Development, evaluation, and comparison of land use regression modelling methods to estimate residential exposure to nitrogen dioxide in a cohort study

Jonathan Gillespie, Iain J. Beverland, Scott Hamilton, Sandosh Padmanabhan

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7 Citations (Scopus)
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Abstract

We used a network of 135 NO2 passive diffusion tube sites to develop land use regression (LUR) models in a UK conurbation. Network sites were divided into 4 groups (32 – 35 sites per group) and models developed using combinations of 1 - 3 groups of 'training' sites to evaluate how the number of training sites influenced model performance and residential NO2 exposure estimates for a cohort of 13,679 participants. All models explained moderate to high variance in training and independent 'hold-out' data (Training adj. R2: 62 – 89%; Hold-out R2: 44 – 85%). Average hold-out R2 increased by 9.5%, while average training adj. R2 decreased by 7.2% when the number of training groups was increased from 1 to 3. Exposure estimate precision improved with increasing number of training sites (median intra-site relative standard deviations of 19.2, 10.3, and 7.7% for 1-group, 2-group and 3-group models respectively). Independent 1-group models gave highly variable exposure estimates suggesting that variations in LUR sampling networks with relatively low numbers of sites (≤ 35) may substantially alter exposure estimates. Collectively, our analyses suggest that use of more than 60 training sites has quantifiable benefits in epidemiological application of LUR models.
Original languageEnglish
Pages (from-to)11085–11093
Number of pages9
JournalEnvironmental Science and Technology
Volume50
Issue number20
Early online date12 Sep 2016
DOIs
Publication statusPublished - 12 Sep 2016

Keywords

  • land use regression
  • nitrogen dioxide
  • air pollution
  • exposure assessment
  • model performance
  • exposure estimate approach

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    LUR Pollution Surfaces

    Gillespie, J. (Creator), University of Strathclyde, 1 Sep 2016

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