Modelling personal exposure to particulate air pollution: An assessment of time-integrated activity modelling,Monte Carlo simulation & artificial neural network approaches |
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| Affiliation: | 1. Braun School of Public Health and Community Medicine, Hebrew University-Hadassah and The Hebrew University Center of Excellence in Agriculture and Environmental Health, Jerusalem, Israel;2. Public Health Services, Ministry of Health, Jerusalem, Israel;3. Institute and Outpatient Clinic of Occupational, Social and Environmental Medicine, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany;4. Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel;5. Department of Management, Bar Ilan University, Ramat Gan, Israel;6. Israel Center for Disease Control, Ministry of Health, Ramat Gan, Israel;1. Bavarian Health and Food Safety Authority, Department of Chemical Safety and Toxicology, Pfarrstrasse 3, D-80538 Munich, Germany;2. Bavarian Health and Food Safety Authority, Department of Pesticides, Contaminants, Nitrosamines, Radioactivity, Dioxins, Irradiation, Veterinaerstrasse 2, D-85764 Oberschleissheim, Germany;3. Bavarian Health and Food Safety Authority, Veterinaerstrasse 2, D-85764 Oberschleissheim, Germany;1. Centre for Environmental Sciences, Hasselt University, Diepenbeek, Belgium;2. Department of Analytical and Environmental Chemistry, Vrije Universiteit Brussel (VUB), Brussel, Belgium;3. Integrated Psychiatric Centre OPZ Geel, Geel, Belgium;4. Flemish Institute for Technological Research, Environmental Risk and Health, Mol, Belgium;5. Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium;6. Department of Health, Provincial Institute for Hygiene, Antwerp, Belgium;7. Interuniversity Institute for Biostatistics and Statistical Bioinformatics, Hasselt University, Diepenbeek, Belgium;8. Department of Public Health, Ghent University, Ghent, Belgium;9. Department of Radiotherapy and Nuclear Medicine, Ghent University, Ghent, Belgium;10. Department of Neurology, Sint Dimphna Hospital, Geel, Belgium;11. School of Public Health and Primary Care, KU Leuven, Leuven, Belgium;1. Slovak Medical University, Limbová 14, 83303 Bratislava, Slovakia;2. Department of Stomatology and Maxillofacial Surgery, Comenius University, Faculty of Medicine in Bratislava, Špitálska 24, 813 72 Bratislava, Slovakia;3. Division of Environmental and Occupational Health, Department of Public Health Sciences, School of Medicine, University of California Davis, One Shields Avenue, Med-Sci 1C, Davis, CA, USA;4. Department of Public Health Sciences, University of Rochester School of Medicine and Dentistry, 265 Crittenden Blvd, CU 420644, Rochester, NY 14642, USA;1. Section of Environmental Health, Department of Public Health, University of Copenhagen, CSS, Oester Farimagsgade 5A, 1014 Copenhagen K, Denmark;2. Institute for Prevention and Occupational Medicine of the German Social Accident Insurance, Institute of the Ruhr-Universität Bochum (IPA), Bürkle-de-la-Camp-Platz 1, 44789 Bochum, Germany;3. Department of Public Health, University of Southern Denmark, J.B. Winsløws Vej 17A, 5000 Odense C, Denmark;1. Department of Hygiene, Social and Environmental Medicine, Ruhr-University Bochum, Universitätsstraße 150, D-44801 Bochum, Germany;2. Department of Developmental Psychology, Ruhr-University Bochum, Universitätsstraße 150, D-44801 Bochum, Germany |
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| Abstract: | An experimental assessment of personal exposure to PM10 in 59 office workers was carried out in Dublin, Ireland. 255 samples of 24-h personal exposure were collected in real time over a 28 month period. A series of modelling techniques were subsequently assessed for their ability to predict 24-h personal exposure to PM10. Artificial neural network modelling, Monte Carlo simulation and time–activity based models were developed and compared. The results of the investigation showed that using the Monte Carlo technique to randomly select concentrations from statistical distributions of exposure concentrations in typical microenvironments encountered by office workers produced the most accurate results, based on 3 statistical measures of model performance. The Monte Carlo simulation technique was also shown to have the greatest potential utility over the other techniques, in terms of predicting personal exposure without the need for further monitoring data. Over the 28 month period only a very weak correlation was found between background air quality and personal exposure measurements, highlighting the need for accurate models of personal exposure in epidemiological studies. |
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| Keywords: | Air pollution modelling Personal exposure Monte Carlo simulation Neural networks Time–activity models |
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