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Development of temporally refined land-use regression models predicting daily household-level air pollution in a panel study of lung function among asthmatic children

Journal of Exposure Science & Environmental Epidemiology volume 23, pages 259–267 (2013)Cite this article

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Abstract

Regulatory monitoring data and land-use regression (LUR) models have been widely used for estimating individual exposure to ambient air pollution in epidemiologic studies. However, LUR models lack fine-scale temporal resolution for predicting acute exposure and regulatory monitoring provides daily concentrations, but fails to capture spatial variability within urban areas. This study coupled LUR models with continuous regulatory monitoring to predict daily ambient nitrogen dioxide (NO2) and particulate matter (PM2.5) at 50 homes in Windsor, Ontario. We compared predicted versus measured daily outdoor concentrations for 5 days in winter and 5 days in summer at each home. We also examined the implications of using modeled versus measured daily pollutant concentrations to predict daily lung function among asthmatic children living in those homes. Mixed effect analysis suggested that temporally refined LUR models explained a greater proportion of the spatial and temporal variance in daily household-level outdoor NO2 measurements compared with daily concentrations based on regulatory monitoring. Temporally refined LUR models captured 40% (summer) and 10% (winter) more of the spatial variance compared with regulatory monitoring data. Ambient PM2.5 showed little spatial variation; therefore, daily PM2.5 models were similar to regulatory monitoring data in the proportion of variance explained. Furthermore, effect estimates for forced expiratory volume in 1 s (FEV1) and peak expiratory flow (PEF) based on modeled pollutant concentrations were consistent with effects based on household-level measurements for NO2 and PM2.5. These results suggest that LUR modeling can be combined with continuous regulatory monitoring data to predict daily household-level exposure to ambient air pollution. Temporally refined LUR models provided a modest improvement in estimating daily household-level NO2 compared with regulatory monitoring data alone, suggesting that this approach could potentially improve exposure estimation for spatially heterogeneous pollutants. These findings have important implications for epidemiologic studies — in particular, for research focused on short-term exposure and health effects.

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Acknowledgements

We would like to thank the scientists and field technicians who contributed to this study including Li Chen, Hongyu You, Ryan Kulka, Keith Van Ryswyk, Corrine Stocco, and Angelos Anastassopoulos (Health Canada). We also received helpful feedback on the draft manuscript from Nina Dobbins and Rick Burnett. Finally, we would like to thank the household participants without whom this study would not have been possible.

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Authors and Affiliations

  1. Air Health Science Division, Water Air and Climate Change Bureau, Health Canada, 269 Laurier Avenue West, Ottawa, Ontario, Canada K1A 0K9,

    Markey Johnson, Morgan MacNeill, Elizabeth Nethery, Robert Dales & Amanda Wheeler

  2. Great Lakes Institute for Environmental Research, University of Windsor, 401 Sunset Avenue, Windsor, Ontario, Canada N9B 3P4,

    Alice Grgicak-Mannion

  3. Department of Civil and Environmental Engineering, University of Windsor, Windsor, N9B 3P4, Ontario, Canada

    Xiaohong Xu

  4. Exposure and Biomonitoring Division, Health Canada, 50 Colombine Driveway, Tunney’s Pasture, Ottawa, Ontario, Canada K1A 0K9,

    Pat Rasmussen

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  1. Markey Johnson
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Correspondence to Markey Johnson.

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Johnson, M., MacNeill, M., Grgicak-Mannion, A. et al. Development of temporally refined land-use regression models predicting daily household-level air pollution in a panel study of lung function among asthmatic children. J Expo Sci Environ Epidemiol 23, 259–267 (2013). https://doi.org/10.1038/jes.2013.1

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