Enhancing water quality in urban areas: A spatiotemporal optimization framework for green-gray infrastructure

Area of Science:

• Environmental Engineering
• Urban Planning
• Water Resource Management

Background:

• Urbanization and climate change exacerbate urban water system challenges, increasing runoff and pollution.
• Green-gray infrastructure offers a promising solution for mitigating these impacts.
• Existing planning methods often lack spatiotemporal optimization and focus on runoff reduction rather than water quality.

Purpose of the Study:

• To develop a novel spatiotemporal optimization framework for planning green-gray infrastructure construction.
• To incorporate construction timing as a key optimization variable.
• To prioritize water quality response over conventional runoff or pollutant load reduction metrics.

Main Methods:

• Developed a novel spatiotemporal optimization framework integrating construction timing.
• Established an integrated water quality model with a machine-learning emulator for efficient lake module simulation.
• Applied the framework to the Caohai catchment, China, to generate Pareto-optimal construction plans.

Main Results:

• The framework achieved synchronized optimization of timing, location, type, and size of green-gray facilities for cost-efficiency.
• Broad construction across diverse facility types was more effective than focusing on a limited selection.
• Optimal construction timing varied based on facility-specific cost dynamics and urbanization pressures.

Conclusions:

• Incorporating strategic construction timing is crucial for effective green-gray infrastructure planning.
• The developed framework provides an integrated ecological and engineering approach for cost-effective urban water management.
• This methodology advances sustainable urban growth by optimizing green-gray infrastructure investments.