Modeling the Intraurban Variability of Ambient Traffic Pollution in Toronto, Canada

Abstract

The objective of this paper is to model determinants of intraurban variation in ambient concentrations of nitrogen dioxide (NO₂) in Toronto, Canada, with a land use regression (LUR) model. Although researchers have conducted similar studies in Europe, this work represents the first attempt in a North American setting to characterize variation in traffic pollution through the LUR method. NO₂ samples were collected over 2 wk using duplicate two-sided Ogawa passive diffusion samplers at 95 locations across Toronto. Independent variables employed in subsequent regression models as predictors of NO₂ were derived by the Arc 8 geographic information system (GIS). Some 85 indicators of land use, traffic, population density, and physical geography were tested. The final regression model yielded a coefficient of determination (R²) of .69. For the traffic variables, density of 24-h traffic counts and road measures display positive associations. For the land use variables, industrial land use and counts of dwellings within 2000 m of the monitoring location were positively associated with NO₂. Locations up to 1500 m downwind of major expressways had elevated NO₂ levels. The results suggest that a good predictive surface can be derived for North American cities with the LUR method. The predictive maps from the LUR appear to capture small-area variation in NO₂ concentrations. These small-area variations in traffic pollution are probably important to the exposure experience of the population and may detect health effects that would have gone unnoticed with other exposure estimates.

Funded by Health Canada and the Canadian Institutes of Health Research. We thank Chris Giovis, Michael Heffernan, Natalia Restrepo, Kelly Williams, and Shuhua Yi, McMaster University, for assistance with deploying the monitors and preparing data. In addition, we thank Dr. Christopher Morgan and his colleagues at the City of Toronto for approval to deploy the monitors on City property and for the traffic data. The Ministry of Transportation for Ontario gave timely approval for entrance onto their highways. We also acknowledge helpful comments from Dr. Mark Goldberg, McGill University, on the cross-validation methods. Additionally we acknowledge funding from U.S. EPA grant RD83186101, NIEHS grants 5P01 ES11627, 5P01 ES09581, and the Southern California Environmental Health Sciences Center funded by NIEHS grant 5P30 ES07048. Any remaining errors or omissions are the responsibility of the authors.

Notes

∗Multiple sites across many cities in the Netherlands, too numerous to mention, were sampled for this study.