Data-Driven Prioritisation Framework for Micropollutants in Tropical Estuaries: Unveiling Hidden Toxicity and Deterministic Health Risks.

Abstract

The rapid expansion of the 'Blue Economy' in tropical estuaries has created complex pollution profiles, necessitating robust methodologies to identify high-risk contaminants. Current prioritisation approaches often suffer from concentration bias, which masks trace-level but highly toxic compounds. This study, centred on a tropical estuary in Sanya, China, established a data-driven prioritisation framework integrating the exposure potential and hazard potential of 144 micropollutants. By employing principal component analysis and K-means clustering, the framework objectively categorised pollutants into distinct management tiers. The incorporation of toxicity equivalency factors corrected the previous underestimation of aryl hydrocarbon receptor-active compounds (e.g. polycyclic aromatic hydrocarbons (PAHs) and polychlorinated biphenyls). Screening results identified 19 Category I (high priority) pollutants, dominated by legacy dichlorodiphenyltrichloroethanes and tourism-derived PAHs, highlighting a 'tropical tourism' signature distinct from that of industrial estuaries. This framework provides a scalable resource-efficient tool for integrated ecological and health risk screening in data-scarce coastal regions. Unlike existing prioritisation approaches that mainly rely on pollutant concentrations or single-risk endpoints, this framework integrates exposure-hazard interactions, TEF-corrected toxicity evaluation, and dual-risk screening to reveal overlooked high-risk contaminants and improve prioritisation accuracy in complex coastal environments.