eCite Digital Repository

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

DOI: doi:10.1038/jes.2013.1

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
Keywords:child exposure/health, criteria pollutants, epidemiology, exposure modeling, environmental monitoring
Research Division:Medical and Health Sciences
Research Group:Public Health and Health Services
Research Field:Environmental and Occupational Health and Safety
Objective Division:Health
Objective Group:Public Health (excl. Specific Population Health)
Objective Field:Environmental Health
Author:Wheeler, A (Dr Amanda Wheeler)
ID Code:101129
Year Published:2013
Web of Science® Times Cited:19
Deposited By:Menzies Institute for Medical Research
Deposited On:2015-06-10
Last Modified:2015-09-15
Downloads:0

Repository Staff Only: item control page