Development of Land Use Regression Models for Elemental, Organic Carbon, PAH, and Hopanes/Steranes in 10 ESCAPE/TRANSPHORM European Study Areas

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• 作者：Aleksandra Jedynska ; Gerard Hoek ; Meng Wang ; Marloes Eeftens ; Josef Cyrys ; Menno Keuken ; Christophe Ampe ; Rob Beelen ; Giulia Cesaroni ; Francesco Forastiere ; Marta Cirach ; Kees de Hoogh ; Audrey De Nazelle ; Wenche Nystad ; Christophe Declercq ; Kirsten T. Eriksen ; Konstantina Dimakopoulou ; Timo Lanki ; Kees Meliefste ; Mark J. Nieuwenhuijsen ; Tarja Yli-Tuomi ; Ole Raaschou-Nielsen ; Bert Brunekreef ; Ingeborg M. Kooter
• 刊名：Environmental Science & Technology
• 出版年：2014
• 出版时间：December 16, 2014
• 年：2014
• 卷：48
• 期：24
• 页码：14435-14444
• 全文大小：292K
• ISSN：1520-5851

文摘

Land use regression (LUR) models have been used to model concentrations of mainly traffic-related air pollutants (nitrogen oxides (NO_x), particulate matter (PM) mass or absorbance). Few LUR models are published of PM composition, whereas the interest in health effects related to particle composition is increasing. The aim of our study was to evaluate LUR models of polycyclic aromatic hydrocarbons (PAH), hopanes/steranes, and elemental and organic carbon (EC/OC) content of PM_2.5. In 10 European study areas, PAH, hopanes/steranes, and EC/OC concentrations were measured at 16鈥?0 sites per study area. LUR models for each study area were developed on the basis of annual average concentrations and predictor variables including traffic, population, industry, natural land obtained from geographic information systems. The highest median model explained variance (R²) was found for EC 鈥?84%. The median R² was 51% for OC, 67% for benzo[a]pyrene, and 38% for sum of hopanes/steranes, with large variability between study areas. Traffic predictors were included in most models. Population and natural land were included frequently as additional predictors. The moderate to high explained variance of LUR models and the overall moderate correlation with PM_2.5 model predictions support the application of especially the OC and PAH models in epidemiological studies.