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Surface water; High-resolution mass spectrometry; Non-target analysis; Exposome; Cheminformatics; Luxembourg
Abstract :
[en] Background
Non-target screening of surface water samples collected over an extended period can reveal interesting temporal patterns in exposome-related pollutants. Additionally, geographical data on pollution sources close to the sampling sites, chemical classification data and the consideration of flow paths can provide valuable information on the origins and potential threat of tentatively identified chemical compounds. In this study, 271 surface water samples from 20 sampling sites across Luxembourg were analysed using high-resolution mass spectrometry, complementing routine target monitoring efforts in 2019–2022. Data analysis was performed using the open source R-package patRoon, which offers a customizable non-target workflow. By employing open source workflows featuring scoring terms, like spectral match and applying identification levels, tentative identifications can be prioritized, e.g. based on spectral similarity. Furthermore, by utilizing supplementary database information such as PubChemLite annotation categories and classification software such as classyFire, an overall assessment of the potential threats posed by the tentatively identified chemicals was conducted, enabling the prioritization of chemicals for future confirmation through targeted approaches.
Results
The study tentatively identified 378 compounds associated with the exposome including benzenoids, organoheterocyclic compounds, and organic phosphoric acids and derivatives (11 classyFire superclasses, 50 subclasses). The classification analysis not only revealed temporal variations in agrochemicals, with the majority of identifications occurring in May to July, but also highlighted the prevalence of pharmaceuticals such as venlafaxine in surface waters. Furthermore, potential sources of pollutants, like metallurgic industry or household products were explored by considering common uses and geographical information, as commercial uses of almost 100% of the identified chemicals are known. 41 chemicals were suggested for potential inclusion to governmental monitoring lists for further investigation.
Conclusions
The findings of this study complement existing knowledge on the pollution status of surface water in Luxembourg and highlight the usefulness of non-target screening for identifying temporal and spatial trends in pollutant levels. This approach, performed in a complementary manner to routine monitoring, can help to tentatively identify chemicals of concern for potential inclusion in target monitoring methods following additional confirmation and quantification efforts.
Research center :
Luxembourg Centre for Systems Biomedicine (LCSB): Environmental Cheminformatics (Schymanski Group) Administration de La Gestion de L’eau (AGE), Avenue du Rock’n’Roll 1, 4361, Esch-Sur-Alzette, Luxembourg Institute for Biodiversity and Ecosystem Dynamics (IBED), University of Amsterdam, Science Park 904, 1098 XH, Amsterdam, The Netherlands
The measurement files in mzML format can be found as dataset MSV000092221 from the [GNPS MassIVE repository] (https://
massi ve. ucsd. edu/ Prote oSAFe/ static/ massi ve. jsp), accessible via ftp:// massi
ve. ucsd. edu/ MSV00 00922 21/
Arp HPH, Aurich D, Schymanski EL et al (2023) Avoiding the next silent spring: our chemical past, present, and future. Environ Sci Technol 57:6355–6359. 10.1021/acs.est.3c01735 DOI: 10.1021/acs.est.3c01735
Agency for Toxic Substances and Disease Registry, U.S. Department of Health and Human Services (2022) Toxicological Profile for DDT, DDE, and DDD. In: https://www.atsdr.cdc.gov/toxprofiles/tp35.pdf. Accessed 23 May 2023
Cocco P, Kazerouni N, Zahm SH (2000) Cancer mortality and environmental exposure to DDE in the United States. Environ Health Perspect 108:1–4. 10.1289/ehp.001081 DOI: 10.1289/ehp.001081
Schymanski EL, Williams AJ (2017) Open science for identifying “Known Unknown” chemicals. Environ Sci Technol 51:5357–5359. 10.1021/acs.est.7b01908 DOI: 10.1021/acs.est.7b01908
Little JL, Cleven CD, Brown SD (2011) Identification of “Known Unknowns” utilizing accurate mass data and chemical abstracts service databases. J Am Soc Mass Spectrom 22:348–359. 10.1007/s13361-010-0034-3 DOI: 10.1007/s13361-010-0034-3
Wang X, Shen Z, Zeng Y et al (2018) Day-night differences, seasonal variations and source apportionment of PM10-Bound PAHs over Xi’an, Northwest China. Atmosphere 9:62. 10.3390/atmos9020062 DOI: 10.3390/atmos9020062
Stamatis N, Hela D, Triantafyllidis V, Konstantinou I (2013) Spatiotemporal variation and risk assessment of pesticides in water of the lower catchment basin of Acheloos River, Western Greece. Sci World J. 10.1155/2013/231610 DOI: 10.1155/2013/231610
Aurich D, Miles O, Schymanski EL (2021) Historical exposomics and high resolution mass spectrometry. Exposome 1:1–15. 10.1093/exposome/osab007 DOI: 10.1093/exposome/osab007
Lickes J-P, L’Administration de la gestion de l’eau (AGE), Luxembourg (2022) Vorstellung des 3. Bewirtschaftungsplans. In: Httpsgouvernementludam-Assetsdocumentsactualites202209-Sept.-Gest.-Dist.-Hydrogr.--3-Wasserbewirtschaftungsplanpdf. https://gouvernement.lu/dam-assets/documents/actualites/2022/09-septembre/22-gestion-districts-hydrographiques/vorstellung-des-3-wasserbewirtschaftungsplan.pdf. Acessed 26 May 2023
Krier J, Singh RR, Kondić T et al (2022) Discovering pesticides and their TPs in Luxembourg waters using open cheminformatics approaches. Environ Int 158:14. 10.1016/j.envint.2021.106885 DOI: 10.1016/j.envint.2021.106885
Singh RR, Lai A, Krier J et al (2021) Occurrence and distribution of pharmaceuticals and their transformation products in Luxembourgish surface waters. ACS Environ Au 1:58–70. 10.1021/acsenvironau.1c00008 DOI: 10.1021/acsenvironau.1c00008
L’Administration de la gestion de l’eau (AGE), Luxembourg (2022) Anhang 14-Überschreitungen der UQN für prioritäre und flussgebietsspezifische Stoffe. In: Httpseaugouvernementludam-Assetsadministrationdocuments3-Cycleelaboration--3e-Plan--Gest.-Doc.-Final.-14-Uberschreitungen--Uqn--Prioritare--Flussgebietsspezifische-Stoffepdf. https://eau.gouvernement.lu/dam-assets/administration/documents/3-cycle/elaboration-du-3e-plan-de-gestion-document-final/anhang-14-uberschreitungen-der-uqn-fur-prioritare-und-flussgebietsspezifische-stoffe.pdf. Accessed 23 May 2023
L’Administration de la gestion de l’eau (AGE), Luxembourg (2022) Elaboration du 3e plan de gestion. In: HttpeaugouvernementlufradministrationdirectivesDirective-Cadre-Sur-Leau3e-Cycle-2021-2027elaboration--3e-Plan--Gest.-Doc.-Final. http://eau.gouvernement.lu/fr/administration/directives/Directive-cadre-sur-leau/3e-cycle-(2021-2027)/elaboration-du-3e-plan-de-gestion-document-final.html. Accessed 23 May 2023
European-Parliament, Council of the European Union (2000) Directive 2000/60/EC of the European Parliament and of the Council of 23 October 2000 establishing a framework for Community action in the field of water policy
European-Parliament, Council of the European Union (2008) Directive 2008/105/EC of the European Parliament and of the Council of 16 December 2008 on environmental quality standards in the field of water policy, amending and subsequently repealing Council Directives 82/176/EEC, 83/513/EEC, 84/156/EEC, 84/491/EEC, 86/280/EEC and amending Directive 2000/60/EC of the European Parliament and of the Council
Directorate-General for Environment (2023) Surface water. In: Eur. Com. - Energy Clim. Change Environ. https://environment.ec.europa.eu/topics/water/surface-water_en. Accessed 11 Oct 2023
European-Parliament, Council of the European Union (2013) Directive 2013/39/EU of the European Parliament and of the Council of 12 August 2013 amending Directives 2000/60/EC and 2008/105/EC as regards priority substances in the field of water policy Text with EEA relevance
European-Parliament, Council of the European Union (2022) Commission Implementing Decision (EU) 2022/1307 of 22 July 2022 establishing a watch list of substances for Union-wide monitoring in the field of water policy pursuant to Directive 2008/105/EC of the European Parliament and of the Council (notified under document C(2022) 5098) (Text with EEA relevance)
Directorate-General for Environment (2022) Proposal amending Water Directives. In: Eur. Com. - Energy Clim. Change Environ. https://environment.ec.europa.eu/publications/proposal-amending-water-directives_en. Accessed 12 Oct 2023
Blum KM, Andersson PL, Renman G et al (2017) Non-target screening and prioritization of potentially persistent, bioaccumulating and toxic domestic wastewater contaminants and their removal in on-site and large-scale sewage treatment plants. Sci Total Environ 575:265–275. 10.1016/j.scitotenv.2016.09.135 DOI: 10.1016/j.scitotenv.2016.09.135
Wang X, Yu N, Qian Y et al (2020) Non-target and suspect screening of per- and polyfluoroalkyl substances in Chinese municipal wastewater treatment plants. Water Res 183:12. 10.1016/j.watres.2020.115989 DOI: 10.1016/j.watres.2020.115989
Suman T-Y, Kim S-Y, Yeom D-H, Jeon J (2022) Transformation products of emerging pollutants explored using non-target screening: perspective in the transformation pathway and toxicity mechanism—a review. Toxics 10:22. 10.3390/toxics10020054 DOI: 10.3390/toxics10020054
Helmus R, van de Velde B, Brunner AM et al (2022) patRoon 2.0: improved non-target analysis workflows including automated transformation product screening. J Open Source Softw. 10.21105/joss.04029 DOI: 10.21105/joss.04029
Helmus R, ter Laak TL, van Wezel AP et al (2021) patRoon: open source software platform for environmental mass spectrometry based non-target screening. J Cheminformatics. 10.1186/s13321-020-00477-w DOI: 10.1186/s13321-020-00477-w
Smith CA, Want EJ, O’Maille G et al (2006) XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal Chem 78:779–787. 10.1021/ac051437y DOI: 10.1021/ac051437y
Tautenhahn R, Böttcher C, Neumann S (2008) Highly sensitive feature detection for high resolution LC/MS. BMC Bioinform. 10.1186/1471-2105-9-504 DOI: 10.1186/1471-2105-9-504
Benton HP, Want EJ, Ebbels TMD (2010) Correction of mass calibration gaps in liquid chromatography–mass spectrometry metabolomics data. Bioinformatics 26:2488–2489. 10.1093/bioinformatics/btq441 DOI: 10.1093/bioinformatics/btq441
Ruttkies C, Schymanski EL, Wolf S et al (2016) MetFrag relaunched: incorporating strategies beyond in silico fragmentation. J Cheminform. 10.1186/s13321-016-0115-9 DOI: 10.1186/s13321-016-0115-9
Libiseller G, Dvorzak M, Kleb U et al (2015) IPO: a tool for automated optimization of XCMS parameters. BMC Bioinformatics 16:118. 10.1186/s12859-015-0562-8 DOI: 10.1186/s12859-015-0562-8
Albóniga OE, González O, Alonso RM et al (2020) Optimization of XCMS parameters for LC–MS metabolomics: an assessment of automated versus manual tuning and its effect on the final results. Metabolomics 16:14. 10.1007/s11306-020-1636-9 DOI: 10.1007/s11306-020-1636-9
Tostengard AR, Smith R (2021) A review and evaluation of techniques for improved feature detection in mass spectrometry data. Grad Stud Theses Diss Prof Pap Univ Mont https://scholarworks.umt.edu/etd/11679:41. Accessed 23 March 2023
Kim S, Chen J, Cheng T et al (2023) PubChem 2023 update. Nucleic Acids Res 51:D1373–D1380. 10.1093/nar/gkac956 DOI: 10.1093/nar/gkac956
Bolton E, Schymanski E, Kondic T, et al (2023) PubChemLite for Exposomics. https://doi.org/10.5281/zenodo.7576412
Schymanski EL, Kondić T, Neumann S et al (2021) Empowering large chemical knowledge bases for exposomics: PubChemLite meets MetFrag. J Cheminformatics 13:19. 10.1186/s13321-021-00489-0 DOI: 10.1186/s13321-021-00489-0
Schymanski EL, Jeon J, Gulde R et al (2014) Identifying small molecules via high resolution mass spectrometry: communicating confidence. Env Sci Technol 48:2097–2098. 10.1021/es5002105 DOI: 10.1021/es5002105
Fiehnlab (2018) MassBank of North America (MoNA). In: https://mona.fiehnlab.ucdavis.edu/. https://mona.fiehnlab.ucdavis.edu/. Accessed 20 Jun 2023
Djoumbou Feunang Y, Eisner R, Knox C et al (2016) ClassyFire: automated chemical classification with a comprehensive, computable taxonomy. J Cheminformatics 8:61. 10.1186/s13321-016-0174-y DOI: 10.1186/s13321-016-0174-y
Kessner D, Chambers M, Burke R et al (2008) ProteoWizard: open source software for rapid proteomics tools development. Bioinformatics 24:2534–2536. 10.1093/bioinformatics/btn323 DOI: 10.1093/bioinformatics/btn323
Chambers MC, Maclean B, Burke R et al (2012) A cross-platform toolkit for mass spectrometry and proteomics. Nat Biotechnol 30:918–920. 10.1038/nbt.2377 DOI: 10.1038/nbt.2377
Martens L, Chambers M, Sturm M et al (2011) mzML - a community standard for mass spectrometry data. Mol Cell Proteomics 10(R110):000133. 10.1074/mcp.R110.000133 DOI: 10.1074/mcp.R110.000133
Pedrioli PGA, Eng JK, Hubley R et al (2004) A common open representation of mass spectrometry data and its application to proteomics research. Nat Biotechnol 22:1459–1466. 10.1038/nbt1031 DOI: 10.1038/nbt1031
Keller A, Eng J, Zhang N et al (2005) A uniform proteomics MS/MS analysis platform utilizing open XML file formats. Mol Syst Biol 1:8. 10.1038/msb4100024 DOI: 10.1038/msb4100024
Talavera Andújar B, Aurich D, Aho VTE et al (2022) Studying the Parkinson’s disease metabolome and exposome in biological samples through different analytical and cheminformatics approaches: a pilot study. Anal Bioanal Chem 414:7399–7419. 10.1007/s00216-022-04207-z DOI: 10.1007/s00216-022-04207-z
Awan M, Buriak I, Fleck R et al (2020) Dimethyl sulfoxide: a central player since the dawn of cryobiology, is efficacy balanced by toxicity? Regen Med 15:1463–1491. 10.2217/rme-2019-0145 DOI: 10.2217/rme-2019-0145
Best C, Melnyk-Lamont N, Gesto M, Vijayan MM (2014) Environmental levels of the antidepressant venlafaxine impact the metabolic capacity of rainbow trout. Aquat Toxicol Amst Neth 155:190–198. 10.1016/j.aquatox.2014.06.014 DOI: 10.1016/j.aquatox.2014.06.014
Maddela NR, Venkateswarlu K, Megharaj M (2020) Tris(2-chloroethyl) phosphate, a pervasive flame retardant: critical perspective on its emissions into the environment and human toxicity. Environ Sci Process Impacts 22:1809–1827. 10.1039/D0EM00222D DOI: 10.1039/D0EM00222D
Wu H, Zhong M, Lu Z et al (2018) Biological effects of tris (1-chloro-2-propyl) phosphate (TCPP) on immunity in mussel Mytilus galloprovincialis. Environ Toxicol Pharmacol 61:102–106. 10.1016/j.etap.2018.05.022 DOI: 10.1016/j.etap.2018.05.022
Ji C, Lu Z, Xu L et al (2020) Global responses to tris(1-chloro-2-propyl)phosphate (TCPP) in rockfish Sebastes schlegeli using integrated proteomic and metabolomic approach. Sci Total Environ. 10.1016/j.scitotenv.2020.138307 DOI: 10.1016/j.scitotenv.2020.138307
Zhang Z-N, Yang D-L, Liu H et al (2023) Effects of TCPP and TCEP exposure on human corneal epithelial cells: oxidative damage, cell cycle arrest, and pyroptosis. Chemosphere. 10.1016/j.chemosphere.2023.138817 DOI: 10.1016/j.chemosphere.2023.138817
Naushad Mu, Ahamad T, Rizwan Khan M (2022) Remediation of wastewater containing 4-nitrophenol using ionic liquid stabilized nanoparticles: Synthesis, characterizations and applications. Chemosphere. 10.1016/j.chemosphere.2022.135173 DOI: 10.1016/j.chemosphere.2022.135173
Lin W, He Y, Li R et al (2023) Adaptive changes of swimming crab (Portunus trituberculatus) associated bacteria helping host against dibutyl phthalate toxification. Environ Pollut. 10.1016/j.envpol.2023.121328 DOI: 10.1016/j.envpol.2023.121328
Fiedler H, Kennedy T, Henry BJ (2021) A critical review of a recommended analytical and classification approach for organic fluorinated compounds with an emphasis on Per- and polyfluoroalkyl substances. Integr Environ Assess Manag 17:331–351. 10.1002/ieam.4352 DOI: 10.1002/ieam.4352
Zhu L, Jiang C, Panthi S et al (2021) Impact of high precipitation and temperature events on the distribution of emerging contaminants in surface water in the Mid-Atlantic, United States. Sci Total Environ. 10.1016/j.scitotenv.2020.142552 DOI: 10.1016/j.scitotenv.2020.142552
statista (2023) Luxembourg: annual rainfall in Luxembourg-City 2021. In: Httpswwwstatistacomstatistics584864annual-Rainfall--Luxemb.-City. https://www.statista.com/statistics/584864/annual-rainfall-in-luxembourg-city/. Accessed 1 Jun 2023
Kuhl C, Tautenhahn R, Böttcher C et al (2012) CAMERA: an integrated strategy for compound spectra extraction and annotation of liquid chromatography/mass spectrometry data sets. Anal Chem 84:283–289. 10.1021/ac202450g DOI: 10.1021/ac202450g
Broeckling CD, Afsar FA, Neumann S et al (2014) RAMClust: a novel feature clustering method enables spectral-matching-based annotation for metabolomics data. Anal Chem 86:6812–6817. 10.1021/ac501530d DOI: 10.1021/ac501530d
Senan O, Aguilar-Mogas A, Navarro M et al (2019) CliqueMS: a computational tool for annotating in-source metabolite ions from LC-MS untargeted metabolomics data based on a coelution similarity network. Bioinformatics 35:4089–4097. 10.1093/bioinformatics/btz207 DOI: 10.1093/bioinformatics/btz207
Meringer M, Reinker S, Zhang J, Muller A (2011) MS/MS data improves automated determination of molecular formulas by mass spectrometry. Match Commun Math Comput Chem 65:259–290
Venditti S, Arenz-Leufen M, Köhler C, et al Treatment of pharmaceutical wastewater by O3 and O3/H2O2 processes: a pilot scale study in Luxembourg. https://www.researchgate.net/publication/260697444. Accessed 19 October 2023
Krein A, Pailler J-Y, Guignard C et al (2012) Determination of estrogen activity in river waters and wastewater in Luxembourg by chemical analysis and the yeast estrogen screen assay. Environ Pollut 1:p86. 10.5539/ep.v1n2p86 DOI: 10.5539/ep.v1n2p86
Pailler J-Y, Krein A, Pfister L et al (2009) Solid phase extraction coupled to liquid chromatography-tandem mass spectrometry analysis of sulfonamides, tetracyclines, analgesics and hormones in surface water and wastewater in Luxembourg. Sci Total Environ 407:4736–4743. 10.1016/j.scitotenv.2009.04.042 DOI: 10.1016/j.scitotenv.2009.04.042
