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San Francisco Bay and its watersheds are polluted by legacy polychlorinated biphenyls (PCBs), resulting in the establishment of a total maximum daily load (TDML) that requires a 90% PCB load reduction from municipal stormwater. Green infrastructure (GI) is a multibenefit solution for stormwater management, potentially addressing the TMDL objectives, but planning and implementing GI cost-effectively to achieve management goals remains a challenge and requires an integrated watershed approach. This study used the nondominated sorting genetic algorithm (NSGA-II) coupled with the Stormwater Management Model (SWMM) to find near-optimal combinations of GIs that maximize PCB load reduction and minimize total relative cost at a watershed scale. The selection and placement of three locally favored GI types (bioretention, infiltration trench, and permeable pavement) were analyzed based on their cost and effectiveness. The results show that between optimal solutions and nonoptimal solutions, the effectiveness in load reduction could vary as much as 30% and the difference in total relative cost could be well over $100 million. Sensitivity analysis of both GI costs and sizing criteria suggest that the assumptions made regarding these parameters greatly influenced the optimal solutions.
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