A Geographical Information Systems Tool for E.coli Monitoring in the Plaster Creek Watershed

Document Type


Lead Author Type

MBI Masters Student


Dr. Jonathan Leidig, jonathan.leidig@gvsu.edu

Embargo Period



Understanding human impacts on water quality continues to be a concern for federal, state, and local agencies. These agencies and organizations are responsible for monitoring and selecting improvement sites in their own watersheds. Improvements to watershed monitoring and intervention site selection are needed. Previous analyses of water quality data have typically been limited to basic metrics such as minimum and maximum, average, and pass/fail. Furthermore, analysis is difficult because data is collected by many stakeholders and not shared. Intervention sites to improve water quality are often selected based on ease of access instead of using best-practices criteria. Previous mapping tools require costly and complicated software packages analysis. This project creates a new tool for water quality data analysis using geographical information systems (GIS). This interactive mapping tool compiles data locations within a watershed, dates and results of testing, locations for potential pollution inputs, and potential monitoring sites. It also defines a list of best practices that can be used to select monitoring sites.

Plaster Creek, a tributary of the Grand River, was chosen because it is currently designated as unsafe for any form of human contact due to high levels of Escherichia coli (E. coli). The tool creates a map that will assist users in selecting monitoring sites and evaluating the efficacy of the current monitoring sites. Compiling data, GIS layers, and other information from multiple stakeholders into one tool will aid in understanding the cause of high levels of E. coli in Plaster Creek. For the first time, Plaster Creek water quality stakeholders can determine if the current testing locations provide adequate information to choose high-impact pollution intervention sites, monitor potential pollution inputs, select additional testing locations, and monitor long-term impacts of improvement efforts. Sustained use of this model to map watersheds will aid stakeholders in further water quality assessments.

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