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

Kees de Hoogh, John Gulliver, Aaron van Donkelaar, Randall V. Martin, Julian D. Marshall, Matthew J. Bechle, Giulia Cesaroni, Marta Cirach Pradas, Audrius Dedele, Marloes Eeftens, Bertil Forsberg, Claudia Galassi, Joachim Heinrich, Barbara Hoffmann, Bénédicte Jacquemin, Klea Katsouyanni, Michal Korek, Nino Künzli, Sarah J. Lindley, Johanna LepeuleFrederik Meleux, Audrey de Nazelle, Mark Nieuwenhuijsen, Wenche Nystad, Ole Raaschou-Nielsen, Annette Peters, Vincent Henri Peuch, Laurence Rouil, Orsolya Udvardy, Rémy Slama, Morgane Stempfelet, Euripides G. Stephanou, Ming Y. Tsai, Tarja Yli-Tuomi, Gudrun Weinmayr, Bert Brunekreef, Danielle Vienneau, Gerard Hoek

Research output: Contribution to journalArticlepeer-review

76 Scopus citations

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 (adjR2: 0.33–0.38). For NO2 CTM improved prediction modestly (adjR2: 0.58) compared to models without SAT and CTM (adjR2: 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.

Original languageEnglish (US)
Pages (from-to)1-10
Number of pages10
JournalEnvironmental Research
Volume151
DOIs
StatePublished - Nov 1 2016

Keywords

  • Air pollution
  • Exposure
  • Fine particulate matter
  • Nitrogen dioxide
  • Spatial modelling

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