Rapid direct analysis of river water and machine learning assisted suspect screening of emerging contaminants in passive sampler extracts. Issue 5 (11th January 2021)

• Record Type: Journal Article
• Authors: Richardson, Alexandra K.; Chadha, Marcus; Rapp-Wright, Helena; Mills, Graham A.; Fones, Gary R.; Gravell, Anthony; Stürzenbaum, Stephen; Cowan, David A.; Neep, David J.; Barron, Leon P.
• Is Part Of: Analytical methods. Volume 13:Issue 5(2021)
• Page Start: 595
• Page End: 606
• Publication Date: 2021-01-11
• Subjects: Chemistry, Analytic -- Periodicals; Analytical biochemistry -- Periodicals; Chemical laboratories -- Standards -- Periodicals; 543.1905
• DOI: 10.1039/d0ay02013c
• Languages: English
• ISSNs: 1759-9660

Abstract

Abstract : Machine-learning assisted suspect screening in passive sampler extracts and rapid direct injection LC-MS/MS of river water from the River Thames, London, UK in 2018/9. 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®) configured with hydrophilic–lipophilic balanced (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.