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Evaluation of land use regression models for NO2 and particulate matter in 20 European study areas : the ESCAPE project

Wang, Meng and Beelen, Rob and Basagana, Xavier and Becker, Thomas and Cesaroni, Giulia and de Hoogh, Kees and Dedele, Audrius and Declercq, Christophe and Dimakopoulou, Konstantina and Eeftens, Marloes and Forastiere, Francesco and Galassi, Claudia and Gražulevičienė, Regina and Hoffmann, Barbara and Heinrich, Joachim and Iakovides, Minas and Künzli, Nino and Korek, Michal and Lindley, Sarah and Mölter, Anna and Mosler, Gioia and Madsen, Christian and Nieuwenhuijsen, Mark and Phuleria, Harish and Pedeli, Xanthi and Raaschou-Nielsen, Ole and Ranzi, Andrea and Stephanou, Euripides and Sugiri, Dorothee and Stempfelet, Morgane and Tsai, Ming-Yi and Lanki, Timo and Udvardy, Orsolya and Varró, Mihály J. and Wolf, Kathrin and Weinmayr, Gudrun and Yli-Tuomi, Tarja and Hoek, Gerard and Brunekreef, Bert. (2013) Evaluation of land use regression models for NO2 and particulate matter in 20 European study areas : the ESCAPE project. Environmental science & technology : ES & T : emphazising, water, air and waste chemistry, Vol. 47, H. 9. pp. 4357-4364.

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Official URL: http://edoc.unibas.ch/dok/A6164963

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Abstract

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.
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 Phuleria, Harish Chandra and Tsai, Ming and Eeftens, Marloes
Item Type:Article, refereed
Article Subtype:Research Article
Bibsysno:Link to catalogue
Publisher:American Chemical Soc.
ISSN:0013-936X
Note:Publication type according to Uni Basel Research Database: Journal article
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Last Modified:27 Mar 2014 13:13
Deposited On:27 Mar 2014 13:13

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