Optimizing Green-Gray Infrastructure for Non-Point Source Pollution Control under Future Uncertainties

Non-Point Source Pollution (NPS) caused by polluted and untreated stormwater runoff discharging into water bodies has become a serious threat to the ecological environment. Green infrastructure and gray infrastructure are considered to be the main stormwater management measures, and the issue of their cost-effectiveness is a widespread concern for decision makers. Multi-objective optimization is one of the most reliable and commonly used approaches in solving cost-effectiveness issues. However, many studies optimized green and gray infrastructure under an invariant condition, and the additional benefits of green infrastructure were neglected. In this study, a simulation-optimization framework was developed by integrated Stormwater Management Model (SWMM) and Non-dominated Sorting Genetic Algorithm (NSGA-II) to optimize green and gray infrastructure for NPS control under future scenarios, and a realistic area of Sponge City in Nanchang, China, was used as a typical case. Different levels of additional benefits of green infrastructure were estimated in the optimizing process. The results demonstrated that green-gray infrastructure can produce a co-benefit if the green infrastructure have appropriate Value of Additional Benefits (VAB), otherwise, gray infrastructure will be a more cost-effectiveness measure. Moreover, gray infrastructure is more sensitive than green infrastructure and green-gray infrastructure under future scenarios. The findings of the study could help decision makers to develop suitable planning for NPS control based on investment cost and water quality objectives.