Development of NO2 and NOx land use regression models for estimating air pollution exposure in 36 study areas in Europe - The ESCAPE project

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

doi:10.1016/j.atmosenv.2013.02.037

Development of NO₂ and NO_x land use regression models for estimating air pollution exposure in 36 study areas in Europe - The ESCAPE project

Rob Beelen
, Gerard Hoek
, Danielle Vienneau
, Marloes Eeftens
, Konstantina Dimakopoulou
, Xanthi Pedeli
, Ming Yi Tsai
, Nino Künzli
, Tamara Schikowski
, Alessandro Marcon
, Kirsten T. Eriksen
, Ole Raaschou-Nielsen
, Euripides Stephanou
, Evridiki Patelarou
, Timo Lanki
, Tarja Yli-Tuomi
, Christophe Declercq
, Grégoire Falq
, Morgane Stempfelet
, Matthias Birk

Josef Cyrys, Stephanie von Klot, Gizella Nádor, Mihály János Varró, Audrius Dedele, Regina Gražulevičiene, Anna Mölter, Sarah Lindley, Christian Madsen, Giulia Cesaroni, Andrea Ranzi, Chiara Badaloni, Barbara Hoffmann, Michael Nonnemacher, Ursula Krämer, Thomas Kuhlbusch, Marta Cirach, Audrey de Nazelle, Mark Nieuwenhuijsen, Tom Bellander, Michal Korek, David Olsson, Magnus Strömgren, Evi Dons, Michael Jerrett, Paul Fischer, Meng Wang, Bert Brunekreef, Kees de Hoogh

Department of Epidemiology and Environmental Health

University at Buffalo

Utrecht University
Imperial College London
National and Kapodistrian University of Athens
Swiss Tropical and Public Health Institute
University of Basel
University of Washington
University of Verona
Danish Cancer Society
University of Crete
National Institute for Health and Welfare
French Institute for Public Health Surveillance
Helmholtz Zentrum München - German Research Center for Environmental Health
Augsburg University
Hungarian National Institute of Environmental Health
Vytautas Magnus University
University of Manchester
Norwegian Institute of Public Health
Department of Epidemiology Lazio Regional Health Service
ARPA Emilia Romagna
Heinrich Heine University Düsseldorf
University of Duisburg-Essen
Air Quality and Sustainable Nanotechnology
(ISGlobal) Instituto de Salud Global de Barcelona
Hospital del Mar
Centro de Investigación Biomédicaen Red de Epidemiología y Salud Pública (CIBERESP)
Karolinska Institutet
Umeå University
Flemish Institute for Technological Research
Hasselt University
University of California at Berkeley
National Institute of Public Health and the Environment

Research output: Contribution to journal › Article › peer-review

886 Scopus citations

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 (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 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 NO_x 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 NO_x. 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 NO_x concentrations. These LUR models are being used to estimate outdoor concentrations at the home addresses of participants in over 30 cohort studies.

Original language	English
Pages (from-to)	10-23
Number of pages	14
Journal	Atmospheric Environment
Volume	72
DOIs	https://doi.org/10.1016/j.atmosenv.2013.02.037
State	Published - Jun 2013

Keywords

Air pollution
CORINE
CV
ESCAPE
GIS
GPS
LUR
Land Use Regression (LUR) model
Mvh
NO
NO
R
RMSE

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10.1016/j.atmosenv.2013.02.037

Cite this

Beelen, R., Hoek, G., Vienneau, D., Eeftens, M., Dimakopoulou, K., Pedeli, X., Tsai, M. Y., Künzli, N., Schikowski, T., Marcon, A., Eriksen, K. T., Raaschou-Nielsen, O., Stephanou, E., Patelarou, E., Lanki, T., Yli-Tuomi, T., Declercq, C., Falq, G., Stempfelet, M., ... de Hoogh, K. (2013). Development of NO₂ and NO_x land use regression models for estimating air pollution exposure in 36 study areas in Europe - The ESCAPE project. Atmospheric Environment, 72, 10-23. https://doi.org/10.1016/j.atmosenv.2013.02.037

@article{b3497e8d474a4a1084342f0c482f46bb,

title = "Development of NO2 and NOx land use regression models for estimating air pollution exposure in 36 study areas in Europe - The ESCAPE project",

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.",

keywords = "Air pollution, CORINE, CV, ESCAPE, GIS, GPS, LUR, Land Use Regression (LUR) model, Mvh, NO, NO, R, RMSE",

author = "Rob Beelen and Gerard Hoek and Danielle Vienneau and Marloes Eeftens and Konstantina Dimakopoulou and Xanthi Pedeli and Tsai, \{Ming Yi\} and Nino K{\"u}nzli and Tamara Schikowski and Alessandro Marcon and Eriksen, \{Kirsten T.\} and Ole Raaschou-Nielsen and Euripides Stephanou and Evridiki Patelarou and Timo Lanki and Tarja Yli-Tuomi and Christophe Declercq and Gr{\'e}goire Falq and Morgane Stempfelet and Matthias Birk and Josef Cyrys and \{von Klot\}, Stephanie and Gizella N{\'a}dor and Varr{\'o}, \{Mih{\'a}ly J{\'a}nos\} and Audrius Dedele and Regina Gra{\v z}ulevi{\v c}iene and Anna M{\"o}lter and Sarah Lindley and Christian Madsen and Giulia Cesaroni and Andrea Ranzi and Chiara Badaloni and Barbara Hoffmann and Michael Nonnemacher and Ursula Kr{\"a}mer and Thomas Kuhlbusch and Marta Cirach and \{de Nazelle\}, Audrey and Mark Nieuwenhuijsen and Tom Bellander and Michal Korek and David Olsson and Magnus Str{\"o}mgren and Evi Dons and Michael Jerrett and Paul Fischer and Meng Wang and Bert Brunekreef and \{de Hoogh\}, Kees",

year = "2013",

month = jun,

doi = "10.1016/j.atmosenv.2013.02.037",

language = "English",

volume = "72",

pages = "10--23",

journal = "Atmospheric Environment",

issn = "1352-2310",

publisher = "Elsevier Ltd.",

}

Beelen, R, Hoek, G, Vienneau, D, Eeftens, M, Dimakopoulou, K, Pedeli, X, Tsai, MY, Künzli, N, Schikowski, T, Marcon, A, Eriksen, KT, Raaschou-Nielsen, O, Stephanou, E, Patelarou, E, Lanki, T, Yli-Tuomi, T, Declercq, C, Falq, G, Stempfelet, M, Birk, M, Cyrys, J, von Klot, S, Nádor, G, Varró, MJ, Dedele, A, Gražulevičiene, R, Mölter, A, Lindley, S, Madsen, C, Cesaroni, G, Ranzi, A, Badaloni, C, Hoffmann, B, Nonnemacher, M, Krämer, U, Kuhlbusch, T, Cirach, M, de Nazelle, A, Nieuwenhuijsen, M, Bellander, T, Korek, M, Olsson, D, Strömgren, M, Dons, E, Jerrett, M, Fischer, P, Wang, M, Brunekreef, B & de Hoogh, K 2013, 'Development of NO₂ and NO_x land use regression models for estimating air pollution exposure in 36 study areas in Europe - The ESCAPE project', Atmospheric Environment, vol. 72, pp. 10-23. https://doi.org/10.1016/j.atmosenv.2013.02.037

TY - JOUR

T1 - Development of NO2 and NOx land use regression models for estimating air pollution exposure in 36 study areas in Europe - The ESCAPE project

AU - Beelen, Rob

AU - Hoek, Gerard

AU - Vienneau, Danielle

AU - Eeftens, Marloes

AU - Dimakopoulou, Konstantina

AU - Pedeli, Xanthi

AU - Tsai, Ming Yi

AU - Künzli, Nino

AU - Schikowski, Tamara

AU - Marcon, Alessandro

AU - Eriksen, Kirsten T.

AU - Raaschou-Nielsen, Ole

AU - Stephanou, Euripides

AU - Patelarou, Evridiki

AU - Lanki, Timo

AU - Yli-Tuomi, Tarja

AU - Declercq, Christophe

AU - Falq, Grégoire

AU - Stempfelet, Morgane

AU - Birk, Matthias

AU - Cyrys, Josef

AU - von Klot, Stephanie

AU - Nádor, Gizella

AU - Varró, Mihály János

AU - Dedele, Audrius

AU - Gražulevičiene, Regina

AU - Mölter, Anna

AU - Lindley, Sarah

AU - Madsen, Christian

AU - Cesaroni, Giulia

AU - Ranzi, Andrea

AU - Badaloni, Chiara

AU - Hoffmann, Barbara

AU - Nonnemacher, Michael

AU - Krämer, Ursula

AU - Kuhlbusch, Thomas

AU - Cirach, Marta

AU - de Nazelle, Audrey

AU - Nieuwenhuijsen, Mark

AU - Bellander, Tom

AU - Korek, Michal

AU - Olsson, David

AU - Strömgren, Magnus

AU - Dons, Evi

AU - Jerrett, Michael

AU - Fischer, Paul

AU - Wang, Meng

AU - Brunekreef, Bert

AU - de Hoogh, Kees

PY - 2013/6

Y1 - 2013/6

N2 - 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.

AB - 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.

KW - Air pollution

KW - CORINE

KW - CV

KW - ESCAPE

KW - GIS

KW - GPS

KW - LUR

KW - Land Use Regression (LUR) model

KW - Mvh

KW - NO

KW - R

KW - RMSE

UR - https://www.scopus.com/pages/publications/84875259343

U2 - 10.1016/j.atmosenv.2013.02.037

DO - 10.1016/j.atmosenv.2013.02.037

M3 - Article

SN - 1352-2310

VL - 72

SP - 10

EP - 23

JO - Atmospheric Environment

JF - Atmospheric Environment

ER -

Development of NO₂ and NO_x land use regression models for estimating air pollution exposure in 36 study areas in Europe - The ESCAPE project

Abstract

Keywords

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