Wastewater-based epidemiology (WBE) has evolved from illicit drug tracking into a population-scale platform for monitoring exposure to hazardous organic contaminants and related health signals. By analyzing composite influent, WBE can quantify trends in pharmaceuticals, personal care products, pesticides, plasticizers, industrial chemicals, and PFAS, while enabling integration with microbial, genetic, and omics indicators. This review synthesizes hazardous-organic classes relevant to WBE and highlights biomarker selection principles that distinguish parent compounds from human metabolites, phase-II conjugates, and sewer- or treatment-derived transformation products. We critically discuss how biomarker specificity, excretion fractions, and in-sewer stability govern back-calculation accuracy and uncertainty. We further examine how wide-scope LC–MS/MS and HRMS workflows, together with automated sampling, IoT context sensors, cloud dashboards, and AI/ML data-fusion, can strengthen multi-biomarker interpretation, anomaly detection, and near-real-time decision support without replacing confirmatory laboratory analysis. Overall, multi-biomarker WBE offers an actionable “wastewater exposome” perspective to support proactive public health protection and environmental risk management. • The novel contribution is reframing WBE toward hazardous-organic-focused, exposome-level surveillance • Provides a critical synthesis of biomarker selection, in-sewer fate, and uncertainty propagation for organics • Integrates multi-omics and chemical biomarkers into a coherent, exposure-to-response WBE framework • Realistically evaluates AI, IoT, and digital twins as decision-support tools, not replacements for analytics
Hridoy et al. (2026) studied this question.