A review of machine learning applications in soil heavy-metal remediation: material design, process optimization, and...

Soil heavy-metal contamination is characterized by toxicity, environmental persistence, and bioaccumulation, and commonly involves multi-metal coexistence and complex co-contamination scenarios. These characteristics, together with strong variations in soil properties and contamination conditions, make efficient remediation difficult to achieve through conventional experimentation and empirical decision-making alone. This review summarizes recent advances in applying machine learning (ML) to soil heavy-metal remediation, with particular emphasis on the development of remediation materials and the optimization of process parameters. We first outline the major classes of remediation materials and their underlying mechanisms, and then discuss how ML has been used to predict remediation performance, optimize process conditions, and support the screening and design of remediation materials. Existing studies show that ML can improve assessment efficiency, reduce trial-and-error costs, and provide new opportunities for data-driven remediation research. However, current progress remains constrained by limited data quality, inconsistent evaluation frameworks, and insufficient model interpretability. Overall, ML offers considerable potential for advancing soil heavy-metal remediation, but further efforts are still needed to strengthen data foundations, improve model reliability, and enhance practical applicability in complex soil environments.Kindly check and confrim the affliations of the authors are correctly processed.Thank you for your query. The author affiliations have been carefully reviewed and the necessary corrections have been made. It is confirmed that the affiliations of all authors have been correctly processed. I would also like to confirm one minor formatting point: according to the general convention in academic publishing, figure captions are usually placed below the figures, while table titles are placed above the tables. Could you please confirm whether the current placement and formatting of the figure captions and table titles are correct according to the journal's style?