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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
Citation
Johnson, M and MacNeill, M and Grgicak-Mannion, A and Nethery, E and Xu, X and Dales, R and Rasmussen, P and Wheeler, A, 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. ISSN 1559-0631 (2013) [Refereed Article]
Copyright Statement
Copyright 2013 Nature America, Inc
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 householdlevel
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
Item Details
Item Type: | Refereed Article |
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Keywords: | child exposure/health, criteria pollutants, epidemiology, exposure modeling, environmental monitoring |
Research Division: | Commerce, Management, Tourism and Services |
Research Group: | Human resources and industrial relations |
Research Field: | Occupational and workplace health and safety |
Objective Division: | Health |
Objective Group: | Public health (excl. specific population health) |
Objective Field: | Public health (excl. specific population health) not elsewhere classified |
UTAS Author: | Wheeler, A (Dr Amanda Wheeler) |
ID Code: | 101129 |
Year Published: | 2013 |
Web of Science® Times Cited: | 40 |
Deposited By: | Menzies Institute for Medical Research |
Deposited On: | 2015-06-10 |
Last Modified: | 2017-11-03 |
Downloads: | 0 |
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