Tea polyphenols transform under processing methods, but a systematic study on their changes in the same large-leaf tea cultivar is lacking. Here, Camellia sinensis var. assamica cv. Yunkang-10 leaves underwent six processing methods and were assessed using optimized nontargeted (UHPLC-Q-Exactive Orbitrap-MS) and targeted (UHPLC-QqQ-MS) polyphenomics, along with molecular networking analysis. 903 and 52 polyphenolic compounds (catechins, flavones and flavonols, and phenolic acids) were respectively relatively and absolutely quantified for the first time. Dark and black teas, with the lowest polyphenol content, differed from the other four tea types, although variations existed among these four teas. However, some flavonol and flavone aglycones (e.g. kaempferol, apigenin), as well as some phenolic acids (e.g. ellagic acid, gallic acid), exhibited higher levels in dark and black teas. Correlations between polyphenolic composition and electronic sensory characteristics were observed using E-tongue and E-eye. This study enriches understanding of polyphenol profiles in Chinese teas post diverse processing.

Pollutants in human milk are critical for evaluating maternal internal exposure and infant external exposure. However, most studies have focused on a limited range of pollutants. Here, 15 pooled samples (prepared from 467 individual samples) of human milk from three areas of the Yangtze River Delta (YRD) in China were analyzed by gas chromatography quadrupole time-of-flight mass spectrometry. In total, 171 compounds of nine types were preliminarily identified. Among these, 16 compounds, including 2,5-di-tert-butylhydroquinone and 2-tert-butyl-1,4-benzoquinone, were detected in human milk for the first time. Partial least-squares discriminant analysis identified ten area-specific pollutants, including 2-naphthylamine, 9-fluorenone, 2-isopropylthianthrone, and benzo[a]pyrene, among pooled human milk samples from Shanghai (n = 3), Jiangsu Province (n = 6), and Zhejiang Province (n = 6). Risk index (RI) values were calculated and indicated that legacy polycyclic aromatic hydrocarbons (PAHs) contributed only 20% of the total RIs for the identified PAHs and derivatives, indicating that more attention should be paid to PAHs with various functional groups. Nine priority pollutants in human milk from the YRD were identified. The most important were 4-tert-amylphenol, caffeine, and 2,6-di-tert-butyl-p-benzoquinone, which are associated with apoptosis, oxidative stress, and other health hazards. The results improve our ability to assess the health risks posed by pollutants in human milk.

4-Hydroxychlorothalonil (4-OH CHT), the main metabolite of chlorothalonil and the most widely used fungicide, has been frequently detected in human samples during monitoring. 4-OH CHT may exhibit higher toxicity and persistence in the environment compared to its prototype. In this study, a total of 540 paired serum and breast milk samples from pregnant women in three provinces in China were monitored for contaminant residues. 4-OH CHT was analyzed in the samples using ultra high-performance liquid chromatography - high-resolution mass spectrometry with a detection limit of 20 ng/L. The study investigated the effects of demographic factors, such as BMI, region of residence, and education level, on the levels of 4-OH CHT residues in serum and breast milk. Among the three provinces, the highest median concentration of 4-OH CHT in serum samples was observed in Hebei (1.04 × 103 ng/L), while the highest median concentration of 4-OH CHT in breast milk samples was observed in Hubei and Guangdong (491 ng/L). Multiple linear regression was used to investigate the significant positive correlation between 4-OH CHT in serum and breast milk (p = 0.000) after adjusting for personal characteristics. Based on this, the study further explored the influencing factors of transfer efficiencies (TEs) in conjunction with the individual TEs and the personal characteristics of the participants. Our results demonstrated that the age of the volunteers and their exercise habits had an effect on TEs, but further studies are needed to determine whether exercise leads to an increase in TEs.

Wastewater-based epidemiology (WBE) has been widely implemented around the world as a complementary tool to conventional surveillance techniques to inform and improve public health responses. Currently, wastewater surveillance programs in the U.S. are evaluating integrated approaches to address public health challenges across multiple domains, including substance abuse. In this work, we demonstrated the potential of online solid-phase extraction coupled with liquid chromatography-high-resolution mass spectrometry to support targeted quantification and nontargeted analysis of psychoactive and lifestyle substances as a step toward understanding the operational feasibility of a statewide wastewater surveillance program for substance use assessment in New York. Target screening confirmed 39 substances in influent samples collected from 10 wastewater treatment plants with varying sewershed characteristics and is anticipated to meet the throughput demands as the statewide program scales up to full capacity. Nontarget screening prioritized additional compounds for identification at three confidence levels, including psychoactive substances, such as opioid analgesics, phenethylamines, and cathinone derivatives. Consumption rates of 12 target substances detected in over 80% of wastewater samples were similar to those reported by previous U.S.-based WBE studies despite the uncertainty associated with back-calculations. For selected substances, the relative bias in consumption estimates was sensitive to variations in monitoring frequency, and factors beyond human excretion (e.g., as indicated by the parent-to-metabolite ratios) might also contribute to their prevalence at the sewershed scale. Overall, our study marks the initial phase of refining analytical workflows and data interpretation in preparation for the incorporation of substance use assessment into the statewide wastewater surveillance program in New York.

Coumarin was detected as one of the most abundant compounds by nontargeted analysis of natural product components in actual water samples prior to disinfection. More importantly, prechlorination of humic acid generated 3-hydroxycoumarin and monohydroxy-monomethyl-substituted coumarin with a total yield of ≤10.1%, which suggested the humic substance in raw water is an important source of coumarins. 7-Hydroxycoumarin, 6-hydroxy-4-methylcoumarin, 6,7-dihydroxycoumarin, and 7-methoxy-4-methylcoumarin were identified in raw water by high-performance liquid chromatography-tandem high-resolution mass spectrometry because only some coumarin standards were commercially available. Their chlorination generated monochlorinated and polychlorinated coumarins, and their structures were confirmed by the synthesized standards. These products could form at various dosages of chlorine and pH levels, and some with a concentration of 600 ng/L can be stable in tap water for days. 3,6,8-Trichloro-7-hydroxycoumarin, 3-chloro-7-methoxy-4-methylcoumarin, and 3,6-dichloro-7-methoxy-4-methylcoumarin were first identified in finished water with concentrations of 0.0670, 78.1, and 14.7 ng/L, respectively, but not in source water, suggesting that they are new DBPs formed during disinfection. The cytotoxicity of 3-chloro-7-methoxy-4-methylcoumarin in CHO-K1 cells was comparable to those of 2,6-dibromo-1,4-benzoquinone and 2,6-dichloro-1,4-benzoquinone in TIC-Tox analyses, suggesting that further investigation of their occurrence and control in drinking water systems is warranted.

Emerging studies implicate fine particulate matter (PM2.5) and its organic components (OCs) as urgent hazard factors for lung cancer progression in nonsmokers. Establishing the adverse outcome pathway (AOP)-directed nontargeted identification method, this study aimed to explore whether PM2.5 exposure in coal-burning areas promoted lung tumor metastasis and how we identify its effective OCs to support traceability and control of regional PM2.5 pollution. First, we used a nude mouse model of lung cancer for PM2.5 exposure and found that the exposure significantly promoted the hematogenous metastases of A549-Luc cells in lung tissues and the adverse outcomes (AOs), with key events (KEs) including the changed expression of epithelial-mesenchymal transition (EMT) markers, such as suppression of E-cad and increased expression of Fib. Subsequently, using AOs and KEs as adverse outcome directors, we identified a total of 35 candidate chemicals based on the in vitro model and nontargeted analysis. Among them, tributyl phosphate (C12H27O4P), 2-bromotetradecane (C14H29Br), and methyl decanoate (C11H22O2) made greater contributions to the AOs. Finally, we clarified the interactions between these OCs and EMT-activating transcription factors (EMT-ATFs) as the molecular initiation event (MIE) to support the feasibility of the above identification strategy. The present study updates a new framework for identifying tumor metastasis-promoting OCs in PM2.5 and provides solid data for screening out chemicals that need priority control in polluted areas posing higher lung cancer risk.

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.

Exposure to environmental endocrine-disrupting chemicals (EDCs) can cause extensive health issues. However, specific EDCs remain elusive. This work aimed at performing nontargeted identification of estrogen receptor α (ERα)-active compounds using an ERα protein affinity assay combined with high-resolution mass spectrometry in the source and drinking water sampled from major rivers in China. Fifty-one potential ERα-active compounds across 13 categories were identified. For the first time, diisodecyl phenyl phosphate was found to have antiestrogenic activity, and three chemicals (galaxolidone, bensulfuron methyl, and UV234) were plausible ERα ligands. Among the 51 identified compounds, 12 were detected in the aquatic environment for the first time, and the concentration of N-phenyl-2-naphthylamine, a widely used antioxidant in rubber products, was up to 1469 and 1190 ng/L in source and drinking water, respectively. This study demonstrated the widespread presence of known and unknown ERα estrogenic and antiestrogenic pollutants in the major rivers that serve as key sources of drinking water in China and the low removal efficiency of these chemicals in drinking water treatment plants.

In an increasingly chemically polluted environment, rapidly characterizing the human chemical exposome (i.e., chemical mixtures accumulating in humans) at the population scale is critical to understand its impact on health. High-resolution mass spectrometry (HRMS) profiling of complex biological matrices can theoretically provide a comprehensive picture of chemical exposures. However, annotating the detected chemical features, particularly low-abundant ones, remains a significant obstacle to implementing such approaches at a large scale. We present Scannotation (https://github.com/scannotation/Scannotation_software), an automated and user-friendly suspect screening tool for the rapid pre-annotation of HRMS preprocessed data sets. This software tool combines several MS1 chemical predictors, i.e., m/z, experimental and predicted retention times, isotopic patterns, and neutral loss patterns, to score the proximity between features and suspects, thus efficiently prioritizing tentative annotations to verify. Scannotation and MS-DIAL4 were used to annotate blood serum samples of 75 Breton adolescents. Scannotation\'s combination of MS1-based chemical predictors allowed us to annotate 89 chemically diverse environmental compounds with high confidence (confirmed by MS2 when available). These compounds included 62% of emerging molecules, for which no toxicological or human biomonitoring data are reported in the literature. The complementarity observed with MS-DIAL4 results demonstrates the relevance of Scannotation for the efficient pre-annotation of large-scale exposomics data sets.

The combination of hydrogen/deuterium (H/D) formaldehyde-based isotopic methyl labeling with solid-phase extraction and high-performance liquid chromatography-high resolution mass spectrometry (HPLC-HRMS) is a powerful analytical solution for nontargeted analysis of trace-level amino-containing chemicals in water samples. Given the huge amount of chemical information generated in HPLC-HRMS analysis, identifying all possible H/D-labeled amino chemicals presents a significant challenge in data processing. To address this, we designed a streamlined data processing pipeline that can automatically extract H/D-labeled amino chemicals from the raw HPLC-HRMS data with high accuracy and efficiency. First, we developed a cross-correlation algorithm to correct the retention time shift resulting from deuterium isotopic effects, which enables reliable pairing of H- and D-labeled peaks. Second, we implemented several bioinformatic solutions to remove false chemical features generated by in-source fragmentation, salt adduction, and natural 13C isotopes. Third, we used a data mining strategy to construct the AMINES library that consists of over 38,000 structure-disjointed primary and secondary amines to facilitate putative compound annotation. Finally, we integrated these modules into a freely available R program, HDPairFinder.R. The rationale of each module was justified and its performance tested using experimental H/D-labeled chemical standards and authentic water samples. We further demonstrated the application of HDPairFinder to effectively extract N-containing contaminants, thus enabling the monitoring of changes of primary and secondary N-compounds in authentic water samples. HDPairFinder is a reliable bioinformatic tool for rapid processing of H/D isotopic methyl labeling-based nontargeted analysis of water samples, and will facilitate a better understanding of N-containing chemical compounds in water.