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Rapid direct analysis of river water and machine learning assisted suspect screening of emerging contaminants in passive sampler extracts

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Rapid direct analysis of river water and machine learning assisted suspect screening of emerging contaminants in passive sampler extracts. / Richardson, Alexandra K. ; Chadha, Marcus; Wright, Helena Rapp; Mills, Graham; Fones, Gary; Gravell, Anthony; Stürzenbaum, Stephen; Cowan, David A.; Neep, David J. ; Barron, Leon P. .

In: Analytical Methods, Vol. 13, 07.02.2021, p. 595-606.

Research output: Contribution to journalArticlepeer-review

Harvard

Richardson, AK, Chadha, M, Wright, HR, Mills, G, Fones, G, Gravell, A, Stürzenbaum, S, Cowan, DA, Neep, DJ & Barron, LP 2021, 'Rapid direct analysis of river water and machine learning assisted suspect screening of emerging contaminants in passive sampler extracts', Analytical Methods, vol. 13, pp. 595-606. https://doi.org/10.1039/D0AY02013C

APA

Richardson, A. K., Chadha, M., Wright, H. R., Mills, G., Fones, G., Gravell, A., Stürzenbaum, S., Cowan, D. A., Neep, D. J., & Barron, L. P. (2021). Rapid direct analysis of river water and machine learning assisted suspect screening of emerging contaminants in passive sampler extracts. Analytical Methods, 13, 595-606. https://doi.org/10.1039/D0AY02013C

Vancouver

Author

Richardson, Alexandra K. ; Chadha, Marcus ; Wright, Helena Rapp ; Mills, Graham ; Fones, Gary ; Gravell, Anthony ; Stürzenbaum, Stephen ; Cowan, David A. ; Neep, David J. ; Barron, Leon P. . / Rapid direct analysis of river water and machine learning assisted suspect screening of emerging contaminants in passive sampler extracts. In: Analytical Methods. 2021 ; Vol. 13. pp. 595-606.

Bibtex

@article{9da583fcd1a2408396143d875e375c5e,
title = "Rapid direct analysis of river water and machine learning assisted suspect screening of emerging contaminants in passive sampler extracts",
abstract = "A novel and rapid approach to characterise the occurrence of contaminants of emerging concern (CECs) in river water is presented using multi-residue targeted analysis and machine learning-assisted in silico suspect screening of passive sampler extracts. Passive samplers (Chemcatcher{\textregistered}) configured with hydrophilic-lipophilic balance (HLB) sorbents were deployed in the Central London region of the tidal River Thames (UK) catchment in winter and summer campaigns in 2018 and 2019. Extracts were analysed by; (a) a rapid 5.5-min direct injection targeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) method for 164 CECs and (b) a full-scan LC coupled to quadrupole time of flight mass spectrometry (QTOF-MS) method using data-independent acquisition over 15 min. From targeted analysis of grab water samples, a total of 33 pharmaceuticals, illicit drugs, drug metabolites, personal care products and pesticides (including several EU Watch-List chemicals) were identified, and mean concentrations determined at 40 ± 37 ng/L-1. For targeted analysis of passive sampler extracts, 65 unique compounds were detected with differences observed between summer and winter campaigns. For suspect screening, 59 additional compounds were shortlisted based on mass spectral database matching, followed by machine learning-assisted retention time prediction. Many of these included additional pharmaceuticals and pesticides, but also new metabolites and industrial chemicals. The novelty in this approach lies in the convenience of using passive samplers together with machine learning-assisted chemical analysis methods for rapid, time-integrated catchment monitoring of CECs.",
keywords = "RCUK, BBSRC, BB/M009513/1",
author = "Richardson, {Alexandra K.} and Marcus Chadha and Wright, {Helena Rapp} and Graham Mills and Gary Fones and Anthony Gravell and Stephen St{\"u}rzenbaum and Cowan, {David A.} and Neep, {David J.} and Barron, {Leon P.}",
year = "2021",
month = feb,
day = "7",
doi = "10.1039/D0AY02013C",
language = "English",
volume = "13",
pages = "595--606",
journal = "Analytical Methods",
issn = "1759-9660",
publisher = "Royal Society of Chemistry",

}

RIS

TY - JOUR

T1 - Rapid direct analysis of river water and machine learning assisted suspect screening of emerging contaminants in passive sampler extracts

AU - Richardson, Alexandra K.

AU - Chadha, Marcus

AU - Wright, Helena Rapp

AU - Mills, Graham

AU - Fones, Gary

AU - Gravell, Anthony

AU - Stürzenbaum, Stephen

AU - Cowan, David A.

AU - Neep, David J.

AU - Barron, Leon P.

PY - 2021/2/7

Y1 - 2021/2/7

N2 - A novel and rapid approach to characterise the occurrence of contaminants of emerging concern (CECs) in river water is presented using multi-residue targeted analysis and machine learning-assisted in silico suspect screening of passive sampler extracts. Passive samplers (Chemcatcher®) configured with hydrophilic-lipophilic balance (HLB) sorbents were deployed in the Central London region of the tidal River Thames (UK) catchment in winter and summer campaigns in 2018 and 2019. Extracts were analysed by; (a) a rapid 5.5-min direct injection targeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) method for 164 CECs and (b) a full-scan LC coupled to quadrupole time of flight mass spectrometry (QTOF-MS) method using data-independent acquisition over 15 min. From targeted analysis of grab water samples, a total of 33 pharmaceuticals, illicit drugs, drug metabolites, personal care products and pesticides (including several EU Watch-List chemicals) were identified, and mean concentrations determined at 40 ± 37 ng/L-1. For targeted analysis of passive sampler extracts, 65 unique compounds were detected with differences observed between summer and winter campaigns. For suspect screening, 59 additional compounds were shortlisted based on mass spectral database matching, followed by machine learning-assisted retention time prediction. Many of these included additional pharmaceuticals and pesticides, but also new metabolites and industrial chemicals. The novelty in this approach lies in the convenience of using passive samplers together with machine learning-assisted chemical analysis methods for rapid, time-integrated catchment monitoring of CECs.

AB - A novel and rapid approach to characterise the occurrence of contaminants of emerging concern (CECs) in river water is presented using multi-residue targeted analysis and machine learning-assisted in silico suspect screening of passive sampler extracts. Passive samplers (Chemcatcher®) configured with hydrophilic-lipophilic balance (HLB) sorbents were deployed in the Central London region of the tidal River Thames (UK) catchment in winter and summer campaigns in 2018 and 2019. Extracts were analysed by; (a) a rapid 5.5-min direct injection targeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) method for 164 CECs and (b) a full-scan LC coupled to quadrupole time of flight mass spectrometry (QTOF-MS) method using data-independent acquisition over 15 min. From targeted analysis of grab water samples, a total of 33 pharmaceuticals, illicit drugs, drug metabolites, personal care products and pesticides (including several EU Watch-List chemicals) were identified, and mean concentrations determined at 40 ± 37 ng/L-1. For targeted analysis of passive sampler extracts, 65 unique compounds were detected with differences observed between summer and winter campaigns. For suspect screening, 59 additional compounds were shortlisted based on mass spectral database matching, followed by machine learning-assisted retention time prediction. Many of these included additional pharmaceuticals and pesticides, but also new metabolites and industrial chemicals. The novelty in this approach lies in the convenience of using passive samplers together with machine learning-assisted chemical analysis methods for rapid, time-integrated catchment monitoring of CECs.

KW - RCUK

KW - BBSRC

KW - BB/M009513/1

U2 - 10.1039/D0AY02013C

DO - 10.1039/D0AY02013C

M3 - Article

VL - 13

SP - 595

EP - 606

JO - Analytical Methods

JF - Analytical Methods

SN - 1759-9660

ER -

ID: 25575158