J. R. Brook

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

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.

The Environment Canada Pan and ParaPan American Science Showcase Project

AbstractThe Pan and Parapan American Games (PA15) are the third largest sporting event in the world and were held in Toronto in the summer of 2015 (10–26 July and 7–15 August). This was used as an ...

High Resolution Model Simulations of the Canadian Oil Sands with Comparisons to Field Study Observations

The governments of Canada and Alberta are implementing a joint plan for oil sands monitoring that includes investigating emissions, transport and downwind chemistry associated with the Canadian oil sands region. As part of that effort, Environment Canada’s Global Environmental Multiscale—Modelling Air-quality And CHemistry (GEM-MACH) system was reconfigured for the first time to create nested forecasts of air quality at model grid resolutions down to 2.5 km, with the highest resolution domain including the Canadian provinces of Alberta and Saskatchewan. The forecasts were used to direct an airborne research platform during a summer 2013 monitoring intensive. Subsequent work with the modelling system has included an in-depth comparison of the model predictions to monitoring network observations, and to field intensive airborne and surface supersite observations. A year of model predictions and monitoring network observations were compared, as were model and aircraft flight track values. The relative impact of different model versions (including modified emissions and feedbacks between weather and air pollution) will be discussed. Model-based predictions of indicators of human-health (i.e., Air Quality Health Index) and ecosystem (i.e. deposition of pollutants) impacts for the region will also be described.