AI-Driven PFAS Monitoring for Sustainable Water Quality Management

Abstract

Per- and polyfluoroalkyl substances (PFAS), which are extremely persistent in the environment and bioaccumulate in humans and animals, have become a serious threat to the environment and human health. Traditional analytical and biochemical techniques, such as chromatographic and mass spectrometric methods, are characterized by high accuracy but are associated with high costs and low feasibility for continuous real-time monitoring. Recent advances in artificial intelligence (AI), machine learning (ML), internet of things (IoT) technologies, smart and biosensors enable novel approaches for the rapid and economic detection of PFAS and assessment of water quality. The current review focuses on recent advances in AI-assisted PFAS detection and monitoring through the use of intelligent sensors, IoT-based water monitoring systems, optical and electrochemical biosensors, and machine learning algorithms. The potential of the discussed approaches for the prediction of PFAS sources and contamination fates, as well as the implementation of smart water management systems for sustainable development, are evaluated. Particular attention is paid to the critical challenges associated with the creation of novel PFAS monitoring concepts, including the availability of high-quality data sets, sensor validation and calibration, issues of cybersecurity and data privacy, and the feasibility of implementing AI-driven approaches in practice. The research directions related to explainable AI, edge intelligence, and digital twins, which can be employed for developing autonomous monitoring systems for smart water management, are highlighted. Overall, the present review aims to provide an insight into the intelligent technologies that support the needs of PFAS recognition, monitoring, and management and promote sustainable development. Keywords: artificial intelligence; machine learning; Water Quality Monitoring; Internet of things; Per- and Polyfluoroalkyl Substances