Decision-oriented evaluation and conceptual framework of bioremediation strategies for heavy metal contamination in aquatic ecosystems

Heavy metal contamination in aquatic environments represents a persistent global challenge due to the toxicity, mobility, and bioaccumulative behaviour of metals such as Pb, Cr, Hg, As, and Cd.Although a wide range of bioremediation approaches has been investigated, the translation of laboratory-scale removal efficiencies into reliable field-scale applications remains limited.This review critically synthesises recent advances in biosorption, microbial remediation, phytoremediation, and nanotechnology-assisted strategies, with emphasis on bridging the gap between experimental performance and operational applicability.Rather than comparing technologies based solely on removal efficiency, the analysis adopts a systems-based perspective that evaluates remediation strategies in relation to environmental heterogeneity, technological readiness, and sustainability-related constraints.The synthesis highlights that biomass-and microbe-based systems often exhibit high removal performance under controlled conditions, while facing challenges associated with scalability, process stability, and biosafety in complex aquatic environments.To address these limitations, the review introduces a decision-oriented evaluation framework and an integrated conceptual architecture that incorporate adaptive monitoring and enabling tools, including omics-informed assessment and Artificial intelligence of things (AIoT)-based sensing, as system-level support mechanisms.By reframing bioremediation assessment from a technology-centred to a context-dependent decision process, this study provides a structured basis for interpreting existing evidence and supporting environmentally defensible remediation planning in real-world aquatic systems.