Decision optimization for inorganic contaminants in water systems using ecological informatics for sustainable management

Urbanization, industrial activity, and climate change are accelerating the accumulation of inorganic contaminants, such as heavy metals, metalloids, radionuclides, nitrates, and phosphates, in global water systems, posing persistent risks to human health and aquatic ecosystems. This review systematically synthesizes evidence from 244 peer-reviewed studies, integrating contaminant sources, exposure pathways, ecological and human-health impacts, monitoring technologies, and remediation strpeer-reviewedategies. In addition, it incorporates decision-optimization frameworks to support the prioritization of monitoring efforts, the selection of cost-effective treatment technologies, and improved resource allocation in settings with limited capacity. By combining analytical advancements (AAS, ICP-MS, biosensors, IoT and remote-sensing systems), emerging remediation approaches, and ecological informatics, the review provides an interdisciplinary assessment of inorganic contaminant dynamics and management options. Decision-optimization methods add value by enabling transparent trade-off evaluation, targeted mitigation, and scenario-based planning under uncertainty. Key takeaways demonstrate that: (i) current regulatory and monitoring capacities remain insufficient in many regions; (ii) scalable, context-appropriate remediation technologies are essential for reducing long-term exposure; and (iii) integration of real-time monitoring, ecological informatics, and optimization-based decision tools can substantially strengthen progress toward SDG-aligned water security.