Retrospective Non-target Analysis to Support Regulatory Water Monitoring: From Masses of Interest to Recommendations via in silico workflows

Abstract

BackgroundApplying non-target analysis (NTA) in regulatory environmental monitoring remains challenging - instead of having exploratory questions, regulators usually already have specific questions related to environmental protection aims. Additionally, data analysis can seem overwhelming because of the large data volumes and many steps required. This work aimed to identify environmental chemical unknowns via retrospective NTA within the scope of a pre-existing Swiss environmental monitoring campaign focusing on industrial chemicals. The research question addressed immediate regulatory priorities: identify pollutants with industrial point sources occurring at the highest intensities over two time points. Samples from 22 wastewater treatment plants obtained in 2018 and measured using liquid chromatography-high resolution mass spectrometry were retrospectively analysed by i) performing peak-picking to identify masses of interest, ii) prescreening and quality-controlling spectra, and iii) tentatively identifying priority “known unknown” pollutants by leveraging environmentally-relevant chemical information provided by Swiss, Swedish, EU-wide, and American regulators. This regulator-supplied information was incorporated into MetFrag, an in silico identification tool replete with “post-relaunch” features used here. This study’s unique regulatory context posed challenges in data quality and volume that were directly addressed with the prescreening, quality control, and identification workflow developed. ResultsOne confirmed and twenty-one tentative identifications were achieved, suggesting the presence of compounds as diverse as manufacturing reagents, adhesives, pesticides, and pharmaceuticals in the samples. More importantly, an in-depth interpretation of the results in the context of environmental regulation and actionable next steps are discussed. The prescreening and quality control workflow is openly accessible within the R package Shinyscreen, and adaptable to any (retrospective) analysis requiring automated quality control of mass spectra and non-target identification, with potential applications in environmental and metabolomics analyses. ConclusionsNTA in regulatory monitoring is critical for environmental protection, but bottlenecks in data analysis and results interpretation remain. The prescreening and quality control workflow, and interpretation work performed here are crucial steps towards scaling up NTA for environmental monitoring.

Occurrence and Distribution of Pharmaceuticals and Their Transformation Products in Luxembourgish Surface Waters

“…A more detailed explanation of the parameters used is available elsewhere. 28,41 Annotation confidence levels were determined using the scheme described by Schymanski et al 42 Level 2a compounds were assigned when the MoNA score was greater than or equal to 0.9. Level 1 identifications were achieved using authentic standards and the ENTACT mixtures, 43 available in-house and analyzed using the same chromatographic method used for sample analysis.…”

Occurrence and Distribution of Pharmaceuticals and their Transformation Products in Luxembourgish Surface Waters

“…A more detailed explanation of the parameters used is available elsewhere. 28,40 Annotation confidence levels were determined using the scheme described by Schymanski et al 41 Level 2a compounds were assigned when the MoNA score was greater than or equal to 0.9. Level 1 identifications were achieved using authentic standards and the ENTACT mixtures 42 , available in-house and analyzed using the same chromatographic method used for sample analysis.…”