Pathway-driven assessment of wastewater contamination in drinking water systems: integrating AI with public health...

Wastewater contamination in drinking water systems arises from failures across interconnected components. Globally, an estimated 2.2 billion people lack safely managed drinking water services, with contamination risks persisting in regulated systems. Despite advances in treatment technologies, contamination events continue due to infrastructure deterioration, hydraulic disturbances, and cross-connections, particularly in rapidly urbanizing and resource-constrained regions. A critical limitation of conventional monitoring is its reliance on periodic sampling and laboratory analysis, which often fails to capture transient contamination events and delays response. To address this challenge, artificial intelligence (AI) enables data-driven surveillance through sensor networks, anomaly detection, and predictive modeling. Machine learning and deep learning approaches can identify multivariate contamination patterns, with several studies reporting R² values exceeding 0.70 under controlled conditions. However, most reported performance metrics originate from experimental, pilot-scale, or benchmark datasets with limited long-term operational validation, indicating that predictive success does not necessarily translate into operational readiness. Consequently, real-world applicability remains constrained by data quality limitations, reliance on non-specific proxy indicators, poor model generalizability, and insufficient validation under operational conditions. The absence of explicit linkage between AI-generated outputs and public health response thresholds further limits the translation of detection into actionable risk mitigation. This review synthesizes current understanding of wastewater contamination pathways and critically evaluates both conventional and AI-based monitoring approaches within an integrated engineering-public health framework. Unlike prior reviews emphasizing generic water-quality prediction, this study focuses on pathway-specific contamination detection, system-level vulnerabilities, and operational deployment constraints. It identifies key translational barriers, including sensor reliability, validation gaps, and limited integration into decision-making workflows. The review further distinguishes between predictive performance, evidence readiness, and operational deployment maturity. The analysis highlights that effective implementation of AI requires alignment with risk-based water safety planning and confirmatory monitoring strategies. Future progress must shift from prediction-focused research toward prevention-oriented monitoring systems that enable early detection, reduce exposure duration, and strengthen public health protection. Operational deployment readiness remains substantially less mature than predictive model development. This review proposes a pathway-driven, AI-integrated monitoring framework linking contamination detection to actionable public health response.