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 article
Johnson, Markey, MacNeill, Morgan, Grgicak-Mannion, Alice, Nethery, Elizabeth, Xu, Xiaohong, Dales, Robert, Rasmussen, Pat and Wheeler, Amanda. (2013). 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 and Environmental Epidemiology. 23, pp. 259-267. https://doi.org/10.1038/jes.2013.1
Authors | Johnson, Markey, MacNeill, Morgan, Grgicak-Mannion, Alice, Nethery, Elizabeth, Xu, Xiaohong, Dales, Robert, Rasmussen, Pat and Wheeler, Amanda |
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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. |
Keywords | child exposure/health; criteria pollutants; epidemiology; exposure modeling; environmental monitoring |
Year | 2013 |
Journal | Journal of Exposure Science and Environmental Epidemiology |
Journal citation | 23, pp. 259-267 |
Publisher | Nature Publishing Group |
ISSN | 1559-0631 |
Digital Object Identifier (DOI) | https://doi.org/10.1038/jes.2013.1 |
Scopus EID | 2-s2.0-84876751481 |
Research or scholarly | Research |
Page range | 259-267 |
Publisher's version | License All rights reserved File Access Level Controlled |
Output status | Published |
Publication dates | |
Online | 27 Mar 2013 |
Publication process dates | |
Accepted | 17 Oct 2012 |
Deposited | 13 Apr 2021 |
https://acuresearchbank.acu.edu.au/item/8vwxz/development-of-temporally-refined-land-use-regression-models-predicting-daily-household-level-air-pollution-in-a-panel-study-of-lung-function-among-asthmatic-children
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