Comparison of land-use regression models between Great Britain and the Netherlands |
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| Authors: | D Vienneau K de Hoogh R Beelen P Fischer G Hoek D Briggs |
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| Institution: | 1. The Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China;2. Joint Center for Global Change Studies (JCGCS), Beijing 100875, China;3. Department of Civil & Environmental Engineering, University of Washington, Seattle, WA 98195, United States;4. Department of Epidemiology and Environmental Health, School of Public Health and Health Professions, University at Buffalo, Buffalo, NY, United States;5. Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA 98195, United States;6. Department of Biostatistics, University of Washington, Seattle, WA 98195, United States;1. Swiss Tropical and Public Health Institute, Socinstrasse 57, 4051 Basel, Switzerland;2. University of Basel, Petersplatz 1, Postfach, 4001 Basel, Switzerland;3. Institute for Risk Assessment Sciences (IRAS), Utrecht University, Postbrus 80125, 3508 TC Utrecht, the Netherlands;4. School of Geography, Geology and the Environment, University of Leicester, University Road, Leicester LE1 7RH, UK;5. Institute for Occupational, Social and Environmental Medicine, Centre for Health and Society, Medical Faculty, Heinrich Heine University Düsseldorf, Universitätsstraße 1, 40225 Düsseldorf, Germany;6. Department of Environmental Science, Aarhus University, 4000 Roskilde, Denmark;7. Interface Demography – Department of Sociology, Vrije Universiteit Brussel, Boulevard de la Plaine 2, 1050 Ixelles, Brussel, Belgium;8. Unit Health & Environment — Sciensano, Rue Juliette Wytsmanstraat 14, 1050, Brussels, Belgium;9. Department of Physics and Atmospheric Science, Dalhousie University, B3H 4R2 Halifax, Nova Scotia, Canada;10. Danish Cancer Society Research Center, Strandboulevarden 49, 2100 Copenhagen, Denmark;11. Department of Hygiene, Epidemiology and Medical Statistics, Medical School, National and Kapodistrian University of Athens, 75 Mikras Asias Str, 115 27 Athens, Greece;12. Department Population Health Sciences, Department of Analytical, Environmental and Forensic Sciences, School of Population Health & Environmental Sciences, King''s College Strand, London WC2R 2LS, UK;13. National Institute for Public Health and the Environment (RIVM), Antonie van Leeuwenhoeklaan 9, 3721 MA Bilthoven, Netherlands;14. Atomic and Molecular Physics Division, Harvard-Smithsonian Center for Astrophysics, 60 Garden St, Cambridge, MA 02138, United States of America;15. Division of Environmental Medicine, Norwegian Institute of Public Health, PO Box 4404, Nydalen, N-0403 Oslo, Norway;p. Department of Epidemiology, Lazio Region Health Service/ASL, Roma 1, Via Cristoforo Colombo, 112 – 00147 Rome, Italy;q. Institute of Environmental Medicine, Karolinska Institutet, SE-171 77 Stockholm, Sweden;r. Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Institute of Epidemiology, Ingolstädter Landstr. 1, D-85764 Neuherberg, Germany;s. Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, Netherlands |
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| Abstract: | Land-use regression models have increasingly been applied for air pollution mapping at typically the city level. Though models generally predict spatial variability well, the structure of models differs widely between studies. The observed differences in the models may be due to artefacts of data and methodology or underlying differences in source or dispersion characteristics. If the former, more standardised methods using common data sets could be beneficial. We compared land-use regression models for NO2 and PM10, developed with a consistent protocol in Great Britain (GB) and the Netherlands (NL).Models were constructed on the basis of 2001 annual mean concentrations from the national air quality networks. Predictor variables used for modelling related to traffic, population, land use and topography. Four sets of models were developed for each country. First, predictor variables derived from data sets common to both countries were used in a pooled analysis, including an indicator for country and interaction terms between country and the identified predictor variables. Second, the common data sets were used to develop individual baseline models for each country. Third, the country-specific baseline models were applied after calibration in the other country to explore transferability. The fourth model was developed using the best possible predictor variables for each country.A common model for GB and NL explained NO2 concentrations well (adjusted R2 0.64), with no significant differences in intercept and slopes between the two countries. The country-specific model developed on common variables for NL but not GB improved the prediction.The performance of models based upon common data was only slightly worse than models optimised with local data. Models transferred to the other country performed substantially worse than the country-specific models. In conclusion, care is needed both in transferring models across different study areas, and in developing large inter-regional LUR models. |
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