Creating National Air Pollution Models for Population Exposure Assessment in Canada

By Hystad, Perry; Setton, Eleanor et al. | Environmental Health Perspectives, August 2011 | Go to article overview

Creating National Air Pollution Models for Population Exposure Assessment in Canada


Hystad, Perry, Setton, Eleanor, Cervantes, Alejandro, Poplawski, Karla, Deschenes, Steeve, Brauer, Michael, Donkelaar, Aaron van, Lamsal, Lok, Martin, Randall, Jerrett, Michael, Demers, Paul, Environmental Health Perspectives


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 particulate matter [PM with aerodynamic diameter <2.5 um (PM2 5)], nitrogen dioxide (N (2)), 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. The national [NO.sub.2] 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 [NO.sub.2] model explained 73% of the variability in fixed-site monitor concentrations, PM2 5 46%, benzene 62%, ethylbenzene 67%, and 1,3-butadiene 68%. The [NO.sub.2] model predicted, on average, 43% of the within-city variability in the independent [NO.sub.2] 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 PM23, 23.37 for [NO.sub.2], 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.

KEY WORDS: air pollution, Canada, fixed-site monitors, gradients, land use regression, population exposure assessment, satellite data. Environ Health Perspect 119:1123-1129 (2011). doi: 10.1289/ ehp. 1002976 [Online 31 March 2011]

Predicting air pollution concentrations at resolutions capable of capturing local-scale pollutant gradients over large geographical areas is becoming increasingly important in multicity and national health studies; in population exposure assessment; and in support of policy, surveillance, and regulatory initiatives. Currently, fixed-site government monitors are the foundation of these activities; however, because of siting criteria, such monitors may fail to fully capture local-scale pollutant variability. In addition, the number of monitors and their spatial distribution may be limited, as is the case in Canada. At present, few methodologies are available that adequately capture local-scale pollutant variability at a national scale when monitor density, distribution, or siting is suboptimal.

A number of approaches may be used to model air pollution over large areas, including interpolation of fixed-site government monitoring data, dispersion modeling, satellite remote sensing, land use regression (LUR), and proximity and deterministic methods. Each approach, however, has inherent limitations that restrict its use for producing local-scale pollution estimates. Interpolation of fixed-site air pollution monitoring data has typically been used to predict pollution concentrations across large areas (Beelen ct al. 2009), with recent interest directed towards kriging methods and spatial smoothing with geographic covariates (Beelen et al. 2009; Hart et al. 2009; Yanosky et al. 2008). Fixed-site monitors may not capture entire populations, and measurements typically represent regional and between-city pollution differences due to monitor siting criteria, which prevent monitors from being placed in proximity to major roads and other pollution sources. …

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