AI-driven predictive models for mapping and mitigating endocrine-disrupting chemicals in water systems

Endocrine-disrupting chemicals (EDCs), which include bisphenols, phthalates, pesticides, and synthetic hormones, are a growing concern in aquatic environments and are quite dangerous to human health and aquatic ecosystems at even low levels. The traditional methods of monitoring and treatment of EDCs tend to have limitations of high cost, slow identification, and spatiotemporal localization. Artificial intelligence (AI) has become a potential instrument in the last few years to improve the identification, forecasting, and elimination of EDC contamination in the water systems. The review will critically discuss the new developments in AI-based approaches used to monitor ECD, predict mapping, and optimize treatment. Artificial intelligence (AI) models of machine learning, big data analytics, and sensor networks based on the Internet-of-Things (IoT) are addressed in relation to the appearance of EDC, fate prediction, hotspots, and the control of mitigation methods of advanced oxidation, adsorption, and membrane-based treatment. The focus is on research that uses real-world data, pilot studies, and performance levels that are described in the literature. Critical problems, such as a lack of data, the interpretability of model, the ability to validate it using real-world samples, and the scalability of AI systems, are identified. The review offers a systematic conglomerate of existing competencies, constraints, and prospects of future research, emphasizing the opportunity of AI-assisted models to assist in the intelligent administration of water quality and risk-related decision-making on the control of endocrine disruptors. • AI will allow real-time detection and prediction of endocrine-disrupting chemicals. • Machine learning algorithms determine EDC hotspots and spatio-temporal trends. • AI is the most efficient at adsorption, membrane, and advanced oxidation treatment. • Smart water quality management is supported with the help of AI, IoT, and GIS integration. • AI tools based on data improve sustainable control of the EDCs in the water systems.