A GIS - environmental justice analysis of particulate air pollution in Hamilton, Canada

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 lev els of particulate air pollution and socioeconomic status to specification of exposure estimates or statistical models? Total suspended particulate (T SP) data from the twenty-three monitoring stations in Hamilton (1985-94) we re interpolated with a universal kriging procedure to develop an estimate o f likely pollution values across the city based on annual geometric means a nd extreme events. Comparing the highest with the lowest exposure zones, th e interpolated surfaces showed more than a twofold increase in TSP concentr ations and more than a twentyfold difference in the probability of exposure to extreme events. Exposure estimates were related to socioeconomic and de mographic data from census tract areas by using ordinary least squares and simultaneous autoregressive (SAR) models. Control for spatial autocorrelati on in the SAR models allowed for tests of how robust specific socioeconomic variables were for predicting pollution exposure. Dwelling values were sig nificantly and negatively associated with pollution exposure, a result robu st 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 mode ls altered the significant variables. This result emphasizes the value of g eographical 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.