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Development of West-European PM2.5 and NO2 land use regression models incorporating satellite-derived and chemical transport modelling data
JournalArticle (Originalarbeit in einer wissenschaftlichen Zeitschrift)
ID 3609545
Author(s) de Hoogh, Kees; Gulliver, John; Donkelaar, Aaron van; Martin, Randall V.; Marshall, Julian D.; Bechle, Matthew J.; Cesaroni, Giulia; Pradas, Marta Cirach; Dedele, Audrius; Eeftens, Marloes; Forsberg, Bertil; Galassi, Claudia; Heinrich, Joachim; Hoffmann, Barbara; Jacquemin, Bénédicte; Katsouyanni, Klea; Korek, Michal; Künzli, Nino; Lindley, Sarah J.; Lepeule, Johanna; Meleux, Frederik; de Nazelle, Audrey; Nieuwenhuijsen, Mark; Nystad, Wenche; Raaschou-Nielsen, Ole; Peters, Annette; Peuch, Vincent-Henri; Rouil, Laurence; Udvardy, Orsolya; Slama, Rémy; Stempfelet, Morgane; Stephanou, Euripides G.; Tsai, Ming Y.; Yli-Tuomi, Tarja; Weinmayr, Gudrun; Brunekreef, Bert; Vienneau, Danielle; Hoek, Gerard
Author(s) at UniBasel Künzli, Nino
de Hoogh, Kees
Eeftens, Marloes
Tsai, Ming-Yi
Vienneau, Danielle
Year 2016
Title Development of West-European PM2.5 and NO2 land use regression models incorporating satellite-derived and chemical transport modelling data
Journal Environmental research
Volume 151
Pages / Article-Number 1-10
Abstract Satellite-derived (SAT) and chemical transport model (CTM) estimates of PM2.5 and NO2 are increasingly used in combination with Land Use Regression (LUR) models. We aimed to compare the contribution of SAT and CTM data to the performance of LUR PM2.5 and NO2 models for Europe. Four sets of models, all including local traffic and land use variables, were compared (LUR without SAT or CTM, with SAT only, with CTM only, and with both SAT and CTM). LUR models were developed using two monitoring data sets: PM2.5 and NO2 ground level measurements from the European Study of Cohorts for Air Pollution Effects (ESCAPE) and from the European AIRBASE network. LUR PM2.5 models including SAT and SAT+CTM explained ~60% of spatial variation in measured PM2.5 concentrations, substantially more than the LUR model without SAT and CTM (adjR(2): 0.33-0.38). For NO2 CTM improved prediction modestly (adjR(2): 0.58) compared to models without SAT and CTM (adjR(2): 0.47-0.51). Both monitoring networks are capable of producing models explaining the spatial variance over a large study area. SAT and CTM estimates of PM2.5 and NO2 significantly improved the performance of high spatial resolution LUR models at the European scale for use in large epidemiological studies.
Publisher Elsevier
ISSN/ISBN 1096-0953
Full Text on edoc No
Digital Object Identifier DOI 10.1016/j.envres.2016.07.005
PubMed ID

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