Multi-criteria decision-making techniques associated with (Q)SAR risk assessment for ranking surface water microcontaminants identified using LC-QTOF MS

FT-GNN Tool for Bridging HRMS Features and Bioactivity: Uncovering Unidentified Estrogen Receptor Agonists in Sewage

Identifying primary estrogen receptor (ER) agonists in municipal sewage is essential for ensuring the health of aquatic environments. Given the complex and variable chemical composition of sewage, the predominant ER agonists remain unclear. High-resolution mass spectrometry (HRMS)-based models have been developed to predict compound bioactivity in complex matrices, but further optimization is needed to effectively bridge HRMS features with ER agonists. To address this challenge, an FT-GNN (fragmentation tree-based graph neural network) model was proposed. Given limited data and class imbalance, data augmentation was performed using model predictions within the applicability domain (AD) and oversampling technique (OTE). Model development results demonstrated that integrating the FT-GNN with data augmentation improved the balanced accuracy (bACC) value by 6%-31%. The developed model, with a high bACC to identify more true ER agonists, efficiently classified tens of thousands of unidentified HRMS features in sewage, reducing postprocessing workload in nontargeted screening. Analysis of ER agonist transformation during sewage treatment revealed the anaerobic stage as key to both their removal and formation. Estrogenic effect balance analysis suggests that α-E2 and 9,11-didehydroestriol may be two previously overlooked key ER agonists. Collectively, the development and application of the FT-GNN model are crucial advancements toward credible tracking and efficient control of estrogenic risks in water.

Prioritization of monitoring compounds from SNTS identified organic micropollutants in contaminated groundwater using a machine learning optimized ToxPi model

Advanced suspect and non-target screening (SNTS) approach can identify a large number of potential hazardous micropollutants in groundwater, underscoring the need for pinpointing priority pollutants among detected chemicals. This present study therefore demonstrates a novel multi-criteria decision making (MCDM) framework utilizing machine learning (ML) algorithms coupled with toxicological prioritization index tool (i.e., ml_ToxPi) to rank 252 chemicals of interest in groundwater for subsequent targeted analysis. The MCDM framework integrated chemical analysis data (i.e., peak area and detection frequency), toxicity profiles (i.e., bioactivity ratio, human exposure metadata, and carcinogenicity metadata), as well as the environmental fate and transport information (i.e., octanol-water partition coefficient (log Kow), water solubility, biodegradation half-life, and soil adsorption coefficient (Koc)) for ranking the identified pollutants, and the random forest machine learning model was useful for systematically determining the weighting factors of each variable according to their variable importance scores (R2 = 0.808 and 0.778 for training and testing datasets, respectively, while RMSE = 0.042 in both cases). A total of 47 unique high priority compounds (i.e., ml_ToxPi score ≥ 0.55) were identified across the investigated sampling regions, which constituted diverse groups of compounds classified according to their chemical uses, such as alkylated polycyclic aromatic hydrocarbons (alkyl-PAHs), organophosphate flame retardants (OPFRs), parent PAHs, personal care products (PCPs), pesticides, pharmaceuticals, phenols, plasticizers, transformation product (TPs), and other industrial use chemicals. By incorporating relevant variables into the proposed ML-optimized ToxPi MCDM framework, the prioritization approach described here may be adopted in future SNTS assessment of environmental and biological media.

Benzotriazoles and bisphenols in wastewater from the food processing industry and the quantitative changes during mechanical/biochemical treatment processes

Benzotriazoles (BTRs) and bisphenols (BPs), categorized as contaminants of emerging concern (CECs), pose significant risks to human health and ecosystems due to their endocrine-disrupting properties and environmental persistence. This study investigates the occurrence and behavior of nine BTRs and ten BPs in wastewater generated in a large-scale meat processing plant, evaluating the effectiveness of a modern mechanical-biological industrial on-site treatment plant in removing these contaminants, and based on the concentration levels from eleven sampling points at different stages of the treatment process. The method used to determine these micropollutants' concentration was ultrasound-assisted emulsification-microextraction for analytes isolation and gas chromatography-mass spectrometry for detection (USAEME-GC/MS). The results indicate that the rigorous quality control processes in the meat processing facility effectively limit the presence of these micropollutants, especially concerning BPs, which are absent or below detection limits in raw wastewater. While the concentrations of some of these micropollutants increased at different points in the treatment process, these values were relatively low, typically below one microgram per liter. Among the compounds analyzed, the only one present after completing the treatment was 5Cl-BTR (maximum concentration: 3007 ng/L), and these contamination levels are around seven times lower than the reference value associated with non-cancer health risk for drinking water. This study contributes to understanding these CECs in industrial wastewater and highlights the importance of effective treatment systems for environmental protection.

Temporal variability and ecological risks of pharmaceuticals and cocaine during the Christmas and New Year holidays in a beach area of North Coast of São Paulo, Brazil

This study assessed the occurrence and ecological potential risk of nine selected pharmaceuticals in water samples from the Juquehy River. The river flows continuously to Juquehy Beach, known as "the jewel of the north coast" of São Paulo, Brazil. Samples were collected during Christmas and the New Year (period of December 2023-January 2024), in addition to a previous baseline weekend, to compare the loads during "celebratory parties" versus "normal operational conditions." The findings indicated that the "mass gathering" during Christmas and New Year holidays contributed significantly to an increase of the mass load of the nine pharmaceuticals flowing along to the Juquehy River, i.e., caffeine (14.40-633.00 ng/L) > losartan ( furosemide (< LOQ to 9.16 ng/L) > diclofenac (0.61-4.55 ng/L) > carbamazepine (< LOQ to 0.73 ng/L) > orphenadrine (< LOQ to 0.11 ng/L) showed higher concentrations during the New Year holiday. Conversely, atenolol (< LOQ to 13.10 ng/L) > benzoylecgonine (0.33-7.23 ng/L) > cocaine (0.12-6.59 ng/L) showed higher concentrations during the Christmas day. The individual ecological risk assessment in the Juquehy River revealed a clear environmental concern for the aquatic ecosystem. The threat to the aquatic biota is significant, with caffeine and losartan presenting a moderate level of risk. Moreover, the mixture ecological risk assessment of nine compounds indicates acute moderate risks to algae, crustaceans, and fishes, as well as chronic low risks to fishes.

Computational modeling (in silico) methods combined with ecotoxicological experiments (in vivo) to predict the environmental risks of an antihistamine drug (loratadine)

This study employed computational modeling (in silico) methods, combined with ecotoxicological experiments (in vivo) to predict the persistence/biodegradability, bioaccumulation, mobility, and ecological risks of an antihistamine drug (Loratadine: LOR) in the aquatic compartment. To achieve these goals, four endpoints of the LOR were obtained from different open-source computational tools, namely: (i) "STP total removal"; (ii) Predicted ready biodegradability; (iii) Octanol-water partition coefficient (KOW); and (iv) Soil organic adsorption coefficient (KOC). Moreover, acute and chronic, ecotoxicological assays using non-target freshwater organisms of different trophic levels (namely, algae Pseudokirchneriella subcapitata; microcrustaceans Daphnia similis and Ceriodaphnia dubia; and fish Danio rerio), were used to predict the ecological risks of LOR. The main results showed that LOR: (i) is considered persistent (after a weight-of-evidence assessment) and highly resistant to biodegradation; (ii) is hydrophobic (LOG KOW = 5.20), immobile (LOG KOC = 5.63), and thus, it can potentially bioaccumulate and/or can cause numerous deleterious effects in aquatic species; and (iii) after ecotoxicological evaluation is considered "toxic" and/or "highly toxic" to the three trophic levels tested. Moreover, both the ecotoxicological assays and risk assessment (RQ), showed that LOR is more harmful for the crustaceans (RQcrustaceans = moderate to high risks) than for algae and fish. Ultimately, this study reinforces the ecological concern due to the indiscriminate disposal of this antihistamine drug in worldwide aquatic ecosystems.

Solar photocatalysis application in UWWTP outlets - simulations based on predictive models in flat-plate reactors and pollutant degradation studies with in silico toxicity assessment

The application of the solar photocatalysis for the degradation of residual pollutants found in surface water was demonstrated. Semi-pilot scale flat-plate cascade reactor (FPCR) was used to study the degradation of model organic pollutants: enrofloxacin (ENRO), 17β-estradiol (E2) and 1H-benzotriazole (1H-BT) over TiO2 thin-film supported on glass fibers. A modular panel with full-spectra solar lamps with appropriate UVB and UVA irradiation levels was used as a simulation of sunlight. Pollutant degradation in FPCR was estimated using predictive models; intrinsic reaction rate constants (ki) for ENRO, E2 and 1H-BT independent of the reactor size, flow rate and irradiation conditions were determined: 9.60, 3.35 and 0.37 109 s-1 W-0.5 m1.5, respectively. Main degradation products (DPs), formed upon hydroxylation, ring opening and oxidation, were identified using LC-QTOF-MS. The ecotoxicological impact was assessed via T.E.S.T. and ECOSAR open-source tools showing the formation of less harmful DPs after sufficient reaction time. Pollutant degradation was simulated at four locations of interest, i.e. exhausts from urban wastewater treatment plants (UWWTPs) in Zagreb, Croatia (45°N), Krakow, Poland (50°N), Sevilla, Spain (37°N) and Ioannina, Greece (39.6°N). Results have proved that a simple flat-plate system with supported photocatalysts can be easily scaled up and incorporated at the outlet of UWWTP for the reduction of pollutant load and related toxicity. The exhaust canal in Zagreb with the estimated length of a photocatalytic layer of 122 m for the > 90% degradation of all target pollutants was discussed as the best installation site among studied locations. ENVIRONMENTAL IMPLICATION: A multi-disciplinary approach to the tentative application of TiO2 solar photocatalysis outdoors to reduce pollutant loads and toxicity in surface waters was demonstrated. Possible application at four selected locations in Europe, as an additional step in water treatment after urban wastewater treatment plants (UWWTPs) was discussed. Target pollutants were studied under environmentally relevant conditions (sunlight levels, water matrix, simulation of process on a real scale at selected geographical location), at both higher and low concentrations.

Prioritizing pharmaceutically active compounds (PhACs) based on occurrence-persistency-mobility-toxicity (OPMT) criteria: an application to the Brazilian scenario

A study of Quantitative Structure Activity Relationship (QSAR) was performed to assess the possible adverse effects of 25 pharmaceuticals commonly found in the Brazilian water compartments and to establish a ranking of environmental concern. The occurrence (O), the persistence (P), the mobility (M), and the toxicity (T) of these compounds in the Brazilian drinking water reservoirs were evaluated. Moreover, to verify the predicted OPMT dataset outcomes, a quality index (QI) was also developed and applied. The main results showed that: (i) after in silico predictions through VEGA QSAR, 19 from 25 pharmaceuticals consumed in Brazil were classified as persistent; (ii) moreover, after in silico predictions through OPERA QSAR, 15 among those 19 compounds considered persistent, were also classified as mobile or very mobile. On the other hand, the results of toxicity indicate that only 9 pharmaceuticals were classified with the highest toxicity level. Ultimately, the QI of 7 from 25 pharmaceuticals were categorized as 'optimal'; 15 pharmaceuticals were categorized as 'good'; and only 3 pharmaceuticals were categorized as 'regular'. Therefore, based on the QI criteria used, it is possible to assume that this OPMT prediction dataset had a good reliability. Efforts to reduce emissions of OPMT-pharmaceuticals in Brazilian drinking water reservoirs are encouraged.

Occurrence, ecological risk assessment and prioritization of pharmaceuticals and abuse drugs in estuarine waters along the São Paulo coast, Brazil

The pollution of the surface waters by pharmaceuticals and personal care products (PPCPs) has attracted worldwide attention, but data regarding their occurrence and potential risks for the aquatic biota on tropical coastal rivers of South America are still scarce. In this context, the occurrence and the preliminary ecological risk assessment of eleven pharmaceuticals of various therapeutic classes (including cocaine and its primary metabolite, benzoylecgonine) were investigated, for the first time, in five rivers of São Paulo, southeast Brazil, covering a coastline of about 140 km, namely Perequê River, Itinga River, Mongaguá River, Itanhaém River and Guaraú River. Although these five rivers are born in well-preserved areas of the Atlantic rainforest biome, on its way to sea and when they cross the urban perimeter, they receive untreated sewage discharges containing a complex mixture of contaminants. In addition, a "persistence, bioaccumulation and toxicity" (PBT) approach allowed to pre-select the priority PPCPs to be monitored in this coastline. Identification of several PPCPs in the samples was done using liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS). Ten PPCPs were successfully quantified in all five rivers, namely caffeine (9.00-560.00 ng/L), acetaminophen ( Collapse

Key Words

• Coastal rivers
• Domestic sewage
• Illicit drugs
• Pharmaceuticals
• Prioritization
• Risk assessment
• Subtropical ecosystem

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MESH Headings

• Environmental Monitoring/methods
• Water Pollutants, Chemical/analysis
• Chromatography, Liquid
• Caffeine/analysis
• Brazil
• Diclofenac
• Ecosystem
• Atenolol
• Orphenadrine/analysis
• Acetaminophen
• Losartan
• Furosemide
• Tandem Mass Spectrometry
• Rivers/chemistry
• Illicit Drugs/analysis
• Cocaine/analysis
• Risk Assessment
• Carbamazepine/analysis
• Pharmaceutical Preparations

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Affiliation(s)

Vinicius Roveri Universidade Metropolitana de Santos (UNIMES), Avenida Conselheiro Nébias, 536 - Encruzilhada, 11045-002, Santos, São Paulo, Brazil Centro Interdisciplinar de Investigação Marinha e Ambiental (CIIMAR/CIMAR), Avenida General Norton de Matos S/N, 4450-208, Matosinhos, Portugal
Luciana Lopes Guimarães Laboratório de Pesquisa em Produtos Naturais, Universidade Santa Cecília, Rua Cesário Mota 8, F83A, 11045-040 Santos, São Paulo, Brazil
Walber Toma Laboratório de Pesquisa em Produtos Naturais, Universidade Santa Cecília, Rua Cesário Mota 8, F83A, 11045-040 Santos, São Paulo, Brazil
Alberto Teodorico Correia Centro Interdisciplinar de Investigação Marinha e Ambiental (CIIMAR/CIMAR), Avenida General Norton de Matos S/N, 4450-208, Matosinhos, Portugal. Faculdade de Ciências da Saúde da Universidade Fernando Pessoa (FCS-UFP), Rua Carlos da Maia 296, 4200-150, Porto, Portugal. Instituto de Ciências Biomédicas Abel Salazar da Universidade do Porto (ICBAS-UP), Rua de Jorge Viterbo Ferreira 228, 4050-313, Porto, Portugal.

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Effect-Directed Analysis Based on the Reduced Human Transcriptome (RHT) to Identify Organic Contaminants in Source and Tap Waters along the Yangtze River

Since a large number of contaminants are detected in source waters (SWs) and tap waters (TWs), it is important to perform a comprehensive effect evaluation and key contributor identification. A reduced human transcriptome (RHT)-based effect-directed analysis, which consisted of a concentration-dependent RHT to reveal the comprehensive effects and noteworthy pathways and systematic identification of key contributors based on the interactions between compounds and pathway effects, was developed and applied to typical SWs and TWs along the Yangtze River. By RHT, 42% more differentially expressed genes and 33% more pathways were identified in the middle and lower reaches, indicating heavier pollution. Hormone and immune pathways were prioritized based on the detection frequency, sensitivity, and removal efficiency, among which the estrogen receptor pathway was the most noteworthy. Consistent with RHT, estrogenic effects were widespread along the Yangtze River based on in vitro evaluations. Furthermore, 38 of 100 targets, 39 pathway-related suspects, and 16 estrogenic nontargets were systematically identified. Among them, diethylstilbestrol was the dominant contributor, with the estradiol equivalent (EEQ) significantly correlated with EEQ_water. In addition, zearalenone and niclosamide explained up to 54% of the EEQ_water. The RHT-based EDA method could support the effect evaluation, contributor identification, and risk management of micropolluted waters.