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

Area of Science:

• Environmental Engineering
• Water Resource Management
• Urban Planning

Background:

• Non-point source pollution (NPS) from stormwater runoff poses a significant ecological threat.
• Cost-effectiveness of green and gray infrastructure for stormwater management is a key concern for decision-makers.
• Previous studies often overlooked the additional benefits of green infrastructure in optimization models.

Purpose of the Study:

• To develop a simulation-optimization framework for optimizing green and gray infrastructure for NPS control.
• To evaluate the cost-effectiveness of combined green-gray infrastructure under future scenarios, considering varying additional benefits of green infrastructure.
• To assess the sensitivity of different infrastructure types to future scenarios.

Main Methods:

• Integrated Stormwater Management Model (SWMM) with Non-dominated Sorting Genetic Algorithm (NSGA-II) for simulation-optimization.
• Case study in a Sponge City area in Nanchang, China.
• Inclusion of different levels of Value of Additional Benefits (VAB) for green infrastructure.

Main Results:

• Green-gray infrastructure yields co-benefits only when green infrastructure has appropriate Value of Additional Benefits (VAB).
• Gray infrastructure emerges as a more cost-effective measure when VAB is insufficient.
• Gray infrastructure demonstrates higher sensitivity to future scenarios compared to green and green-gray infrastructure.

Conclusions:

• The optimal choice between green, gray, or combined infrastructure depends on the VAB of green infrastructure and future scenario conditions.
• Decision-makers can utilize these findings for tailored NPS control planning, balancing investment costs with water quality objectives.
• The developed framework provides a robust approach for evaluating sustainable stormwater management strategies.