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Evaluation of land use regression models for NO2 and particulate matter in 20 European study areas : the ESCAPE project
JournalArticle (Originalarbeit in einer wissenschaftlichen Zeitschrift)
ID 2017099
Author(s) Wang, Meng; Beelen, Rob; Basagana, Xavier; Becker, Thomas; Cesaroni, Giulia; de Hoogh, Kees; Dedele, Audrius; Declercq, Christophe; Dimakopoulou, Konstantina; Eeftens, Marloes; Forastiere, Francesco; Galassi, Claudia; Gražulevičienė, Regina; Hoffmann, Barbara; Heinrich, Joachim; Iakovides, Minas; Künzli, Nino; Korek, Michal; Lindley, Sarah; Mölter, Anna; Mosler, Gioia; Madsen, Christian; Nieuwenhuijsen, Mark; Phuleria, Harish; Pedeli, Xanthi; Raaschou-Nielsen, Ole; Ranzi, Andrea; Stephanou, Euripides; Sugiri, Dorothee; Stempfelet, Morgane; Tsai, Ming-Yi; Lanki, Timo; Udvardy, Orsolya; Varró, Mihály J; Wolf, Kathrin; Weinmayr, Gudrun; Yli-Tuomi, Tarja; Hoek, Gerard; Brunekreef, Bert
Author(s) at UniBasel Künzli, Nino
Phuleria, Harish Chandra
Tsai, Ming-Yi
Eeftens, Marloes
Year 2013
Title Evaluation of land use regression models for NO2 and particulate matter in 20 European study areas : the ESCAPE project
Journal Environmental science & technology : ES & T : emphazising, water, air and waste chemistry
Volume 47
Number 9
Pages / Article-Number 4357-64

Land use regression models (LUR) frequently use leave-one-out-cross-validation (LOOCV) to assess model fit, but recent studies suggested that this may overestimate predictive ability in independent data sets. Our aim was to evaluate LUR models for nitrogen dioxide (NO2) and particulate matter (PM) components exploiting the high correlation between concentrations of PM metrics and NO2. LUR models have been developed for NO2, PM2.5 absorbance, and copper (Cu) in PM10 based on 20 sites in each of the 20 study areas of the ESCAPE project. Models were evaluated with LOOCV and "hold-out evaluation (HEV)" using the correlation of predicted NO2 or PM concentrations with measured NO2 concentrations at the 20 additional NO2 sites in each area. For NO2, PM2.5 absorbance and PM10 Cu, the median LOOCV R(2)s were 0.83, 0.81, and 0.76 whereas the median HEV R(2) were 0.52, 0.44, and 0.40. There was a positive association between the LOOCV R(2) and HEV R(2) for PM2.5 absorbance and PM10 Cu. Our results confirm that the predictive ability of LUR models based on relatively small training sets is overestimated by the LOOCV R(2)s. Nevertheless, in most areas LUR models still explained a substantial fraction of the variation of concentrations measured at independent sites.

Publisher American Chemical Soc.
ISSN/ISBN 0013-936X
Full Text on edoc No
Digital Object Identifier DOI 10.1021/es305129t
PubMed ID
ISI-Number WOS:000318756000052
Document type (ISI) Journal Article

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