Development of Land Use Regression Models for PM2.5, PM2.5 Absorbance, PM10 and PMcoarse in 20 European Study Areas; Results of the ESCAPE Project

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• 作者：Marloes Eeftens ; Rob Beelen ; Kees de Hoogh ; Tom Bellander ; Giulia Cesaroni ; Marta Cirach ; Christophe Declercq ; Audrius D臈del臈 ; Evi Dons ; Audrey de Nazelle ; Konstantina Dimakopoulou ; Kirsten Eriksen ; Gr茅goire Falq ; Paul Fischer ; Claudia Galassi ; Regina Gra啪ulevi膷ien臈 ; Joachim Heinrich ; Barbara Hoffmann ; Michael Jerrett ; Dirk Keidel ; Michal Korek ; Timo Lanki ; Sarah Lindley ; Christian Madsen ; Anna M枚lter ; Gizella N谩dor ; Mark Nieuwenhuijsen ; Michael Nonnemacher ; Xanthi Pedeli ; Ole Raaschou-Nielsen ; Evridiki Patelarou ; Ulrich Quass ; Andrea Ranzi ; Christian Schindler ; Morgane Stempfelet ; Euripides Stephanou ; Dorothea Sugiri ; Ming-Yi Tsai ; Tarja Yli-Tuomi ; Mih谩ly J Varr贸 ; Danielle Vienneau ; Stephanie von Klot ; Kathrin Wolf ; Bert Brunekreef ; Gerard Hoek
• 刊名：Environmental Science & Technology (ES&T)
• 出版年：2012
• 出版时间：October 16, 2012
• 年：2012
• 卷：46
• 期：20
• 页码：11195-11205
• 全文大小：325K
• 年卷期：v.46,no.20(October 16, 2012)
• ISSN：1520-5851

文摘

Land Use Regression (LUR) models have been used increasingly for modeling small-scale spatial variation in air pollution concentrations and estimating individual exposure for participants of cohort studies. Within the ESCAPE project, concentrations of PM_2.5, PM_2.5 absorbance, PM₁₀, and PM_coarse were measured in 20 European study areas at 20 sites per area. GIS-derived predictor variables (e.g., traffic intensity, population, and land-use) were evaluated to model spatial variation of annual average concentrations for each study area. The median model explained variance (R²) was 71% for PM_2.5 (range across study areas 35鈥?4%). Model R² was higher for PM_2.5 absorbance (median 89%, range 56鈥?7%) and lower for PM_coarse (median 68%, range 32鈥?81%). Models included between two and five predictor variables, with various traffic indicators as the most common predictors. Lower R² was related to small concentration variability or limited availability of predictor variables, especially traffic intensity. Cross validation R² results were on average 8鈥?1% lower than model R². Careful selection of monitoring sites, examination of influential observations and skewed variable distributions were essential for developing stable LUR models. The final LUR models are used to estimate air pollution concentrations at the home addresses of participants in the health studies involved in ESCAPE.