Modelling air pollution for epidemiologic research--part II: predicting temporal variation through land use regression

A Mölter, S Lindley, F de Vocht, A Simpson, R Agius

Research output: Contribution to journalArticle (Academic Journal)peer-review

35 Citations (Scopus)


Over recent years land use regression (LUR) has become a frequently used method in air pollution exposure studies, as it can model intra-urban variation in pollutant concentrations at a fine spatial scale. However, very few studies have used the LUR methodology to also model the temporal variation in air pollution exposure. The aim of this study is to estimate annual mean NO(2) and PM(10) concentrations from 1996 to 2008 for Greater Manchester using land use regression models. The results from these models will be used in the Manchester Asthma and Allergy Study (MAAS) birth cohort to determine health effects of air pollution exposure. The Greater Manchester LUR model for 2005 was recalibrated using interpolated and adjusted NO(2) and PM(10) concentrations as dependent variables for 1996-2008. In addition, temporally resolved variables were available for traffic intensity and PM(10) emissions. To validate the resulting LUR models, they were applied to the locations of automatic monitoring stations and the estimated concentrations were compared against measured concentrations. The 2005 LUR models were successfully recalibrated, providing individual models for each year from 1996 to 2008. When applied to the monitoring stations the mean prediction error (MPE) for NO(2) concentrations for all stations and years was -0.8μg/m³ and the root mean squared error (RMSE) was 6.7μg/m³. For PM(10) concentrations the MPE was 0.8μg/m³ and the RMSE was 3.4μg/m³. These results indicate that it is possible to model temporal variation in air pollution through LUR with relatively small prediction errors. It is likely that most previous LUR studies did not include temporal variation, because they were based on short term monitoring campaigns and did not have historic pollution data. The advantage of this study is that it uses data from an air dispersion model, which provided concentrations for 2005 and 2010, and therefore allowed extrapolation over a longer time period.

Original languageEnglish
Pages (from-to)211-7
Number of pages7
JournalScience of The Total Environment
Issue number1
Publication statusPublished - 1 Dec 2010

Bibliographical note

Copyright © 2010 Elsevier B.V. All rights reserved.


  • Air Pollutants
  • Air Pollution
  • Environmental Monitoring
  • Epidemiologic Studies
  • Models, Chemical
  • Nitrogen Dioxide
  • Particulate Matter
  • Regression Analysis


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