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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/15322

Title: Development of NO2 and NOx land use regression models for estimating air pollution exposure in 36 study areas in Europe - The ESCAPE project
Authors: Beelen, Rob
Hoek, Gerard
Vienneau, Danielle
Eeftens, Marloes
Dimakopoulou, Konstantina
Pedeli, Xanthi
Tsai, Ming-Yi
Kuenzli, Nino
Schikowski, Tamara
Marcon, Alessandro
Eriksen, Kirsten T.
Raaschou-Nielsen, Ole
Stephanou, Euripides
Patelarou, Evridiki
Lanki, Timo
Yli-Toumi, Tarja
Declercq, Christophe
Falq, Gregoire
Stempfelet, Morgane
Birk, Matthias
Cyrys, Josef
von Klot, Stephanie
Nador, Gizella
Varro, Mihaly Janos
Dedele, Audrius
Grazuleviciene, Regina
Moelter, Anna
Lindley, Sarah
Madsen, Christian
Cesaroni, Giulia
Ranzi, Andrea
Badaloni, Chiara
Hoffmann, Barbara
Nonnemacher, Michael
Kraemer, Ursula
Kuhlbusch, Thomas
Cirach, Marta
de Nazelle, Audrey
Nieuwenhuijsen, Mark
Bellander, Tom
Korek, Michal
Olsson, David
Stromgren, Magnus
DONS, Evi
Jerrett, Michael
Fischer, Paul
Wang, Meng
Brunekreef, Bert
de Hoogh, Kees
Issue Date: 2013
Publisher: PERGAMON-ELSEVIER SCIENCE LTD
Citation: ATMOSPHERIC ENVIRONMENT, 72, p. 10-23
Abstract: Estimating within-city variability in air pollution concentrations is important. Land use regression (LUR) models are able to explain such small-scale within-city variations. Transparency in LUR model development methods is important to facilitate comparison of methods between different studies. We therefore developed LUR models in a standardized way in 36 study areas in Europe for the ESCAPE (European Study of Cohorts for Air Pollution Effects) project. Nitrogen dioxide (NO2) and nitrogen oxides (NOx) were measured with Ogawa passive samplers at 40 or 80 sites in each of the 36 study areas. The spatial variation in each area was explained by LUR modelling. Centrally and locally available Geographic Information System (GIS) variables were used as potential predictors. A leave-one out cross-validation procedure was used to evaluate the model performance. There was substantial contrast in annual average NO2 and NOx concentrations within the study areas. The model explained variances (R-2) of the LUR models ranged from 55% to 92% (median 82%) for NO2 and from 49% to 91% (median 78%) for NOx. For most areas the cross-validation R-2 was less than 10% lower than the model R-2. Small-scale traffic and population/household density were the most common predictors. The magnitude of the explained variance depended on the contrast in measured concentrations as well as availability of GIS predictors, especially traffic intensity data were important. In an additional evaluation, models in which local traffic intensity was not offered had 10% lower R-2 compared to models in the same areas in which these variables were offered. Within the ESCAPE project it was possible to develop LUR models that explained a large fraction of the spatial variance in measured annual average NO2 and NOx concentrations. These LUR models are being used to estimate outdoor concentrations at the home addresses of participants in over 30 cohort studies. (C) 2013 Elsevier Ltd. All rights reserved.
Notes: Beelen, R (reprint author), Univ Utrecht, Inst Risk Assessment Sci, NL-3508 TD Utrecht, Netherlands. Univ London Imperial Coll Sci Technol & Med, Dept Epidemiol & Biostat, MRC HPA Ctr Environm & Hlth, London, England. Univ Athens, Sch Med, Dept Hyg Epidemiol & Med Stat, Athens 11528, Greece. Swiss Trop & Publ Hlth Inst, Basel, Switzerland. Univ Basel, Basel, Switzerland. Univ Washington, Dept Environm & Occupat Hlth Sci, Seattle, WA 98195 USA. Univ Verona, Dept Publ Hlth & Community Med, Unit Epidemiol & Med Stat, I-37100 Verona, Italy. Danish Canc Soc, Copenhagen, Denmark. Univ Crete, Environm Chem Proc Lab, Iraklion, Greece. Natl Inst Hlth & Welf, Dept Environm Hlth, Kuopio, Finland. French Inst Publ Hlth Surveillance, St Maurice, France. Helmholtz Zentrum Munchen, German Res Ctr Environm Hlth, Inst Epidemiol 1, Neuherberg, Germany. Inst Epidemiol II, Neuherberg, Germany. Univ Augsburg, Ctr Environm Sci, D-86159 Augsburg, Germany. Natl Inst Environm Hlth, Dept Environm Epidemiol, Budapest, Hungary. Vytautas Magnus Univ, Kaunas, Lithuania. Univ Manchester, Ctr Occupat & Environm Hlth, Manchester, Lancs, England. Univ Manchester, Sch Environm & Dev Geog, Manchester, Lancs, England. Norwegian Inst Publ Hlth, Div Epidemiol, Oslo, Norway. Lazio Reg Hlth Serv, Dept Epidemiol, Rome, Italy. ARPA Emilia Romagna, Reg Reference Ctr Environm & Hlth, Modena, Italy. Univ Dusseldorf, IUF Leibniz Res Inst Environm Med, D-40225 Dusseldorf, Germany. Univ Duisburg Essen, Inst Med Informat Biometry & Epidemiol, Essen, Germany. Inst Energy & Environm Technol IUTA eV, Duisburg, Germany. Ctr Res Environm Epidemiol CREAL, Barcelona, Spain. IMIM Hosp del Mar Res Inst, Barcelona, Spain. CIBER Epidemiol & Salud Publ CIBERESP, Madrid, Spain. Karolinska Inst, Inst Environm Med, S-10401 Stockholm, Sweden. Umea Univ, Dept Publ Hlth & Clin Med, Div Occupat & Environm Med, S-90187 Umea, Sweden. Umea Univ, Dept Geog & Econ Hist, S-90187 Umea, Sweden. VITO MRG Flemish Inst Technol Res, Environm Risk & Hlth Unit, Mol, Belgium. Hasselt Univ, Diepenbeek, Belgium. Univ Calif Berkeley, Sch Publ Hlth, Berkeley, CA 94720 USA. Natl Inst Publ Hlth & Environm, Ctr Environm Hlth, NL-3720 BA Bilthoven, Netherlands. Univ Med Ctr Utrecht, Julius Ctr Hlth Sci & Primary Care, Utrecht, Netherlands.
URI: http://hdl.handle.net/1942/15322
DOI: 10.1016/j.atmosenv.2013.02.037
ISI #: 000318262000002
ISSN: 1352-2310
Category: A1
Type: Journal Contribution
Validation: ecoom, 2014
Appears in Collections: Research publications

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