Smart Water, Smart Models: Algorithmic Assessment of Water Quality under Evolving Chemical and Industrial Stressors

The growing use of recycled materials and the expansion of chemical recycling activities are introducing new, poorly understood, and potentially hazardous pollutants into global water systems. This makes it harder to monitor, anticipate, and control. Machine learning (ML) offers unprecedented power to analyze heterogeneous aquatic datasets, infer pollutant dynamics, and support adaptive water treatment; yet its application remains primarily focused on conventional parameters. This review critically examines how advanced ML spanning deep neural networks, hybrid physics–data approaches, and reinforcement learning can anticipate and manage pollution from recycling-driven industrial processes, including microplastics, per- and polyfluoroalkyl substances (PFAS), textile dyes, and emerging solvents. We evaluate data-centric workflows derived from high-frequency sensors, remote sensing, and socio-hydrological indicators, with a focus on the challenges of data sparsity, uncertainty, and the evolving chemical space. Unlike prior reviews, we highlight the growing regulatory gap: models increasingly predict new risk scenarios but lack validation pathways compatible with chemical testing norms and policy instruments. We propose an integrated approach that combines interpretable ML, uncertainty quantification, and mechanistic coupling to support anticipatory governance from early warning systems to risk-based water quality standards. This synthesis positions ML not just as a computational upgrade, but also as a strategic tool to manage the next wave of anthropogenic pollutants emerging from circular-economy and advanced-recycling initiatives.