Development of land use regression models for particle composition in twenty study areas in Europe

de Hoogh, Kees and Wang, Meng and Adam, Martin and Badaloni, Chiara and Beelen, Rob and Birk, Matthias and Cesaroni, Giulia and Cirach, Marta and Declercq, Christophe and Dėdelė, Audrius and Dons, Evi and de Nazelle, Audrey and Eeftens, Marloes and Eriksen, Kirsten and Eriksson, Charlotta and Fischer, Paul and Gražulevičienė, Regina and Gryparis, Alexandros and Hoffmann, Barbara and Jerrett, Michael and Katsouyanni, Klea and Iakovides, Minas and Lanki, Timo and Lindley, Sarah and Madsen, Christian and Mölter, Anna and Mosler, Gioia and Nádor, Gizella and Nieuwenhuijsen, Mark and Pershagen, Göran and Peters, Annette and Phuleria, Harisch and Probst-Hensch, Nicole and Raaschou-Nielsen, Ole and Quass, Ulrich and Ranzi, Andrea and Stephanou, Euripides and Sugiri, Dorothea and Schwarze, Per and Tsai, Ming-Yi and Yli-Tuomi, Tarja and Varró, Mihály J. and Vienneau, Danielle and Weinmayr, Gudrun and Brunekreef, Bert and Hoek, Gerard. (2013) Development of land use regression models for particle composition in twenty study areas in Europe. Environmental science & technology, Vol. 47, H. 11. pp. 5778-5786.

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Official URL: http://edoc.unibas.ch/dok/A6146259

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Abstract

Land Use Regression (LUR) models have been used to describe and model spatial variability of annual mean concentrations of traffic related pollutants such as nitrogen dioxide (NO2), nitrogen oxides (NOx) and particulate matter (PM). No models have yet been published of elemental composition. As part of the ESCAPE project, we measured the elemental composition in both the PM10 and PM2.5 fraction sizes at 20 sites in each of 20 study areas across Europe. LUR models for eight a priori selected elements (copper (Cu), iron (Fe), potassium (K), nickel (Ni), sulfur (S), silicon (Si), vanadium (V), and zinc (Zn)) were developed. Good models were developed for Cu, Fe, and Zn in both fractions (PM10 and PM2.5) explaining on average between 67 and 79% of the concentration variance (R(2)) with a large variability between areas. Traffic variables were the dominant predictors, reflecting nontailpipe emissions. Models for V and S in the PM10 and PM2.5 fractions and Si, Ni, and K in the PM10 fraction performed moderately with R(2) ranging from 50 to 61%. Si, NI, and K models for PM2.5 performed poorest with R(2) under 50%. The LUR models are used to estimate exposures to elemental composition in the health studies involved in ESCAPE.

Faculties and Departments:	09 Associated Institutions > Swiss Tropical and Public Health Institute (Swiss TPH) > Department of Epidemiology and Public Health (EPH) > Chronic Disease Epidemiology > Exposome Science (Probst-Hensch) 03 Faculty of Medicine > Departement Public Health > Sozial- und Präventivmedizin > Exposome Science (Probst-Hensch) 09 Associated Institutions > Swiss Tropical and Public Health Institute (Swiss TPH) > Former Units within Swiss TPH > Exposure Science (Tsai)
UniBasel Contributors:	Tsai, Ming and Probst Hensch, Nicole and Phuleria, Harish Chandra
Item Type:	Article, refereed
Article Subtype:	Research Article
Publisher:	American Chemical Society
ISSN:	0013-936X
Note:	Publication type according to Uni Basel Research Database: Journal article
Related URLs:	Document Document
Identification Number:	pmid: 23651082 doi: 10.1021/es400156t
Last Modified:	25 Oct 2013 08:33
Deposited On:	25 Oct 2013 08:33

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