A miniaturized passive sampling-based workflow for monitoring chemicals of emerging concern in water.

The miniaturization of a full workflow for identification and monitoring of contaminants of emerging concern (CECs) is presented. Firstly, successful development of a low-cost small 3D-printed passive sampler device (3D-PSD), based on a two-piece methacrylate housing that held up to five separate 9 mm disk sorbents, is discussed. Secondly, a highly sensitive liquid chromatography-tandem mass spectrometry (LC-MS/MS) method reduced the need for large scale in-laboratory apparatus, solvent, reagents and reference material quantities for in-laboratory passive sampler device (PSD) calibration and extraction. Using hydrophilic-lipophilic balanced sorbents, sampling rates (Rs) were determined after a low 50 ng L-1 exposure over seven days for 39 pesticides, pharmaceuticals, drug metabolites and illicit drugs over the range 0.3 to 12.3 mL day-1. The high sensitivity LC-MS/MS method enabled rapid analysis of river water using only 10 µL of directly injected sample filtrate to measure occurrence of 164 CECs and sources along 19 sites on the River Wandle, (London, UK). The new 3D-PSD was then field-tested over seven days at the site with the highest number and concentration of CECs, which was down-river from a wastewater treatment plant. Almost double the number of CECs were identified in 3D-PSD extracts across sites in comparison to water samples (80 versus 42 CECs, respectively). Time-weighted average CEC concentrations ranged from 8.2 to 845 ng L-1, which were generally comparable to measured concentrations in grab samples. Lastly, high resolution mass spectrometry-based suspect screening of 3D-PSD extracts enabled 113 additional compounds to be tentatively identified via library matching, many of which are currently or are under consideration for the EU Watch List. This miniaturized workflow represents a new, cost-effective, and more practically efficient means to perform passive sampling chemical monitoring at a large scale. SYNOPSIS: Miniaturized, low cost, multi-disk passive samplers enabled more efficient multi-residue chemical contaminant characterization, potentially for large-scale monitoring programs.

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

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.