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Development of land use regression models for PM2.5, PM2.5 absorbance, PM10 and PM(coarse) in 20 European study areas; results of the ESCAPE project
Type of publication
Straipsnis Web of Science ir Scopus duomenų bazėje / Article in Web of Science and Scopus database (S1)
Author(s)
Eeftens, Marloes | Utrecht University, Netherlands |
Beelen, Rob | Utrecht University, Netherlands |
Hoogh, Kees de | Imperial College London, United Kingdom |
Bellander, Tom | Institute of Environmental Medicine, Stockholm, Sweden |
Cesaroni, Giulia | Lazio Regional Health Service, Rome, Italy |
Cirach, Marta | Center for Research in Environmental Epidemiology, Barcelona, Spain |
Declercq, Christophe | French Institute for Public Health Surveillance, Saint-Maurice Cedex, France |
Dons, Evi | Flemish Institute for Technological Research, Environmental Risk and Health unit, Mol, Belgium |
Nazelle, Audrey de | Center for Research in Environmental Epidemiology, Barcelona, Spain |
Dimakopoulou, Konstantina | National and Kapodistrian University of Athens, Greece |
Eriksen, Kirsten | Danish Cancer Society Research Center, Copenhagen, Denmark |
Falq, Grégoire | French Institute for Public Health Surveillance, Saint-Maurice, France |
Fischer, Paul | National Institute for Public Health and the Environment, Bilthoven, Netherlands |
Galassi, Claudia | AOU San Giovanni Battista - CPO Piedmont, Turin, Italy |
Heinrich, Joachim | HMGU Institute of Epidemiology I, Neuherberg, Germany |
Hoffmann, Barbara | Heinrich-Heine University of Düsseldorf, Germany |
Jerrett, Michael | University of California, Berkeley, California, US |
Keidel, Dirk | Swiss Tropical & Public Health Institute, Basel, Switzerland |
Korek, Michal | Institute of Environmental Medicine, Stockholm, Sweden |
Lanki, Timo | National Institute for Health and Welfare, Kuopio, Finland |
Lindley, Sarah | University of Manchester, UK |
Madsen, Christian | Norwegian Institute of Public Health, Oslo, Norway |
Mölter, Anna | University of Manchester, England, UK |
Nádor, Gizella | National Institute of Environmental Health, Budapest, Hungary |
Nieuwenhuijsen, Mark | Center for Research in Environmental Epidemiology, Barcelona, Spain |
Nonnemacher, Michael | University of Duisburg-Essen, Germany |
Pedeli, Xanthi | National and Kapodistrian University of Athens, Medical School, Greece |
Raaschou-Nielsen, Ole | Danish Cancer Society Research Center, Copenhagen, Denmark |
Patelarou, Evridiki | University of Crete, Heraklion, Greece |
Quass, Ulrich | Institut für Energie- und Umwelttechnik, Duisburg, Germany |
Ranzi, Andrea | Regional Reference Centre on Environment and Health, ARPA Emilia Romagna, Modena, Italy |
Schindler, Christian | Swiss Tropical & Public Health Institute, Basel, Switzerland |
Stempfelet, Morgane | French Institute for Public Health Surveillance, Saint-Maurice, France |
Stephanou, Euripides | University of Washington, USA |
Sugiri, Dorothee | University of Düsseldorf, Germany |
Tsai, Ming-Yi | Swiss Tropical & Public Health Institute, Basel, Switzerland |
Yli-Tuomi, Tarja | National Institute for Health and Welfare, Kuopio, Finland |
Varró, Mihály J. | National Institute of Environmental Health, Budapest, Hungary |
Vienneau, Danielle | Imperial College London, UK |
Klot, Stephanie von | HMGU Institute of Epidemiology II, Neuherberg, Germany |
Wolf, Kathrin | HMGU Institute of Epidemiology II, Neuherberg, Germany |
Brunekreef, Bert | Utrecht University, Netherlands |
Hoek, Gerard | Utrecht University, Netherlands |
Title
Development of land use regression models for PM2.5, PM2.5 absorbance, PM10 and PM(coarse) in 20 European study areas; results of the ESCAPE project
Publisher
Washington : American Chemical Society
Date Issued
2012
Extent
p. 11195-11205
Is part of
Environmental science and technology. Washington : American Chemical Society, 2012, vol. 46, iss. 20
Field of Science
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 PM(2.5), PM(2.5) absorbance, PM(10), 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 (R(2)) was 71% for PM(2.5) (range across study areas 35-94%). Model R(2) was higher for PM(2.5) absorbance (median 89%, range 56-97%) and lower for PM(coarse) (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.
Is Referenced by
Is Referenced by |
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Type of document
type::text::journal::journal article::research article
ISSN (of the container)
0013-936X
WOS
WOS:000309805000045
Coverage Spatial
Jungtinės Amerikos Valstijos / United States of America (US)
Language
Anglų / English (en)
Bibliographic Details
35
Journal | IF | AIF | AIF (min) | AIF (max) | Cat | AV | Year | Quartile |
---|---|---|---|---|---|---|---|---|
ENVIRONMENTAL SCIENCE & TECHNOLOGY | 5.257 | 2.956 | 2.678 | 3.235 | 2 | 1.907 | 2012 | Q1 |
Journal | IF | AIF | AIF (min) | AIF (max) | Cat | AV | Year | Quartile |
---|---|---|---|---|---|---|---|---|
ENVIRONMENTAL SCIENCE & TECHNOLOGY | 5.257 | 2.956 | 2.678 | 3.235 | 2 | 1.907 | 2012 | Q1 |
2.956 | ||||||||
2.956 |
Journal | Cite Score | SNIP | SJR | Year | Quartile |
---|---|---|---|---|---|
Environmental Science and Technology | 8.6 | 2.043 | 3.115 | 2012 | Q1 |