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  • de Hoogh, Kees (author)

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

  • Article/chapterEnglish2016

Publisher, publication year, extent ...

  • Elsevier BV,2016
  • printrdacarrier

Numbers

  • LIBRIS-ID:oai:DiVA.org:umu-125054
  • https://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-125054URI
  • https://doi.org/10.1016/j.envres.2016.07.005DOI
  • http://kipublications.ki.se/Default.aspx?queryparsed=id:134451097URI

Supplementary language notes

  • Language:English
  • Summary in:English

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Classification

  • Subject category:ref swepub-contenttype
  • Subject category:art swepub-publicationtype

Notes

  • 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.

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Added entries (persons, corporate bodies, meetings, titles ...)

  • Gulliver, John (author)
  • Donkelaar, Aaron van (author)
  • Martin, Randall V (author)
  • Marshall, Julian D (author)
  • Bechle, Matthew J (author)
  • Cesaroni, Giulia (author)
  • Pradas, Marta Cirach (author)
  • Dedele, Audrius (author)
  • Eeftens, Marloes (author)
  • Forsberg, BertilUmeå universitet,Yrkes- och miljömedicin(Swepub:umu)befo0001 (author)
  • Galassi, Claudia (author)
  • Heinrich, Joachim (author)
  • Hoffmann, Barbara (author)
  • Jacquemin, Bénédicte (author)
  • Katsouyanni, Klea (author)
  • Korek, Michal (author)
  • Künzli, Nino (author)
  • Lindley, Sarah J (author)
  • Lepeule, Johanna (author)
  • Meleux, Frederik (author)
  • de Nazelle, Audrey (author)
  • Nieuwenhuijsen, Mark (author)
  • Nystad, Wenche (author)
  • Raaschou-Nielsen, Ole (author)
  • Peters, Annette (author)
  • Peuch, Vincent-Henri (author)
  • Rouil, Laurence (author)
  • Udvardy, Orsolya (author)
  • Slama, Rémy (author)
  • Stempfelet, Morgane (author)
  • Stephanou, Euripides G (author)
  • Tsai, Ming Y (author)
  • Yli-Tuomi, Tarja (author)
  • Weinmayr, Gudrun (author)
  • Brunekreef, Bert (author)
  • Vienneau, Danielle (author)
  • Hoek, Gerard (author)
  • Umeå universitetYrkes- och miljömedicin (creator_code:org_t)

Related titles

  • In:Environmental Research: Elsevier BV151, s. 1-100013-93511096-0953

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