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Development of West-European PM2.5 and NO2 land use regression models incorporating satellite-derived and chemical transport modelling data

de Hoogh, Kees and Gulliver, John and Donkelaar, Aaron van and Martin, Randall V. and Marshall, Julian D. and Bechle, Matthew J. and Cesaroni, Giulia and Pradas, Marta Cirach and Dedele, Audrius and Eeftens, Marloes and Forsberg, Bertil and Galassi, Claudia and Heinrich, Joachim and Hoffmann, Barbara and Jacquemin, Bénédicte and Katsouyanni, Klea and Korek, Michal and Künzli, Nino and Lindley, Sarah J. and Lepeule, Johanna and Meleux, Frederik and de Nazelle, Audrey and Nieuwenhuijsen, Mark and Nystad, Wenche and Raaschou-Nielsen, Ole and Peters, Annette and Peuch, Vincent-Henri and Rouil, Laurence and Udvardy, Orsolya and Slama, Rémy and Stempfelet, Morgane and Stephanou, Euripides G. and Tsai, Ming Y. and Yli-Tuomi, Tarja and Weinmayr, Gudrun and Brunekreef, Bert and Vienneau, Danielle and Hoek, Gerard. (2016) Development of West-European PM2.5 and NO2 land use regression models incorporating satellite-derived and chemical transport modelling data. Environmental research, 151. pp. 1-10.

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Official URL: http://edoc.unibas.ch/44206/

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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.
Faculties and Departments:09 Associated Institutions > Swiss Tropical and Public Health Institute (Swiss TPH)
09 Associated Institutions > Swiss Tropical and Public Health Institute (Swiss TPH) > Former Units within Swiss TPH > Exposure Science (Tsai)
UniBasel Contributors:Künzli, Nino and de Hoogh, Kees and Eeftens, Marloes and Tsai, Ming and Vienneau, Danielle
Item Type:Article, refereed
Article Subtype:Research Article
Publisher:Elsevier
ISSN:1096-0953
Note:Publication type according to Uni Basel Research Database: Journal article
Identification Number:
Last Modified:05 Jan 2017 08:59
Deposited On:27 Oct 2016 13:39

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