Advancements in artificial intelligence-based technologies for PFAS detection, monitoring, and management

Per- and polyfluoroalkyl substances (PFAS) are persistent environmental contaminants with strong carbon‑fluorine (CF) bonds that contribute to bioaccumulation and long-term environmental and health risks. Traditional PFAS detection and treatment methods are often time-consuming, costly, and limited in scope. Recently, artificial intelligence (AI)-based technologies, particularly machine learning (ML), have emerged as powerful tools for enhancing PFAS monitoring, source identification, and remediation. ML models such as random forest (RF), gradient boosting decision trees (GBDT), support vector machines (SVM), and artificial neural networks (ANN) have been successfully applied to classify PFAS contamination sources with over 96 % accuracy, predict PFAS concentrations in groundwater with an AUC of 0.90, and optimize removal processes such as nanofiltration and adsorption with R² values exceeding 0.93. Despite these advancement, challenges remain in ensuring high-quality datasets, addressing data imbalance and improving model interpretability. Future research should focus on expanding public datasets, leveraging Automated ML (AutoML) for optimization, and integrating Al-driven sensors for real-time detection. AI-based approaches present a transformative opportunity to enhance efficiency, accuracy, and cost-effectiveness in PFAS management, aiding regulatory decision-making and environmental protection.