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.