Water pollution is an ongoing problem that can be attributed to human activities. As world population increases and countries become more developed, this problem intensifies. Fortunately, the causes and solutions of water pollution are documented and have been implemented with various levels of success. These solutions, or Best Management Practices (BMPs), vary in type and function and remove pollutants from runoff prior to it reaching rivers, lakes, and other bodies of water. This study investigates bioretention basins, a specific group of BMPs, and presents analysis and prediction of their performance, of which our knowledge is incomplete in the existing literature. To fill this knowledge gap, this study examined mean pollutant removal rates for 25 separate pollutants and developed a series of regression models and nomographs to predict pollutant removal rates given an influent pollutant concentration, rainfall depth, and bioretention basin geometry. Results indicate that a wide variety of factors influence the pollutant removal rates that can be achieved using bioretention basins. This study was performed to gain a better understanding of the processes that define pollutant removal and to develop predictive models that could be used to estimate potential pollutant removal rates provided by bioretention basins. Given the ongoing water pollution problem, this study aims to evaluate the effectiveness of bioretention basins as a possible solution. The predictive models are likely to be the first of their kind and will contribute to the improvement of the design and engineering of bioretention basins.
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