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Development of Land Use Regression Models for PM 2.5 , PM 2.5 Absorbance, PM 10 and PM coarse in 20 European Study Areas; Results of the ESCAPE Project

Eeftens, Marloes, Beelen, Rob, de Hoogh, Kees, Bellander, Tom, Cesaroni, Giulia, Cirach, Marta, Declercq, Christophe, Dėdelė, Audrius, Dons, Evi, de Nazelle, Audrey, Dimakopoulou, Konstantina, Eriksen, Kirsten, Falq, Grégoire, Fischer, Paul, Galassi, Claudia, Gražulevičienė, Regina, Heinrich, Joachim, Hoffmann, Barbara, Jerrett, Michael, Keidel, Dirk, Korek, Michal, Lanki, Timo, Lindley, Sarah, Madsen, Christian, Mölter, Anna, Nádor, Gizella, Nieuwenhuijsen, Mark, Nonnemacher, Michael, Pedeli, Xanthi, Raaschou-Nielsen, Ole, Patelarou, Evridiki, Quass, Ulrich, Ranzi, Andrea, Schindler, Christian, Stempfelet, Morgane, Stephanou, Euripides, Sugiri, Dorothea, Tsai, Ming-Yi, Yli-Tuomi, Tarja, Varró, Mihály J, Vienneau, Danielle, Klot, Stephanie von, Wolf, Kathrin, Brunekreef, Bert, Hoek, Gerard·2012·Environmental Science & Technology
DOI 10.1021/es301948k
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Abstract

Land Use Regression (LUR) models have been used increasingly for modeling small-scale spatial variation in air pollution concentrations and estimating individual exposure for participants of cohort studies. Within the ESCAPE project, concentrations of PM(2.5), PM(2.5) absorbance, PM(10), and PM(coarse) were measured in 20 European study areas at 20 sites per area. GIS-derived predictor variables (e.g., traffic intensity, population, and land-use) were evaluated to model spatial variation of annual average concentrations for each study area. The median model explained variance (R(2)) was 71% for PM(2.5) (range across study areas 35-94%). Model R(2) was higher for PM(2.5) absorbance (median 89%, range 56-97%) and lower for PM(coarse) (median 68%, range 32- 81%). Models included between two and five predictor variables, with various traffic indicators as the most common predictors. Lower R(2) was related to small concentration variability or limited availability of predictor variables, especially traffic intensity. Cross validation R(2) results were on average 8-11% lower than model R(2). Careful selection of monitoring sites, examination of influential observations and skewed variable distributions were essential for developing stable LUR models. The final LUR models are used to estimate air pollution concentrations at the home addresses of participants in the health studies involved in ESCAPE.

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Research works printed but not linked: 32

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Metadata

Authors
Eeftens, M., Beelen, R., de Hoogh, K., Bellander, T., Cesaroni, G., Cirach, M., Declercq, C., Dėdelė, A., Dons, E., de Nazelle, A., Dimakopoulou, K., Eriksen, K., Falq, G., Fischer, P., Galassi, C., Gražulevičienė, R., Heinrich, J., Hoffmann, B., Jerrett, M., Keidel, D., Korek, M., Lanki, T., Lindley, S., Madsen, C., Mölter, A., Nádor, G., Nieuwenhuijsen, M., Nonnemacher, M., Pedeli, X., Raaschou-Nielsen, O., Patelarou, E., Quass, U., Ranzi, A., Schindler, C., Stempfelet, M., Stephanou, E., Sugiri, D., Tsai, M.-Y., Yli-Tuomi, T., Varró, M. J., Vienneau, D., Klot, S. V., Wolf, K., Brunekreef, B., & Hoek, G.
Affiliations
  • Institute for Risk Assessment Sciences, Utrecht University, P.O. Box 80178, 3508 TD Utrecht, The Netherlands
  • MRC-HPA Centre for Environment and Health, Department of Epidemiology and Biostatistics, Imperial College London, London, United Kingdom
  • Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
  • Epidemiology Department, Lazio Regional Health Service, Rome, Italy
  • Center for Research in Environmental Epidemiology (CREAL), Barcelona, Spain
  • French Institute for Public Health Surveillance, Saint-Maurice, France
  • Vytautas Magnus University, Kaunas, Lithuania
  • VITO-MRG (Flemish Institute for Technological Research), Environmental Risk and Health unit, Mol, Belgium
  • Department of Hygiene, Epidemiology & Medical Statistics, National and Kapodistrian University of Athens, Medical School, Athens, Greece
  • Danish Cancer Society Research Center, Copenhagen, Denmark
  • Centre for Environmental Health, National Institute for Public Health and the Environment, Bilthoven, The Netherlands
  • AOU San Giovanni Battista − CPO Piedmont, Turin, Italy
  • HMGU Institute of Epidemiology I, Neuherberg, Germany
  • School of Public Health, University of California, Berkeley, California, United States
  • Department of Epidemiology and Public Health, Swiss Tropical & Public Health Institute, Basel, Switzerland
  • Department of Environmental Health, National Institute for Health and Welfare, Kuopio, Finland
  • School of Environment and Development (Geography), The University of Manchester, Manchester, England
  • Division of Epidemiology, Norwegian Institute of Public Health, Oslo, Norway
  • Centre for Occupational and Environmental Health, The University of Manchester, Manchester, England
  • Department of Environmental Epidemiology, National Institute of Environmental Health, Budapest, Hungary
  • Institute for Medical Informatics, Biometry and Epidemiology, University of Duisburg-Essen, Essen, Germany
  • Department of Social Medicine, Medical School, University of Crete, Greece
  • Air Quality & Sustainable Nanotechnology, IUTA Institüt für Energie- und Umwelttechnik e.V., Duisburg, Germany
  • Regional Reference Centre on Environment and Health, ARPA Emilia Romagna, Modena, Italy
  • Department of Environmental & Occupational Health Sciences, University of Washington, Seattle, Washington, United States
  • HMGU Institute of Epidemiology II, Neuherberg, Germany
Venue
Environmental Science & Technology
Publisher
American Chemical Society (ACS)
Published
2012-10-01
Volume
46
Issue
20
Pages
11195-11205
ISSN
  • 0013-936X
  • 1520-5851
DOI
10.1021/es301948k
Cited by
1,214
References
0
Language
en

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