Transforming PFAS management: A critical review of machine learning applications for enhanced monitoring and treatment

Traditional approaches for managing per- and polyfluoroalkyl substances (PFAS) face significant limitations in cost, efficiency, and predictive capability, despite recent advances in monitoring and remediation technologies. While machine learning (ML) shows promise in environmental applications, its systematic implementation in PFAS management remains unexplored. This review synthesizes and evaluates recent advances in ML applications for PFAS management, addressing two key questions: (1) How effectively can ML predict PFAS contamination and identify sources? and (2) How can ML optimize PFAS removal technologies? Our methodology combines systematic literature review with two case studies, examining ML algorithm selection, implementation strategies, and performance metrics across PFAS applications. Results demonstrate that ML models achieve over 90 % accuracy in contamination prediction and source identification and improve treatment efficiency through automated parameter optimization. Our original contributions include: (1) developing a comprehensive framework for ML-enabled PFAS monitoring that integrates source identification and concentration prediction, (2) providing a systematic analysis of ML applications across PFAS treatment technologies, and (3) creating a roadmap for addressing critical challenges in data quality and model interpretability. We propose specific strategies for data standardization and model validation to accelerate real-world implementation of ML in PFAS management.