science.gov (United States)

    Hyder, Ayaz; Buckeridge, David L.; Leung, Brian

    2013-01-01

    Background Modeling plays a critical role in mitigating impacts of seasonal influenza epidemics. Complex simulation models are currently at the forefront of evaluating optimal mitigation strategies at multiple scales and levels of organization. Given their evaluative role, these models remain limited in their ability to predict and forecast future epidemics leading some researchers and public-health practitioners to question their usefulness. The objective of this study is to evaluate the predictive ability of an existing complex simulation model of influenza spread. Methods and Findings We used extensive data on past epidemics to demonstrate the process of predictive validation. This involved generalizing an individual-based model for influenza spread and fitting it to laboratory-confirmed influenza infection data from a single observed epidemic (1998–1999). Next, we used the fitted model and modified two of its parameters based on data on real-world perturbations (vaccination coverage by age group and strain type). Simulating epidemics under these changes allowed us to estimate the deviation/error between the expected epidemic curve under perturbation and observed epidemics taking place from 1999 to 2006. Our model was able to forecast absolute intensity and epidemic peak week several weeks earlier with reasonable reliability and depended on the method of forecasting-static or dynamic. Conclusions Good predictive ability of influenza epidemics is critical for implementing mitigation strategies in an effective and timely manner. Through the process of predictive validation applied to a current complex simulation model of influenza spread, we provided users of the model (e.g. public-health officials and policy-makers) with quantitative metrics and practical recommendations on mitigating impacts of seasonal influenza epidemics. This methodology may be applied to other models of communicable infectious diseases to test and potentially improve their predictive

  13. 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.

  14. Differing Air Traffic Controller Responses to Similar Trajectory Prediction Errors

    Science.gov (United States)

    Mercer, Joey; Hunt-Espinosa, Sarah; Bienert, Nancy; Laraway, Sean

    2016-01-01

    A Human-In-The-Loop simulation was conducted in January of 2013 in the Airspace Operations Laboratory at NASA's Ames Research Center. The simulation airspace included two en route sectors feeding the northwest corner of Atlanta's Terminal Radar Approach Control. The focus of this paper is on how uncertainties in the study's trajectory predictions impacted the controllers ability to perform their duties. Of particular interest is how the controllers interacted with the delay information displayed in the meter list and data block while managing the arrival flows. Due to wind forecasts with 30-knot over-predictions and 30-knot under-predictions, delay value computations included errors of similar magnitude, albeit in opposite directions. However, when performing their duties in the presence of these errors, did the controllers issue clearances of similar magnitude, albeit in opposite directions?

  15. 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.

  16. 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.

  17. Posterior predictive checking of multiple imputation models.

    Science.gov (United States)

    Nguyen, Cattram D; Lee, Katherine J; Carlin, John B

    2015-07-01

    Multiple imputation is gaining popularity as a strategy for handling missing data, but there is a scarcity of tools for checking imputation models, a critical step in model fitting. Posterior predictive checking (PPC) has been recommended as an imputation diagnostic. PPC involves simulating "replicated" data from the posterior predictive distribution of the model under scrutiny. Model fit is assessed by examining whether the analysis from the observed data appears typical of results obtained from the replicates produced by the model. A proposed diagnostic measure is the posterior predictive "p-value", an extreme value of which (i.e., a value close to 0 or 1) suggests a misfit between the model and the data. The aim of this study was to evaluate the performance of the posterior predictive p-value as an imputation diagnostic. Using simulation methods, we deliberately misspecified imputation models to determine whether posterior predictive p-values were effective in identifying these problems. When estimating the regression parameter of interest, we found that more extreme p-values were associated with poorer imputation model performance, although the results highlighted that traditional thresholds for classical p-values do not apply in this context. A shortcoming of the PPC method was its reduced ability to detect misspecified models with increasing amounts of missing data. Despite the limitations of posterior predictive p-values, they appear to have a valuable place in the imputer's toolkit. In addition to automated checking using p-values, we recommend imputers perform graphical checks and examine other summaries of the test quantity distribution. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  18. Experimental analysis and regression prediction of desiccant wheel behavior in high temperature heat pump and desiccant wheel air-conditioning system

    DEFF Research Database (Denmark)

    Sheng, Ying; Zhang, Yufeng; Sun, Yuexia

    2014-01-01

    and the ratio between regeneration and process air flow rates. A simple method based on multiple linear regression theory for predicting the performance of the wheel has been proposed. The predicted values and the experimental data are compared and good agreements are obtained. Regression models are established...

  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. 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...... in the Markov model for this task. Classifications that are purely based on statistical models might not always be biologically meaningful. We present combinatorial methods to incorporate biological background knowledge to enhance the prediction performance....

  1. 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

    2018-04-01

    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.

  2. Determination and prediction of octanol-air partition coefficients for organophosphate flame retardants.

    Science.gov (United States)

    Wang, Qingzhi; Zhao, Hongxia; Wang, Yan; Xie, Qing; Chen, Jingwen; Quan, Xie

    2017-11-01

    Organophosphate flame retardants (OPFRs) have attracted wide concerns due to their toxicities and ubiquitous occurrence in the environment. In this work, Octanol-air partition coefficient (K OA ) for 14 OPFRs including 4 halogenated alkyl-, 5 aryl- and 5 alkyl-OPFRs, were estimated as a function of temperature using a gas chromatographic retention time (GC-RT) method. Their log K OA-GC values and internal energies of phase transfer (Δ OA U/kJmol -1 ) ranged from 8.03 to 13.0 and from 69.7 to 149, respectively. Substitution pattern and molar volume (V M ) were found to be capable of influencing log K OA-GC values of OPFRs. The halogenated alkyl-OPFRs had higher log K OA-GC values than aryl- or alkyl-OPFRs. The bigger the molar volume was, the greater the log K OA-GC values increased. In addition, a predicted model of log K OA-GC versus different relative retention times (RRTs) was developed with a high cross-validated value (Q 2 (cum) ) of 0.951, indicating a good predictive ability and stability. Therefore, the log K OA-GC values of the remaining OPFRs can be predicted by using their RRTs on different GC columns. Copyright © 2017 Elsevier Inc. All rights reserved.

  3. 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...... on underlying basic assumptions, such as diffuse fields, high modal overlap, resonant field being dominant, etc., and the consequences of these in terms of limitations in the theory and in the practical use of the models....

  4. Comparative Study of Bancruptcy Prediction Models

    Directory of Open Access Journals (Sweden)

    Isye Arieshanti

    2013-09-01

    Full Text Available Early indication of bancruptcy is important for a company. If companies aware of  potency of their bancruptcy, they can take a preventive action to anticipate the bancruptcy. In order to detect the potency of a bancruptcy, a company can utilize a a model of bancruptcy prediction. The prediction model can be built using a machine learning methods. However, the choice of machine learning methods should be performed carefully. Because the suitability of a model depends on the problem specifically. Therefore, in this paper we perform a comparative study of several machine leaning methods for bancruptcy prediction. According to the comparative study, the performance of several models that based on machine learning methods (k-NN, fuzzy k-NN, SVM, Bagging Nearest Neighbour SVM, Multilayer Perceptron(MLP, Hybrid of MLP + Multiple Linear Regression, it can be showed that fuzzy k-NN method achieve the best performance with accuracy 77.5%

  5. A comparative study of turbulence models for dissolved air flotation flow analysis

    Energy Technology Data Exchange (ETDEWEB)

    Park, Min A; Lee, Kyun Ho; Chung, Jae Dong [School of Mechanical and Aerospace Engineering, Sejong University, Seoul (Korea, Republic of); Seo, Seung Ho [Tops Engineering Co, Ltd., Gwangmyeong (Korea, Republic of)

    2015-07-15

    The dissolved air flotation (DAF) system is a water treatment process that removes contaminants by attaching micro bubbles to them, causing them to float to the water surface. In the present study, two-phase flow of air-water mixture is simulated to investigate changes in the internal flow analysis of DAF systems caused by using different turbulence models. Internal micro bubble distribution, velocity, and computation time are compared between several turbulence models for a given DAF geometry and condition. As a result, it is observed that the standard κ-ε model, which has been frequently used in previous research, predicts somewhat different behavior than other turbulence models.

  6. A comparative study of turbulence models for dissolved air flotation flow analysis

    International Nuclear Information System (INIS)

    Park, Min A; Lee, Kyun Ho; Chung, Jae Dong; Seo, Seung Ho

    2015-01-01

    The dissolved air flotation (DAF) system is a water treatment process that removes contaminants by attaching micro bubbles to them, causing them to float to the water surface. In the present study, two-phase flow of air-water mixture is simulated to investigate changes in the internal flow analysis of DAF systems caused by using different turbulence models. Internal micro bubble distribution, velocity, and computation time are compared between several turbulence models for a given DAF geometry and condition. As a result, it is observed that the standard κ-ε model, which has been frequently used in previous research, predicts somewhat different behavior than other turbulence models

  7. 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.

  8. 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...

  9. Long-Term Calculations with Large Air Pollution Models

    DEFF Research Database (Denmark)

    Ambelas Skjøth, C.; Bastrup-Birk, A.; Brandt, J.

    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...

  10. 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...

  11. Prediction Models for Dynamic Demand Response

    Energy Technology Data Exchange (ETDEWEB)

    Aman, Saima; Frincu, Marc; Chelmis, Charalampos; Noor, Muhammad; Simmhan, Yogesh; Prasanna, Viktor K.

    2015-11-02

    As Smart Grids move closer to dynamic curtailment programs, Demand Response (DR) events will become necessary not only on fixed time intervals and weekdays predetermined by static policies, but also during changing decision periods and weekends to react to real-time demand signals. Unique challenges arise in this context vis-a-vis demand prediction and curtailment estimation and the transformation of such tasks into an automated, efficient dynamic demand response (D2R) process. While existing work has concentrated on increasing the accuracy of prediction models for DR, there is a lack of studies for prediction models for D2R, which we address in this paper. Our first contribution is the formal definition of D2R, and the description of its challenges and requirements. Our second contribution is a feasibility analysis of very-short-term prediction of electricity consumption for D2R over a diverse, large-scale dataset that includes both small residential customers and large buildings. Our third, and major contribution is a set of insights into the predictability of electricity consumption in the context of D2R. Specifically, we focus on prediction models that can operate at a very small data granularity (here 15-min intervals), for both weekdays and weekends - all conditions that characterize scenarios for D2R. We find that short-term time series and simple averaging models used by Independent Service Operators and utilities achieve superior prediction accuracy. We also observe that workdays are more predictable than weekends and holiday. Also, smaller customers have large variation in consumption and are less predictable than larger buildings. Key implications of our findings are that better models are required for small customers and for non-workdays, both of which are critical for D2R. Also, prediction models require just few days’ worth of data indicating that small amounts of

  12. Are animal models predictive for humans?

    Directory of Open Access Journals (Sweden)

    Greek Ray

    2009-01-01

    Full Text Available Abstract It is one of the central aims of the philosophy of science to elucidate the meanings of scientific terms and also to think critically about their application. The focus of this essay is the scientific term predict and whether there is credible evidence that animal models, especially in toxicology and pathophysiology, can be used to predict human outcomes. Whether animals can be used to predict human response to drugs and other chemicals is apparently a contentious issue. However, when one empirically analyzes animal models using scientific tools they fall far short of being able to predict human responses. This is not surprising considering what we have learned from fields such evolutionary and developmental biology, gene regulation and expression, epigenetics, complexity theory, and comparative genomics.

  13. 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.

  14. 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.

  15. 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.

  16. 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

  17. Model predictive controller design of hydrocracker reactors

    OpenAIRE

    GÖKÇE, Dila

    2014-01-01

    This study summarizes the design of a Model Predictive Controller (MPC) in Tüpraş, İzmit Refinery Hydrocracker Unit Reactors. Hydrocracking process, in which heavy vacuum gasoil is converted into lighter and valuable products at high temperature and pressure is described briefly. Controller design description, identification and modeling studies are examined and the model variables are presented. WABT (Weighted Average Bed Temperature) equalization and conversion increase are simulate...

  18. 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.

  19. Impact of preferential sampling on exposure prediction and health effect inference in the context of air pollution epidemiology.

    Science.gov (United States)

    Lee, A; Szpiro, A; Kim, S Y; Sheppard, L

    2015-06-01

    Preferential sampling has been defined in the context of geostatistical modeling as the dependence between the sampling locations and the process that describes the spatial structure of the data. It can occur when networks are designed to find high values. For example, in networks based on the U.S. Clean Air Act monitors are sited to determine whether air quality standards are exceeded. We study the impact of the design of monitor networks in the context of air pollution epidemiology studies. The effect of preferential sampling has been illustrated in the literature by highlighting its impact on spatial predictions. In this paper, we use these predictions as input in a second stage analysis, and we assess how they affect health effect inference. Our work is motivated by data from two United States regulatory networks and health data from the Multi-Ethnic Study of Atherosclerosis and Air Pollution. The two networks were designed to monitor air pollution in urban and rural areas respectively, and we found that the health analysis results based on the two networks can lead to different scientific conclusions. We use preferential sampling to gain insight into these differences. We designed a simulation study, and found that the validity and reliability of the health effect estimate can be greatly affected by how we sample the monitor locations. To better understand its effect on second stage inference, we identify two components of preferential sampling that shed light on how preferential sampling alters the properties of the health effect estimate.

  20. Impact of preferential sampling on exposure prediction and health effect inference in the context of air pollution epidemiology

    Science.gov (United States)

    Lee, A.; Szpiro, A.; Kim, S.Y.; Sheppard, L.

    2018-01-01

    Summary Preferential sampling has been defined in the context of geostatistical modeling as the dependence between the sampling locations and the process that describes the spatial structure of the data. It can occur when networks are designed to find high values. For example, in networks based on the U.S. Clean Air Act monitors are sited to determine whether air quality standards are exceeded. We study the impact of the design of monitor networks in the context of air pollution epidemiology studies. The effect of preferential sampling has been illustrated in the literature by highlighting its impact on spatial predictions. In this paper, we use these predictions as input in a second stage analysis, and we assess how they affect health effect inference. Our work is motivated by data from two United States regulatory networks and health data from the Multi-Ethnic Study of Atherosclerosis and Air Pollution. The two networks were designed to monitor air pollution in urban and rural areas respectively, and we found that the health analysis results based on the two networks can lead to different scientific conclusions. We use preferential sampling to gain insight into these differences. We designed a simulation study, and found that the validity and reliability of the health effect estimate can be greatly affected by how we sample the monitor locations. To better understand its effect on second stage inference, we identify two components of preferential sampling that shed light on how preferential sampling alters the properties of the health effect estimate. PMID:29576734

  1. Air

    Science.gov (United States)

    ... gov/ Home The environment and your health Air Air While we don’t often think about the ... do to protect yourself from dirty air . Indoor air pollution and outdoor air pollution Air can be ...

  2. 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

    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.......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...

  3. Multi-Model Ensemble Wake Vortex Prediction

    Science.gov (United States)

    Koerner, Stephan; Holzaepfel, Frank; Ahmad, Nash'at N.

    2015-01-01

    Several multi-model ensemble methods are investigated for predicting wake vortex transport and decay. This study is a joint effort between National Aeronautics and Space Administration and Deutsches Zentrum fuer Luft- und Raumfahrt to develop a multi-model ensemble capability using their wake models. An overview of different multi-model ensemble methods and their feasibility for wake applications is presented. The methods include Reliability Ensemble Averaging, Bayesian Model Averaging, and Monte Carlo Simulations. The methodologies are evaluated using data from wake vortex field experiments.

  4. Thermodynamic modeling of activity coefficient and prediction of solubility: Part 1. Predictive models.

    Science.gov (United States)

    Mirmehrabi, Mahmoud; Rohani, Sohrab; Perry, Luisa

    2006-04-01

    A new activity coefficient model was developed from excess Gibbs free energy in the form G(ex) = cA(a) x(1)(b)...x(n)(b). The constants of the proposed model were considered to be function of solute and solvent dielectric constants, Hildebrand solubility parameters and specific volumes of solute and solvent molecules. The proposed model obeys the Gibbs-Duhem condition for activity coefficient models. To generalize the model and make it as a purely predictive model without any adjustable parameters, its constants were found using the experimental activity coefficient and physical properties of 20 vapor-liquid systems. The predictive capability of the proposed model was tested by calculating the activity coefficients of 41 binary vapor-liquid equilibrium systems and showed good agreement with the experimental data in comparison with two other predictive models, the UNIFAC and Hildebrand models. The only data used for the prediction of activity coefficients, were dielectric constants, Hildebrand solubility parameters, and specific volumes of the solute and solvent molecules. Furthermore, the proposed model was used to predict the activity coefficient of an organic compound, stearic acid, whose physical properties were available in methanol and 2-butanone. The predicted activity coefficient along with the thermal properties of the stearic acid were used to calculate the solubility of stearic acid in these two solvents and resulted in a better agreement with the experimental data compared to the UNIFAC and Hildebrand predictive models.

  5. 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.

  6. 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.

  7. 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.

  8. A revised prediction model for natural conception.

    Science.gov (United States)

    Bensdorp, Alexandra J; van der Steeg, Jan Willem; Steures, Pieternel; Habbema, J Dik F; Hompes, Peter G A; Bossuyt, Patrick M M; van der Veen, Fulco; Mol, Ben W J; Eijkemans, Marinus J C

    2017-06-01

    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 was to assess whether additional predictors can refine the Hunault model and extend its applicability. Consecutive subfertile couples with unexplained and mild male subfertility presenting in fertility clinics were asked to participate in a prospective cohort study. We constructed a multivariable prediction model with the predictors from the Hunault model and new potential predictors. The primary outcome, natural conception leading to an ongoing pregnancy, was observed in 1053 women of the 5184 included couples (20%). All predictors of the Hunault model were selected into the revised model plus an additional seven (woman's body mass index, cycle length, basal FSH levels, tubal status,history of previous pregnancies in the current relationship (ongoing pregnancies after natural conception, fertility treatment or miscarriages), semen volume, and semen morphology. Predictions from the revised model seem to concur better with observed pregnancy rates compared with the Hunault model; c-statistic of 0.71 (95% CI 0.69 to 0.73) compared with 0.59 (95% CI 0.57 to 0.61). Copyright © 2017. Published by Elsevier Ltd.

  9. A simulation Model of the Reactor Hall Ventilation and air Conditioning Systems of ETRR-2

    International Nuclear Information System (INIS)

    Abd El-Rahman, M.F.

    2004-01-01

    Although the conceptual design for any system differs from one designer to another. each of them aims to achieve the function of the system required. the ventilation and air conditioning system of reactors hall is one of those systems that really differs but always dose its function for which it is designed. thus, ventilation and air conditioning in some reactor hall constitute only one system whereas in some other ones, they are separate systems. the Egypt Research Reactor-2 (ETRR-2)represents the second type. most studies conducted on ventilation and air conditioning simulation models either in traditional building or for research rectors show that those models were not designed similarly to the model of the hall of ETRR-2 in which ventilation and air conditioning constitute two separate systems.besides, those studies experimented on ventilation and air conditioning simulation models of reactor building predict the temperature and humidity inside these buildings at certain outside condition and it is difficult to predict when the outside conditions are changed . also those studies do not discuss the influences of reactor power changes. therefore, the present work deals with a computational study backed by infield experimental measurements of the performance of the ventilation and air conditioning systems of reactor hall during normal operation at different outside conditions as well as at different levels of reactor power

  10. Air Pollution Modeling at Road Sides Using the Operational Street Pollution Model-A Case Study in Hanoi, Vietnam

    DEFF Research Database (Denmark)

    Hung, Ngo Tho; Ketzel, Matthias; Jensen, Steen Solvang

    2010-01-01

    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...... 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), sulfur dioxide (SO2), carbon monoxide...

  11. A Spatiotemporal Prediction Framework for Air Pollution Based on Deep RNN

    Science.gov (United States)

    Fan, J.; Li, Q.; Hou, J.; Feng, X.; Karimian, H.; Lin, S.

    2017-10-01

    Time series data in practical applications always contain missing values due to sensor malfunction, network failure, outliers etc. In order to handle missing values in time series, as well as the lack of considering temporal properties in machine learning models, we propose a spatiotemporal prediction framework based on missing value processing algorithms and deep recurrent neural network (DRNN). By using missing tag and missing interval to represent time series patterns, we implement three different missing value fixing algorithms, which are further incorporated into deep neural network that consists of LSTM (Long Short-term Memory) layers and fully connected layers. Real-world air quality and meteorological datasets (Jingjinji area, China) are used for model training and testing. Deep feed forward neural networks (DFNN) and gradient boosting decision trees (GBDT) are trained as baseline models against the proposed DRNN. Performances of three missing value fixing algorithms, as well as different machine learning models are evaluated and analysed. Experiments show that the proposed DRNN framework outperforms both DFNN and GBDT, therefore validating the capacity of the proposed framework. Our results also provides useful insights for better understanding of different strategies that handle missing values.

  12. A Spatiotemporal Prediction Framework for Air Pollution Based on Deep RNN

    Directory of Open Access Journals (Sweden)

    J. Fan

    2017-10-01

    Full Text Available Time series data in practical applications always contain missing values due to sensor malfunction, network failure, outliers etc. In order to handle missing values in time series, as well as the lack of considering temporal properties in machine learning models, we propose a spatiotemporal prediction framework based on missing value processing algorithms and deep recurrent neural network (DRNN. By using missing tag and missing interval to represent time series patterns, we implement three different missing value fixing algorithms, which are further incorporated into deep neural network that consists of LSTM (Long Short-term Memory layers and fully connected layers. Real-world air quality and meteorological datasets (Jingjinji area, China are used for model training and testing. Deep feed forward neural networks (DFNN and gradient boosting decision trees (GBDT are trained as baseline models against the proposed DRNN. Performances of three missing value fixing algorithms, as well as different machine learning models are evaluated and analysed. Experiments show that the proposed DRNN framework outperforms both DFNN and GBDT, therefore validating the capacity of the proposed framework. Our results also provides useful insights for better understanding of different strategies that handle missing values.

  13. Remote Sensing Characterization of the Urban Landscape for Improvement of Air Quality Modeling

    Science.gov (United States)

    Quattrochi, Dale A.; Estes, Maurice G., Jr.; Khan, Maudood

    2005-01-01

    The urban landscape is inherently complex and this complexity is not adequately captured in air quality models, particularly the Community Multiscale Air Quality (CMAQ) model that is used to assess whether urban areas are in attainment of EPA air quality standards, primarily for ground level ozone. This inadequacy of the CMAQ model to sufficiently respond to the heterogeneous nature of the urban landscape can impact how well the model predicts ozone pollutant levels over metropolitan areas and ultimately, whether cities exceed EPA ozone air quality standards. We are exploring the utility of high-resolution remote sensing data and urban growth projections as improved inputs to the meteorology component of the CMAQ model focusing on the Atlanta, Georgia metropolitan area as a case study. These growth projections include "business as usual" and "smart growth" scenarios out to 2030. The growth projections illustrate the effects of employing urban heat island mitigation strategies, such as increasing tree canopy and albedo across the Atlanta metro area, in moderating ground-level ozone and air temperature, compared to "business as usual" simulations in which heat island mitigation strategies are not applied. The National Land Cover Dataset at 30m resolution is being used as the land use/land cover input and aggregated to the 4km scale for the MM5 mesoscale meteorological model and the (CMAQ) modeling schemes. Use of these data has been found to better characterize low densityhburban development as compared with USGS 1 km land use/land cover data that have traditionally been used in modeling. Air quality prediction for fiture scenarios to 2030 is being facilitated by land use projections using a spatial growth model. Land use projections were developed using the 2030 Regional Transportation Plan developed by the Atlanta Regional Commission, the regional planning agency for the area. This allows the state Environmental Protection agency to evaluate how these

  14. Mathematical modeling of hot air/electrohydrodynamic (EHD) drying kinetics of mushroom slices

    International Nuclear Information System (INIS)

    Taghian Dinani, Somayeh; Hamdami, Nasser; Shahedi, Mohammad; Havet, Michel

    2014-01-01

    Highlights: • Hot air/EHD drying behavior of thin layer mushroom slices was evaluated. • A new empirical model was proposed for drying kinetics modeling of mushroom slices. • The new model presents excellent predictions for hot air/EHD drying of mushroom. - Abstract: Researches about mathematical modeling of electrohydrodynamic (EHD) drying are rare. In this study, hot air combined with electrohydrodynamic (EHD) drying behavior of thin layer mushroom slices was evaluated in a laboratory scale dryer at voltages of 17, 19, and 21 kV and electrode gaps of 5, 6, and 7 cm. The drying curves were fitted to ten different mathematical models (Newton, Page, Modified Page, Henderson and Pabis, Logarithmic, Two-term exponential, Midilli and Kucuk, Wang and Singh, Weibull and Parabolic models) and a proposed new empirical model to select a suitable drying equation for drying mushroom slices in a hot air combined with EHD dryer. Coefficients of the models were determined by non-linear regression analysis and the models were compared based on their coefficient of determination (R 2 ), sum of square errors (SSE) and root mean square error (RMSE) between experimental and predicted moisture ratios. According to the results, the proposed model that contains only three parameters provided the best fit with the experimental data. It was closely followed by the Midilli and Kucuk model that contains four parameters. Therefore, the proposed model can present comfortable usage and excellent predictions for the moisture content changes of mushroom slices in the hot air combined with EHD drying system

  15. 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.

  16. 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

  17. Modelling the predictive performance of credit scoring

    Directory of Open Access Journals (Sweden)

    Shi-Wei Shen

    2013-07-01

    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.

  18. 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.

  19. Prediction of traffic-related nitrogen oxides concentrations using Structural Time-Series models

    Science.gov (United States)

    Lawson, Anneka Ruth; Ghosh, Bidisha; Broderick, Brian

    2011-09-01

    Ambient air quality monitoring, modeling and compliance to the standards set by European Union (EU) directives and World Health Organization (WHO) guidelines are required to ensure the protection of human and environmental health. Congested urban areas are most susceptible to traffic-related air pollution which is the most problematic source of air pollution in Ireland. Long-term continuous real-time monitoring of ambient air quality at such urban centers is essential but often not realistic due to financial and operational constraints. Hence, the development of a resource-conservative ambient air quality monitoring technique is essential to ensure compliance with the threshold values set by the standards. As an intelligent and advanced statistical methodology, a Structural Time Series (STS) based approach has been introduced in this paper to develop a parsimonious and computationally simple air quality model. In STS methodology, the different components of a time-series dataset such as the trend, seasonal, cyclical and calendar variations can be modeled separately. To test the effectiveness of the proposed modeling strategy, average hourly concentrations of nitrogen dioxide and nitrogen oxides from a congested urban arterial in Dublin city center were modeled using STS methodology. The prediction error estimates from the developed air quality model indicate that the STS model can be a useful tool in predicting nitrogen dioxide and nitrogen oxides concentrations in urban areas and will be particularly useful in situations where the information on external variables such as meteorology or traffic volume is not available.

  20. 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.

  1. Model Predictive Control of Sewer Networks

    DEFF Research Database (Denmark)

    Pedersen, Einar B.; Herbertsson, Hannes R.; Niemann, Henrik

    2016-01-01

    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 cont...... benchmark model. Due to the inherent constraints the applied approach is based on Model Predictive Control....... and controlled have thus become essential factors for efficient 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...

  2. Bayesian Predictive Models for Rayleigh Wind Speed

    DEFF Research Database (Denmark)

    Shahirinia, Amir; Hajizadeh, Amin; Yu, David C

    2017-01-01

    predictive model of the wind speed aggregates the non-homogeneous distributions into a single continuous distribution. Therefore, the result is able to capture the variation among the probability distributions of the wind speeds at the turbines’ locations in a wind farm. More specifically, instead of using...... a wind speed distribution whose parameters are known or estimated, the parameters are considered as random whose variations are according to probability distributions. The Bayesian predictive model for a Rayleigh which only has a single model scale parameter has been proposed. Also closed-form posterior......One of the major challenges with the increase in wind power generation is the uncertain nature of wind speed. So far the uncertainty about wind speed has been presented through probability distributions. Also the existing models that consider the uncertainty of the wind speed primarily view...

  3. Comparison of two ordinal prediction models

    DEFF Research Database (Denmark)

    Kattan, Michael W; Gerds, Thomas A

    2015-01-01

    system (i.e. old or new), such as the level of evidence for one or more factors included in the system or the general opinions of expert clinicians. However, given the major objective of estimating prognosis on an ordinal scale, we argue that the rival staging system candidates should be compared...... on their ability to predict outcome. We sought to outline an algorithm that would compare two rival ordinal systems on their predictive ability. RESULTS: We devised an algorithm based largely on the concordance index, which is appropriate for comparing two models in their ability to rank observations. We...... demonstrate our algorithm with a prostate cancer staging system example. CONCLUSION: We have provided an algorithm for selecting the preferred staging system based on prognostic accuracy. It appears to be useful for the purpose of selecting between two ordinal prediction models....

  4. Predictive analytics can support the ACO model.

    Science.gov (United States)

    Bradley, Paul

    2012-04-01

    Predictive analytics can be used to rapidly spot hard-to-identify opportunities to better manage care--a key tool in accountable care. When considering analytics models, healthcare providers should: Make value-based care a priority and act on information from analytics models. Create a road map that includes achievable steps, rather than major endeavors. Set long-term expectations and recognize that the effectiveness of an analytics program takes time, unlike revenue cycle initiatives that may show a quick return.

  5. Predictive modeling in homogeneous catalysis: a tutorial

    NARCIS (Netherlands)

    Maldonado, A.G.; Rothenberg, G.

    2010-01-01

    Predictive modeling has become a practical research tool in homogeneous catalysis. It can help to pinpoint ‘good regions’ in the catalyst space, narrowing the search for the optimal catalyst for a given reaction. Just like any other new idea, in silico catalyst optimization is accepted by some

  6. Model predictive control of smart microgrids

    DEFF Research Database (Denmark)

    Hu, Jiefeng; Zhu, Jianguo; Guerrero, Josep M.

    2014-01-01

    required to realise high-performance of distributed generations and will realise innovative control techniques utilising model predictive control (MPC) to assist in coordinating the plethora of generation and load combinations, thus enable the effective exploitation of the clean renewable energy sources...

  7. Feedback model predictive control by randomized algorithms

    NARCIS (Netherlands)

    Batina, Ivo; Stoorvogel, Antonie Arij; Weiland, Siep

    2001-01-01

    In this paper we present a further development of an algorithm for stochastic disturbance rejection in model predictive control with input constraints based on randomized algorithms. The algorithm presented in our work can solve the problem of stochastic disturbance rejection approximately but with

  8. A Robustly Stabilizing Model Predictive Control Algorithm

    Science.gov (United States)

    Ackmece, A. Behcet; Carson, John M., III

    2007-01-01

    A model predictive control (MPC) algorithm that differs from prior MPC algorithms has been developed for controlling an uncertain nonlinear system. This algorithm guarantees the resolvability of an associated finite-horizon optimal-control problem in a receding-horizon implementation.

  9. 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...

  10. 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 ...

  11. Seasonal Prediction of Regional Surface Air Temperature and First-flowering Date in South Korea using Dynamical Downscaling

    Science.gov (United States)

    Ahn, J. B.; Hur, J.

    2015-12-01

    The seasonal prediction of both the surface air temperature and the first-flowering date (FFD) over South Korea are produced using dynamical downscaling (Hur and Ahn, 2015). Dynamical downscaling is performed using Weather Research and Forecast (WRF) v3.0 with the lateral forcing from hourly outputs of Pusan National University (PNU) coupled general circulation model (CGCM) v1.1. Gridded surface air temperature data with high spatial (3km) and temporal (daily) resolution are obtained using the physically-based dynamical models. To reduce systematic bias, simple statistical correction method is then applied to the model output. The FFDs of cherry, peach and pear in South Korea are predicted for the decade of 1999-2008 by applying the corrected daily temperature predictions to the phenological thermal-time model. The WRF v3.0 results reflect the detailed topographical effect, despite having cold and warm biases for warm and cold seasons, respectively. After applying the correction, the mean temperature for early spring (February to April) well represents the general pattern of observation, while preserving the advantages of dynamical downscaling. The FFD predictabilities for the three species of trees are evaluated in terms of qualitative, quantitative and categorical estimations. Although FFDs derived from the corrected WRF results well predict the spatial distribution and the variation of observation, the prediction performance has no statistical significance or appropriate predictability. The approach used in the study may be helpful in obtaining detailed and useful information about FFD and regional temperature by accounting for physically-based atmospheric dynamics, although the seasonal predictability of flowering phenology is not high enough. Acknowledgements This work was carried out with the support of the Rural Development Administration Cooperative Research Program for Agriculture Science and Technology Development under Grant Project No. PJ009953 and

  12. Development of a distributed air pollutant dry deposition modeling framework

    Science.gov (United States)

    Satoshi Hirabayashi; Charles N. Kroll; David J. Nowak

    2012-01-01

    A distributed air pollutant dry deposition modeling systemwas developed with a geographic information system (GIS) to enhance the functionality of i-Tree Eco (i-Tree, 2011). With the developed system, temperature, leaf area index (LAI) and air pollutant concentration in a spatially distributed form can be estimated, and based on these and other input variables, dry...

  13. P161 Improved Impact of Atmospheric Infrared Sounder (AIRS) Radiance Assimilation in Numerical Weather Prediction

    Science.gov (United States)

    Zavodsky, Bradley T.; Chou, Shih-Hung; Jedlovec, Gary J.

    2012-01-01

    For over 6 years, AIRS radiances have been assimilated operationally into National (e.g. Environmental Modeling Center (EMC)) and International (e.g. European Centre for Medium-Range Weather Forecasts (ECMWF)), operational centers; assimilated in the North American Mesoscale (NAM) since 2008. Due partly to data latency and operational constraints, hyperspectral radiance assimilation has had less impact on the Gridpoint Statistical Interpolation (GSI) system used in the NAM and GFS. Objective of this project is to use AIRS retrieved profiles as a proxy for the AIRS radiances in situations where AIRS radiances are unable to be assimilated in the current operational system by evaluating location and magnitude of analysis increments.

  14. 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

  15. 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)

  16. 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)

  17. Prediction of outdoor air concentrations and implied exposure of 1,3-dichloro-propene following its agricultural use as a soil fumigant

    Energy Technology Data Exchange (ETDEWEB)

    Houtman, B.A.; Knuteson, J.A. [DowElanco, Indianapolis, IN (United States)] [and others

    1996-10-01

    Off-field air concentrations of 1,3-Dichloro-propene associated with the agricultural use of TELONE II soil fumigant can be predicted by integrating the use of the Industrial Source Complex Short Term (ISCST) air dispersion model and a field-source strength term derived from aerodynamic field volatilization flux evaluations. The predicted air concentration distributions associated with single field and regional TELONE II soil fumigant use scenarios have been tested and validated with ambient air sampling under various environmental and edaphic conditions. An understanding of the temporal and spatial distributions of air concentrations allows refined exposure and risk estimates for populations that reside in areas of soil fumigation activity to be determined. In addition, the impact of various residential exposure mitigation measures can be evaluated.

  18. 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

  19. 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.

  20. 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...

  1. Air pollution exposure modeling of individuals

    Science.gov (United States)

    Air pollution epidemiology studies of ambient fine particulate matter (PM2.5) often use outdoor concentrations as exposure surrogates. These surrogates can induce exposure error since they do not account for (1) time spent indoors with ambient PM2.5 levels attenuated from outdoor...

  2. Occupant feedback based model predictive control for thermal comfort and energy optimization: A chamber experimental evaluation

    International Nuclear Information System (INIS)

    Chen, Xiao; Wang, Qian; Srebric, Jelena

    2016-01-01

    Highlights: • This study evaluates an occupant-feedback driven Model Predictive Controller (MPC). • The MPC adjusts indoor temperature based on a dynamic thermal sensation (DTS) model. • A chamber model for predicting chamber air temperature is developed and validated. • Experiments show that MPC using DTS performs better than using Predicted Mean Vote. - Abstract: In current centralized building climate control, occupants do not have much opportunity to intervene the automated control system. This study explores the benefit of using thermal comfort feedback from occupants in the model predictive control (MPC) design based on a novel dynamic thermal sensation (DTS) model. This DTS model based MPC was evaluated in chamber experiments. A hierarchical structure for thermal control was adopted in the chamber experiments. At the high level, an MPC controller calculates the optimal supply air temperature of the chamber heating, ventilation, and air conditioning (HVAC) system, using the feedback of occupants’ votes on thermal sensation. At the low level, the actual supply air temperature is controlled by the chiller/heater using a PI control to achieve the optimal set point. This DTS-based MPC was also compared to an MPC designed based on the Predicted Mean Vote (PMV) model for thermal sensation. The experiment results demonstrated that the DTS-based MPC using occupant feedback allows significant energy saving while maintaining occupant thermal comfort compared to the PMV-based MPC.

  3. Link Prediction via Sparse Gaussian Graphical Model

    Directory of Open Access Journals (Sweden)

    Liangliang Zhang

    2016-01-01

    Full Text Available Link prediction is an important task in complex network analysis. Traditional link prediction methods are limited by network topology and lack of node property information, which makes predicting links challenging. In this study, we address link prediction using a sparse Gaussian graphical model and demonstrate its theoretical and practical effectiveness. In theory, link prediction is executed by estimating the inverse covariance matrix of samples to overcome information limits. The proposed method was evaluated with four small and four large real-world datasets. The experimental results show that the area under the curve (AUC value obtained by the proposed method improved by an average of 3% and 12.5% compared to 13 mainstream similarity methods, respectively. This method outperforms the baseline method, and the prediction accuracy is superior to mainstream methods when using only 80% of the training set. The method also provides significantly higher AUC values when using only 60% in Dolphin and Taro datasets. Furthermore, the error rate of the proposed method demonstrates superior performance with all datasets compared to mainstream methods.

  4. 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. Copyright © 2015 Elsevier Ltd. All rights reserved.

  5. 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 Precipitation, which was found to be the most important variable for short-term air quality conditions, and air pressure are not important for long-term air quality. The lagged 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.

  6. 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.)

  7. 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.

  8. Genetic models of homosexuality: generating testable predictions

    Science.gov (United States)

    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 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. PMID:17015344

  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. 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

    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......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...... wave) shallow flow and two-phase flow in a porous medium. The simultaneous mass transfer between the soil, overland, and atmosphere compartments is achieved by upgrading a fully established leakance concept for overland-soil liquid exchange to an air exchange flux between soil and atmosphere. In a new...

  11. Predicting Cigarette Initiation and Re-Initiation Among Active Duty Air Force Recruits

    Science.gov (United States)

    2017-10-17

    REPORT TYPE 3. DATES COVERED (From- To) 10/17/2017 Abstract 4. TITLE AND SUBTITLE sa. CONTRACT NUMBER Predicting Cigarette Initiation and Re...NOTES Nicotine and Tobacco Research 14. ABSTRACT PREDICTING CIGARETTE INITIATION AND RE-INITIATION AMONG ACTIVE DUTY AIR FORCE RECRUITS Little, M.A...nonsmokers initiate once the ban is lifted. Understanding the factors associated with cigarette smoking initiation among non-users and re-initiation

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

    DEFF Research Database (Denmark)

    Sørensen, Karl Grau; Rode, Carsten

    2009-01-01

    phenomena that 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 a ventilated...... cavity such as behind the exterior cladding of a building envelope, 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 constructionplane. The paper presents the models and how they have......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...

  13. 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.

  14. 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)

  15. A statistical model for predicting muscle performance

    Science.gov (United States)

    Byerly, Diane Leslie De Caix

    The objective of these studies was to develop a capability for predicting muscle performance and fatigue to be utilized for both space- and ground-based applications. To develop this predictive model, healthy test subjects performed a defined, repetitive dynamic exercise to failure using a Lordex spinal machine. Throughout the exercise, surface electromyography (SEMG) data were collected from the erector spinae using a Mega Electronics ME3000 muscle tester and surface electrodes placed on both sides of the back muscle. These data were analyzed using a 5th order Autoregressive (AR) model and statistical regression analysis. It was determined that an AR derived parameter, the mean average magnitude of AR poles, significantly correlated with the maximum number of repetitions (designated Rmax) that a test subject was able to perform. Using the mean average magnitude of AR poles, a test subject's performance to failure could be predicted as early as the sixth repetition of the exercise. This predictive model has the potential to provide a basis for improving post-space flight recovery, monitoring muscle atrophy in astronauts and assessing the effectiveness of countermeasures, monitoring astronaut performance and fatigue during Extravehicular Activity (EVA) operations, providing pre-flight assessment of the ability of an EVA crewmember to perform a given task, improving the design of training protocols and simulations for strenuous International Space Station assembly EVA, and enabling EVA work task sequences to be planned enhancing astronaut performance and safety. Potential ground-based, medical applications of the predictive model include monitoring muscle deterioration and performance resulting from illness, establishing safety guidelines in the industry for repetitive tasks, monitoring the stages of rehabilitation for muscle-related injuries sustained in sports and accidents, and enhancing athletic performance through improved training protocols while reducing

  16. Prediction models : the right tool for the right problem

    NARCIS (Netherlands)

    Kappen, Teus H.; Peelen, Linda M.

    2016-01-01

    PURPOSE OF REVIEW: Perioperative prediction models can help to improve personalized patient care by providing individual risk predictions to both patients and providers. However, the scientific literature on prediction model development and validation can be quite technical and challenging to

  17. 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.

  18. 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.

  19. Reduced-form air quality modeling for community-scale ...

    Science.gov (United States)

    Transportation plays an important role in modern society, but its impact on air quality has been shown to have significant adverse effects on public health. Numerous reviews (HEI, CDC, WHO) summarizing findings of hundreds of studies conducted mainly in the last decade, conclude that exposures to traffic emissions near roads are a public health concern. The Community LINE Source Model (C-LINE) is a web-based model designed to inform the community user of local air quality impacts due to roadway vehicles in their region of interest using a simplified modeling approach. Reduced-form air quality modeling is a useful tool for examining what-if scenarios of changes in emissions, such as those due to changes in traffic volume, fleet mix, or vehicle speed. Examining various scenarios of air quality impacts in this way can identify potentially at-risk populations located near roadways, and the effects that a change in traffic activity may have on them. C-LINE computes dispersion of primary mobile source pollutants using meteorological conditions for the region of interest and computes air-quality concentrations corresponding to these selected conditions. C-LINE functionality has been expanded to model emissions from port-related activities (e.g. ships, trucks, cranes, etc.) in a reduced-form modeling system for local-scale near-port air quality analysis. This presentation describes the Community modeling tools C-LINE and C-PORT that are intended to be used by local gove

  20. Modeling for pollution dispersion and air quality 4.: the Gaussian model

    International Nuclear Information System (INIS)

    Bertagna, Silvia

    2005-01-01

    The Gaussian Model is the simulation model for atmospheric pollutant dispersion most used in practice, in particular for engineering applications; it has been the first model used in the United States to predict the impact of pollutant sources on air quality and for many years it has constituted the projecting instrument in environmental and territory planning; today it is still a very useful instrument, above all when the meteorological input data are not so abundant. In recent year, great efforts have been made to extend the original Gaussian model to different typologies of sources and to make it able to treat more detailed effects, as, for example, a complex terrain, the dry deposition, the gravity effect on heavy particulate matter and other microscale effects. In this work, the main characteristics of the Gaussian model and the equations which govern its description of the dispersion of air pollutants are discussed; moreover, the main used codices which implement Gaussian models which can be easily found in commerce or, sometimes, in the net, are briefly described [it

  1. 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.

  2. SAFARI 2000 Modeled Tropospheric Air Mass Trajectories, Dry Season 2000

    Data.gov (United States)

    National Aeronautics and Space Administration — The ETA Forecast Trajectory Model was used to produce forecasts of air-parcel trajectories twice a day at three pressure levels over seven sites in Southern Africa...

  3. SAFARI 2000 Modeled Tropospheric Air Mass Trajectories, Dry Season 2000

    Data.gov (United States)

    National Aeronautics and Space Administration — ABSTRACT: The ETA Forecast Trajectory Model was used to produce forecasts of air-parcel trajectories twice a day at three pressure levels over seven sites in...

  4. Development and application of air quality models at the US ...

    Science.gov (United States)

    Overview of the development and application of air quality models at the U.S. EPA, particularly focused on the development and application of the Community Multiscale Air Quality (CMAQ) model developed within the Computation Exposure Division (CED) of the National Exposure Research Laboratory (NERL). This presentation will provide a simple overview of air quality model development and application geared toward a non-technical student audience. The National Exposure Research Laboratory (NERL) Computational Exposure Division (CED) develops and evaluates data, decision-support tools, and models to be applied to media-specific or receptor-specific problem areas. CED uses modeling-based approaches to characterize exposures, evaluate fate and transport, and support environmental diagnostics/forensics with input from multiple data sources. It also develops media- and receptor-specific models, process models, and decision support tools for use both within and outside of EPA.

  5. Air quality model studies with application for southeastern Virginia

    Science.gov (United States)

    Brewer, D. A.; Remsberg, E. E.

    1980-01-01

    A single-cell photochemical air quality model incorporating (1) a published chemical mechanism, (2) advection, and (3) entrainment and emissions processes was constructed and compared with data from the EPA Regional Air Pollution Study. While agreement with measured CO and NO2 was established, O3 production was found to occur too rapidly and in excess. Calculated O3 levels improved when a 20% reduction in photolytic rate constants and a doubling of wind speed were applied. The results of the model sensitivity studies are being incorporated into the design and conduct of field measurement programs for the characterization of the vertical and horizontal homogeneity of an air quality region.

  6. Predictive Models for Carcinogenicity and Mutagenicity ...

    Science.gov (United States)

    Mutagenicity and carcinogenicity are endpoints of major environmental and regulatory concern. These endpoints are also important targets for development of alternative methods for screening and prediction due to the large number of chemicals of potential concern and the tremendous cost (in time, money, animals) of rodent carcinogenicity bioassays. Both mutagenicity and carcinogenicity involve complex, cellular processes that are only partially understood. Advances in technologies and generation of new data will permit a much deeper understanding. In silico methods for predicting mutagenicity and rodent carcinogenicity based on chemical structural features, along with current mutagenicity and carcinogenicity data sets, have performed well for local prediction (i.e., within specific chemical classes), but are less successful for global prediction (i.e., for a broad range of chemicals). The predictivity of in silico methods can be improved by improving the quality of the data base and endpoints used for modelling. In particular, in vitro assays for clastogenicity need to be improved to reduce false positives (relative to rodent carcinogenicity) and to detect compounds that do not interact directly with DNA or have epigenetic activities. New assays emerging to complement or replace some of the standard assays include VitotoxTM, GreenScreenGC, and RadarScreen. The needs of industry and regulators to assess thousands of compounds necessitate the development of high-t

  7. Evaluation of the meteorological forcing used for the Air Quality Model Evaluation International Initiative (AQMEII) air quality simulations

    NARCIS (Netherlands)

    Vautard, R.; Moran, M.D.; Solazzo, E.; Gilliam, R.C.; Matthias, V.; Bianconi, R.; Chemel, C.; Ferreira, J.; Geyer, B.; Hansen, A.B.; Jericevic, A.; Prank, M.; Segers, A.; Silver, J.D.; Werhahn, J.; Wolke, R.; Rao, S.T.; Galmarini, S.

    2012-01-01

    Accurate regional air pollution simulation relies strongly on the accuracy of the mesoscale meteorological simulation used to drive the air quality model. The framework of the Air Quality Model Evaluation International Initiative (AQMEII), which involved a large international community of modeling

  8. Evaluation of the meteorological forcing used the Air Quality Model Evaluation International Initiative (AQMEII) air quality simulations

    NARCIS (Netherlands)

    Segers, A.J.; Vautard, R.; Moran, M.D.; Solazzo, E.; Gilliam, R.C.; Matthias, V.; Bianconi, R.; Chemel, C.; Ferreira, J.; Geyer, B.; Hansen, A.B.; Jericevic, A.; Prank, M.; Silver, J.D.; Werhahn, J.; Wolke, R.; Rao, S.T.; Galmarini, S.

    2011-01-01

    Accurate regional air pollution simulation relies strongly on the accuracy of the mesoscale meteorological simulation used to drive the air quality model. The framework of the Air Quality Model Evaluation International Initiative (AQMEII), which involved a large international community of modeling

  9. A simple approach to the prediction of waterhammer transients in a pipe line with entrapped air

    International Nuclear Information System (INIS)

    Epstein, Michael

    2008-01-01

    The pressure histories within entrapped air bubbles in a pipe line during a waterhammer transient are treated theoretically. A convenient integral method is introduced, which takes full account of air/water interface movement and liquid compressibility. The significance of the method is that it provides a simple equation set for approximating, with good accuracy and with a small degree of conservatism, the solution to a problem that otherwise involves coupled partial differential equations on time dependent domains with non-linear boundary conditions. The accuracy of the method is defined by its comparison with available numerical-solution-predictions and measurements of the pressure within an entrapped-air-bubble at a dead end in a pipe. The method is shown to be a computationally simple and efficient way of assessing the impact of liquid compressibility on pressure rise when multiple water columns and air pockets are present in a pipe line

  10. 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

  11. Analytical orbit predictions with air drag using K-S uniformly regular canonical elements

    Science.gov (United States)

    Xavier James Raj, M.; Sharma, R. K.

    Accurate orbit prediction of the Earth's satellites is an important requirement for mission planning, satellite geodesy, spacecraft navigation, re-entry and orbital lifetime estimates. For this purpose, it has become necessary to use extremely complex force models to match with the present operational requirements and observational techniques. The problem becomes all the more complicated in the near-Earth environment due to the fact that the satellite is influenced by the non-spherical effects of the Earth's gravitational field as well as the dissipative effects of the Earth's atmosphere. The effects of the atmosphere are difficult to determine since the atmospheric density, and hence the drag, undergoes large modelled fluctuations. Though the accurate ephemeris of a near-Earth satellite can be generated by the numerical integration methods with respect to a complex force model, the analytical solutions, though difficult to obtain for complex force models and limited to relatively simple models, represent a manifold of solutions for a large domain of initial conditions and find indispensable application to mission planning and qualitative analysis. The method of the K-S total-energy element equations (Stiefel & Scheifele, 1971) is a powerful method for numerical solution with respect to any type of perturbing forces, as the equations are less sensitive to round-off and truncation errors in the numerical algorithm. The equations are everywhere regular in contrast with the classical Newtonian equations, which are singular at the collision of the two bodies. The equations are smoothed for eccentric orbits because eccentric anomaly is the independent variable. These equations have been used effectively to generate analytical solution with respect to Earth's zonal harmonic term J2 (Sharma 1997) and air drag perturbations (Sharma 1992). A particular canonical form of the K-S differential equations, known as K-S uniform regular canonical equations, where all the ten

  12. 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

  13. 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...

  14. A predictive model for dimensional errors in fused deposition modeling

    DEFF Research Database (Denmark)

    Stolfi, A.

    2015-01-01

    values of L (0.254 mm, 0.330 mm) was produced by comparing predicted values with external face-to-face measurements. After removing outliers, the results show that the developed two-parameter model can serve as tool for modeling the FDM dimensional behavior in a wide range of deposition angles....

  15. 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...... values of L (0.254 mm, 0.330 mm) was produced by comparing predicted values with external face-to-face measurements. After removing outliers, the results show that the developed two-parameter model can serve as tool for modeling the FDM dimensional behavior in a wide range of deposition angles....

  16. 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.

  17. Predictive modelling of evidence informed teaching

    OpenAIRE

    Zhang, Dell; Brown, C.

    2017-01-01

    In this paper, we analyse the questionnaire survey data collected from 79 English primary schools about the situation of evidence informed teaching, where the evidences could come from research journals or conferences. Specifically, we build a predictive model to see what external factors could help to close the gap between teachers’ belief and behaviour in evidence informed teaching, which is the first of its kind to our knowledge. The major challenge, from the data mining perspective, is th...

  18. A Predictive Model for Cognitive Radio

    Science.gov (United States)

    2006-09-14

    response in a given situation. Vadde et al. interest and produce a model for prediction of the response. have applied response surface methodology and...34 2000. [3] K. K. Vadde and V. R. Syrotiuk, "Factor interaction on service configurations to those that best meet our communication delivery in mobile ad...resulting set of configurations randomly or apply additional 2004. screening criteria. [4] K. K. Vadde , M.-V. R. Syrotiuk, and D. C. Montgomery

  19. Tectonic predictions with mantle convection models

    Science.gov (United States)

    Coltice, Nicolas; Shephard, Grace E.

    2018-04-01

    Over the past 15 yr, numerical models of convection in Earth's mantle have made a leap forward: they can now produce self-consistent plate-like behaviour at the surface together with deep mantle circulation. These digital tools provide a new window into the intimate connections between plate tectonics and mantle dynamics, and can therefore be used for tectonic predictions, in principle. This contribution explores this assumption. First, initial conditions at 30, 20, 10 and 0 Ma are generated by driving a convective flow with imposed plate velocities at the surface. We then compute instantaneous mantle flows in response to the guessed temperature fields without imposing any boundary conditions. Plate boundaries self-consistently emerge at correct locations with respect to reconstructions, except for small plates close to subduction zones. As already observed for other types of instantaneous flow calculations, the structure of the top boundary layer and upper-mantle slab is the dominant character that leads to accurate predictions of surface velocities. Perturbations of the rheological parameters have little impact on the resulting surface velocities. We then compute fully dynamic model evolution from 30 and 10 to 0 Ma, without imposing plate boundaries or plate velocities. Contrary to instantaneous calculations, errors in kinematic predictions are substantial, although the plate layout and kinematics in several areas remain consistent with the expectations for the Earth. For these calculations, varying the rheological parameters makes a difference for plate boundary evolution. Also, identified errors in initial conditions contribute to first-order kinematic errors. This experiment shows that the tectonic predictions of dynamic models over 10 My are highly sensitive to uncertainties of rheological parameters and initial temperature field in comparison to instantaneous flow calculations. Indeed, the initial conditions and the rheological parameters can be good enough

  20. 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.

  1. Modeling air-quality in complex terrain using mesoscale and ...

    African Journals Online (AJOL)

    Air-quality in a complex terrain (Colorado-River-Valley/Grand-Canyon Area, Southwest U.S.) is modeled using a higher-order closure mesoscale model and a higher-order closure dispersion model. Non-reactive tracers have been released in the Colorado-River valley, during winter and summer 1992, to study the ...

  2. Modeling Air-Quality in Complex Terrain Using Mesoscale and ...

    African Journals Online (AJOL)

    Air-quality in a complex terrain (Colorado-River-Valley/Grand-Canyon Area, Southwest U.S.) is modeled using a higher-order closure mesoscale model and a higher-order closure dispersion model. Non-reactive tracers have been released in the Colorado-River valley, during winter and summer 1992, to study the ...

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

    DEFF Research Database (Denmark)

    Plejdrup, Marlene Schmidt; Gyldenkærne, Steen

    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......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...

  4. When relationships estimated in the past cannot be used to predict the future: using mechanistic models to predict landscape ecological dynamics in a changing world

    Science.gov (United States)

    Eric J. Gustafson

    2013-01-01

    Researchers and natural resource managers need predictions of how multiple global changes (e.g., climate change, rising levels of air pollutants, exotic invasions) will affect landscape composition and ecosystem function. Ecological predictive models used for this purpose are constructed using either a mechanistic (process-based) or a phenomenological (empirical)...

  5. Predictive Modeling of the CDRA 4BMS

    Science.gov (United States)

    Coker, Robert F.; Knox, James C.

    2016-01-01

    As part of NASA's Advanced Exploration Systems (AES) program and the Life Support Systems Project (LSSP), fully predictive models of the Four Bed Molecular Sieve (4BMS) of the Carbon Dioxide Removal Assembly (CDRA) on the International Space Station (ISS) 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.

  6. Inverse modelling of air quality data through a neural network approach

    Science.gov (United States)

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

    2009-04-01

    Air quality is usually driven by a complex combination of factors where meteorology, physical obstacles and interaction between pollutants play significant roles. Considering the characteristics of the atmospheric circulation and also the residence times of certain pollutants in the atmosphere, air pollution is, nowadays, considered to be a global problem that affects everyone. As a result, 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. 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 neural network modelling. In this paper, we describe the development of a neural network tool to forecast the daily average NO2 concentrations in Lisbon, Portugal, one day ahead. This research is based upon measurements from 22 air quality monitoring stations during the period 2001-2005. The analysis revealed that the most significant variable in predicting NO2 daily concentration is the previous day value of NO2 concentration followed by the 5a.m. NO2 concentration. This approach shows to be very promising for urban air quality characterization, allowing further developments in order to produce an integrated air quality and health surveillance/monitoring system in the area of Lisbon.

  7. Integrating a human thermoregulatory model with a clothing model to predict core and skin temperatures.

    Science.gov (United States)

    Yang, Jie; Weng, Wenguo; Wang, Faming; Song, Guowen

    2017-05-01

    This paper aims to integrate a human thermoregulatory model with a clothing model to predict core and skin temperatures. The human thermoregulatory model, consisting of an active system and a passive system, was used to determine the thermoregulation and heat exchanges within the body. The clothing model simulated heat and moisture transfer from the human skin to the environment through the microenvironment and fabric. In this clothing model, the air gap between skin and clothing, as well as clothing properties such as thickness, thermal conductivity, density, porosity, and tortuosity were taken into consideration. The simulated core and mean skin temperatures were compared to the published experimental results of subject tests at three levels of ambient temperatures of 20 °C, 30 °C, and 40 °C. Although lower signal-to-noise-ratio was observed, the developed model demonstrated positive performance at predicting core temperatures with a maximum difference between the simulations and measurements of no more than 0.43 °C. Generally, the current model predicted the mean skin temperatures with reasonable accuracy. It could be applied to predict human physiological responses and assess thermal comfort and heat stress. Copyright © 2017 Elsevier Ltd. All rights reserved.

  8. Review of air quality modeling techniques. Volume 8

    International Nuclear Information System (INIS)

    Rosen, L.C.

    1977-01-01

    Air transport and diffusion models which are applicable to the assessment of the environmental effects of nuclear, geothermal, and fossil-fuel electric generation are reviewed. The general classification of models and model inputs are discussed. A detailed examination of the statistical, Gaussian plume, Gaussian puff, one-box and species-conservation-of-mass models is given. Representative models are discussed with attention given to the assumptions, input data requirement, advantages, disadvantages and applicability of each

  9. An evaluation of air quality modeling over the Pearl River Delta during November 2006

    Science.gov (United States)

    Wu, Qizhong; Wang, Zifa; Chen, Huansheng; Zhou, Wen; Wenig, Mark

    2012-05-01

    In this paper, we evaluate the performance of several air quality models using the Pearl River Delta (PRD) region, including the Nested Air Quality Prediction Modeling System (NAQPMS), the Community Multiscale Air Quality (CMAQ) model, and the Comprehensive Air Quality Model with extensions (CAMx). All three model runs are based on the same meteorological fields generated by the Fifth-Generation Pennsylvania State University/National Center for Atmospheric Research (PSU/NCAR) Mesoscale Model (MM5) and the same emission inventories. The emission data are processed by the Sparse Matrix Operator Kernel Emissions (SMOKE) model, with the inventories generated from the Transport and Chemical Evolution over the Pacific/Intercontinental Chemical Transport Experiment Phase B (TRACE-P/INTEX-B) and local emission inventory data. The results show that: (1) the meteorological simulation of the MM5 model is reasonable compared with the observations at the regional background and urban stations. (2) The models have different advantages at different stations. The CAMx model has the best performance for SO2 simulation, with the lowest mean normalized bias (MNB) and mean normalized error (MNE) at most of the Guangzhou stations, while the CMAQ model has the lowest normalized mean square error (NMSE) value for SO2 simulation at most of the other PRD urban stations. The NAQPMS model has the best performance in the NO2 simulation at most of the Guangzhou stations. (3) The model performance at the Guangzhou stations is better than that at the other stations, and the emissions may be underestimated in the other PRD cities. (4) The PM10 simulation has the best model measures of FAC2 (fraction of predictions within a factor of two of the observations) (average 53-56%) and NMSE (0.904-1.015), while the SO2 simulation has the best concentration distribution compared with the observations, according to the quantile-quantile (Q-Q) plots.

  10. A desiccant-enhanced evaporative air conditioner: Numerical model and experiments

    International Nuclear Information System (INIS)

    Woods, Jason; Kozubal, Eric

    2013-01-01

    Highlights: ► We studied a new process combining liquid desiccants and evaporative cooling. ► We modeled the process using a finite-difference numerical model. ► We measured the performance of the process with experimental prototypes. ► Results show agreement between model and experiment of ±10%. ► Results add confidence to previous modeled energy savings estimates of 40–85%. - Abstract: This article presents modeling and experimental results on a recently proposed liquid desiccant air conditioner, which consists of two stages: a liquid desiccant dehumidifier and an indirect evaporative cooler. Each stage is a stack of channel pairs, where a channel pair is a process air channel separated from an exhaust air channel with a thin plastic plate. In the first stage, a liquid desiccant film, which lines the process air channels, removes moisture from the air through a porous hydrophobic membrane. An evaporating water film wets the surface of the exhaust channels and transfers the enthalpy of vaporization from the liquid desiccant into an exhaust airstream, cooling the desiccant and enabling lower outlet humidity. The second stage is a counterflow indirect evaporative cooler that siphons off and uses a portion of the cool-dry air exiting the second stage as the evaporative sink. The objectives of this article are to (1) present fluid-thermal numerical models for each stage, (2) present experimental results of prototypes for each stage, and (3) compare the modeled and experimental results. Several experiments were performed on the prototypes over a range of inlet temperatures and humidities, process and exhaust air flow rates, and desiccant concentrations and flow rates. The model predicts the experiments within ±10%.

  11. 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

    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......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...... agrees well with quality field studies in non-air-conditioned buildings of three continents....

  12. Trends of air pollution in Denmark - Normalised by a simple weather index model

    International Nuclear Information System (INIS)

    Kiilsholm, S.; Rasmussen, A.

    2000-01-01

    station at Kastrup Airport just outside Copenhagen. The mixing height was calculated using a bulk Richardson method on vertical profiles provided by the Numerical Weather Prediction model DMI-HIRLAM (Danish Meteorological Institute - High Resolution Limited Area Model). The model in general gives a good explanation of variations from year to year in the air quality. (au)

  13. Predictive Modeling by the Cerebellum Improves Proprioception

    Science.gov (United States)

    Bhanpuri, Nasir H.; Okamura, Allison M.

    2013-01-01

    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. PMID:24005283

  14. The ASAC Air Carrier Investment Model (Third Generation)

    Science.gov (United States)

    Wingrove, Earl R., III; Gaier, Eric M.; Santmire, Tara E.

    1998-01-01

    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 model 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 flat are envisioned for ASAC.

  15. Prediction of Particle Concentration using Traffic Emission Model

    Science.gov (United States)

    He, Hong-di; Lu, Jane Wei-zhen

    2010-05-01

    Vehicle emission is regarded as one of major sources of air pollution in urban area. Much attention has been addressed on it especially at traffic intersection. At intersection, vehicles frequently stop with idling engine during the red time and speed-up rapidly in the green time, which result in a high velocity fluctuation and produce extra pollutants to the surrounding air. To deeply understand such process, a semi-empirical model for predicting the changing effect of traffic flow patterns on particulate concentrations is proposed. The performance of the model is evaluated using the correlation coefficient and other parameters. From the results, the correlation coefficients in morning and afternoon data were found to be 0.86 an 0.73 respectively, which implies that the semi-empirical model for morning and afternoon data are 86% and 73% error free. Due to less affected by possible factors such as traffic volume and movement of pedestrian, the dispersion of the particulate matter in the morning is smaller and then contributes to higher performance than that in the afternoon.

  16. Prediction of Chemical Function: Model Development and ...

    Science.gov (United States)

    The United States Environmental Protection Agency’s Exposure Forecaster (ExpoCast) project is developing both statistical and mechanism-based computational models for predicting exposures to thousands of chemicals, including those in consumer products. The high-throughput (HT) screening-level exposures developed under ExpoCast can be combined with HT screening (HTS) bioactivity data for the risk-based prioritization of chemicals for further evaluation. The functional role (e.g. solvent, plasticizer, fragrance) that a chemical performs can drive both the types of products in which it is found and the concentration in which it is present and therefore impacting exposure potential. However, critical chemical use information (including functional role) is lacking for the majority of commercial chemicals for which exposure estimates are needed. A suite of machine-learning based models for classifying chemicals in terms of their likely functional roles in products based on structure were developed. This effort required collection, curation, and harmonization of publically-available data sources of chemical functional use information from government and industry bodies. Physicochemical and structure descriptor data were generated for chemicals with function data. Machine-learning classifier models for function were then built in a cross-validated manner from the descriptor/function data using the method of random forests. The models were applied to: 1) predict chemi

  17. 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...

  18. 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.

  19. 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.

  20. Evaluating predictive models of software quality

    International Nuclear Information System (INIS)

    Ciaschini, V; Canaparo, M; Ronchieri, E; Salomoni, D

    2014-01-01

    Applications from High Energy Physics scientific community are constantly growing and implemented by a large number of developers. This implies a strong churn on the code and an associated risk of faults, which is unavoidable as long as the software undergoes active evolution. However, the necessities of production systems run counter to this. Stability and predictability are of paramount importance; in addition, a short turn-around time for the defect discovery-correction-deployment cycle is required. A way to reconcile these opposite foci is to use a software quality model to obtain an approximation of the risk before releasing a program to only deliver software with a risk lower than an agreed threshold. In this article we evaluated two quality predictive models to identify the operational risk and the quality of some software products. We applied these models to the development history of several EMI packages with intent to discover the risk factor of each product and compare it with its real history. We attempted to determine if the models reasonably maps reality for the applications under evaluation, and finally we concluded suggesting directions for further studies.

  1. 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

  2. PREDICTION MODELS OF GRAIN YIELD AND CHARACTERIZATION

    Directory of Open Access Journals (Sweden)

    Narciso Ysac Avila Serrano

    2009-06-01

    Full Text Available With the objective to characterize the grain yield of five cowpea cultivars and to find linear regression models to predict it, a study was developed in La Paz, Baja California Sur, Mexico. A complete randomized blocks design was used. Simple and multivariate analyses of variance were carried out using the canonical variables to characterize the cultivars. The variables cluster per plant, pods per plant, pods per cluster, seeds weight per plant, seeds hectoliter weight, 100-seed weight, seeds length, seeds wide, seeds thickness, pods length, pods wide, pods weight, seeds per pods, and seeds weight per pods, showed significant differences (P≤ 0.05 among cultivars. Paceño and IT90K-277-2 cultivars showed the higher seeds weight per plant. The linear regression models showed correlation coefficients ≥0.92. In these models, the seeds weight per plant, pods per cluster, pods per plant, cluster per plant and pods length showed significant correlations (P≤ 0.05. In conclusion, the results showed that grain yield differ among cultivars and for its estimation, the prediction models showed determination coefficients highly dependable.

  3. Chloroform in indoor swimming-pool air: monitoring and modeling coupled with the effects of environmental conditions and occupant activities.

    Science.gov (United States)

    Hsu, H T; Chen, M J; Lin, C H; Chou, W S; Chen, J H

    2009-08-01

    Human exposure to chloroform in indoor swimming pools has been recognized as a potential health concern. Although environmental monitoring is a useful technique to investigate chloroform concentrations in indoor swimming-pool air, in practice, the interpretations of measured data would inevitably run into difficulties due to the complex interactions among the numerous variables, including environmental conditions and occupant activities. Considering of the relevant variables of environmental conditions and occupant activities, a mathematical model was first proposed to predict the chloroform concentration in indoor swimming-pool air. The developed model provides a straightforward, conceptually simple way to predict the indoor air chloroform concentration by calculating the mass flux, J, and the Péclet number, Pe, and by using a heuristic value of the indoor airflow recycle ratio, R. The good agreement between model simulation and measured data demonstrates the feasibility of using the presented model for indoor air quality management, operational guidelines and health-related risk assessment.

  4. Modelling and design of a novel air-spring for a suspension seat

    Science.gov (United States)

    Holtz, Marco W.; van Niekerk, Johannes L.

    2010-10-01

    Air-springs used in conjunction with auxiliary volumes provide both spring stiffness and damping. The damping is introduced through the flow restriction connecting the two air volumes. This article presents a simplified model of an air-spring with an auxiliary volume derived from first principles for simulation and design of an air-spring coupled to an auxiliary volume for a suspension seat. Tests were performed on an experimental apparatus to validate the model. The simulation model of the air-spring and auxiliary volume followed the trend predicted by the literature but showed approximately 27% lower transmissibility amplitude and 21% lower system natural frequency than that obtained by tests when using large diameter flow restrictions. This inaccuracy is assumed to be introduced by the simplified mass transfer equations defining the flow restriction between air-spring and auxiliary volume. The model showed closer correlation to the experimental results when the auxiliary volume size was decreased by two-thirds of the volume actually used for the experiment. A procedure, using the developed simulation model, for the design of a prototype air-spring and auxiliary volume, is presented for application in a typical articulated or rigid frame dump truck. The goal of the study was to design a suspension seat for this application and to obtain a SEAT value below 1.1. The design was optimised by varying auxiliary volume size and flow restriction diameters for different loads. A SEAT value of less than 0.9 was achieved, clearly indicating the effectiveness of using an auxiliary volume with an air-spring as seat suspension.

  5. USAF Enlisted Air Traffic Controller Selection: Examination of the Predictive Validity of the FAA Air Traffic Selection and Training Battery versus Training Performance

    National Research Council Canada - National Science Library

    Carretta, Thomas R; King, Raymond E

    2008-01-01

    .... The current study examined the utility of the FAA Air Traffic Selection and Training (AT-SAT) battery for incrementing the predictiveness of the ASVAB versus several enlisted ATC training criteria...

  6. Predictive Models for Normal Fetal Cardiac Structures.

    Science.gov (United States)

    Krishnan, Anita; Pike, Jodi I; McCarter, Robert; Fulgium, Amanda L; Wilson, Emmanuel; Donofrio, Mary T; Sable, Craig A

    2016-12-01

    Clinicians rely on age- and size-specific measures of cardiac structures to diagnose cardiac disease. No universally accepted normative data exist for fetal cardiac structures, and most fetal cardiac centers do not use the same standards. The aim of this study was to derive predictive models for Z scores for 13 commonly evaluated fetal cardiac structures using a large heterogeneous population of fetuses without structural cardiac defects. The study used archived normal fetal echocardiograms in representative fetuses aged 12 to 39 weeks. Thirteen cardiac dimensions were remeasured by a blinded echocardiographer from digitally stored clips. Studies with inadequate imaging views were excluded. Regression models were developed to relate each dimension to estimated gestational age (EGA) by dates, biparietal diameter, femur length, and estimated fetal weight by the Hadlock formula. Dimension outcomes were transformed (e.g., using the logarithm or square root) as necessary to meet the normality assumption. Higher order terms, quadratic or cubic, were added as needed to improve model fit. Information criteria and adjusted R 2 values were used to guide final model selection. Each Z-score equation is based on measurements derived from 296 to 414 unique fetuses. EGA yielded the best predictive model for the majority of dimensions; adjusted R 2 values ranged from 0.72 to 0.893. However, each of the other highly correlated (r > 0.94) biometric parameters was an acceptable surrogate for EGA. In most cases, the best fitting model included squared and cubic terms to introduce curvilinearity. For each dimension, models based on EGA provided the best fit for determining normal measurements of fetal cardiac structures. Nevertheless, other biometric parameters, including femur length, biparietal diameter, and estimated fetal weight provided results that were nearly as good. Comprehensive Z-score results are available on the basis of highly predictive models derived from gestational

  7. Developing of a New Atmospheric Ionizing Radiation (AIR) Model

    Science.gov (United States)

    Clem, John M.; deAngelis, Giovanni; Goldhagen, Paul; Wilson, John W.

    2003-01-01

    As a result of the research leading to the 1998 AIR workshop and the subsequent analysis, the neutron issues posed by Foelsche et al. and further analyzed by Hajnal have been adequately resolved. We are now engaged in developing a new atmospheric ionizing radiation (AIR) model for use in epidemiological studies and air transportation safety assessment. A team was formed to examine a promising code using the basic FLUKA software but with modifications to allow multiple charged ion breakup effects. A limited dataset of the ER-2 measurements and other cosmic ray data will be used to evaluate the use of this code.

  8. An analytical model for climatic predictions

    International Nuclear Information System (INIS)

    Njau, E.C.

    1990-12-01

    A climatic model based upon analytical expressions is presented. This model is capable of making long-range predictions of heat energy variations on regional or global scales. These variations can then be transformed into corresponding variations of some other key climatic parameters since weather and climatic changes are basically driven by differential heating and cooling around the earth. On the basis of the mathematical expressions upon which the model is based, it is shown that the global heat energy structure (and hence the associated climatic system) are characterized by zonally as well as latitudinally propagating fluctuations at frequencies downward of 0.5 day -1 . We have calculated the propagation speeds for those particular frequencies that are well documented in the literature. The calculated speeds are in excellent agreement with the measured speeds. (author). 13 refs

  9. An Anisotropic Hardening Model for Springback Prediction

    International Nuclear Information System (INIS)

    Zeng, Danielle; Xia, Z. Cedric

    2005-01-01

    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

  10. A role of the Atlantic Ocean in predicting summer surface air temperature over North East Asia?

    Science.gov (United States)

    Monerie, Paul-Arthur; Robson, Jon; Dong, Buwen; Dunstone, Nick

    2017-10-01

    We assess the ability of the DePreSys3 prediction system to predict the summer (JJAS) surface-air temperature over North East Asia. DePreSys3 is based on a high resolution ocean-atmosphere coupled climate prediction system ( 60 km in the atmosphere and 25 km in the ocean), which is full-field initialized from 1960 to 2014 (26 start-dates). We find skill in predicting surface-air temperature, relative to a long-term trend, for 1 and 2-5 year lead-times over North East Asia, the North Atlantic Ocean and Eastern Europe. DePreSys3 also reproduces the interdecadal evolution of surface-air temperature over the North Atlantic subpolar gyre and North East Asia for both lead times, along with the strong warming that occurred in the mid-1990s over both areas. Composite analysis reveals that the skill at capturing interdecadal changes in North East Asia is associated with the propagation of an atmospheric Rossby wave, which follows the subtropical jet and modulates surface-air temperature from Europe to Eastern Asia. We hypothesise that this `circumglobal teleconnection' pattern is excited over the Atlantic Ocean and is related to Atlantic multi-decadal variability and the associated changes in precipitation over the Sahel and the subtropical Atlantic Ocean. This mechanism is robust for the 2-5 year lead-time. For the 1 year lead-time the Pacific Ocean also plays an important role in leading to skill in predicting SAT over Northeast Asia. Increased temperatures and precipitation over the western Pacific Ocean was found to be associated with a Pacific-Japan like-pattern, which can affect East Asia's climate.

  11. A demand model for domestic air travel in Sweden

    OpenAIRE

    Kopsch, Fredrik

    2011-01-01

     The aim of this study is to estimate the price elasticity of demand for domestic air travel in Sweden. Using national aggregated data on passenger quantities and fares, price elasticities of demand are estimated with an unbalanced, in terms of stationarity, yet well performing model. The analysis also includes estimates of cross-price elasticities for the main transport substitutes to air travel, rail and road. The robustness of the results is enforced by a primitive division of business and...

  12. Development of a model for radon concentration in indoor air

    International Nuclear Information System (INIS)

    Jelle, Bjørn Petter

    2012-01-01

    A model is developed for calculation of the radon concentration in indoor air. The model takes into account various important parameters, e.g. radon concentration in ground, radon diffusion resistance of radon barrier, air permeance of ground, air pressure difference between outdoor ground and indoor at ground level, ventilation of the building ground and number of air changes per hour due to ventilation. Characteristic case studies are depicted in selected 2D and 3D graphical plots for easy visualization and interpretation. The radon transport into buildings might be dominated by diffusion, pressure driven flow or a mixture of both depending on the actual values of the various parameters. The results of our work indicate that with realistic or typical values of the parameters, most of the transport of radon from the building ground to the indoor air is due to air leakage driven by pressure differences through the construction. By incorporation of various and realistic values in the radon model, valuable information about the miscellaneous parameters influencing the indoor radon level is gained. Hence, the presented radon model may be utilized as a simple yet versatile and powerful tool for examining which preventive or remedial measures should be carried out to achieve an indoor radon level below the reference level as set by the authorities. - Highlights: ► Model development for calculation of radon concentration in indoor air. ► Radon model accounting for various important parameters. ► Characteristic case studies depicted in 2D and 3D graphical plots. ► May be utilized for examining radon preventive measures.

  13. 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

  14. Fast predictive control for air-fuel ratio of SI engines using a ...

    African Journals Online (AJOL)

    In this paper MPC based on an adaptive neural network model is attempted for air fuel ratio (AFR), in which the model is adapted on-line to cope with nonlinear dynamics and parameter uncertainties. A radial basis function (RBF) network is employed and the recursive least squares (RLS) algorithm is used for weight ...

  15. Web tools for predictive toxicology model building.

    Science.gov (United States)

    Jeliazkova, Nina

    2012-07-01

    The development and use of web tools in chemistry has accumulated more than 15 years of history already. Powered by the advances in the Internet technologies, the current generation of web systems are starting to expand into areas, traditional for desktop applications. The web platforms integrate data storage, cheminformatics and data analysis tools. The ease of use and the collaborative potential of the web is compelling, despite the challenges. The topic of this review is a set of recently published web tools that facilitate predictive toxicology model building. The focus is on software platforms, offering web access to chemical structure-based methods, although some of the frameworks could also provide bioinformatics or hybrid data analysis functionalities. A number of historical and current developments are cited. In order to provide comparable assessment, the following characteristics are considered: support for workflows, descriptor calculations, visualization, modeling algorithms, data management and data sharing capabilities, availability of GUI or programmatic access and implementation details. The success of the Web is largely due to its highly decentralized, yet sufficiently interoperable model for information access. The expected future convergence between cheminformatics and bioinformatics databases provides new challenges toward management and analysis of large data sets. The web tools in predictive toxicology will likely continue to evolve toward the right mix of flexibility, performance, scalability, interoperability, sets of unique features offered, friendly user interfaces, programmatic access for advanced users, platform independence, results reproducibility, curation and crowdsourcing utilities, collaborative sharing and secure access.

  16. [Endometrial cancer: Predictive models and clinical impact].

    Science.gov (United States)

    Bendifallah, Sofiane; Ballester, Marcos; Daraï, Emile

    2017-12-01

    In France, in 2015, endometrial cancer (CE) is the first gynecological cancer in terms of incidence and the fourth cause of cancer of the woman. About 8151 new cases and nearly 2179 deaths have been reported. Treatments (surgery, external radiotherapy, brachytherapy and chemotherapy) are currently delivered on the basis of an estimation of the recurrence risk, an estimation of lymph node metastasis or an estimate of survival probability. This risk is determined on the basis of prognostic factors (clinical, histological, imaging, biological) taken alone or grouped together in the form of classification systems, which are currently insufficient to account for the evolutionary and prognostic heterogeneity of endometrial cancer. For endometrial cancer, the concept of mathematical modeling and its application to prediction have developed in recent years. These biomathematical tools have opened a new era of care oriented towards the promotion of targeted therapies and personalized treatments. Many predictive models have been published to estimate the risk of recurrence and lymph node metastasis, but a tiny fraction of them is sufficiently relevant and of clinical utility. The optimization tracks are multiple and varied, suggesting the possibility in the near future of a place for these mathematical models. The development of high-throughput genomics is likely to offer a more detailed molecular characterization of the disease and its heterogeneity. Copyright © 2017 Société Française du Cancer. Published by Elsevier Masson SAS. All rights reserved.

  17. 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.

  18. Predictions of models for environmental radiological assessment

    International Nuclear Information System (INIS)

    Peres, Sueli da Silva; Lauria, Dejanira da Costa; Mahler, Claudio Fernando

    2011-01-01

    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 137 Cs and 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)

  19. Prediction of CO concentrations based on a hybrid Partial Least Square and Support Vector Machine model

    Science.gov (United States)

    Yeganeh, B.; Motlagh, M. Shafie Pour; Rashidi, Y.; Kamalan, H.

    2012-08-01

    Due to the health impacts caused by exposures to air pollutants in urban areas, monitoring and forecasting of air quality parameters have become popular as an important topic in atmospheric and environmental research today. The knowledge on the dynamics and complexity of air pollutants behavior has made artificial intelligence models as a useful tool for a more accurate pollutant concentration prediction. This paper focuses on an innovative method of daily air pollution prediction using combination of Support Vector Machine (SVM) as predictor and Partial Least Square (PLS) as a data selection tool based on the measured values of CO concentrations. The CO concentrations of Rey monitoring station in the south of Tehran, from Jan. 2007 to Feb. 2011, have been used to test the effectiveness of this method. The hourly CO concentrations have been predicted using the SVM and the hybrid PLS-SVM models. Similarly, daily CO concentrations have been predicted based on the aforementioned four years measured data. Results demonstrated that both models have good prediction ability; however the hybrid PLS-SVM has better accuracy. In the analysis presented in this paper, statistic estimators including relative mean errors, root mean squared errors and the mean absolute relative error have been employed to compare performances of the models. It has been concluded that the errors decrease after size reduction and coefficients of determination increase from 56 to 81% for SVM model to 65-85% for hybrid PLS-SVM model respectively. Also it was found that the hybrid PLS-SVM model required lower computational time than SVM model as expected, hence supporting the more accurate and faster prediction ability of hybrid PLS-SVM model.

  20. Assessing The Policy Relevance of Regional Air Quality Models

    Science.gov (United States)

    Holloway, T.

    This work presents a framework for discussing the policy relevance of models, and regional air quality models in particular. We define four criteria: 1) The scientific status of the model; 2) Its ability to address primary environmental concerns; 3) The position of modeled environmental issues on the political agenda; and 4) The role of scientific input into the policy process. This framework is applied to current work simulating the transport of nitric acid in Asia with the ATMOS-N model, to past studies on air pollution transport in Europe with the EMEP model, and to future applications of the United States Environmental Protection Agency (US EPA) Models-3. The Lagrangian EMEP model provided critical input to the development of the 1994 Oslo and 1999 Gothenburg Protocols to the Convention on Long-Range Transbound- ary Air Pollution, as well as to the development of EU directives, via its role as a component of the RAINS integrated assessment model. Our work simulating reactive nitrogen in Asia follows the European example in part, with the choice of ATMOS-N, a regional Lagrangian model to calculate source-receptor relationships for the RAINS- Asia integrated assessment model. However, given differences between ATMOS-N and the EMEP model, as well as differences between the scientific and political cli- mates facing Europe ten years ago and Asia today, the role of these two models in the policy process is very different. We characterize the different aspects of policy relevance between these models using our framework, and consider how the current generation US EPA air quality model compares, in light of its Eulerian structure, dif- ferent objectives, and the policy context of the US.

  1. 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.

  2. Prediction of fog/visibility over India using NWP Model

    Science.gov (United States)

    Singh, Aditi; George, John P.; Iyengar, Gopal Raman

    2018-03-01

    Frequent occurrence of fog in different parts of northern India is common during the winter months of December and January. Low visibility conditions due to fog disrupt normal public life. Visibility conditions heavily affect both surface and air transport. A number of flights are either diverted or cancelled every year during the winter season due to low visibility conditions, experienced at different airports of north India. Thus, fog and visibility forecasts over plains of north India become very important during winter months. This study aims to understand the ability of a NWP model (NCMRWF, Unified Model, NCUM) with a diagnostic visibility scheme to forecast visibility over plains of north India. The present study verifies visibility forecasts obtained from NCUM against the INSAT-3D fog images and visibility observations from the METAR reports of different stations in the plains of north India. The study shows that the visibility forecast obtained from NCUM can provide reasonably good indication of the spatial extent of fog in advance of one day. The fog intensity is also predicted fairly well. The study also verifies the simple diagnostic model for fog which is driven by NWP model forecast of surface relative humidity and wind speed. The performance of NWP model forecast of visibility is found comparable to that from simple fog model driven by NWP forecast of relative humidity and wind speed.

  3. Model Reference Adaptive Control of the Air Flow Rate of Centrifugal Compressor Using State Space Method

    International Nuclear Information System (INIS)

    Han, Jaeyoung; Jung, Mooncheong; Yu, Sangseok; Yi, Sun

    2016-01-01

    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.

  4. 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.

  5. Mathematical models for atmospheric pollutants. Appendix D. Available air quality models. Final report

    International Nuclear Information System (INIS)

    Drake, R.L.; McNaughton, D.J.; Huang, C.

    1979-08-01

    Models that are available for the analysis of airborne pollutants are summarized. In addition, recommendations are given concerning the use of particular models to aid in particular air quality decision making processes. The air quality models are characterized in terms of time and space scales, steady state or time dependent processes, reference frames, reaction mechanisms, treatment of turbulence and topography, and model uncertainty. Using these characteristics, the models are classified in the following manner: simple deterministic models, such as air pollution indices, simple area source models and rollback models; statistical models, such as averaging time models, time series analysis and multivariate analysis; local plume and puff models; box and multibox models; finite difference or grid models; particle models; physical models, such as wind tunnels and liquid flumes; regional models; and global models

  6. Model-generated air quality statistics for application in vegetation response models in Alberta

    International Nuclear Information System (INIS)

    McVehil, G.E.; Nosal, M.

    1990-01-01

    To test and apply vegetation response models in Alberta, air pollution statistics representative of various parts of the Province are required. At this time, air quality monitoring data of the requisite accuracy and time resolution are not available for most parts of Alberta. Therefore, there exists a need to develop appropriate air quality statistics. The objectives of the work reported here were to determine the applicability of model generated air quality statistics and to develop by modelling, realistic and representative time series of hourly SO 2 concentrations that could be used to generate the statistics demanded by vegetation response models

  7. Development of Air Dispersion Modeling for Future Nuclear Plant in Malaysia

    International Nuclear Information System (INIS)

    Mohd Nahar Othman

    2011-01-01

    The impact development of Nuclear power plant in Malaysia, can be very negative to the nearby population, causing public restlessness and consequently affecting the image of the authorities in the countries. The precise source of the pollution from the nuclear power plant must be determined, pollution emission level and the meteorological conditions are needed to predict and established the ambient air to the save level at the perimeter fence of the plant and address it with respect to the radiological ambient standards. Upon modeling using an established package as well as site measurements, the radiological level at the perimeter fence of the plant must deduced and lower than the normal ambient level. Based on this issue, a modeling study was made in vicinity of Malaysian Nuclear Agency TRIGA reactor in the area of Bangi, Selangor to evaluate the possibility of movement of air around the area and their impact. This paper will address and discuss the modeling base on the data getting from Meteorological Department such as measurement of wind speed, temperature, humidity, ambient air radiological concentration and ect. The purpose of Air Dispersion Modeling is to establish the critical ambient emission level, as well as radiological modeling. The focus will be made on exploring the use of Ausplume modeling to develop correlations between the radiological concentrations, chemical compositions and ambient model for emission controls. (author)

  8. [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.

  9. Combining GPS measurements and IRI model predictions

    International Nuclear Information System (INIS)

    Hernandez-Pajares, M.; Juan, J.M.; Sanz, J.; Bilitza, D.

    2002-01-01

    The free electrons distributed in the ionosphere (between one hundred and thousands of km in height) produce a frequency-dependent effect on Global Positioning System (GPS) signals: a delay in the pseudo-orange and an advance in the carrier phase. These effects are proportional to the columnar electron density between the satellite and receiver, i.e. the integrated electron density along the ray path. Global ionospheric TEC (total electron content) maps can be obtained with GPS data from a network of ground IGS (international GPS service) reference stations with an accuracy of few TEC units. The comparison with the TOPEX TEC, mainly measured over the oceans far from the IGS stations, shows a mean bias and standard deviation of about 2 and 5 TECUs respectively. The discrepancies between the STEC predictions and the observed values show an RMS typically below 5 TECUs (which also includes the alignment code noise). he existence of a growing database 2-hourly global TEC maps and with resolution of 5x2.5 degrees in longitude and latitude can be used to improve the IRI prediction capability of the TEC. When the IRI predictions and the GPS estimations are compared for a three month period around the Solar Maximum, they are in good agreement for middle latitudes. An over-determination of IRI TEC has been found at the extreme latitudes, the IRI predictions being, typically two times higher than the GPS estimations. Finally, local fits of the IRI model can be done by tuning the SSN from STEC GPS observations

  10. A Predictive Maintenance Model for Railway Tracks

    DEFF Research Database (Denmark)

    Li, Rui; Wen, Min; Salling, Kim Bang

    2015-01-01

    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......). Five technical and economic aspects are taken into account to schedule tamping: (1) track degradation of the standard deviation of the longitudinal level over time; (2) track geometrical alignment; (3) track quality thresholds based on the train speed limits; (4) the dependency of the track quality...... 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. An inexact fuzzy-chance-constrained air quality management model.

    Science.gov (United States)

    Xu, Ye; Huang, Guohe; Qin, Xiaosheng

    2010-07-01

    Regional air pollution is a major concern for almost every country because it not only directly relates to economic development, but also poses significant threats to environment and public health. In this study, an inexact fuzzy-chance-constrained air quality management model (IFAMM) was developed for regional air quality management under uncertainty. IFAMM was formulated through integrating interval linear programming (ILP) within a fuzzy-chance-constrained programming (FCCP) framework and could deal with uncertainties expressed as not only possibilistic distributions but also discrete intervals in air quality management systems. Moreover, the constraints with fuzzy variables could be satisfied at different confidence levels such that various solutions with different risk and cost considerations could be obtained. The developed model was applied to a hypothetical case of regional air quality management. Six abatement technologies and sulfur dioxide (SO2) emission trading under uncertainty were taken into consideration. The results demonstrated that IFAMM could help decision-makers generate cost-effective air quality management patterns, gain in-depth insights into effects of the uncertainties, and analyze tradeoffs between system economy and reliability. The results also implied that the trading scheme could achieve lower total abatement cost than a nontrading one.

  12. An Operational Model for the Prediction of Jet Blast

    Science.gov (United States)

    2012-01-09

    This paper presents an operational model for the prediction of jet blast. The model was : developed based upon three modules including a jet exhaust model, jet centerline decay : model and aircraft motion model. The final analysis was compared with d...

  13. MODELING OF AIR POLLUTION DISPERSION EMITTED FROM POINT SOURCES

    Directory of Open Access Journals (Sweden)

    Monika Wierzbińska

    2017-04-01

    Full Text Available In this paper, the modeling results of parameters and factors which determine spread of contamination in atmospheric air, are presented. These factors are: aerodynamic coefficient of area roughness, emitters location, exhaust temperature and velocity at the end of emitter. Computer program Ek100w calculates concentration of pollutants in the air on different distance from the emitter. We use calculation results to prepare charts with contour lines of air pollutions concentration. In this article contamination spread from emitters with different work parameters is analyzed. It follows that these parameters and factors have an important effect on contamination spreading in the atmospheric air. We can use such programs for emission design in practice and reduce impurities and immission on area where people are especially endanger for industrial emission.

  14. Mathematical modeling of compression processes in air-driven boosters

    International Nuclear Information System (INIS)

    Li Zeyu; Zhao Yuanyang; Li Liansheng; Shu Pengcheng

    2007-01-01

    The compressed air in normal pressure is used as the source of power of the air-driven booster. The continuous working of air-driven boosters relies on the difference of surface area between driven piston and driving piston, i.e., the different forces acting on the pistons. When the working surface area of the driving piston for providing power is greater than that of the driven piston for compressing gas, the gas in compression chamber will be compressed. On the basis of the first law of thermodynamics, the motion regulation of piston is analyzed and the mathematical model of compression processes is set up. Giving a calculating example, the vary trends of gas pressure and pistons' move in working process of booster have been gotten. The change of parameters at different working conditions is also calculated and compared. And the corresponding results can be referred in the design of air-driven boosters

  15. Transient modeling of an air conditioner with a rapid cycling compressor and multi-indoor units

    International Nuclear Information System (INIS)

    Zhang Weijiang; Zhang Chunlu

    2011-01-01

    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.

  16. 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.

  17. Economic Model Predictive Control for Hot Water Based Heating Systems in Smart Buildings

    DEFF Research Database (Denmark)

    Awadelrahman, M. A. Ahmed; Zong, Yi; Li, Hongwei

    2017-01-01

    This paper presents a study to optimize the heating energy costs in a residential building with varying electricity price signals based on an Economic Model Predictive Controller (EMPC). The investigated heating system consists of an air source heat pump (ASHP) incorporated with a hot water tank...

  18. 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

    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...... 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...

  19. Prediction of seasonal water-table fluctuations in La Pampa and Buenos Aires, Argentina

    Science.gov (United States)

    Tanco, Raúl; Kruse, Eduardo

    2001-07-01

    The fluctuation of the water table east of La Pampa province and northwest of Buenos Aires province, Argentina, influences agricultural production in the region because it is closely related to the alternation of dry and wet periods. Sea-surface temperature (SST) anomalies have been used as predictors to forecast atmospheric variables in different regions of the world. The objective of this work is to present a simple model to forecast seasonal rainfall using SST distribution in the Pacific Ocean as a predictor. Once the relationship between precipitation and water-table fluctuations was established, a methodology for the prediction of water-table fluctuations was developed. A good agreement between observed and predicted water-table fluctuations was found when estimating water-table fluctuations in the summer and autumn seasons. Résumé. Les fluctuations de la nappe à l'est de la province de La Pampa et au nord-ouest de la province de Buenos Aires (Argentine) influence la production agricole de la région parce qu'elle est étroitement liée à l'alternance de saisons sèches et humides. Les anomalies de la température de surface de l'océan (SST) ont été utilisées comme prédicteurs pour prévoir les variables atmosphériques dans différentes régions du monde. L'objectif de ce travail est de présenter un modèle simple de prévision des précipitations saisonnières en utilisant comme prédicteur la distribution des SST dans l'Océan Pacifique. Une fois que la relation entre les fluctuations des précipitations et celles de la nappe a été établie, une méthodologie de prédiction des variations de la nappe a été mise au point. Un bon accord entre les variations de la nappe observées et celles prédites a été trouvé pour les estimations des variations de nappe en été et en automne. Resumen. La fluctuación del nivel freático al este de la provincia de La Pampa y al nordeste de la de Buenos Aires (Argentina) repercute en la producción agr

  20. Modeling breathing-zone concentrations of airborne contaminants generated during compressed air spray painting.

    Science.gov (United States)

    Flynn, M R; Gatano, B L; McKernan, J L; Dunn, K H; Blazicko, B A; Carlton, G N

    1999-01-01

    This paper presents a mathematical model to predict breathing-zone concentrations of airborne contaminants generated during compressed air spray painting in cross-flow ventilated booths. The model focuses on characterizing the generation and transport of overspray mist. It extends previous work on conventional spray guns to include exposures generated by HVLP guns. Dimensional analysis and scale model wind-tunnel studies are employed using non-volatile oils, instead of paint, to produce empirical equations for estimating exposure to total mass. Results indicate that a dimensionless breathing zone concentration is a nonlinear function of the ratio of momentum flux of air from the spray gun to the momentum flux of air passing through the projected area of the worker's body. The orientation of the spraying operation within the booth is also very significant. The exposure model requires an estimate of the contaminant generation rate, which is approximated by a simple impactor model. The results represent an initial step in the construction of more realistic models capable of predicting exposure as a mathematical function of the governing parameters.

  1. CREATION OF OPTIMIZATION MODEL OF STEAM BOILER RECUPERATIVE AIR HEATER

    Directory of Open Access Journals (Sweden)

    N. B. Carnickiy

    2006-01-01

    Full Text Available The paper proposes to use a mathematical modeling as one of the ways intended to improve quality of recuperative air heater design (RAH without significant additional costs, connected with the change of design materials or fuel type. The described conceptual mathematical AHP optimization model of RAH consists of optimized and constant parameters, technical limitations and optimality criteria.The paper considers a methodology for search of design and regime parameters of an air heater which is based on the methods of multi-criteria optimization. Conclusions for expediency of the given approach usage are made in the paper.

  2. Mathematical model of one-man air revitalization system

    Science.gov (United States)

    1976-01-01

    A mathematical model was developed for simulating the steady state performance in electrochemical CO2 concentrators which utilize (NMe4)2 CO3 (aq.) electrolyte. This electrolyte, which accommodates a wide range of air relative humidity, is most suitable for one-man air revitalization systems. The model is based on the solution of coupled nonlinear ordinary differential equations derived from mass transport and rate equations for the processes which take place in the cell. The boundary conditions are obtained by solving the mass and energy transport equations. A shooting method is used to solve the differential equations.

  3. ANL/HIWAY: an air pollution evaluation model for roadways

    Energy Technology Data Exchange (ETDEWEB)

    Concaildi, G. A.; Cohen, A. S.; King, R. F.

    1976-12-01

    This report describes a computer program, called ANL/HIWAY, for estimating air quality levels of nonreactive pollutants produced by vehicular sources. It is valid for receptors at distances of tens to hundreds of meters, at an angle, downwind of the roadway, in relatively uncomplicated terrain. It may be used by planners to analyze the effects of a proposed roadway on adjacent air quality. The ANL/HIWAY model expands the evaluation capabilities of the EPA/HIWAY dispersion model. This report also serves as a user's manual for running the ANL/HIWAY PROGRAM. All command structures are described in detail, with sample problems exemplifying their use.

  4. Modeling of a hybrid ejector air conditioning system using artificial neural networks

    International Nuclear Information System (INIS)

    Wang, Hao; Cai, Wenjian; Wang, Youyi

    2016-01-01

    Highlights: • We apply three different types of neural network for a hybrid system components modeling. • We vary the activation function, network structure and training testing ratio to find the most optimal combination. • We apply data-mining algorithm for parameter selection. • We choose the neural network with best performance to model the whole system. • The result shows a good agreement between predicted and measured value within ±10% error. - Abstract: In order to predict the performance of a hybrid ejector air conditioning system, neural network is chosen to model the proposed platform. First, three different types of neural networks, namely multi-layer perceptron (MLP), radial basis function (RBF) and support vector machine (SVM) are applied to model the component of a hybrid ejector air conditioning system. The MLP outperforms other two networks in this research and therefore it is selected to model the whole system. Since there is no formal criterion about input selection so far, a date-mining algorithm, boosting tree, is employed before system modeling to search the most significant parameters among the 19 input variables and the five most influential parameters of them are selected to be the final input of the system model. And the result shows a good agreement between predicted and measured value which indicates the excellent ability of MLP.

  5. 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.

  6. 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.

  7. Progress on Implementing Additional Physics Schemes into MPAS-A v5.1 for Next Generation Air Quality Modeling

    Science.gov (United States)

    The U.S. Environmental Protection Agency (USEPA) has a team of scientists developing a next generation air quality modeling system employing the Model for Prediction Across Scales – Atmosphere (MPAS-A) as its meteorological foundation. Several preferred physics schemes and ...

  8. 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...

  9. PREDICTION OF ATMOSPHERIC AIR POLLUTION BY EMISSIONS OF MOTOR TRANSPORT TAKING INTO ACCOUNT THE CHEMICAL TRANSFORMATION OF HARMFUL SUBSTANCES

    Directory of Open Access Journals (Sweden)

    M. M. Biliaiev

    2017-06-01

    Full Text Available Purpose. Development of 3D numerical models, which allow us to calculate air pollution process from road transport emissions based on chemical transformation of pollutants. Creating numerical models, which would give the opportunity to predict the level of air pollution in urban areas. Methodology. To address the evaluation of the air pollution problem of emissions of vehicles the equations of aerodynamics and mass transfer were used. In order to solve differential equations of aerodynamics and mass transfer the finite difference methods are used. For the numerical integration of the equation for the velocity potential the method of conditional approximation was applied. The equation for the velocity potential written in difference form, is being split into two equations, and at each step of splitting the unknown value of the potential speed is determined by the explicit scheme of running account and the difference scheme itself is implicit. For the numerical integration of the equation of dispersion of emissions in the atmosphere is used implicit alternating-triangular difference splitting scheme. Emissions from the road are simulated by a series of point sources of a given intensity. The developed numerical models are the basis of established software package.Findings. There were developed 3D numerical models, which belong to the class «diagnostic models». These models take into account the main physical factors affecting the process of dispersion of pollutants in the atmosphere when emissions from road transport taking into account the chemical transformation of pollutants. On the basis of the constructed numerical models a computational experiment to assess the level of air pollution in the street was carried out. Originality. Numerical models that allow you to calculate the 3D aerodynamic of wind flow in urban areas and the process of mass transfer of emissions from the road were developed. The models make it possible to account the

  10. 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

  11. 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.

  12. Modeling and Analysing of Air Filter in Air Intake System in Automobile Engine

    Directory of Open Access Journals (Sweden)

    R. Manikantan

    2013-01-01

    Full Text Available As the legislations on the emission and performance of automobiles are being made more stringent, the expected performance of all the subsystems of an internal combustion engine is also becoming crucial. Nowadays the engines are downsized, and their power increased the demand on the air intake system that has increased phenomenally. Hence, an analysis was carried on a typical air filter fitted into the intake system to determine its flow characteristics. In the present investigation, a CAD model of an existing air filter was designed, and CFD analysis was done pertaining to various operating regimes of an internal combustion engine. The numerical results were validated with the experimental data. From the postprocessed result, we can see that there is a deficit in the design of the present filter, as the bottom portion of the filter is preventing the upward movement of air. Hence, the intake passage can be rearranged to provide an upward tangential motion, which can enhance the removal of larger dust and soot particles effectively by the inertial action of air alone.

  13. Predicted impact of thermal power generation emission control measures in the Beijing-Tianjin-Hebei region on air pollution over Beijing, China.

    Science.gov (United States)

    Wang, Liqiang; Li, Pengfei; Yu, Shaocai; Mehmood, Khalid; Li, Zhen; Chang, Shucheng; Liu, Weiping; Rosenfeld, Daniel; Flagan, Richard C; Seinfeld, John H

    2018-01-17

    Widespread economic growth in China has led to increasing episodes of severe air pollution, especially in major urban areas. Thermal power plants represent a particularly important class of emissions. Here we present an evaluation of the predicted effectiveness of a series of recently proposed thermal power plant emission controls in the Beijing-Tianjin-Hebei (BTH) region on air quality over Beijing using the Community Multiscale Air Quality(CMAQ) atmospheric chemical transport model to predict CO, SO 2 , NO 2 , PM 2.5 , and PM 10 levels. A baseline simulation of the hypothetical removal of all thermal power plants in the BTH region is predicted to lead to 38%, 23%, 23%, 24%, and 24% reductions in current annual mean levels of CO, SO 2 , NO 2 , PM 2.5 , and PM 10 in Beijing, respectively. Similar percentage reductions are predicted in the major cities in the BTH region. Simulations of the air quality impact of six proposed thermal power plant emission reduction strategies over the BTH region provide an estimate of the potential improvement in air quality in the Beijing metropolitan area, as a function of the time of year.

  14. Predictive modeling: potential application in prevention services.

    Science.gov (United States)

    Wilson, Moira L; Tumen, Sarah; Ota, Rissa; Simmers, Anthony G

    2015-05-01

    In 2012, the New Zealand Government announced a proposal to introduce predictive risk models (PRMs) to help professionals identify and assess children at risk of abuse or neglect as part of a preventive early intervention strategy, subject to further feasibility study and trialing. The purpose of this study is to examine technical feasibility and predictive validity of the proposal, focusing on a PRM that would draw on population-wide linked administrative data to identify newborn children who are at high priority for intensive preventive services. Data analysis was conducted in 2013 based on data collected in 2000-2012. A PRM was developed using data for children born in 2010 and externally validated for children born in 2007, examining outcomes to age 5 years. Performance of the PRM in predicting administratively recorded substantiations of maltreatment was good compared to the performance of other tools reviewed in the literature, both overall, and for indigenous Māori children. Some, but not all, of the children who go on to have recorded substantiations of maltreatment could be identified early using PRMs. PRMs should be considered as a potential complement to, rather than a replacement for, professional judgment. Trials are needed to establish whether risks can be mitigated and PRMs can make a positive contribution to frontline practice, engagement in preventive services, and outcomes for children. Deciding whether to proceed to trial requires balancing a range of considerations, including ethical and privacy risks and the risk of compounding surveillance bias. Crown Copyright © 2015. Published by Elsevier Inc. All rights reserved.

  15. Preprocessing and Optimization of Smooth Data-driven Model for Emergency Conditions Against Air Pollution

    Directory of Open Access Journals (Sweden)

    Ali Ardalan

    2016-10-01

    Full Text Available Magnitudes of the air pollution depend on various variables. Preprocessing and optimisation processes are necessary to discover the complexity of the relationship of the data for more accurate and efficient predictions. These techniques help to clean the datasets and to find the best structure of the smooth data model. The Gamma test (GT and Genetic Algorithm (GA are practical tools which can be applied for preprocessing and optimising data models. Regarding building a smooth data model, the developed artificial neural networks are functional optimisation strategies which are suitable for ANN training. Moreover, local linear regression (LLR and dynamic local linear regression (DLLR models are effective due to the high density of our normalised dataset. In this regard, we developed a process to construct a smooth data model to support environmental decision making in air pollution emergency conditions. The main objective of this work was to set an appropriate algorithm by preprocessing and optimising a set of the data model for developing smooth data-driven models which could play a significant role in early warning systems in regard to the human health. The data sets included the meteorological and air pollutant variables as inputs/predictors and emergency medical service clients as outputs. The GT and GA were applied to analyse and optimise the input variables. Three types of ANNS (ANN1, ANN2, and ANN3, (LLR, and (DLLR techniques were used to establish the models. Finally, a smooth data model was constructed and evaluated.

  16. Heuristic Modeling for TRMM Lifetime Predictions

    Science.gov (United States)

    Jordan, P. S.; Sharer, P. J.; DeFazio, R. L.

    1996-01-01

    Analysis time for computing the expected mission lifetimes of proposed frequently maneuvering, tightly altitude constrained, Earth orbiting spacecraft have been significantly reduced by means of a heuristic modeling method implemented in a commercial-off-the-shelf spreadsheet product (QuattroPro) running on a personal computer (PC). The method uses a look-up table to estimate the maneuver frequency per month as a function of the spacecraft ballistic coefficient and the solar flux index, then computes the associated fuel use by a simple engine model. Maneuver frequency data points are produced by means of a single 1-month run of traditional mission analysis software for each of the 12 to 25 data points required for the table. As the data point computations are required only a mission design start-up and on the occasion of significant mission redesigns, the dependence on time consuming traditional modeling methods is dramatically reduced. Results to date have agreed with traditional methods to within 1 to 1.5 percent. The spreadsheet approach is applicable to a wide variety of Earth orbiting spacecraft with tight altitude constraints. It will be particularly useful to such missions as the Tropical Rainfall Measurement Mission scheduled for launch in 1997, whose mission lifetime calculations are heavily dependent on frequently revised solar flux predictions.

  17. A Computational Model for Predicting Gas Breakdown

    Science.gov (United States)

    Gill, Zachary

    2017-10-01

    Pulsed-inductive discharges are a common method of producing a plasma. They provide a mechanism for quickly and efficiently generating a large volume of plasma for rapid use and are seen in applications including propulsion, fusion power, and high-power lasers. However, some common designs see a delayed response time due to the plasma forming when the magnitude of the magnetic field in the thruster is at a minimum. New designs are difficult to evaluate due to the amount of time needed to construct a new geometry and the high monetary cost of changing the power generation circuit. To more quickly evaluate new designs and better understand the shortcomings of existing designs, a computational model is developed. This model uses a modified single-electron model as the basis for a Mathematica code to determine how the energy distribution in a system changes with regards to time and location. By analyzing this energy distribution, the approximate time and location of initial plasma breakdown can be predicted. The results from this code are then compared to existing data to show its validity and shortcomings. Missouri S&T APLab.

  18. Distributed model predictive control made easy

    CERN Document Server

    Negenborn, Rudy

    2014-01-01

    The rapid evolution of computer science, communication, and information technology has enabled the application of control techniques to systems beyond the possibilities of control theory just a decade ago. Critical infrastructures such as electricity, water, traffic and intermodal transport networks are now in the scope of control engineers. The sheer size of such large-scale systems requires the adoption of advanced distributed control approaches. Distributed model predictive control (MPC) is one of the promising control methodologies for control of such systems.   This book provides a state-of-the-art overview of distributed MPC approaches, while at the same time making clear directions of research that deserve more attention. The core and rationale of 35 approaches are carefully explained. Moreover, detailed step-by-step algorithmic descriptions of each approach are provided. These features make the book a comprehensive guide both for those seeking an introduction to distributed MPC as well as for those ...

  19. 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…

  20. Detonation cell size measurements and predictions in hydrogen-air-steam mixtures at elevated temperatures

    International Nuclear Information System (INIS)

    Ciccarelli, G.; Ginsberg, T.; Boccio, J.; Economos, C.; Sato, K.; Kinoshita, M.

    1994-01-01

    The effect of initial mixture temperature on the experimentally measured detonation cell size for hydrogen-air-steam mixtures at 0.1 MPa has been investigated. Experiments were carried out in a 10-cm-inner-diameter, 6.1-m-long heated detonation tube with a maximum operating temperature of 700 K and spatial temperature uniformity of ± 14 K. Detonation cell size measurements provide clear evidence that the effect of hydrogen-air initial gas mixture temperature, in the range 300--650 K, is to decrease cell size and, hence, to increase the sensitivity of the mixture to undergo detonations. The effect of steam content, at ay given temperature, is to increase the cell size and, thereby, to decrease the sensitivity of stoichiometric hydrogen-air mixtures. The hydrogen-air detonability limits for the 10-cm-inside-diameter test vessel, based upon the onset of single-head spin, decreased from 15% hydrogen at 300 K down to about 9% hydrogen at 650 K. The experimental detonation cell size data were correlated suing a Zel'dovich-von Neumann-Doering (ZND) model for the detonation using detailed chemical-kinetic reaction mechanisms. The proportionality constants used to scale the reaction zone length calculations from the ZND model varied from 3o to 51 for the hydrogen-air cell size data at 650 and 300 K, respectively

  1. Detonation cell size measurements and predictions in hydrogen-air-steam mixtures at elevated temperatures

    Energy Technology Data Exchange (ETDEWEB)

    Ciccarelli, G.; Ginsberg, T.; Boccio, J.; Economos, C.; Sato, K.; Kinoshita, M. (Brookhaven National Lab., Upton, NY (United States). Safety and Risk Evaluation Division)

    1994-11-01

    The effect of initial mixture temperature on the experimentally measured detonation cell size for hydrogen-air-steam mixtures at 0.1 MPa has been investigated. Experiments were carried out in a 10-cm-inner-diameter, 6.1-m-long heated detonation tube with a maximum operating temperature of 700 K and spatial temperature uniformity of [+-] 14 K. Detonation cell size measurements provide clear evidence that the effect of hydrogen-air initial gas mixture temperature, in the range 300--650 K, is to decrease cell size and, hence, to increase the sensitivity of the mixture to undergo detonations. The effect of steam content, at ay given temperature, is to increase the cell size and, thereby, to decrease the sensitivity of stoichiometric hydrogen-air mixtures. The hydrogen-air detonability limits for the 10-cm-inside-diameter test vessel, based upon the onset of single-head spin, decreased from 15% hydrogen at 300 K down to about 9% hydrogen at 650 K. The experimental detonation cell size data were correlated suing a Zel'dovich-von Neumann-Doering (ZND) model for the detonation using detailed chemical-kinetic reaction mechanisms. The proportionality constants used to scale the reaction zone length calculations from the ZND model varied from 3o to 51 for the hydrogen-air cell size data at 650 and 300 K, respectively.

  2. Economic damages of ozone air pollution to crops using combined air quality and GIS modelling

    Science.gov (United States)

    Vlachokostas, Ch.; Nastis, S. A.; Achillas, Ch.; Kalogeropoulos, K.; Karmiris, I.; Moussiopoulos, N.; Chourdakis, E.; Banias, G.; Limperi, N.

    2010-09-01

    This study aims at presenting a combined air quality and GIS modelling methodological approach in order to estimate crop damages from photochemical air pollution, depict their spatial resolution and assess the order of magnitude regarding the corresponding economic damages. The analysis is conducted within the Greater Thessaloniki Area, Greece, a Mediterranean territory which is characterised by high levels of photochemical air pollution and considerable agricultural activity. Ozone concentration fields for 2002 and for specific emission reduction scenarios for the year 2010 were estimated with the Ozone Fine Structure model in the area under consideration. Total economic damage to crops turns out to be significant and estimated to be approximately 43 M€ for the reference year. Production of cotton presents the highest economic loss, which is over 16 M€, followed by table tomato (9 M€), rice (4.2 M€), wheat (4 M€) and oilseed rape (2.8 M€) cultivations. Losses are not spread uniformly among farmers and the major losses occur in areas with valuable ozone-sensitive crops. The results are very useful for highlighting the magnitude of the total economic impacts of photochemical air pollution to the area's agricultural sector and can potentially be used for comparison with studies worldwide. Furthermore, spatial analysis of the economic damage could be of importance for governmental authorities and decision makers since it provides an indicative insight, especially if the economic instruments such as financial incentives or state subsidies to farmers are considered.

  3. Which method predicts recidivism best?: A comparison of statistical, machine learning, and data mining predictive models

    OpenAIRE

    Tollenaar, N.; van der Heijden, P.G.M.

    2012-01-01

    Using criminal population conviction histories of recent offenders, prediction mod els are developed that predict three types of criminal recidivism: general recidivism, violent recidivism and sexual recidivism. The research question is whether prediction techniques from modern statistics, data mining and machine learning provide an improvement in predictive performance over classical statistical methods, namely logistic regression and linear discrim inant analysis. These models are compared ...

  4. MODEL PENGELOLAAN AIR BERSIH DESA DI BANTUL YOGYAKARTA

    Directory of Open Access Journals (Sweden)

    Hardjono -

    2014-02-01

    Full Text Available AbstrakArtikel ini bertujuan mendeskripsikan model pengelolaan air minum desa dan permasalah yang dihadapinya. Penelitian dilakukan di wilayah Pucung Desa Wukirsari Bantul Yogyakarta. Permasalahan yang dikaji adalah bagaimana pengelolaan  ditinjau dari aspek kelembagaan, ketersediaan air, jumlah pengguna, kebutuhan air bersih, pedoman yang mengatur dan manajemen keuangannya. Jenis penelitian survai dengan pendekatan deskriptif kualitatif dan kuantitatif. Subyek penelitian kepala keluarga. Hasil penelitian Pengelolaan Air Bersih (PAB Pucung dikelola berbasis masyarakat (tipe C, namun belum melibatkan pelanggan dalam pengelolaannya. Ketersediaan air sangat cukup, tetapi kebutuhan pelanggan belum terpenuhi secara maksimal. Apabila PAB Pucung dapat beroperasi secara efektif dan efisien masyarakat Pucung tidak akan kekurangan air bersih karena dalam satu bulan masih tersedia 13.445 m3, yang setara  dengan pemenuhan kebutuhan air bersih rata–rata 259 jiwa/bulan.AbstractThis article aims to describe a village water management model and the problems it faces. The study was conducted in the area of ​​Bantul, Yogyakarta, to be exactly in Wukirsari village. The article studies water management in the aspect of institutional management, water availability, number of users, the need for clean water, and guidelines governing financial management. The results of the study reveals that the water is managed by the community (type C, and do not involve the customer in its management. Though water is abundant, the management does not meet customer needs to the fullest. If PAB Pucung can operate effectively and efficiently Pucung people will not lack of clean water because of lack of clean water is still available in a month 13 445 m3, which is equivalent to a clean water supply on average 259 people/month.© 2013 Universitas Negeri Semarang

  5. Climate Predictions: The Chaos and Complexity in Climate Models

    Directory of Open Access Journals (Sweden)

    D. T. Mihailović

    2014-01-01

    Full Text Available Some issues which are relevant for the recent state in climate modeling have been considered. A detailed overview of literature related to this subject is given. The concept in modeling of climate, as a complex system, seen through Gödel’s theorem and Rosen’s definition of complexity and predictability is discussed. Occurrence of chaos in computing the environmental interface temperature from the energy balance equation given in a difference form is pointed out. A coupled system of equations, often used in climate models, was analyzed. It is shown that the Lyapunov exponent mostly has positive values allowing presence of chaos in this system. The horizontal energy exchange between environmental interfaces, which is described by the dynamics of driven coupled oscillators, was analyzed. Their behavior and synchronization, when a perturbation is introduced in the system, as a function of the coupling parameter, the logistic parameter, and the parameter of exchange, were studied calculating the Lyapunov exponent under simulations with the closed contour of N=100 environmental interfaces. Finally, we have explored possible differences in complexities of two global and two regional climate models using their air temperature and precipitation output time series. The complexities were obtained with the algorithm for calculating the Kolmogorov complexity.

  6. 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

  7. Pollutant dispersion models for issues of air pollution control

    International Nuclear Information System (INIS)

    1985-01-01

    14 papers entered separately into the data base were presented at the meeting for application-oriented dispersion models for issues of air pollution control. These papers focus on fields of application, availability of required input data relevant to emissions and meteorology, performance and accuracy of these methods and their practicability. (orig./PW) [de

  8. Solving vertical transport and chemistry in air pollution models

    NARCIS (Netherlands)

    P.J.F. Berkvens (Patrick); M.A. Botchev; J.G. Verwer (Jan); M.C. Krol; W. Peters

    2000-01-01

    textabstractFor the time integration of stiff transport-chemistry problems from air pollution modelling, standard ODE solvers are not feasible due to the large number of species and the 3D nature. The popular alternative, standard operator splitting, introduces artificial transients for short-lived

  9. Solving Vertical Transport and Chemistry in Air Pollution Models.

    NARCIS (Netherlands)

    Berkvens, P.J.F.; Bochev, Mikhail A.; Verwer, J.G.; Krol, M.C.; Peters, W.

    For the time integration of stiff transport-chemistry problems from air pollution modelling, standard ODE solvers are not feasible due to the large number of species and the 3D nature. The popular alternative, standard operator splitting, introduces artificial transients for short-lived species.

  10. Solving vertical transport and chemistry in air pollution models

    NARCIS (Netherlands)

    Berkvens, P.J.F.; Bochev, M.A.; Krol, M.C.; Peters, W.; Verwer, J.G.; Chock, David P.; Carmichael, Gregory R.; Brick, Patricia

    2002-01-01

    For the time integration of stiff transport-chemistry problems from air pollution modelling, standard ODE solvers are not feasible due to the large number of species and the 3D nature. The popular alternative, standard operator splitting, introduces artificial transients for short-lived species.

  11. REKABENTUK MODEL SISTEM GUNA SEMULA AIR WUDHUK

    Directory of Open Access Journals (Sweden)

    Misbahul Muneer Abd Rahman

    2015-08-01

    Full Text Available Ablution is an essential practice as a Muslim because it is an abligatory requiredto perform prayer. A Muslim use approximately 5 litres of water per singleablution. Approximately, a Muslim use 25 litres of treated water to performablution. Islam categorized the used water produced from an ablution asMusta’mal water. Normally Musta'mal water will be left to flow into the drainagesystem. The accumulated amount of wasted water is significant when it ismeasured at a mosque or surau. The quality of Musta'mal water is far better than the typical quality of the waste water produced from washing activities because there were no oil, grease, soap and dirt except for small quantities ofmiccroorganisms. To overcome this problem, this study focused on thediscussion of reusing ablution water based on Shari'ah law which led to thedevelopment of a system (or model to reuse ablution water. This ablution water reuse system consists of several parts including ablution water collection tanks, filters, storage tanks, filling tank, water pump and water sensor. This system runs automatically using a water pump and water sensor. The study found that the Shari'ah law allow reuse water to be used again as ablution water. Based on this study, it is found that the ablution water reused system is feasible and is able to be produced from the engineering aspect.

  12. Developing a Predictive for Unscheduled Maintenance Requirements on United States Air Force Installations

    National Research Council Canada - National Science Library

    Kovich, Matthew D; Norton, J. D

    2008-01-01

    .... This paper develops one such method by using linear regression and time series analysis to develop a predictive model to forecast future year man-hour and funding requirements for unscheduled maintenance...

  13. Air Quality Forecasts Using the NASA GEOS Model

    Science.gov (United States)

    Keller, Christoph A.; Knowland, K. Emma; Nielsen, Jon E.; Orbe, Clara; Ott, Lesley; Pawson, Steven; Saunders, Emily; Duncan, Bryan; Follette-Cook, Melanie; Liu, Junhua; hide

    2018-01-01

    We provide an introduction to a new high-resolution (0.25 degree) global composition forecast produced by NASA's Global Modeling and Assimilation office. The NASA Goddard Earth Observing System version 5 (GEOS-5) model has been expanded to provide global near-real-time forecasts of atmospheric composition at a horizontal resolution of 0.25 degrees (25 km). Previously, this combination of detailed chemistry and resolution was only provided by regional models. This system combines the operational GEOS-5 weather forecasting model with the state-of-the-science GEOS-Chem chemistry module (version 11) to provide detailed chemical analysis of a wide range of air pollutants such as ozone, carbon monoxide, nitrogen oxides, and fine particulate matter (PM2.5). The resolution of the forecasts is the highest resolution compared to current, publically-available global composition forecasts. Evaluation and validation of modeled trace gases and aerosols compared to surface and satellite observations will be presented for constituents relative to health air quality standards. Comparisons of modeled trace gases and aerosols against satellite observations show that the model produces realistic concentrations of atmospheric constituents in the free troposphere. Model comparisons against surface observations highlight the model's capability to capture the diurnal variability of air pollutants under a variety of meteorological conditions. The GEOS-5 composition forecasting system offers a new tool for scientists and the public health community, and is being developed jointly with several government and non-profit partners. Potential applications include air quality warnings, flight campaign planning and exposure studies using the archived analysis fields.

  14. Modeling the residential infiltration of outdoor PM(2.5) in the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air).

    Science.gov (United States)

    Allen, Ryan W; Adar, Sara D; Avol, Ed; Cohen, Martin; Curl, Cynthia L; Larson, Timothy; Liu, L-J Sally; Sheppard, Lianne; Kaufman, Joel D

    2012-06-01

    Epidemiologic studies of fine particulate matter [aerodynamic diameter ≤ 2.5 μm (PM(2.5))] typically use outdoor concentrations as exposure surrogates. Failure to account for variation in residential infiltration efficiencies (F(inf)) will affect epidemiologic study results. We aimed to develop models to predict F(inf) for > 6,000 homes in the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air), a prospective cohort study of PM(2.5) exposure, subclinical cardiovascular disease, and clinical outcomes. We collected 526 two-week, paired indoor-outdoor PM(2.5) filter samples from a subset of study homes. PM(2.5) elemental composition was measured by X-ray fluorescence, and F(inf) was estimated as the indoor/outdoor sulfur ratio. We regressed F(inf) on meteorologic variables and questionnaire-based predictors in season-specific models. Models were evaluated using the R² and root mean square error (RMSE) from a 10-fold cross-validation. The mean ± SD F(inf) across all communities and seasons was 0.62 ± 0.21, and community-specific means ranged from 0.47 ± 0.15 in Winston-Salem, North Carolina, to 0.82 ± 0.14 in New York, New York. F(inf) was generally greater during the warm (> 18°C) season. Central air conditioning (AC) use, frequency of AC use, and window opening frequency were the most important predictors during the warm season; outdoor temperature and forced-air heat were the best cold-season predictors. The models predicted 60% of the variance in 2-week F(inf), with an RMSE of 0.13. We developed intuitive models that can predict F(inf) using easily obtained variables. Using these models, MESA Air will be the first large epidemiologic study to incorporate variation in residential F(inf) into an exposure assessment.

  15. Model calculations of the age of firn air across the Antarctic continent

    Directory of Open Access Journals (Sweden)

    K. A. Kaspers

    2004-01-01

    Full Text Available The age of firn air in Antarctica at pore close-off depth is only known for a few specific sites where firn air has been sampled for analyses. We present a model that calculates the age of firn air at pore close-off depth for the entire Antarctic continent. The model basically uses four meteorological parameters as input (surface temperature, pressure, accumulation rate and wind speed. Using parameterisations for surface snow density, pore close-off density and tortuosity, in combination with a density-depth model and data of a regional atmospheric climate model, distribution of pore close-off depth for the entire Antarctic continent is determined. The deepest pore close-off depth was found for the East Antarctic Plateau near 72° E, 82° S, at 150±15 m (2σ. A firn air diffusion model was applied to calculate the age of CO2 at pore close-off depth. The results predict that the oldest firn gas (CO2 age is located between Dome Fuji, Dome Argos and Vostok at 43° E, 78° S being 148±23 (1σ or 38 for 2σ years old. At this location an atmospheric trace gas record should be obtained. In this study we show that methyl chloride could be recorded with a predicted length of 125 years as an example for trace gas records at this location. The longest record currently available from firn air is derived at South Pole, being 80 years. Sensitivity tests reveal that the locations with old firn air (East Antarctic Plateau have an estimated uncertainty (2σ for the modelled CO2 age at pore close-off depth of 30% and of about 40% for locations with younger firn air (CO2 age typically 40 years. Comparing the modelled age of CO2 at pore close-off depth with directly determined ages at seven sites yielded a correlation coefficient of 0.90 and a slope close to 1, suggesting a high level of confidence for the modelled results in spite of considerable remaining uncertainties.

  16. Wind Speed Prediction Using a Univariate ARIMA Model and a Multivariate NARX Model

    Directory of Open Access Journals (Sweden)

    Erasmo Cadenas

    2016-02-01

    Full Text Available Two on step ahead wind speed forecasting models were compared. A univariate model was developed using a linear autoregressive integrated moving average (ARIMA. This method’s performance is well studied for a large number of prediction problems. The other is a multivariate model developed using a nonlinear autoregressive exogenous artificial neural network (NARX. This uses the variables: barometric pressure, air temperature, wind direction and solar radiation or relative humidity, as well as delayed wind speed. Both models were developed from two databases from two sites: an hourly average measurements database from La Mata, Oaxaca, Mexico, and a ten minute average measurements database from Metepec, Hidalgo, Mexico. The main objective was to compare the impact of the various meteorological variables on the performance of the multivariate model of wind speed prediction with respect to the high performance univariate linear model. The NARX model gave better results with improvements on the ARIMA model of between 5.5% and 10. 6% for the hourly database and of between 2.3% and 12.8% for the ten minute database for mean absolute error and mean squared error, respectively.

  17. Modeling of Complex Adaptive Systems in Air Operations

    National Research Council Canada - National Science Library

    Busch, Timothy E; Trevisani, Dawn A

    2006-01-01

    .... Model predictive control theory provides the basis for this investigation. Given some set of objectives the military commander must devise a sequence of actions that transform the current state to the desired one...

  18. Fuzzy predictive filtering in nonlinear economic model predictive control for demand response

    DEFF Research Database (Denmark)

    Santos, Rui Mirra; Zong, Yi; Sousa, Joao M. C.

    2016-01-01

    The performance of a model predictive controller (MPC) is highly correlated with the model's accuracy. This paper introduces an economic model predictive control (EMPC) scheme based on a nonlinear model, which uses a branch-and-bound tree search for solving the inherent non-convex optimization...

  19. Simplified Model and Response Analysis for Crankshaft of Air Compressor

    Science.gov (United States)

    Chao-bo, Li; Jing-jun, Lou; Zhen-hai, Zhang

    2017-11-01

    The original model of crankshaft is simplified to the appropriateness to balance the calculation precision and calculation speed, and then the finite element method is used to analyse the vibration response of the structure. In order to study the simplification and stress concentration for crankshaft of air compressor, this paper compares calculative mode frequency and experimental mode frequency of the air compressor crankshaft before and after the simplification, the vibration response of reference point constraint conditions is calculated by using the simplified model, and the stress distribution of the original model is calculated. The results show that the error between calculative mode frequency and experimental mode frequency is controlled in less than 7%, the constraint will change the model density of the system, the position between the crank arm and the shaft appeared stress concentration, so the part of the crankshaft should be treated in the process of manufacture.

  20. Development of a model for activated sludge aeration systems: linking air supply, distribution, and demand.

    Science.gov (United States)

    Schraa, Oliver; Rieger, Leiv; Alex, Jens

    2017-02-01

    During the design of a water resource recovery facility, it is becoming industry practice to use simulation software to assist with process design. Aeration is one of the key components of the activated sludge process, and is one of the most important aspects of modelling wastewater treatment systems. However, aeration systems are typically not modelled in detail in most wastewater treatment process modelling studies. A comprehensive dynamic aeration system model has been developed that captures both air supply and demand. The model includes sub-models for blowers, pipes, fittings, and valves. An extended diffuser model predicts both oxygen transfer efficiency within an aeration basin and pressure drop across the diffusers. The aeration system model allows engineers to analyse aeration systems as a whole to determine biological air requirements, blower performance, air distribution, control valve impacts, controller design and tuning, and energy costs. This enables engineers to trouble-shoot the entire aeration system including process, equipment and controls. It also allows much more realistic design of these highly complex systems.

  1. MELSAR: a mesoscale air quality model for complex terrain. Volume 2. Appendices

    Energy Technology Data Exchange (ETDEWEB)

    Allwine, K.J.; Whiteman, C.D.

    1985-04-01

    This final report is submitted as part of the Green River Ambient Model Assessment (GRAMA) project conducted at the US Department of Energy's Pacific Northwest Laboratory for the US Environmental Protection Agency. The GRAMA Program has, as its ultimate goal, the development of validated air quality models that can be applied to the complex terrain of the Green River Formation of western Colorado, eastern Utah and southern Wyoming. The Green River Formation is a geologic formation containing large reserves of oil shale, coal, and other natural resources. Development of these resources may lead to a degradation of the air quality of the region. Air quality models are needed immediately for planning and regulatory purposes to assess the magnitude of these regional impacts. This report documents one of the models being developed for this purpose within GRAMA - specifically a model to predict short averaging time (less than or equal to 24 h) pollutant concentrations resulting from the mesoscale transport of pollutant releases from multiple sources. MELSAR has not undergone any rigorous operational testing, sensitivity analyses, or validation studies. Testing and evaluation of the model are needed to gain a measure of confidence in the model's performance. This report consists of two volumes. This volume contains the Appendices, which include listings of the FORTRAN code and Volume 1 contains the model overview, technical description, and user's guide. 13 figs., 10 tabs.

  2. Spatial distribution of emissions to air - the SPREAD model

    Energy Technology Data Exchange (ETDEWEB)

    Plejdrup, M.S.; Gyldenkaerne, S.

    2011-04-15

    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, a distribution on the 17x17 km EMEP grid has been set up and used in research projects combined with detailed distributions for a few sectors or sub-sectors e.g. a distribution for emissions from road traffic on 1x1 km resolution. SPREAD is developed to generate improved spatial emission data for e.g. air quality modelling in exposure studies. SPREAD includes emission distributions for each sector in the Danish inventory system; stationary combustion, mobile sources, fugitive emissions from fuels, industrial processes, solvents and other product use, agriculture and waste. This model enables generation of distributions for single sectors and for a number of sub-sectors and single sources as well. This report documents the methodologies in this first version of SPREAD and presents selected results. Further, a number of potential improvements for later versions of SPREAD are addressed and discussed. (Author)

  3. Modeling of air pollution from the power plant ash dumps

    Science.gov (United States)

    Aleksic, Nenad M.; Balać, Nedeljko

    A simple model of air pollution from power plant ash dumps is presented, with emission rates calculated from the Bagnold formula and transport simulated by the ATDL type model. Moisture effects are accounted for by assumption that there is no pollution on rain days. Annual mean daily sedimentation rates, calculated for the area around the 'Nikola Tesla' power plants near Belgrade for 1987, show reasonably good agreement with observations.

  4. 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...... that involve several types of numerical computations. The computers considered in our study are the Connection Machines CM-200 and CM-5, and the MasPar MP-2216...

  5. Air oxidation of Zircaloy, Part 2: New model for Zry-4 oxidation

    Energy Technology Data Exchange (ETDEWEB)

    Stempniewicz, M.M., E-mail: stempniewicz@nrg.eu

    2016-05-15

    Highlights: • Recommended set of correlations proposed for air oxidation of Zircaloy-4. • New breakaway correlation for air oxidation of Zircaloy-4. • Improved accuracy of predicting air oxidation of Zircaloy-4. • Models applicable to analyses of accidents in Spent Fuel Pool. - Abstract: The accident in Fukushima brought up new issues in the area of safety of nuclear reactors. Among others, Spent Fuel Pool accidents gained new focus. The computer codes applicable for safety analyses of Nuclear Power Plants have limited verification and validation in this area and their applicability remains still to be proven. An important phenomenon occurring during loss of water in SFP is air oxidation of Zircaloy cladding material. Mathematical modeling of this phenomenon in computer codes has been under development during the last years. This document presents a review of models for air oxidation of Zircaloy, including: up to date models available in open literature, as well as models available in computer codes: ASTEC, MELCOR, and SPECTRA. The models were tested by performing simulations of a number air oxidation experiments from ANL, KIT, and IRSN. As a result of this work, a recommended set of correlations, applicable for wide range of temperatures, including pre- and post-breakaway reaction, has been selected. For the pre-breakaway (parabolic) regime the correlation of Benjamin et al. (Sandia National Laboratories, Albuquerque, NM, 1979) was selected for the low temperatures and a new correlation has been proposed for the high temperatures. For the post-breakaway (linear) regime, Boase and Vandergraaf (Nucl. Technol., 1977;32:60–71) were selected for the low temperatures and a new correlation has been proposed for the high temperatures. Furthermore, a new model for the breakaway transition has been proposed. The correlation set is applicable for Zircaloy-4, for practically the entire temperature range. The recommended set provides an improved accuracy of results

  6. Plutonium air transportable package Model PAT-1. Safety analysis report

    International Nuclear Information System (INIS)

    1978-02-01

    The document is a Safety Analysis Report for the Plutonium Air Transportable Package, Model PAT-1, which was developed by Sandia Laboratories under contract to the Nuclear Regulatory Commission (NRC). The document describes the engineering tests and evaluations that the NRC staff used as a basis to determine that the package design meets the requirements specified in the NRC ''Qualification Criteria to Certify a Package for Air Transport of Plutonium'' (NUREG-0360). By virtue of its ability to meet the NRC Qualification Criteria, the package design is capable of safely withstanding severe aircraft accidents. The document also includes engineering drawings and specifications for the package. 92 figs, 29 tables

  7. A geographic approach to modelling human exposure to traffic air pollution using GIS. Separate appendix report

    Energy Technology Data Exchange (ETDEWEB)

    Solvang Jensen, S.

    1998-10-01

    A new exposure model has been developed that is based on a physical, single media (air) and single source (traffic) micro environmental approach that estimates traffic related exposures geographically with the postal address as exposure indicator. The micro environments: residence, workplace and street (road user exposure) may be considered. The model estimates outdoor levels for selected ambient air pollutants (benzene, CO, NO{sub 2} and O{sub 3}). The influence of outdoor air pollution on indoor levels can be estimated using average (I/O-ratios. The model has a very high spatial resolution (the address), a high temporal resolution (one hour) and may be used to predict past, present and future exposures. The model may be used for impact assessment of control measures provided that the changes to the model inputs are obtained. The exposure model takes advantage of a standard Geographic Information System (GIS) (ArcView and Avenue) for generation of inputs, for visualisation of input and output, and uses available digital maps, national administrative registers and a local traffic database, and the Danish Operational Street Pollution Model (OSPM). The exposure model presents a new approach to exposure determination by integration of digital maps, administrative registers, a street pollution model and GIS. New methods have been developed to generate the required input parameters for the OSPM model: to geocode buildings using cadastral maps and address points, to automatically generate street configuration data based on digital maps, the BBR and GIS; to predict the temporal variation in traffic and related parameters; and to provide hourly background levels for the OSPM model. (EG)

  8. A geographic approach to modelling human exposure to traffic air pollution using GIS

    Energy Technology Data Exchange (ETDEWEB)

    Solvang Jensen, S.

    1998-10-01

    A new exposure model has been developed that is based on a physical, single media (air) and single source (traffic) micro environmental approach that estimates traffic related exposures geographically with the postal address as exposure indicator. The micro environments: residence, workplace and street (road user exposure) may be considered. The model estimates outdoor levels for selected ambient air pollutants (benzene, CO, NO{sub 2} and O{sub 3}). The influence of outdoor air pollution on indoor levels can be estimated using average (I/O-ratios. The model has a very high spatial resolution (the address), a high temporal resolution (one hour) and may be used to predict past, present and future exposures. The model may be used for impact assessment of control measures provided that the changes to the model inputs are obtained. The exposure model takes advantage of a standard Geographic Information System (GIS) (ArcView and Avenue) for generation of inputs, for visualisation of input and output, and uses available digital maps, national administrative registers and a local traffic database, and the Danish Operational Street Pollution Model (OSPM). The exposure model presents a new approach to exposure determination by integration of digital maps, administrative registers, a street pollution model and GIS. New methods have been developed to generate the required input parameters for the OSPM model: to geocode buildings using cadastral maps and address points, to automatically generate street configuration data based on digital maps, the BBR and GIS; to predict the temporal variation in traffic and related parameters; and to provide hourly background levels for the OSPM model. (EG) 109 refs.

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

    Science.gov (United States)

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

    2017-10-01

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

  10. Coupling model of aerobic waste degradation considering temperature, initial moisture content and air injection volume.

    Science.gov (United States)

    Ma, Jun; Liu, Lei; Ge, Sai; Xue, Qiang; Li, Jiangshan; Wan, Yong; Hui, Xinminnan

    2018-03-01

    A quantitative description of aerobic waste degradation is important in evaluating landfill waste stability and economic management. This research aimed to develop a coupling model to predict the degree of aerobic waste degradation. On the basis of the first-order kinetic equation and the law of conservation of mass, we first developed the coupling model of aerobic waste degradation that considered temperature, initial moisture content and air injection volume to simulate and predict the chemical oxygen demand in the leachate. Three different laboratory experiments on aerobic waste degradation were simulated to test the model applicability. Parameter sensitivity analyses were conducted to evaluate the reliability of parameters. The coupling model can simulate aerobic waste degradation, and the obtained simulation agreed with the corresponding results of the experiment. Comparison of the experiment and simulation demonstrated that the coupling model is a new approach to predict aerobic waste degradation and can be considered as the basis for selecting the economic air injection volume and appropriate management in the future.

  11. Incremental Validity of Biographical Data in the Prediction of En Route Air Traffic Control Specialist Technical Skills

    Science.gov (United States)

    2012-07-01

    Previous research demonstrated that an empirically-keyed, response-option scored biographical data (biodata) : scale predicted supervisory ratings of air traffic control specialist (ATCS) job performance (Dean & Broach, : 2011). This research f...

  12. Differential Prediction of FAA Academy Performance on the Basis of Race and Written Air Traffic Control Specialist Aptitude Test Scores

    National Research Council Canada - National Science Library

    Broach, Dana

    1999-01-01

    The written air traffic control specialist (ATCS) aptitude test battery was evaluated for evidence of predictive bias within the framework of the Uniform Guidelines on Employee Selection Procedures (29 CFR 1607...

  13. Modeling and Economics of Air Pollution Abatement Policies in the Valley of Mexico

    Science.gov (United States)

    Jazcilevich, A.; Garcia-Reynoso, A.

    2008-05-01

    Using meteorological and air quality models it has been possible to study the air pollution phenomenon in the Valley of Mexico. This capability together with the development of a system to obtain vehicular emissions in Mexico City, allow estimating the possible reductions in Ozone concentrations because of the introduction of new car technologies such as Hybrid Electric Vehicles (HEV´s) in Mexico City. Using this data together with epidemiological studies, a prediction on avoided cases of mortality and morbidity due to reduction in ambient concentrations of Ozone are obtained. Monetary values of these reductions are calculated valuating this car technology change. This methodology will allow the prediction on health benefits because of the introduction of bio fuels and other vehicular technologies in Mexico City.

  14. Calibration of the heat balance model for prediction of car climate

    OpenAIRE

    Jícha Miroslav; Fišer Jan; Pokorný Jan

    2012-01-01

    In the paper, the authors refer to development a heat balance model to predict car climate and power heat load. Model is developed in Modelica language using Dymola as interpreter. It is a dynamical system, which describes a heat exchange between car cabin and ambient. Inside a car cabin, there is considered heat exchange between air zone, interior and air-conditioning system. It is considered 1D heat transfer with a heat accumulation and a relative movement Sun respect to the car cabin, whil...

  15. Impact of satellite data assimilation on the predictability of monsoon intraseasonal oscillations in a regional model

    KAUST Repository

    Parekh, Anant

    2017-04-07

    This study reports the improvement in the predictability of circulation and precipitation associated with monsoon intraseasonal oscillations (MISO) when the initial state is produced by assimilating Atmospheric Infrared Sounder (AIRS) retrieved temperature and water vapour profiles in Weather Research Forecast (WRF) model. Two separate simulations are carried out for nine years (2003 to 2011) . In the first simulation, forcing is from National Centers for Environmental Prediction (NCEP, CTRL) and in the second, apart from NCEP forcing, AIRS temperature and moisture profiles are assimilated (ASSIM). Ten active and break cases are identified from each simulation. Three dimensional temperature states of identified active and break cases are perturbed using twin perturbation method and carried out predictability tests. Analysis reveals that the limit of predictability of low level zonal wind is improved by four (three) days during active (break) phase. Similarly the predictability of upper level zonal wind (precipitation) is enhanced by four (two) and two (four) days respectively during active and break phases. This suggests that the initial state using AIRS observations could enhance predictability limit of MISOs in WRF. More realistic baroclinic response and better representation of vertical state of atmosphere associated with monsoon enhance the predictability of circulation and rainfall.

  16. Development of a distributed air pollutant dry deposition modeling framework

    International Nuclear Information System (INIS)

    Hirabayashi, Satoshi; Kroll, Charles N.; Nowak, David J.

    2012-01-01

    A distributed air pollutant dry deposition modeling system was developed with a geographic information system (GIS) to enhance the functionality of i-Tree Eco (i-Tree, 2011). With the developed system, temperature, leaf area index (LAI) and air pollutant concentration in a spatially distributed form can be estimated, and based on these and other input variables, dry deposition of carbon monoxide (CO), nitrogen dioxide (NO 2 ), sulfur dioxide (SO 2 ), and particulate matter less than 10 microns (PM10) to trees can be spatially quantified. Employing nationally available road network, traffic volume, air pollutant emission/measurement and meteorological data, the developed system provides a framework for the U.S. city managers to identify spatial patterns of urban forest and locate potential areas for future urban forest planting and protection to improve air quality. To exhibit the usability of the framework, a case study was performed for July and August of 2005 in Baltimore, MD. - Highlights: ► A distributed air pollutant dry deposition modeling system was developed. ► The developed system enhances the functionality of i-Tree Eco. ► The developed system employs nationally available input datasets. ► The developed system is transferable to any U.S. city. ► Future planting and protection spots were visually identified in a case study. - Employing nationally available datasets and a GIS, this study will provide urban forest managers in U.S. cities a framework to quantify and visualize urban forest structure and its air pollution removal effect.

  17. Urban compaction or dispersion? An air quality modelling study

    Science.gov (United States)

    Martins, Helena

    2012-07-01

    Urban sprawl is altering the landscape, with current trends pointing to further changes in land use that will, in turn, lead to changes in population, energy consumption, atmospheric emissions and air quality. Urban planners have debated on the most sustainable urban structure, with arguments in favour and against urban compaction and dispersion. However, it is clear that other areas of expertise have to be involved. Urban air quality and human exposure to atmospheric pollutants as indicators of urban sustainability can contribute to the discussion, namely through the study of the relation between urban structure and air quality. This paper addresses the issue by analysing the impacts of alternative urban growth patterns on the air quality of Porto urban region in Portugal, through a 1-year simulation with the MM5-CAMx modelling system. This region has been experiencing one of the highest European rates of urban sprawl, and at the same time presents a poor air quality. As part of the modelling system setup, a sensitivity study was conducted regarding different land use datasets and spatial distribution of emissions. Two urban development scenarios were defined, SPRAWL and COMPACT, together with their new land use and emission datasets; then meteorological and air quality simulations were performed. Results reveal that SPRAWL land use changes resulted in an average temperature increase of 0.4 °C, with local increases reaching as high as 1.5 °C. SPRAWL results also show an aggravation of PM10 annual average values and an increase in the exceedances to the daily limit value. For ozone, differences between scenarios were smaller, with SPRAWL presenting larger concentration differences than COMPACT. Finally, despite the higher concentrations found in SPRAWL, population exposure to the pollutants is higher for COMPACT because more inhabitants are found in areas of highest concentration levels.

  18. Regional air-quality and acid-deposition modeling and the role for visualization

    International Nuclear Information System (INIS)

    Novak, J.H.; Dennis, R.L.

    1991-11-01

    The U.S. Environmental Protection Agency (EPA) uses air quality and deposition models to advance the scientific understanding of basic physical and chemical processes related to air pollution, and to assess the effectiveness of alternative emissions control strategies. The paper provides a brief technical description of several regional scale atmospheric models, their current use within EPA, and related data analysis issues. Spatial analysis is a key component in the evaluation and interpretation of the model predictions. Thus, the authors highlight several types of analysis enhancements focusing on those related to issues of spatial scale, user access to models and analysis tools, and consolidation of air quality modeling and graphical analysis capabilities. They discuss their initial experience with a Geographical Information System (GIS) pilot project that generated the initial concepts for the design of an integrated modeling and analysis environment. And finally, they present current plans to evolve this modeling/visualization approach to a distributed, heterogeneous computing environment which enables any research scientist or policy analyst to use high performance visualization techniques from his/her desktop

  19. Model Predictive Control for an Industrial SAG Mill

    DEFF Research Database (Denmark)

    Ohan, Valeriu; Steinke, Florian; Metzger, Michael

    2012-01-01

    We discuss Model Predictive Control (MPC) based on ARX models and a simple lower order disturbance model. The advantage of this MPC formulation is that it has few tuning parameters and is based on an ARX prediction model that can readily be identied using standard technologies from system identic...

  20. Uncertainties in spatially aggregated predictions from a logistic regression model

    NARCIS (Netherlands)

    Horssen, P.W. van; Pebesma, E.J.; Schot, P.P.

    2002-01-01

    This paper presents a method to assess the uncertainty of an ecological spatial prediction model which is based on logistic regression models, using data from the interpolation of explanatory predictor variables. The spatial predictions are presented as approximate 95% prediction intervals. The