Graduation Year


Document Type




Degree Granting Department


Major Professor

Jayajit Chakraborty, Ph.D.


Environmental justice, Pollution, GIS, Spatial statistics, Health disparities


This dissertation seeks to extend quantitative research on environmental justice and address methodological limitations of previous studies by: (a) using new indicators of exposure to air pollution and contemporary risk modeling techniques; (b) assessing disparities in human health risks, instead of focusing only on potential exposure or proximity to pollution sources; and (c) using multivariate regression models that consider the effects of spatial dependence. The case study examines racial/ethnic and socioeconomic disparities in the geographic distribution of exposure to airborne toxic emissions from industrial point sources in the Houston-Galveston-Brazoria metropolitan statistical area. Industrial pollution sources for this study comprise facilities listed in the US EPA's Toxic Release Inventory (TRI).

The Risk-Screening Environmental Indicator (RSEI) model is used to estimate potential human health risks from air pollutants based on data on toxicity and dispersion of chemical releases from TRI facilities. The analyses utilize four indicators of potential exposure to industrial pollution: (a) presence or absence of air emissions, (b) total quantities (pounds) of air emissions, (c) toxicity-weighted quantities of emissions and (d) modeled risk scores based on the cumulative health risk posed by air emissions. Traditional linear regression and spatial autoregressive techniques based on several neighborhood configurations are used to model the occurrence and magnitude of these four indicators, using relevant explanatory variables from the 2000 census, at the census tract and block groups levels of aggregation. Results indicate a disproportionate pattern of health risks from TRI facilities in the HGB-MSA, with the Hispanic population facing the highest exposure.

The locations and magnitude of toxic pollution are significantly statistical effected by the presence of minority residents and population density. Additionally, key differences in the significance of explanatory variables between the spatial and conventional regression models demonstrate the importance of correcting for spatial dependence in environmental justice analysis. The analytical results for several variables are also sensitive to the choice of data resolution (tract or block group). Overall, this study indicates that more flexible spatial analytic techniques are required to improve the identification of environmental injustice and past studies utilizing conventional statistical methods should be revisited to explicitly account for spatial effects.