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Hulleman T, Turkina V, O’Brien JW, Chojnacka A, Thomas KV, Samanipour S. Critical Assessment of the Chemical Space Covered by LC-HRMS Non-Targeted Analysis. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:14101-14112. [PMID: 37704971 PMCID: PMC10537454 DOI: 10.1021/acs.est.3c03606] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 08/29/2023] [Accepted: 08/30/2023] [Indexed: 09/15/2023]
Abstract
Non-targeted analysis (NTA) has emerged as a valuable approach for the comprehensive monitoring of chemicals of emerging concern (CECs) in the exposome. The NTA approach can theoretically identify compounds with diverse physicochemical properties and sources. Even though they are generic and have a wide scope, non-targeted analysis methods have been shown to have limitations in terms of their coverage of the chemical space, as the number of identified chemicals in each sample is very low (e.g., ≤5%). Investigating the chemical space that is covered by each NTA assay is crucial for understanding the limitations and challenges associated with the workflow, from the experimental methods to the data acquisition and data processing techniques. In this review, we examined recent NTA studies published between 2017 and 2023 that employed liquid chromatography-high-resolution mass spectrometry. The parameters used in each study were documented, and the reported chemicals at confidence levels 1 and 2 were retrieved. The chosen experimental setups and the quality of the reporting were critically evaluated and discussed. Our findings reveal that only around 2% of the estimated chemical space was covered by the NTA studies investigated for this review. Little to no trend was found between the experimental setup and the observed coverage due to the generic and wide scope of the NTA studies. The limited coverage of the chemical space by the reviewed NTA studies highlights the necessity for a more comprehensive approach in the experimental and data processing setups in order to enable the exploration of a broader range of chemical space, with the ultimate goal of protecting human and environmental health. Recommendations for further exploring a wider range of the chemical space are given.
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Affiliation(s)
- Tobias Hulleman
- Van
’t Hoff Institute for Molecular Sciences (HIMS), University of Amsterdam, 1090 GD Amsterdam, The Netherlands
| | - Viktoriia Turkina
- Van
’t Hoff Institute for Molecular Sciences (HIMS), University of Amsterdam, 1090 GD Amsterdam, The Netherlands
| | - Jake W. O’Brien
- Van
’t Hoff Institute for Molecular Sciences (HIMS), University of Amsterdam, 1090 GD Amsterdam, The Netherlands
- Queensland
Alliance for Environmental Health Sciences (QAEHS), The University of Queensland, 20 Cornwall Street, Woolloongabba, Queensland 4102, Australia
| | - Aleksandra Chojnacka
- Van
’t Hoff Institute for Molecular Sciences (HIMS), University of Amsterdam, 1090 GD Amsterdam, The Netherlands
| | - Kevin V. Thomas
- Queensland
Alliance for Environmental Health Sciences (QAEHS), The University of Queensland, 20 Cornwall Street, Woolloongabba, Queensland 4102, Australia
| | - Saer Samanipour
- Van
’t Hoff Institute for Molecular Sciences (HIMS), University of Amsterdam, 1090 GD Amsterdam, The Netherlands
- UvA
Data Science Center, University of Amsterdam, 1012 WP Amsterdam, The Netherlands
- Queensland
Alliance for Environmental Health Sciences (QAEHS), 20 Cornwall Street, Woolloongabba, Queensland 4102, Australia
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2
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van Herwerden D, O’Brien JW, Lege S, Pirok BWJ, Thomas KV, Samanipour S. Cumulative Neutral Loss Model for Fragment Deconvolution in Electrospray Ionization High-Resolution Mass Spectrometry Data. Anal Chem 2023; 95:12247-12255. [PMID: 37549176 PMCID: PMC10448439 DOI: 10.1021/acs.analchem.3c00896] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 07/03/2023] [Indexed: 08/09/2023]
Abstract
Clean high-resolution mass spectra (HRMS) are essential to a successful structural elucidation of an unknown feature during nontarget analysis (NTA) workflows. This is a crucial step, particularly for the spectra generated during data-independent acquisition or during direct infusion experiments. The most commonly available tools only take advantage of the time domain for spectral cleanup. Here, we present an algorithm that combines the time domain and mass domain information to perform spectral deconvolution. The algorithm employs a probability-based cumulative neutral loss (CNL) model for fragment deconvolution. The optimized model, with a mass tolerance of 0.005 Da and a scoreCNL threshold of 0.00, was able to achieve a true positive rate (TPr) of 95.0%, a false discovery rate (FDr) of 20.6%, and a reduction rate of 35.4%. Additionally, the CNL model was extensively tested on real samples containing predominantly pesticides at different concentration levels and with matrix effects. Overall, the model was able to obtain a TPr above 88.8% with FD rates between 33 and 79% and reduction rates between 9 and 45%. Finally, the CNL model was compared with the retention time difference method and peak shape correlation analysis, showing that a combination of correlation analysis and the CNL model was the most effective for fragment deconvolution, obtaining a TPr of 84.7%, an FDr of 54.4%, and a reduction rate of 51.0%.
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Affiliation(s)
- Denice van Herwerden
- Van
’t Hoff Institute for Molecular Sciences (HIMS), University of Amsterdam, Amsterdam 1012 WX, The Netherlands
| | - Jake W. O’Brien
- Van
’t Hoff Institute for Molecular Sciences (HIMS), University of Amsterdam, Amsterdam 1012 WX, The Netherlands
- Queensland
Alliance for Environmental Health Sciences (QAEHS), The University of Queensland, Brisbane 4102, Australia
| | - Sascha Lege
- Agilent
Technologies Deutschland GmbH, Waldbronn 76337, Germany
| | - Bob W. J. Pirok
- Van
’t Hoff Institute for Molecular Sciences (HIMS), University of Amsterdam, Amsterdam 1012 WX, The Netherlands
| | - Kevin V. Thomas
- Queensland
Alliance for Environmental Health Sciences (QAEHS), The University of Queensland, Brisbane 4102, Australia
| | - Saer Samanipour
- Van
’t Hoff Institute for Molecular Sciences (HIMS), University of Amsterdam, Amsterdam 1012 WX, The Netherlands
- Queensland
Alliance for Environmental Health Sciences (QAEHS), The University of Queensland, Brisbane 4102, Australia
- UvA
Data Science Center, University of Amsterdam, Amsterdam 1012 WP, The Netherlands
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3
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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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4
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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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5
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Dürig W, Alygizakis NA, Menger F, Golovko O, Wiberg K, Ahrens L. Novel prioritisation strategies for evaluation of temporal trends in archived white-tailed sea eagle muscle tissue in non-target screening. JOURNAL OF HAZARDOUS MATERIALS 2022; 424:127331. [PMID: 34879552 DOI: 10.1016/j.jhazmat.2021.127331] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Revised: 09/20/2021] [Accepted: 09/21/2021] [Indexed: 06/13/2023]
Abstract
Environmental monitoring studies based on target analysis capture only a small fraction of contaminants of emerging concern (CECs) and miss pollutants potentially harmful to wildlife. Environmental specimen banks, with their archived samples, provide opportunities to identify new CECs by temporal trend analysis and non-target screening. In this study, archived white-tailed sea eagle (Haliaeetus albicilla) muscle tissue was analysed by non-targeted high-resolution mass spectrometry. Univariate statistical tests (Mann-Kendall and Spearman rank) for temporal trend analysis were applied as prioritisation methods. A workflow for non-target data was developed and validated using an artificial time series spiked at five levels with gradient concentrations of selected CECs (n = 243). Pooled eagle muscle tissues collected 1965-2017 were then investigated with an eight-point time series using the validated screening workflow. Following peak detection, peak alignment, and blank subtraction, 14 409 features were considered for statistical analysis. Prioritisation by time-trend analysis detected 207 features with increasing trends. Following unequivocal molecular formula assignment to prioritised features and further elucidation with MetFrag and EU Massbank, 13 compounds were tentatively identified, of which four were of anthropogenic origin. These results show that it is possible to prioritise new CECs in archived biological samples using univariate statistical approaches.
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Affiliation(s)
- Wiebke Dürig
- Department of Aquatic Sciences and Assessment, Swedish University of Agricultural Sciences (SLU), Box 7050, SE-750 07 Uppsala, Sweden.
| | - Nikiforos A Alygizakis
- Environmental Institute, Okruzná 784/42, 97241 Koš, Slovak Republic; Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Greece.
| | - Frank Menger
- Department of Aquatic Sciences and Assessment, Swedish University of Agricultural Sciences (SLU), Box 7050, SE-750 07 Uppsala, Sweden.
| | - Oksana Golovko
- Department of Aquatic Sciences and Assessment, Swedish University of Agricultural Sciences (SLU), Box 7050, SE-750 07 Uppsala, Sweden.
| | - Karin Wiberg
- Department of Aquatic Sciences and Assessment, Swedish University of Agricultural Sciences (SLU), Box 7050, SE-750 07 Uppsala, Sweden.
| | - Lutz Ahrens
- Department of Aquatic Sciences and Assessment, Swedish University of Agricultural Sciences (SLU), Box 7050, SE-750 07 Uppsala, Sweden.
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6
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Samanipour S, Choi P, O'Brien JW, Pirok BWJ, Reid MJ, Thomas KV. From Centroided to Profile Mode: Machine Learning for Prediction of Peak Width in HRMS Data. Anal Chem 2021; 93:16562-16570. [PMID: 34843646 PMCID: PMC8674881 DOI: 10.1021/acs.analchem.1c03755] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Centroiding is one of the major approaches used for size reduction of the data generated by high-resolution mass spectrometry. During centroiding, performed either during acquisition or as a pre-processing step, the mass profiles are represented by a single value (i.e., the centroid). While being effective in reducing the data size, centroiding also reduces the level of information density present in the mass peak profile. Moreover, each step of the centroiding process and their consequences on the final results may not be completely clear. Here, we present Cent2Prof, a package containing two algorithms that enables the conversion of the centroided data to mass peak profile data and vice versa. The centroiding algorithm uses the resolution-based mass peak width parameter as the first guess and self-adjusts to fit the data. In addition to the m/z values, the centroiding algorithm also generates the measured mass peak widths at half-height, which can be used during the feature detection and identification. The mass peak profile prediction algorithm employs a random-forest model for the prediction of mass peak widths, which is consequently used for mass profile reconstruction. The centroiding results were compared to the outputs of the MZmine-implemented centroiding algorithm. Our algorithm resulted in rates of false detection ≤5% while the MZmine algorithm resulted in 30% rate of false positive and 3% rate of false negative. The error in profile prediction was ≤56% independent of the mass, ionization mode, and intensity, which was 6 times more accurate than the resolution-based estimated values.
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Affiliation(s)
- Saer Samanipour
- Van't Hoff Institute for Molecular Sciences (HIMS), University of Amsterdam, Science Park 904, Amsterdam 1098 XH, The Netherlands.,Queensland Alliance for Environmental Health Sciences (QAEHS), The University of Queensland, Woolloongabba, Queensland 4102, Australia.,Norwegian Institute for Water Research (NIVA), Økernveien 94, Oslo 0579, Norway
| | - Phil Choi
- Queensland Alliance for Environmental Health Sciences (QAEHS), The University of Queensland, Woolloongabba, Queensland 4102, Australia.,Water Unit, Health Protection Branch, Prevention Division, Queensland Department of Health, Brisbane, Queensland 4000, Australia
| | - Jake W O'Brien
- Queensland Alliance for Environmental Health Sciences (QAEHS), The University of Queensland, Woolloongabba, Queensland 4102, Australia
| | - Bob W J Pirok
- Van't Hoff Institute for Molecular Sciences (HIMS), University of Amsterdam, Science Park 904, Amsterdam 1098 XH, The Netherlands
| | - Malcolm J Reid
- Norwegian Institute for Water Research (NIVA), Økernveien 94, Oslo 0579, Norway
| | - Kevin V Thomas
- Queensland Alliance for Environmental Health Sciences (QAEHS), The University of Queensland, Woolloongabba, Queensland 4102, Australia
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7
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Inter-laboratory mass spectrometry dataset based on passive sampling of drinking water for non-target analysis. Sci Data 2021; 8:223. [PMID: 34429429 PMCID: PMC8384892 DOI: 10.1038/s41597-021-01002-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Accepted: 07/12/2021] [Indexed: 11/09/2022] Open
Abstract
Non-target analysis (NTA) employing high-resolution mass spectrometry is a commonly applied approach for the detection of novel chemicals of emerging concern in complex environmental samples. NTA typically results in large and information-rich datasets that require computer aided (ideally automated) strategies for their processing and interpretation. Such strategies do however raise the challenge of reproducibility between and within different processing workflows. An effective strategy to mitigate such problems is the implementation of inter-laboratory studies (ILS) with the aim to evaluate different workflows and agree on harmonized/standardized quality control procedures. Here we present the data generated during such an ILS. This study was organized through the Norman Network and included 21 participants from 11 countries. A set of samples based on the passive sampling of drinking water pre and post treatment was shipped to all the participating laboratories for analysis, using one pre-defined method and one locally (i.e. in-house) developed method. The data generated represents a valuable resource (i.e. benchmark) for future developments of algorithms and workflows for NTA experiments. Measurement(s) | chemical • drinking water | Technology Type(s) | high resolution mass spectrometry • non-target analysis • Interlaboratory | Factor Type(s) | method | Sample Characteristic - Environment | laboratory environment |
Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.15028665
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8
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An assessment of quality assurance/quality control efforts in high resolution mass spectrometry non-target workflows for analysis of environmental samples. Trends Analyt Chem 2020. [DOI: 10.1016/j.trac.2020.116063] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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9
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Beyer J, Goksøyr A, Hjermann DØ, Klungsøyr J. Environmental effects of offshore produced water discharges: A review focused on the Norwegian continental shelf. MARINE ENVIRONMENTAL RESEARCH 2020; 162:105155. [PMID: 32992224 DOI: 10.1016/j.marenvres.2020.105155] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Revised: 09/15/2020] [Accepted: 09/17/2020] [Indexed: 06/11/2023]
Abstract
Produced water (PW), a large byproduct of offshore oil and gas extraction, is reinjected to formations or discharged to the sea after treatment. The discharges contain dispersed crude oil, polycyclic aromatic hydrocarbons (PAHs), alkylphenols (APs), metals, and many other constituents of environmental relevance. Risk-based regulation, greener offshore chemicals and improved cleaning systems have reduced environmental risks of PW discharges, but PW is still the largest operational source of oil pollution to the sea from the offshore petroleum industry. Monitoring surveys find detectable exposures in caged mussel and fish several km downstream from PW outfalls, but biomarkers indicate only mild acute effects in these sentinels. On the other hand, increased concentrations of DNA adducts are found repeatedly in benthic fish populations, especially in haddock. It is uncertain whether increased adducts could be a long-term effect of sediment contamination due to ongoing PW discharges, or earlier discharges of oil-containing drilling waste. Another concern is uncertainty regarding the possible effect of PW discharges in the sub-Arctic Southern Barents Sea. So far, research suggests that sub-arctic species are largely comparable to temperate species in their sensitivity to PW exposure. Larval deformities and cardiac toxicity in fish early life stages are among the biomarkers and adverse outcome pathways that currently receive much attention in PW effect research. Herein, we summarize the accumulated ecotoxicological knowledge of offshore PW discharges and highlight some key remaining knowledge needs.
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Affiliation(s)
- Jonny Beyer
- Norwegian Institute for Water Research (NIVA), Oslo, Norway.
| | - Anders Goksøyr
- Department of Biological Sciences, University of Bergen, Norway; Institute of Marine Research (IMR), Bergen, Norway
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Samanipour S, Reid MJ, Rundberget JT, Frost TK, Thomas KV. Concentration and Distribution of Naphthenic Acids in the Produced Water from Offshore Norwegian North Sea Oilfields. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2020; 54:2707-2714. [PMID: 32019310 DOI: 10.1021/acs.est.9b05784] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Naphthenic acids (NAs) constitute one of the toxic components of the produced water (PW) from offshore oil platforms discharged into the marine environment. We employed liquid chromatography (LC) coupled to high-resolution mass spectrometry with electrospray ionization (ESI) in negative mode for the comprehensive chemical characterization and quantification of NAs in PW samples from six different Norwegian offshore oil platforms. In total, we detected 55 unique NA isomer groups, out of the 181 screened homologous groups, across all tested samples. The frequency of detected NAs in the samples varied between 14 and 44 isomer groups. Principal component analysis (PCA) indicated a clear distinction of the PW from the tested platforms based on the distribution of NAs in these samples. The averaged total concentration of NAs varied between 6 and 56 mg L-1, among the tested platforms, whereas the concentrations of the individual NA isomer groups ranged between 0.2 and 44 mg L-1. Based on both the distribution and the concentration of NAs in the samples, the C8H14O2 isomer group appeared to be a reasonable indicator of the presence and the total concentration of NAs in the samples with a Pearson correlation coefficient of 0.89.
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Affiliation(s)
- Saer Samanipour
- Norwegian Institute for Water Research (NIVA), Gaustadalléen 21, Oslo 0349, Norway
- Queensland Alliance for Environmental Health Sciences (QAEHS), The University of Queensland, 20 Cornwall St, Woolloongabba, Queensland 4102, Australia
| | - Malcolm J Reid
- Norwegian Institute for Water Research (NIVA), Gaustadalléen 21, Oslo 0349, Norway
| | | | - Tone K Frost
- Equinor, Arkitekt Ebbels veg 10, Rotvoll, Trondheim 7005, Norway
| | - Kevin V Thomas
- Norwegian Institute for Water Research (NIVA), Gaustadalléen 21, Oslo 0349, Norway
- Queensland Alliance for Environmental Health Sciences (QAEHS), The University of Queensland, 20 Cornwall St, Woolloongabba, Queensland 4102, Australia
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11
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Jewell KS, Kunkel U, Ehlig B, Thron F, Schlüsener M, Dietrich C, Wick A, Ternes TA. Comparing mass, retention time and tandem mass spectra as criteria for the automated screening of small molecules in aqueous environmental samples analyzed by liquid chromatography/quadrupole time-of-flight tandem mass spectrometry. RAPID COMMUNICATIONS IN MASS SPECTROMETRY : RCM 2020; 34:e8541. [PMID: 31364212 DOI: 10.1002/rcm.8541] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/09/2019] [Revised: 06/27/2019] [Accepted: 07/22/2019] [Indexed: 06/10/2023]
Abstract
RATIONALE The adoption of database screening using high-resolution liquid chromatography/mass spectrometry data is promising as a river water monitoring and surveillance tool but depends on the ability to perform reliable data processing on a large number of samples in a unified workflow. Strategies to minimize errors have been proposed but automated procedures are rare. METHODS High-resolution LC/ESI-QTOFMS/MS in data-dependent MS2 acquisition mode was performed for the analysis of surface water samples by direct injection. Data processing was achieved with software tools written in R. A database containing MS2 spectra of 693 compounds formed the basis of the workflow. Standard mixes and a time series of 361 samples of river water were analyzed and processed with the optimized workflow. RESULTS Using the database and a mix of 70 standards for testing, it was found that an identification strategy including (i) mass, (ii) retention time, and (iii) MS2 spectral matching achieved a two- to three-fold improvement in the fraction of false positives compared with using only two criteria, while the number of false negatives remained low. The optimized workflow was applied to the sample series of river water. In total, 135 compounds were identified by a library match. CONCLUSIONS The developed automated database screening approach minimizes the proportion of false positives, while still allowing for the screening of hundreds of water samples for hundreds of compounds in a single run.
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Affiliation(s)
- Kevin S Jewell
- Federal Institute of Hydrology, Am Mainzer Tor 1, 56068, Koblenz, Germany
| | - Uwe Kunkel
- Federal Institute of Hydrology, Am Mainzer Tor 1, 56068, Koblenz, Germany
| | - Björn Ehlig
- Federal Institute of Hydrology, Am Mainzer Tor 1, 56068, Koblenz, Germany
| | - Franziska Thron
- Federal Institute of Hydrology, Am Mainzer Tor 1, 56068, Koblenz, Germany
| | - Michael Schlüsener
- Federal Institute of Hydrology, Am Mainzer Tor 1, 56068, Koblenz, Germany
| | - Christian Dietrich
- Federal Institute of Hydrology, Am Mainzer Tor 1, 56068, Koblenz, Germany
| | - Arne Wick
- Federal Institute of Hydrology, Am Mainzer Tor 1, 56068, Koblenz, Germany
| | - Thomas A Ternes
- Federal Institute of Hydrology, Am Mainzer Tor 1, 56068, Koblenz, Germany
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12
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Samanipour S, O’Brien JW, Reid MJ, Thomas KV. Self Adjusting Algorithm for the Nontargeted Feature Detection of High Resolution Mass Spectrometry Coupled with Liquid Chromatography Profile Data. Anal Chem 2019; 91:10800-10807. [DOI: 10.1021/acs.analchem.9b02422] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Affiliation(s)
- Saer Samanipour
- Norwegian Institute for Water Research (NIVA), Gaustadalléen 21, Oslo 0349, Norway
- Queensland Alliance for Environmental Health Sciences (QAEHS), The University of Queensland, 20 Cornwall St., Woolloongabba, Qld 4102, Australia
| | - Jake W. O’Brien
- Queensland Alliance for Environmental Health Sciences (QAEHS), The University of Queensland, 20 Cornwall St., Woolloongabba, Qld 4102, Australia
| | - Malcolm J. Reid
- Norwegian Institute for Water Research (NIVA), Gaustadalléen 21, Oslo 0349, Norway
| | - Kevin V. Thomas
- Norwegian Institute for Water Research (NIVA), Gaustadalléen 21, Oslo 0349, Norway
- Queensland Alliance for Environmental Health Sciences (QAEHS), The University of Queensland, 20 Cornwall St., Woolloongabba, Qld 4102, Australia
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13
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Samanipour S, Kaserzon S, Vijayasarathy S, Jiang H, Choi P, Reid MJ, Mueller JF, Thomas KV. Machine learning combined with non-targeted LC-HRMS analysis for a risk warning system of chemical hazards in drinking water: A proof of concept. Talanta 2019; 195:426-432. [DOI: 10.1016/j.talanta.2018.11.039] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2018] [Revised: 11/10/2018] [Accepted: 11/13/2018] [Indexed: 10/27/2022]
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14
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Samanipour S, Hooshyari M, Baz-Lomba JA, Reid MJ, Casale M, Thomas KV. The effect of extraction methodology on the recovery and distribution of naphthenic acids of oilfield produced water. THE SCIENCE OF THE TOTAL ENVIRONMENT 2019; 652:1416-1423. [PMID: 30586826 DOI: 10.1016/j.scitotenv.2018.10.264] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/28/2018] [Revised: 10/15/2018] [Accepted: 10/19/2018] [Indexed: 06/09/2023]
Abstract
Comprehensive chemical characterization of naphthenic acids (NAs) in oilfield produced water is a challenging task due to sample complexity. The recovery of NAs from produced water, and the corresponding distribution of detectable NAs are strongly influenced by sample extraction methodologies. In this study, we evaluated the effect of the extraction method on chemical space (i.e. the total number of chemicals present in a sample), relative recovery, and the distribution of NAs in a produced water sample. Three generic and pre-established extraction methods (i.e. liquid-liquid extraction (Lq), and solid phase extraction using HLB cartridges (HLB), and the combination of ENV+ and C8 (ENV) cartridges) were employed for our evaluation. The ENV method produced the largest number of detected NAs (134 out of 181) whereas the HLB and Lq methods produced 108 and 91 positive detections, respectively, in the tested produced water sample. For the relative recoveries, the ENV performed better than the other two methods. The uni-variate and multi-variate statistical analysis of our results indicated that the ENV and Lq methods explained most of the variance observed in our data. When looking at the distribution of NAs in our sample the ENV method appeared to provide a more complete picture of the chemical diversity of NAs in that sample. Finally, the results are further discussed.
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Affiliation(s)
- Saer Samanipour
- Norwegian Institute for Water Research (NIVA), Oslo 0349, Norway.
| | - Maryam Hooshyari
- Department of Pharmacy, Genova University, Viale Cembrano 4, Genova 16147, Italy
| | - Jose A Baz-Lomba
- Norwegian Institute for Water Research (NIVA), Oslo 0349, Norway
| | - Malcolm J Reid
- Norwegian Institute for Water Research (NIVA), Oslo 0349, Norway
| | - Monica Casale
- Department of Pharmacy, Genova University, Viale Cembrano 4, Genova 16147, Italy
| | - Kevin V Thomas
- Norwegian Institute for Water Research (NIVA), Oslo 0349, Norway; Queensland Alliance for Environmental Health Science (QAEHS), University of Queensland, 20 Cornwall Street, Woolloongabba, QLD 4102, Australia
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15
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Cheng Z, Zhang X, Geng X, Organtini KL, Dong F, Xu J, Liu X, Wu X, Zheng Y. A target screening method for detection of organic pollutants in fruits and vegetables by atmospheric pressure gas chromatography quadrupole-time-of-flight mass spectrometry combined with informatics platform. J Chromatogr A 2018; 1577:82-91. [DOI: 10.1016/j.chroma.2018.09.043] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2018] [Revised: 08/23/2018] [Accepted: 09/21/2018] [Indexed: 12/12/2022]
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16
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Colby JM, Thoren KL, Lynch KL. Suspect Screening Using LC-QqTOF Is a Useful Tool for Detecting Drugs in Biological Samples. J Anal Toxicol 2018; 42:207-213. [PMID: 29309651 DOI: 10.1093/jat/bkx107] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2017] [Accepted: 12/06/2017] [Indexed: 11/12/2022] Open
Abstract
High-resolution mass spectrometers (HRMS), including quadrupole time of flight mass analyzers (QqTOF), are becoming more prevalent as screening tools in clinical and forensic toxicology laboratories. Among other advantages, HRMS instruments can collect untargeted, full-scan mass spectra. These datasets can be analyzed retrospectively using a combination of techniques, which can extend the drug detection capabilities. Most laboratories using HRMS in production settings perform untargeted data collection, but analyze data in a targeted manner. To perform targeted analysis, a laboratory must first analyze a reference standard to determine the expected characteristics of a given compound. In an alternate technique known as suspect screening, compounds can be tentatively identified without the use of reference standards. Instead, predicted and/or intrinsic characteristics of a compound, such as the accurate mass, isotope pattern, and product ion spectrum are used to determine its presence in a sample. The fact that reference standards are not required a priori makes this data analysis approach very attractive, especially for the ever-changing landscape of novel psychoactive substances. In this work, we compared the performance of four data analysis workflows (targeted and three suspect screens) for a panel of 170 drugs and metabolites, detected by LC-QqTOF. We found that retention time was not required for drug identification; the suspect screen using accurate mass, isotope pattern, and product ion library matching was able to identify more than 80% of the drugs that were present in human urine samples. We showed that the inclusion of product ion spectral matching produced the largest decrease in false discovery and false negative rates, as compared to suspect screening using mass alone or using just mass and isotope pattern. Our results demonstrate the promise that suspect screening holds for building large, economical drug screens, which may be a key tool to monitor the use of emerging drugs of abuse, including novel psychoactive substances.
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Affiliation(s)
- Jennifer M Colby
- Department of Pathology, Microbiology, and Immunology, Vanderbilt University, 1301 Medical Center Drive, Nashville, TN 37232, USA
| | - Katie L Thoren
- Department of Laboratory Medicine, University of California San Francisco, 1001 Potrero Avenue NH 2M16, San Francisco, CA 94110, USA
| | - Kara L Lynch
- Department of Laboratory Medicine, University of California San Francisco, 1001 Potrero Avenue NH 2M16, San Francisco, CA 94110, USA
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17
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Samanipour S, Baz-Lomba JA, Reid MJ, Ciceri E, Rowland S, Nilsson P, Thomas KV. Assessing sample extraction efficiencies for the analysis of complex unresolved mixtures of organic pollutants: A comprehensive non-target approach. Anal Chim Acta 2018; 1025:92-98. [DOI: 10.1016/j.aca.2018.04.020] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2017] [Revised: 04/10/2018] [Accepted: 04/14/2018] [Indexed: 12/12/2022]
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18
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Albergamo V, Helmus R, de Voogt P. Direct injection analysis of polar micropollutants in natural drinking water sources with biphenyl liquid chromatography coupled to high-resolution time-of-flight mass spectrometry. J Chromatogr A 2018; 1569:53-61. [PMID: 30017221 DOI: 10.1016/j.chroma.2018.07.036] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2018] [Revised: 06/30/2018] [Accepted: 07/06/2018] [Indexed: 12/31/2022]
Abstract
A method for the trace analysis of polar micropollutants (MPs) by direct injection of surface water and groundwater was validated with ultrahigh-performance liquid chromatography using a core-shell biphenyl stationary phase coupled to time-of-flight high-resolution mass spectrometry. The validation was successfully conducted with 33 polar MPs representative for several classes of emerging contaminants. Identification and quantification were achieved by semi-automated processing of full-scan and data-independent acquisition MS/MS spectra. In most cases good linearity (R2 ≥ 0.99), recovery (75% to 125%) and intra-day (RSD < 20%) and inter-day precision (RSD < 10%) values were observed. Detection limits of 9 to 83 ng/L and 9 to 93 ng/L could be achieved in riverbank filtrate and surface water, respectively. A solid-phase extraction was additionally validated to screen samples from full-scale reverse osmosis drinking water treatment at sub-ng/L levels and overall satisfactory analytical performance parameters were observed for RBF and reverse osmosis permeate. Applicability of the direct injection method is shown for surface water and riverbank filtrate samples from an actual drinking water source. Several targets linkable to incomplete removal in wastewater treatment and farming activities were detected and quantified in concentrations between 28 ng/L for saccharine in riverbank filtrate and up to 1 μg/L for acesulfame in surface water. The solid phase extraction method applied to samples from full-scale reverse osmosis drinking water treatment plant led to quantification of 8 targets between 6 and 57 ng/L in the feed water, whereas only diglyme was detected and quantified in reverse osmosis permeate. Our study shows that combining the chromatographic resolution of biphenyl stationary phase with the performance of time-of-flight high-resolution tandem mass spectrometry resulted in a fast, accurate and robust method to monitor polar MPs in source waters by direct injection with high efficiency.
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Affiliation(s)
- Vittorio Albergamo
- Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, The Netherlands.
| | - Rick Helmus
- Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, The Netherlands
| | - Pim de Voogt
- Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, The Netherlands; KWR Watercycle Research Institute, Nieuwegein, The Netherlands
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19
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Alygizakis NA, Samanipour S, Hollender J, Ibáñez M, Kaserzon S, Kokkali V, van Leerdam JA, Mueller JF, Pijnappels M, Reid MJ, Schymanski EL, Slobodnik J, Thomaidis NS, Thomas KV. Exploring the Potential of a Global Emerging Contaminant Early Warning Network through the Use of Retrospective Suspect Screening with High-Resolution Mass Spectrometry. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2018; 52:5135-5144. [PMID: 29651850 DOI: 10.1021/acs.est.8b00365] [Citation(s) in RCA: 73] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
A key challenge in the environmental and exposure sciences is to establish experimental evidence of the role of chemical exposure in human and environmental systems. High resolution and accurate tandem mass spectrometry (HRMS) is increasingly being used for the analysis of environmental samples. One lauded benefit of HRMS is the possibility to retrospectively process data for (previously omitted) compounds that has led to the archiving of HRMS data. Archived HRMS data affords the possibility of exploiting historical data to rapidly and effectively establish the temporal and spatial occurrence of newly identified contaminants through retrospective suspect screening. We propose to establish a global emerging contaminant early warning network to rapidly assess the spatial and temporal distribution of contaminants of emerging concern in environmental samples through performing retrospective analysis on HRMS data. The effectiveness of such a network is demonstrated through a pilot study, where eight reference laboratories with available archived HRMS data retrospectively screened data acquired from aqueous environmental samples collected in 14 countries on 3 different continents. The widespread spatial occurrence of several surfactants (e.g., polyethylene glycols ( PEGs ) and C12AEO-PEGs ), transformation products of selected drugs (e.g., gabapentin-lactam, metoprolol-acid, carbamazepine-10-hydroxy, omeprazole-4-hydroxy-sulfide, and 2-benzothiazole-sulfonic-acid), and industrial chemicals (3-nitrobenzenesulfonate and bisphenol-S) was revealed. Obtaining identifications of increased reliability through retrospective suspect screening is challenging, and recommendations for dealing with issues such as broad chromatographic peaks, data acquisition, and sensitivity are provided.
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Affiliation(s)
- Nikiforos A Alygizakis
- Laboratory of Analytical Chemistry, Department of Chemistry , University of Athens , Panepistimiopolis Zografou, 15771 Athens , Greece
- Environmental Institute, s.r.o. , Okružná 784/42 , 972 41 Koš , Slovak Republic
| | - Saer Samanipour
- Norwegian Institute for Water Research (NIVA) , Gaustadalléen 21 , 0349 Oslo , Norway
| | - Juliane Hollender
- Eawag: Swiss Federal Institute of Aquatic Science and Technology , 8600 Dübendorf , Switzerland
- Institute of Biogeochemistry and Pollutant Dynamics , ETH Zürich , 8092 Zürich , Switzerland
| | - María Ibáñez
- Research Institute for Pesticides and Water , University Jaume I , Avda. Sos Baynat s/n , 12071 Castellón de la Plana , Spain
| | - Sarit Kaserzon
- Queensland Alliance for Environmental Health Sciences (QAEHS) , The University of Queensland , 20 Cornwall Street , Woolloongabba , Queensland 4102 , Australia
| | - Varvara Kokkali
- Vitens Laboratory , Snekertrekweg 61 , 8912 AA Leeuwarden , The Netherlands
| | - Jan A van Leerdam
- KWR Watercycle Research Institute , P.O. Box 1072, 3430 BB Nieuwegein , The Netherlands
| | - Jochen F Mueller
- Queensland Alliance for Environmental Health Sciences (QAEHS) , The University of Queensland , 20 Cornwall Street , Woolloongabba , Queensland 4102 , Australia
| | - Martijn Pijnappels
- Rijkswaterstaat , Ministry of Infrastructure and the Environment , Zuiderwagenplein 2 , 8224 AD Lelystad , The Netherlands
| | - Malcolm J Reid
- Norwegian Institute for Water Research (NIVA) , Gaustadalléen 21 , 0349 Oslo , Norway
| | - Emma L Schymanski
- Eawag: Swiss Federal Institute of Aquatic Science and Technology , 8600 Dübendorf , Switzerland
- Luxembourg Centre for Systems Biomedicine (LCSB) , University of Luxembourg , 7 Avenue des Hauts Fourneaux , L-4362 Esch-sur-Alzette , Luxembourg
| | - Jaroslav Slobodnik
- Environmental Institute, s.r.o. , Okružná 784/42 , 972 41 Koš , Slovak Republic
| | - Nikolaos S Thomaidis
- Laboratory of Analytical Chemistry, Department of Chemistry , University of Athens , Panepistimiopolis Zografou, 15771 Athens , Greece
| | - Kevin V Thomas
- Norwegian Institute for Water Research (NIVA) , Gaustadalléen 21 , 0349 Oslo , Norway
- Queensland Alliance for Environmental Health Sciences (QAEHS) , The University of Queensland , 20 Cornwall Street , Woolloongabba , Queensland 4102 , Australia
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20
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Samanipour S, Reid MJ, Bæk K, Thomas KV. Combining a Deconvolution and a Universal Library Search Algorithm for the Nontarget Analysis of Data-Independent Acquisition Mode Liquid Chromatography-High-Resolution Mass Spectrometry Results. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2018; 52:4694-4701. [PMID: 29561135 DOI: 10.1021/acs.est.8b00259] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
Nontarget analysis is considered one of the most comprehensive tools for the identification of unknown compounds in a complex sample analyzed via liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS). Due to the complexity of the data generated via LC-HRMS, the data-dependent acquisition mode, which produces the MS2 spectra of a limited number of the precursor ions, has been one of the most common approaches used during nontarget screening. However, data-independent acquisition mode produces highly complex spectra that require proper deconvolution and library search algorithms. We have developed a deconvolution algorithm and a universal library search algorithm (ULSA) for the analysis of complex spectra generated via data-independent acquisition. These algorithms were validated and tested using both semisynthetic and real environmental data. A total of 6000 randomly selected spectra from MassBank were introduced across the total ion chromatograms of 15 sludge extracts at three levels of background complexity for the validation of the algorithms via semisynthetic data. The deconvolution algorithm successfully extracted more than 60% of the added ions in the analytical signal for 95% of processed spectra (i.e., 3 complexity levels multiplied by 6000 spectra). The ULSA ranked the correct spectra among the top three for more than 95% of cases. We further tested the algorithms with 5 wastewater effluent extracts for 59 artificial unknown analytes (i.e., their presence or absence was confirmed via target analysis). These algorithms did not produce any cases of false identifications while correctly identifying ∼70% of the total inquiries. The implications, capabilities, and the limitations of both algorithms are further discussed.
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Affiliation(s)
- Saer Samanipour
- Norwegian Institute for Water Research (NIVA) , 0349 Oslo , Norway
| | - Malcolm J Reid
- Norwegian Institute for Water Research (NIVA) , 0349 Oslo , Norway
| | - Kine Bæk
- Norwegian Institute for Water Research (NIVA) , 0349 Oslo , Norway
| | - Kevin V Thomas
- Norwegian Institute for Water Research (NIVA) , 0349 Oslo , Norway
- Queensland Alliance for Environmental Health Science (QAEHS) , University of Queensland , 39 Kessels Road , Coopers Plains , Queensland 4108 , Australia
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