A GIS–Environmental Justice Analysis of Particulate Air Pollution in Hamilton, Canada

Michael Jerrett, Richard T Burnett, Pavlos Kanaroglou, John Eyles, Norm Finkelstein, Chris Giovis and Jeffrey R Brook
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Michael Jerrett: School of Geography and Geology, and McMaster Institute of Environment and Health, McMaster University, 1280 Main Street, West Hamilton, Ontario, Canada L8S 4K1
Richard T Burnett: Health Canada, 200 Environmental Health Centre, Health Canada, Tunney's Pasture, Ottawa, Canada K1A 0L2
Jeffrey R Brook: Air Quality Processes Research Division, Meteorological Service of Canada, 4905 Dufferin Street, Toronto, Ontario, Canada M3H 5T4

Environment and Planning A, 2001, vol. 33, issue 6, 955-973

Abstract: The authors address two research questions: (1) Are populations with lower socioeconomic status, compared with people of higher socioeconomic status, more likely to be exposed to higher levels of particulate air pollution in Hamilton, Ontario, Canada? (2) How sensitive is the association between levels of particulate air pollution and socioeconomic status to specification of exposure estimates or statistical models? Total suspended particulate (TSP) data from the twenty-three monitoring stations in Hamilton (1985–94) were interpolated with a universal kriging procedure to develop an estimate of likely pollution values across the city based on annual geometric means and extreme events. Comparing the highest with the lowest exposure zones, the interpolated surfaces showed more than a twofold increase in TSP concentrations and more than a twentyfold difference in the probability of exposure to extreme events. Exposure estimates were related to socioeconomic and demographic data from census tract areas by using ordinary least squares and simultaneous autoregressive (SAR) models. Control for spatial autocorrelation in the SAR models allowed for tests of how robust specific socioeconomic variables were for predicting pollution exposure. Dwelling values were significantly and negatively associated with pollution exposure, a result robust to the method of statistical analysis. Low income and unemployment were also significant predictors of exposure, although results varied depending on the method of analysis. Relatively minor changes in the statistical models altered the significant variables. This result emphasizes the value of geographical information systems (GIS) and spatial statistical techniques in modelling exposure. The result also shows the importance of taking spatial autocorrelation into account in future justice – health studies.

Date: 2001
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Persistent link: https://EconPapers.repec.org/RePEc:sae:envira:v:33:y:2001:i:6:p:955-973

DOI: 10.1068/a33137

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