Advances in Water Quality Assessment & Treatment Technologies for Safe & Sustainable Water Supply

Abstract

Global freshwater systems face compounding pressure from population growth, industrial pollution, climate variability, and emerging contaminants, driving parallel innovation across water quality assessment and water treatment technology. This paper reviews recent advances in both domains, synthesizing the machine-learning-and-Internet-of-Things (IoT) literature on real-time water quality monitoring and prediction with the materials-science literature on membrane-based desalination, multifunctional filtration, and emerging-contaminant removal. Particular attention is given to the specific technical mechanisms, IoT sensor networks, machine-learning-based parameter prediction, electrochemical heavy-metal sensing, and advanced membrane and nanomaterial treatment processes, that the reviewed studies identify as driving measurable improvements in monitoring accuracy and treatment efficiency. Comparative tables map water quality parameters to the sensing and machine-learning methods used to monitor them, cross-reference treatment technologies against the specific contaminant classes each addresses, and set reported performance metrics, detection limits, classification accuracy, and monitoring accuracy gains, against the technological approach that produced them. The review finds that IoT-integrated machine-learning monitoring systems now achieve substantially higher measurement accuracy than traditional manual methods, while membrane, nanomaterial, and hybrid treatment architectures are increasingly capable of addressing previously intractable emerging contaminants such as per- and polyfluoroalkyl substances (PFAS), but that regional infrastructure constraints, particularly in low-resource settings, and the sustainability of treatment by-products remain significant unresolved barriers to universal safe water supply.