Which Nutrient Criteria Should States and Tribes Choose to Determine Waterbody Impairment?: Using Science and Judgments to Inform Decision-making
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Nutrients are the number one water pollution problem for U.S. lakes, reservoirs, and ponds. Excessive nutrients, such as nitrogen and phosphorus, lead to eutrophication, a condition that can include low oxygen levels, noxious algal blooms, and fish kills. Since eutrophication is a condition that manifests itself differently in different systems, there is not a criterion variable with a clear threshold that can be used to set the criterion level. This dissertation presents an approach to address the question: How should States and Tribes choose nutrient criteria to determine eutrophication-related impairments of the designated use? To address this question I used a combination of water quality modeling and decision analysis to determine the optimal nutrient criterion variables and levels. To choose criterion variables that are predictive of the designated use, I utilized statistical models (structural equation models, multiple regression, and binomial regression model) to link the measured water quality variables to expert elicited categories of eutrophication and the designated uses. These models were applied successfully to single waterbodies, the Kissimmee Chain-of-Lakes region, and the State of North Carolina to assess which candidate criterion variables were the most predictive. Additionally, the models indicated that the variables that were most predictive of eutrophication were also the most predictive of the designated use. Using the predictive nutrient criteria variables, I applied a decision-analytic approach to nutrient criteria setting in North Carolina. I developed a nutrient criteria value model that included two submodels, a water quality model and a multiattribute value model. The submodels were parameterized using a combination of water quality data, expert elicitation data, and utility assessments. The outcome of the nutrient criteria value model is the overall expected value for a criterion level choice; the optimal criterion level would be the choice that maximized the expected value. Using the preferences of North Carolina environmental decision-makers and a total phosphorus criterion variable, the optimal criterion level was between 0.03 mg/L and 0.07 mg/L. Ultimately, I hope this research will establish methodology used to set appropriate water quality criteria.
Business Administration, Management
Political Science, General
water quality modeling
multiattribute utility analysis
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