| Summary: | A Thesis Submitted In Partial Fulfillment of the Requirements for the Degree of MASTER OF SCIENCE in Environmental Science from Texas A&M University-Corpus Christi. Water quality is a key factor in ecosystem health. While physical and ecological models of pollution have been widely used to determine water quality, there is a significant gap in the use of socioeconomic metrics in these models. The purpose of this study was to identify and visualize social, cultural, behavioral, and economic drivers of surface water quality impairment. Standardized socio-economic data with links to water quality data were mapped across two study sites: San Antonio Bay and Baffin Bay. A binomial logistic regression model was utilized to identify connections between socio-economic metrics and water quality impairment status. In the San Antonio Bay study site, important predictors of surface water quality impairment were housing type and transportation vulnerability, percentage of 5% or more impervious land cover within 30m if shoreline, amount of developed area, and forest and woody wetland cover. In the Baffin Bay study site, important predictors were minority status and language vulnerability, percentage of 5% or more impervious land cover within 30m of shoreline, forest and woody wetland cover, and amount of cropland. Understanding the interactions between communities and local water quality will allow for more thorough and effective management of water resources. Environmental Science College of Science
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