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.