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Coupling Suspect and Nontarget Screening with Mass Balance Modeling to Characterize Organic Micropollutants in the Onondaga Lake–Three Rivers System
[Image: see text] Characterizing the occurrence, sources, and fate of organic micropollutants (OMPs) in lake–river systems serves as an important foundation for constraining the potential impacts of OMPs on the ecosystem functions of these critical landscape features. In this work, we combined suspe...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Chemical Society
2021
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8600663/ https://www.ncbi.nlm.nih.gov/pubmed/34730951 http://dx.doi.org/10.1021/acs.est.1c04699 |
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author | Wang, Shiru Perkins, MaryGail Matthews, David A. Zeng, Teng |
author_facet | Wang, Shiru Perkins, MaryGail Matthews, David A. Zeng, Teng |
author_sort | Wang, Shiru |
collection | PubMed |
description | [Image: see text] Characterizing the occurrence, sources, and fate of organic micropollutants (OMPs) in lake–river systems serves as an important foundation for constraining the potential impacts of OMPs on the ecosystem functions of these critical landscape features. In this work, we combined suspect and nontarget screening with mass balance modeling to investigate OMP contamination in the Onondaga Lake–Three Rivers system of New York. Suspect and nontarget screening enabled by liquid chromatography–high-resolution mass spectrometry led to the confirmation and quantification of 105 OMPs in water samples collected throughout the lake–river system, which were grouped by their concentration patterns into wastewater-derived and mixed-source clusters via hierarchical cluster analysis. Four of these OMPs (i.e., galaxolidone, diphenylphosphinic acid, N-butylbenzenesulfonamide, and triisopropanolamine) were prioritized and identified by nontarget screening based on their characteristic vertical distribution patterns during thermal stratification in Onondaga Lake. Mass balance modeling performed using the concentration and discharge data highlighted the export of OMPs from Onondaga Lake to the Three Rivers as a major contributor to the OMP budget in this lake–river system. Overall, this work demonstrated the utility of an integrated screening and modeling framework that can be adapted for OMP characterization, fate assessment, and load apportionment in similar surface water systems. |
format | Online Article Text |
id | pubmed-8600663 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | American Chemical Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-86006632021-11-18 Coupling Suspect and Nontarget Screening with Mass Balance Modeling to Characterize Organic Micropollutants in the Onondaga Lake–Three Rivers System Wang, Shiru Perkins, MaryGail Matthews, David A. Zeng, Teng Environ Sci Technol [Image: see text] Characterizing the occurrence, sources, and fate of organic micropollutants (OMPs) in lake–river systems serves as an important foundation for constraining the potential impacts of OMPs on the ecosystem functions of these critical landscape features. In this work, we combined suspect and nontarget screening with mass balance modeling to investigate OMP contamination in the Onondaga Lake–Three Rivers system of New York. Suspect and nontarget screening enabled by liquid chromatography–high-resolution mass spectrometry led to the confirmation and quantification of 105 OMPs in water samples collected throughout the lake–river system, which were grouped by their concentration patterns into wastewater-derived and mixed-source clusters via hierarchical cluster analysis. Four of these OMPs (i.e., galaxolidone, diphenylphosphinic acid, N-butylbenzenesulfonamide, and triisopropanolamine) were prioritized and identified by nontarget screening based on their characteristic vertical distribution patterns during thermal stratification in Onondaga Lake. Mass balance modeling performed using the concentration and discharge data highlighted the export of OMPs from Onondaga Lake to the Three Rivers as a major contributor to the OMP budget in this lake–river system. Overall, this work demonstrated the utility of an integrated screening and modeling framework that can be adapted for OMP characterization, fate assessment, and load apportionment in similar surface water systems. American Chemical Society 2021-11-03 2021-11-16 /pmc/articles/PMC8600663/ /pubmed/34730951 http://dx.doi.org/10.1021/acs.est.1c04699 Text en © 2021 The Authors. Published by American Chemical Society https://creativecommons.org/licenses/by/4.0/Permits the broadest form of re-use including for commercial purposes, provided that author attribution and integrity are maintained (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Wang, Shiru Perkins, MaryGail Matthews, David A. Zeng, Teng Coupling Suspect and Nontarget Screening with Mass Balance Modeling to Characterize Organic Micropollutants in the Onondaga Lake–Three Rivers System |
title | Coupling
Suspect and Nontarget Screening with Mass
Balance Modeling to Characterize Organic Micropollutants in the Onondaga
Lake–Three Rivers System |
title_full | Coupling
Suspect and Nontarget Screening with Mass
Balance Modeling to Characterize Organic Micropollutants in the Onondaga
Lake–Three Rivers System |
title_fullStr | Coupling
Suspect and Nontarget Screening with Mass
Balance Modeling to Characterize Organic Micropollutants in the Onondaga
Lake–Three Rivers System |
title_full_unstemmed | Coupling
Suspect and Nontarget Screening with Mass
Balance Modeling to Characterize Organic Micropollutants in the Onondaga
Lake–Three Rivers System |
title_short | Coupling
Suspect and Nontarget Screening with Mass
Balance Modeling to Characterize Organic Micropollutants in the Onondaga
Lake–Three Rivers System |
title_sort | coupling
suspect and nontarget screening with mass
balance modeling to characterize organic micropollutants in the onondaga
lake–three rivers system |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8600663/ https://www.ncbi.nlm.nih.gov/pubmed/34730951 http://dx.doi.org/10.1021/acs.est.1c04699 |
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