Please use this identifier to cite or link to this item:https://hdl.handle.net/20.500.12259/48770
Type of publication: Straipsnis Clarivate Analytics Web of Science ar/ir Scopus / Article in Clarivate Analytics Web of Science or / and Scopus (S1)
Field of Science: Ekologija ir aplinkotyra / Ecology and environmental sciences (N012)
Author(s): Wang, Meng;Beelen, Rob;Basagana, Xavier;Becker, Thomas;Cesaroni, Giulia;Hoogh, Kees de;Dėdelė, Audrius;Declercq, Christophe;Dimakopoulou, Konstantina;Eeftens, Marloes;Forastiere, Francesco;Claudia Galassi;Gražulevičienė, Regina;Hoffmann, Barbara;Heinrich, Joachim;Iakovides, Minas;Künzli, Nino;Korek, Michal;Lindley, Sarah;Mölter, Anna;Mosler, Gioia;Nieuwenhuijsen, Mark;Phuleria, Harish;Pedeli, Xanthi;Raaschou-Nielsen, Ole;Ranzi, Andrea;Sugiri, Dorothee;Stephanou, Euripides G;Stempfelet, Morgane;Tsai, Ming-Yi;Lanki, Timo;Udvardy, Orsolya;Varró, Mihály J;Wolf, Kathrin;Weinmayr, Gudrun;Yli-Tuomi, Tarja;Brunekreef, Bert;Hoek, Gerard
Title: Evaluation of land use regression models for NO2 and particulate matter in 20 European study areas : the ESCAPE project
Is part of: Environmental science and technology. Washington, USA : American chemical society, 2013, vol. 47, no. 9
Extent: p. 4357-4364
Date: 2013
Keywords: Land use regression model;Evaluation;NO2;Articulate matter;ESCAPE study
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 R2s were 0.83, 0.81, and 0.76 whereas the median HEV R2 were 0.52, 0.44, and 0.40. There was a positive association between the LOOCV R2 and HEV R2 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 R2s. Nevertheless, in most areas LUR models still explained a substantial fraction of the variation of concentrations measured at independent sites
Internet: http://pubs.acs.org/doi/abs/10.1021/es305129t
Affiliation(s): Aplinkotyros katedra
Gamtos mokslų fakultetas
Vytauto Didžiojo universitetas
Appears in Collections:Universiteto mokslo publikacijos / University Research Publications

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