Next-Generation AI-IoT Integrated Systems for Dynamic Optimization of Water Disinfection and Removal of Emerging Contaminants

The increasing complexity of water quality challenges, including the need for effective disinfection and the removal of emerging contaminants, necessitates innovative solutions. This paper explores the integration of Artificial Intelligence and the Internet of Things into water management systems, presenting a next-generation approach to dynamic optimization. AI-driven algorithms and IoT-enabled sensors facilitate real-time monitoring, precise detection, and adaptive responses to varying water quality conditions. These systems address the limitations of traditional methods, offering enhanced efficiency, reduced operational costs, and improved sustainability. Furthermore, their scalability and adaptability make them suitable for diverse environments, from urban water treatment facilities to rural decentralized systems. The paper also examines the role of AI-IoT technologies in mitigating emerging contaminants, such as pharmaceuticals and microplastics, while proposing recommendations for advancing sensor technologies, enhancing AI models, and promoting policy support. This study highlights a pathway to more resilient, sustainable, and equitable water management solutions by leveraging these transformative tools.