Retrospective non-target analysis to support regulatory water monitoring: from masses of interest to recommendations via in silico workflows
|Author(s)||Lai Adelene1, 2, Singh Randolph1, Kovalova Lubomira3, Jaeggi Oliver3, Kondić Todor1, Schymanski Emma L.1|
|Affiliation(s)||1 : Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 6 Avenue du Swing, 4367, Belvaux, Luxembourg
2 : Institute for Inorganic and Analytical Chemistry, Friedrich-Schiller University, Lessing Strasse 8, 07743, Jena, Germany
3 : Amt Für Abfall, Wasser, Energie Und Luft (AWEL), Walcheplatz 2, 8090, Zurich, Switzerland
|Source||Environmental Sciences Europe (2190-4707) (Springer Science and Business Media LLC), 2021-04 , Vol. 33 , N. 1 , P. 43 (21p.)|
|WOS© Times Cited||15|
|Keyword(s)||Non-target analysis, Suspect screening, Retrospective, Wastewater, Micropollutants, Cheminformatics, Identification, Monitoring, Regulation|
Applying 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 establish an open in silico workflow 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.
One confirmed and 21 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.
NTA 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.