Development of West-European PM2.5 and NO2 land use regression models incorporating satellite-derived and chemical transport modelling data - Archive ouverte HAL Access content directly
Journal Articles Environmental Research Year : 2016

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

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Kees de Hoogh
  • Function : Author
John Gulliver
Aaron van Donkelaar
  • Function : Author
Randall V. Martin
  • Function : Author
Julian D. Marshall
  • Function : Author
Matthew J. Bechle
  • Function : Author
Giula Cesaroni
  • Function : Author
Audrius Dedele
  • Function : Author
Marloes Eeftens
  • Function : Author
Bertil Forsberg
  • Function : Author
Claudia Galassi
  • Function : Author
Joachim Heinrich
  • Function : Author
Barbara Hoffmann
  • Function : Author
Klea Katsouyanni
  • Function : Author
Michal Korek
  • Function : Author
Nino Kunzli
  • Function : Author
Sarah J. Lindley
  • Function : Author
Wenche Nystad
  • Function : Author
Annette Peters
Vincent-Henri Peuch
Orsolya Udvardy
  • Function : Author
Euripides G. Stephanou
  • Function : Author
Ming Y. Tsai
  • Function : Author
Tarja Yli-Tuomi
  • Function : Author
Gudrun Weinmayr
  • Function : Author
Bert Brunekreef
Danielle Vienneau
Gerard Hoek
  • Function : Author

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.

Dates and versions

ineris-01854319 , version 1 (06-08-2018)

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Cite

Kees de Hoogh, John Gulliver, Aaron van Donkelaar, Randall V. Martin, Julian D. Marshall, et al.. Development of West-European PM2.5 and NO2 land use regression models incorporating satellite-derived and chemical transport modelling data. Environmental Research, 2016, 151, pp.1-10. ⟨10.1016/j.envres.2016.07.005⟩. ⟨ineris-01854319⟩
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