Development of Land Use Regression Models for PM2.5, PM2.5 Absorbance, PM10 and PMcoarse in 20 European Study Areas; Results of the ESCAPE ProjectClick to copy article linkArticle link copied!
- Marloes Eeftens
- Rob Beelen
- Kees de Hoogh
- Tom Bellander
- Giulia Cesaroni
- Marta Cirach
- Christophe Declercq
- Audrius Dėdelė
- Evi Dons
- Audrey de Nazelle
- Konstantina Dimakopoulou
- Kirsten Eriksen
- Grégoire Falq
- Paul Fischer
- Claudia Galassi
- Regina Gražulevičienė
- Joachim Heinrich
- Barbara Hoffmann
- Michael Jerrett
- Dirk Keidel
- Michal Korek
- Timo Lanki
- Sarah Lindley
- Christian Madsen
- Anna Mölter
- Gizella Nádor
- 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
- Mihály J Varró
- Danielle Vienneau
- Stephanie von Klot
- Kathrin Wolf
- Bert Brunekreef
- Gerard Hoek
Abstract
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 PM2.5, PM2.5 absorbance, PM10, and PMcoarse 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 PM2.5 (range across study areas 35–94%). Model R2 was higher for PM2.5 absorbance (median 89%, range 56–97%) and lower for PMcoarse (median 68%, range 32– 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 8–11% 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.
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