Περίληψη:
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. © 2012 American Chemical Society.
Συγγραφείς:
Eeftens, M.
Beelen, R.
De Hoogh, K.
Bellander, T.
Cesaroni, G.
Cirach, M.
Declercq, C.
Dedele, A.
Dons, E.
De Nazelle, A.
Dimakopoulou, K.
Eriksen, K.
Falq, G.
Fischer, P.
Galassi, C.
Gražulevičiene, R.
Heinrich, J.
Hoffmann, B.
Jerrett, M.
Keidel, D.
Korek, M.
Lanki, T.
Lindley, S.
Madsen, C.
Mölter, A.
Nádor, G.
Nieuwenhuijsen, M.
Nonnemacher, M.
Pedeli, X.
Raaschou-Nielsen, O.
Patelarou, E.
Quass, U.
Ranzi, A.
Schindler, C.
Stempfelet, M.
Stephanou, E.
Sugiri, D.
Tsai, M.-Y.
Yli-Tuomi, T.
Varró, M.J.
Vienneau, D.
Klot, S.V.
Wolf, K.
Brunekreef, B.
Hoek, G.
Λέξεις-κλειδιά:
Absorbances; Annual average concentration; Cohort studies; Cross validation; Health-study; Home address; Influential observations; Land use regression; Land-use regression models; Monitoring sites; Pollution concentration; Predictor variables; Small concentration; Spatial variations; Study areas; Traffic intensity; Variable distribution, Air pollution; Regression analysis, Land use, absorbance; atmospheric pollution; GIS; land use change; particulate matter; regression analysis; spatial variation; traffic emission, air pollution; air pollution control; article; Europe; land use; particulate matter, Absorbent Pads; Air Pollutants; Air Pollution; Environmental Monitoring; Europe; Geographic Information Systems; Models, Chemical; Particulate Matter; Regression Analysis, Europe
