Biotransformation kinetics and pathways of typical synthetic progestins in soil microcosms

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.

Occurrence and spatiotemporal distribution of natural and synthetic steroid hormones in soil, water, and sediment systems in suburban agricultural area of Guangzhou City, China

Steroid hormones are highly potent compounds that can disrupt the endocrine systems of aquatic organisms. This study explored the spatiotemporal distribution of 49 steroid hormones in agricultural soils, ditch water, and sediment from suburban areas of Guangzhou City, China. The average concentrations of Σsteroid hormones in the water, soils, and sediment were 97.7 ng/L, 4460 ng/kg, and 9140 ng/kg, respectively. Elevated hormone concentrations were notable in water during the flood season compared to the dry season, whereas an inverse trend was observed in soils and sediment. These observations were attributed to illegal wastewater discharge during the flood season, and sediment partitioning of hormones and manure fertilization during the dry season. Correlation analysis further showed that population, precipitation, and number of slaughtered animals significantly influenced the spatial distribution of steroid hormones across various districts. Moreover, there was substantial mass transfer among the three media, with steroid hormones predominantly distributed in the sediment (60.8 %) and soils (34.4 %). Risk quotients, calculated as the measured concentration and predicted no-effect concentration, exceeded 1 at certain sites for some hormones, indicating high risks. This study reveals that the risk assessment of steroid hormones requires consideration of their spatiotemporal variability and inter-media mass transfer dynamics in agroecosystems.

Non-targeted screening and photolysis transformation of tire-related compounds in roadway runoff

Roadway runoff serves as a crucial pathway for transporting contaminants of emerging concern (CECs) from urban environments to receiving water bodies. Tire-related compounds originating from tire wear particles (TWPs) have been frequently detected, posing a potential ecological threat. Yet, the photolysis of tire-related compounds within roadway runoff remains inadequately acknowledged. Addressing this deficit, our study utilized high-resolution mass spectrometry (HRMS) to characterize the chemical profile of roadway runoff across eight strategically selected sites in Guangzhou, China. 219 chemicals were identified or detected within different confidence levels. Among them, 29 tire-related contaminants were validated with reference standards, including hexa(methoxymethyl)melamine (HMMM), 1,3-diphenylguanidine (DPG), dicyclohexylurea (DCU), and N-cyclohexyl-2-benzothiazol-amine (DCMA). HMMM exhibited with the abundance ranging from 2.30 × 104-3.10 × 106, followed by DPG, 1.69 × 104-8.34 × 106. Runoff sample were exposed to irradiation of 500 W mercury lamp for photodegradation experiment. Photolysis results indicated that tire-related compounds with a low photolysis rate, notably DCU, DCMA, and DPG, are more likely to persist within the runoff. The photolytic rates were significantly correlated with the spatial distribution patterns of these contaminants. Our findings underscore TWPs as a significant source of pollution in water bodies, emphasizing the need for enhanced environmental monitoring and assessment strategies.

Enhancing compound confidence in suspect and non-target screening through machine learning-based retention time prediction

The retention time (RT) of contaminants of emerging concern (CECs) in liquid chromatography-high-resolution mass spectrometry (LC-HRMS) is crucial for database matching in non-targeted screening (NTS) analysis. In this study, we developed a machine learning (ML) model to predict RTs of CECs in NTS analysis. Using 1051 CEC standards, we evaluated Random Forest (RF), XGBoost, Support Vector Regression (SVR), and Artificial Neural Network (ANN) with molecular fingerprints and chemical descriptors to establish an optimal model. The SVR model utilizing chemical descriptors resulted in good predictive capacity with R2ext = 0.850 and r2 = 0.925. The model was further validated through laboratory NTS compound characterization. When applied to examine CEC occurrence in a large wastewater treatment plant, we identified 40 level S1 CECs (confirmed structure by reference standard) and 234 level S2 compounds (probable structure by library spectrum match). The model predicted RTs for level S2 compounds, leading to the classification of 153 level S2 compounds with high confidence (ΔRT <2 min). The model served as a robust filtering mechanism within the analytical framework. This study emphasizes the importance of predicted RTs in NTS analysis and highlights the potential of prediction models. Our research introduces a workflow that enhances NTS analysis by utilizing RT prediction models to determine compound confidence levels.

Application of Nontarget High-Resolution Mass Spectrometry Fingerprints for Qualitative and Quantitative Source Apportionment: A Real Case Study

High-resolution mass spectrometry (HRMS) provides extensive chemical data, facilitating the differentiation and quantification of contaminants of emerging concerns (CECs) in aquatic environments. This study utilizes liquid chromatography-HRMS for source apportionment in Chebei Stream, an urban water stream in Guangzhou, South China. Initially, 254 features were identified as potential CECs by the nontarget screening (NTS) method. We then established 1689, 1317, and 15,759 source-specific HRMS fingerprints for three distinct sources, the mainstream (C3), the tributary (T2), and the rain runoff (R1), qualitatively assessing the contribution from each source downstream. Subsequently, 32, 55, and 3142 quantitative fingerprints were isolated for sites C3, T2, and R1, respectively, employing dilution curve screening for source attribution. The final contribution estimates downstream from sites C3, T2, and R1 span 32-96, 12-23, and 8-23%, respectively. Cumulative contributions from these sources accurately mirrored actual conditions, fluctuating between 103 and 114% across C6 to C8 sites. Yet, with further tributary integration, the overall source contribution dipped to 52%. The findings from this research present a pioneering instance of applying HRMS fingerprints for qualitative and quantitative source tracking in real-world scenarios, which empowers the development of more effective strategies for environmental protection.

Biotransformation of cyproterone acetate, drospirenone, and megestrol acetate in agricultural soils: Kinetics, microbial community dynamics, transformation products, and mechanisms

The occurrence of biologically active synthetic progestins in agricultural soils is of growing concern due to their potential to disrupt the endocrine function of aquatic fish in nearby surface waters. This study investigated the biotransformation outcomes of cyproterone acetate (CPA), drospirenone (DRO), and megestrol acetate (MGA) in four agricultural soils. The biotransformation data were fitted to a first-order decay model (R2 = 0.93-0.99), with half-lives and first-order decay coefficients ranging from 76.2-217 h and 9.10 × 10-3-3.20 × 10-3 (h-1), respectively. Abundant biotransformation products (TPs) were generated during incubation, with the number and yields varying across the four soils. 1,2-Dehydrogenation was the main transformation pathway of DRO in the four soils (yields of 32.3-214 %). Similarly, 1,2-dehydrogenation was the most relevant transformation pathway of MGA in the four soils (yields of 21.8-417 %). C3 reduction was the major transformation pathway of CPA in soils B, C, and D (yields of 114-245 %). Hydrogenation (yield of 133 %) and hydroxylation (yield of 21.0 %) were the second major transformation pathway of CPA in soil B and C, respectively. In particular, several TPs exhibited progestogenic and antimineralocorticoid activity, as well as genotoxicity. The high-throughput sequencing indicated that interactions between microorganisms and soil properties may affect biotransformation. Spearman correlation and bidirectional network correlation analysis further revealed that soil properties can directly interfere with the soil sorption capacity for the progestins, thus affecting biotransformation. In particular, soil properties can also limit or promote biotransformation and the formation of TPs (i.e., biotransformation pathways) by affecting the relative abundances of relevant microorganisms. The results of this study indicate that the ecotoxicity of synthetic progestins and related TPs can vary across soils and that the assessment of environmental risks associated with these compounds requires special consideration of both soil properties and microbial communities.