Model-Based Multi-Objective Optimization of Heavy Metal Remediation Solutions

Remediating polluted sites involves complex trade-offs between competing objectives, such as balancing performance with resource expense. Yet, remediation strategies are designed often subjectively, without the guidance of formal optimization methods. Here, we propose a nonsubjective, pragmatic framework that integrates a reactive transport model (RTM) with a multiobjective particle swarm optimization (MOPSO) algorithm to address this issue. An antimony (Sb)-polluted site was chosen as an illustrative example, and physicochemical treatment and bioremediation techniques were considered. The RTM simulation simulated the fate of Sb, which was validated against site observations. Then, RTM and MOPSO were combined for optimization of four interrelated objectives, including minimizing (1) [Sb]aq within the site, (2) off-site migration, (3) treatment cost, and (4) time. [Sb]aq mitigation was shown to require strategic coordination between efficient but capital-intensive physicochemical intervention on high-risk hotspots and economical but slow bioremediation on low-risk zones, whereas outflux could be curbed by targeting hotspots without overusing resources. Compared with a traditional scalarization approach, our true multiobjective optimization prevented crowded solution distributions and produced a more diverse Pareto front that could best achieve all or selective objectives. This RTM-MOPSO framework is broadly applicable to identifying solutions for heavy metal pollution and navigating the interplay between technical and practical factors.