Nilima Bhoi

Date of Award


Degree Type


Degree Name

Master of Science




Dr. Jamie Baxter


This thesis explores the existence of environmental injustice in five Canadian census metropolitan areas (CMA): Edmonton, London, Montreal, Toronto and Victoria. Spatial/distributive environmental injustice refers to situations where specific, already disadvantaged, groups bear a disproportionate share of pollution exposure. Several studies in the United States have found that pollution is often more prevalent in areas that are predominantly low income backgrounds or are visible minorities (UCC, 1987; Mohai and Bryant, 1992; Szasz and Meuser, 1997; Ringquist, 2005). Though there are limitations to the ecological approach typically used in these cases, important lessons from the US research (e.g., Bowen, 2000; Maantay 2002; Mohai and Saha 2006) can be

applied in the Canadian context. The present study builds on these lessons to contribute to a growing set of Canadian studies by ‘weighting’ pollution values according to toxic intensity. Data from 2001 Canadian Censuses are regressed against GIS-derived census tract exposures from the National Pollutant Release Inventory (NPRI) (point source industrial pollution) and from DMTI’s road network (traffic pollution). The independent variables are percentage of manufacturing employment, percentage of visible minority, percentage of aboriginal identity, percentage of recent immigrants, percentage of immigrants, percentage of lone-parent families, percentage of low-income families, median household income, average dwelling value and population density. The results indicate statistically significant correlations between median household income, percentage of immigrants and percentage of visible minorities, and industrial pollution exposure. However, in general, there is no consistent evidence of environmental injustice


with respect to the other variables across the five cities as the result varies widely across the CMAs and buffer sizes. The control variable, population density is the strongest (negative) and most consistent predictor of pollution across the CMAs



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