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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; Dedele, Audrius; Dons, Evi; de Nazelle, Audrey; Dimakopoulou, Konstantina; Eriksen, Kirsten; Falq, Grégoire; Fischer, Paul; Galassi, Claudia; Grazuleviciene, 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.
Afiliación
  • Eeftens M; Institute for Risk Assessment Sciences, Utrecht University, P.O. Box 80178, 3508 TD Utrecht, The Netherlands. m.r.eeftens@uu.nl
Environ Sci Technol ; 46(20): 11195-205, 2012 Oct 16.
Article en En | MEDLINE | ID: mdl-22963366
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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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Contaminantes Atmosféricos / Contaminación del Aire / Material Particulado / Modelos Químicos Tipo de estudio: Diagnostic_studies / Observational_studies / Prognostic_studies / Risk_factors_studies País/Región como asunto: Europa Idioma: En Revista: Environ Sci Technol Año: 2012 Tipo del documento: Article País de afiliación: Países Bajos Pais de publicación: Estados Unidos
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Contaminantes Atmosféricos / Contaminación del Aire / Material Particulado / Modelos Químicos Tipo de estudio: Diagnostic_studies / Observational_studies / Prognostic_studies / Risk_factors_studies País/Región como asunto: Europa Idioma: En Revista: Environ Sci Technol Año: 2012 Tipo del documento: Article País de afiliación: Países Bajos Pais de publicación: Estados Unidos