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<p>Urban stormwater runoff is a dominant non-point source of heavy metal contamination, threatening aquatic ecosystems and human health, particularly in rapidly urbanizing semi-arid regions. This study develops a novel, risk-informed optimization framework integrating the Storm Water Management Model (SWMM) with two metaheuristic algorithms, the Slime Mould Algorithm (SMA) and the Non-Dominated Sorting Genetic Algorithm III (NSGA-III), to enhance the performance of Low Impact Development (LID) practices. Five scenarios were simulated, including baseline, bioretention cells, vegetated swales, permeable pavements, and an integrated LID configuration. The dual-objective framework simultaneously maximized pollutant removal efficiency and minimized human health risks derived from heavy metal exposure pathways. Results indicated that SMA outperformed NSGA-III in convergence stability and computational efficiency, achieving significant reductions in pollutant loads: mercury (21%), arsenic (26%), cadmium (25%), lead (27%), and copper (22%). These decreases corresponded to reductions of 13–18% in cumulative non-carcinogenic and carcinogenic risks. The integrated LID scenario produced the highest mitigation efficiency through synergistic hydrologic and adsorptive mechanisms. The proposed framework provides a flexible decision-support approach for improving stormwater quality and public health protection in urban environments experiencing ongoing land-use intensification and increased hydrologic pressure.</p>

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Risk-Informed Optimization of Low-Impact Development Strategies Using Slime Mould and NSGA-III Algorithms for Heavy Metal Mitigation in Urban Runoff

  • 期刊论文

  • 发表日期：

    2026年2月7日

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  • Mehrtash Eskandaripour,
  • Shahrokh Soltaninia

摘要

Urban stormwater runoff is a dominant non-point source of heavy metal contamination, threatening aquatic ecosystems and human health, particularly in rapidly urbanizing semi-arid regions. This study develops a novel, risk-informed optimization framework integrating the Storm Water Management Model (SWMM) with two metaheuristic algorithms, the Slime Mould Algorithm (SMA) and the Non-Dominated Sorting Genetic Algorithm III (NSGA-III), to enhance the performance of Low Impact Development (LID) practices. Five scenarios were simulated, including baseline, bioretention cells, vegetated swales, permeable pavements, and an integrated LID configuration. The dual-objective framework simultaneously maximized pollutant removal efficiency and minimized human health risks derived from heavy metal exposure pathways. Results indicated that SMA outperformed NSGA-III in convergence stability and computational efficiency, achieving significant reductions in pollutant loads: mercury (21%), arsenic (26%), cadmium (25%), lead (27%), and copper (22%). These decreases corresponded to reductions of 13–18% in cumulative non-carcinogenic and carcinogenic risks. The integrated LID scenario produced the highest mitigation efficiency through synergistic hydrologic and adsorptive mechanisms. The proposed framework provides a flexible decision-support approach for improving stormwater quality and public health protection in urban environments experiencing ongoing land-use intensification and increased hydrologic pressure.