Details

Autor(en) / Beteiligte

• Beelen, Rob
• Hoek, Gerard
• Vienneau, Danielle
• Eeftens, Marloes
• Dimakopoulou, Konstantina
• Pedeli, Xanthi
• Tsai, Ming-Yi
• Künzli, Nino
• Schikowski, Tamara
• Marcon, Alessandro
• Eriksen, Kirsten
• Raaschou-Nielsen, Ole
• Stephanou, Euripides
• Evridiki, Patelarou
• Lanki, Timo
• Yli-Tuomi, Tarja
• Declercq, Christophe
• Falq, Grégoire
• Stempfelet, Morgane
• Birk, Matthias
• Cyrys, Josef
• von Klot, Stephanie
• Nádor, Gizella
• Varró, Mihály János
• Dėdelė, Audrius
• Gražulevičienė, Regina
• Mölter, Anna
• Lindley, Sarah
• Madsen, Christian
• Cesaroni, Giulia
• Ranzi, Andrea
• Badaloni, Chiara
• Hoffmann, Barbara
• Nonnemacher, Michael
• Krämer, Ursula
• Kuhlbusch, Thomas
• Cirach, Marta
• de Nazelle, Audrey
• Nieuwenhuijsen, Mark
• Bellander, Tom
• Korek, Michal
• Olsson, David
• Strömgren, Magnus
• Dons, Evi
• Jerrett, Michael
• Fischer, Paul
• Brunekreef, Bert
• de Hoogh, Kees

Titel

Development of NO 2 and NO x land use regression models for estimating air pollution exposure in 36 study areas in Europe: the ESCAPE project

Ist Teil von

• Atmospheric environment (1994), 2013, Vol.72, p.10

Erscheinungsjahr

2013

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• 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 (NO 2 ) and nitrogen oxides (NO x ) 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 modeling. 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 NO 2 and NO x concentrations within the study areas. The model explained variances (R 2 ) of the LUR models ranged from 55% to 92% (median 82%) for NO 2 and from 49% to 91% (median 78%) for NO x . 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 NO 2 and NO x concentrations. These LUR models are being used to estimate outdoor concentrations at the home addresses of participants in over 30 cohort studies.