Text Mining-Based Suspect Screening for Aquatic Risk Assessment in the Big Data Era: Event-Driven Taxonomy Links Chemical Exposures and Hazards

To improve the accuracy of mixture risk assessment, researchers are employing suspect analysis with expanded lists of contaminants in addition to conventional target lists. However, there are some inherent challenges for these instrument-based analyses, including subjective selection of suspect contaminants, no information for chemical bioactivity, requirements for costly verification, and limited regional coverage. As a supplementary approach, we propose a data-driven suspect screening and risk assessment method informed by mining big data from high-throughput screening bioassay platforms and the refereed literature. The Pearl River Delta (PRD) with main event drivers of arylhydrocarbon receptor (AhR) and oxidative stress (ARE) response was examined. Bioactivity concentrations were collected from the CompTox Chemicals Dashboard, which contained more than 900 000 substances. In addition, exposure metadata from 24 986 literature entries for the environmental occurrence and distribution of contaminants in the PRD over the past three decades were mined. Collectively, a regional distribution map of aquatic hazards induced by AhR- and ARE-active compounds was generated, indicating gradients of low to moderate risks. This study specifically reports a novel big data approach for addressing the increasingly common challenge of objectively selecting analytes during suspect screening, which was recently identified as an urgent research question to advance more sustainable environmental quality.