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Huang J, Cheng F, He L, Lou X, Li H, You J. Effect driven prioritization of contaminants in wastewater treatment plants across China: A data mining-based toxicity screening approach. WATER RESEARCH 2024; 264:122223. [PMID: 39116614 DOI: 10.1016/j.watres.2024.122223] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/09/2024] [Revised: 07/08/2024] [Accepted: 08/04/2024] [Indexed: 08/10/2024]
Abstract
A diversity of contaminants of emerging concern (CECs) are present in wastewater effluent, posing potential threats to receiving waters. It is urgent for a holistic assessment of the occurrence and risk of CECs related to wastewater treatment plants (WWTP) on national and regional scales. A data mining-based risk prioritization method was developed to collect the reported contaminants and their respective concentrations in municipal and industrial WWTPs and their receiving waters across China over the past 20 years. A total of 10,781 chemicals were reported in 8336 publications, of which 1037 contaminants were reported with environmental concentrations. While contaminant categories varied across WWTP types (municipal vs. industrial) and regions, pharmaceuticals and cyclic hydrocarbons were the most studied CECs. Contaminant composition in receiving water was closer to that in municipal than industrial WWTPs. Publications on legacy pesticides and polycyclic aromatic hydrocarbons in WWTP decreased recently compared to the past, while pharmaceuticals and perfluorochemicals have received increasing attention, showing a changing concern over time. Detection frequency, concentration, removal efficiency, and toxicity data were integrated for assessing potential risks and prioritizing CECs on national and regional scales using an environmental health prioritization index (EHPi) approach. Among 666 contaminants in municipal WWTP effluent, trichlorfon and perfluorooctanesulfonic acid were with the highest EHPi scores, while 17ɑ-ethinylestradiol and bisphenol A had the highest EHPi scores among 304 contaminants in industrial WWTPs. The prioritized contaminants varied across regions, suggesting a need for tailoring regional measures of wastewater treatment and control.
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Affiliation(s)
- Jiehui Huang
- Guangdong Key Laboratory of Environmental Pollution and Health, School of Environment, Jinan University, Guangzhou, 511443, China
| | - Fei Cheng
- State Key Laboratory of Organic Geochemistry, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou, 510640, China
| | - Liwei He
- Guangdong Key Laboratory of Environmental Pollution and Health, School of Environment, Jinan University, Guangzhou, 511443, China
| | - Xiaohan Lou
- Guangdong Key Laboratory of Environmental Pollution and Health, School of Environment, Jinan University, Guangzhou, 511443, China
| | - Huizhen Li
- Guangdong Key Laboratory of Environmental Pollution and Health, School of Environment, Jinan University, Guangzhou, 511443, China.
| | - Jing You
- Guangdong Key Laboratory of Environmental Pollution and Health, School of Environment, Jinan University, Guangzhou, 511443, China.
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Sahoo AK, Chivukula N, Madgaonkar SR, Ramesh K, Marigoudar SR, Sharma KV, Samal A. Leveraging integrative toxicogenomic approach towards development of stressor-centric adverse outcome pathway networks for plastic additives. Arch Toxicol 2024:10.1007/s00204-024-03825-z. [PMID: 39097536 DOI: 10.1007/s00204-024-03825-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2024] [Accepted: 07/24/2024] [Indexed: 08/05/2024]
Abstract
Plastics are widespread pollutants found in atmospheric, terrestrial and aquatic ecosystems due to their extensive usage and environmental persistence. Plastic additives, that are intentionally added to achieve specific functionality in plastics, leach into the environment upon plastic degradation and pose considerable risk to ecological and human health. Limited knowledge concerning the presence of plastic additives throughout plastic life cycle has hindered their effective regulation, thereby posing risks to product safety. In this study, we leveraged the adverse outcome pathway (AOP) framework to understand the mechanisms underlying plastic additives-induced toxicities. We first identified an exhaustive list of 6470 plastic additives from chemicals documented in plastics. Next, we leveraged heterogenous toxicogenomics and biological endpoints data from five exposome-relevant resources, and identified associations between 1287 plastic additives and 322 complete and high quality AOPs within AOP-Wiki. Based on these plastic additive-AOP associations, we constructed a stressor-centric AOP network, wherein the stressors are categorized into ten priority use sectors and AOPs are linked to 27 disease categories. We visualized the plastic additives-AOP network for each of the 1287 plastic additives and made them available in a dedicated website: https://cb.imsc.res.in/saopadditives/ . Finally, we showed the utility of the constructed plastic additives-AOP network by identifying highly relevant AOPs associated with benzo[a]pyrene (B[a]P), bisphenol A (BPA), and bis(2-ethylhexyl) phthalate (DEHP) and thereafter, explored the associated toxicity pathways in humans and aquatic species. Overall, the constructed plastic additives-AOP network will assist regulatory risk assessment of plastic additives, thereby contributing towards a toxic-free circular economy for plastics.
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Affiliation(s)
- Ajaya Kumar Sahoo
- Computational Biology Group, The Institute of Mathematical Sciences (IMSc), CIT Campus, Taramani, Chennai, 600113, India
- Homi Bhabha National Institute (HBNI), Mumbai, 400094, India
| | - Nikhil Chivukula
- Computational Biology Group, The Institute of Mathematical Sciences (IMSc), CIT Campus, Taramani, Chennai, 600113, India
- Homi Bhabha National Institute (HBNI), Mumbai, 400094, India
| | - Shreyes Rajan Madgaonkar
- Computational Biology Group, The Institute of Mathematical Sciences (IMSc), CIT Campus, Taramani, Chennai, 600113, India
- Homi Bhabha National Institute (HBNI), Mumbai, 400094, India
| | - Kundhanathan Ramesh
- Computational Biology Group, The Institute of Mathematical Sciences (IMSc), CIT Campus, Taramani, Chennai, 600113, India
| | | | - Krishna Venkatarama Sharma
- Ministry of Earth Sciences, National Centre for Coastal Research, Government of India, Pallikaranai, Chennai, 600100, India
| | - Areejit Samal
- Computational Biology Group, The Institute of Mathematical Sciences (IMSc), CIT Campus, Taramani, Chennai, 600113, India.
- Homi Bhabha National Institute (HBNI), Mumbai, 400094, India.
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Britton KN, Judson RS, Hill BN, Jarema KA, Olin JK, Knapp BR, Lowery M, Feshuk M, Brown J, Padilla S. Using Zebrafish to Screen Developmental Toxicity of Per- and Polyfluoroalkyl Substances (PFAS). TOXICS 2024; 12:501. [PMID: 39058153 PMCID: PMC11281043 DOI: 10.3390/toxics12070501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/30/2024] [Revised: 07/01/2024] [Accepted: 07/02/2024] [Indexed: 07/28/2024]
Abstract
Per- and polyfluoroalkyl substances (PFAS) are found in many consumer and industrial products. While some PFAS, notably perfluorooctanoic acid (PFOA) and perfluorooctanesulfonic acid (PFOS), are developmentally toxic in mammals, the vast majority of PFAS have not been evaluated for developmental toxicity potential. A concentration-response study of 182 unique PFAS chemicals using the zebrafish medium-throughput, developmental vertebrate toxicity assay was conducted to investigate chemical structural identifiers for toxicity. Embryos were exposed to each PFAS compound (≤100 μM) beginning on the day of fertilization. At 6 days post-fertilization (dpf), two independent observers graded developmental landmarks for each larva (e.g., mortality, hatching, swim bladder inflation, edema, abnormal spine/tail, or craniofacial structure). Thirty percent of the PFAS were developmentally toxic, but there was no enrichment of any OECD structural category. PFOS was developmentally toxic (benchmark concentration [BMC] = 7.48 μM); however, other chemicals were more potent: perfluorooctanesulfonamide (PFOSA), N-methylperfluorooctane sulfonamide (N-MeFOSA), ((perfluorooctyl)ethyl)phosphonic acid, perfluoro-3,6,9-trioxatridecanoic acid, and perfluorohexane sulfonamide. The developmental toxicity profile for these more potent PFAS is largely unexplored in mammals and other species. Based on these zebrafish developmental toxicity results, additional screening may be warranted to understand the toxicity profile of these chemicals in other species.
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Affiliation(s)
- Katy N. Britton
- Oak Ridge Associated Universities Research Participation Program Hosted by EPA, Center for Computational Toxicology and Exposure, Biomolecular and Computational Toxicology Division, Rapid Assay Development Branch, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Richard S. Judson
- Center for Computational Toxicology and Exposure, Computational Toxicology and Bioinformatics Branch, Research Triangle Park, NC 27711, USA;
| | - Bridgett N. Hill
- Oak Ridge Institute for Science and Education Research Participation Program Hosted by EPA, Center for Computational Toxicology and Exposure, Biomolecular and Computational Toxicology Division, Rapid Assay Development Branch, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA; (B.N.H.); (B.R.K.)
| | - Kimberly A. Jarema
- Center for Public Health and Environmental Assessment, Immediate Office, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA;
| | - Jeanene K. Olin
- Center for Computational Toxicology and Exposure, Biomolecular and Computational Toxicology Division, Rapid Assay Development Branch, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA; (J.K.O.); (M.L.)
| | - Bridget R. Knapp
- Oak Ridge Institute for Science and Education Research Participation Program Hosted by EPA, Center for Computational Toxicology and Exposure, Biomolecular and Computational Toxicology Division, Rapid Assay Development Branch, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA; (B.N.H.); (B.R.K.)
| | - Morgan Lowery
- Center for Computational Toxicology and Exposure, Biomolecular and Computational Toxicology Division, Rapid Assay Development Branch, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA; (J.K.O.); (M.L.)
| | - Madison Feshuk
- Center for Computational Toxicology and Exposure, Scientific Computing and Data Curation Division, Data Extraction and Quality Evaluation Branch, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA;
| | - Jason Brown
- Center for Computational Toxicology and Exposure, Scientific Computing and Data Curation Division, Application Development Branch, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA;
| | - Stephanie Padilla
- Center for Computational Toxicology and Exposure, Biomolecular and Computational Toxicology Division, Rapid Assay Development Branch, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA; (J.K.O.); (M.L.)
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Pronk TE, Hoondert RPJ, Kools SAE, Kumar V, de Baat ML. Bioassay predictive values for chemical health risks in drinking water. ENVIRONMENT INTERNATIONAL 2024; 188:108733. [PMID: 38744044 DOI: 10.1016/j.envint.2024.108733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/03/2024] [Revised: 04/17/2024] [Accepted: 05/08/2024] [Indexed: 05/16/2024]
Abstract
Bioanalytical tools can be used for assessment of the chemical quality of drinking water and its sources. For water managers it is important to know the probability that a bioassay response above an established health-based 'effect-based trigger value' (EBT) indeed implies a harmful chemical (mixture) concentration. This study presents and applies a framework, based on Bayes' theorem, to derive such risk probabilities for bioassay responses. These were evaluated under varying (in silico) chemical mixture concentrations relevant to drinking water (sources), with toxicity data for six in vitro assays from the ToxCast database. For single chemicals and in silico mixtures, the negative predictive value (NPV) was 100 % for all assays. For water managers, this means that when a bioassay response is below the EBT, a chemical risk is reliably absent, and no further action is required. The positive predictive value (PPV) increased with increasing chemical concentrations (2 µg/L) up to 40-80 %, depending on the assay. For in silico mixtures of increasing numbers of chemicals, the PPV did not increase until higher sum concentrations (>2-10 µg/L). Hence, the ability to accurately signal a harmful chemical (mixture) using bioassays will be lowest for highly diverse, low-concentration chemical mixtures. For water managers, this means in practice that further investigations after an EBT exceedance will, in many cases, not reveal chemicals at harmful concentrations. A solution offered is to increase the trigger value for positive responses to achieve a higher PPV and maintain the EBT for negative responses to ensure an optimal NPV.
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Affiliation(s)
- Tessa E Pronk
- KWR Water Research Institute, Groningenhaven 7, 3433 PE Nieuwegein, the Netherlands.
| | - Renske P J Hoondert
- KWR Water Research Institute, Groningenhaven 7, 3433 PE Nieuwegein, the Netherlands
| | - Stefan A E Kools
- KWR Water Research Institute, Groningenhaven 7, 3433 PE Nieuwegein, the Netherlands
| | - Vikas Kumar
- Environmental Engineering Laboratory, Departament d' Enginyeria Quimica, Universitat Rovira i Virgili, Av. Països Catalans 26 43007, Tarragona, Catalonia, Spain; IISPV, Hospital Universitari Sant Joan de Reus, Universitat Rovira i Virgili, Reus, Spain; German Federal Institute for Risk Assessment (BfR), Max-Dohrn-Str. 8-10 10589, Berlin, Germany
| | - Milo L de Baat
- Dept. of Freshwater and Marine Ecology, Inst. for Biodiversity and Ecosystem Dynamics, University of Amsterdam, Science Park 904 1098XH, Amsterdam, the Netherlands
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Sahoo AK, Chivukula N, Ramesh K, Singha J, Marigoudar SR, Sharma KV, Samal A. An integrative data-centric approach to derivation and characterization of an adverse outcome pathway network for cadmium-induced toxicity. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 920:170968. [PMID: 38367714 DOI: 10.1016/j.scitotenv.2024.170968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 01/28/2024] [Accepted: 02/05/2024] [Indexed: 02/19/2024]
Abstract
Cadmium is a prominent toxic heavy metal that contaminates both terrestrial and aquatic environments. Owing to its high biological half-life and low excretion rates, cadmium causes a variety of adverse biological outcomes. Adverse outcome pathway (AOP) networks were envisioned to systematically capture toxicological information to enable risk assessment and chemical regulation. Here, we leveraged AOP-Wiki and integrated heterogeneous data from four other exposome-relevant resources to build the first AOP network relevant for inorganic cadmium-induced toxicity. From AOP-Wiki, we filtered 309 high confidence AOPs, identified 312 key events (KEs) associated with inorganic cadmium from five exposome-relevant databases using a data-centric approach, and thereafter, curated 30 cadmium relevant AOPs (cadmium-AOPs). By constructing the undirected AOP network, we identified a large connected component of 18 cadmium-AOPs. Further, we analyzed the directed network of 59 KEs and 82 key event relationships (KERs) in the largest component using graph-theoretic approaches. Subsequently, we mined published literature using artificial intelligence-based tools to provide auxiliary evidence of cadmium association for all KEs in the largest component. Finally, we performed case studies to verify the rationality of cadmium-induced toxicity in humans and aquatic species. Overall, cadmium-AOP network constructed in this study will aid ongoing research in systems toxicology and chemical exposome.
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Affiliation(s)
- Ajaya Kumar Sahoo
- The Institute of Mathematical Sciences (IMSc), Chennai, India; Homi Bhabha National Institute (HBNI), Mumbai, India
| | - Nikhil Chivukula
- The Institute of Mathematical Sciences (IMSc), Chennai, India; Homi Bhabha National Institute (HBNI), Mumbai, India
| | | | - Jasmine Singha
- National Centre for Coastal Research, Ministry of Earth Sciences, Government of India, Pallikaranai, Chennai, India
| | | | - Krishna Venkatarama Sharma
- National Centre for Coastal Research, Ministry of Earth Sciences, Government of India, Pallikaranai, Chennai, India
| | - Areejit Samal
- The Institute of Mathematical Sciences (IMSc), Chennai, India; Homi Bhabha National Institute (HBNI), Mumbai, India.
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