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Creating National Air Pollution Models for Population Exposure Assessment in Canada

Perry Hystad, Eleanor Setton, Alejandro Cervantes, Karla Poplawski, Steeve Deschenes, Michael Brauer, Aaron van Donkelaar, Lok Lamsal, Randall Martin, Michael Jerrett and Paul Demers
Environmental Health Perspectives
Vol. 119, No. 8 (AUGUST 2011), pp. 1123-1129
Stable URL: http://www.jstor.org/stable/41233467
Page Count: 7
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Abstract

Background: Population exposure assessment methods that capture local-scale pollutant variability are needed for large-scale epidemiological studies and surveillance, policy, and regulatory purposes. Currently, such exposure methods are limited. Methods: We created 2006 national pollutant models for fine paniculate matter [PM with aerodynamic diameter ≤ 2.5 um (PM2.5)], nitrogen dioxide (NO₂), benzene, ethylbenzene, and 1,3-butadiene from routinely collected fixed-site monitoring data in Canada. In multiple regression models, we incorporated satellite estimates and geographic predictor variables to capture background and regional pollutant variation and used deterministic gradients to capture local-scale variation, lhe national NO₂ and benzene models are evaluated with independent measurements from previous land use regression models that were conducted in seven Canadian cities. National models are applied to census block-face points, each of which represents the location of approximately 89 individuals, to produce estimates of population exposure. Results: The national NO₂ model explained 73% of the variability in fixed-site monitor concentrations, PM2.5 46%, benzene 62%, ethylbenzene 67%, and 1,3-butadiene 68%. The NO₂ model predicted, on average, 43% of the within-city variability in the independent NO₂ data compared with 18% when using inverse distance weighting of fixed-site monitoring data. Benzene models performed poorly in predicting within-city benzene variability. Based on our national models, we estimated Canadian ambient annual average population-weighted exposures (in micrograms per cubic meter) of 8.39 for PM2.5, 23.37 for NO₂, 1.04 for benzene, 0.63 for ethylbenzene, and 0.09 for 1,3-butadiene. Conclusions: The national pollutant models created here improve exposure assessment compared with traditional monitor-based approaches by capturing both regional and local-scale pollution variation. Applying national models to routinely collected population location data can extend land use modeling techniques to population exposure assessment and to informing surveillance, policy, and regulation.

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