Improving Target and Suspect Screening High-Resolution Mass Spectrometry Workflows in Environmental Analysis by Ion Mobility Separation

Unveiling Impurity Profiling of Synthetic Pathways of Organophosphorus Chlorpyrifos Through LC-HRMS Metabolomics-Based Approaches

Sourcing in chemical forensic science refers to the attribution of a sample to a specific source using a characteristic signature. It relies on the identification of chemical attribution signatures (CAS), including chemical markers such as residual synthetic precursors, impurities, reaction by-products and degradation products, or even metabolites. Undertaking CAS for chemical threat agents (CTA) can be used to provide an evidentiary link between the use of a given chemical and its precursor(s) to support forensic investigations. Organophosphorus compounds, a class of nerve agents, can be produced by different, more or less complex synthesis routes that can lead to specific CAS. Chlorpyrifos (CPF), an organophosphorus pesticide, was selected as model compound. To assess the specificity of impurity markers originated from a chemical synthesis, untargeted fingerprints of crude CPF from different synthesis pathways were analyzed as a first use-case using metabolomics-based trace discovery strategies. Seven different CPF synthesis routes were considered, and their crude mixtures were analyzed with a minimal sample preparation. Analyses were performed on a trapped ion mobility spectrometry (TIMS) coupled to liquid chromatography (LC) and high-resolution mass spectrometry (HRMS). Chemometrics analyses were conducted with multivariate methods to extract discriminating features (i.e., relevant impurities), annotate, and identify them. Then, unknown samples were analyzed in blind conditions without any information of the synthesis pathway employed. The aim is to validate the methodology seeking some discriminating impurities identified in the first section to attribute and classify them according to the synthesis route.

Introducing "Identification Probability" for Automated and Transferable Assessment of Metabolite Identification Confidence in Metabolomics and Related Studies

Methods for assessing compound identification confidence in metabolomics and related studies have been debated and actively researched for the past two decades. The earliest effort in 2007 focused primarily on mass spectrometry and nuclear magnetic resonance spectroscopy and resulted in four recommended levels of metabolite identification confidence─the Metabolite Standards Initiative (MSI) Levels. In 2014, the original MSI Levels were expanded to five levels (including two sublevels) to facilitate communication of compound identification confidence in high resolution mass spectrometry studies. Further refinement in identification levels have occurred, for example to accommodate use of ion mobility spectrometry in metabolomics workflows, and alternate approaches to communicate compound identification confidence also have been developed based on identification points schema. However, neither qualitative levels of identification confidence nor quantitative scoring systems address the degree of ambiguity in compound identifications in the context of the chemical space being considered. Neither are they easily automated nor transferable between analytical platforms. In this perspective, we propose that the metabolomics and related communities consider identification probability as an approach for automated and transferable assessment of compound identification and ambiguity in metabolomics and related studies. Identification probability is defined simply as 1/N, where N is the number of compounds in a database that matches an experimentally measured molecule within user-defined measurement precision(s), for example mass measurement or retention time accuracy, etc. We demonstrate the utility of identification probability in an in silico analysis of multiproperty reference libraries constructed from a subset of the Human Metabolome Database and computational property predictions, provide guidance to the community in transparent implementation of the concept, and invite the community to further evaluate this concept in parallel with their current preferred methods for assessing metabolite identification confidence.

Critical review on in silico methods for structural annotation of chemicals detected with LC/HRMS non-targeted screening

Non-targeted screening with liquid chromatography coupled to high-resolution mass spectrometry (LC/HRMS) is increasingly leveraging in silico methods, including machine learning, to obtain candidate structures for structural annotation of LC/HRMS features and their further prioritization. Candidate structures are commonly retrieved based on the tandem mass spectral information either from spectral or structural databases; however, the vast majority of the detected LC/HRMS features remain unannotated, constituting what we refer to as a part of the unknown chemical space. Recently, the exploration of this chemical space has become accessible through generative models. Furthermore, the evaluation of the candidate structures benefits from the complementary empirical analytical information such as retention time, collision cross section values, and ionization type. In this critical review, we provide an overview of the current approaches for retrieving and prioritizing candidate structures. These approaches come with their own set of advantages and limitations, as we showcase in the example of structural annotation of ten known and ten unknown LC/HRMS features. We emphasize that these limitations stem from both experimental and computational considerations. Finally, we highlight three key considerations for the future development of in silico methods.

Progress, applications, and challenges in high-throughput effect-directed analysis for toxicity driver identification - is it time for HT-EDA?

The rapid increase in the production and global use of chemicals and their mixtures has raised concerns about their potential impact on human and environmental health. With advances in analytical techniques, in particular, high-resolution mass spectrometry (HRMS), thousands of compounds and transformation products with potential adverse effects can now be detected in environmental samples. However, identifying and prioritizing the toxicity drivers among these compounds remain a significant challenge. Effect-directed analysis (EDA) emerged as an important tool to address this challenge, combining biotesting, sample fractionation, and chemical analysis to unravel toxicity drivers in complex mixtures. Traditional EDA workflows are labor-intensive and time-consuming, hindering large-scale applications. The concept of high-throughput (HT) EDA has recently gained traction as a means of accelerating these workflows. Key features of HT-EDA include the combination of microfractionation and downscaled bioassays, automation of sample preparation and biotesting, and efficient data processing workflows supported by novel computational tools. In addition to microplate-based fractionation, high-performance thin-layer chromatography (HPTLC) offers an interesting alternative to HPLC in HT-EDA. This review provides an updated perspective on the state-of-the-art in HT-EDA, and novel methods/tools that can be incorporated into HT-EDA workflows. It also discusses recent studies on HT-EDA, HT bioassays, and computational prioritization tools, along with considerations regarding HPTLC. By identifying current gaps in HT-EDA and proposing new approaches to overcome them, this review aims to bring HT-EDA a step closer to monitoring applications.

Target, suspect, and non-target screening of per- and poly-fluoroalkyl substances in wastewater treatment plant effluents in South Korea using ion mobility spectrometry-mass spectrometry

This study used target, suspect, and non-target screening, to assess the presence of per- and polyfluoroalkyl substances (PFASs) in domestic (municipal) and industrial wastewater treatment plants (WWTPs) in South Korea. Target analysis quantified 20 PFASs in the WWTP effluents. Total concentration of PFASs ranged from 69.1 to 79.6 ng/L and the concentrations of perfluorobutanoic acid (PFBA) (mean: 15.6 ng/L, median: 14.9 ng/L) and perfluorooctanoic acid (PFOA) (mean: 14.7 ng/L, median: 12.7 ng/L) were higher than those of other PFASs. Compared to 2010, there was an overall increase in perfluoroalkyl carboxylic acids (PFCAs), particularly perfluoroheptanoic acid, (PFHpA), which showed a nearly 10-fold increase, with current concentrations reaching 9.5 ng/L. Suspect and non-target screening with ion mobility spectrometry (IMS)-mass spectrometry was used to identify additional PFASs based on their exact mass, collision cross-section (CCS), and tandem mass spectrometry fragmentation patterns. Twenty compounds were identified as PFAS compounds through suspect screening at a confidence level (CL) of 3 or higher, with five compounds identified at CL 2. Additionally, fragment-based, suspect and non-target screening identified emerging PFASs, including FBSA, a n:2 fluorotelomer-based non-polymer, and bistriflimide, all with CL 2. Semi-quantification of identified PFASs revealed that the concentrations of PFASs identified by suspect and non-target screening were higher than those of the target PFASs, especially in industrial wastewater effluents.

Trapped and drift-tube ion-mobility spectrometry for the analysis of environmental contaminants: Comparability of collision cross-section values and resolving power

RATIONALE

Ion-mobility (IM)-derived collision cross-section (CCS) values can serve as a valuable additional identification parameter within suspect and non-target screening studies of environmental contaminants. However, these applications require to assess the reproducibility of CCS calculations between different IM set-ups. Especially for the comparison of trapped and drift-tube IM (TIMS/DTIM) derived CCS values, data for environmental applications is lacking.

METHODS

The presented study assessed the bias of TIMS derived CCSN2 (TIMSCCSN2) values of 48 environmental contaminants from three classes in comparison to a previously established DTIM database. Based on two sets of isomeric bisphenols, the resolving power of both systems was compared, addressing the instrumental settings which influence the resolution of TIMS measurements.

RESULTS

For 91% of the datapoints, bias between TIMSCCSN2 and DTCCSN2 values (latter set as reference) were < 2%, indicating a good inter-platform reproducibility. TIMS resolving power was dependent on the selected mobility window and ramping times whereby a resolution of up to 116 was achieved. Similar resolving power was observed for multiplexed DTIMS data if a high-resolution post-processing step was implemented.

CONCLUSIONS

These results provide valuable insights in CCSN2 reproducibility facilitating database transfer in future TIMS based studies. Knowledge on the influence of acquisition settings on robustness of TIMSCCSN2 calculations and resolving power can ease method development supporting efficient development and reliable identifications of emerging environmental contaminants.

Applications of artificial intelligence for chemical analysis and monitoring of pharmaceutical and personal care products in water and wastewater: A review

Specifying and interpreting the occurrence of emerging pollutants is essential for assessing treatment processes and plants, conducting wastewater-based epidemiology, and advancing environmental toxicology research. In recent years, artificial intelligence (AI) has been increasingly applied to enhance chemical analysis and monitoring of contaminants in environmental water and wastewater. However, their specific roles targeting pharmaceuticals and personal care products (PPCPs) have not been reviewed sufficiently. This review aims to narrow the gap by highlighting, scoping, and discussing the incorporation of AI during the detection and quantification of PPCPs when utilising chemical analysis equipment and interpreting their monitoring data for the first time. In the chemical analysis of PPCPs, AI-assisted prediction of chromatographic retention times and collision cross-sections (CCS) in suspect and non-target screenings using high-resolution mass spectrometry (HRMS) enhances detection confidence, reduces analysis time, and lowers costs. AI also aids in interpreting spectroscopic analysis results. However, this approach still cannot be applied in all matrices, as it offers lower sensitivity than liquid chromatography coupled with tandem or HRMS. For the interpretation of monitoring of PPCPs, unsupervised AI methods have recently presented the capacity to survey regional or national community health and socioeconomic factors. Nevertheless, as a challenge, long-term monitoring data sources are not given in the literature, and more comparative AI studies are needed for both chemical analysis and monitoring. Finally, AI assistance anticipates more frequent applications of CCS prediction to enhance detection confidence and the use of AI methods in data processing for wastewater-based epidemiology and community health surveillance.

Ecological consequences of antimicrobial residues and bioactive chemicals on antimicrobial resistance in agroecosystems

BACKGROUND

The widespread use of antimicrobials in agriculture, coupled with bioactive chemicals like pesticides and growth-promoting agents, has accelerated the global crisis of antimicrobial resistance (AMR). Agroecosystems provides a platform in the evolution and dissemination of antibiotic-resistant bacteria (ARB) and antibiotic resistance genes (ARGs), which pose significant threats to both environmental and public health.

AIM OF REVIEW

This review explores the ecological consequences of antimicrobial residues and bioactive chemicals in agroecosystems, with a focus on their role in shaping AMR. It delves into the mechanisms by which these substances enter agricultural environments, their interactions with soil microbiomes, and the subsequent impacts on microbial community structure.

KEY SCIENTIFIC CONCEPTS OF REVIEW

Evidence indicates that the accumulation of antimicrobials promotes resistance gene transfer among microorganisms, potentially compromising ecosystem health and agricultural productivity. By synthesizing current research, we identify critical gaps in knowledge and propose strategies for mitigating the ecological risks associated with antimicrobial residues. Moreover, this review highlights the urgent need for integrated management approaches to preserve ecosystem health and combat the spread of AMR in agricultural settings.

In Vitro Metabolism of Quaternary Ammonium Compounds and Confirmation in Human Urine by Liquid Chromatography Ion-Mobility High-Resolution Mass Spectrometry

Quaternary ammonium compounds (QACs) are high-production chemicals used as cleaning and disinfecting agents. Due to their ubiquitous presence in the environment and several toxic effects described, human exposure to these chemicals gained increasing attention in recent years. However, very limited data on the biotransformation of QACs is available, hampering exposure assessment. In this study, three QACs (dimethyl dodecyl ammonium, C10-DDAC; benzyldimethyl dodecylammonium, C12-BAC; cetyltrimethylammonium, C16-ATMAC) commonly detected in indoor microenvironments were incubated with human liver microsomes and cytosol (HLM/HLC) simulating Phase I and II metabolism. Thirty-one Phase I metabolites were annotated originating from 19 biotransformation reactions. Four metabolites of C10-DDAC were described for the first time. A detailed assessment of experimental fragmentation spectra allowed to characterize potential oxidation sites. For each annotated metabolite, drift-tube ion-mobility derived collision cross section (DTCCSN2) values were reported, serving as an additional identification parameter and allowing the characterization of changes in DTCCSN2 values following metabolism. Lastly, eight metabolites, including four metabolites of both C12-BAC and C10-DDAC, were confirmed in human urine samples showing high oxidation states through introduction of up to four oxygen atoms. This is the first report of higher oxidized C10-DDAC metabolites in human urine facilitating future biomonitoring studies on QACs.

Predicting Collision Cross-Section Values for Small Molecules through Chemical Class-Based Multimodal Graph Attention Network

Libraries of collision cross-section (CCS) values have the potential to facilitate compound identification in metabolomics. Although computational methods provide an opportunity to increase library size rapidly, accurate prediction of CCS values remains challenging due to the structural diversity of small molecules. Here, we developed a machine learning (ML) model that integrates graph attention networks and multimodal molecular representations to predict CCS values on the basis of chemical class. Our approach, referred to as MGAT-CCS, had superior performance in comparison to other ML models in CCS prediction. MGAT-CCS achieved a median relative error of 0.47%/1.14% (positive/negative mode) and 1.40%/1.63% (positive/negative mode) for lipids and metabolites, respectively. When MGAT-CCS was applied to real-world metabolomics data, it reduced the number of false metabolite candidates by roughly 25% across multiple sample types ranging from plasma and urine to cells. To facilitate its application, we developed a user-friendly stand-alone web server for MGAT-CCS that is freely available at https://mgat-ccs-web.onrender.com. This work represents a step forward in predicting CCS values and can potentially facilitate the identification of small molecules when using ion mobility spectrometry coupled with mass spectrometry.

Exposure of Polycyclic Aromatic Hydrocarbons (PAHs) and Crude Oil to Atlantic Haddock (Melanogrammus aeglefinus): A Unique Snapshot of the Mercapturic Acid Pathway

Fish exposed to xenobiotics like petroleum-derived polycyclic aromatic hydrocarbons (PAHs) will immediately initiate detoxification systems through effective biotransformation reactions. Yet, there is a discrepancy between recognized metabolic pathways and the actual metabolites detected in fish following PAH exposure like oil pollution. To deepen our understanding of PAH detoxification, we conducted experiments exposing Atlantic haddock (Melanogrammus aeglefinus) to individual PAHs or complex oil mixtures. Bile extracts, analyzed by using an ion mobility quadrupole time-of-flight mass spectrometer, revealed novel metabolites associated with the mercapturic acid pathway. A dominant spectral feature recognized as PAH thiols set the basis for a screening strategy targeting (i) glutathione-, (ii) cysteinylglycine-, (iii) cysteine-, and (iv) mercapturic acid S-conjugates. Based on controlled single-exposure experiments, we constructed an interactive library of 33 metabolites originating from 8 PAHs (anthracene, phenanthrene, 1-methylphenanthrene, 1,4-dimethylphenanthrene, chrysene, benz[a]anthracene, benzo[a]pyrene, and dibenz[a,h]anthracene). By incorporation of the library in the analysis of samples from crude oil exposed fish, PAHs conjugated with glutathione and cysteinylglycine were uncovered. This qualitative study offers an exclusive glimpse into the rarely acknowledged mercapturic acid detoxification pathway in fish. Furthermore, this furnishes evidence that this metabolic pathway also succeeds for PAHs in complex pollution sources, a notable discovery not previously reported.

Analytical Methods for the Determination of Pharmaceuticals and Personal Care Products in Solid and Liquid Environmental Matrices: A Review

Among the various compounds regarded as emerging contaminants (ECs), pharmaceuticals and personal care products (PPCPs) are of particular concern. Their continuous release into the environment has a negative global impact on human life. This review summarizes the sources, occurrence, persistence, consequences of exposure, and toxicity of PPCPs, and evaluates the various analytical methods used in the identification and quantification of PPCPs in a variety of solid and liquid environmental matrices. The current techniques of choice for the analysis of PPCPs are state-of-the-art liquid chromatography coupled to mass spectrometry (LC-MS) or tandem mass spectrometry (LC-MS2). However, the complexity of the environmental matrices and the trace levels of micropollutants necessitate the use of advanced sample treatments before these instrumental analyses. Solid-phase extraction (SPE) with different sorbents is now the predominant method used for the extraction of PPCPs from environmental samples. This review also addresses the ongoing analytical method challenges, including sample clean-up and matrix effects, focusing on the occurrence, sample preparation, and analytical methods presently available for the determination of environmental residues of PPCPs. Continuous development of innovative analytical methods is essential for overcoming existing limitations and ensuring the consistency and diversity of analytical methods used in investigations of environmental multi-class compounds.

Assessing Per- and Polyfluoroalkyl Substances in Fish Fillet Using Non-Targeted Analyses

Per- and polyfluoroalkyl substances (PFAS) are a class of thousands of man-made chemicals that are persistent and highly stable in the environment. Fish consumption has been identified as a key route of PFAS exposure for humans. However, routine fish monitoring targets only a handful of PFAS, and non-targeted analyses have largely only evaluated fish from heavily PFAS-impacted waters. Here, we evaluated PFAS in fish fillets from recreational and drinking water sources in central North Carolina to assess whether PFAS are present in these fillets that would not be detected by conventional targeted methods. We used liquid chromatography, ion mobility spectrometry, and mass spectrometry (LC-IMS-MS) to collect full scan feature data, performed suspect screening using an in-house library of 100 PFAS for high confidence feature identification, searched for additional PFAS features using non-targeted data analyses, and quantified perfluorooctanesulfonic acid (PFOS) in the fillet samples. A total of 36 PFAS were detected in the fish fillets, including 19 that would not be detected using common targeted methods, with a minimum of 6 and a maximum of 22 in individual fish. Median fillet PFOS levels were concerningly high at 11.6 to 42.3 ppb, and no significant correlation between PFOS levels and number of PFAS per fish was observed. Future PFAS monitoring in this region should target more of these 36 PFAS, and other regions not considered heavily PFAS contaminated should consider incorporating non-targeted analyses into ongoing fish monitoring studies.

Introducing 'identification probability' for automated and transferable assessment of metabolite identification confidence in metabolomics and related studies

Methods for assessing compound identification confidence in metabolomics and related studies have been debated and actively researched for the past two decades. The earliest effort in 2007 focused primarily on mass spectrometry and nuclear magnetic resonance spectroscopy and resulted in four recommended levels of metabolite identification confidence - the Metabolite Standards Initiative (MSI) Levels. In 2014, the original MSI Levels were expanded to five levels (including two sublevels) to facilitate communication of compound identification confidence in high resolution mass spectrometry studies. Further refinement in identification levels have occurred, for example to accommodate use of ion mobility spectrometry in metabolomics workflows, and alternate approaches to communicate compound identification confidence also have been developed based on identification points schema. However, neither qualitative levels of identification confidence nor quantitative scoring systems address the degree of ambiguity in compound identifications in context of the chemical space being considered, are easily automated, or are transferable between analytical platforms. In this perspective, we propose that the metabolomics and related communities consider identification probability as an approach for automated and transferable assessment of compound identification and ambiguity in metabolomics and related studies. Identification probability is defined simply as 1/N, where N is the number of compounds in a reference library or chemical space that match to an experimentally measured molecule within user-defined measurement precision(s), for example mass measurement or retention time accuracy, etc. We demonstrate the utility of identification probability in an in silico analysis of multi-property reference libraries constructed from the Human Metabolome Database and computational property predictions, provide guidance to the community in transparent implementation of the concept, and invite the community to further evaluate this concept in parallel with their current preferred methods for assessing metabolite identification confidence.

High-Resolution Mass Spectrometry for Human Exposomics: Expanding Chemical Space Coverage

In the modern "omics" era, measurement of the human exposome is a critical missing link between genetic drivers and disease outcomes. High-resolution mass spectrometry (HRMS), routinely used in proteomics and metabolomics, has emerged as a leading technology to broadly profile chemical exposure agents and related biomolecules for accurate mass measurement, high sensitivity, rapid data acquisition, and increased resolution of chemical space. Non-targeted approaches are increasingly accessible, supporting a shift from conventional hypothesis-driven, quantitation-centric targeted analyses toward data-driven, hypothesis-generating chemical exposome-wide profiling. However, HRMS-based exposomics encounters unique challenges. New analytical and computational infrastructures are needed to expand the analysis coverage through streamlined, scalable, and harmonized workflows and data pipelines that permit longitudinal chemical exposome tracking, retrospective validation, and multi-omics integration for meaningful health-oriented inferences. In this article, we survey the literature on state-of-the-art HRMS-based technologies, review current analytical workflows and informatic pipelines, and provide an up-to-date reference on exposomic approaches for chemists, toxicologists, epidemiologists, care providers, and stakeholders in health sciences and medicine. We propose efforts to benchmark fit-for-purpose platforms for expanding coverage of chemical space, including gas/liquid chromatography-HRMS (GC-HRMS and LC-HRMS), and discuss opportunities, challenges, and strategies to advance the burgeoning field of the exposome.

Ion Mobility Separations Using Cocentric Architecture

Ion mobility separations, especially using drift tube ion mobility spectrometers, are usually performed in linear channels, which can have a large footprint when extended to achieve higher resolving powers. In this work, we explored the performance of an ion mobility device with a curved architecture, which can have a more compact form. The cocentric ion mobility spectrometer (CoCIMS) manipulates ions between two cocentric surfaces containing a serpentine track. The mobility separation inside the CoCIMS is achieved using traveling waveforms (TWs). We initially evaluated the device using ion trajectory simulations using SIMION, which indicated that when ions traveled circularly inside the CoCIMS they resulted in similar resolving powers and transmitted m/z range as traveling in a straight path. We then performed experimental validation of the CoCIMS in conjunction with a TOF MS. The CoCIMS was made of two flexible printed circuit board materials folded into cocentric cylinders separated by a gap of 2.8 mm. The device was about 50 mm diameter ×152 mm long and provided 1.846 m of serpentine path length. Three sets of mixtures (Agilent tune mixture, tetraalkylammonium salts, and an eight-peptide mixture) and four traveling waveform profiles (square, sine, triangle, and sawtooth) were used. The sawtooth TW profile produced a slightly higher resolving power for the Agilent tuning mixture and tetraalkylammonium ions. The average resolving power for Agilent tune mixture ions ranged from 37 (using sawtooth TW) to 27 (using square TW). The average resolving powers ranged from 45 (sawtooth TW) to 31 (square TW) for tetraalkylammonium ions. The resolving power of the peptide mixture ions was similar among the four TW profiles and ranged from 51 to 56. The average percent error in TWCCS for the peptide mixture ions was about 0.4%. The new device showed promising results, but improvements are needed to further increase the resolving power.

Workflow to facilitate the detection of new psychoactive substances and drugs of abuse in influent urban wastewater

The complexity around the dynamic markets for new psychoactive substances (NPS) forces researchers to develop and apply innovative analytical strategies to detect and identify them in influent urban wastewater. In this work a comprehensive suspect screening workflow following liquid chromatography - high resolution mass spectrometry analysis was established utilising the open-source InSpectra data processing platform and the HighResNPS library. In total, 278 urban influent wastewater samples from 47 sites in 16 countries were collected to investigate the presence of NPS and other drugs of abuse. A total of 50 compounds were detected in samples from at least one site. Most compounds found were prescription drugs such as gabapentin (detection frequency 79%), codeine (40%) and pregabalin (15%). However, cocaine was the most found illicit drug (83%), in all countries where samples were collected apart from the Republic of Korea and China. Eight NPS were also identified with this protocol: 3-methylmethcathinone 11%), eutylone (6%), etizolam (2%), 3-chloromethcathinone (4%), mitragynine (6%), phenibut (2%), 25I-NBOH (2%) and trimethoxyamphetamine (2%). The latter three have not previously been reported in municipal wastewater samples. The workflow employed allowed the prioritisation of features to be further investigated, reducing processing time and gaining in confidence in their identification.

Toward Part-per-Million Precision in the Determination of an Ion's Collision Cross Section Using Multipass Cyclic Ion Mobility

In cyclic ion mobility (cIMS), ions are permitted to travel multiple passes around the drift cell, increasing the distance traveled and the relative separation between ions. This study tests the hypothesis that multiple passes around the cell can also result in improved precision when measuring an ion's mobility and the collision cross section (TWCCS) derived therefrom. Experiments were performed with a diverse set of compounds, including 16 polycyclic aromatic hydrocarbons using gas chromatographic atmospheric pressure chemical ionization and a set of drug molecules by direct infusion electrospray ionization. The average periodic drift time, viz., the average time required for the ion to travel around the cIMS cell once, shifts dramatically, approaching part-per-million (ppm) precision as the number of passes increases to ∼100. Extrapolation of the precision of the CCS values with respect to the number of passes led to the prediction that the precision will reach 1000 ppm after 50 passes, 100 ppm after 100 passes, and <10 ppm after 150 passes. Experiments wherein the number of passes exceeded 100 produced TWCCS values having within-run precisions ranging between 15 and 117 ppm. The improved precision with an increasing number of passes may be a consequence of mitigating space-charge effects by allowing the ions to occupy a larger region of the cIMS cell. A method is proposed to enable practical measurements of TWCCS with ppm precision and is demonstrated to characterize an unknown drug mixture.

Innovative analytical methodologies for characterizing chemical exposure with a view to next-generation risk assessment

The chemical burden on the environment and human population is increasing. Consequently, regulatory risk assessment must keep pace to manage, reduce, and prevent adverse impacts on human and environmental health associated with hazardous chemicals. Surveillance of chemicals of known, emerging, or potential future concern, entering the environment-food-human continuum is needed to document the reality of risks posed by chemicals on ecosystem and human health from a one health perspective, feed into early warning systems and support public policies for exposure mitigation provisions and safe and sustainable by design strategies. The use of less-conventional sampling strategies and integration of full-scan, high-resolution mass spectrometry and effect-directed analysis in environmental and human monitoring programmes have the potential to enhance the screening and identification of a wider range of chemicals of known, emerging or potential future concern. Here, we outline the key needs and recommendations identified within the European Partnership for Assessment of Risks from Chemicals (PARC) project for leveraging these innovative methodologies to support the development of next-generation chemical risk assessment.

Discovery of polycyclic aromatic acid metabolites in fish exposed to the petroleum compounds 1-methylphenanthrene and 1,4-dimethylphenanthrene

Most of the polycyclic aromatic hydrocarbons (PAHs) in petroleum are alkylated (alkyl PAHs), still the metabolism of these alkyl PAHs to the expected acid products (polycyclic aromatic acids; PAAs) has yet to be demonstrated in oil-exposed fish. Should these compounds be discovered in fish as they have in ragworm, rodents, and humans, they could present an indicative biomarker for assessing oil pollution. In this study, the ability to biotransform alkyl PAHs to PAAs was examined on Atlantic haddock (Melanogrammus aeglefinus). Exposure to phenanthrene, 1-methyphenanthrene or 1,4-dimethylphenanthrene was performed via intraperitoneal injection. An Ion Mobility Quadrupole Time-Of-Flight Mass Spectrometer (IMS-Q-TOF MS) was used in exploratory analysis of extracted bile samples. Acquisition of four-dimensional information by coupling liquid chromatography with the IMS-Q-TOF MS and in-silico prediction for feature prioritization in the data processing workflow allowed several tentative identifications with high degree of confidence. This work presents the first detection of PAAs in fish and suggests the importance of investigating alkyl PAHs in ecotoxicological studies of oil-polluted fish environments.

Untargeted 4D-metabolomics using Trapped Ion Mobility combined with LC-HRMS in extra virgin olive oil adulteration study with lower-quality olive oils

Metabolomics is widely established in the field of food authenticity to address demanding issues, such as adulteration cases. Trapped ion mobility spectrometry (TIMS) coupled to liquid chromatography (LC) and high-resolution mass spectrometry (HRMS) provides an additional analytical dimension, introducing mobility-enhanced metabolomics in four dimensions (4D). In the present work, the potential of LC-TIMS-HRMS as a reliable analytical platform for authenticity studies is being explored, applied in extra virgin olive oil (EVOO) adulteration study. An integrated untargeted 4D-metabolomics approach is being implemented to investigate adulteration, with refined olive oils (ROOs) and olive pomace oils (OPOs) set as adulterants. Robust prediction models are built, successfully discriminating authentic EVOOs from adulterated ones and highlighting markers in each group. Noteworthy outcomes are retrieved regarding TIMS added value in LC-HRMS workflows, resulting in a significant increase of metabolic coverage, while, thanks to platform's enhanced sensitivity, detection of adulteration is being achieved down to 1%.

Accumulation of Large Ion Populations with High Ion Densities and Effects Due to Space Charge in Traveling Wave-Based Structures for Lossless Ion Manipulations (SLIM) IMS-MS

The accumulation of very large ion populations in traveling wave (TW)-based Structures for Lossless ion Manipulations (SLIM) has been studied to better understand aspects of "in-SLIM" ion accumulation, and particularly its use in conjunction with ion mobility spectrometry (IMS). A linear SLIM ion path was implemented that had a "gate" for blocking and accumulating ions for arbitrary time periods. Removing the gate potential caused ions to exit, and the spatial distributions of accumulated ions examined. The ion populations for a set of peptides increased approximately linearly with increased accumulation times until space change effects became significant, after which the peptide precursor ion populations decreased due to growing space charge-related ion activation, reactions, and losses. Ion activation increased with added storage times and the TW amplitude. Lower amplitude TWs in the accumulation/storage region prevented or minimized ion losses or ion heating effects that can also lead to fragmentation. Our results supported the use of an accumulation region close to the SLIM entrance for speeding accumulation, minimizing ion heating, and avoiding ion population profiles that result in IMS peak tailing. Importantly, space charge-driven separations were observed for large populations of accumulated species and attributed to the opposing effects of space charge and the TW. In these separations, ion species form distributions or peaks, sometimes moving against the TW, and are ordered in the SLIM based on their mobilities. Only the highest mobility ions located closest to the gate in the trapped ion population (and where the highest ion densities were achieved) were significantly activated. The observed separations may offer utility for ion prefractionation of ions and increasing the dynamic range measurements, increasing the resolving power of IMS separations by decreasing peak widths for accumulated ion populations, and other purposes benefiting from separations of extremely large ion populations.

Hydroxylated transformation products of pharmaceutical active compounds: Generation from processes used in wastewater treatment plants and its environmental monitoring

Pharmaceutical active compounds (PhACs) are organic pollutants detected in wastewater and aquatic environments worldwide in concentrations ranging from ng L-1 to μg L-1. Wastewater effluents containing PhACs residues is discharged in municipal sewage and, subsequently collected in municipal wastewater treatment plants (WWTPs) where are not entirely removed. Thus, PhACs and its transformation products (TPs) are discharged into water bodies. In the current work, the transformation of PhACs under treatments used in municipal WWTPs such as biological, photolysis, chlorination, and ozonation processes was reviewed. Data set of the major transformation pathways were obtained of studies that performed the PhACs removal and TPs monitoring during batch-scale experiments using gas and liquid chromatography coupled with tandem mass spectrometry (GC/LC-MS/MS). Several transformation pathways as dealkylation, hydroxylation, oxidation, acetylation, aromatic ring opening, chlorination, dehalogenation, photo-substitution, and ozone attack reactions were identified during the transformation of PhACs. Especially, hydroxylation reaction was identified as transformation pathway in all the processes. During the elucidation of hydroxylated TPs several isobaric compounds as monohydroxylated and dihydroxylated were identified. However, hydroxylated TPs monitoring in wastewater and aquatic environments is a topic scarcely studied due to that has no environmental significance, lack of available analytic standars of hydroxylated TPs and lack of analytic methods for their identification. Thus, screening strategy for environmental monitoring of hydroxylated TPs was proposed through target and suspect screening using GC/LC-MS/MS systems. In the next years, more studies on the hydroxylated TPs monitoring are necessary for its detection in WWTPs effluents as well as studies on their environmental effects in aquatic environments.

Suspect and nontarget screening of per- and polyfluoroalkyl substances based on ion mobility mass spectrometry and machine learning techniques

High-resolution mass spectrometry (HRMS)-based suspect and nontarget screening techniques are powerful tools for the comprehensive identification of per- and polyfluoroalkyl substances (PFASs), but the interference of complex matrices (especially for wastewater) pose an analytical challenge. This study explored the potential of combining ion mobility spectrometry (IMS) with HRMS and machine learning techniques to achieve the rapid and accurate suspect and nontarget screening of PFAS in wastewater. There were fewer interfering peaks and a clearer spectrum in the data acquired by IMS-HRMS than conventional HRMS. The introduction of collision cross section (CCS) in PFAS homologous series search could filter out 63% of false positive results. Retention time and CCS prediction models were helpful in improving the confidence for PFAS qualitative identification and the random forest algorithm combined with RDKit descriptor performed best for CCS prediction. With the inclusion of extra dimensional information, this study also proposed a comprehensive and concise confidence assignment criterion to better convey the certainty of the qualitative identification of PFAS. Finally, a total of 56 potential PFASs were identified in the wastewater sample using the newly developed method and 45 of them were identified outside reference standards, emphasizing the importance of suspect and nontarget screening for PFAS.

Comprehensive study on the potential environmental risk of temporal antibiotic usage through wastewater discharges

Antibiotic residues can reach aquatic ecosystems through urban wastewater discharges, posing an ecotoxicological risk for aquatic organisms and favoring the development of bacterial resistance. To assess the emission rate and hazardousness of these compounds, it is important to carry out periodic chemical monitoring campaigns that provide information regarding the actual performance of wastewater treatment plants (WWTPs) and the potential impact of the treated wastewater in the aquatic environment. In this study, 18 of the most widely consumed antibiotics in Spain were determined by liquid chromatography-tandem mass spectrometry in both influent (IWW) and effluent wastewater (EWW) samples collected over four seasons along 2021-2022. Eleven antibiotics were detected in EWW with azithromycin, ciprofloxacin and levofloxacin showing the highest concentration levels (around 2 μg L-1 of azithromycin and 0.4 μg L-1 of quinolone compounds). Data showed that only 4 out of the 11 compounds were removed by more than 50 % in the WWTP, with sulfamethoxazole standing out with an average removal efficiency >80 %. The risk that treated water could pose to the aquatic environment was also assessed, with 6 compounds indicating a potential environmental risk by exceeding established ecotoxicological and resistance thresholds. Based on the risk assessment, the WWTP removal efficiency required to reduce such risk for antibiotics was estimated. In addition, pooled wastewater samples were screened by LC coupled to high resolution mass spectrometry with ion mobility separation, searching for metabolites and transformation products of the antibiotics investigated to widen future research. Studies like this are crucial to map the impact of antibiotic pollution and to provide the basis for designing water quality and risk prevention monitoring programs.

Nontargeted Screening of Contaminants of Emerging Concern in the Glen Valley Wastewater Treatment Plant, Botswana

There is growing concern about the prevalence and impact of contaminants of emerging concern (CECs). The environmental monitoring of CECs has, however, been limited in low- and middle-income countries due to the lack of advanced analytical instrumentation locally. In the present study we employed a nontargeted and suspect screening workflow via liquid chromatography coupled with high-resolution mass spectrometry (HRMS) to identify known and unknown pollutants in the Glen Valley wastewater treatment plant, Botswana, complemented by analysis of groundwater samples. The present study represents the first HRMS analysis of CECs in water samples obtained in Botswana. Suspect screening of 5942 compounds qualitatively identified 28 compounds, including 26 pharmaceuticals and two illicit drugs (2-ethylmethcathinone and 11-nor-9-carboxy-Δ9-tetrahydrocannabinol). Nontargeted analysis tentatively identified the presence of 34 more compounds including (5ξ)-12,13-dihydroxypodocarpa-8,11,13-trien-7-one, 12-aminododecanoic acid, atenolol acid, brilliant blue, cyclo leucylprolyl, decanophenone, DL-carnitine, N,N'-dicyclohexylurea, N4-acetylsulfamethoxazole, NP-003672, and 24 polyethylene glycol polymers. The highest number of detections were in influent wastewater (26 CECs) followed by effluent wastewater (10 CECs) and, lastly, groundwater (4 CECs). Seventeen CECs detected in the influent water were not detected in the effluent waters, suggesting reduced emissions due to wastewater treatment. Two antiretroviral compounds (abacavir and tenofovir) were detected in the influent and effluent sources. This suggests that wastewater treatment plants are a major pathway of chemical pollution to the environment in Botswana and will help inform prioritization efforts for monitoring and remediation that is protective of these key ecosystems. Environ Toxicol Chem 2024;43:52-61. © 2023 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Application of Ion Mobility Spectrometry and the Derived Collision Cross Section in the Analysis of Environmental Organic Micropollutants

Ion mobility spectrometry (IMS) is a rapid gas-phase separation technique, which can distinguish ions on the basis of their size, shape, and charge. The IMS-derived collision cross section (CCS) can serve as additional identification evidence for the screening of environmental organic micropollutants (OMPs). In this work, we summarize the published experimental CCS values of environmental OMPs, introduce the current CCS prediction tools, summarize the use of IMS and CCS in the analysis of environmental OMPs, and finally discussed the benefits of IMS and CCS in environmental analysis. An up-to-date CCS compendium for environmental contaminants was produced by combining CCS databases and data sets of particular types of environmental OMPs, including pesticides, drugs, mycotoxins, steroids, plastic additives, per- and polyfluoroalkyl substances (PFAS), polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), and polybrominated diphenyl ethers (PBDEs), as well as their well-known transformation products. A total of 9407 experimental CCS values from 4170 OMPs were retrieved from 23 publications, which contain both drift tube CCS in nitrogen (DTCCSN2) and traveling wave CCS in nitrogen (TWCCSN2). A selection of publicly accessible and in-house CCS prediction tools were also investigated; the chemical space covered by the training set and the quality of CCS measurements seem to be vital factors affecting the CCS prediction accuracy. Then, the applications of IMS and the derived CCS in the screening of various OMPs were summarized, and the benefits of IMS and CCS, including increased peak capacity, the elimination of interfering ions, the separation of isomers, and the reduction of false positives and false negatives, were discussed in detail. With the improvement of the resolving power of IMS and enhancements of experimental CCS databases, the practicability of IMS in the analysis of environmental OMPs will continue to improve.

Sliding Windows in Ion Mobility (SWIM): A New Approach to Increase the Resolving Power in Trapped Ion Mobility-Mass Spectrometry Hyphenated with Chromatography

Over the past decade, the separation efficiency achieved by linear IMS instruments has increased substantially, with state-of-the-art IM technologies, such as the trapped ion mobility (TIMS), the cyclic traveling wave ion mobility (cTWIMS), and the structure for lossless ion manipulation (SLIM) platforms commonly demonstrating resolving powers in excess of 200. However, for complex sample analysis that require front end separation, the achievement of such high resolving power in TIMS is significantly hampered, since the ion mobility range must be broad enough to analyze all the classes of compounds of interest, whereas the IM analysis time must be short enough to cope with the time scale of the preseparation technique employed. In this paper, we introduce the concept of sliding windows in ion mobility (SWIM) for chromatography hyphenated TIMS applications that bypasses the need to use a wide and fixed IM range by using instead narrow and mobile ion mobility windows that adapt to the analytes' ion mobility during chromatographic separation. GC-TIMS-MS analysis of a mixture of 174 standards from several halogenated persistent organic pollutant (POP) classes, including chlorinated and brominated dioxins, biphenyls, and PBDEs, demonstrated that the average IM resolving power could be increased up to 40% when the SWIM mode was used, thereby greatly increasing the method selectivity for the analysis of complex samples.

Water, Water Everywhere, but Every Drop Unique: Challenges in the Science to Understand the Role of Contaminants of Emerging Concern in the Management of Drinking Water Supplies

The protection and management of water resources continues to be challenged by multiple and ongoing factors such as shifts in demographic, social, economic, and public health requirements. Physical limitations placed on access to potable supplies include natural and human-caused factors such as aquifer depletion, aging infrastructure, saltwater intrusion, floods, and drought. These factors, although varying in magnitude, spatial extent, and timing, can exacerbate the potential for contaminants of concern (CECs) to be present in sources of drinking water, infrastructure, premise plumbing and associated tap water. This monograph examines how current and emerging scientific efforts and technologies increase our understanding of the range of CECs and drinking water issues facing current and future populations. It is not intended to be read in one sitting, but is instead a starting point for scientists wanting to learn more about the issues surrounding CECs. This text discusses the topical evolution CECs over time (Section 1), improvements in measuring chemical and microbial CECs, through both analysis of concentration and toxicity (Section 2) and modeling CEC exposure and fate (Section 3), forms of treatment effective at removing chemical and microbial CECs (Section 4), and potential for human health impacts from exposure to CECs (Section 5). The paper concludes with how changes to water quantity, both scarcity and surpluses, could affect water quality (Section 6). Taken together, these sections document the past 25 years of CEC research and the regulatory response to these contaminants, the current work to identify and monitor CECs and mitigate exposure, and the challenges facing the future.

Screening for new psychoactive substances in wastewater from educational institutions

Drug (ab)use among young people is a serious issue, negatively impacting their well-being and prospects. The emergence of new psychoactive substances (NPS) further complicates the situation as they are easily accessible (e.g., online), but users are at high risk of intoxication as their chemical identity is often unknown and toxicity poorly understood. While surveys and drug testing are traditionally used in educational institutions to comprehend drug use trends and establish effective prevention programs, they are not without their limitations. Accordingly, we investigated the occurrence of NPS in educational institutions through wastewater analysis and critically evaluated the viability of the approach. The study included eight wastewater samples from primary schools (ages 6-15 years), six from secondary schools (ages 15-19 years), three from institutions for both secondary and higher education (ages 15+), and six from higher educational institutions (ages 19+). Samples were obtained mid-week and evaluated in two Slovenian municipalities; the capital Ljubljana and a smaller one (M1). Samples were screened using liquid chromatography-ion mobility-high-resolution mass spectrometry (LC-IMS-HRMS), and NPS identified at three levels of confidence (Level 1: unequivocal, Level 2: probable, Level 3: tentative) from a suspect list containing over 5600 entries. NPS were identified in all types of educational institutions. Most were synthetic stimulants, with 3-MMC, ephedrine, 4-chloro-α-PPP, and ethcathinone being unequivocally identified. Also, NPS were present in wastewater from all educational institution types revealing potential spatial but no inter-institutional trends. Although specific groups cannot be targeted, the study, as a proof-of-concept, demonstrates that a suspect screening of wastewater employing LC-IMS-HRMS can be used as a radar for NPS in educational institutions and potentially replace invasive drug testing.

Identification and Prioritization of Environmental Organic Pollutants: From an Analytical and Toxicological Perspective

Exposure to environmental organic pollutants has triggered significant ecological impacts and adverse health outcomes, which have been received substantial and increasing attention. The contribution of unidentified chemical components is considered as the most significant knowledge gap in understanding the combined effects of pollutant mixtures. To address this issue, remarkable analytical breakthroughs have recently been made. In this review, the basic principles on recognition of environmental organic pollutants are overviewed. Complementary analytical methodologies (i.e., quantitative structure-activity relationship prediction, mass spectrometric nontarget screening, and effect-directed analysis) and experimental platforms are briefly described. The stages of technique development and/or essential parts of the analytical workflow for each of the methodologies are then reviewed. Finally, plausible technique paths and applications of the future nontarget screening methods, interdisciplinary techniques for achieving toxicant identification, and burgeoning strategies on risk assessment of chemical cocktails are discussed.

Complementary methods for structural assignment of isomeric candidate structures in non-target liquid chromatography ion mobility high-resolution mass spectrometric analysis

Non-target screening with LC/IMS/HRMS is increasingly employed for detecting and identifying the structure of potentially hazardous chemicals in the environment and food. Structural assignment relies on a combination of multidimensional instrumental methods and computational methods. The candidate structures are often isomeric, and unfortunately, assigning the correct structure among a number of isomeric candidate structures still is a key challenge both instrumentally and computationally. While practicing non-target screening, it is usually impossible to evaluate separately the limitations arising from (1) the inability of LC/IMS/HRMS to resolve the isomeric candidate structures and (2) the uncertainty of in silico methods in predicting the analytical information of isomeric candidate structures due to the lack of analytical standards for all candidate structures. Here we evaluate the feasibility of structural assignment of isomeric candidate structures based on in silico-predicted retention time and database collision cross-section (CCS) values as well as based on matching the empirical analytical properties of the detected feature with those of the analytical standards. For this, we investigated 14 candidate structures corresponding to five features detected with LC/HRMS in a spiked surface water sample. Considering the predicted retention times and database CCS values with the accompanying uncertainty, only one of the isomeric candidate structures could be deemed as unlikely; therefore, the annotation of the LC/IMS/HRMS features remained ambiguous. To further investigate if unequivocal annotation is possible via analytical standards, the reversed-phase LC retention times and low- and high-resolution ion mobility spectrometry separation, as well as high-resolution MS2 spectra of analytical standards were studied. Reversed-phase LC separated the highest number of candidate structures while low-resolution ion mobility and high-resolution MS2 spectra provided little means for pinpointing the correct structure among the isomeric candidate structures even if analytical standards were available for comparison. Furthermore, the question arises which prediction accuracy is required from the in silico methods to par the analytical separation. Based on the experimental data of the isomeric candidate structures studied here and previously published in the literature (516 retention time and 569 CCS values), we estimate that to reduce the candidate list by 95% of the structures, the confidence interval of the predicted retention times would need to decrease to below 0.05 min for a 15-min gradient while that of CCS values would need to decrease to 0.15%. Hereby, we set a clear goal to the in silico methods for retention time and CCS prediction.

Cracked and shucked: GC-APCI-IMS-HRMS facilitates identification of unknown halogenated organic chemicals in French marine bivalves

High resolution mass spectrometry (HRMS)-based non-target analysis coupled with ion mobility spectrometry (IMS) is gaining momentum due to its ability to provide complementary information which can be useful in the identification of unknown organic chemicals in support of efforts in unraveling the complexity of the chemical exposome. The chemical exposome in the marine environment, though not as well studied as its freshwater counterparts, is not foreign to chemical diversity specially when it comes to potentially bioaccumulative and bioactive polyhalogenated organic contaminants and natural products. In this work we present in detail how we utilized IMS-HRMS coupled with gas chromatographic separation and atmospheric pressure chemical ionization (APCI) to annotate polyhalogenated organic chemicals in French bivalves collected from 25 sites along the French coasts. We describe how we used open cheminformatic tools to exploit isotopologue patterns, isotope ratios, Kendrick mass defect (Cl scale), and collisional cross section (CCS), in order to annotate 157 halogenated features (level 1: 54, level 2: 47, level 3: 50, and level 4: 6). Grouping the features into 11 compound classes was facilitated by a KMD vs CCS plot which showed co-clustering of potentially structurally-related compounds. The features were semi-quantified to gain insight into the distribution of these halogenated features along the French coast, ultimately allowing us to differentiate between sites that are more anthropologically impacted versus sites that are potentially biodiverse.

Searching for pharmaceutically active products and metabolites in environmental waters of Peru by HRMS-based screening: Proposal for future monitoring and environmental risk assessment

The presence of pharmaceutical active products (PhACs) in the aquatic environment is a matter of current concern, and there is an increasing trend to include these compounds in water quality monitoring programs and environmental risk assessments. Several studies have reported the presence of PhACs in environmental waters worldwide, but only a few studies have focused on Latin American countries. Thus, available information on the occurrence of parent pharmaceuticals, especially their metabolites, is very scarce. Peru is one of the less monitored countries in terms of contaminants of emerging concern (CECs) in waters, and only one study has been found, which was focused on the quantification of selected PhACs in urban wastewater and surface water. The aim of this work is to complement the previous data reported on PhACs in the aquatic environment by application of a wide-scope high-resolution (HRMS)-based screening, making use of target and suspect approaches. In the present work, 30 pharmaceuticals, drugs or other compounds (sweeteners, UV filters, etc.) and 21 metabolites have been identified, with antibiotics (and metabolites) being the most prevalent compounds. The use of liquid chromatography (LC) coupled to ion mobility-HRMS allowed the tentative identification of parent compounds and metabolites, for which the analytical reference standard was not available, with a high level of confidence in their identification. Based on the results obtained, a strategy for the monitoring of PhACs and relevant metabolites in environmental waters from Peru and for subsequent risk assessment is proposed. Our data will also help to focus future studies to evaluate the removal efficiency of wastewater treatment plants and the impact of treated water in receiving water bodies.

Efficient Validation Strategies in Environmental Analytical Chemistry: A Focus on Organic Micropollutants in Water Samples

This article critically reviews analytical method validation and quality control applied to the environmental chemistry field. The review focuses on the determination of organic micropollutants (OMPs), specifically emerging contaminants and pesticides, in the aquatic environment. The analytical technique considered is (gas and liquid) chromatography coupled to mass spectrometry (MS), including high-resolution MS for wide-scope screening purposes. An analysis of current research practices outlined in the literature has been performed, and key issues and analytical challenges are identified and critically discussed. It is worth emphasizing the lack of specific guidelines applied to environmental analytical chemistry and the minimal regulation of OMPs in waters, which greatly affect method development and performance, requirements for method validation, and the subsequent application to samples. Finally, a proposal is made for method validation and data reporting, which can be understood as starting points for further discussion with specialists in environmental analytical chemistry.

Identification and characterization of quaternary ammonium compounds in Flemish indoor dust by ion-mobility high-resolution mass spectrometry

Quaternary ammonium compounds (QACs) are a class of surfactants commonly used in disinfecting and cleaning products. Their use has substantially increased during the COVID-19 pandemic leading to increasing human exposure. QACs have been associated with hypersensitivity reactions and an increased risk of asthma. This study introduces the first identification, characterization and semi-quantification of QACs in European indoor dust using ion-mobility high-resolution mass spectrometry (IM-HRMS), including the acquisition of collision cross section values (^DTCCS_N2) for targeted and suspect QACs. A total of 46 indoor dust samples collected in Belgium were analyzed using target and suspect screening. Targeted QACs (n = 21) were detected with detection frequencies ranging between 4.2 and 100 %, while 15 QACs showed detection frequencies > 90 %. Semi-quantified concentrations of individual QACs showed a maximum of 32.23 µg/g with a median ∑QAC concentration of 13.05 µg/g and allowed the calculation of Estimated Daily Intakes for adults and toddlers. Most abundant QACs matched the patterns reported in indoor dust collected in the United States. Suspect screening allowed the identification of 17 additional QACs. A dialkyl dimethyl ammonium compound with mixed chain lengths (C16:C18) was characterized as a major QAC homologue with a maximum semi-quantified concentration of 24.90 µg/g. The high detection frequencies and structural variabilities observed call for more European studies on potential human exposure to these compounds. For all targeted QACs, drift tube IM-HRMS derived collision cross section values (^DTCCS_N2) are reported. Reference ^DTCCS_N2 values allowed the characterization of CCS-m/z trendlines for each of the targeted QAC classes. Experimental CCS-m/z ratios of suspect QACs were compared with the CCS-m/z trendlines. The alignment between the two datasets served as an additional confirmation of the assigned suspect QACs. The use of the 4bit multiplexing acquisition mode with consecutive high-resolution demultiplexing confirmed the presence of isomers for two of the suspect QACs.

Effects of structural characteristics of (un)conjugated steroid metabolites in their collision cross section value

In this work, the collision cross section (CCS) value of 103 steroids (including unconjugated metabolites and phase II metabolites conjugated with sulfate and glucuronide groups) was determined by liquid chromatography coupled to traveling wave ion mobility spectrometry (LC-TWIMS). A time of flight (QTOF) mass analyzer was used to perform the analytes determination at high-resolution mass spectrometry. An electrospray ionization source (ESI) was used to generate [M+H]⁺, [M + NH₄]⁺ and/or [M - H]^- ions. High reproducibility was observed for the CCS determination in both urine and standard solutions, obtaining RSD lower than 0.3% and 0.5% in all cases respectively. CCS determination in matrix was in accordance with the CCS measured in standards solution showing deviations below 2%. In general, CCS values were directly correlated with the ion mass and allowed differentiating between glucuronides, sulfates and free steroids although differences among steroids of the same group were less significant. However, more specific information was obtained for phase II metabolites observing differences in the CCS value of isomeric pairs concerning the conjugation position or the α/β configuration, which could be useful in the structural elucidation of new steroid metabolites in the anti-doping field. Finally, the potential of IMS reducing interferences from the sample matrix was also tested for the analysis of a glucuronide metabolite of bolasterone (5β-androstan-7α,17α-dimethyl-3α,17β-diol-3-glucuronide) in urine samples.

LC-IMS-HRMS for identification of biomarkers in untargeted metabolomics: The effects of pterostilbene and resveratrol consumption in liver steatosis, animal model

Untargeted metabolomics with the combination of ion mobility separation coupled to high resolution mass spectrometry (IMS-HRMS) was applied to investigate the impact of resveratrol and pterostilbene supplementation on the metabolic fingerprint of the Wistar rats liver with induced liver steatosis. RP-LC and HILIC in both ionisation modes were employed to analyse the liver samples (n = 40) from Wistar rats fed with a high-fat and high-fructose diet, supplemented or not with resveratrol and pterostilbene. After univariate and multivariate statistical analysis, 34 metabolites were highlighted in the different diets and elucidated. Despite the structural similarity, different alterations in liver metabolism were observed by the supplementations. Resveratrol treatment was characterised by the alteration in metabolism of 17 lysophospholipids, while pterostilbene affected some vitamins and derivatives, among others. IMS has demonstrated great potential in the elucidation process thanks to the additional structural descriptor the CCS (Ų), providing more confidence in the identification.

Untargeted metabolomics approach using UHPLC-IMS-QTOF MS for surface body samples to identify low-volatility chemosignals related to maternal care in mice

The present study is focused on the determination of low-volatile chemosignals excreted or secreted by mouse pups in their early days of life involved in maternal care induction in mice adult females. Untargeted metabolomics was employed to differentiate between samples collected with swabs from facial and anogenital area from neonatal mouse pups receiving maternal care (first two weeks of life) and the elder mouse pups in the weaning period (4th week old). The sample extracts were analysed by ultra-high pressure liquid chromatography (UHPLC) coupled to ion mobility separation (IMS) in combination with high resolution mass spectrometry (HRMS). After data processing with Progenesis QI and multivariate statistical analysis, five markers present in the first two weeks of mouse pups life and putatively involved in materno-filial chemical communication were tentatively identified: arginine, urocanic acid, erythro-sphingosine (d17:1), sphingosine (d18:1) and sphinganine. The four-dimensional data and the tools associated to the additional structural descriptor obtained by IMS separation were of great help in the compound identification. The results demonstrated the great potential of UHPLC-IMS-HRMS based untargeted metabolomics to identity putative pheromones in mammals.

Ultra-high performance liquid chromatography coupled to ion mobility quadrupole time-of-flight mass spectrometry for the identification of non-volatile compounds migrating from 'natural' dishes

Although most new biomaterials for food industry applications are labelled '100% natural fabrication' and 'chemical-free', certain compounds may migrate from those materials to the food, compromising the organoleptic characteristics and safety of the product. In this work, the degree of compound migration from dishes made with four different biomaterials: bamboo, palm leaf, wood and wheat pulp was investigated. Migration tests were carried out using three food simulants, 10% ethanol (simulant A), 3% acetic acid (simulant B), and 95% ethanol (simulant D2). Unequivocal identification of non-intentionally added substances (NIAS) is challenging even when using high-resolution mass spectrometry techniques however, a total of 25 different non-volatile compounds from the migration tests were identified and quantified using Ultra-high performance liquid chromatography coupled to ion mobility quadrupole time-of-flight mass spectrometry (UPLC-IMS-MS). In the bamboo samples three oligomers, cyclic diethylene glycol adipate, 3,6,9,16,19,22-hexaoxabicyclo[22.3.1]-octacosa-1(28),24,26-triene-2,10,15,23-tetrone and 1,4,7,14,17,20-hexaoxacyclohexacosane-8,13,21,26-tetrone exceeded the specified limits of migration.

Analytical key issues and challenges in the LC-MS/MS determination of antibiotics in wastewater

The research on antibiotics occurrence in the aquatic environment has become a hot topic in the last years due to their potential negative effects, associated to possible bacterial antibiotic-resistance, after continuous exposure to these compounds. Most of antibiotic residues are not completely removed in the wastewater treatment plants (WWTPs) and end up in the aquatic environment through treated wastewater (WW). The development of reliable analytical methodologies for the determination of antibiotics in influent (IWW) and effluent wastewater (EWW) is needed with different purposes, among others: monitoring their occurrence in the aquatic environment, performing environmental risk assessment, estimating removal efficiencies of WWTPs, or estimating the consumption of these compounds. In this paper, we perform an in-depth investigation on analytical key issues that pose difficulties in the determination of antibiotics in complex matrices, such as WW, and we identify challenges to be properly addressed for successful analysis. The analytical technique selected was liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS), as it is the most powerful and widely applied at present for antibiotic residues determination. The mass spectrometric behavior of 18 selected antibiotics, the chromatographic performance, ion ratio variations associated to the sample matrix when using different precursor ions or protomers, and the macrolides adsorption to glass vial, were some of the issues studied in this work. On the basis of the detailed study performed, an analytical LC-MS/MS method based on sample direct injection has been developed for quantification of 18 antibiotics in IWW and EWW, allowing their determination at low ng L^-1 levels.

Guidelines and considerations for building multidimensional libraries for untargeted MS-based metabolomics

INTRODUCTION

Feature annotation is crucial in untargeted metabolomics but remains a major challenge. The large pool of metabolites collected under various instrumental conditions is underrepresented in publicly available databases. Retention time (RT) and collision cross section (CCS) measurements from liquid chromatography ion mobility high-resolution mass spectrometers can be employed in addition to MS/MS spectra to improve the confidence of metabolite annotation. Recent advancements in machine learning focus on improving the accuracy of predictions for CCS and RT values. Therefore, high-quality experimental data are crucial to be used either as training datasets or as a reference for high-confidence matching.

METHODS

This manuscript provides an easy-to-use workflow for the creation of an in-house metabolite library, offers an overview of alternative solutions, and discusses the challenges and advantages of using open-source software. A total of 100 metabolite standards from various classes were analyzed and subjected to the described workflow for library generation.

RESULTS AND DISCUSSION

The outcome was an open-access available NIST format metabolite library (.msp) with multidimensional information. The library was used to evaluate CCS prediction tools, MS/MS spectra heterogeneities (e.g., multiple adducts, in-source fragmentation, radical fragment ions using collision-induced dissociation), and the reporting of RT.

Degradation of Kresoxim-Methyl in Different Soils: Kinetics, Identification of Transformation Products, and Pathways Using High-Resolution-Mass-Spectrometry-Based Suspect and Non-Target Screening Approaches

This study investigated the degradation of strobilurin fungicide kresoxim-methyl (KM) in three typical agricultural soils from China by aerobic and anaerobic degradation experiments, focusing on degradation kinetics of KM, identification of transformation products (TPs), and prediction of toxicity end points via in silico approaches. KM showed a pronounced biphasic degradation in different soils and could rapidly degrade, with DT₅₀ of <3 days. Four TPs were identified by high-resolution mass spectrometry (HRMS), and three of them have never been reported before. Possible degradation pathways of KM in soil were proposed, including hydrolysis, oxidation, and reduction, and the main mechanism involved in the biodegradation of KM was the hydrolysis of methyl ester regardless of aerobic or anaerobic conditions. The results of toxicity evaluation indicated that some TPs are more toxic than KM and may have a developmental toxicity and mutagenicity, and further risk assessment should be carried out.

Enhancing spectral quality in complex environmental matrices: Supporting suspect and non-target screening in zebra mussels with ion mobility

Identification of bioaccumulating contaminants of emerging concern (CECs) via suspect and non-target screening remains a challenging task. In this study, ion mobility separation with high-resolution mass spectrometry (IM-HRMS) was used to investigate the effects of drift time (DT) alignment on spectrum quality and peak annotation for screening of CECs in complex sample matrices using data independent acquisition (DIA). Data treatment approaches (Binary Sample Comparison) and prioritisation strategies (Halogen Match, co-occurrence of features in biota and the water phase) were explored in a case study on zebra mussel (Dreissena polymorpha) in Lake Mälaren, Sweden's largest drinking water reservoir. DT alignment evidently improved the fragment spectrum quality by increasing the similarity score to reference spectra from on average (±standard deviation) 0.33 ± 0.31 to 0.64 ± 0.30 points, thus positively influencing structure elucidation efforts. Thirty-two features were tentatively identified at confidence level 3 or higher using MetFrag coupled with the new PubChemLite database, which included predicted collision cross-section values from CCSbase. The implementation of predicted mobility data was found to support compound annotation. This study illustrates a quantitative assessment of the benefits of IM-HRMS on spectral quality, which will enhance the performance of future screening studies of CECs in complex environmental matrices.

Prediction of Retention Time and Collision Cross Section (CCSH+, CCSH-, and CCSNa+) of Emerging Contaminants Using Multiple Adaptive Regression Splines

Ultra-high performance liquid chromatography coupled to ion mobility separation and high-resolution mass spectrometry instruments have proven very valuable for screening of emerging contaminants in the aquatic environment. However, when applying suspect or nontarget approaches (i.e., when no reference standards are available), there is no information on retention time (RT) and collision cross-section (CCS) values to facilitate identification. In silico prediction tools of RT and CCS can therefore be of great utility to decrease the number of candidates to investigate. In this work, Multiple Adaptive Regression Splines (MARS) were evaluated for the prediction of both RT and CCS. MARS prediction models were developed and validated using a database of 477 protonated molecules, 169 deprotonated molecules, and 249 sodium adducts. Multivariate and univariate models were evaluated showing a better fit for univariate models to the experimental data. The RT model (R² = 0.855) showed a deviation between predicted and experimental data of ±2.32 min (95% confidence intervals). The deviation observed for CCS data of protonated molecules using the CCS_H model (R² = 0.966) was ±4.05% with 95% confidence intervals. The CCS_H model was also tested for the prediction of deprotonated molecules, resulting in deviations below ±5.86% for the 95% of the cases. Finally, a third model was developed for sodium adducts (CCS_Na, R² = 0.954) with deviation below ±5.25% for 95% of the cases. The developed models have been incorporated in an open-access and user-friendly online platform which represents a great advantage for third-party research laboratories for predicting both RT and CCS data.

The NORMAN Suspect List Exchange (NORMAN-SLE): facilitating European and worldwide collaboration on suspect screening in high resolution mass spectrometry

Background

The NORMAN Association (https://www.norman-network.com/) initiated the NORMAN Suspect List Exchange (NORMAN-SLE; https://www.norman-network.com/nds/SLE/) in 2015, following the NORMAN collaborative trial on non-target screening of environmental water samples by mass spectrometry. Since then, this exchange of information on chemicals that are expected to occur in the environment, along with the accompanying expert knowledge and references, has become a valuable knowledge base for "suspect screening" lists. The NORMAN-SLE now serves as a FAIR (Findable, Accessible, Interoperable, Reusable) chemical information resource worldwide.

Results

The NORMAN-SLE contains 99 separate suspect list collections (as of May 2022) from over 70 contributors around the world, totalling over 100,000 unique substances. The substance classes include per- and polyfluoroalkyl substances (PFAS), pharmaceuticals, pesticides, natural toxins, high production volume substances covered under the European REACH regulation (EC: 1272/2008), priority contaminants of emerging concern (CECs) and regulatory lists from NORMAN partners. Several lists focus on transformation products (TPs) and complex features detected in the environment with various levels of provenance and structural information. Each list is available for separate download. The merged, curated collection is also available as the NORMAN Substance Database (NORMAN SusDat). Both the NORMAN-SLE and NORMAN SusDat are integrated within the NORMAN Database System (NDS). The individual NORMAN-SLE lists receive digital object identifiers (DOIs) and traceable versioning via a Zenodo community (https://zenodo.org/communities/norman-sle), with a total of > 40,000 unique views, > 50,000 unique downloads and 40 citations (May 2022). NORMAN-SLE content is progressively integrated into large open chemical databases such as PubChem (https://pubchem.ncbi.nlm.nih.gov/) and the US EPA's CompTox Chemicals Dashboard (https://comptox.epa.gov/dashboard/), enabling further access to these lists, along with the additional functionality and calculated properties these resources offer. PubChem has also integrated significant annotation content from the NORMAN-SLE, including a classification browser (https://pubchem.ncbi.nlm.nih.gov/classification/#hid=101).

Conclusions

The NORMAN-SLE offers a specialized service for hosting suspect screening lists of relevance for the environmental community in an open, FAIR manner that allows integration with other major chemical resources. These efforts foster the exchange of information between scientists and regulators, supporting the paradigm shift to the "one substance, one assessment" approach. New submissions are welcome via the contacts provided on the NORMAN-SLE website (https://www.norman-network.com/nds/SLE/).

Supplementary Information

The online version contains supplementary material available at 10.1186/s12302-022-00680-6.

Revealing the differences in collision cross section values of small organic molecules acquired by different instrumental designs and prediction models

The number of open access databases containing experimental and predicted collision cross section (CCS) values is rising and leads to their increased use for compound identification. However, the reproducibility of reference values with different instrumental designs and the comparison between predicted and experimental CCS values is still under evaluation. This study compared experimental CCS values of 56 small molecules (Contaminants of Emerging Concern) acquired by both drift tube (DT) and travelling wave (TW) ion mobility mass spectrometry (IM-MS). The TWIM-MS included two instrumental designs (Synapt G2 and VION). The experimental ^TWCCS_N2 values obtained by the TWIM-MS systems showed absolute percent errors (APEs) < 2% in comparison to experimental DTIMS data, indicating a good correlation between the datasets. Furthermore, ^TWCCS_N2 values of [M - H]^- ions presented the lowest APEs. An influence of the compound class on APEs was observed. The applicability of prediction models based on artificial neural networks (ANN) and multivariate adaptive regression splines (MARS), both built using TWIM-MS data, was investigated for the first time for the prediction of ^DTCCS_N2 values. For [M+H]⁺ and [M - H]^- ions, the 95th percentile confidence intervals of observed APEs were comparable to values reported for both models indicating a good applicability for DTIMS predictions. For the prediction of ^DTCCS_N2 values of [M+Na]⁺ ions, the MARS based model provided the best results with 73.9% of the ions showing APEs below the threshold reported for [M+Na]⁺. Finally, recommendations for database transfer and applications of prediction models for future DTIMS studies are made.

Traveling Wave Ion Mobility-Derived Collision Cross Section Database for Plant Specialized Metabolites: An Application to Ventilago harmandiana Pierre

The combination of ion mobility mass spectrometry (IM-MS) and chromatography is a valuable tool for identifying compounds in natural products. In this study, using an ultra-performance liquid chromatography system coupled to a high-resolution quadrupole/traveling wave ion mobility spectrometry/time-of-flight MS (UPLC-TWIMS-QTOF), we have established and validated a comprehensive TWCCSN2 and MS database for 112 plant specialized metabolites. The database included 15 compounds that were isolated and purified in-house and are not commercially available. We obtained accurate m/z, retention times, fragment ions, and TWIMS-derived CCS (TWCCSN2) values for 207 adducts (ESI+ and ESI-). The database included novel 158 TWCCSN2 values from 79 specialized metabolites. In the presence of plant matrix, the CCS measurement was reproducible and robust. Finally, we demonstrated the application of the database to extend the metabolite coverage of Ventilago harmandiana Pierre. In addition to pyranonaphthoquinones, a group of known specialized metabolites in V. harmandiana, we identified flavonoids, xanthone, naphthofuran, and protocatechuic acid for the first time through targeted analysis. Interestingly, further investigation using IM-MS of unknown features suggested the presence of organonitrogen compounds and lipid and lipid-like molecules, which is also reported for the first time. Data are available on the MassIVE (https://massive.ucsd.edu, data set identifier MSV000090213).

Collision Cross Section Prediction with Molecular Fingerprint Using Machine Learning

High-resolution mass spectrometry is a promising technique in non-target screening (NTS) to monitor contaminants of emerging concern in complex samples. Current chemical identification strategies in NTS experiments typically depend on spectral libraries, chemical databases, and in silico fragmentation tools. However, small molecule identification remains challenging due to the lack of orthogonal sources of information (e.g., unique fragments). Collision cross section (CCS) values measured by ion mobility spectrometry (IMS) offer an additional identification dimension to increase the confidence level. Thanks to the advances in analytical instrumentation, an increasing application of IMS hybrid with high-resolution mass spectrometry (HRMS) in NTS has been reported in the recent decades. Several CCS prediction tools have been developed. However, limited CCS prediction methods were based on a large scale of chemical classes and cross-platform CCS measurements. We successfully developed two prediction models using a random forest machine learning algorithm. One of the approaches was based on chemicals' super classes; the other model was direct CCS prediction using molecular fingerprint. Over 13,324 CCS values from six different laboratories and PubChem using a variety of ion-mobility separation techniques were used for training and testing the models. The test accuracy for all the prediction models was over 0.85, and the median of relative residual was around 2.2%. The models can be applied to different IMS platforms to eliminate false positives in small molecule identification.

Halocyclopentadienes: An Emerging Class of Toxic DBPs in Chlor(am)inated Drinking Water

Although >700 disinfection by-products (DBPs) have been identified to date, most DBPs in drinking water are still unknown. Identifying unknown DBPs is an important step for improving drinking water quality because known DBPs do not fully account for the adverse health effects noted in epidemiologic studies. Using gas chromatography high-resolution mass spectrometry, six chloro- and bromo-halocyclopentadienes (HCPDs) were identified in chlorinated and chloraminated drinking water via non-target analysis; five HCPDs are reported for the first time as new alicyclic DBPs. Formation pathways were also proposed. Simulated disinfection experiments with Suwannee River natural organic matter (NOM) confirm that NOM is a precursor for these new DBPs. Further, HCPDs are more abundant in chlorinated drinking water (real and simulated) when compared to chloraminated drinking water due to the higher reactivity of chlorine. Of these new DBPs, 1,2,3,4,5,5-hexachloro-1,3-cyclopentadiene is approximately 100,000× more toxic (in vivo) than regulated trihalomethanes (THMs) and haloacetic acids (HAAs) and 20-2000× more toxic than halobenzoquinones, halophenols, and halogenated pyridinols using the available median lethal dose (LD50) and concentration for 50% of maximal effective concentration (EC50) of DBPs to aquatic organisms. The predicted bioconcentration factors of these HCPDs range from 384 to 3980, which are 2-3 orders of magnitude higher than those for regulated and priority DBPs (including THMs, HAAs, halobenzoquinones, haloacetonitriles, haloacetamides, halonitromethanes, haloacetaldehydes, iodo-THMs, and iodo-HAAs). Thus, HCPDs are an important emerging class of DBPs that should be studied to better understand their impact on drinking water quality and long-term human health exposure.