Artificial Intelligence Framework Predicts Residual Ecotoxicity During Advanced Oxidation Water Treatment

General Background Advanced oxidation processes (AOPs) efficiently degrade structurally resistant contaminants of emerging concern (CECs) in tertiary wastewater treatment. Specific Background Conventional performance metrics rely on the disappearance of the parent compound, ignoring the formation of transformation products (TPs) whose persistence, mobility, and toxicity often exceed those of the parent structure. Knowledge Gap Current targeted analytical workflows frequently leave transient TPs undetected, and existing predictive models lack a cohesive, validated framework that integrates radical reactivity, in silico enumeration, and toxicity predictions to actively manage residual risk. Aims This critical review evaluates the mechanistic origins of toxic TPs and proposes a conceptual tiered framework that couples density functional theory (DFT) reactivity descriptors, graph neural networks (GNNs), high-resolution mass spectrometry (HRMS), and machine-learning toxicity predictions into a mixture-aware Net Ecotoxicity Index (NEI). Results Radical-mediated electrophilic addition, single-electron transfer, and hydrogen abstraction generate reactive electrophiles and halogenated or nitrated TPs that significantly raise residual mixture toxicity. The proposed AI-assisted framework integrates these chemical pathways to predict TP formation and structure, prioritizing analytical screening through HRMS, and subsequently assessing ecotoxicity using QSAR models to inform supervisory oxidant dosing. Novelty This framework shifts the AOP control paradigm from parent-compound degradation tracking to prospective, AI-driven residual ecotoxicity prediction and mitigation. Implications Adopting mixture-aware predictive control strategies requires establishing open TP datasets, applying precise applicability domains in modeling, and shifting regulatory targets toward effect-based risk assessments. Highlights: Evaluating advanced oxidation performance solely by parent compound degradation masks the potential toxicity of transformation products. Integrating DFT descriptors and graph neural networks enables the prospective in silico identification of toxic transformation products. AI-assisted toxicity predictions integrated with HRMS screening can dynamically control oxidant dosing to minimize residual ecotoxicity.