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Next-Generation AI-IoT Integrated Systems for Dynamic Optimization of Water Disinfection and Removal of Emerging Contaminants
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Researchers explored the integration of artificial intelligence and Internet of Things technologies into water management systems to improve disinfection and removal of emerging contaminants. The study found that AI-IoT integrated systems enable dynamic, real-time optimization of water treatment processes, offering more effective responses to complex water quality challenges.
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.
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This comprehensive review integrates physical, chemical, and biological water quality parameters, examines major pollution sources including emerging contaminants like microplastics, and surveys advances in real-time IoT-enabled monitoring and integrated treatment approaches.
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This review examined how artificial intelligence can be integrated into wastewater treatment systems to improve removal of emerging contaminants including PFAS, heavy metals, microplastics, and antibiotics. The authors conclude that AI-driven optimization offers significant potential for a circular economy approach to water treatment.
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Monitoring Water Quality: Suggestions and Prospects
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This review examined real-time water quality monitoring systems, evaluating sensors, data transmission technologies, and AI approaches for continuous assessment of physical, chemical, and biological parameters at scale. The authors proposed integrating IoT-connected sensor networks with machine learning to enable early warning of contamination events including microplastic and pathogen loads.
Monitoring Water Quality: Suggestions and Prospects
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This review examined real-time water quality monitoring systems, evaluating sensors, data transmission technologies, and AI approaches for continuous assessment of physical, chemical, and biological parameters at scale. The authors proposed integrating IoT-connected sensor networks with machine learning to enable early warning of contamination events including microplastic and pathogen loads.
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When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.