Nontarget Discovery of Antimicrobial Transformation Products in Wastewater Based on Molecular Networks

Tracing contaminants of emerging concern and their transformations in the whole treatment process of a municipal wastewater treatment plant using nontarget screening and molecular networking strategies

This study employed nontarget screening with high-resolution mass spectrometry and molecular network strategy to characterize the occurrence and tranformation of contaminants of emerging concern (CECs) through a wastewater treatment plant in Guangzhou. We detected 70,631 compounds in positive mode and 14,423 in negative mode in influent, from which 94.5 % of these compounds were successfully eliminated after treatment. Among them, 510 chemicals were identified, with pharmaceuticals being the largest category excluding natural products, accounting for 146 compounds. And 29 CECs were semiquantified with concentrations ranging from 2.80 ng/L (Fluconazole) to 10,351 ng/L (Nicotine). The removal efficiency varied: 60 compounds were easily removable (>90 % removal), 17 were partially removable (40-90 % removal), and 44 were non-degradable (<40 % removal). Additionally, we tentatively identified transformation products (TPs) of CECs using a molecular network analysis, revealing over 20,000 compound pairs sharing common fragments, with 191 compounds potentially linked to 47 level 1 compounds, suggesting their role as TPs of CECs. These findings illuminated the actual treatment efficiency of wastewater treatment plants for CECs and the potential TPs, offering valuable insights for future improvements in wastewater management practices.

Identification of Key Toxic Substances Considering Metabolic Activation: A Combination of Transcriptome and Nontarget Analysis

There have been numerous studies using effect-directed analysis (EDA) to identify key toxic substances present in source and drinking water, but none of these studies have considered the effects of metabolic activation. This study developed a comprehensive method including a pretreatment process based on an in vitro metabolic activation system, a comprehensive biological effect evaluation based on concentration-dependent transcriptome (CDT), and a chemical feature identification based on nontarget chemical analysis (NTA), to evaluate the changes in the toxic effects and differences in the chemical composition after metabolism. Models for matching metabolites and precursors as well as data-driven identification methods were further constructed to identify toxic metabolites and key toxic precursor substances in drinking water samples from the Yangtze River. After metabolism, the metabolic samples showed a general trend of reduced toxicity in terms of overall biological potency (mean: 3.2-fold). However, metabolic activation led to an increase in some types of toxic effects, including pathways such as excision repair, mismatch repair, protein processing in endoplasmic reticulum, nucleotide excision repair, and DNA replication. Meanwhile, metabolic samples showed a decrease (17.8%) in the number of peaks and average peak area after metabolism, while overall polarity, hydrophilicity, and average molecular weight increased slightly (10.3%). Based on the models for matching of metabolites and precursors and the data-driven identification methods, 32 chemicals were efficiently identified as key toxic substances as main contributors to explain the different transcriptome biological effects such as cellular component, development, and DNA damage related, including 15 industrial compounds, 7 PPCPs, 6 pesticides, and 4 natural products. This study avoids the process of structure elucidation of toxic metabolites and can trace them directly to the precursors based on MS spectra, providing a new idea for the identification of key toxic pollutants of metabolites.

Regular Tetrahedron Model for the Assessment of High-Resolution Mass Spectrometry Data of Four-Way Fractionated Dissolved Organic Matter

A regular tetrahedron model was established to pierce the fractionation of dissolved organic matter (DOM) among quaternary components by using high-resolution mass spectrometry. The model can stereoscopically visualize molecular formulas of DOM to show the preference to each component according to the position in a regular tetrahedron. A classification method was subsequently developed to divide molecular formulas into 15 categories related to fractionation ratios, the relative change of which was demonstrated to be convergent with the uncertainty of mass peak area. The practicality of the regular tetrahedron model was verified by seven kinds of sludge from waste leachate treatment and sewage wastewater treatment plants by using stratification of extracellular polymeric substances coupled with Orbitrap MS as an example, presenting the DOM chemodiversity in stratified sludge flocs. Sensitivity analysis proved that classification results were relatively stable with the perturbation of four model parameters. Multinomial logistic regression analysis could further help identify the effect of molecular properties on the fractionation of DOM based on the classification results of the regular tetrahedron model. This model offers a methodology for the assessment of specificity of sequential extraction on DOM from solid or semisolid components and simplifies the complex mathematical expression of fractionation coefficients for quaternary components.

Machine learning-enhanced molecular network reveals global exposure to hundreds of unknown PFAS

Unknown forever chemicals like per- and polyfluoroalkyl substances (PFASs) are difficult to identify. Current platforms designed for metabolites and natural products cannot capture the diverse structural characteristics of PFAS. Here, we report an automatic PFAS identification platform (APP-ID) that screens for PFAS in environmental samples using an enhanced molecular network and identifies unknown PFAS structures using machine learning. Our networking algorithm, which enhances characteristic fragment matches, has lower false-positive rate (0.7%) than current algorithms (2.4 to 46%). Our support vector machine model identified unknown PFAS in test set with 58.3% accuracy, surpassing current software. Further, APP-ID detected 733 PFASs in real fluorochemical wastewater, 39 of which are previously unreported in environmental media. Retrospective screening of 126 PFASs against public data repository from 20 countries show PFAS substitutes are prevalent worldwide.

Should Transformation Products Change the Way We Manage Chemicals?

When chemical pollutants enter the environment, they can undergo diverse transformation processes, forming a wide range of transformation products (TPs), some of them benign and others more harmful than their precursors. To date, the majority of TPs remain largely unrecognized and unregulated, particularly as TPs are generally not part of routine chemical risk or hazard assessment. Since many TPs formed from oxidative processes are more polar than their precursors, they may be especially relevant in the context of persistent, mobile, and toxic (PMT) and very persistent and very mobile (vPvM) substances, which are two new hazard classes that have recently been established on a European level. We highlight herein that as a result, TPs deserve more attention in research, chemicals regulation, and chemicals management. This perspective summarizes the main challenges preventing a better integration of TPs in these areas: (1) the lack of reliable high-throughput TP identification methods, (2) uncertainties in TP prediction, (3) inadequately considered TP formation during (advanced) water treatment, and (4) insufficient integration and harmonization of TPs in most regulatory frameworks. A way forward to tackle these challenges and integrate TPs into chemical management is proposed.

Identify organic contaminants of high-concern based on non-targeted toxicity testing and non-targeted LC-HRMS analysis in tap water and source water along the Yangtze River

Many organic pollutants were detected in tap water (TW) and source water (SW) along the Yangtze River. However, the potential toxic effects and the high-concern organics (HCOs) which drive the effect are still unknown. Here, a non-targeted toxicity testing method based on the concentration-dependent transcriptome and non-targeted LC-HRMS analysis combining tiered filtering were used to reveal the overall biological effects and chemical information. Subsequently, we developed a qualitative pathway-structure relationship (QPSR) model to effectively match the biological and chemical information and successfully identified HCOs in TW and SW along the Yangtze River by potential substructures of HCOs. Non-targeted toxicity testing found that the biological potency of both TW and SW was stronger in the downstream of the Yangtze River, and disruption of the endocrine system and cancer were the main drivers of the effect. In addition, non-targeted LC-HRMS analysis combined with retention time prediction results identified 3220 and 631 high-confidence compound structures in positive and negative ion modes, respectively. Then, QPSR model was further implied and identified a total of 103 HCOs, containing 35 industrial chemicals, 30 PPCPs, 26 pesticides, and 12 hormones in TW and SW, respectively. Among them, the neuroactive and hormonal compounds oxoamide, 8-iso-16-cyclohexyl-tetranor prostaglandin E2, E Keppra, and Tocris-0788 showed the highest frequency of detection, which were identified in more than 1/3 of the samples. The strategy of combining non-targeted toxicity testing and non-targeted LC-HRMS analysis will support comprehensive biological effect assessment, identification of HCOs, and risk control of mixtures.

Suspect and Nontarget Screening Reveal the Underestimated Risks of Antibiotic Transformation Products in Wastewater Treatment Plant Effluents

Antibiotics are anthropogenic contaminants with a global presence and of deep concern in aquatic environments, while less is known about the occurrence and risks of their transformation products (TPs). Herein, we developed a comprehensive suspect and nontarget screening workflow based on high-resolution mass spectrometry to identify unknown antibiotic TPs in wastewater treatment plant effluents. We identified 211 compounds (35 parent antibiotics and 176 TPs) at confidence levels of ≥3 and 107 TPs originated from macrolides. TPs were quantified by 17 TPs standards and semiquantified by the predicted response factors and accounted for 55.6-95.1% (76.7% on average) of the total concentrations of parents and TPs. 22.2%, 63.1%, and 18.8% of the identified TPs were estimated to be more persistent, mobile, and toxic than their parent antibiotics, respectively. Further ecological risk assessment based on concentrations and toxicity to aquatic organisms revealed that the cumulative risks of TPs were generally higher than those of parents. Despite the newly formed N-oxide TPs, the tertiary treatment process (mainly ozonation) could decrease the averaged 20.3% of concentrations and 36.2% of the risks of antibiotic-related compounds. This study highlights the necessity to include antibiotic TPs in environmental scrutiny and risk assessment of antibiotics in different aquatic environments.

Comprehensive discovery and migration evaluation of antimicrobial drugs and their transformation products in a swine farm by target, suspect, and nontarget screening

Swine farms contaminated the surrounding environment through manure application and biogas slurry irrigation, hence causing the wide residual of multiple antimicrobial drugs (ADs) and their transformation products (TPs). This study performed target, suspect, and nontarget screening methods to comprehensively investigate the pollution profiles of ADs in a typical swine farm, and characterize the potential transformed pathway of TPs and distinguish specific reactions of different catalog of ADs. Samples of fresh feces, compost, biogas slurry, topsoil, column soil, groundwater and plants were analyzed using the database containing 98 target analytes, 679 suspected parent ADs, and ∼ 107 TPs. In total, 29 ADs were quantitively detected, and tetracyclines (TCs) were mostly frequently detected ADs with the concentrations up to 4251 ng/g in topsoil. Soil column investigation revealed that doxycycline (DOX) and tetracycline (TC) in soil could migrate to depths of approximately 1 m in soil. Suspect screening identified 75 parent ADs, with 10 being reported for the first time in environmental media. Semi-quantification of ADs revealed that one of the less-concerned ADs, clinafloxacin, was detected to exceed 5000 ng/L in biogas slurry, suggesting that significant attentions should be paid to these less-concerned ADs. Moreover, 314 TPs was identified, and most of them were found to undergo microbial/enzymatic metabolism pathways. Overall, our study displays a comprehensive overview of ADs and their TPs in swine farming environments, and provides an inventory of crucial list that worthy of concern. The results emphasize the need to quantify the levels and distribution of previously overlooked ADs and their TPs in livestock farms.