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

Dados Bibliográficos

ID4139916
AutoresMichael Jerrett (0000-0002-4121-0587, McMaster University, autor correspondente), Richard T Burnett (0000-0001-6772-4239, Health Canada, 200 Environmental Health Centre, Health Canada, Tunney's Pasture, Ottawa, Canada K1A 0L2, autor correspondente), Pavlos Kanaroglou (McMaster University, autor correspondente), John Eyles (0000-0002-7348-4005, McMaster University, autor correspondente), Norm Finkelstein (McMaster University, autor correspondente), Chris Giovis (McMaster University, autor correspondente), Jeffrey R Brooks (0000-0002-4165-2190, autor correspondente), Jeffrey R Brook (Air Quality Processes Research Division, Meteorological Service of Canada, 4905 Dufferin Street, Toronto, Ontario, Canada M3H 5T4)
Ano2001
Volume33
Fascículo6
Páginas955-973
Data de publicação2001-06-01
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoEnvironment and Planning A Economy and Space (JOURNAL)
Identificadores do periódicoISSN: 0308-518X • E-ISSN: 1472-3409
EditoraSAGE Publications Inc (PUBLISHER)
DOI10.1068/a33137
OpenAlexW1970393924
IdiomaEN
Citações recebidas116
Referências citadas34

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

Air pollution · Biology · Environmental health · Environmental justice · Geography · Particulates · Population · Socioeconomic status · Spatial analysis · Statistical model · Statistics · Air Quality and Health Impacts · Climate Change and Health Impacts · Environmental Justice and Health Disparities · Environmental Science · Mathematics · Ecology · Pollution