The use of wind fields in a land use regression model to predict air pollution concentrations for health exposure studies |
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| Institution: | 1. School of Geography and Earth Sciences, McMaster University, Hamilton, Ontario, Canada;2. Meteorological Service of Canada, Environment Canada, Toronto, Ontario, Canada;3. Division of Environmental Health Sciences, School of Public Health, University of California, Berkeley, CA, USA;1. Swiss Tropical and Public Health Institute, Socinstrasse 57, 4051 Basel, Switzerland;2. University of Basel, Petersplatz 1, 4001 Basel, Switzerland;3. MRC-PHE Centre for Environment and Health, Department of Epidemiology and Biostatistics, Imperial College London, St Mary’s Campus, Norfolk Place, W2 1PG London, United Kingdom;4. Department of Physics and Atmospheric Science, Dalhousie University, 6310 Coburg Rd., Halifax, NS, Canada B3H 4R2;5. Harvard-Smithsonian Center for Astrophysics, 60 Garden Street, Cambridge, MA 02138, USA;6. John R. Kiely Professor of Civil & Environmental Engineering, University of Washington, Wilcox 268, Seattle, WA 98195, USA;7. Department of Epidemiology, Lazio Regional Health Service, Via Cristoforo Colombo, 112-00147 Rome, Italy;8. Centre for Research in Environmental Epidemiology (CREAL), Doctor Aiguader 88, E-08003 Barcelona, Spain;9. CIBER Epidemiología y Salud Pública (CIBERESP), Av. Monforte de Lemos, 3-5 Pabellón 11. Planta 0, 28029 Madrid, Spain;10. Department of Environmental Sciences, Vytauto Didziojo Universitetas, K. Donelaicio 58, Kaunas 44248, Lithuania;11. Department of Public Health and Clinical Medicine, Occupational and Environmental Medicine, Umea University, SE-901 87 Umea, Sweden;12. Unit of Cancer Epidemiology, Citta’ della Salute e della Scienza University Hospital and Centre for Cancer Prevention, Corso Bramante, 88, 10126 Turin, Italy;13. Ludwig Maximilians University Munich, University Hospital, Munich Institute and Outpatient Clinic for Occupational, Social and Environmental Medicine, Ziemssenstr. 1, d-80336 Munich, Germany;14. Helmholtz Zentrum München - German Research Center for Environmental Health, Institute of Epidemiology I, Ingolstädter Landstr. 1, d-85764 Neuherberg, Germany;p. Medical Faculty, Heinrich-Heine University of Düsseldorf, Universitätsstr. 1, 40225 Düsseldorf, Germany;q. INSERM, U1168, VIMA: Aging and Chronic Diseases, Epidemiological and Public Health Approaches, 16, Avenue Paul Vaillant Couturier, 94807 Villejuif, France;r. Université Versailles St-Quentin-en-Yvelines, UMR-S 1168, 2 Avenue de la Source de la Bièvre, 78180 Montigny le Bretonneux, France;s. Universitat Pompeu Fabra (UPF), Plaça de la Mercè, 10-12, 08002 Barcelona, Spain;t. Department of Hygiene, Epidemiology and Medical Statistics, University of Athens Medical School, 75, Mikras Asias Street, 115 27 Athens, Greece;u. Department of Primary Care & Public Health Sciences and Environmental Research Group, King’s College London, Franklin-Wilkins Building, 150 Stamford Street, London SE1 9NH, UK;v. Institute of Environmental Medicine, Karolinska Institutet, Nobels väg 13, Solna, 171 65 Stockholm, Sweden;w. Geography, School of Environment, Education and Development, University of Manchester, Manchester M13 3PL, UK;x. Inserm and Univ. Grenoble-Alpes, IAB (U1209), Team of Environmental Epidemiology, 38000 Grenoble, France;y. National Institute for industrial Environment and Risks (INERIS), Parc Technologique ALATA, 60550 Verneuil en Halatte, France;z. Centre for Environmental Policy, Imperial College London, South Kensington Campus, London SW7 2AZ, UK;11. IMIM (Hospital del Mar Research Institute), Dr. Aiguader, 88, 08003 Barcelona, Spain;12. Division of Environmental Medicine, Norwegian Institute of Public Health, PO Box 4404, Nydalen, N-0403 Oslo, Norway;13. Danish Cancer Society Research Center, Strandboulevarden 49, DK-2100 Copenhagen, Denmark;14. Department of Environmental Science, Aarhus University, Frederiksborgvej 399, P.O. Box 358, DK-4000 Roskilde, Denmark;15. Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstr. 1, d-85764 Neuherberg, Germany;16. ECMWF, Shinfield Park, Reading RG2 9AX, United Kingdom;17. National Public Health Center, Albert Flórián út 2-6, H-1097 Budapest, Hungary;18. French Institut for Public Health, 12, rue du Val d′Osne, 94415 Saint-Maurice, France;19. Environmental Chemical Processes Laboratory (ECPL), Department of Chemistry, University of Crete, 71003 Heraklion, Greece;110. Department of Environmental and Occupational Health Sciences, University of Washington, Box 357234, Seattle, WA 98195, USA;111. National Institute for Health and Welfare (THL), Department of Health Protection, Living Environment and Health Unit, P.O. Box 95, FI-70701 Kuopio, Finland;112. Institute of Epidemiology and Medical Biometry, Ulm University, Helmholtzstr. 22, 89081 Ulm, Germany;113. Institute for Risk Assessment Sciences, Utrecht University, Yalelaan 2, 3584 CM Utrecht, The Netherlands;114. Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Universiteitsweg 100, 3584 CG Utrecht, The Netherlands;1. State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Xianlin Campus, Box 624, 163 Xianlin Avenue, Nanjing 210023, China;2. Lamont-Doherty Earth Observatory, Columbia University, P.O. Box 1000, 61 Rt. 9W, Palisades, NY 10964, USA;1. School of Population Health, The University of Queensland, Brisbane, Australia;2. School of Geography, Planning and Environmental Management, The University of Queensland, Brisbane, Australia;3. Department of Civil Engineering, The University of Minnesota, Minneapolis, USA;4. School of Public Health and Social Work & Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Australia;1. Institute for Risk Assessment Sciences (IRAS), Utrecht University, P.O. Box 80178, 3508 TD Utrecht, The Netherlands;2. MRC-HPA Centre for Environment and Health, Department of Epidemiology and Biostatistics, Imperial College London, London, United Kingdom;3. Department of Hygiene, Epidemiology & Medical Statistics, Medical School, National and Kapodistrian University of Athens, Medical School, Athens, Greece;4. Swiss Tropical & Public Health Institute, Basel, Switzerland;5. University of Basel, Basel, Switzerland;6. Department of Environmental & Occupational Health Sciences, University of Washington, Seattle, USA;7. Unit of Epidemiology & Medical Statistics, Department of Public Health and Community Medicine, University of Verona, Italy;8. Danish Cancer Society, Copenhagen, Denmark;9. Environmental Chemical Processes Laboratory, University of Crete, Heraklion, Greece;10. Department of Environmental Health, National Institute for Health and Welfare, Kuopio, Finland;11. French Institute for Public Health Surveillance, Saint-Maurice, France;12. Helmholtz Zentrum München, German Research Center for Environmental Health, Institutes of Epidemiology I and II, Neuherberg, Germany;13. University of Augsburg, Environmental Science Center, Augsburg, Germany;14. Department of Environmental Epidemiology, National Institute of Environmental Health, Budapest, Hungary;15. Vytautas Magnus University, Kaunas, Lithuania;p. Centre for Occupational and Environmental Health, The University of Manchester, Manchester, England;q. School of Environment and Development (Geography), The University of Manchester, Manchester, England;r. Division of Epidemiology, Norwegian Institute of Public Health, Oslo, Norway;s. Epidemiology Department, Lazio Regional Health Service, Rome, Italy;t. Regional Reference Centre on Environment and Health, ARPA Emilia Romagna, Modena, Italy;u. IUF Leibniz Research Institute for Environmental Medicine, University of Düsseldorf, Düsseldorf, Germany;v. Institute for Medical Informatics, Biometry and Epidemiology, University of Duisburg-Essen, Essen, Germany;w. Air Quality & Sustainable Nanotechnology, Institute for Energy and Environmental Technology (IUTA) e.V., Duisburg, Germany;x. Center for Research in Environmental Epidemiology (CREAL), Barcelona, Spain;y. IMIM (Hospital del Mar Research Institute), Barcelona, Spain;z. CIBER Epidemiología y Salud Pública (CIBERESP), Spain;11. Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden;22. Division of Occupational and Environmental Medicine, Department of Public Health and Clinical Medicine, Umeå University, Sweden;33. Department of Geography and Economic History, Umeå University, Sweden;44. Environmental Risk and Health Unit, VITO-MRG (Flemish Institute for Technological Research), Mol, Belgium;55. Hasselt University, Diepenbeek, Belgium;66. School of Public Health, University of California, Berkeley, USA;77. Centre for Environmental Health, National Institute for Public Health and the Environment, Bilthoven, The Netherlands;88. Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands;1. School of Architecture, Southeast University, 2nd Sipailou Street, Nanjing, 210096, China;2. Urban Institute, ICOSS, 219 Portobello, Sheffield, S1 4DP, United Kingdom |
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| Abstract: | A methodology is developed to include wind flow effects in land use regression (LUR) models for predicting nitrogen dioxide (NO2) concentrations for health exposure studies. NO2 is widely used in health studies as an indicator of traffic-generated air pollution in urban areas. Incorporation of high-resolution interpolated observed wind direction from a network of 38 weather stations in a LUR model improved NO2 concentration estimates in densely populated, high traffic and industrial/business areas in Toronto-Hamilton urban airshed (THUA) of Ontario, Canada. These small-area variations in air pollution concentrations that are probably more important for health exposure studies may not be detected by sparse continuous air pollution monitoring network or conventional interpolation methods. Observed wind fields were also compared with wind fields generated by Global Environmental Multiscale-High resolution Model Application Project (GEM-HiMAP) to explore the feasibility of using regional weather forecasting model simulated wind fields in LUR models when observed data are either sparse or not available. While GEM-HiMAP predicted wind fields well at large scales, it was unable to resolve wind flow patterns at smaller scales. These results suggest caution and careful evaluation of regional weather forecasting model simulated wind fields before incorporating into human exposure models for health studies. This study has demonstrated that wind fields may be integrated into the land use regression framework. Such integration has a discernable influence on both the overall model prediction and perhaps more importantly for health effects assessment on the relative spatial distribution of traffic pollution throughout the THUA. Methodology developed in this study may be applied in other large urban areas across the world. |
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