Toxicity identification evaluation (TIE) and effect-directed analysis (EDA) were integrated to diagnose toxicity drivers in a complex system, such as sediment. In TIE manipulation, XAD resin was utilized as an amending agent for characterizing organic toxicants, which also facilitate a large-volume bioaccessibility-based extraction for EDA purposes. Both raw sediments in TIE and extract fractions in EDA were tested with Chironomus dilutus for toxicity using whole-sediment testing and a high-throughput microplate assay. This allowed for a direct link between wholesediment TIE and EDA, which strongly strengthened the characterization and identification of toxicants. Sediments amended with XAD resin, as part of the TIE, significantly reduced midge mortality compared with unamended sediments, suggesting that organics were one class of main toxicants. On the basis of bioaccessible concentrations in sediment measured by XAD extraction, a group of previously unidentified contaminants, synthetic polycyclic musks (versalide, tonalide, and galaxolide), were found to explain 32−73% of the observed toxicity in test sediments. Meanwhile, three pyrethroids contributed to an additional 17−35% of toxicity. Surprisingly, the toxicity contribution of musks and pyrethroids reached 58−442 and 56−1625%, respectively, based on total sediment concentrations measured by exhaustive extraction. This suggested that total sediment concentrations significantly overestimated toxicity and that bioavailability should be considered in toxicity identification. Identifying nontarget toxicants sheds a light on application of the integrated TIE and EDA method in defining causality in a complex environment.

Per- and polyfluoroalkyl substances (PFASs) have attracted worldwide attention due to their ubiquitous occurrence, bioaccumulation, and toxicological effects, yet the fate of PFASs in a lotic ecosystem is largely unknown. To elucidate spatial distribution and multimedia partitioning of legacy and emerging PFASs in a lotic river flowing into an estuary, PFASs were synchronously analyzed in water, suspended particulate matter (SPM), sediment, and biota samples collected along Guangzhou reach of the Pearl River, South China. Geographically, the concentrations of PFASs in the water phase showed a decreasing trend from the upper and middle sections (urban area) to the down section (suburban area close to estuary) of the river. While perfluorooctanoic acid predominated in water and SPM, more diverse compositions were observed in sediment and biota with the increase in contributions of long-chain PFASs. Field-derived sediment–water partitioning coefficients (K d) and bioaccumulation factors (BAFs) of PFASs increased with the increase in perfluorinated carbons. Besides hydrophobicity, water pH and salinity significantly affected the multimedia partitioning of PFASs in a lotic ecosystem. In addition, 87 homologues (63 classes) were identified as emerging PFASs in four media using suspect analysis. Interestingly, K d and BAF of the emerging PFASs were often higher than legacy PFASs containing the same perfluorinated carbons, raising a special concern on the environmental risk of emerging PFASs.

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.

No abstract

No abstract