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 (R2) 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 R2 was less than 10% lower than the model R2. 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 R2 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.
Description
Development of NO2 and NOx land use regression models for estimating air pollution exposure in 36 study areas in Europe – The ESCAPE project
%0 Journal Article
%1 BEELEN201310
%A Beelen, Rob
%A Hoek, Gerard
%A Vienneau, Danielle
%A Eeftens, Marloes
%A Dimakopoulou, Konstantina
%A Pedeli, Xanthi
%A Tsai, Ming-Yi
%A Künzli, Nino
%A Schikowski, Tamara
%A Marcon, Alessandro
%A Eriksen, Kirsten T.
%A Raaschou-Nielsen, Ole
%A Stephanou, Euripides
%A Patelarou, Evridiki
%A Lanki, Timo
%A Yli-Tuomi, Tarja
%A Declercq, Christophe
%A Falq, Grégoire
%A Stempfelet, Morgane
%A Birk, Matthias
%A Cyrys, Josef
%A von Klot, Stephanie
%A Nádor, Gizella
%A Varró, Mihály János
%A Dėdelė, Audrius
%A Gražulevičienė, Regina
%A Mölter, Anna
%A Lindley, Sarah
%A Madsen, Christian
%A Cesaroni, Giulia
%A Ranzi, Andrea
%A Badaloni, Chiara
%A Hoffmann, Barbara
%A Nonnemacher, Michael
%A Krämer, Ursula
%A Kuhlbusch, Thomas
%A Cirach, Marta
%A de Nazelle, Audrey
%A Nieuwenhuijsen, Mark
%A Bellander, Tom
%A Korek, Michal
%A Olsson, David
%A Strömgren, Magnus
%A Dons, Evi
%A Jerrett, Michael
%A Fischer, Paul
%A Wang, Meng
%A Brunekreef, Bert
%A de Hoogh, Kees
%D 2013
%J Atmospheric Environment
%K air land pollution regression thema thema:LUR use
%P 10 - 23
%R http://dx.doi.org/10.1016/j.atmosenv.2013.02.037
%T Development of NO2 and NOx land use regression models for estimating air pollution exposure in 36 study areas in Europe – The ESCAPE project
%U http://www.sciencedirect.com/science/article/pii/S1352231013001386
%V 72
%X 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 (R2) 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 R2 was less than 10% lower than the model R2. 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 R2 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.
@article{BEELEN201310,
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 (R2) 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 R2 was less than 10% lower than the model R2. 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 R2 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.},
added-at = {2017-04-19T21:11:34.000+0200},
author = {Beelen, Rob and Hoek, Gerard and Vienneau, Danielle and Eeftens, Marloes and Dimakopoulou, Konstantina and Pedeli, Xanthi and Tsai, Ming-Yi and Künzli, Nino and Schikowski, Tamara and Marcon, Alessandro and Eriksen, Kirsten T. and Raaschou-Nielsen, Ole and Stephanou, Euripides and Patelarou, Evridiki and Lanki, Timo and Yli-Tuomi, Tarja and Declercq, Christophe and Falq, Grégoire and Stempfelet, Morgane and Birk, Matthias and Cyrys, Josef and von Klot, Stephanie and Nádor, Gizella and Varró, Mihály János and Dėdelė, Audrius and Gražulevičienė, Regina and Mölter, Anna and Lindley, Sarah and Madsen, Christian and Cesaroni, Giulia and Ranzi, Andrea and Badaloni, Chiara and Hoffmann, Barbara and Nonnemacher, Michael and Krämer, Ursula and Kuhlbusch, Thomas and Cirach, Marta and de Nazelle, Audrey and Nieuwenhuijsen, Mark and Bellander, Tom and Korek, Michal and Olsson, David and Strömgren, Magnus and Dons, Evi and Jerrett, Michael and Fischer, Paul and Wang, Meng and Brunekreef, Bert and de Hoogh, Kees},
biburl = {https://www.bibsonomy.org/bibtex/241156763262193b80f8907c41fec1617/lautenschlager},
description = {Development of NO2 and NOx land use regression models for estimating air pollution exposure in 36 study areas in Europe – The ESCAPE project},
doi = {http://dx.doi.org/10.1016/j.atmosenv.2013.02.037},
interhash = {173e87b47ad6c3179400c971ee7fa2a3},
intrahash = {41156763262193b80f8907c41fec1617},
issn = {1352-2310},
journal = {Atmospheric Environment},
keywords = {air land pollution regression thema thema:LUR use},
pages = {10 - 23},
timestamp = {2017-04-25T13:28:04.000+0200},
title = {Development of NO2 and NOx land use regression models for estimating air pollution exposure in 36 study areas in Europe – The ESCAPE project},
url = {http://www.sciencedirect.com/science/article/pii/S1352231013001386},
volume = 72,
year = 2013
}