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Development of Land Use Regression Models for PM2.5, PM2.5 Absorbance, PM10 and PMcoarse in 20 European Study Areas; Results of the ESCAPE Project

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† 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
# IMIM (Hospital del Mar Research Institute), Barcelona, Spain
▽ CIBER Epidemiología y Salud Pública (CIBERESP), 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
∞ Hasselt University, Diepenbeek, 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
☆ ☆IUF Leibniz Research Institute for Environmental Medicine, and √Medical Faculty, Heinrich-Heine, University of Düsseldorf, Düsseldorf, 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
∀ University of Basel, 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
Я Air Quality & Sustainable Nanotechnology, IUTA Institüt für Energie- und Umwelttechnik e.V., Duisburg, Germany
Å Regional Reference Centre on Environment and Health, ARPA Emilia Romagna, Modena, Italy
¢ Environmental Chemical Processes Laboratory, University of Crete, Heraklion, Greece
⬕ Department of Environmental & Occupational Health Sciences, University of Washington, Seattle, Washington, United States
∑ HMGU Institute of Epidemiology II, Neuherberg, Germany
∮ Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands
*Phone: +31 30 253 94 74; fax: +31 30 253 94 99; e-mail: [email protected]
Cite this: Environ. Sci. Technol. 2012, 46, 20, 11195–11205
Publication Date (Web):September 10, 2012
https://doi.org/10.1021/es301948k
Copyright © 2012 American Chemical Society
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    Abstract

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    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 PM2.5, PM2.5 absorbance, PM10, and PMcoarse 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 (R2) was 71% for PM2.5 (range across study areas 35–94%). Model R2 was higher for PM2.5 absorbance (median 89%, range 56–97%) and lower for PMcoarse (median 68%, range 32– 81%). Models included between two and five predictor variables, with various traffic indicators as the most common predictors. Lower R2 was related to small concentration variability or limited availability of predictor variables, especially traffic intensity. Cross validation R2 results were on average 8–11% lower than model R2. 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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    Supporting Information is available on (1) the derivation of predictor variables, (2) description of local GIS variables, (3) description of PM10 LUR models, (4) distribution of the predictor variables, (5) the development of combined LUR models for London/Oxford, The Netherlands/Belgium and Ruhr Area and (6) description of excluded sites. This material is available free of charge via the Internet at http://pubs.acs.org.

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