Stormwater reuse is a key strategy for advancing integrated urban water management under the One Water framework, yet identifying sustainable treatment trains remains challenging due to trade-offs among cost, energy, and emissions. This study presents an integrated techno-economic and environmental optimization framework to identify optimal end-use-specific stormwater treatment systems for the eastern United States. A mixed-integer nonlinear programming superstructure integrates cost, energy, and life cycle greenhouse gas emissions data for a suite of technologies—including coagulation, sedimentation, constructed wetlands, filtration, membranes, and disinfection—across five end uses: drinking, industrial, irrigation, infrastructure, and surface-water recharge. Results reveal that cost-optimal treatment trains generally align with emission-optimal trains for drinking water, irrigation, and infrastructure, as treatment flexibility is constrained by either stringent (drinking water) or lenient (irrigation, infrastructure) quality requirements that limit flexibility. Industrial and surface-water recharge pathways exhibit greater differentiation among objectives, reflecting trade-offs between costs and emissions. Under typical influent conditions, levelized costs range from 0. 06–0. 20 m⁻³, life cycle GHG emissions from 0. 14–0. 35 kg CO₂-eq m⁻³, and energy consumption from 0. 3–0. 8 kWh m⁻³, with all metrics increasing alongside treatment complexity. Scaling analysis indicates diminishing cost benefits beyond ~50 MGD, while sensitivity analysis highlights annual operating days and electricity price as dominant uncertainty drivers. This framework provides a transferable decision-support tool for designing resilient, low-emission, and economically viable stormwater reuse systems tailored to diverse end uses.
Haq et al. (Wed,) studied this question.
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