1
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Aggerbeck MR, Frøkjær EE, Johansen A, Ellegaard-Jensen L, Hansen LH, Hansen M. Non-target analysis of Danish wastewater treatment plant effluent: Statistical analysis of chemical fingerprinting as a step toward a future monitoring tool. ENVIRONMENTAL RESEARCH 2024; 257:119242. [PMID: 38821457 DOI: 10.1016/j.envres.2024.119242] [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: 01/11/2024] [Revised: 04/25/2024] [Accepted: 05/26/2024] [Indexed: 06/02/2024]
Abstract
In an attempt to discover and characterize the plethora of xenobiotic substances, this study investigates chemical compounds released into the environment with wastewater effluents. A novel non-targeted screening methodology based on ultra-high resolution Orbitrap mass spectrometry and nanoflow ultra-high performance liquid chromatography together with a newly optimized data-processing pipeline were applied to effluent samples from two state-of-the-art and one small wastewater treatment facility. In total, 785 molecular structures were obtained, of which 38 were identified as single compounds, while 480 structures were identified at a putative level. Most of these substances were therapeutics and drugs, present as parent compounds and metabolites. Using R packages Phyloseq and MetacodeR, originally developed for bioinformatics, significant differences in xenobiotic presence in the wastewater effluents between the three sites were demonstrated.
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Affiliation(s)
- Marie Rønne Aggerbeck
- Department of Environmental Science, Aarhus University, Frederiksborgvej 399, 4000, Roskilde, Denmark; Aarhus University Centre for Water Technology (WATEC), Aarhus University, Vejlsøvej 25, 8600, Silkeborg, Denmark.
| | - Emil Egede Frøkjær
- Department of Environmental Science, Aarhus University, Frederiksborgvej 399, 4000, Roskilde, Denmark
| | - Anders Johansen
- Department of Environmental Science, Aarhus University, Frederiksborgvej 399, 4000, Roskilde, Denmark; Aarhus University Centre for Water Technology (WATEC), Aarhus University, Vejlsøvej 25, 8600, Silkeborg, Denmark; Department of Plant and Environmental Sciences, University of Copenhagen, Thorvaldsensvej 40, 1871, Frederiksberg, Denmark; Aarhus University Centre for Circular Bioeconomy, Aarhus University, 8830 Tjele, Denmark
| | - Lea Ellegaard-Jensen
- Department of Environmental Science, Aarhus University, Frederiksborgvej 399, 4000, Roskilde, Denmark; Aarhus University Centre for Water Technology (WATEC), Aarhus University, Vejlsøvej 25, 8600, Silkeborg, Denmark
| | - Lars Hestbjerg Hansen
- Department of Plant and Environmental Sciences, University of Copenhagen, Thorvaldsensvej 40, 1871, Frederiksberg, Denmark
| | - Martin Hansen
- Department of Environmental Science, Aarhus University, Frederiksborgvej 399, 4000, Roskilde, Denmark; Aarhus University Centre for Water Technology (WATEC), Aarhus University, Vejlsøvej 25, 8600, Silkeborg, Denmark.
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2
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van Herwerden D, Nikolopoulos A, Barron LP, O'Brien JW, Pirok BWJ, Thomas KV, Samanipour S. Exploring the chemical subspace of RPLC: A data driven approach. Anal Chim Acta 2024; 1317:342869. [PMID: 39029998 DOI: 10.1016/j.aca.2024.342869] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Revised: 06/11/2024] [Accepted: 06/11/2024] [Indexed: 07/21/2024]
Abstract
BACKGROUND The chemical space is comprised of a vast number of possible structures, of which an unknown portion comprises the human and environmental exposome. Such samples are frequently analyzed using non-targeted analysis via liquid chromatography (LC) coupled to high-resolution mass spectrometry often employing a reversed phase (RP) column. However, prior to analysis, the contents of these samples are unknown and could be comprised of thousands of known and unknown chemical constituents. Moreover, it is unknown which part of the chemical space is sufficiently retained and eluted using RPLC. RESULTS We present a generic framework that uses a data driven approach to predict whether molecules fall 'inside', 'maybe' inside, or 'outside' of the RPLC subspace. Firstly, three retention index random forest (RF) regression models were constructed that showed that molecular fingerprints are able to predict RPLC retention behavior. Secondly, these models were used to set up the dataset for building an RPLC RF classification model. The RPLC classification model was able to correctly predict whether a chemical belonged to the RPLC subspace with an accuracy of 92% for the testing set. Finally, applying this model to the 91 737 small molecules (i.e., ≤1 000 Da) in NORMAN SusDat showed that 19.1% fall 'outside' of the RPLC subspace. SIGNIFICANCE AND NOVELTY The RPLC chemical space model provides a major step towards mapping the chemical space and is able to assess whether chemicals can potentially be measured with an RPLC method (i.e., not every RPLC method) or if a different selectivity should be considered. Moreover, knowing which chemicals are outside of the RPLC subspace can assist in reducing potential candidates for library searching and avoid screening for chemicals that will not be present in RPLC data.
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Affiliation(s)
- Denice van Herwerden
- Van 't Hoff Institute for Molecular Sciences (HIMS), University of Amsterdam, Amsterdam, 1098 XH, the Netherlands.
| | - Alexandros Nikolopoulos
- Van 't Hoff Institute for Molecular Sciences (HIMS), University of Amsterdam, Amsterdam, 1098 XH, the Netherlands
| | - Leon P Barron
- Van 't Hoff Institute for Molecular Sciences (HIMS), University of Amsterdam, Amsterdam, 1098 XH, the Netherlands; MRC Centre for Environment and Health, Environmental Research Group, School of Public Health, Faculty of Medicine, Imperial College London, London, W12 0BZ, United Kingdom
| | - Jake W O'Brien
- Van 't Hoff Institute for Molecular Sciences (HIMS), University of Amsterdam, Amsterdam, 1098 XH, the Netherlands; Queensland Alliance for Environmental Health Sciences (QAEHS), The University of Queensland, Brisbane, QLD, 4102, Australia
| | - Bob W J Pirok
- Van 't Hoff Institute for Molecular Sciences (HIMS), University of Amsterdam, Amsterdam, 1098 XH, the Netherlands
| | - Kevin V Thomas
- Queensland Alliance for Environmental Health Sciences (QAEHS), The University of Queensland, Brisbane, QLD, 4102, Australia
| | - Saer Samanipour
- Van 't Hoff Institute for Molecular Sciences (HIMS), University of Amsterdam, Amsterdam, 1098 XH, the Netherlands; UvA Data Science Center, University of Amsterdam, Amsterdam, 1012 WP, the Netherlands.
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3
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Hupatz H, Rahu I, Wang WC, Peets P, Palm EH, Kruve A. Critical review on in silico methods for structural annotation of chemicals detected with LC/HRMS non-targeted screening. Anal Bioanal Chem 2024:10.1007/s00216-024-05471-x. [PMID: 39138659 DOI: 10.1007/s00216-024-05471-x] [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: 04/30/2024] [Revised: 07/22/2024] [Accepted: 07/24/2024] [Indexed: 08/15/2024]
Abstract
Non-targeted screening with liquid chromatography coupled to high-resolution mass spectrometry (LC/HRMS) is increasingly leveraging in silico methods, including machine learning, to obtain candidate structures for structural annotation of LC/HRMS features and their further prioritization. Candidate structures are commonly retrieved based on the tandem mass spectral information either from spectral or structural databases; however, the vast majority of the detected LC/HRMS features remain unannotated, constituting what we refer to as a part of the unknown chemical space. Recently, the exploration of this chemical space has become accessible through generative models. Furthermore, the evaluation of the candidate structures benefits from the complementary empirical analytical information such as retention time, collision cross section values, and ionization type. In this critical review, we provide an overview of the current approaches for retrieving and prioritizing candidate structures. These approaches come with their own set of advantages and limitations, as we showcase in the example of structural annotation of ten known and ten unknown LC/HRMS features. We emphasize that these limitations stem from both experimental and computational considerations. Finally, we highlight three key considerations for the future development of in silico methods.
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Affiliation(s)
- Henrik Hupatz
- Department of Materials and Environmental Chemistry, Stockholm University, Svante Arrhenius Väg 16, 114 18, Stockholm, Sweden
- Stockholm University Center for Circular and Sustainable Systems (SUCCeSS), Stockholm University, 106 91, Stockholm, Sweden
| | - Ida Rahu
- Department of Materials and Environmental Chemistry, Stockholm University, Svante Arrhenius Väg 16, 114 18, Stockholm, Sweden.
| | - Wei-Chieh Wang
- Department of Materials and Environmental Chemistry, Stockholm University, Svante Arrhenius Väg 16, 114 18, Stockholm, Sweden
| | - Pilleriin Peets
- Institute of Biodiversity, Faculty of Biological Science, Cluster of Excellence Balance of the Microverse, Friedrich Schiller University Jena, 07743, Jena, Germany
| | - Emma H Palm
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 6 Avenue du Swing, 4367, Belvaux, Luxembourg
| | - Anneli Kruve
- Department of Materials and Environmental Chemistry, Stockholm University, Svante Arrhenius Väg 16, 114 18, Stockholm, Sweden.
- Stockholm University Center for Circular and Sustainable Systems (SUCCeSS), Stockholm University, 106 91, Stockholm, Sweden.
- Department of Environmental Science, Stockholm University, Svante Arrhenius Väg 8, 114 18, Stockholm, Sweden.
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4
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Samanipour S, Barron LP, van Herwerden D, Praetorius A, Thomas KV, O’Brien JW. Exploring the Chemical Space of the Exposome: How Far Have We Gone? JACS AU 2024; 4:2412-2425. [PMID: 39055136 PMCID: PMC11267556 DOI: 10.1021/jacsau.4c00220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/08/2024] [Revised: 05/29/2024] [Accepted: 05/31/2024] [Indexed: 07/27/2024]
Abstract
Around two-thirds of chronic human disease can not be explained by genetics alone. The Lancet Commission on Pollution and Health estimates that 16% of global premature deaths are linked to pollution. Additionally, it is now thought that humankind has surpassed the safe planetary operating space for introducing human-made chemicals into the Earth System. Direct and indirect exposure to a myriad of chemicals, known and unknown, poses a significant threat to biodiversity and human health, from vaccine efficacy to the rise of antimicrobial resistance as well as autoimmune diseases and mental health disorders. The exposome chemical space remains largely uncharted due to the sheer number of possible chemical structures, estimated at over 1060 unique forms. Conventional methods have cataloged only a fraction of the exposome, overlooking transformation products and often yielding uncertain results. In this Perspective, we have reviewed the latest efforts in mapping the exposome chemical space and its subspaces. We also provide our view on how the integration of data-driven approaches might be able to bridge the identified gaps.
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Affiliation(s)
- Saer Samanipour
- Van’t
Hoff Institute for Molecular Sciences (HIMS), University of Amsterdam, Amsterdam 1090 GD, The Netherlands
- UvA
Data Science Center, University of Amsterdam, Amsterdam 1090 GD, The Netherlands
- Queensland
Alliance for Environmental Health Sciences (QAEHS), The University of Queensland, Cornwall Street, Woolloongabba, Queensland 4102, Australia
| | - Leon Patrick Barron
- Van’t
Hoff Institute for Molecular Sciences (HIMS), University of Amsterdam, Amsterdam 1090 GD, The Netherlands
- MRC
Centre for Environment and Health, Environmental Research Group, School
of Public Health, Faculty of Medicine, Imperial
College London, London W12 0BZ, United Kingdom
| | - Denice van Herwerden
- Van’t
Hoff Institute for Molecular Sciences (HIMS), University of Amsterdam, Amsterdam 1090 GD, The Netherlands
| | - Antonia Praetorius
- Institute
for Biodiversity and Ecosystem Dynamics (IBED), University of Amsterdam, Amsterdam 1090 GD, The Netherlands
| | - Kevin V. Thomas
- Queensland
Alliance for Environmental Health Sciences (QAEHS), The University of Queensland, Cornwall Street, Woolloongabba, Queensland 4102, Australia
| | - Jake William O’Brien
- Van’t
Hoff Institute for Molecular Sciences (HIMS), University of Amsterdam, Amsterdam 1090 GD, The Netherlands
- Queensland
Alliance for Environmental Health Sciences (QAEHS), The University of Queensland, Cornwall Street, Woolloongabba, Queensland 4102, Australia
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5
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Li Z, Zhang J, Yang L, Li X, Meng Q, Li Y, Yao S, Wei W, Bi Q, Qu H, An Y, Guo DA. Intelligent chemical profiling of 73 edible flowers by liquid chromatography-high resolution mass spectrometry combined with HRMS database and their authentication based on large-scale fingerprints. Food Chem 2024; 446:138683. [PMID: 38428081 DOI: 10.1016/j.foodchem.2024.138683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 01/31/2024] [Accepted: 02/04/2024] [Indexed: 03/03/2024]
Abstract
A commercial high-resolution MS database "TCM-PCDL" was innovatively introduced to automatically identify multi-components in 73 edible flowers rapidly and accurately by liquid chromatography-high resolution mass spectrometry, which can be time-consuming and labor-intensive in traditional manual method. The database encompasses over 2565 natural products with various energy levels. Unknown compounds can be identified through direct matching and scoring MS2 spectra with database. A total of 870 compounds were identified from 73 flowers, with polyphenols constituting up to 75%. Focusing on polyphenols, a high performance liquid chromatography (HPLC) method was developed to generate fingerprints from 510 batches, establishing an "HPLC database" that enabled accurate authentication using similarity scores and rankings. This method demonstrated an accuracy rate of 100% when applied to 30 unknown samples. For flowers prone to confusion, additional statistical analysis methods could be employed as aids in authentication. This study provides valuable insights for large-scale sample chemical profiling and authentication.
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Affiliation(s)
- Ziqing Li
- National Engineering Research Center of TCM Standardization Technology, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Haike Road #501, Shanghai 201203, China
| | - Jianqing Zhang
- National Engineering Research Center of TCM Standardization Technology, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Haike Road #501, Shanghai 201203, China
| | - Lin Yang
- School of Chinese Materia Medica, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Xiaolan Li
- National Engineering Research Center of TCM Standardization Technology, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Haike Road #501, Shanghai 201203, China
| | - Qian Meng
- National Engineering Research Center of TCM Standardization Technology, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Haike Road #501, Shanghai 201203, China
| | - Yun Li
- National Engineering Research Center of TCM Standardization Technology, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Haike Road #501, Shanghai 201203, China
| | - Shuai Yao
- National Engineering Research Center of TCM Standardization Technology, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Haike Road #501, Shanghai 201203, China
| | - Wenlong Wei
- National Engineering Research Center of TCM Standardization Technology, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Haike Road #501, Shanghai 201203, China
| | - Qirui Bi
- National Engineering Research Center of TCM Standardization Technology, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Haike Road #501, Shanghai 201203, China
| | - Hua Qu
- National Engineering Research Center of TCM Standardization Technology, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Haike Road #501, Shanghai 201203, China
| | - Yaling An
- Zhongshan Institute for Drug Discovery, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Zhongshan 528400, China
| | - De-An Guo
- National Engineering Research Center of TCM Standardization Technology, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Haike Road #501, Shanghai 201203, China; School of Chinese Materia Medica, Nanjing University of Chinese Medicine, Nanjing 210023, China; Zhongshan Institute for Drug Discovery, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Zhongshan 528400, China.
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6
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Wang F, Xiang L, Sze-Yin Leung K, Elsner M, Zhang Y, Guo Y, Pan B, Sun H, An T, Ying G, Brooks BW, Hou D, Helbling DE, Sun J, Qiu H, Vogel TM, Zhang W, Gao Y, Simpson MJ, Luo Y, Chang SX, Su G, Wong BM, Fu TM, Zhu D, Jobst KJ, Ge C, Coulon F, Harindintwali JD, Zeng X, Wang H, Fu Y, Wei Z, Lohmann R, Chen C, Song Y, Sanchez-Cid C, Wang Y, El-Naggar A, Yao Y, Huang Y, Cheuk-Fung Law J, Gu C, Shen H, Gao Y, Qin C, Li H, Zhang T, Corcoll N, Liu M, Alessi DS, Li H, Brandt KK, Pico Y, Gu C, Guo J, Su J, Corvini P, Ye M, Rocha-Santos T, He H, Yang Y, Tong M, Zhang W, Suanon F, Brahushi F, Wang Z, Hashsham SA, Virta M, Yuan Q, Jiang G, Tremblay LA, Bu Q, Wu J, Peijnenburg W, Topp E, Cao X, Jiang X, Zheng M, Zhang T, Luo Y, Zhu L, Li X, Barceló D, Chen J, Xing B, Amelung W, Cai Z, Naidu R, Shen Q, Pawliszyn J, Zhu YG, Schaeffer A, Rillig MC, Wu F, Yu G, Tiedje JM. Emerging contaminants: A One Health perspective. Innovation (N Y) 2024; 5:100612. [PMID: 38756954 PMCID: PMC11096751 DOI: 10.1016/j.xinn.2024.100612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Accepted: 03/10/2024] [Indexed: 05/18/2024] Open
Abstract
Environmental pollution is escalating due to rapid global development that often prioritizes human needs over planetary health. Despite global efforts to mitigate legacy pollutants, the continuous introduction of new substances remains a major threat to both people and the planet. In response, global initiatives are focusing on risk assessment and regulation of emerging contaminants, as demonstrated by the ongoing efforts to establish the UN's Intergovernmental Science-Policy Panel on Chemicals, Waste, and Pollution Prevention. This review identifies the sources and impacts of emerging contaminants on planetary health, emphasizing the importance of adopting a One Health approach. Strategies for monitoring and addressing these pollutants are discussed, underscoring the need for robust and socially equitable environmental policies at both regional and international levels. Urgent actions are needed to transition toward sustainable pollution management practices to safeguard our planet for future generations.
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Affiliation(s)
- Fang Wang
- State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Leilei Xiang
- State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Kelvin Sze-Yin Leung
- Department of Chemistry, Hong Kong Baptist University, Hong Kong, China
- HKBU Institute of Research and Continuing Education, Shenzhen Virtual University Park, Shenzhen, China
| | - Martin Elsner
- Technical University of Munich, TUM School of Natural Sciences, Institute of Hydrochemistry, 85748 Garching, Germany
| | - Ying Zhang
- School of Resources & Environment, Northeast Agricultural University, Harbin 150030, China
| | - Yuming Guo
- Climate, Air Quality Research Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | - Bo Pan
- Faculty of Environmental Science & Engineering, Kunming University of Science & Technology, Kunming 650500, China
| | - Hongwen Sun
- Ministry of Education Key Laboratory of Pollution Processes and Environmental Criteria, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
| | - Taicheng An
- Guangdong Key Laboratory of Environmental Catalysis and Health Risk Control, School of Environmental Science and Engineering, Institute of Environmental Health and Pollution Control, Guangdong University of Technology, Guangzhou 510006, China
| | - Guangguo Ying
- Ministry of Education Key Laboratory of Environmental Theoretical Chemistry, South China Normal University, Guangzhou, Guangdong 510006, China
| | - Bryan W. Brooks
- Department of Environmental Science, Baylor University, Waco, TX, USA
- Center for Reservoir and Aquatic Systems Research (CRASR), Baylor University, Waco, TX, USA
| | - Deyi Hou
- School of Environment, Tsinghua University, Beijing 100084, China
| | - Damian E. Helbling
- School of Civil and Environmental Engineering, Cornell University, Ithaca, NY 14853, USA
| | - Jianqiang Sun
- Key Laboratory of Microbial Technology for Industrial Pollution Control of Zhejiang Province, Zhejiang University of Technology, Hangzhou 310014, China
| | - Hao Qiu
- School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Timothy M. Vogel
- Laboratoire d’Ecologie Microbienne, Universite Claude Bernard Lyon 1, UMR CNRS 5557, UMR INRAE 1418, VetAgro Sup, 69622 Villeurbanne, France
| | - Wei Zhang
- Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI 48824, USA
| | - Yanzheng Gao
- Institute of Organic Contaminant Control and Soil Remediation, College of Resources and Environmental Sciences, Nanjing Agricultural University, Weigang Road 1, Nanjing 210095, China
| | - Myrna J. Simpson
- Environmental NMR Centre and Department of Physical and Environmental Sciences, University of Toronto Scarborough, 1265 Military Trail, Toronto, ON M1C 1A4, Canada
| | - Yi Luo
- Ministry of Education Key Laboratory of Pollution Processes and Environmental Criteria, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210093, China
| | - Scott X. Chang
- Department of Renewable Resources, University of Alberta, 442 Earth Sciences Building, Edmonton, AB T6G 2E3, Canada
| | - Guanyong Su
- Jiangsu Key Laboratory of Chemical Pollution Control and Resources Reuse, School of Environmental and Biological Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
| | - Bryan M. Wong
- Materials Science & Engineering Program, Department of Chemistry, and Department of Physics & Astronomy, University of California-Riverside, Riverside, CA, USA
| | - Tzung-May Fu
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
| | - Dong Zhu
- Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
| | - Karl J. Jobst
- Department of Chemistry, Memorial University of Newfoundland, 45 Arctic Avenue, St. John’s, NL A1C 5S7, Canada
| | - Chengjun Ge
- Key Laboratory of Agro-Forestry Environmental Processes and Ecological Regulation of Hainan Province, School of Ecological and Environmental Sciences, Hainan University, Haikou 570228, China
| | - Frederic Coulon
- School of Water, Energy and Environment, Cranfield University, Cranfield MK43 0AL, UK
| | - Jean Damascene Harindintwali
- State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xiankui Zeng
- Key Laboratory of Surficial Geochemistry, Ministry of Education, School of Earth Sciences and Engineering, Nanjing University, Nanjing 210023, China
| | - Haijun Wang
- Institute for Ecological Research and Pollution Control of Plateau Lakes, School of Ecology and Environmental Science, Yunnan University, Kunming 650504, China
| | - Yuhao Fu
- State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zhong Wei
- Jiangsu Provincial Key Lab for Organic Solid Waste Utilization, Jiangsu Collaborative Innovation Center for Solid Organic Waste Resource Utilization, Nanjing Agricultural University, Nanjing 210095, China
| | - Rainer Lohmann
- Graduate School of Oceanography, University of Rhode Island, Narragansett, RI, USA
| | - Changer Chen
- Ministry of Education Key Laboratory of Environmental Theoretical Chemistry, South China Normal University, Guangzhou, Guangdong 510006, China
| | - Yang Song
- State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Concepcion Sanchez-Cid
- Environmental Microbial Genomics, UMR 5005 Laboratoire Ampère, CNRS, École Centrale de Lyon, Université de Lyon, Écully, France
| | - Yu Wang
- State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Ali El-Naggar
- Department of Renewable Resources, University of Alberta, 442 Earth Sciences Building, Edmonton, AB T6G 2E3, Canada
- Department of Soil Sciences, Faculty of Agriculture, Ain Shams University, Cairo 11241, Egypt
| | - Yiming Yao
- Ministry of Education Key Laboratory of Pollution Processes and Environmental Criteria, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
| | - Yanran Huang
- Applied Biology and Chemical Technology, Hong Kong Polytechnic University, Hong Kong, China
| | | | - Chenggang Gu
- State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Huizhong Shen
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
| | - Yanpeng Gao
- Guangdong Key Laboratory of Environmental Catalysis and Health Risk Control, School of Environmental Science and Engineering, Institute of Environmental Health and Pollution Control, Guangdong University of Technology, Guangzhou 510006, China
| | - Chao Qin
- Institute of Organic Contaminant Control and Soil Remediation, College of Resources and Environmental Sciences, Nanjing Agricultural University, Weigang Road 1, Nanjing 210095, China
| | - Hao Li
- Faculty of Environmental Science & Engineering, Kunming University of Science & Technology, Kunming 650500, China
| | - Tong Zhang
- Environmental Microbiome Engineering and Biotechnology Laboratory, Center for Environmental Engineering Research, Department of Civil Engineering, The University of Hong Kong, Hong Kong, China
| | - Natàlia Corcoll
- Department of Biological and Environmental Sciences, University of Gothenburg, Gothenburg, Sweden
| | - Min Liu
- Key Laboratory of Geographic Information Science of the Ministry of Education, School of Geographic Sciences, East China Normal University, Shanghai 200241, China
| | - Daniel S. Alessi
- Department of Earth and Atmospheric Sciences, University of Alberta, Edmonton, AB T6G 2E3, Canada
| | - Hui Li
- Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI 48824, USA
| | - Kristian K. Brandt
- Section for Microbial Ecology and Biotechnology, Department of Plant and Environmental Sciences, University of Copenhagen, Thorvaldsensvej 40, 1871 Frederiksberg C, Denmark
- Sino-Danish Center (SDC), Beijing, China
| | - Yolanda Pico
- Food and Environmental Safety Research Group of the University of Valencia (SAMA-UV), Desertification Research Centre - CIDE (CSIC-UV-GV), Road CV-315 km 10.7, 46113 Moncada, Valencia, Spain
| | - Cheng Gu
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210093, China
| | - Jianhua Guo
- Australian Centre for Water and Environmental Biotechnology, The University of Queensland, Brisbane, QLD 4072, Australia
| | - Jianqiang Su
- Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
| | - Philippe Corvini
- School of Life Sciences, University of Applied Sciences and Arts Northwestern Switzerland, 4132 Muttenz, Switzerland
| | - Mao Ye
- State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Teresa Rocha-Santos
- Centre for Environmental and Marine Studies (CESAM) & Department of Chemistry, University of Aveiro, 3810-193 Aveiro, Portugal
| | - Huan He
- Jiangsu Engineering Laboratory of Water and Soil Eco-remediation, School of Environment, Nanjing Normal University, Nanjing 210023, China
| | - Yi Yang
- Key Laboratory of Geographic Information Science of the Ministry of Education, School of Geographic Sciences, East China Normal University, Shanghai 200241, China
| | - Meiping Tong
- College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
| | - Weina Zhang
- Guangdong Key Laboratory of Environmental Catalysis and Health Risk Control, School of Environmental Science and Engineering, Institute of Environmental Health and Pollution Control, Guangdong University of Technology, Guangzhou 510006, China
| | - Fidèle Suanon
- Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
- Laboratory of Physical Chemistry, Materials and Molecular Modeling (LCP3M), University of Abomey-Calavi, Republic of Benin, Cotonou 01 BP 526, Benin
| | - Ferdi Brahushi
- Department of Environment and Natural Resources, Agricultural University of Tirana, 1029 Tirana, Albania
| | - Zhenyu Wang
- Institute of Environmental Processes and Pollution Control, and School of Environment & Ecology, Jiangnan University, Wuxi 214122, China
| | - Syed A. Hashsham
- Center for Microbial Ecology, Department of Plant, Soil, and Microbial Sciences, Michigan State University, East Lansing, MI 48824, USA
- Department of Civil and Environmental Engineering, Michigan State University, East Lansing, MI 48824, USA
| | - Marko Virta
- Department of Microbiology, University of Helsinki, 00010 Helsinki, Finland
| | - Qingbin Yuan
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210093, China
| | - Gaofei Jiang
- Jiangsu Provincial Key Lab for Organic Solid Waste Utilization, Jiangsu Collaborative Innovation Center for Solid Organic Waste Resource Utilization, Nanjing Agricultural University, Nanjing 210095, China
| | - Louis A. Tremblay
- School of Biological Sciences, University of Auckland, Auckland, Aotearoa 1142, New Zealand
| | - Qingwei Bu
- School of Chemical & Environmental Engineering, China University of Mining & Technology - Beijing, Beijing 100083, China
| | - Jichun Wu
- Key Laboratory of Surficial Geochemistry, Ministry of Education, School of Earth Sciences and Engineering, Nanjing University, Nanjing 210023, China
| | - Willie Peijnenburg
- National Institute of Public Health and the Environment, Center for the Safety of Substances and Products, 3720 BA Bilthoven, The Netherlands
- Leiden University, Center for Environmental Studies, Leiden, the Netherlands
| | - Edward Topp
- Agroecology Mixed Research Unit, INRAE, 17 rue Sully, 21065 Dijon Cedex, France
| | - Xinde Cao
- School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Xin Jiang
- State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Minghui Zheng
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
| | - Taolin Zhang
- State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
| | - Yongming Luo
- State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Lizhong Zhu
- Department of Environmental Science, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Xiangdong Li
- Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China
| | - Damià Barceló
- Chemistry and Physics Department, University of Almeria, 04120 Almeria, Spain
| | - Jianmin Chen
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China
| | - Baoshan Xing
- Stockbridge School of Agriculture, University of Massachusetts, Amherst, MA 01003, USA
| | - Wulf Amelung
- Institute of Crop Science and Resource Conservation (INRES), Soil Science and Soil Ecology, University of Bonn, 53115 Bonn, Germany
- Agrosphere Institute (IBG-3), Forschungszentrum Jülich GmbH, 52428 Jülich, Germany
| | - Zongwei Cai
- State Key Laboratory of Environmental and Biological Analysis, Department of Chemistry, Hong Kong Baptist University, Hong Kong, China
| | - Ravi Naidu
- Global Centre for Environmental Remediation (GCER), The University of Newcastle (UON), Newcastle, NSW 2308, Australia
- Cooperative Research Centre for Contamination Assessment and Remediation of the Environment (CRC CARE), The University of Newcastle (UON), Newcastle, NSW 2308, Australia
| | - Qirong Shen
- Jiangsu Provincial Key Lab for Organic Solid Waste Utilization, Jiangsu Collaborative Innovation Center for Solid Organic Waste Resource Utilization, Nanjing Agricultural University, Nanjing 210095, China
| | - Janusz Pawliszyn
- Department of Chemistry, University of Waterloo, Waterloo, ON N2L 3G1, Canada
| | - Yong-guan Zhu
- University of Chinese Academy of Sciences, Beijing 100049, China
- Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
- State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
| | - Andreas Schaeffer
- Institute for Environmental Research, RWTH Aachen University, 52074 Aachen, Germany
| | - Matthias C. Rillig
- Institute of Biology, Freie Universität Berlin, Berlin, Germany
- Berlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB), Berlin, Germany
| | - Fengchang Wu
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Gang Yu
- Advanced Interdisciplinary Institute of Environment and Ecology, Beijing Normal University, Zhuhai, China
| | - James M. Tiedje
- Center for Microbial Ecology, Department of Plant, Soil, and Microbial Sciences, Michigan State University, East Lansing, MI 48824, USA
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7
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Luo W, Chou L, Cui Q, Wei S, Zhang X, Guo J. High-efficiency effect-directed analysis (EDA) advancing toxicant identification in aquatic environments: Latest progress and application status. ENVIRONMENT INTERNATIONAL 2024; 190:108855. [PMID: 38945088 DOI: 10.1016/j.envint.2024.108855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Revised: 05/21/2024] [Accepted: 06/26/2024] [Indexed: 07/02/2024]
Abstract
Facing the great threats to ecosystems and human health posed by the continuous release of chemicals into aquatic environments, effect-directed analysis (EDA) has emerged as a powerful tool for identifying causative toxicants. However, traditional EDA shows problems of low-coverage, labor-intensive and low-efficiency. Currently, a number of high-efficiency techniques have been integrated into EDA to improve toxicant identification. In this review, the latest progress and current limitations of high-efficiency EDA, comprising high-coverage effect evaluation, high-resolution fractionation, high-coverage chemical analysis, high-automation causative peak extraction and high-efficiency structure elucidation, are summarized. Specifically, high-resolution fractionation, high-automation data processing algorithms and in silico structure elucidation techniques have been well developed to enhance EDA. While high-coverage effect evaluation and chemical analysis should be further emphasized, especially omics tools and data-independent mass acquisition. For the application status in aquatic environments, high-efficiency EDA is widely applied in surface water and wastewater. Estrogenic, androgenic and aryl hydrocarbon receptor-mediated activities are the most concerning, with causative toxicants showing the typical structural features of steroids and benzenoids. A better understanding of the latest progress and application status of EDA would be beneficial to further advance in the field and greatly support aquatic environment monitoring.
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Affiliation(s)
- Wenrui Luo
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China
| | - Liben Chou
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China
| | - Qinglan Cui
- Bluestar Lehigh Engineering Institute Co., Ltd., Lianyungang 222004, China
| | - Si Wei
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China
| | - Xiaowei Zhang
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China
| | - Jing Guo
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China; Jiangsu Province Ecology and Environment Protection Key Laboratory of Chemical Safety and Health Risk, Nanjing 210023, China.
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8
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Meyer C, Stravs MA, Hollender J. How Wastewater Reflects Human Metabolism─Suspect Screening of Pharmaceutical Metabolites in Wastewater Influent. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:9828-9839. [PMID: 38785362 PMCID: PMC11154963 DOI: 10.1021/acs.est.4c00968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 04/13/2024] [Accepted: 04/17/2024] [Indexed: 05/25/2024]
Abstract
Pharmaceuticals and their human metabolites are contaminants of emerging concern in the aquatic environment. Most monitoring studies focus on a limited set of parent compounds and even fewer metabolites. However, more than 50% of the most consumed pharmaceuticals are excreted in higher amounts as metabolites than as parents, as confirmed by a literature analysis within this study. Hence, we applied a wide-scope suspect screening approach to identify human pharmaceutical metabolites in wastewater influent from three Swiss treatment plants. Based on consumption amounts and human metabolism data, a suspect list comprising 268 parent compounds and over 1500 metabolites was compiled. Online solid phase extraction combined with liquid chromatography coupled to high-resolution tandem mass spectrometry was used to analyze the samples. Data processing, annotation, and structure elucidation were achieved with various tools, including molecular networking as well as SIRIUS/CSI:FingerID and MetFrag for MS2 spectra rationalization. We confirmed 37 metabolites with reference standards and 16 by human liver S9 incubation experiments. More than 25 metabolites were detected for the first time in influent wastewater. Semiquantification with MS2Quant showed that metabolite to parent concentration ratios were generally lower compared to literature expectations, probably due to further metabolite transformation in the sewer system or limitations in the metabolite detection. Nonetheless, metabolites pose a large fraction to the total pharmaceutical contribution in wastewater, highlighting the need for metabolite inclusion in chemical risk assessment.
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Affiliation(s)
- Corina Meyer
- Eawag:
Swiss Federal Institute of Aquatic Science and Technology, Ueberlandstrasse 133, 8600 Duebendorf, Switzerland
- Institute
of Biogeochemistry and Pollutant Dynamics, ETH Zurich, Universitaetstrasse
16, 8092 Zurich, Switzerland
| | - Michael A. Stravs
- Eawag:
Swiss Federal Institute of Aquatic Science and Technology, Ueberlandstrasse 133, 8600 Duebendorf, Switzerland
| | - Juliane Hollender
- Eawag:
Swiss Federal Institute of Aquatic Science and Technology, Ueberlandstrasse 133, 8600 Duebendorf, Switzerland
- Institute
of Biogeochemistry and Pollutant Dynamics, ETH Zurich, Universitaetstrasse
16, 8092 Zurich, Switzerland
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9
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Song D, Tang T, Wang R, Liu H, Xie D, Zhao B, Dang Z, Lu G. Enhancing compound confidence in suspect and non-target screening through machine learning-based retention time prediction. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2024; 347:123763. [PMID: 38492749 DOI: 10.1016/j.envpol.2024.123763] [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: 01/26/2024] [Revised: 02/26/2024] [Accepted: 03/09/2024] [Indexed: 03/18/2024]
Abstract
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.
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Affiliation(s)
- Dehao Song
- School of Environment and Energy, South China University of Technology, Guangzhou, 510006, China
| | - Ting Tang
- School of Environment and Energy, South China University of Technology, Guangzhou, 510006, China; The Key Lab of Pollution Control and Ecosystem Restoration in Industry Clusters, Ministry of Education, South China University of Technology, Guangzhou, 510006, China.
| | - Rui Wang
- South China Institute of Environmental Sciences, Ministry of Ecology and Environment, Guangzhou, 510655, China; Guangxi Key Laboratory of Emerging Contaminants Monitoring, Early Warning and Environmental Health Risk Assessment, Nanning, 530000, China
| | - He Liu
- South China Institute of Environmental Sciences, Ministry of Ecology and Environment, Guangzhou, 510655, China; Guangxi Key Laboratory of Emerging Contaminants Monitoring, Early Warning and Environmental Health Risk Assessment, Nanning, 530000, China
| | - Danping Xie
- South China Institute of Environmental Sciences, Ministry of Ecology and Environment, Guangzhou, 510655, China; Guangxi Key Laboratory of Emerging Contaminants Monitoring, Early Warning and Environmental Health Risk Assessment, Nanning, 530000, China
| | - Bo Zhao
- South China Institute of Environmental Sciences, Ministry of Ecology and Environment, Guangzhou, 510655, China; Guangxi Key Laboratory of Emerging Contaminants Monitoring, Early Warning and Environmental Health Risk Assessment, Nanning, 530000, China
| | - Zhi Dang
- School of Environment and Energy, South China University of Technology, Guangzhou, 510006, China; Guangdong Provincial Key Laboratory of Solid Wastes Pollution Control and Recycling, South China University of Technology, Guangzhou, 510006, China; The Key Lab of Pollution Control and Ecosystem Restoration in Industry Clusters, Ministry of Education, South China University of Technology, Guangzhou, 510006, China
| | - Guining Lu
- School of Environment and Energy, South China University of Technology, Guangzhou, 510006, China; The Key Lab of Pollution Control and Ecosystem Restoration in Industry Clusters, Ministry of Education, South China University of Technology, Guangzhou, 510006, China
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10
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Altamirano JC, Yin S, Belova L, Poma G, Covaci A. Exploring the hidden chemical landscape: Non-target and suspect screening analysis for investigating solid waste-associated environments. ENVIRONMENTAL RESEARCH 2024; 245:118006. [PMID: 38154568 DOI: 10.1016/j.envres.2023.118006] [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: 09/01/2023] [Revised: 12/11/2023] [Accepted: 12/20/2023] [Indexed: 12/30/2023]
Abstract
Solid waste is an inevitable consequence of urbanization. It can be safely managed in municipal landfills and processing plants for volume reduction or material reuse, including organic solid waste. However, solid waste can also be discarded in (un-)authorized dumping sites or inadvertently released into the environment. Legacy and emerging contaminants have the potential to leach from solid waste, making it a significant pathway to the environment. Non-target screening (NTS) and suspect screening analysis (SSA) have become helpful tools in environmental science for the simultaneous analysis of a wide range of chemical compounds. However, the application of these analytical approaches to environmental samples related to Raw or Processed Solid Waste (RPSW) has been largely neglected so far. This perspective review examines the potential and policy relevance of NTS and SSA applied to waste-related samples (liquid, gaseous and solid). It addresses the hurdles associated with the chemical safety of solid waste accumulation, processing, and reuse, and the need for landfill traceability, as well as effectiveness of leachate treatments. We reviewed the current applications of NTS and SSA to environmental samples of RPSW, as well as the potential adaptation of NTS and SSA techniques from related fields, such as oilfield and metabolomics, to the solid waste domain. Despite the ongoing technical challenges, this review highlights the significant potential for the implementation of NTS and SSA approaches in solid waste management and related scientific fields and provides support and guidance to the regulatory authorities.
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Affiliation(s)
- Jorgelina Cecilia Altamirano
- Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales (IANIGLA), CONICET-UNCuyo-Government of Mendoza, P.O. Box. 331, 5500, Mendoza, Argentina; Universidad Nacional de Cuyo, Facultad de Ciencias Exactas y Naturales, Padre Jorge Contreras 1300, 5500, Mendoza, Argentina; Toxicological Centre, University of Antwerp, Universiteitsplein 1, 2610, Wilrijk, Antwerp, Belgium.
| | - Shanshan Yin
- Toxicological Centre, University of Antwerp, Universiteitsplein 1, 2610, Wilrijk, Antwerp, Belgium; Key Laboratory of Pollution Exposure and Health Intervention of Zhejiang Province, Interdisciplinary Research Academy (IRA), Zhejiang Shuren University, Hangzhou, 310015, China
| | - Lidia Belova
- Toxicological Centre, University of Antwerp, Universiteitsplein 1, 2610, Wilrijk, Antwerp, Belgium
| | - Giulia Poma
- Toxicological Centre, University of Antwerp, Universiteitsplein 1, 2610, Wilrijk, Antwerp, Belgium
| | - Adrian Covaci
- Toxicological Centre, University of Antwerp, Universiteitsplein 1, 2610, Wilrijk, Antwerp, Belgium.
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11
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Zhang Y, Liu F, Li XQ, Gao Y, Li KC, Zhang QH. Generic and accurate prediction of retention times in liquid chromatography by post-projection calibration. Commun Chem 2024; 7:54. [PMID: 38459241 PMCID: PMC10923921 DOI: 10.1038/s42004-024-01135-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Accepted: 02/21/2024] [Indexed: 03/10/2024] Open
Abstract
Retention time predictions from molecule structures in liquid chromatography (LC) are increasingly used in MS-based targeted and untargeted analyses, providing supplementary evidence for molecule annotation and reducing experimental measurements. Nevertheless, different LC setups (e.g., differences in gradient, column, and/or mobile phase) give rise to many prediction models that can only accurately predict retention times for a specific chromatographic method (CM). Here, a generic and accurate method is present to predict retention times across different CMs, by introducing the concept of post-projection calibration. This concept builds on the direct projections of retention times between different CMs and uses 35 external calibrants to eliminate the impact of LC setups on projection accuracy. Results showed that post-projection calibration consistently achieved a median projection error below 3.2% of the elution time. The ranking results of putative candidates reached similar levels among different CMs. This work opens up broad possibilities for coordinating retention times between different laboratories and developing extensive retention databases.
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Affiliation(s)
- Yan Zhang
- Key Laboratory of Groundwater Conservation of MWR, China University of Geosciences, Beijing, 100083, People's Republic of China
- Division of Chemical Metrology and Analytical Science, National Institute of Metrology, Beijing, 100029, People's Republic of China
- Key Laboratory of Chemical Metrology and Applications on Nutrition and Health for State Market Regulation, Beijing, 100029, China
| | - Fei Liu
- Key Laboratory of Groundwater Conservation of MWR, China University of Geosciences, Beijing, 100083, People's Republic of China.
| | - Xiu Qin Li
- Division of Chemical Metrology and Analytical Science, National Institute of Metrology, Beijing, 100029, People's Republic of China
- Key Laboratory of Chemical Metrology and Applications on Nutrition and Health for State Market Regulation, Beijing, 100029, China
| | - Yan Gao
- Division of Chemical Metrology and Analytical Science, National Institute of Metrology, Beijing, 100029, People's Republic of China
- Key Laboratory of Chemical Metrology and Applications on Nutrition and Health for State Market Regulation, Beijing, 100029, China
| | - Kang Cong Li
- Division of Chemical Metrology and Analytical Science, National Institute of Metrology, Beijing, 100029, People's Republic of China
- Key Laboratory of Chemical Metrology and Applications on Nutrition and Health for State Market Regulation, Beijing, 100029, China
| | - Qing He Zhang
- Division of Chemical Metrology and Analytical Science, National Institute of Metrology, Beijing, 100029, People's Republic of China.
- Key Laboratory of Chemical Metrology and Applications on Nutrition and Health for State Market Regulation, Beijing, 100029, China.
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12
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Szabo D, Falconer TM, Fisher CM, Heise T, Phillips AL, Vas G, Williams AJ, Kruve A. Online and Offline Prioritization of Chemicals of Interest in Suspect Screening and Non-targeted Screening with High-Resolution Mass Spectrometry. Anal Chem 2024; 96:3707-3716. [PMID: 38380899 PMCID: PMC10918621 DOI: 10.1021/acs.analchem.3c05705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Revised: 01/30/2024] [Accepted: 02/07/2024] [Indexed: 02/22/2024]
Abstract
Recent advances in high-resolution mass spectrometry (HRMS) have enabled the detection of thousands of chemicals from a single sample, while computational methods have improved the identification and quantification of these chemicals in the absence of reference standards typically required in targeted analysis. However, to determine the presence of chemicals of interest that may pose an overall impact on ecological and human health, prioritization strategies must be used to effectively and efficiently highlight chemicals for further investigation. Prioritization can be based on a chemical's physicochemical properties, structure, exposure, and toxicity, in addition to its regulatory status. This Perspective aims to provide a framework for the strategies used for chemical prioritization that can be implemented to facilitate high-quality research and communication of results. These strategies are categorized as either "online" or "offline" prioritization techniques. Online prioritization techniques trigger the isolation and fragmentation of ions from the low-energy mass spectra in real time, with user-defined parameters. Offline prioritization techniques, in contrast, highlight chemicals of interest after the data has been acquired; detected features can be filtered and ranked based on the relative abundance or the predicted structure, toxicity, and concentration imputed from the tandem mass spectrum (MS2). Here we provide an overview of these prioritization techniques and how they have been successfully implemented and reported in the literature to find chemicals of elevated risk to human and ecological environments. A complete list of software and tools is available from https://nontargetedanalysis.org/.
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Affiliation(s)
- Drew Szabo
- Department
of Materials and Environmental Chemistry, Stockholm University, Stockholm 106 91, Sweden
| | - Travis M. Falconer
- Forensic
Chemistry Center, Office of Regulatory Science, Office of Regulatory
Affairs, US Food and Drug Administration, Cincinnati, Ohio 45237, United States
| | - Christine M. Fisher
- Center
for Food Safety and Applied Nutrition, US Food and Drug Administration, College Park, Maryland 20740, United States
| | - Ted Heise
- MED
Institute Inc, West Lafayette, Indiana 47906, United States
| | - Allison L. Phillips
- Center
for Public Health and Environmental Assessment, US Environmental Protection Agency, Corvallis, Oregon 97333, United States
| | - Gyorgy Vas
- VasAnalytical, Flemington, New Jersey 08822, United States
- Intertek
Pharmaceutical Services, Whitehouse, New Jersey 08888, United States
| | - Antony J. Williams
- Center
for Computational Toxicology and Exposure, Office of Research and
Development, US Environmental Protection
Agency, Durham, North Carolina 27711, United States
| | - Anneli Kruve
- Department
of Materials and Environmental Chemistry, Stockholm University, Stockholm 106 91, Sweden
- Department
of Environmental Science, Stockholm University, Stockholm 106 91, Sweden
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13
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Musatadi M, Baciero-Hernández I, Prieto A, Olivares M, Etxebarria N, Zuloaga O. Development and evaluation of a comprehensive workflow for suspect screening of exposome-related xenobiotics and phase II metabolites in diverse human biofluids. CHEMOSPHERE 2024; 351:141221. [PMID: 38224745 DOI: 10.1016/j.chemosphere.2024.141221] [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: 09/30/2023] [Revised: 12/07/2023] [Accepted: 01/12/2024] [Indexed: 01/17/2024]
Abstract
Suspect and non-target screening (SNTS) methods are being promoted in order to decode the human exposome since a wide chemical space can be analysed in a diversity of human biofluids. However, SNTS approaches in the exposomics field are infra-studied in comparison to environmental or food monitoring studies. In this work, a comprehensive suspect screening workflow was developed to annotate exposome-related xenobiotics and phase II metabolites in diverse human biofluids. Precisely, human urine, breast milk, saliva and ovarian follicular fluid were employed as samples and analysed by means of ultra-high performance liquid chromatography coupled with high resolution tandem mass spectrometry (UHPLC-HRMS/MS). To automate the workflow, the "peak rating" parameter implemented in Compound Discoverer 3.3.2 was optimized to avoid time-consuming manual revision of chromatographic peaks. In addition, the presence of endogenous molecules that might interfere with the annotation of xenobiotics was carefully studied as the employment of inclusion and exclusion suspect lists. To evaluate the workflow, limits of identification (LOIs) and type I and II errors (i.e., false positives and negatives, respectively) were calculated in both standard solutions and spiked biofluids using 161 xenobiotics and 22 metabolites. For 80.3 % of the suspects, LOIs below 15 ng/mL were achieved. In terms of type I errors, only two cases were identified in standards and spiked samples. Regarding type II errors, the 7.7 % errors accounted in standards increased to 17.4 % in real samples. Lastly, the use of an inclusion list for endogens was favoured since it avoided 18.7 % of potential type I errors, while the exclusion list caused 7.2 % of type II errors despite making the annotation workflow less time-consuming.
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Affiliation(s)
- Mikel Musatadi
- Department of Analytical Chemistry, University of the Basque Country (UPV/EHU), 48940, Leioa, Basque Country, Spain; Research Centre for Experimental Marine Biology and Biotechnology, University of the Basque Country (UPV/EHU), 48620, Plentzia, Basque Country, Spain.
| | - Inés Baciero-Hernández
- Department of Analytical Chemistry, University of the Basque Country (UPV/EHU), 48940, Leioa, Basque Country, Spain; Research Centre for Experimental Marine Biology and Biotechnology, University of the Basque Country (UPV/EHU), 48620, Plentzia, Basque Country, Spain
| | - Ailette Prieto
- Department of Analytical Chemistry, University of the Basque Country (UPV/EHU), 48940, Leioa, Basque Country, Spain; Research Centre for Experimental Marine Biology and Biotechnology, University of the Basque Country (UPV/EHU), 48620, Plentzia, Basque Country, Spain
| | - Maitane Olivares
- Department of Analytical Chemistry, University of the Basque Country (UPV/EHU), 48940, Leioa, Basque Country, Spain; Research Centre for Experimental Marine Biology and Biotechnology, University of the Basque Country (UPV/EHU), 48620, Plentzia, Basque Country, Spain
| | - Nestor Etxebarria
- Department of Analytical Chemistry, University of the Basque Country (UPV/EHU), 48940, Leioa, Basque Country, Spain; Research Centre for Experimental Marine Biology and Biotechnology, University of the Basque Country (UPV/EHU), 48620, Plentzia, Basque Country, Spain
| | - Olatz Zuloaga
- Department of Analytical Chemistry, University of the Basque Country (UPV/EHU), 48940, Leioa, Basque Country, Spain; Research Centre for Experimental Marine Biology and Biotechnology, University of the Basque Country (UPV/EHU), 48620, Plentzia, Basque Country, Spain
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14
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Hattaway M, Alaimo C, Wong L, Teerlink J, Young TM. Spatial and temporal variability of micropollutants within a wastewater catchment system. ENVIRONMENTAL SCIENCE. PROCESSES & IMPACTS 2024; 26:357-367. [PMID: 38170844 PMCID: PMC10922816 DOI: 10.1039/d3em00361b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
Treated wastewater effluent is a major contributor to concentrations of many anthropogenic chemicals in the environment. Examining patterns of these compounds measured from different catchment areas comprising the influent to a wastewater treatment plant, across many months, may reveal patterns in compound sources and seasonality helpful to management efforts. This study considers a wastewater catchment system that was sampled at six sub-catchment sites plus the treatment plant influent and effluent at seven time points spanning nine months. Wastewater samples were analyzed with LC-QTOF-MS using positive electrospray ionization and GC-QTOF-MS using negative chemical ionization and electron ionization. MS data were screened against spectral libraries to identify micropollutants. As expected, multiple classes of chemicals were represented, including pharmaceuticals, plasticizers, personal care products, and flame retardants. Patterns in the compounds seen at different sampling sites and dates reflect the varying uses and down-the-drain routes that influence micropollutant loading in sewer systems. Patterns in examined compounds revealed little spatial variation, and greater temporal variation. For example, the greatest loads of DEET were found to occur in the summer months. Additionally, groups of compounds exhibited strong correlation with each other, which could be indicative of similar down-the-drain routes (such as a group intercorrelated chemicals that are components of cleaning products) or the influence of similar physicochemical processes within the sewer system. This study contributes to the understanding of dynamics of micropollutants in sewer systems.
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Affiliation(s)
- Madison Hattaway
- Department of Civil and Environmental Engineering, University of California, Davis, Davis, CA 95616, USA.
| | - Chris Alaimo
- Department of Civil and Environmental Engineering, University of California, Davis, Davis, CA 95616, USA.
| | - Luann Wong
- Department of Civil and Environmental Engineering, University of California, Davis, Davis, CA 95616, USA.
| | - Jennifer Teerlink
- California Department of Pesticide Regulation, Sacramento, CA 95618, USA
| | - Thomas M Young
- Department of Civil and Environmental Engineering, University of California, Davis, Davis, CA 95616, USA.
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15
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Gelao V, Fornasaro S, Briguglio SC, Mattiussi M, De Martin S, Astel AM, Barbieri P, Licen S. Self-Organizing Maps: An AI Tool for Identifying Unexpected Source Signatures in Non-Target Screening Analysis of Urban Wastewater by HPLC-HRMS. TOXICS 2024; 12:113. [PMID: 38393208 PMCID: PMC10891566 DOI: 10.3390/toxics12020113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/17/2023] [Revised: 01/23/2024] [Accepted: 01/24/2024] [Indexed: 02/25/2024]
Abstract
(1) Background: Monitoring effluent in water treatment plants has a key role in identifying potential pollutants that might be released into the environment. A non-target analysis approach can be used for identifying unknown substances and source-specific multipollutant signatures. (2) Methods: Urban and industrial wastewater effluent were analyzed by HPLC-HRMS for non-target analysis. The anomalous infiltration of industrial wastewater into urban wastewater was investigated by analyzing the mass spectra data of "unknown common" compounds using principal component analysis (PCA) and the Self-Organizing Map (SOM) AI tool. The outcomes of the models were compared. (3) Results: The outlier detection was more straightforward in the SOM model than in the PCA one. The differences among the samples could not be completely perceived in the PCA model. Moreover, since PCA involves the calculation of new variables based on the original experimental ones, it is not possible to reconstruct a chromatogram that displays the recurring patterns in the urban WTP samples. This can be achieved using the SOM outcomes. (4) Conclusions: When comparing a large number of samples, the SOM AI tool is highly efficient in terms of calculation, visualization, and identifying outliers. Interpreting PCA visualization and outlier detection becomes challenging when dealing with a large sample size.
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Affiliation(s)
- Vito Gelao
- Regional Environmental Protection Agency—ARPA-FVG, Via Cairoli 14, 33057 Palmanova, Italy; (V.G.); (S.C.B.); (M.M.); (S.D.M.)
| | - Stefano Fornasaro
- Department of Chemical and Pharmaceutical Sciences, University of Trieste, Via Giorgieri 1, 34127 Trieste, Italy; (S.F.); (P.B.)
| | - Sara C. Briguglio
- Regional Environmental Protection Agency—ARPA-FVG, Via Cairoli 14, 33057 Palmanova, Italy; (V.G.); (S.C.B.); (M.M.); (S.D.M.)
| | - Michele Mattiussi
- Regional Environmental Protection Agency—ARPA-FVG, Via Cairoli 14, 33057 Palmanova, Italy; (V.G.); (S.C.B.); (M.M.); (S.D.M.)
| | - Stefano De Martin
- Regional Environmental Protection Agency—ARPA-FVG, Via Cairoli 14, 33057 Palmanova, Italy; (V.G.); (S.C.B.); (M.M.); (S.D.M.)
| | - Aleksander M. Astel
- Department of Environmental Chemistry and Toxicology, Pomeranian University in Słupsk, 22a Arciszewskiego Str., 76-200 Słupsk, Poland
| | - Pierluigi Barbieri
- Department of Chemical and Pharmaceutical Sciences, University of Trieste, Via Giorgieri 1, 34127 Trieste, Italy; (S.F.); (P.B.)
| | - Sabina Licen
- Department of Chemical and Pharmaceutical Sciences, University of Trieste, Via Giorgieri 1, 34127 Trieste, Italy; (S.F.); (P.B.)
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16
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Ma Y, Cao Y, Song X, Min C, Man Z, Li Z. BART: A transferable liquid chromatography retention time library for bile acids. J Chromatogr A 2024; 1715:464602. [PMID: 38159405 DOI: 10.1016/j.chroma.2023.464602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 12/21/2023] [Accepted: 12/22/2023] [Indexed: 01/03/2024]
Abstract
Identification of unknown bile acids, especially the distinguishment between isomers, requires retention times of a large number of reference standards, which are often not commercially available. Meanwhile, published retention information cannot be directly transferred across labs due to the differences between liquid chromatography (LC) systems, such as different extra column volume and dwell volume. To improve this situation, a transferrable retention time library for bile acids named BART was developed. BART was composed of isocratic retention models of 272 bile acids and a software tool to predict their gradient retention times on various LC systems. The isocratic retention times of bile acids were acquired on a Waters BEH C18 column with mobile phases of acidic ammonium acetate buffer and acetonitrile, and fit to the quadratic solvent strength model (QSSM). Segmented linear gradient retention times were calculated with holdup time (t0), dwell time (tD) and actual gradient profile corrected using 21 bile acid calibration standards. In addition to the reference system where the isocratic retention times were acquired, this approach has been validated on four other LC-MS systems in four labs with two gradient methods. Average root mean square errors (RMSE) between predicted and experimental retention times were 0.052 and 0.054 min for the two gradients tested, which were 9-fold more accurate than referring to a static retention time library. The library is freely available at https://bafinder.github.io/.
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Affiliation(s)
- Yan Ma
- National Institute of Biological Sciences, Beijing 102206, China; Tsinghua Institute of Multidisciplinary Biomedical Research, Tsinghua University, Beijing 100084, China.
| | - Yang Cao
- National Institute of Biological Sciences, Beijing 102206, China
| | - Xiaocui Song
- National Institute of Biological Sciences, Beijing 102206, China
| | - Chunyan Min
- Suzhou Institute for Drug Control, Suzhou 215104, China
| | - Zhuo Man
- SCIEX China, Beijing 100015, China
| | - Zhen Li
- State Key Laboratory of Plant Environmental Resilience, College of Biological Sciences, China Agricultural University, Beijing 100193, China
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17
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Takeda K, Saito T, Sasaki S, Eguchi A, Sugiyama M, Eto S, Suzuki K, Kamata R. Toxicity Assessment of Mixed Exposure of Nine Perfluoroalkyl Substances at Concentrations Relevant to Daily Intake. TOXICS 2024; 12:52. [PMID: 38251008 PMCID: PMC10819949 DOI: 10.3390/toxics12010052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 12/27/2023] [Accepted: 01/08/2024] [Indexed: 01/23/2024]
Abstract
Per- and poly-fluoroalkyl substances (PFAS) exhibit high persistence in the environment and accumulate within the human body, warranting a thorough assessment of their toxicity. In this study, we exposed mice (male C57BL/6J mice aged 8 weeks) to a composite of nine PFAS, encompassing both long-chain PFAS (e.g., perfluorooctanoic acid and perfluorooctanesulfonic acid) and short-chain PFAS (e.g., perfluorobutanoic acid and perfluorobutanesulfonic acid). The exposure concentrations of PFAS were equivalent to the estimated daily human intake in the composition reported (1 µg/L (sum of the nine compounds), representing the maximum reported exposure concentration). Histological examination revealed hepatocyte vacuolization and irregular hepatocyte cord arrangement, indicating that exposure to low levels of the PFAS mixture causes morphological changes in liver tissues. Transcriptome analysis revealed that PFAS exposure mainly altered a group of genes related to metabolism and chemical carcinogenesis. Machine learning analysis of the liver metabolome showed a typical concentration-independent alteration upon PFAS exposure, with the annotation of substances such as glutathione and 5-aminovaleric acid. This study demonstrates that daily exposure to PFAS leads to morphological changes in liver tissues and alters the expression of metabolism- and cancer-related genes as well as phospholipid metabolism.
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Affiliation(s)
- Kazuki Takeda
- Laboratory of Toxicology, School of Veterinary Medicine, Kitasato University, E23-35-1, Towada, Aomori 034-0021, Japan
- Department of Computer Science, Tokyo Institute of Technology, 4259-J3-1818, Nagatsuta-cho, Midori-ku, Yokohama-shi 226-0026, Kanagawa, Japan
- Laboratory of Toxicology, Department of Environmental Veterinary Sciences, Faculty of Veterinary Medicine, Hokkaido University, N18 W9, Kita-ku, Sapporo 060-0818, Japan
| | - Taki Saito
- Laboratory of Toxicology, School of Veterinary Medicine, Kitasato University, E23-35-1, Towada, Aomori 034-0021, Japan
| | - Sakura Sasaki
- Laboratory of Toxicology, School of Veterinary Medicine, Kitasato University, E23-35-1, Towada, Aomori 034-0021, Japan
| | - Akifumi Eguchi
- Department of Sustainable Health Science, Center for Preventive Medical Sciences, Chiba University, Chiba 263-8522, Japan
| | - Makoto Sugiyama
- Laboratory of Anatomy, School of Veterinary Medicine, Kitasato University, Aomori 034-0021, Japan
| | - Saeka Eto
- Laboratory of Toxicology, School of Veterinary Medicine, Kitasato University, E23-35-1, Towada, Aomori 034-0021, Japan
| | - Kio Suzuki
- Laboratory of Toxicology, School of Veterinary Medicine, Kitasato University, E23-35-1, Towada, Aomori 034-0021, Japan
| | - Ryo Kamata
- Laboratory of Toxicology, School of Veterinary Medicine, Kitasato University, E23-35-1, Towada, Aomori 034-0021, Japan
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18
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Kwon Y, Kwon H, Han J, Kang M, Kim JY, Shin D, Choi YS, Kang S. Retention Time Prediction through Learning from a Small Training Data Set with a Pretrained Graph Neural Network. Anal Chem 2023; 95:17273-17283. [PMID: 37955847 DOI: 10.1021/acs.analchem.3c03177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2023]
Abstract
Graph neural networks (GNNs) have shown remarkable performance in predicting the retention time (RT) for small molecules. However, the training data set for a particular target chromatographic system tends to exhibit scarcity, which poses a challenge because the experimental process for measuring RT is costly. To address this challenge, transfer learning has been used to leverage an abundant training data set from a related source task. In this study, we present an improved transfer learning method to better predict the RT of molecules for a target chromatographic system by learning from a small training data set with a pretrained GNN. We use a graph isomorphism network as the architecture of the GNN. The GNN is pretrained on the METLIN-SMRT data set and is then fine-tuned on the target training data set for a fixed number of training iterations using the limited-memory Broyden-Fletcher-Goldfarb-Shanno optimizer with a learning rate decay. We demonstrate that the proposed method achieves superior predictive performance on various chromatographic systems compared with that of the existing transfer learning methods, especially when only a small training data set is available for use. A potential avenue for future research is to leverage multiple small training data sets from different chromatographic systems to further enhance the generalization performance.
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Affiliation(s)
- Youngchun Kwon
- Samsung Advanced Institute of Technology, Samsung Electronics Co. Ltd., 130 Samsung-ro, Yeongtong-gu, Suwon 16678, Republic of Korea
| | - Hyukju Kwon
- Samsung Advanced Institute of Technology, Samsung Electronics Co. Ltd., 130 Samsung-ro, Yeongtong-gu, Suwon 16678, Republic of Korea
- Department of Chemistry, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon 16419, Republic of Korea
| | - Jongmin Han
- Department of Industrial Engineering, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon 16419, Republic of Korea
| | - Myeonginn Kang
- Department of Industrial Engineering, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon 16419, Republic of Korea
| | - Ji-Yeong Kim
- Samsung Advanced Institute of Technology, Samsung Electronics Co. Ltd., 130 Samsung-ro, Yeongtong-gu, Suwon 16678, Republic of Korea
| | - Dongyeeb Shin
- Samsung Advanced Institute of Technology, Samsung Electronics Co. Ltd., 130 Samsung-ro, Yeongtong-gu, Suwon 16678, Republic of Korea
| | - Youn-Suk Choi
- Samsung Advanced Institute of Technology, Samsung Electronics Co. Ltd., 130 Samsung-ro, Yeongtong-gu, Suwon 16678, Republic of Korea
| | - Seokho Kang
- Department of Industrial Engineering, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon 16419, Republic of Korea
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19
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Dürig W, Lindblad S, Golovko O, Gkotsis G, Aalizadeh R, Nika MC, Thomaidis N, Alygizakis NA, Plassmann M, Haglund P, Fu Q, Hollender J, Chaker J, David A, Kunkel U, Macherius A, Belova L, Poma G, Preud'Homme H, Munschy C, Aminot Y, Jaeger C, Lisec J, Hansen M, Vorkamp K, Zhu L, Cappelli F, Roscioli C, Valsecchi S, Bagnati R, González B, Prieto A, Zuloaga O, Gil-Solsona R, Gago-Ferrero P, Rodriguez-Mozaz S, Budzinski H, Devier MH, Dierkes G, Boulard L, Jacobs G, Voorspoels S, Rüdel H, Ahrens L. What is in the fish? Collaborative trial in suspect and non-target screening of organic micropollutants using LC- and GC-HRMS. ENVIRONMENT INTERNATIONAL 2023; 181:108288. [PMID: 37918065 DOI: 10.1016/j.envint.2023.108288] [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: 07/25/2023] [Revised: 10/04/2023] [Accepted: 10/23/2023] [Indexed: 11/04/2023]
Abstract
A collaborative trial involving 16 participants from nine European countries was conducted within the NORMAN network in efforts to harmonise suspect and non-target screening of environmental contaminants in whole fish samples of bream (Abramis brama). Participants were provided with freeze-dried, homogenised fish samples from a contaminated and a reference site, extracts (spiked and non-spiked) and reference sample preparation protocols for liquid chromatography (LC) and gas chromatography (GC) coupled to high resolution mass spectrometry (HRMS). Participants extracted fish samples using their in-house sample preparation method and/or the protocol provided. Participants correctly identified 9-69 % of spiked compounds using LC-HRMS and 20-60 % of spiked compounds using GC-HRMS. From the contaminated site, suspect screening with participants' own suspect lists led to putative identification of on average ∼145 and ∼20 unique features per participant using LC-HRMS and GC-HRMS, respectively, while non-target screening identified on average ∼42 and ∼56 unique features per participant using LC-HRMS and GC-HRMS, respectively. Within the same sub-group of sample preparation method, only a few features were identified by at least two participants in suspect screening (16 features using LC-HRMS, 0 features using GC-HRMS) and non-target screening (0 features using LC-HRMS, 2 features using GC-HRMS). The compounds identified had log octanol/water partition coefficient (KOW) values from -9.9 to 16 and mass-to-charge ratios (m/z) of 68 to 761 (LC-HRMS and GC-HRMS). A significant linear trend was found between log KOW and m/z for the GC-HRMS data. Overall, these findings indicate that differences in screening results are mainly due to the data analysis workflows used by different participants. Further work is needed to harmonise the results obtained when applying suspect and non-target screening approaches to environmental biota samples.
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Affiliation(s)
- Wiebke Dürig
- Department of Aquatic Sciences and Assessment, Swedish University of Agricultural Sciences (SLU), Box 7050, 75007 Uppsala, Sweden.
| | - Sofia Lindblad
- Department of Aquatic Sciences and Assessment, Swedish University of Agricultural Sciences (SLU), Box 7050, 75007 Uppsala, Sweden.
| | - Oksana Golovko
- Department of Aquatic Sciences and Assessment, Swedish University of Agricultural Sciences (SLU), Box 7050, 75007 Uppsala, Sweden.
| | - Georgios Gkotsis
- Department of Chemistry, National and Kapodistrian University of Athens, 15771 Athens, Greece.
| | - Reza Aalizadeh
- Department of Chemistry, National and Kapodistrian University of Athens, 15771 Athens, Greece.
| | - Maria-Christina Nika
- Department of Chemistry, National and Kapodistrian University of Athens, 15771 Athens, Greece.
| | - Nikolaos Thomaidis
- Department of Chemistry, National and Kapodistrian University of Athens, 15771 Athens, Greece.
| | - Nikiforos A Alygizakis
- Department of Chemistry, National and Kapodistrian University of Athens, 15771 Athens, Greece; Environmental Institute, Okružná 784/42, 97241 Koš, Slovakia.
| | - Merle Plassmann
- Department of Environmental Science, Stockholm University, 10691 Stockholm, Sweden.
| | - Peter Haglund
- Department of Chemistry, Chemical Biological Centre (KBC), Umeå University, Linnaeus väg 6, 90187 Umeå, Sweden.
| | - Qiuguo Fu
- Eawag: Swiss Federal Institute of Aquatic Science and Technology, Überlandstrasse 133, 8600 Dübendorf, Switzerland; Helmholtz Centre for Environmental Research - UFZ, Permoserstraße 15, 04318 Leipzig, Germany.
| | - Juliane Hollender
- Eawag: Swiss Federal Institute of Aquatic Science and Technology, Überlandstrasse 133, 8600 Dübendorf, Switzerland; Institute of Biogeochemistry and Pollutant Dynamics, ETH Zürich, Universitätstrasse 16, 8092 Zürich, Switzerland.
| | - Jade Chaker
- Université de Rennes, Inserm, EHESP, Irset - UMR_S, 1085 Rennes, France.
| | - Arthur David
- Université de Rennes, Inserm, EHESP, Irset - UMR_S, 1085 Rennes, France.
| | - Uwe Kunkel
- Bavarian Environment Agency, Bürgermeister-Ulrich-Straße 160, 86179 Augsburg, Germany.
| | - André Macherius
- Bavarian Environment Agency, Bürgermeister-Ulrich-Straße 160, 86179 Augsburg, Germany.
| | - Lidia Belova
- Toxicological Centre, University of Antwerp, Universiteitsplein 1, 2610 Wilrijk, Belgium.
| | - Giulia Poma
- Toxicological Centre, University of Antwerp, Universiteitsplein 1, 2610 Wilrijk, Belgium.
| | | | - Catherine Munschy
- Ifremer, CCEM Contamination Chimique des Écosystèmes Marins, 44000 Nantes, France.
| | - Yann Aminot
- Ifremer, CCEM Contamination Chimique des Écosystèmes Marins, 44000 Nantes, France.
| | - Carsten Jaeger
- Bundesanstalt für Materialforschung und -prüfung (BAM), Analytical Chemistry, Richard-Willstätter-Straße 11, 12489 Berlin, Germany.
| | - Jan Lisec
- Bundesanstalt für Materialforschung und -prüfung (BAM), Analytical Chemistry, Richard-Willstätter-Straße 11, 12489 Berlin, Germany.
| | - Martin Hansen
- Department of Environmental Science, Aarhus University, Frederiksborgvej 399, 4000 Roskilde, Denmark.
| | - Katrin Vorkamp
- Department of Environmental Science, Aarhus University, Frederiksborgvej 399, 4000 Roskilde, Denmark.
| | - Linyan Zhu
- Department of Environmental Science, Aarhus University, Frederiksborgvej 399, 4000 Roskilde, Denmark.
| | - Francesca Cappelli
- Water Research Institute, National Research Council of Italy, Via del Mulino 19, 20861 Brugherio MB, Italy.
| | - Claudio Roscioli
- Water Research Institute, National Research Council of Italy, Via del Mulino 19, 20861 Brugherio MB, Italy.
| | - Sara Valsecchi
- Water Research Institute, National Research Council of Italy, Via del Mulino 19, 20861 Brugherio MB, Italy.
| | - Renzo Bagnati
- Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milan, Italy.
| | - Belén González
- Department of Analytical Chemistry, Faculty of Science and Technology, University of the Basque Country (UPV/EHU), Barrio Sarriena s/n, 48940 Leioa, Spain; Research Centre for Experimental Marine Biology and Biotechnology (PIE), University of the Basque Country (UPV/EHU), Areatza Pasealekua 47, 48620 Plentzia, Spain.
| | - Ailette Prieto
- Department of Analytical Chemistry, Faculty of Science and Technology, University of the Basque Country (UPV/EHU), Barrio Sarriena s/n, 48940 Leioa, Spain; Research Centre for Experimental Marine Biology and Biotechnology (PIE), University of the Basque Country (UPV/EHU), Areatza Pasealekua 47, 48620 Plentzia, Spain.
| | - Olatz Zuloaga
- Department of Analytical Chemistry, Faculty of Science and Technology, University of the Basque Country (UPV/EHU), Barrio Sarriena s/n, 48940 Leioa, Spain; Research Centre for Experimental Marine Biology and Biotechnology (PIE), University of the Basque Country (UPV/EHU), Areatza Pasealekua 47, 48620 Plentzia, Spain.
| | - Ruben Gil-Solsona
- Catalan Institute for Water Research (ICRA), Carrer Emili Grahit 101, 17003 Girona, Spain; Universitat de Girona, Girona, Spain; Institute of Environmental Assessment and Water Research - Severo Ochoa Excellence Center (IDAEA), Spanish Council of Scientific Research (CSIC), Barcelona 08034, Spain.
| | - Pablo Gago-Ferrero
- Catalan Institute for Water Research (ICRA), Carrer Emili Grahit 101, 17003 Girona, Spain; Institute of Environmental Assessment and Water Research - Severo Ochoa Excellence Center (IDAEA), Spanish Council of Scientific Research (CSIC), Barcelona 08034, Spain.
| | - Sara Rodriguez-Mozaz
- Catalan Institute for Water Research (ICRA), Carrer Emili Grahit 101, 17003 Girona, Spain; Universitat de Girona, Girona, Spain.
| | - Hélène Budzinski
- University Bordeaux, CNRS, Bordeaux INP, EPOC, UMR 5805, 33600 Pessac, France.
| | - Marie-Helene Devier
- University Bordeaux, CNRS, Bordeaux INP, EPOC, UMR 5805, 33600 Pessac, France.
| | - Georg Dierkes
- Federal Institute of Hydrology, Am Mainzer Tor 1, 56068 Koblenz, Germany.
| | - Lise Boulard
- Federal Institute of Hydrology, Am Mainzer Tor 1, 56068 Koblenz, Germany; Metabolomics Core Facility, Centre de Ressources et Recherches Technologiques (C2RT), Institut Pasteur, 25-28 Rue du Dr Roux, 75015 Paris, France.
| | - Griet Jacobs
- Flemish Institute for Technological Research (VITO), Unit Separation and Conversion Technology, Boeretang 200, 2400 Mol, Belgium.
| | - Stefan Voorspoels
- Flemish Institute for Technological Research (VITO), Unit Separation and Conversion Technology, Boeretang 200, 2400 Mol, Belgium.
| | - Heinz Rüdel
- Fraunhofer Institute for Molecular Biology and Applied Ecology (Fraunhofer IME), Auf dem Aberg 1, 57392 Schmallenberg, Germany.
| | - Lutz Ahrens
- Department of Aquatic Sciences and Assessment, Swedish University of Agricultural Sciences (SLU), Box 7050, 75007 Uppsala, Sweden.
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20
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Phungsiangdee Y, Chaothong P, Karnpanit W, Tanaviyutpakdee P. Validation of UHPLC-ESI-MS/MS Method for Determining Steviol Glycoside and Its Derivatives in Foods and Beverages. Foods 2023; 12:3941. [PMID: 37959060 PMCID: PMC10647612 DOI: 10.3390/foods12213941] [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: 09/19/2023] [Revised: 10/13/2023] [Accepted: 10/15/2023] [Indexed: 11/15/2023] Open
Abstract
The aim of this study was to validate a method for determining nine types of steviol glycoside and its derivatives in food and beverage products, using ultrahigh-performance liquid chromatography tandem mass spectrometry with electrospray ionization (UHPLC ESI MS/MS). The performance characteristics of the analysis method were determined along with their suitability for the intended use. Coefficient of determination (R2) calibration curves from 0.2 to 1.0 mg L-1 were in the ranges of 0.9911-0.9990, 0.9939-1.0000 and 0.9973-0.9999 for a beverage, yogurt and snack, respectively. Intra-day precisions in terms of percent relative standard deviation (% RSD) of concentration, at 0.2, 0.5 and 1.0 mg L-1, for the beverage, yogurt and snack were lower than 15% (1.1-9.3%). At all concentrations, percentage recoveries were in the accepted range of 70-120%. For the matrix effect study, matrix-matched calibration was used for all compounds, obtaining a linear concentration range from 0.2 mg L-1 to 1.0 mg L-1. Almost all matrix-matched results presented as percentage recoveries were within the accepted range of 80-120%. The limit of detection (LOD) for steviol glycosides ranged from 0.003 to 0.078 μg g-1, while the limit of quantitation (LOQ) ranged from 0.011 to 0.261 μg g-1. These results indicate that the modified test method can be applied to determine the presence of steviol glycoside and its derivatives in a wide range of sample matrices.
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Affiliation(s)
- Yollada Phungsiangdee
- Master of Science Program in Toxicology and Nutrition for Food Safety, Institute of Nutrition, Mahidol University, Nakhon Pathom 73170, Thailand;
- Food Toxicology Unit, Institute of Nutrition, Mahidol University, Nakhon Pathom 73170, Thailand;
| | - Pimpuk Chaothong
- Food Toxicology Unit, Institute of Nutrition, Mahidol University, Nakhon Pathom 73170, Thailand;
| | - Weeraya Karnpanit
- School of Science, Western Sydney University, Locked Bag 1797, Penrith, NSW 2751, Australia;
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21
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García-Vara M, Orlando-Véliz D, Bonansea RI, Postigo C, López de Alda M. Prioritization of organic contaminants in a reclaimed water irrigation system using wide-scope LC-HRMS screening. JOURNAL OF HAZARDOUS MATERIALS 2023; 459:132119. [PMID: 37543020 DOI: 10.1016/j.jhazmat.2023.132119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 07/19/2023] [Accepted: 07/20/2023] [Indexed: 08/07/2023]
Abstract
A prioritization procedure was developed and implemented at the local level to identify the most relevant organic contaminants of emerging concern (CECs) in an agricultural area irrigated with reclaimed water. A wide-scope screening methodology based on UPLC-HRMS analysis was applied to holistically characterize the CEC footprint in water and its spatial and temporal variations. One hundred and fifty-eight CECs, including pharmaceuticals, industrial chemicals, and pesticides, among others, were identified with a confidence level of 2 in the water samples investigated. After water treatment in the reclamation plant and transport within the irrigation channel network, more than a hundred compounds were still detected at the location where water is abstracted for crop irrigation. Compound ecotoxicity and occurrence (semi-quantified concentrations or peak intensity) were the parameters used to prioritize CECs in the water used for irrigation. Results pointed at venlafaxine, O-desmethyl-venlafaxine, galaxolidone, theophylline/paraxanthine, oxybenzone, and N-phenyl-1-naphtylamine, among others, as CECs of concern in the investigated area. This study provides a simple and cost-effective approach to detecting site-specific priority pollutants that could otherwise be overlooked by national or European regulations. The prioritization tool provided contributes to rationally designing monitoring and attenuation programs and efficiently managing water resources, by ensuring the safety of reclaimed water applications.
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Affiliation(s)
- Manuel García-Vara
- Water, Environmental and Food Chemistry Unit (ENFOCHEM), Department of Environmental Chemistry, Institute of Environmental Assessment and Water Research (IDAEA-CSIC), C/ Jordi Girona 18-26, 08034 Barcelona, Spain
| | - Dana Orlando-Véliz
- Water, Environmental and Food Chemistry Unit (ENFOCHEM), Department of Environmental Chemistry, Institute of Environmental Assessment and Water Research (IDAEA-CSIC), C/ Jordi Girona 18-26, 08034 Barcelona, Spain; PhD student in the Analytical and Environmental Chemistry PhD Program at the University of Barcelona, C/ Martí i Franquès, 08028 Barcelona, Spain
| | - Rocío Inés Bonansea
- Water, Environmental and Food Chemistry Unit (ENFOCHEM), Department of Environmental Chemistry, Institute of Environmental Assessment and Water Research (IDAEA-CSIC), C/ Jordi Girona 18-26, 08034 Barcelona, Spain
| | - Cristina Postigo
- Technologies for Water Management and Treatment Research Group, Department of Civil Engineering, University of Granada, Campus de Fuentenueva, Avda Severo Ocha s/n, Granada 18071, Spain; Institute for Water Research, University of Granada, C/ Ramón y Cajal 4, Granada 18071, Spain
| | - Miren López de Alda
- Water, Environmental and Food Chemistry Unit (ENFOCHEM), Department of Environmental Chemistry, Institute of Environmental Assessment and Water Research (IDAEA-CSIC), C/ Jordi Girona 18-26, 08034 Barcelona, Spain.
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22
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Kajtazi A, Russo G, Wicht K, Eghbali H, Lynen F. Facilitating structural elucidation of small environmental solutes in RPLC-HRMS by retention index prediction. CHEMOSPHERE 2023; 337:139361. [PMID: 37392796 DOI: 10.1016/j.chemosphere.2023.139361] [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: 03/14/2023] [Revised: 06/06/2023] [Accepted: 06/26/2023] [Indexed: 07/03/2023]
Abstract
Implementing effective environmental management strategies requires a comprehensive understanding of the chemical composition of environmental pollutants, particularly in complex mixtures. Utilizing innovative analytical techniques, such as high-resolution mass spectrometry and predictive retention index models, can provide valuable insights into the molecular structures of environmental contaminants. Liquid Chromatography-High-Resolution Mass Spectrometry is a powerful tool for the identification of isomeric structures in complex samples. However, there are some limitations that can prevent accurate isomeric structure identification, particularly in cases where the isomers have similar mass and fragmentation patterns. Liquid chromatographic retention, determined by the size, shape, and polarity of the analyte and its interactions with the stationary phase, contains valuable 3D structural information that is vastly underutilized. Therefore, a predictive retention index model is developed which is transferrable to LC-HRMS systems and can assist in the structural elucidation of unknowns. The approach is currently restricted to carbon, hydrogen, and oxygen-based molecules <500 g mol-1. The methodology facilitates the acceptance of accurate structural formulas and the exclusion of erroneous hypothetical structural representations by leveraging retention time estimations, thereby providing a permissible tolerance range for a given elemental composition and experimental retention time. This approach serves as a proof of concept for the development of a Quantitative Structure-Retention Relationship model using a generic gradient LC approach. The use of a widely used reversed-phase (U)HPLC column and a relatively large set of training (101) and test compounds (14) demonstrates the feasibility and potential applicability of this approach for predicting the retention behaviour of compounds in complex mixtures. By providing a standard operating procedure, this approach can be easily replicated and applied to various analytical challenges, further supporting its potential for broader implementation.
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Affiliation(s)
- Ardiana Kajtazi
- Separation Science Group, Department of Organic and Macromolecular Chemistry, Ghent University, Krijgslaan 281 S4bis, B-9000 Ghent, Belgium
| | - Giacomo Russo
- School of Applied Sciences, Sighthill Campus, Edinburgh Napier University, 9 Sighthill Ct, EH11 4BN, Edinburgh, United Kingdom
| | - Kristina Wicht
- Separation Science Group, Department of Organic and Macromolecular Chemistry, Ghent University, Krijgslaan 281 S4bis, B-9000 Ghent, Belgium
| | - Hamed Eghbali
- Packaging and Specialty Plastics R&D, Dow Benelux B.V., Terneuzen, 4530 AA, the Netherlands
| | - Frédéric Lynen
- Separation Science Group, Department of Organic and Macromolecular Chemistry, Ghent University, Krijgslaan 281 S4bis, B-9000 Ghent, Belgium.
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23
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Egede Frøkjær E, Rüsz Hansen H, Hansen M. Non-targeted and suspect screening analysis using ion exchange chromatography-Orbitrap tandem mass spectrometry reveals polar and very mobile xenobiotics in Danish drinking water. CHEMOSPHERE 2023; 339:139745. [PMID: 37558003 DOI: 10.1016/j.chemosphere.2023.139745] [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: 03/29/2023] [Revised: 06/21/2023] [Accepted: 08/04/2023] [Indexed: 08/11/2023]
Abstract
Non-targeted and suspect screening analysis is gaining approval across the scientific and regulatory community to monitor the chemical status in the environment and thus environmental quality. These holistic screening analyses provides the means to perform suspect screening and go beyond to discover previously undescribed chemical pollutants in environmental samples. In a case study, we developed and optimized a high-resolution tandem mass spectrometry platform hyphenated with anion exchange chromatography to screen drinking water samples in Denmark. The optimized non-targeted screening method was able to detect anionic and polar compounds and was successfully applied to drinking water from two drinking water facilities. Following a data analysis pipeline optimization, anionic pesticide residues and other environmental contaminants were detected at confidence identification level 1 such as dimethachlor ESA, mecoprop, and dichlorprop in drinking water. In addition to these three substances, it was possible to detect another 1662 compounds, of which 97 were annotated at confidence identification level 2. More research is urgently needed to health risk prioritize the detected substances and to determine their concentrations.
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Affiliation(s)
- Emil Egede Frøkjær
- Environmental Metabolomics Lab, Department of Environmental Science, Aarhus University, Frederiksborgvej 399, 4000, Roskilde, Denmark.
| | - Helle Rüsz Hansen
- Danish Environmental Protection Agency, Tolderlundsvej 5, 5000, Odense C, Denmark
| | - Martin Hansen
- Environmental Metabolomics Lab, Department of Environmental Science, Aarhus University, Frederiksborgvej 399, 4000, Roskilde, Denmark.
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24
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Ruan T, Li P, Wang H, Li T, Jiang G. Identification and Prioritization of Environmental Organic Pollutants: From an Analytical and Toxicological Perspective. Chem Rev 2023; 123:10584-10640. [PMID: 37531601 DOI: 10.1021/acs.chemrev.3c00056] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/04/2023]
Abstract
Exposure to environmental organic pollutants has triggered significant ecological impacts and adverse health outcomes, which have been received substantial and increasing attention. The contribution of unidentified chemical components is considered as the most significant knowledge gap in understanding the combined effects of pollutant mixtures. To address this issue, remarkable analytical breakthroughs have recently been made. In this review, the basic principles on recognition of environmental organic pollutants are overviewed. Complementary analytical methodologies (i.e., quantitative structure-activity relationship prediction, mass spectrometric nontarget screening, and effect-directed analysis) and experimental platforms are briefly described. The stages of technique development and/or essential parts of the analytical workflow for each of the methodologies are then reviewed. Finally, plausible technique paths and applications of the future nontarget screening methods, interdisciplinary techniques for achieving toxicant identification, and burgeoning strategies on risk assessment of chemical cocktails are discussed.
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Affiliation(s)
- Ting Ruan
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Pengyang Li
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Haotian Wang
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Tingyu Li
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Guibin Jiang
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
- University of Chinese Academy of Sciences, Beijing 100049, China
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25
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Choi E, Yoo WJ, Jang HY, Kim TY, Lee SK, Oh HB. Machine learning liquid chromatography retention time prediction model augments the dansylation strategy for metabolite analysis of urine samples. J Chromatogr A 2023; 1705:464167. [PMID: 37348224 DOI: 10.1016/j.chroma.2023.464167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Revised: 06/10/2023] [Accepted: 06/15/2023] [Indexed: 06/24/2023]
Abstract
Herein, a standalone software equipped with a graphic user interface (GUI) is developed to predict liquid chromatography mass spectrometry (LC-MS) retention times (RTs) of dansylated metabolites. Dansylation metabolomics strategy developed by Li et al. narrows down a vast chemical space of metabolites into the metabolites containing amines and phenolic hydroxyls. Combined with differential isotope labeling, e.g., 12C-reagent labeled individual samples spiked with a 13C-reagent labeled reference or pooled sample, LC-MS analysis of the dansylated samples enables accurate relative quantification of all labeled metabolites. Herein, the LC-RTs for dansylated metabolites are predicted using an artificial neural network (ANN) machine-learning model. For the ANN modeling, 315 dansylated urine metabolites obtained from the DnsID database are used. The ANN LC-RT prediction model was reliable, with a mean absolute deviation of 0.74 min for the 30 min LC run. In the RT model, a deviation of more than 2 min was observed in only 3.2% of the total 315 metabolites, while a deviation of 1.5 min or more was observed in 11% of the metabolites. Furthermore, it was found that the LC-RT prediction was also reliable even for metabolites containing both amine and phenolic functional groups that can undergo dansylation on either one of the two functional groups, resulting in the generation of two isomeric forms. This RT-prediction model is embedded into a user-friendly GUI and can be used for identifying nontargeted dansylated metabolites with unknown RTs, along with accurate mass measurements. Furthermore, it is demonstrated that the developed software can help identify metabolites from a urine sample of an anonymous healthy pregnant woman.
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Affiliation(s)
- Eunwoo Choi
- Department of Chemistry, Sogang University, Seoul 04107, Republic of Korea
| | - Won Jun Yoo
- Department of Chemistry, Sogang University, Seoul 04107, Republic of Korea
| | - Hwa-Yong Jang
- Department of Chemistry, Sogang University, Seoul 04107, Republic of Korea
| | - Tae-Young Kim
- School of Earth Sciences and Environmental Engineering, Gwangju Institute of Science and Technology, Gwangju 61005, Republic of Korea
| | - Sung Ki Lee
- Department of Obstetrics and Gynecology, College of Medicine, Konyang University, Daejeon 35365, Republic of Korea.
| | - Han Bin Oh
- Department of Chemistry, Sogang University, Seoul 04107, Republic of Korea.
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26
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Sepman H, Malm L, Peets P, MacLeod M, Martin J, Breitholtz M, Kruve A. Bypassing the Identification: MS2Quant for Concentration Estimations of Chemicals Detected with Nontarget LC-HRMS from MS 2 Data. Anal Chem 2023; 95:12329-12338. [PMID: 37548594 PMCID: PMC10448440 DOI: 10.1021/acs.analchem.3c01744] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Accepted: 07/23/2023] [Indexed: 08/08/2023]
Abstract
Nontarget analysis by liquid chromatography-high-resolution mass spectrometry (LC-HRMS) is now widely used to detect pollutants in the environment. Shifting away from targeted methods has led to detection of previously unseen chemicals, and assessing the risk posed by these newly detected chemicals is an important challenge. Assessing exposure and toxicity of chemicals detected with nontarget HRMS is highly dependent on the knowledge of the structure of the chemical. However, the majority of features detected in nontarget screening remain unidentified and therefore the risk assessment with conventional tools is hampered. Here, we developed MS2Quant, a machine learning model that enables prediction of concentration from fragmentation (MS2) spectra of detected, but unidentified chemicals. MS2Quant is an xgbTree algorithm-based regression model developed using ionization efficiency data for 1191 unique chemicals that spans 8 orders of magnitude. The ionization efficiency values are predicted from structural fingerprints that can be computed from the SMILES notation of the identified chemicals or from MS2 spectra of unidentified chemicals using SIRIUS+CSI:FingerID software. The root mean square errors of the training and test sets were 0.55 (3.5×) and 0.80 (6.3×) log-units, respectively. In comparison, ionization efficiency prediction approaches that depend on assigning an unequivocal structure typically yield errors from 2× to 6×. The MS2Quant quantification model was validated on a set of 39 environmental pollutants and resulted in a mean prediction error of 7.4×, a geometric mean of 4.5×, and a median of 4.0×. For comparison, a model based on PaDEL descriptors that depends on unequivocal structural assignment was developed using the same dataset. The latter approach yielded a comparable mean prediction error of 9.5×, a geometric mean of 5.6×, and a median of 5.2× on the validation set chemicals when the top structural assignment was used as input. This confirms that MS2Quant enables to extract exposure information for unidentified chemicals which, although detected, have thus far been disregarded due to lack of accurate tools for quantification. The MS2Quant model is available as an R-package in GitHub for improving discovery and monitoring of potentially hazardous environmental pollutants with nontarget screening.
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Affiliation(s)
- Helen Sepman
- Department
of Materials and Environmental Chemistry, Stockholm University, Svante Arrhenius väg 16, 106
91 Stockholm, Sweden
- Department
of Environmental Science, Stockholm University, Svante Arrhenius väg 8, 106 91 Stockholm, Sweden
| | - Louise Malm
- Department
of Materials and Environmental Chemistry, Stockholm University, Svante Arrhenius väg 16, 106
91 Stockholm, Sweden
| | - Pilleriin Peets
- Department
of Materials and Environmental Chemistry, Stockholm University, Svante Arrhenius väg 16, 106
91 Stockholm, Sweden
| | - Matthew MacLeod
- Department
of Environmental Science, Stockholm University, Svante Arrhenius väg 8, 106 91 Stockholm, Sweden
| | - Jonathan Martin
- Science
for Life Laboratory, Department of Environmental Science, Stockholm University, Svante Arrhenius väg 8, 106 91 Stockholm, Sweden
| | - Magnus Breitholtz
- Department
of Environmental Science, Stockholm University, Svante Arrhenius väg 8, 106 91 Stockholm, Sweden
| | - Anneli Kruve
- Department
of Materials and Environmental Chemistry, Stockholm University, Svante Arrhenius väg 16, 106
91 Stockholm, Sweden
- Department
of Environmental Science, Stockholm University, Svante Arrhenius väg 8, 106 91 Stockholm, Sweden
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27
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Buckley TJ, Egeghy PP, Isaacs K, Richard AM, Ring C, Sayre RR, Sobus JR, Thomas RS, Ulrich EM, Wambaugh JF, Williams AJ. Cutting-edge computational chemical exposure research at the U.S. Environmental Protection Agency. ENVIRONMENT INTERNATIONAL 2023; 178:108097. [PMID: 37478680 PMCID: PMC10588682 DOI: 10.1016/j.envint.2023.108097] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 06/05/2023] [Accepted: 07/12/2023] [Indexed: 07/23/2023]
Abstract
Exposure science is evolving from its traditional "after the fact" and "one chemical at a time" approach to forecasting chemical exposures rapidly enough to keep pace with the constantly expanding landscape of chemicals and exposures. In this article, we provide an overview of the approaches, accomplishments, and plans for advancing computational exposure science within the U.S. Environmental Protection Agency's Office of Research and Development (EPA/ORD). First, to characterize the universe of chemicals in commerce and the environment, a carefully curated, web-accessible chemical resource has been created. This DSSTox database unambiguously identifies >1.2 million unique substances reflecting potential environmental and human exposures and includes computationally accessible links to each compound's corresponding data resources. Next, EPA is developing, applying, and evaluating predictive exposure models. These models increasingly rely on data, computational tools like quantitative structure activity relationship (QSAR) models, and machine learning/artificial intelligence to provide timely and efficient prediction of chemical exposure (and associated uncertainty) for thousands of chemicals at a time. Integral to this modeling effort, EPA is developing data resources across the exposure continuum that includes application of high-resolution mass spectrometry (HRMS) non-targeted analysis (NTA) methods providing measurement capability at scale with the number of chemicals in commerce. These research efforts are integrated and well-tailored to support population exposure assessment to prioritize chemicals for exposure as a critical input to risk management. In addition, the exposure forecasts will allow a wide variety of stakeholders to explore sustainable initiatives like green chemistry to achieve economic, social, and environmental prosperity and protection of future generations.
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Affiliation(s)
- Timothy J Buckley
- U.S. Environmental Protection Agency, Office of Research & Development, Center for Computational Toxicology & Exposure (CCTE), 109 TW Alexander Drive, Research Triangle Park, NC 27711, United States.
| | - Peter P Egeghy
- U.S. Environmental Protection Agency, Office of Research & Development, Center for Computational Toxicology & Exposure (CCTE), 109 TW Alexander Drive, Research Triangle Park, NC 27711, United States
| | - Kristin Isaacs
- U.S. Environmental Protection Agency, Office of Research & Development, Center for Computational Toxicology & Exposure (CCTE), 109 TW Alexander Drive, Research Triangle Park, NC 27711, United States
| | - Ann M Richard
- U.S. Environmental Protection Agency, Office of Research & Development, Center for Computational Toxicology & Exposure (CCTE), 109 TW Alexander Drive, Research Triangle Park, NC 27711, United States
| | - Caroline Ring
- U.S. Environmental Protection Agency, Office of Research & Development, Center for Computational Toxicology & Exposure (CCTE), 109 TW Alexander Drive, Research Triangle Park, NC 27711, United States
| | - Risa R Sayre
- U.S. Environmental Protection Agency, Office of Research & Development, Center for Computational Toxicology & Exposure (CCTE), 109 TW Alexander Drive, Research Triangle Park, NC 27711, United States
| | - Jon R Sobus
- U.S. Environmental Protection Agency, Office of Research & Development, Center for Computational Toxicology & Exposure (CCTE), 109 TW Alexander Drive, Research Triangle Park, NC 27711, United States
| | - Russell S Thomas
- U.S. Environmental Protection Agency, Office of Research & Development, Center for Computational Toxicology & Exposure (CCTE), 109 TW Alexander Drive, Research Triangle Park, NC 27711, United States
| | - Elin M Ulrich
- U.S. Environmental Protection Agency, Office of Research & Development, Center for Computational Toxicology & Exposure (CCTE), 109 TW Alexander Drive, Research Triangle Park, NC 27711, United States
| | - John F Wambaugh
- U.S. Environmental Protection Agency, Office of Research & Development, Center for Computational Toxicology & Exposure (CCTE), 109 TW Alexander Drive, Research Triangle Park, NC 27711, United States
| | - Antony J Williams
- U.S. Environmental Protection Agency, Office of Research & Development, Center for Computational Toxicology & Exposure (CCTE), 109 TW Alexander Drive, Research Triangle Park, NC 27711, United States
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28
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Akhlaqi M, Wang WC, Möckel C, Kruve A. Complementary methods for structural assignment of isomeric candidate structures in non-target liquid chromatography ion mobility high-resolution mass spectrometric analysis. Anal Bioanal Chem 2023:10.1007/s00216-023-04852-y. [PMID: 37452839 PMCID: PMC10404200 DOI: 10.1007/s00216-023-04852-y] [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: 05/12/2023] [Revised: 07/03/2023] [Accepted: 07/06/2023] [Indexed: 07/18/2023]
Abstract
Non-target screening with LC/IMS/HRMS is increasingly employed for detecting and identifying the structure of potentially hazardous chemicals in the environment and food. Structural assignment relies on a combination of multidimensional instrumental methods and computational methods. The candidate structures are often isomeric, and unfortunately, assigning the correct structure among a number of isomeric candidate structures still is a key challenge both instrumentally and computationally. While practicing non-target screening, it is usually impossible to evaluate separately the limitations arising from (1) the inability of LC/IMS/HRMS to resolve the isomeric candidate structures and (2) the uncertainty of in silico methods in predicting the analytical information of isomeric candidate structures due to the lack of analytical standards for all candidate structures. Here we evaluate the feasibility of structural assignment of isomeric candidate structures based on in silico-predicted retention time and database collision cross-section (CCS) values as well as based on matching the empirical analytical properties of the detected feature with those of the analytical standards. For this, we investigated 14 candidate structures corresponding to five features detected with LC/HRMS in a spiked surface water sample. Considering the predicted retention times and database CCS values with the accompanying uncertainty, only one of the isomeric candidate structures could be deemed as unlikely; therefore, the annotation of the LC/IMS/HRMS features remained ambiguous. To further investigate if unequivocal annotation is possible via analytical standards, the reversed-phase LC retention times and low- and high-resolution ion mobility spectrometry separation, as well as high-resolution MS2 spectra of analytical standards were studied. Reversed-phase LC separated the highest number of candidate structures while low-resolution ion mobility and high-resolution MS2 spectra provided little means for pinpointing the correct structure among the isomeric candidate structures even if analytical standards were available for comparison. Furthermore, the question arises which prediction accuracy is required from the in silico methods to par the analytical separation. Based on the experimental data of the isomeric candidate structures studied here and previously published in the literature (516 retention time and 569 CCS values), we estimate that to reduce the candidate list by 95% of the structures, the confidence interval of the predicted retention times would need to decrease to below 0.05 min for a 15-min gradient while that of CCS values would need to decrease to 0.15%. Hereby, we set a clear goal to the in silico methods for retention time and CCS prediction.
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Affiliation(s)
- Masoumeh Akhlaqi
- Department of Materials and Environmental Chemistry, Svante Arrhenius väg 16C, 114 18, Stockholm, Sweden
| | - Wei-Chieh Wang
- Department of Materials and Environmental Chemistry, Svante Arrhenius väg 16C, 114 18, Stockholm, Sweden
| | - Claudia Möckel
- Department of Materials and Environmental Chemistry, Svante Arrhenius väg 16C, 114 18, Stockholm, Sweden
| | - Anneli Kruve
- Department of Materials and Environmental Chemistry, Svante Arrhenius väg 16C, 114 18, Stockholm, Sweden.
- Department of Environmental Science, Svante Arrhenius väg 8, 114 18, Stockholm, Sweden.
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29
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Renner G, Reuschenbach M. Critical review on data processing algorithms in non-target screening: challenges and opportunities to improve result comparability. Anal Bioanal Chem 2023; 415:4111-4123. [PMID: 37380744 PMCID: PMC10328864 DOI: 10.1007/s00216-023-04776-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 04/23/2023] [Accepted: 05/15/2023] [Indexed: 06/30/2023]
Abstract
Non-target screening (NTS) is a powerful environmental and analytical chemistry approach for detecting and identifying unknown compounds in complex samples. High-resolution mass spectrometry has enhanced NTS capabilities but created challenges in data analysis, including data preprocessing, peak detection, and feature extraction. This review provides an in-depth understanding of NTS data processing methods, focusing on centroiding, extracted ion chromatogram (XIC) building, chromatographic peak characterization, alignment, componentization, and prioritization of features. We discuss the strengths and weaknesses of various algorithms, the influence of user input parameters on the results, and the need for automated parameter optimization. We address uncertainty and data quality issues, emphasizing the importance of incorporating confidence intervals and raw data quality assessment in data processing workflows. Furthermore, we highlight the need for cross-study comparability and propose potential solutions, such as utilizing standardized statistics and open-access data exchange platforms. In conclusion, we offer future perspectives and recommendations for developers and users of NTS data processing algorithms and workflows. By addressing these challenges and capitalizing on the opportunities presented, the NTS community can advance the field, improve the reliability of results, and enhance data comparability across different studies.
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Affiliation(s)
- Gerrit Renner
- Instrumental Analytical Chemistry, University of Duisburg-Essen, Universitätsstr. 5, Essen, D-45141, NRW, Germany.
- Centre for Water and Environmental Research (ZWU), University of Duisburg-Essen, Universitätsstr. 2, Essen, D-45141, NRW, Germany.
| | - Max Reuschenbach
- Instrumental Analytical Chemistry, University of Duisburg-Essen, Universitätsstr. 5, Essen, D-45141, NRW, Germany
- Centre for Water and Environmental Research (ZWU), University of Duisburg-Essen, Universitätsstr. 2, Essen, D-45141, NRW, Germany
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30
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Phillips AL, Peter KT, Sobus JR, Fisher CM, Manzano CA, McEachran AD, Williams AJ, Knolhoff AM, Ulrich EM. Standardizing non-targeted analysis reporting to advance exposure science and environmental epidemiology. JOURNAL OF EXPOSURE SCIENCE & ENVIRONMENTAL EPIDEMIOLOGY 2023; 33:501-504. [PMID: 36813888 PMCID: PMC10631379 DOI: 10.1038/s41370-022-00490-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Revised: 10/19/2022] [Accepted: 10/20/2022] [Indexed: 05/11/2023]
Affiliation(s)
- Allison L Phillips
- Center for Public Health and Environmental Assessment, U.S. Environmental Protection Agency, Corvallis, OR, 97333, USA
| | - Katherine T Peter
- Center for Urban Waters, Tacoma, WA, 98421, USA
- Interdisciplinary Arts and Sciences, University of Washington Tacoma, Tacoma, WA, 98402, USA
| | - Jon R Sobus
- Center for Computational Toxicology and Exposure, U.S. Environmental Protection Agency, Research Triangle Park, NC, 27711, USA
| | - Christine M Fisher
- Center for Food Safety and Applied Nutrition, U.S. Food and Drug Administration, College Park, MD, 20740, USA
| | - Carlos A Manzano
- School of Public Health, San Diego State University, San Diego, CA, 92182, USA
- Faculty of Science, University of Chile, 7750000, Nunoa, RM, Chile
| | | | - Antony J Williams
- Center for Computational Toxicology and Exposure, U.S. Environmental Protection Agency, Research Triangle Park, NC, 27711, USA
| | - Ann M Knolhoff
- Center for Food Safety and Applied Nutrition, U.S. Food and Drug Administration, College Park, MD, 20740, USA
| | - Elin M Ulrich
- Center for Computational Toxicology and Exposure, U.S. Environmental Protection Agency, Research Triangle Park, NC, 27711, USA.
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31
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Panara A, Gikas E, Tzavellas I, Thomaidis NS. Comprehensive HRMS Chemical Characterization of Pomegranate-Based Antioxidant Drinks via a Newly Developed Suspect and Target Screening Workflow. Molecules 2023; 28:4986. [PMID: 37446648 DOI: 10.3390/molecules28134986] [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: 05/09/2023] [Revised: 06/20/2023] [Accepted: 06/22/2023] [Indexed: 07/15/2023] Open
Abstract
Antioxidants play a significant role in human health, protecting against a variety of diseases. Therefore, the development of products with antioxidant activity is becoming increasingly prominent in the human lifestyle. New antioxidant drinks containing different percentages of pomegranate, blackberries, red grapes, and aronia have been designed, developed, and manufactured by a local industry. The comprehensive characterization of the drinks' constituents has been deemed necessary to evaluate their bioactivity. Thus, LC-qTOFMS has been selected, due to its sensitivity and structure identification capability. Both data-dependent and -independent acquisition modes have been utilized. The data have been treated according to a novel, newly designed workflow based on MS-DIAL and MZmine for suspect, as well as target screening. The classical MS-DIAL workflow has been modified to perform suspect and target screening in an automatic way. Furthermore, a novel methodology based on a compiled bioactivity-driven suspect list was developed and expanded with combinatorial enumeration to include metabolism products of the highlighted metabolites. Compounds belonging to ontologies with possible antioxidant capacity have been identified, such as flavonoids, amino acids, and fatty acids, which could be beneficial to human health, revealing the importance of the produced drinks as well as the efficacy of the new in-house developed workflow.
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Affiliation(s)
- Anthi Panara
- Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Panepistimiopolis Zografou, 15771 Athens, Greece
| | - Evagelos Gikas
- Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Panepistimiopolis Zografou, 15771 Athens, Greece
| | - Ilias Tzavellas
- Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Panepistimiopolis Zografou, 15771 Athens, Greece
| | - Nikolaos S Thomaidis
- Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Panepistimiopolis Zografou, 15771 Athens, Greece
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32
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Jonkers TJH, Houtman CJ, van Oorschot Y, Lamoree MH, Hamers T. Identification of antimicrobial and glucocorticoid compounds in wastewater effluents with effect-directed analysis. ENVIRONMENTAL RESEARCH 2023; 231:116117. [PMID: 37178748 DOI: 10.1016/j.envres.2023.116117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 05/03/2023] [Accepted: 05/11/2023] [Indexed: 05/15/2023]
Abstract
Pharmaceuticals, such as glucocorticoids and antibiotics, are inadequately removed from wastewater and may cause unwanted toxic effects in the receiving environment. This study aimed to identify contaminants of emerging concern in wastewater effluent with antimicrobial or glucocorticoid activity by applying effect-directed analysis (EDA). Effluent samples from six wastewater treatment plants (WWTPs) in the Netherlands were collected and analyzed with unfractionated and fractionated bioassay testing. Per sample, 80 fractions were collected and in parallel high-resolution mass spectrometry (HRMS) data were recorded for suspect and nontarget screening. The antimicrobial activity of the effluents was determined with an antibiotics assay and ranged from 298 to 711 ng azithromycin equivalents·L-1. Macrolide antibiotics were identified in each effluent and found to significantly contribute to the antimicrobial activity of each sample. Agonistic glucocorticoid activity determined with the GR-CALUX assay ranged from 98.1 to 286 ng dexamethasone equivalents·L-1. Bioassay testing of several tentatively identified compounds to confirm their activity revealed inactivity in the assay or the incorrect identification of a feature. Effluent concentrations of glucocorticoid active compounds were estimated from the fractionated GR-CALUX bioassay response. Subsequently, the biological and chemical detection limits were compared and a sensitivity gap between the two monitoring approaches was identified. Overall, these results emphasize that combining sensitive effect-based testing with chemical analysis can more accurately reflect environmental exposure and risk than chemical analysis alone.
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Affiliation(s)
- Tim J H Jonkers
- Amsterdam Institute for Life and Environment, Department of Environment & Health, Faculty of Science, Vrije Universiteit Amsterdam, De Boelelaan 1085, 1081 HV, Amsterdam, the Netherlands
| | - Corine J Houtman
- The Water Laboratory, J.W. Lucasweg 2, 2031 BE, Haarlem, the Netherlands
| | | | - Marja H Lamoree
- Amsterdam Institute for Life and Environment, Department of Environment & Health, Faculty of Science, Vrije Universiteit Amsterdam, De Boelelaan 1085, 1081 HV, Amsterdam, the Netherlands
| | - Timo Hamers
- Amsterdam Institute for Life and Environment, Department of Environment & Health, Faculty of Science, Vrije Universiteit Amsterdam, De Boelelaan 1085, 1081 HV, Amsterdam, the Netherlands.
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Bonnefille B, Karlsson O, Rian MB, Raqib R, Parvez F, Papazian S, Islam MS, Martin JW. Nontarget Analysis of Polluted Surface Waters in Bangladesh Using Open Science Workflows. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:6808-6824. [PMID: 37083417 PMCID: PMC10157886 DOI: 10.1021/acs.est.2c08200] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Nontarget mass spectrometry has great potential to reveal patterns of water contamination globally through community science, but few studies are conducted in low-income countries, nor with open-source workflows, and few datasets are FAIR (Findable, Accessible, Interoperable, Reusable). Water was collected from urban and rural rivers around Dhaka, Bangladesh, and analyzed by liquid chromatography high-resolution mass spectrometry in four ionization modes (electrospray ionization ±, atmospheric pressure chemical ionization ±) with data-independent MS2 acquisition. The acquisition strategy was complementary: 19,427 and 7365 features were unique to ESI and APCI, respectively. The complexity of water pollution was revealed by >26,000 unique molecular features resolved by MS-DIAL, among which >20,000 correlated with urban sources in Dhaka. A major wastewater treatment plant was not a dominant pollution source, consistent with major contributions from uncontrolled urban drainage, a result that encourages development of further wastewater infrastructures. Matching of deconvoluted MS2 spectra to public libraries resulted in 62 confident annotations (i.e., Level 1-2a) and allowed semiquantification of 42 analytes including pharmaceuticals, pesticides, and personal care products. In silico structure prediction for the top 100 unknown molecular features associated with an urban source allowed 15 additional chemicals of anthropogenic origin to be annotated (i.e., Level 3). The authentic MS2 spectra were uploaded to MassBank Europe, mass spectral data were openly shared on the MassIVE repository, a tool (i.e., MASST) that could be used for community science environmental surveillance was demonstrated, and current limitations were discussed.
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Affiliation(s)
- Bénilde Bonnefille
- Department of Environmental Science, Exposure and Effects Unit, Science for Life Laboratory, Stockholm University, Stockholm 106 91, Sweden
| | - Oskar Karlsson
- Department of Environmental Science, Exposure and Effects Unit, Science for Life Laboratory, Stockholm University, Stockholm 106 91, Sweden
| | - May Britt Rian
- Department of Environmental Science, Exposure and Effects Unit, Science for Life Laboratory, Stockholm University, Stockholm 106 91, Sweden
| | - Rubhana Raqib
- Immunobiology, Nutrition and Toxicology Unit, Infectious Diseases Division, International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b), Dhaka 1212, Bangladesh
| | - Faruque Parvez
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York 10032, United States
| | - Stefano Papazian
- Department of Environmental Science, Exposure and Effects Unit, Science for Life Laboratory, Stockholm University, Stockholm 106 91, Sweden
- National Facility for Exposomics, Metabolomics Platform, Science for Life Laboratory, Stockholm University, Solna 171 65, Sweden
| | - M Sirajul Islam
- Laboratory of Food Safety and One Health, Laboratory Sciences and Services Division, International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b), Dhaka 1212, Bangladesh
| | - Jonathan W Martin
- Department of Environmental Science, Exposure and Effects Unit, Science for Life Laboratory, Stockholm University, Stockholm 106 91, Sweden
- National Facility for Exposomics, Metabolomics Platform, Science for Life Laboratory, Stockholm University, Solna 171 65, Sweden
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Yang J, Zhao F, Zheng J, Wang Y, Fei X, Xiao Y, Fang M. An automated toxicity based prioritization framework for fast chemical characterization in non-targeted analysis. JOURNAL OF HAZARDOUS MATERIALS 2023; 448:130893. [PMID: 36746086 DOI: 10.1016/j.jhazmat.2023.130893] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 01/13/2023] [Accepted: 01/26/2023] [Indexed: 06/18/2023]
Abstract
Identification of environmental pollutants with harmful effects is commonly conducted by non-targeted analysis (NTA) using liquid chromatography coupled with high-resolution mass spectrometry. Prioritization of possible candidates is important yet challenging because of the large number of candidates from MS acquisitions. We aimed to prioritize candidates to the exposure potential of organic chemicals by their toxicity and identification evidence in the matrix. We have developed an R package application, "NTAprioritization.R", for fast prioritization of suspect lists. In this workflow, the identification levels of candidates were first rated according to spectral matching and retention time prediction. The toxicity levels were rated according to candidates' toxicity of different endpoints or ToxPi score. Finally, the various levels of candidates were identified as Tier 1 - 5 descending in priority. For validation, we used this workflow to identify pollutants in a sludge water sample spiked with 28 environmental pollutants. The workflow reduced the candidate list of over 6,982 candidates to a final list of 2,779 compounds and prioritized them to 5 tiers (Tier 1 - 5), including 21 out of 28 spiked standards. Overall, this study shows the added value of an automated prioritization R package for the fast screening of environmental pollutants based on the NTA method.
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Affiliation(s)
- Junjie Yang
- School of Civil and Environmental Engineering, Nanyang Technological University, 639798, Singapore; Singapore Phenome Center, Lee Kong Chian School of Medicine, Nanyang Technological University, 636921, Singapore
| | - Fanrong Zhao
- School of Civil and Environmental Engineering, Nanyang Technological University, 639798, Singapore
| | - Jie Zheng
- Singapore Phenome Center, Lee Kong Chian School of Medicine, Nanyang Technological University, 636921, Singapore
| | - Yulan Wang
- Singapore Phenome Center, Lee Kong Chian School of Medicine, Nanyang Technological University, 636921, Singapore
| | - Xunchang Fei
- School of Civil and Environmental Engineering, Nanyang Technological University, 639798, Singapore
| | - Yongjun Xiao
- International Food & Water Research Centre, Waters Pacific Pte Ltd, 117528, Singapore.
| | - Mingliang Fang
- School of Civil and Environmental Engineering, Nanyang Technological University, 639798, Singapore; Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China; Institute of Eco-Chongming, 3663 Zhongshan Road, Shanghai 200062, China.
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Jonkers TJH, Keizers PHJ, Béen F, Meijer J, Houtman CJ, Al Gharib I, Molenaar D, Hamers T, Lamoree MH. Identifying antimicrobials and their metabolites in wastewater and surface water with effect-directed analysis. CHEMOSPHERE 2023; 320:138093. [PMID: 36758810 DOI: 10.1016/j.chemosphere.2023.138093] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 02/04/2023] [Accepted: 02/06/2023] [Indexed: 06/18/2023]
Abstract
This study aimed to identify antimicrobial contaminants in the aquatic environment with effect-directed analysis. Wastewater influent, effluent, and surface water (up- and downstream of the discharge location) were sampled at two study sites. The samples were enriched, subjected to high-resolution fractionation, and the resulting 80 fractions were tested in an antibiotics bioassay. The resulting bioactive fractions guided the suspect and nontargeted identification strategy in the high-resolution mass spectrometry data that was recorded in parallel. Chemical features were annotated with reference databases, assessed on annotation quality, and assigned identification confidence levels. To identify antibiotic metabolites, Phase I metabolites were predicted in silico for over 500 antibiotics and included as a suspect list. Predicted retention times and fragmentation patterns reduced the number of annotations to consider for confirmation testing. Overall, the bioactivity of three fractions could be explained by the identified antibiotics (clarithromycin and azithromycin) and an antibiotic metabolite (14-OH(R) clarithromycin), explaining 78% of the bioactivity measured at one study site. The applied identification strategy successfully identified antibiotic metabolites in the aquatic environment, emphasizing the need to include the toxic effects of bioactive metabolites in environmental risk assessments.
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Affiliation(s)
- Tim J H Jonkers
- Department of Environment & Health, Faculty of Science, Amsterdam Institute for Life and Environment, Vrije Universiteit Amsterdam, De Boelelaan 1085, 1081 HV, Amsterdam, the Netherlands.
| | - Peter H J Keizers
- National Institute for Public Health and the Environment RIVM, A. van Leeuwenhoeklaan 9, 3721MA, Bilthoven, the Netherlands.
| | - Frederic Béen
- Department of Environment & Health, Faculty of Science, Amsterdam Institute for Life and Environment, Vrije Universiteit Amsterdam, De Boelelaan 1085, 1081 HV, Amsterdam, the Netherlands; KWR Water Research Institute, Groningenhaven 7, 3430 BB, Nieuwegein, the Netherlands.
| | - Jeroen Meijer
- Department of Environment & Health, Faculty of Science, Amsterdam Institute for Life and Environment, Vrije Universiteit Amsterdam, De Boelelaan 1085, 1081 HV, Amsterdam, the Netherlands; Institute for Risk Assessment Sciences (IRAS), Utrecht University, Yalelaan 2, 3584 CM, Utrecht, the Netherlands.
| | - Corine J Houtman
- The Water Laboratory, J.W. Lucasweg 2, 2031 BE, Haarlem, the Netherlands.
| | - Imane Al Gharib
- Systems Biology Lab, Faculty of Science, Amsterdam Institute for Life and Environment, Vrije Universiteit Amsterdam, De Boelelaan 1085, 1081 HV, Amsterdam, the Netherlands
| | - Douwe Molenaar
- Systems Biology Lab, Faculty of Science, Amsterdam Institute for Life and Environment, Vrije Universiteit Amsterdam, De Boelelaan 1085, 1081 HV, Amsterdam, the Netherlands.
| | - Timo Hamers
- Department of Environment & Health, Faculty of Science, Amsterdam Institute for Life and Environment, Vrije Universiteit Amsterdam, De Boelelaan 1085, 1081 HV, Amsterdam, the Netherlands.
| | - Marja H Lamoree
- Department of Environment & Health, Faculty of Science, Amsterdam Institute for Life and Environment, Vrije Universiteit Amsterdam, De Boelelaan 1085, 1081 HV, Amsterdam, the Netherlands.
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Movalli P, Biesmeijer K, Gkotsis G, Alygizakis N, Nika MC, Vasilatos K, Kostakis M, Thomaidis NS, Oswald P, Oswaldova M, Slobodnik J, Glowacka N, Hooijmeijer JCEW, Howison RA, Dekker RWRJ, van den Brink N, Piersma T. High resolution mass spectrometric suspect screening, wide-scope target analysis of emerging contaminants and determination of legacy pollutants in adult black-tailed godwit Limosa limosa limosa in the Netherlands - A pilot study. CHEMOSPHERE 2023; 321:138145. [PMID: 36791819 DOI: 10.1016/j.chemosphere.2023.138145] [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: 07/08/2022] [Revised: 01/22/2023] [Accepted: 02/12/2023] [Indexed: 06/18/2023]
Abstract
The Dutch breeding population of the black-tailed godwit Limosa limosa limosa has declined substantially over recent decades; the role of contaminants is unknown. We analysed liver samples from 11 adult birds found dead on their breeding grounds in SW Friesland 2016-2020, six from extensive, herb-rich grasslands, five from intensive grasslands. We carried out LC and GC wide-scope target analysis of more than 2400 substances, LC suspect screening for more than 60,000 substances, target analysis for Cd, Hg, Ni and Pb, organo-phosphate flame retardants (OPFRs), dechlorane plus compounds and selected polybrominated diphenyl ether flame retardants (PBDEs), and bioassay for polybrominated dibenzo-p-dioxins and dibenzofurans (PBDDs/PDBFs) and dioxin-like polychlorinated biphenyls (dl-PCBs). Residues of 29 emerging contaminants (ECs) were determined through wide-scope target analysis. Another 20 were tentatively identified through suspect screening. These contaminants include industrial chemicals (personal care products, surfactants, PAHs and others), plant protection products (PPPs) and pharmaceuticals and their transformation products. Total contaminant load detected by wide-scope target analysis ranged from c. 155 to c. 1400 ng g-1 and was generally lower in birds from extensive grasslands. Heatmaps suggest that birds from intensive grasslands have a greater mix and higher residue concentrations of PPPs, while birds from extensive grasslands have a greater mix and higher residue concentrations of per- and polyfluoroalkyl substances (PFAS). All four metals and two OPFRs were detected. All tested PBDEs were below the respective LODs. Bioassay revealed presence of PBDDs, PBDFs and dl-PCBs. Further research is required to elucidate potential health risks to godwits and contaminant sources.
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Affiliation(s)
- P Movalli
- Naturalis Biodiversity Center, P.O. Box 9517, 2300 RA, Leiden, the Netherlands.
| | - K Biesmeijer
- Laboratory of Analytical Chemistry, Department of Chemistry, University of Athens, Panepistimiopolis Zografou, 15771, Athens, Greece
| | - G Gkotsis
- Laboratory of Analytical Chemistry, Department of Chemistry, University of Athens, Panepistimiopolis Zografou, 15771, Athens, Greece
| | - N Alygizakis
- Laboratory of Analytical Chemistry, Department of Chemistry, University of Athens, Panepistimiopolis Zografou, 15771, Athens, Greece; Environmental Institute, Okružná 784/42, 97241, Koš, Slovak Republic
| | - M C Nika
- Laboratory of Analytical Chemistry, Department of Chemistry, University of Athens, Panepistimiopolis Zografou, 15771, Athens, Greece
| | - K Vasilatos
- Laboratory of Analytical Chemistry, Department of Chemistry, University of Athens, Panepistimiopolis Zografou, 15771, Athens, Greece
| | - M Kostakis
- Laboratory of Analytical Chemistry, Department of Chemistry, University of Athens, Panepistimiopolis Zografou, 15771, Athens, Greece
| | - N S Thomaidis
- Laboratory of Analytical Chemistry, Department of Chemistry, University of Athens, Panepistimiopolis Zografou, 15771, Athens, Greece
| | - P Oswald
- Environmental Institute, Okružná 784/42, 97241, Koš, Slovak Republic
| | - M Oswaldova
- Environmental Institute, Okružná 784/42, 97241, Koš, Slovak Republic
| | - J Slobodnik
- Environmental Institute, Okružná 784/42, 97241, Koš, Slovak Republic
| | - N Glowacka
- Environmental Institute, Okružná 784/42, 97241, Koš, Slovak Republic
| | - J C E W Hooijmeijer
- Conservation Ecology Group, Groningen Institute for Evolutionary Science (GELIFES), University of Groningen, PO Box 11103, 9700 CC, Groningen, the Netherlands
| | - R A Howison
- Knowledge Infrastructures Department, Campus Fryslân, University of Groningen, Wirdumerdijk 34, 8911 CE Leeuwarden, The Netherlands
| | - R W R J Dekker
- Naturalis Biodiversity Center, P.O. Box 9517, 2300 RA, Leiden, the Netherlands
| | - N van den Brink
- Wageningen University, Division of Toxicology, Box 8000, NL6700 EA, Wageningen, the Netherlands
| | - T Piersma
- Conservation Ecology Group, Groningen Institute for Evolutionary Science (GELIFES), University of Groningen, PO Box 11103, 9700 CC, Groningen, the Netherlands; NIOZ Royal Netherlands Institute for Sea Research, Department of Coastal Systems, PO Box 59, 1790 AB Den Burg, Texel, the Netherlands
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Sunyer-Caldú A, Peiró A, Díaz M, Ibáñez L, Gil-Solsona R, Gago-Ferrero P, Silvia Diaz-Cruz M. Target analysis and suspect screening of UV filters, parabens and other chemicals used in personal care products in human cord blood: Prenatal exposure by mother-fetus transfer. ENVIRONMENT INTERNATIONAL 2023; 173:107834. [PMID: 36893631 DOI: 10.1016/j.envint.2023.107834] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 02/15/2023] [Accepted: 02/15/2023] [Indexed: 06/18/2023]
Abstract
Prenatal exposure to certain organic chemicals like pesticides and phenols has been lifelong associated with birth outcomes and health disorders. Many personal care product (PCP) ingredients have similar properties or structures to those chemicals. Previous studies have documented the occurrence of UV filters (UVFs) and paraben preservatives (PBs) in the placenta, but observational studies concerning PCPs chemicals and foetal exposure are particularly scarce. Thus, this work aimed to assess the presence of a wide range of PCPs chemicals using target and suspect screening in the umbilical cord blood of new born babies to evaluate their potential transfer to the fetus. To do so, we analysed 69 umbilical cord blood plasma samples from a mother-child cohort from Barcelona (Spain). We quantified 8 benzophenone-type UVFs and their metabolites, and 4 PBs using validated analytical methodologies based on target screening using liquid chromatography-tandem mass spectrometry (HPLC-MS/MS). Then, we screened for additional 3246 substances using high-resolution mass spectrometry (HRMS) and advanced suspect analysis strategies. Six UVFs and three parabens were detected in the plasma with frequencies between 1.4% and 17.4% and concentrations up to 53.3 ng/mL (benzophenone-2). Thirteen additional chemicals were tentatively identified in the suspect screening, and ten were further confirmed with the corresponding standards. Among them, we found the organic solvent N-methyl-2-pyrrolidone, the chelating agent 8-hydroxyquinoline, and the antioxidant 2,2'-methylenebis(4-methyl-6-tert-butylphenol), which have been demonstrated to display reproductive toxicity. UVFs and PBs presence in the umbilical cord blood demonstrates mother-fetus transfer through the placental barrier and prenatal exposure to these PCPs chemicals, which may lead to adverse effects in the early stages of fetal development. Considering the small cohort used in this study, the reported results should be interpreted as a preliminary reference for the background umbilical cord transfer levels of the target PCPs chemicals. Further research is needed to determine the long-term consequences of prenatal exposure to PCPs chemicals.
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Affiliation(s)
- Adrià Sunyer-Caldú
- Department of Environmental Chemistry, Institute of Environmental Assessment and Water Research - Severo Ochoa Excellence Center (IDAEA), Spanish Council of Scientific Research (CSIC), Jordi Girona 18-26, 08034 Barcelona, Spain
| | - Amelia Peiró
- Department of Environmental Chemistry, Institute of Environmental Assessment and Water Research - Severo Ochoa Excellence Center (IDAEA), Spanish Council of Scientific Research (CSIC), Jordi Girona 18-26, 08034 Barcelona, Spain
| | - Marta Díaz
- Endocrinology, Institut de Recerca Pediàtrica Sant Joan de Déu, University of Barcelona, Pg. Sant Joan de Déu, 2, E-08950 Esplugues (Barcelona), Spain; CIBERDEM, Instituto de Salud Carlos III, Madrid, Spain
| | - Lourdes Ibáñez
- Endocrinology, Institut de Recerca Pediàtrica Sant Joan de Déu, University of Barcelona, Pg. Sant Joan de Déu, 2, E-08950 Esplugues (Barcelona), Spain; CIBERDEM, Instituto de Salud Carlos III, Madrid, Spain
| | - Ruben Gil-Solsona
- Department of Environmental Chemistry, Institute of Environmental Assessment and Water Research - Severo Ochoa Excellence Center (IDAEA), Spanish Council of Scientific Research (CSIC), Jordi Girona 18-26, 08034 Barcelona, Spain
| | - Pablo Gago-Ferrero
- Department of Environmental Chemistry, Institute of Environmental Assessment and Water Research - Severo Ochoa Excellence Center (IDAEA), Spanish Council of Scientific Research (CSIC), Jordi Girona 18-26, 08034 Barcelona, Spain
| | - M Silvia Diaz-Cruz
- Department of Environmental Chemistry, Institute of Environmental Assessment and Water Research - Severo Ochoa Excellence Center (IDAEA), Spanish Council of Scientific Research (CSIC), Jordi Girona 18-26, 08034 Barcelona, Spain.
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38
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Boelrijk J, van Herwerden D, Ensing B, Forré P, Samanipour S. Predicting RP-LC retention indices of structurally unknown chemicals from mass spectrometry data. J Cheminform 2023; 15:28. [PMID: 36829215 PMCID: PMC9960388 DOI: 10.1186/s13321-023-00699-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Accepted: 02/13/2023] [Indexed: 02/26/2023] Open
Abstract
Non-target analysis combined with liquid chromatography high resolution mass spectrometry is considered one of the most comprehensive strategies for the detection and identification of known and unknown chemicals in complex samples. However, many compounds remain unidentified due to data complexity and limited number structures in chemical databases. In this work, we have developed and validated a novel machine learning algorithm to predict the retention index (r[Formula: see text]) values for structurally (un)known chemicals based on their measured fragmentation pattern. The developed model, for the first time, enabled the predication of r[Formula: see text] values without the need for the exact structure of the chemicals, with an [Formula: see text] of 0.91 and 0.77 and root mean squared error (RMSE) of 47 and 67 r[Formula: see text] units for the NORMAN ([Formula: see text]) and amide ([Formula: see text]) test sets, respectively. This fragment based model showed comparable accuracy in r[Formula: see text] prediction compared to conventional descriptor-based models that rely on known chemical structure, which obtained an [Formula: see text] of 0.85 with an RMSE of 67.
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Affiliation(s)
- Jim Boelrijk
- AI4Science Lab, University of Amsterdam, Amsterdam, The Netherlands. .,Institute for Informatics, University of Amsterdam, Amsterdam, The Netherlands.
| | - Denice van Herwerden
- grid.7177.60000000084992262Van’t Hoff Institute for Molecular Sciences (HIMS), University of Amsterdam, Amsterdam, The Netherlands
| | - Bernd Ensing
- grid.7177.60000000084992262AI4Science Lab, University of Amsterdam, Amsterdam, The Netherlands ,Computational Chemistry Group, Van’t Hoff Institute for Molecular Sciences (HIMS), Amsterdam, The Netherlands
| | - Patrick Forré
- grid.7177.60000000084992262AI4Science Lab, University of Amsterdam, Amsterdam, The Netherlands ,grid.7177.60000000084992262Institute for Informatics, University of Amsterdam, Amsterdam, The Netherlands
| | - Saer Samanipour
- Van't Hoff Institute for Molecular Sciences (HIMS), University of Amsterdam, Amsterdam, The Netherlands. .,UvA Data Science Center, University of Amsterdam, Amsterdam, The Netherlands. .,Queensland Alliance for Environmental Health Sciences (QAEHS), The University of Queensland, Woolloongabba, Australia.
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Xu Z, Chughtai H, Tian L, Liu L, Roy JF, Bayen S. Development of quantitative structure-retention relationship models to improve the identification of leachables in food packaging using non-targeted analysis. Talanta 2023; 253:123861. [PMID: 36095943 DOI: 10.1016/j.talanta.2022.123861] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 08/15/2022] [Accepted: 08/17/2022] [Indexed: 12/13/2022]
Abstract
Quantitative structure-retention relationship (QSRR) models can be used to predict the chromatographic retention time of chemicals and facilitate the identification of unknown compounds, notably with non-targeted analysis. In this study, QSRR models were developed from the data obtained for 178 pure chemical standards and four types of analytical columns (C18, phenylhexyl, pentafluorophenyl, cyano) in liquid chromatography quadrupole time-of-flight mass spectrometry (LC-Q-TOF-MS). First, different data partitioning ratios and feature selection methods [random forest (RF) and support vector machine (SVM)] were tested to build models to predict chromatographic retention times based on 2D molecular descriptors. The internal and external performances of the non-linear (RF) and corresponding linear predictive models were systematically compared, and RF models resulted in better predictive capacities [p < 0.05, with an average PVE (proportion of variance explained) value of 0.89 ± 0.02] than linear models (0.79 ± 0.03). For each column, the resulting model was applied to identify leachables from actual plastic packaging samples. An in-depth investigation of the top 20 most intense molecular features revealed that all false-positives could be identified as outliers in the QSRR models (outside of the 95% prediction bands). Furthermore, analyzing a sample on multiple chromatographic columns and applying the associated QSRR models increased the capacity to filter false positives. Such an approach will contribute to a more effective identification of unknown or unexpected leachables in plastics (e.g. non-intended added substances), therefore refining our understanding of the chemical risks associated with food contact materials.
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Affiliation(s)
- Ziyun Xu
- Department of Food Science and Agricultural Chemistry, McGill University, Ste-Anne-de-Bellevue, QC, Canada
| | - Hamza Chughtai
- Department of Food Science and Agricultural Chemistry, McGill University, Ste-Anne-de-Bellevue, QC, Canada
| | - Lei Tian
- Department of Food Science and Agricultural Chemistry, McGill University, Ste-Anne-de-Bellevue, QC, Canada
| | - Lan Liu
- Department of Food Science and Agricultural Chemistry, McGill University, Ste-Anne-de-Bellevue, QC, Canada
| | | | - Stéphane Bayen
- Department of Food Science and Agricultural Chemistry, McGill University, Ste-Anne-de-Bellevue, QC, Canada.
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40
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Arvaniti OS, Arvaniti ES, Gyparakis S, Sabathianakis I, Karagiannis E, Pettas E, Gkotsis G, Nika MC, Thomaidis NS, Manios T, Fountoulakis MS, Stasinakis AS. Occurrence of pharmaceuticals in the wastewater of a Greek hospital: Combining consumption data collection and LC-QTOF-MS analysis. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 858:160153. [PMID: 36379345 DOI: 10.1016/j.scitotenv.2022.160153] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Revised: 10/22/2022] [Accepted: 11/09/2022] [Indexed: 06/16/2023]
Abstract
In this article we applied drug consumption approach and chemical analysis in parallel to investigate the concentrations of a large number of pharmaceuticals in different streams of a General Hospital. Drugs consumption data was collected during two periods (Period 1, 2) and the predicted environmental concentrations (PECs) were estimated for the wastewater of a building housing specific medical services (Point A) and for the entire hospital (Point B). Hospital wastewater samples (HWW) samples were also collected from these points and periods and the measured environmental concentrations (MEC) were determined using UHPLC-ESI-QTOF-MS/MS. According to consumption data, the highest number of drugs was consumed in the departments of Hematology, Intensive Care Unit, Cardiology, Internal Medicine, and Oncology, while the number of active substances used in the hospital was 413 (Period 1) and 362 (Period 2). For most substances, much higher PEC and MEC values were found at the HWW of Point A indicating that on-site treatment of this stream could be examined in the future. The application of wide-scope target analysis allowed the quantification of 122 compounds, while 21 additional substances were identified using suspect screening. The highest mean concentrations in Period 1 were found for acetaminophen (1100 μg/L) and rifaximin (723 μg/L), while in Period 2 for iopromide (458 μg/L) and acyclovir (408 μg/L). Among the detected compounds, 19 metabolites were determined. Atenolol acid, 1-hydroxy-midazolam and clopidogrel carboxylic acid were quantified at concentrations much higher than parent compounds indicating the importance of metabolites' monitoring in HWW. Calculation of PEC/MEC ratio for 36 pharmaceuticals showed sufficient correlation of these values for 19 % to 33 % of the substances depending on the examined period and sampling point. The parallel collection of drugs consumption data and chemical analysis give a thorough picture of the substances present in HWW and their main sources, facilitating decision-making for their better management.
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Affiliation(s)
- O S Arvaniti
- Water and Air Quality Laboratory, Department of Environment, University of the Aegean, University Hill, Mytilene 81100, Greece; Department of Agricultural Development, Agrofood and Management of Natural Resources, National and Kapodistrian University of Athens, Psachna 34400, Greece.
| | - E S Arvaniti
- Water and Air Quality Laboratory, Department of Environment, University of the Aegean, University Hill, Mytilene 81100, Greece
| | - S Gyparakis
- Department of Agriculture, Hellenic Mediterranean University, Estavromenos, Heraklion 71410, Crete, Greece
| | - I Sabathianakis
- Department of Agriculture, Hellenic Mediterranean University, Estavromenos, Heraklion 71410, Crete, Greece
| | - E Karagiannis
- Medical Waste SA, Heraklion Industrial Area, Heraklion 71601, Greece
| | - E Pettas
- Medical Waste SA, Heraklion Industrial Area, Heraklion 71601, Greece
| | - G Gkotsis
- Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Athens 15771, Greece
| | - M C Nika
- Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Athens 15771, Greece
| | - N S Thomaidis
- Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Athens 15771, Greece
| | - T Manios
- Department of Agriculture, Hellenic Mediterranean University, Estavromenos, Heraklion 71410, Crete, Greece
| | - M S Fountoulakis
- Water and Air Quality Laboratory, Department of Environment, University of the Aegean, University Hill, Mytilene 81100, Greece
| | - A S Stasinakis
- Water and Air Quality Laboratory, Department of Environment, University of the Aegean, University Hill, Mytilene 81100, Greece.
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Nikolopoulou V, Ajibola AS, Aalizadeh R, Thomaidis NS. Wide-scope target and suspect screening of emerging contaminants in sewage sludge from Nigerian WWTPs by UPLC-qToF-MS. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 857:159529. [PMID: 36270367 DOI: 10.1016/j.scitotenv.2022.159529] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 10/12/2022] [Accepted: 10/13/2022] [Indexed: 06/16/2023]
Abstract
There is currently a paucity of scientific data in Africa on the analysis and occurrence of emerging contaminants in sewage sludge. In this work, the occurrence of European Union (EU) Water Framework Directive priority substances and wide-range emerging contaminants were investigated and discussed comprehensively in the sewage sludge samples from three different wastewater treatment plants (WWTPs) in Lagos, Nigeria. The identification strategy was implemented by target and suspect screening in liquid chromatography-high resolution mass spectrometry. 250 compounds were identified in the sewage sludge samples from the investigated WWTPs. From 250 detected compounds, 182 compounds were quantified, and 78 compounds significantly show high environmental risk score (calculated from provisional no-effect concentrations values (PNEC) as well as their environmental quality data (EQs)). Most of contaminants detected at high amount belong to pharmaceuticals and are from hospital WWTP. While the highest concentration (72.4 mg kg-1) was measured for salicylic acid (a non-steroidal anti-inflammatory drug), antibiotics showed high concentrations up to 24.4 and 28.4 mg kg-1 for ciprofloxacin and ofloxacin, respectively. Three simple factors including frequency of exceedance, frequency of occurrence and extent of exceedance were used to aid prioritization of these substances in future monitoring campaigns. This work presents the first comprehensive and wide-scope screening of a large number of emerging contaminants in sewage sludge from Nigerian WWTPs.
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Affiliation(s)
- Varvara Nikolopoulou
- Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Panepistimiopolis Zografou, 15771 Athens, Greece
| | - Akinranti S Ajibola
- Analytical/Environmental Unit, Department of Chemistry, University of Ibadan, Ibadan, Nigeria
| | - Reza Aalizadeh
- Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Panepistimiopolis Zografou, 15771 Athens, Greece
| | - Nikolaos S Thomaidis
- Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Panepistimiopolis Zografou, 15771 Athens, Greece.
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Ng K, Alygizakis NA, Thomaidis NS, Slobodnik J. Wide-Scope Target and Suspect Screening of Antibiotics in Effluent Wastewater from Wastewater Treatment Plants in Europe. Antibiotics (Basel) 2023; 12:antibiotics12010100. [PMID: 36671300 PMCID: PMC9854574 DOI: 10.3390/antibiotics12010100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 01/02/2023] [Accepted: 01/04/2023] [Indexed: 01/09/2023] Open
Abstract
The occurrence of antibiotics in the environment could result in the development of antibiotic-resistant bacteria, which could result in a public health crisis. The occurrence of 676 antibiotics and the main transformation products (TPs) was investigated in the 48 wastewater treatment plants (WWTPs) from 11 countries (Germany, Romania, Serbia, Croatia, Slovenia, Hungary, Slovakia, Czechia, Austria, Cyprus, and Greece) by target and suspect screening. Target screening involved the investigation of antibiotics with reference standards (40 antibiotics). Suspect screening covered 676 antibiotics retrieved from the NORMAN Substance Database (antibiotic list on NORMAN network). Forty-seven antibiotics were detected in effluent wastewater samples: thirty-two by target screening and fifteen additional ones by suspect screening. An ecotoxicological risk assessment was performed based on occurrence data and predicted no effect concentration (PNEC), which involved the derivation of frequency of appearance (FoA), frequency of PNEC exceedance (FoE), and extent of PNEC exceedance (EoE). Azithromycin, erythromycin, clarithromycin, ofloxacin, and ciprofloxacin were prioritized as the calculated risk score was above 1. The median of antibiotics' load to freshwater ecosystems was 0.59 g/day/WWTP. The detection of antibiotics across countries indicates the presence of antibiotics in the ecosystems of Europe, which may trigger unwanted responses from the ecosystem, including antibiotic resistance.
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Affiliation(s)
- Kelsey Ng
- Environmental Institute, Okružná 784/42, 97241 Koš, Slovakia
- RECETOX, Faculty of Science, Masaryk University, Kotlářská 2, 60200 Brno, Czech Republic
| | - Nikiforos A. Alygizakis
- Environmental Institute, Okružná 784/42, 97241 Koš, Slovakia
- Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Panepistimiopolis Zografou, 15771 Athens, Greece
- Correspondence:
| | - Nikolaos S. Thomaidis
- Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Panepistimiopolis Zografou, 15771 Athens, Greece
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Towards a harmonized identification scoring system in LC-HRMS/MS based non-target screening (NTS) of emerging contaminants. Trends Analyt Chem 2023. [DOI: 10.1016/j.trac.2023.116944] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
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Li R, Ren K, Su H, Wei Y, Su G. Target and suspect analysis of liquid crystal monomers in soil from different urban functional zones. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 854:158408. [PMID: 36057313 DOI: 10.1016/j.scitotenv.2022.158408] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 08/24/2022] [Accepted: 08/26/2022] [Indexed: 06/15/2023]
Abstract
Recent studies have reported the occurrence of liquid crystal monomers (LCMs) in sediment, indoor dust, hand wipes, and human serum samples; however, information regarding their contamination status in soil is currently unavailable. The concentrations of 39 target LCMs were determined in n = 96 surface soil samples collected from five different urban functional zones including agricultural, scenic, industrial, commercial, and residential zones. We observed that 76 of 96 surface soil samples contained at least 19, 13, 16, 19, and 14 of the 39 target LCMs that were detectable in samples from agricultural, scenic, industrial, commercial, and residential zones, respectively. The LCMs in the samples from the agricultural zone exhibited the highest mean concentrations of 12.9 ng/g dry weight (dw), followed by those from commercial (5.23 ng/g dw), residential (3.30 ng/g dw), industrial (2.48 ng/g dw), and scenic zones (0.774 ng/g dw). Furthermore, strong and statistically significant (p < 0.05) correlations were observed for several pairs of LCMs (3cH2B vs. 5bcHdFB in the agricultural zone; 5bcHdFB vs. 2bcHdFB, 5bcHdFB vs. 3cH2B in the commercial zone; 5bcHdFB vs. 2bcHdFB in the industrial zone), indicating that they might have similar commercial applications and sources. Based on a newly established database containing 1173 LCMs, suspect screening was applied to discover other LCMs in these 96 soil samples using gas chromatograph coupled with quadrupole-time-of-flight mass spectrometry (GC-QTOF/MS). We tentatively identified 51 LCM formulas with 69 chemical structures. Collectively, this study provides the first evidence for the occurrence of LCMs in soil samples, and suggests that LCMs could be widely distributed across all five urban functional zones.
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Affiliation(s)
- Rongrong Li
- Jiangsu Key Laboratory of Chemical Pollution Control and Resources Reuse, School of Environmental and Biological Engineering, Nanjing University of Science and Technology, Nanjing 210094, PR China
| | - Kefan Ren
- Jiangsu Key Laboratory of Chemical Pollution Control and Resources Reuse, School of Environmental and Biological Engineering, Nanjing University of Science and Technology, Nanjing 210094, PR China
| | - Huijun Su
- Jiangsu Key Laboratory of Chemical Pollution Control and Resources Reuse, School of Environmental and Biological Engineering, Nanjing University of Science and Technology, Nanjing 210094, PR China
| | - Yu Wei
- Jiangsu Key Laboratory of Chemical Pollution Control and Resources Reuse, School of Environmental and Biological Engineering, Nanjing University of Science and Technology, Nanjing 210094, PR China
| | - Guanyong Su
- Jiangsu Key Laboratory of Chemical Pollution Control and Resources Reuse, School of Environmental and Biological Engineering, Nanjing University of Science and Technology, Nanjing 210094, PR China.
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Gkotsis G, Nika MC, Athanasopoulou AI, Vasilatos K, Alygizakis N, Boschert M, Osterauer R, Höpker KA, Thomaidis NS. Advanced throughput analytical strategies for the comprehensive HRMS screening of organic micropollutants in eggs of different bird species. CHEMOSPHERE 2023; 312:137092. [PMID: 36332731 DOI: 10.1016/j.chemosphere.2022.137092] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 10/11/2022] [Accepted: 10/30/2022] [Indexed: 06/16/2023]
Abstract
Raptors are ideal indicators for biomonitoring studies using wildlife in order to assess the environmental pollution in the terrestrial ecosystem, since they are placed in the highest trophic position in the food webs and their life expectancy is relatively long. In this study, 26 eggs of 4 bird species (Peregrine falcon, Eurasian curlew, Little owl and Eagle owl) collected in Germany, were investigated for the presence of persistent organic pollutants (POPs) and thousands of contaminants of emerging concern (CECs). Generic sample preparation protocols were followed for the extraction of the analytes and the purification of the extracts, and the samples were analyzed both by liquid (LC) and gas chromatography (GC) coupled to high resolution mass spectrometry (HRMS), for capturing a wide range of organic micropollutants with different physicochemical properties. State-of-the-art screening methodologies were applied in the acquired HRMS data, including wide-scope target analysis of 2448 known pollutants and suspect screening of over 65,000 environmentally relevant compounds. Overall, 58 pollutants from different chemical classes, such as plant protection products, per- and polyfluoroalkyl substances and medicinal products, as well as their transformation products, were determined through target analysis. Most of the detected compounds were lipophilic (logP>2), although the presence of (semi)polar contaminants should not be overlooked, underlying the need for holistic analytical approaches in environmental monitoring studies. p,p'-DDE, PCB 153 and PCB138, PFOS and methylparaben were the most frequently detected compounds. 50 additional substances were identified and semi-quantified through suspect screening workflows, including mainly compounds of industrial use with high production volume.
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Affiliation(s)
- Georgios Gkotsis
- National and Kapodistrian University of Athens, Panepistimiopolis Zographou, 15771, Athens, Greece
| | - Maria-Christina Nika
- National and Kapodistrian University of Athens, Panepistimiopolis Zographou, 15771, Athens, Greece.
| | - Antonia I Athanasopoulou
- National and Kapodistrian University of Athens, Panepistimiopolis Zographou, 15771, Athens, Greece
| | - Konstantinos Vasilatos
- National and Kapodistrian University of Athens, Panepistimiopolis Zographou, 15771, Athens, Greece
| | - Nikiforos Alygizakis
- National and Kapodistrian University of Athens, Panepistimiopolis Zographou, 15771, Athens, Greece; Environmental Institute s.r.o., Okruzna 784/42, 97241, Kos, Slovak Republic
| | | | - Raphaela Osterauer
- State Institute for Environment Baden-Wuerttemberg (LUBW), Griesbachstr. 1, 76185 Karlsruhe, Germany
| | - Kai-Achim Höpker
- State Institute for Environment Baden-Wuerttemberg (LUBW), Griesbachstr. 1, 76185 Karlsruhe, Germany
| | - Nikolaos S Thomaidis
- National and Kapodistrian University of Athens, Panepistimiopolis Zographou, 15771, Athens, Greece.
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Badry A, Rüdel H, Göckener B, Nika MC, Alygizakis N, Gkotsis G, Thomaidis NS, Treu G, Dekker RWRJ, Movalli P, Walker LA, Potter ED, Cincinelli A, Martellini T, Duke G, Slobodnik J, Koschorreck J. Making use of apex predator sample collections: an integrated workflow for quality assured sample processing, analysis and digital sample freezing of archived samples. CHEMOSPHERE 2022; 309:136603. [PMID: 36174727 DOI: 10.1016/j.chemosphere.2022.136603] [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: 06/20/2022] [Revised: 09/21/2022] [Accepted: 09/22/2022] [Indexed: 06/16/2023]
Abstract
Using monitoring data from apex predators for chemicals risk assessment can provide important information on bioaccumulating as well as biomagnifying chemicals in food webs. A survey among European institutions involved in chemical risk assessment on their experiences with apex predator data in chemical risk assessment revealed great interest in using such data. However, the respondents indicated that constraints were related to expected high costs, lack of standardisation and harmonised quality criteria for exposure assessment, data access, and regulatory acceptance/application. During the Life APEX project, we demonstrated that European sample collections (i.e. environmental specimen banks (ESBs), research collection (RCs), natural history museums (NHMs)) archive a large variety of biological samples that can be readily used for chemical analysis once appropriate quality assurance/control (QA/QC) measures have been developed and implemented. We therefore issued a second survey on sampling, processing and archiving procedures in European sample collections to derive key quality QA/QC criteria for chemical analysis. The survey revealed great differences in QA/QC measures between ESBs, NHMs and RCs. Whereas basic information such as sampling location, date and biometric data were mostly available across institutions, protocols to accompany the sampling strategy with respect to chemical analysis were only available for ESBs. For RCs, the applied QA/QC measures vary with the respective research question, whereas NHMs are generally less aware of e.g. chemical cross-contamination issues. Based on the survey we derived key indicators for assessing the quality of biota samples that can be easily implemented in online databases. Furthermore, we provide a QA/QC workflow not only for sampling and processing but also for the chemical analysis of biota samples. We focussed on comprehensive analytical techniques such as non-target screening and provided insights into subsequent storage of high-resolution chromatograms in online databases (i.e. digital sample freezing platform) to ultimately support chemicals risk assessment.
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Affiliation(s)
- Alexander Badry
- German Environment Agency (Umweltbundesamt), 06813, Dessau-Roßlau, Germany.
| | - Heinz Rüdel
- Fraunhofer Institute for Molecular Biology and Applied Ecology (Fraunhofer IME), 57392, Schmallenberg, Germany
| | - Bernd Göckener
- Fraunhofer Institute for Molecular Biology and Applied Ecology (Fraunhofer IME), 57392, Schmallenberg, Germany
| | - Maria-Christina Nika
- Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Panepistimiopolis Zografou, 15771, Athens, Greece
| | - Nikiforos Alygizakis
- Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Panepistimiopolis Zografou, 15771, Athens, Greece; Environmental Institute, Okružná 784/42, 97241, Koš, Slovak Republic
| | - Georgios Gkotsis
- Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Panepistimiopolis Zografou, 15771, Athens, Greece
| | - Nikolaos S Thomaidis
- Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Panepistimiopolis Zografou, 15771, Athens, Greece
| | - Gabriele Treu
- German Environment Agency (Umweltbundesamt), 06813, Dessau-Roßlau, Germany
| | - Rene W R J Dekker
- Naturalis Biodiversity Center, Darwinweg 2, 2333, CR, Leiden, the Netherlands
| | - Paola Movalli
- Naturalis Biodiversity Center, Darwinweg 2, 2333, CR, Leiden, the Netherlands
| | - Lee A Walker
- UK Centre for Ecology & Hydrology, Lancaster Environment Centre, Lancaster, LA1 4PQ, United Kingdom
| | - Elaine D Potter
- UK Centre for Ecology & Hydrology, Lancaster Environment Centre, Lancaster, LA1 4PQ, United Kingdom
| | - Alessandra Cincinelli
- Department of Chemistry "Ugo Schiff", University of Florence, 50019, Sesto Fiorentino, Italy
| | - Tania Martellini
- Department of Chemistry "Ugo Schiff", University of Florence, 50019, Sesto Fiorentino, Italy
| | - Guy Duke
- UK Centre for Ecology & Hydrology, MacLean Bldg, Benson Ln, Crowmarsh Gifford, Wallingford, OX10 8BB, United Kingdom
| | | | - Jan Koschorreck
- German Environment Agency (Umweltbundesamt), 06813, Dessau-Roßlau, Germany
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Gkotsis G, Nika MC, Nikolopoulou V, Alygizakis N, Bizani E, Aalizadeh R, Badry A, Chadwick E, Cincinelli A, Claßen D, Danielsson S, Dekker R, Duke G, Drost W, Glowacka N, Göckener B, Jansman HAH, Juergens M, Knopf B, Koschorreck J, Krone O, Martellini T, Movalli P, Persson S, Potter ED, Rohner S, Roos A, O' Rourke E, Siebert U, Treu G, van den Brink NW, Walker LA, Williams R, Slobodnik J, Thomaidis NS. Assessment of contaminants of emerging concern in European apex predators and their prey by LC-QToF MS wide-scope target analysis. ENVIRONMENT INTERNATIONAL 2022; 170:107623. [PMID: 36379200 DOI: 10.1016/j.envint.2022.107623] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Revised: 10/23/2022] [Accepted: 11/07/2022] [Indexed: 06/16/2023]
Abstract
Apex predators are good indicators of environmental pollution since they are relatively long-lived and their high trophic position and spatiotemporal exposure to chemicals provides insights into the persistent, bioaccumulative and toxic (PBT) properties of chemicals. Although monitoring data from apex predators can considerably support chemicals' management, there is a lack of pan-European studies, and longer-term monitoring of chemicals in organisms from higher trophic levels. The present study investigated the occurrence of contaminants of emerging concern (CECs) in 67 freshwater, marine and terrestrial apex predators and in freshwater and marine prey, gathered from four European countries. Generic sample preparation protocols for the extraction of CECs with a broad range of physicochemical properties and the purification of the extracts were used. The analysis was performed utilizing liquid (LC) chromatography coupled to high resolution mass spectrometry (HRMS), while the acquired chromatograms were screened for the presence of more than 2,200 CECs through wide-scope target analysis. In total, 145 CECs were determined in the apex predator and their prey samples belonging in different categories, such as pharmaceuticals, plant protection products, per- and polyfluoroalkyl substances, their metabolites and transformation products. Higher concentration levels were measured in predators compared to prey, suggesting that biomagnification of chemicals through the food chain occurs. The compounds were prioritized for further regulatory risk assessment based on their frequency of detection and their concentration levels. The majority of the prioritized CECs were lipophilic, although the presence of more polar contaminants should not be neglected. This indicates that holistic analytical approaches are required to fully characterize the chemical universe of biota samples. Therefore, the present survey is an attempt to systematically investigate the presence of thousands of chemicals at a European level, aiming to use these data for better chemicals management and contribute to EU Zero Pollution Ambition.
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Affiliation(s)
- Georgios Gkotsis
- National and Kapodistrian University of Athens, Panepistimiopolis Zografou, 15771 Athens, Greece
| | - Maria-Christina Nika
- National and Kapodistrian University of Athens, Panepistimiopolis Zografou, 15771 Athens, Greece.
| | - Varvara Nikolopoulou
- National and Kapodistrian University of Athens, Panepistimiopolis Zografou, 15771 Athens, Greece
| | - Nikiforos Alygizakis
- National and Kapodistrian University of Athens, Panepistimiopolis Zografou, 15771 Athens, Greece; Environmental Institute, s.r.o., Okružná 784/42, 972 41 Koš, Slovak Republic
| | - Erasmia Bizani
- National and Kapodistrian University of Athens, Panepistimiopolis Zografou, 15771 Athens, Greece
| | - Reza Aalizadeh
- National and Kapodistrian University of Athens, Panepistimiopolis Zografou, 15771 Athens, Greece
| | - Alexander Badry
- German Environment Agency (Umweltbundesamt), Wörlitzer Pl. 1, 06844 Dessau-Roßlau, Germany
| | - Elizabeth Chadwick
- Cardiff University, Biomedical Science Building, Museum Avenue, Postal Code: CF10 3AX Cardiff, United Kingdom
| | - Alessandra Cincinelli
- University of Florence, Department of Chemistry, Via della Lastruccia 3, 50019 Sesto Fiorentino (Firenze), Italy
| | - Daniela Claßen
- German Environment Agency (Umweltbundesamt), Wörlitzer Pl. 1, 06844 Dessau-Roßlau, Germany
| | - Sara Danielsson
- Swedish Museum of Natural History, Frescativägen 40, 114 18 Stockholm, Sweden
| | - René Dekker
- Naturalis Biodiversity Center, Darwinweg 2, 2333 CR Leiden, Netherlands
| | - Guy Duke
- Environmental Change Institute, University of Oxford, University of Oxford, 3 S Parks Rd, OX1 3QY Oxford, United Kingdom; UK Centre for Ecology & Hydrology, Maclean Building, Benson Lane, Crowmarsh Gifford, Wallingford, Oxfordshire OX10 8BB, United Kingdom
| | - Wiebke Drost
- German Environment Agency (Umweltbundesamt), Wörlitzer Pl. 1, 06844 Dessau-Roßlau, Germany
| | - Natalia Glowacka
- Environmental Institute, s.r.o., Okružná 784/42, 972 41 Koš, Slovak Republic
| | - Bernd Göckener
- Fraunhofer Institute for Molecular Biology and Applied Ecology, Auf dem Aberg 1, 57392 Schmallenberg, Germany
| | - Hugh A H Jansman
- Wageningen University & Research, Wageningen Environmental Research, Droevendaalsesteeg 3-3 A, 6708 PB Wageningen, the Netherlands
| | - Monika Juergens
- Center for Ecology and Hydrology, Library Ave, Bailrigg, LA1 4AP Lancaster, United Kingdom
| | - Burkhard Knopf
- Fraunhofer Institute for Molecular Biology and Applied Ecology, Auf dem Aberg 1, 57392 Schmallenberg, Germany
| | - Jan Koschorreck
- German Environment Agency (Umweltbundesamt), Wörlitzer Pl. 1, 06844 Dessau-Roßlau, Germany
| | - Oliver Krone
- Leibniz Institute for Zoo and Wildlife Research, Department of Wildlife Diseases, Alfred-Kowalke-Strasse 17, 10315 Berlin, Germany
| | - Tania Martellini
- University of Florence, Department of Chemistry, Via della Lastruccia 3, 50019 Sesto Fiorentino (Firenze), Italy
| | - Paola Movalli
- Naturalis Biodiversity Center, Darwinweg 2, 2333 CR Leiden, Netherlands
| | - Sara Persson
- Swedish Museum of Natural History, Frescativägen 40, 114 18 Stockholm, Sweden
| | - Elaine D Potter
- Center for Ecology and Hydrology, Library Ave, Bailrigg, LA1 4AP Lancaster, United Kingdom
| | - Simon Rohner
- University of Veterinary Medicine Hannover, Foundation, Bünteweg 9, 30559 Hannover, Germany
| | - Anna Roos
- Swedish Museum of Natural History, Frescativägen 40, 114 18 Stockholm, Sweden
| | - Emily O' Rourke
- Cardiff University, Biomedical Science Building, Museum Avenue, Postal Code: CF10 3AX Cardiff, United Kingdom
| | - Ursula Siebert
- University of Veterinary Medicine Hannover, Foundation, Bünteweg 9, 30559 Hannover, Germany
| | - Gabriele Treu
- German Environment Agency (Umweltbundesamt), Wörlitzer Pl. 1, 06844 Dessau-Roßlau, Germany
| | - Nico W van den Brink
- Wageningen University & Research, Division of Toxicology, Stippeneng 4, 6700EA Wageningen, the Netherlands
| | - Lee A Walker
- Center for Ecology and Hydrology, Library Ave, Bailrigg, LA1 4AP Lancaster, United Kingdom
| | - Rosie Williams
- Zoological Society of London, Institute of Zoology, Regent's Park, NW1 4RY London, United Kingdom
| | - Jaroslav Slobodnik
- Environmental Institute, s.r.o., Okružná 784/42, 972 41 Koš, Slovak Republic
| | - Nikolaos S Thomaidis
- National and Kapodistrian University of Athens, Panepistimiopolis Zografou, 15771 Athens, Greece.
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Lopez-Herguedas N, González-Gaya B, Cano A, Alvarez-Mora I, Mijangos L, Etxebarria N, Zuloaga O, Olivares M, Prieto A. Effect-directed analysis of a hospital effluent sample using A-YES for the identification of endocrine disrupting compounds. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 850:157985. [PMID: 35985602 DOI: 10.1016/j.scitotenv.2022.157985] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 07/29/2022] [Accepted: 08/08/2022] [Indexed: 06/15/2023]
Abstract
An effect-directed analysis (EDA) approach was used to identify the compounds responsible for endocrine disruption in a hospital effluent (Basque Country). In order to facilitate the identification of the potentially toxic substances, a sample was collected using an automated onsite large volume solid phase extraction (LV-SPE) system. Then, it was fractionated with a two-step orthogonal chromatographic separation and tested for estrogenic effects with a recombinant yeast (A-YES) in-vitro bioassay. The fractionation method was optimized and validated for 184 compounds, and its application to the hospital effluent sample allowed reducing the number of unknowns from 292 in the raw sample to 35 after suspect analysis of the bioactive fractions. Among those, 7 of them were confirmed with chemical standards. In addition, target analysis of the raw sample confirmed the presence of mestranol, estrone and dodemorph in the fractions showing estrogenic activity. Predictive estrogenic activity modelling using quantitative structure-activity relationships indicated that the hormones mestranol (5840 ng/L) and estrone (128 ng/L), the plasticiser bisphenol A (9219 ng/L) and the preservative butylparaben (1224 ng/L) were the main contributors of the potential toxicity. Derived bioanalytical equivalents (BEQs) pointed mestranol and estrone as the main contributors (56 % and 43 %, respectively) of the 50 % of the sample's explained total estrogenic activity.
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Affiliation(s)
- Naroa Lopez-Herguedas
- Department of Analytical Chemistry, Faculty of Science and Technology, University of the Basque Country (UPV/EHU), Leioa, Basque Country, Spain; Research Centre for Experimental Marine Biology and Biotechnology (PIE), University of the Basque Country (UPV/EHU), Plentzia, Basque Country, Spain.
| | - Belén González-Gaya
- Department of Analytical Chemistry, Faculty of Science and Technology, University of the Basque Country (UPV/EHU), Leioa, Basque Country, Spain; Research Centre for Experimental Marine Biology and Biotechnology (PIE), University of the Basque Country (UPV/EHU), Plentzia, Basque Country, Spain.
| | - Alicia Cano
- Department of Analytical Chemistry, Faculty of Science and Technology, University of the Basque Country (UPV/EHU), Leioa, Basque Country, Spain
| | - Iker Alvarez-Mora
- Department of Analytical Chemistry, Faculty of Science and Technology, University of the Basque Country (UPV/EHU), Leioa, Basque Country, Spain; Research Centre for Experimental Marine Biology and Biotechnology (PIE), University of the Basque Country (UPV/EHU), Plentzia, Basque Country, Spain.
| | - Leire Mijangos
- Department of Analytical Chemistry, Faculty of Science and Technology, University of the Basque Country (UPV/EHU), Leioa, Basque Country, Spain; Research Centre for Experimental Marine Biology and Biotechnology (PIE), University of the Basque Country (UPV/EHU), Plentzia, Basque Country, Spain.
| | - Nestor Etxebarria
- Department of Analytical Chemistry, Faculty of Science and Technology, University of the Basque Country (UPV/EHU), Leioa, Basque Country, Spain; Research Centre for Experimental Marine Biology and Biotechnology (PIE), University of the Basque Country (UPV/EHU), Plentzia, Basque Country, Spain.
| | - Olatz Zuloaga
- Department of Analytical Chemistry, Faculty of Science and Technology, University of the Basque Country (UPV/EHU), Leioa, Basque Country, Spain; Research Centre for Experimental Marine Biology and Biotechnology (PIE), University of the Basque Country (UPV/EHU), Plentzia, Basque Country, Spain.
| | - Maitane Olivares
- Department of Analytical Chemistry, Faculty of Science and Technology, University of the Basque Country (UPV/EHU), Leioa, Basque Country, Spain; Research Centre for Experimental Marine Biology and Biotechnology (PIE), University of the Basque Country (UPV/EHU), Plentzia, Basque Country, Spain.
| | - Ailette Prieto
- Department of Analytical Chemistry, Faculty of Science and Technology, University of the Basque Country (UPV/EHU), Leioa, Basque Country, Spain; Research Centre for Experimental Marine Biology and Biotechnology (PIE), University of the Basque Country (UPV/EHU), Plentzia, Basque Country, Spain.
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Celma A, Bade R, Sancho JV, Hernandez F, Humphries M, Bijlsma L. Prediction of Retention Time and Collision Cross Section (CCS H+, CCS H-, and CCS Na+) of Emerging Contaminants Using Multiple Adaptive Regression Splines. J Chem Inf Model 2022; 62:5425-5434. [PMID: 36280383 PMCID: PMC9709913 DOI: 10.1021/acs.jcim.2c00847] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Ultra-high performance liquid chromatography coupled to ion mobility separation and high-resolution mass spectrometry instruments have proven very valuable for screening of emerging contaminants in the aquatic environment. However, when applying suspect or nontarget approaches (i.e., when no reference standards are available), there is no information on retention time (RT) and collision cross-section (CCS) values to facilitate identification. In silico prediction tools of RT and CCS can therefore be of great utility to decrease the number of candidates to investigate. In this work, Multiple Adaptive Regression Splines (MARS) were evaluated for the prediction of both RT and CCS. MARS prediction models were developed and validated using a database of 477 protonated molecules, 169 deprotonated molecules, and 249 sodium adducts. Multivariate and univariate models were evaluated showing a better fit for univariate models to the experimental data. The RT model (R2 = 0.855) showed a deviation between predicted and experimental data of ±2.32 min (95% confidence intervals). The deviation observed for CCS data of protonated molecules using the CCSH model (R2 = 0.966) was ±4.05% with 95% confidence intervals. The CCSH model was also tested for the prediction of deprotonated molecules, resulting in deviations below ±5.86% for the 95% of the cases. Finally, a third model was developed for sodium adducts (CCSNa, R2 = 0.954) with deviation below ±5.25% for 95% of the cases. The developed models have been incorporated in an open-access and user-friendly online platform which represents a great advantage for third-party research laboratories for predicting both RT and CCS data.
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Affiliation(s)
- Alberto Celma
- Environmental
and Public Health Analytical
Chemistry, Research Institute for Pesticides
and Water, University Jaume I, E-12071Castelló, Spain,Department
of Aquatic Sciences and Assessment, Swedish
University of Agricultural Sciences (SLU), SE-750 07Uppsala, Sweden
| | - Richard Bade
- University
of South Australia, Adelaide, UniSA: Clinical and Health Sciences,
Health and Biomedical Innovation, AdelaideSA-5000, South
Australia, Australia,Queensland
Alliance for Environmental Health Sciences (QAEHS), The University of Queensland, 20 Cornwall Street, WoolloongabbaAUS-4102, Queensland, Australia
| | - Juan Vicente Sancho
- Environmental
and Public Health Analytical
Chemistry, Research Institute for Pesticides
and Water, University Jaume I, E-12071Castelló, Spain
| | - Félix Hernandez
- Environmental
and Public Health Analytical
Chemistry, Research Institute for Pesticides
and Water, University Jaume I, E-12071Castelló, Spain
| | - Melissa Humphries
- School
of Mathematical Sciences, University of
Adelaide, Ingkarni Wardli Building, North Terrace Campus, SA-5005Adelaide, Australia,
| | - Lubertus Bijlsma
- Environmental
and Public Health Analytical
Chemistry, Research Institute for Pesticides
and Water, University Jaume I, E-12071Castelló, Spain,
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50
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Alygizakis N, Giannakopoulos T, Τhomaidis NS, Slobodnik J. Detecting the sources of chemicals in the Black Sea using non-target screening and deep learning convolutional neural networks. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 847:157554. [PMID: 35878861 DOI: 10.1016/j.scitotenv.2022.157554] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 07/17/2022] [Accepted: 07/18/2022] [Indexed: 06/15/2023]
Abstract
The Black Sea is an important ecosystem, which is affected by various anthropogenic pressures, such as shipping activities and wastewater inputs from large coastal cities. Significant loads of chemical pollutants are being continuously brought in by major European rivers. This study investigated the spatial distribution of chemicals in the Ukrainian shelf (the northwestern part of the Black Sea) and their main sources. Chemical occurrence data used in the study was generated within the Joint Black Sea Surveys (JBSS), which took place in 2016 and 2017 as a part of the EU/UNDP EMBLAS II project (www.emblasproject.org). During the JBSS, seawater samples were analyzed by a non-target screening workflow using liquid chromatography high-resolution mass spectrometry (LC-HRMS). Open-source algorithms were applied to generate a combined dataset of 30,489 detected chemical signals and their intensities. Out of these, 35 compounds were tentatively identified by the application of a non-target screening identification workflow based on automated matching of their mass spectra against those in available mass spectral libraries. The dataset was used to generate images, representing spatial distribution of each of the signals. These images were then used as an input to a deep learning convolutional neural network classification model. The study resulted in the development of an open-source end-to-end workflow for the estimation of the pollution load by chemicals contributed by the two major inflowing rivers (Danube and Dnieper) and other, so far unidentified, sources. A dedicated dashboard was built to facilitate data visualization per detected signal/compound. The presented model proved to be especially useful at the prioritization of signals of unknown compounds, which is of key importance for the follow up structure elucidation efforts of bulky non-target screening data. The deep learning approach for peak prioritization of unknown chemicals in the environment has been used for the first time.
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Affiliation(s)
- Nikiforos Alygizakis
- Laboratory of Analytical Chemistry, Department of Chemistry, University of Athens, Panepistimiopolis Zografou, 15771 Athens, Greece; Environmental Institute, Okružná 784/42, 97241 Koš, Slovak Republic.
| | | | - Nikolaos S Τhomaidis
- Laboratory of Analytical Chemistry, Department of Chemistry, University of Athens, Panepistimiopolis Zografou, 15771 Athens, Greece.
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