Please use this identifier to cite or link to this item:https://hdl.handle.net/20.500.12259/48060
Type of publication: Straipsnis Clarivate Analytics Web of Science ar/ir Scopus / Article in Clarivate Analytics Web of Science or / and Scopus (S1)
Field of Science: Ekologija ir aplinkotyra / Ecology and environmental sciences (N012)
Author(s): Eeftens, Marloes;Beelen, Rob;Hoogh, Kees de;Bellander, Tom;Cesaroni, Giulia;Cirach, Marta;Declercq, Christophe;Dėdelė, Audrius;Dons, Evi;Nazelle, Audrey de;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;Nonnemacher, Michael;Pedeli, Xanthi;Raaschou-Nielsen, Ole;Patelarou, Evridiki;Quass, Ulrich;Ranzi, Andrea;Schindler, Christian;Stempfelet, Morgane;Stephanou, Euripides;Sugiri, Dorothee;Tsai, Ming-Yi;Yli-Tuomi, Tarja;Varró, Mihály J;Vienneau, Danielle;Klot, Stephanie von;Wolf, Kathrin;Brunekreef, Bert;Hoek, Gerard
Contributor(s): Nieuwenhuijsen, Mark
Title: Development of land use regression models for PM2.5, PM2.5 absorbance, PM10 and PM(coarse) in 20 European study areas; results of the ESCAPE project
Is part of: Environmental science and technology. Washington : American Chemical Society, 2012, vol. 46, iss. 20
Extent: p. 11195-11205
Date: 2012
Keywords: LUR model;Pollution;PM2.5;PM10
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
Internet: https://hdl.handle.net/20.500.12259/48060
Affiliation(s): Aplinkotyros katedra
Gamtos mokslų fakultetas
Vytauto Didžiojo universitetas
Appears in Collections:Universiteto mokslo publikacijos / University Research Publications

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