Comparing land use regression and dispersion modelling to assess residential exposure to ambient air pollution for epidemiological studies

Kees de Hoogh*, Michal Korek, Danielle Vienneau, Menno Keuken, Jaakko Kukkonen, Mark J. Nieuwenhuijsen, Chiara Badaloni, Rob Beelen, Andrea Bolignano, Giulia Cesaroni, Marta Cirach Pradas, Josef Cyrys, John Douros, Marloes Eeftens, Francesco Forastiere, Bertil Forsberg, Kateryna Fuks, Ulrike Gehring, Alexandros Gryparis, John GulliverAnna L. Hansell, Barbara Hoffmann, Christer Johansson, Sander Jonkers, Leena Kangas, Klea Katsouyanni, Nino Künzli, Timo Lanki, Michael Memmesheimer, Nicolas Moussiopoulos, Lars Modig, Göran Pershagen, Nicole Probst-Hensch, Christian Schindler, Tamara Schikowski, Dorothee Sugiri, Oriol Teixidó, Ming Yi Tsai, Tarja Yli-Tuomi, Bert Brunekreef, Gerard Hoek, Tom Bellander

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

93 Citations (Scopus)


Background: Land-use regression (LUR) and dispersion models (DM) are commonly used for estimating individual air pollution exposure in population studies. Few comparisons have however been made of the performance of these methods. Objectives: Within the European Study of Cohorts for Air Pollution Effects (ESCAPE) we explored the differences between LUR and DM estimates for NO2, PM10 and PM2.5. Methods: The ESCAPE study developed LUR models for outdoor air pollution levels based on a harmonised monitoring campaign. In thirteen ESCAPE study areas we further applied dispersion models. We compared LUR and DM estimates at the residential addresses of participants in 13 cohorts for NO2; 7 for PM10 and 4 for PM2.5. Additionally, we compared the DM estimates with measured concentrations at the 20-40 ESCAPE monitoring sites in each area. Results: The median Pearson R (range) correlation coefficients between LUR and DM estimates for the annual average concentrations of NO2, PM10 and PM2.5 were 0.75 (0.19-0.89), 0.39 (0.23-0.66) and 0.29 (0.22-0.81) for 112,971 (13 study areas), 69,591 (7) and 28,519 (4) addresses respectively. The median Pearson R correlation coefficients (range) between DM estimates and ESCAPE measurements were of 0.74 (0.09-0.86) for NO2; 0.58 (0.36-0.88) for PM10 and 0.58 (0.39-0.66) for PM2.5. Conclusions: LUR and dispersion model estimates correlated on average well for NO2 but only moderately for PM10 and PM2.5, with large variability across areas. DM predicted a moderate to large proportion of the measured variation for NO2 but less for PM10 and PM2.5.

Original languageEnglish
Pages (from-to)382-392
Number of pages11
JournalEnvironment International
Publication statusPublished - 1 Dec 2014
Externally publishedYes

Bibliographical note

Funding Information:
The research leading to these results was funded by the European Community's Seventh Framework Program (FP7/2007–2011) projects ESCAPE (grant agreement number: 211250 ) and TRANSPHORM ( ENV.2009. ). We also like to thank José Lao from the Energy & Air Quality Department, Barcelona Regional, Barcelona, Spain for his help in the Barcelona dispersion modelling; Pekka Taimisto and Arto Pennanen for their field work in Helsinki Vantaa region: and Christine McHugh from CERC, UK for her help in dispersion modelling for London. We thank all study participants and the dedicated personnel of the Heinz Nixdorf Recall Study. We gratefully acknowledge the collaboration with K.-H. Jöckel, D. Grönemeyer, R. Seibel, K. Mann, L. Vollbracht, and K. Lauterbach. We thank the North Rhine-Westphalia State Agency for Nature, Environment and Consumer Protection for providing road maps with traffic data and emission data from the reference sites for back-extrapolation. The study was supported by the Heinz Nixdorf Foundation [chairman: M. Nixdorf; former chairman: G. Schmidt (deceased)], the German Ministry of Education and Science , the German Research Foundation (DFG; projects JO-170/8-1 , HO 3314/2-1 , SI 236/8-1 , and SI236/9-1 ). SAPALDIA: we thank Study directorate: NM Probst Hensch, T Rochat, N Künzli, C Schindler, JM Gaspoz, the Swiss National Science Foundation (grant nos 33CSCO-134276/1 , 33CSCO-108796 , 3247BO-104283 , 3247BO-104288 , 3247BO-104284 , 3247-065896 , 3100-059302 , 3200-052720 , 3200-042532 , 4026-028099 , PMPDP3_129021/1 , PMPDP3_141671/1 ), and the Federal Office for Forest, Environment and Landscape ( 10.0022.PJ/J112-0392 ). For full study team: see Appendix A (p. 2).

Publisher Copyright:
© 2014 Elsevier Ltd.


  • Air pollution
  • Cohort
  • Dispersion modelling
  • Exposure
  • Land use regression


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