Details

Autor(en) / Beteiligte

• Eeftens, Marloes
• Beelen, Rob
• de Hoogh, Kees
• Bellander, Tom
• Cesaroni, Giulia
• Cirach, Marta
• Declercq, Christophe
• Dėdelė, Audrius
• Dons, Evi
• de Nazelle, Audrey
• Dimakopoulou, Konstantina
• Eriksen, Kirsten
• Falq, Grégoire
• Fischer, Paul
• Galassi, Claudia
• Gražulevičienė, Regina
• Heinrich, Joachim
• Hoffmann, Barbara
• Jerrett, Michael
• Keidel, Dirk
• Korek, Michal
• Lanki, Timo
• Lindley, Sarah
• Madsen, Christian
• Mölter, Anna
• Nádor, Gizella
• Nieuwenhuijsen, Mark
• Nonnemacher, Michael
• Pedeli, Xanthi
• Raaschou-Nielsen, Ole
• Patelarou, Evridiki
• Quass, Ulrich
• Ranzi, Andrea
• Schindler, Christian
• Stempfelet, Morgane
• Stephanou, Euripides
• Sugiri, Dorothea
• Tsai, Ming-Yi
• Yli-Tuomi, Tarja
• Varró, Mihály J
• Vienneau, Danielle
• Klot, Stephanie von
• Wolf, Kathrin
• Brunekreef, Bert
• Hoek, Gerard

Titel

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

Ist Teil von

• Environmental science & technology, 2012-10, Vol.46 (20), p.11195-11205

Ort / Verlag

Washington, DC: American Chemical Society

Erscheinungsjahr

2012

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• 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 (R 2) was 71% for PM2.5 (range across study areas 35–94%). Model R 2 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 R 2 was related to small concentration variability or limited availability of predictor variables, especially traffic intensity. Cross validation R 2 results were on average 8–11% lower than model R 2. 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.