Identification and characterization of diverse isomers of per- and polyfluoroalkyl substances in Chinese municipal wastewater

Occurrence and profiles of perfluoroalkyl substances in wastewaters of chemical industrial parks and receiving river waters: Implications for the environmental impact of wastewater discharge

A total of 17 groups of wastewaters from the chemical industrial parks and matched receiving river waters were collected in the east of China. The measured total concentrations of 21 analyzed PFAS analogues (∑21PFAS) in the influents and effluents of the wastewater treatment plants (WWTPs) were in the range of 0.172-20.6 μg/L (mean: 18.2 μg/L, median: 3.9 μg/L) and 0.167-93.6 μg/L (mean: 10.8 μg/L, median: 1.12 μg/L), respectively, which were significantly higher than those observed in the upstream (range: 0.0158-7.05 μg/L, mean: 1.09 μg/L, median: 0.482 μg/L) and downstream (range: 0.0237-1.82 μg/L, mean: 0.697 μg/L, median: 0.774 μg/L) receiving waters. Despite the concentrations and composition profiles of PFAS varied in the water samples from different sampling sites, PFOA was generally the major PFAS analogue in the research areas, mainly due to the history of PFOA production and usage as well as the specific exemptions. The calculated concentration ratios of the short-chain PFCAs and PFSAs to their respective predecessors (PFOA and PFOS) in most of the samples far exceeded 1, indicating a shift from legacy PFOA and PFOS to short-chain PFAS in the research areas. Correlation network analysis and the calculated concentration ratios of PFAS in the effluents versus influents indicated transformation may have occurred during the water treatment processes and PFAS could not be efficiently removed in the WWTPs. Wastewater discharge of chemical industrial parks is a vital source of PFAS dispersed into the aquatic environment.

Suspect and nontarget screening of per- and polyfluoroalkyl substances based on ion mobility mass spectrometry and machine learning techniques

High-resolution mass spectrometry (HRMS)-based suspect and nontarget screening techniques are powerful tools for the comprehensive identification of per- and polyfluoroalkyl substances (PFASs), but the interference of complex matrices (especially for wastewater) pose an analytical challenge. This study explored the potential of combining ion mobility spectrometry (IMS) with HRMS and machine learning techniques to achieve the rapid and accurate suspect and nontarget screening of PFAS in wastewater. There were fewer interfering peaks and a clearer spectrum in the data acquired by IMS-HRMS than conventional HRMS. The introduction of collision cross section (CCS) in PFAS homologous series search could filter out 63% of false positive results. Retention time and CCS prediction models were helpful in improving the confidence for PFAS qualitative identification and the random forest algorithm combined with RDKit descriptor performed best for CCS prediction. With the inclusion of extra dimensional information, this study also proposed a comprehensive and concise confidence assignment criterion to better convey the certainty of the qualitative identification of PFAS. Finally, a total of 56 potential PFASs were identified in the wastewater sample using the newly developed method and 45 of them were identified outside reference standards, emphasizing the importance of suspect and nontarget screening for PFAS.

Application of Ion Mobility Spectrometry and the Derived Collision Cross Section in the Analysis of Environmental Organic Micropollutants

Ion mobility spectrometry (IMS) is a rapid gas-phase separation technique, which can distinguish ions on the basis of their size, shape, and charge. The IMS-derived collision cross section (CCS) can serve as additional identification evidence for the screening of environmental organic micropollutants (OMPs). In this work, we summarize the published experimental CCS values of environmental OMPs, introduce the current CCS prediction tools, summarize the use of IMS and CCS in the analysis of environmental OMPs, and finally discussed the benefits of IMS and CCS in environmental analysis. An up-to-date CCS compendium for environmental contaminants was produced by combining CCS databases and data sets of particular types of environmental OMPs, including pesticides, drugs, mycotoxins, steroids, plastic additives, per- and polyfluoroalkyl substances (PFAS), polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), and polybrominated diphenyl ethers (PBDEs), as well as their well-known transformation products. A total of 9407 experimental CCS values from 4170 OMPs were retrieved from 23 publications, which contain both drift tube CCS in nitrogen (DTCCSN2) and traveling wave CCS in nitrogen (TWCCSN2). A selection of publicly accessible and in-house CCS prediction tools were also investigated; the chemical space covered by the training set and the quality of CCS measurements seem to be vital factors affecting the CCS prediction accuracy. Then, the applications of IMS and the derived CCS in the screening of various OMPs were summarized, and the benefits of IMS and CCS, including increased peak capacity, the elimination of interfering ions, the separation of isomers, and the reduction of false positives and false negatives, were discussed in detail. With the improvement of the resolving power of IMS and enhancements of experimental CCS databases, the practicability of IMS in the analysis of environmental OMPs will continue to improve.

Effects of point and nonpoint source pollution on urban rivers: From the perspective of pollutant composition and toxicity

Wastewater discharge is considered to be one of the anthropogenic factors affecting the water quality of urban rivers. The source and composition of wastewater are complex and diverse, and it is difficult to evaluate its effect on water quality and ecological health of receiving waters. Environmental DNA method can determine all species living in waters by examining DNA sequences, reflecting the impact of water quality changes on aquatic systems. In this study, water samples from two urban rivers were collected in dry and wet seasons, and the composition of pollutants was investigated by nontarget screening. Based on the pollutant composition, compound toxicity prediction and concentration addition model were used to predict the toxicity changes of pollutants in the urban rivers. More than 1500 suspect organic pollutants were nontarget screened, and silafluofen was found to be a major toxicity contributor. Environmental DNA analysis was combined with water quality measure and pollutant toxicity prediction to reveal the effects of pollutants from different sources on aquatic ecosystems. Fish diversity was negatively correlated with the mixed toxicity of organic pollutants, suggesting potential ecological risk in these two urban rivers. Our study developed a water quality assessment method based on pollutant composition and toxicity, and the potential risk of nonpoint source pollutants on aquatic ecosystems should not be neglected.