| Abstract Title: | Analysis of river water using passive samplers and machine learning tools to aid screening of novel compounds |
| Presenter Name: | Mr Richardson AK |
| Co-authors: | Chadha M Rapp-Wright H Mills G.A Fones G.R Gravell A Stürzenbaum S Cowan D.A Neep D.J Barron L.P |
| Company/Organisation: | Imperial College London |
| Country: | United Kingdom |
Abstract Information :
A novel and rapid approach to characterise the occurrence of contaminants of emerging concern (CECs) in river water is presented using machine learning-assisted in silico screening of suspect findings from the analysis 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. Samplers were configured with a 47 mm divinylbenzene-co-N-vinylpyrollidone (HLB) sorbent disk and a polyethersulfone (PES) diffusion limiting membrane. Extracts were analysed by a screening workflow using full-scan reversed-phase liquid chromatography (LC) coupled to quadrupole time of flight mass spectrometry (QTOF-MS) method using data-independent acquisition over 15 min. For screening of suspect results, 59 compounds were curated from a shortlist of 237, including 95 multiple matches, based on mass spectral database matching, followed by machine learning-assisted retention time prediction. Many of these compounds included pharmaceuticals and pesticides, as well as 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.
