Non-targeted screening (NTS) with reversed phase liquid chromatography electrospray ionization high resolution mass spectrometry (LC/ESI/HRMS) is increasingly employed as an alternative to targeted analysis; however, it is not possible to quantify all compounds found in a sample with analytical standards. As an alternative, semi-quantification strategies are, or at least should be, used to estimate the concentrations of the unknown compounds before final decision making. All steps in the analytical chain, from sample preparation to ionization conditions and data processing can influence the signals obtained, and thus the estimated concentrations. Therefore, each step needs to be considered carefully. Generally, less is more when it comes to choosing sample preparation as well as chromatographic and ionization conditions in NTS. By combining the positive and negative ionization mode, the performance of NTS can be improved, since different compounds ionize better in one or the other mode. Furthermore, NTS gives opportunities for retrospective analysis. In this tutorial, strategies for semi-quantification are described, sources potentially decreasing the signals are identified and possibilities to improve NTS are discussed. Additionally, examples of retrospective analysis are presented. Finally, we present a checklist for carrying out semi-quantitative NTS.

Background: Persistent, mobile and toxic (PMT), or very persistent and very mobile (vPvM) substances are a wide class of chemicals that are recalcitrant to degradation, easily transported, and potentially harmful to humans and the environment. Due to their persistence and mobility, these substances are often widespread in the environment once emitted, particularly in water resources, causing increased challenges during water treatment processes. Some PMT/vPvM substances such as GenX and perfluorobutane sulfonic acid have been identified as substances of very high concern (SVHCs) under the European Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH) regulation. With hundreds to thousands of potential PMT/vPvM substances yet to be assessed and managed, effective and efficient approaches that avoid a case-by-case assessment and prevent regrettable substitution are necessary to achieve the European Union's zero-pollution goal for a non-toxic environment by 2050. Main: Substance grouping has helped global regulation of some highly hazardous chemicals, e.g., through the Montreal Protocol and the Stockholm Convention. This article explores the potential of grouping strategies for identifying, assessing and managing PMT/vPvM substances. The aim is to facilitate early identification of lesser-known or new substances that potentially meet PMT/vPvM criteria, prompt additional testing, avoid regrettable use or substitution, and integrate into existing risk management strategies. Thus, this article provides an overview of PMT/vPvM substances and reviews the definition of PMT/vPvM criteria and various lists of PMT/vPvM substances available. It covers the current definition of groups, compares the use of substance grouping for hazard assessment and regulation, and discusses the advantages and disadvantages of grouping substances for regulation. The article then explores strategies for grouping PMT/vPvM substances, including read-across, structural similarity and commonly retained moieties, as well as the potential application of these strategies using cheminformatics to predict P, M and T properties for selected examples. Conclusions: Effective substance grouping can accelerate the assessment and management of PMT/vPvM substances, especially for substances that lack information. Advances to read-across methods and cheminformatics tools are needed to support efficient and effective chemical management, preventing broad entry of hazardous chemicals into the global market and favouring safer and more sustainable alternatives.

LC/ESI/HRMS is increasingly employed for monitoring chemical pollutants in water samples, with non-targeted analysis becoming more common. Unfortunately, due to the lack of analytical standards, non-targeted analysis is mostly qualitative. To remedy this, models have been developed to evaluate the response of compounds from their structure, which can then be used for quantification in non-targeted analysis. Still, these models rely on tentatively known structures while for most detected compounds, a list of structural candidates, or sometimes only exact mass and retention time are identified. In this study, a quantification approach was developed, where LC/ESI/HRMS descriptors are used for quantification of compounds even if the structure is unknown. The approach was developed based on 92 compounds analyzed in parallel in both positive and negative ESI mode with mobile phases at pH 2.7, 8.0, and 10.0. The developed approach was compared with two baseline approaches— one assuming equal response factors for all compounds and one using the response factor of the closest eluting standard. The former gave a mean prediction error of a factor of 29, while the latter gave a mean prediction error of a factor of 1300. In the machine learning-based quantification approach developed here, the corresponding prediction error was a factor of 10. Furthermore, the approach was validated by analyzing two blind samples containing 48 compounds spiked into tap water and ultrapure water. The obtained mean prediction error was lower than a factor of 6.0 for both samples. The errors were found to be comparable to approaches using structural information.

Transformation product (TP) information is essential to accurately evaluate the hazards compounds pose to human health and the environment. However, information about TPs is often limited, and existing data is often not fully Findable, Accessible, Interoperable, and Reusable (FAIR). FAIRifying existing TP knowledge is a relatively easy path toward improving access to data for identification workflows and for machine-learning-based algorithms. ShinyTPs was developed to curate existing transformation information derived from text-mined data within the PubChem database. The application (available as an R package) visualizes the text-mined chemical names to facilitate the user validation of the automatically extracted reactions. ShinyTPs was applied to a case study using 436 tentatively identified compounds to prioritize TP retrieval. This resulted in the extraction of 645 reactions (associated with 496 compounds), of which 319 were not previously available in PubChem. The curated reactions were added to the PubChem Transformations library, which was used as a TP suspect list for identification of TPs using the open-source workflow patRoon. In total, 72 compounds from the library were tentatively identified, 18% of which were curated using ShinyTPs, showing that the app can help support TP identification in non-target analysis workflows.

Organic micropollutant (OMP) biodegradation rates and pathways in environmental and engineered systems depend on several factors, including redox conditions and microbial diversity. We investigated the impact of these two variables on the degradation of 20 OMPs. Soil microbial communities with three diversity levels were generated via solid-phase dilution-to-extinction. After a regrowth period, communities were incubated with OMPs under aerobic and nitrate-reducing conditions. High and medium microbial diversity led to more OMPs being degraded, up to 15 and 5 under aerobic and nitrate-reducing conditions, respectively, compared to 11 and 4 under low diversity. Higher diversity also led to increased biodegradation rates and formation of a broader range of transformation products (TPs), including later-generation TPs. For TPs detected across multiple diversity levels and redox conditions, lag phases before initial detection were shorter in higher diversity and aerobic conditions. Biodegradation under nitrate-reducing conditions was slower and less efficient, yet the positive correlation with microbial diversity remained (mean r_aerobic = 0.28 and r_{nitrate-reducing} = 0.26, p < 0.01). Redox-specific TP production and taxonomic differences suggested distinct biodegradation pathways under different redox conditions. While some TPs persisted until the end of the incubation, none were predicted to be dead-end TPs according to BioTransformer. However, predictions with OPERA's CERAPP and CoMPARA modules revealed two TPs with potential endocrine-disrupting effects. Our study highlights that microbial diversity and redox conditions strongly influence OMP degradation rates and pathways, and thus TP formation. Moreover, our results suggest a threshold in microbial diversity below which functional capacity is affected. Diversity should therefore be considered in standardized tests evaluating OMP persistence in the environment.

Considering transformation products (TPs) in environmental studies remains a huge challenge for scientists, from identification in samples via mass spectrometry to inclusion in chemical regulation. This article introduces FAIR-TPs, a Web site to browse openly available TP data collated from literature sources. The data is sourced from several community-contributed environmental data sets on the NORMAN Suspect List Exchange (NORMAN-SLE), plus a data set from ChEMBL available through PubChem, with links to templates and contact details to encourage further community contributions. The data are compiled regularly using an open source workflow, archived and versioned on Zenodo under a CC-BY license, then processed and displayed on FAIR-TPs. FAIR-TPs currently contains 11,190 reactions involving 9,435 compounds from 11 sources, covering human and environmental transformations through to high energy water treatment reactions. A graph-based representation of the transformations (compounds as nodes and reactions as edges) is stored in a Neo4j database as a Directed Graph and made publicly accessible online through a Django Web Application. Users can retrieve the shortest directed pathways between predecessors (parents) and successors (transformation products/metabolites), search by SMARTS substructures, or explore local reaction neighborhood data on individual compounds. Interactive network visualizations provide ways for users to view multistep transformations in a smooth, user-friendly interface, while exploring transformation pathways. The compound/reaction metadata provide links to further information about the chemicals and data sources. Key statistics, including number of reactions/compounds, top compounds, reaction types, and mass differences are summarized from the current data set. FAIR-TPs is designed to be a public resource to support suspect and nontarget screening workflows, helping scientists identify data gaps and interpret complex transformation reactions. The FAIR-TPs website is openly available at https://fairtps.lcsb.uni.lu.