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