Suspect screening of large numbers of emerging contaminants in environmental waters using artificial neural networks for chromatographic retention time prediction and high resolution mass spectrometry data analysis

ABCoRT: Retention Time Prediction for Metabolite Identification via Atom-Bond Co-Learning

Liquid chromatography retention time (RT) prediction plays a crucial role in metabolite identification, a challenging and essential task in untargeted metabolomics. Accurate molecular representation is vital for reliable RT prediction. To address this, we propose a novel molecular representation learning framework, ABCoRT(Atom-Bond Co-learning for Retention Time prediction), designed for predicting metabolite retention times. Our model transforms molecular graphs into dual hypergraphs, enabling the collaborative updating of atomic and bond information within both molecular graphs and hypergraphs, thereby producing highly informative molecular representations. We evaluated ABCoRT on a large-scale Small Molecule Retention Time (SMRT) data set comprising 80,038 molecules. Our model achieved a mean absolute error (MAE) of 25.75 s and a mean relative error (MRE) of 3.24% after removing nonretained molecules. Additionally, we fine-tuned pretrained ABCoRT models on six additional data sets from PredRet, achieving the lowest MAEs on five of them. Additionally, in metabolite screening conducted on the MetaboBASE and RIKEN_PlaSM data sets from the MassBank of North America, ABCoRT demonstrates its capability to filter out 38.35 and 28.46% of candidate compounds, respectively.

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.

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.

Retention time dataset for heterogeneous molecules in reversed-phase liquid chromatography

Quantitative structure-property relationships have been extensively studied in the field of predicting retention times in liquid chromatography (LC). However, making transferable predictions is inherently complex because retention times are influenced by both the structure of the molecule and the chromatographic method used. Despite decades of development and numerous published machine learning models, the practical application of predicting small molecule retention time remains limited. The resulting models are typically limited to specific chromatographic conditions and the molecules used in their training and evaluation. Here, we have developed a comprehensive dataset comprising over 10,000 experimental retention times. These times were derived from 30 different reversed-phase liquid chromatography methods and pertain to a collection of 343 small molecules representing a wide range of chemical structures. These chromatographic methods encompass common LC setups for studying the retention behavior of small molecules. They offer a wide range of examples for modeling retention time with different LC setups.

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.

Enhancing compound confidence in suspect and non-target screening through machine learning-based retention time prediction

The retention time (RT) of contaminants of emerging concern (CECs) in liquid chromatography-high-resolution mass spectrometry (LC-HRMS) is crucial for database matching in non-targeted screening (NTS) analysis. In this study, we developed a machine learning (ML) model to predict RTs of CECs in NTS analysis. Using 1051 CEC standards, we evaluated Random Forest (RF), XGBoost, Support Vector Regression (SVR), and Artificial Neural Network (ANN) with molecular fingerprints and chemical descriptors to establish an optimal model. The SVR model utilizing chemical descriptors resulted in good predictive capacity with R2ext = 0.850 and r2 = 0.925. The model was further validated through laboratory NTS compound characterization. When applied to examine CEC occurrence in a large wastewater treatment plant, we identified 40 level S1 CECs (confirmed structure by reference standard) and 234 level S2 compounds (probable structure by library spectrum match). The model predicted RTs for level S2 compounds, leading to the classification of 153 level S2 compounds with high confidence (ΔRT <2 min). The model served as a robust filtering mechanism within the analytical framework. This study emphasizes the importance of predicted RTs in NTS analysis and highlights the potential of prediction models. Our research introduces a workflow that enhances NTS analysis by utilizing RT prediction models to determine compound confidence levels.

Transformation products of antibacterial drugs in environmental water: Identification approaches based on liquid chromatography-high resolution mass spectrometry

In recent years, the presence of antibiotics in the aquatic environment has caused increasing concern for the possible consequences on human health and ecosystems, including the development of antibiotic-resistant bacteria. However, once antibiotics enter the environment, mainly through hospital and municipal discharges and the effluents of wastewater treatment plants, they can be subject to transformation reactions, driven by both biotic (e.g. microorganism and mammalian metabolisms) and abiotic factors (e.g. oxidation, photodegradation, and hydrolysis). The resulting transformation products (TPs) can be less or more active than their parent compounds, therefore the inclusion of TPs in monitoring programs should be mandatory. However, only the reference standards of a few known TPs are available, whereas many other TPs are still unknown, due to the high diversity of possible transformation reactions in the environment. Modern high-resolution mass spectrometry (HRMS) instrumentation is now ready to tackle this problem through suspect and untargeted screening approaches. However, for handling the large amount of data typically encountered in the analysis of environmental samples, these approaches also require suitable processing workflows and accurate tandem mass spectra interpretation. The compilation of a suspect list containing the possible monoisotopic masses of TPs retrieved from the literature and/or from laboratory simulated degradation experiments showed unique advantages. However, the employment of in silico prediction tools could improve the identification reliability. In this review, the most recent strategies relying on liquid chromatography-HRMS for the analysis of environmental TPs of the main antibiotic classes were examined, whereas TPs formed during water treatments or disinfection were not included.

Liquid chromatographic retention time prediction models to secure and improve the feature annotation process in high-resolution mass spectrometry

The development of quantitative structure-retention relationship (QSRR) models has, until recently, required an adequate selection of molecular descriptors necessarily obtained based on a known chemical structure. However, these complex descriptors are not always available nor calculable when the high-resolution mass spectrometry (HRMS) annotation process is underway. Depending on the level of annotation, many structures or even various molecular formulas could be candidates. To secure and improve the annotation process and to save time, a QSRR model (using only 0D molecular descriptors) to predict retention times in reverse-phase liquid chromatography (RPLC) based on the molecular formula was developed, and a general QSRR annotation-based methodology was also proposed.

Photo-assisted transformation of furosemide: Exploring transformation pathways, structure database and suspect and non-target workflows for comprehensive screening of unknown transformation products in wastewaters and landfill leachates

In recent years, transformation products-(TPs) of pharmaceuticals in the environment have received considerable attention. In this context, here, a customized overview of transformation of Furosemide-(FRS) in aqueous matrices treated by photo-oxidation is provided as a proof of concept. Hence, the primary goal of the study was to display an integrated strategy by combining the target (parent-molecule) and suspect screening-(SS) approaches (TPs) in order to build an in-house High-Resolution mass spectrometry (HRMS) database able to provide reference information (chromatographic/spectral) for environmental investigations in complex matrices (wastewaters/landfill leachates). Data analysis was performed by optimizing a SS workflow. Additional confirmation for the proposed structural elucidation was provided by correlating retention time to the proposed structure employing three prediction models. This approach was applied for the tentative identification of 35 TPs of FRS, 28 of which are reported herein for the first time. Finally, SS and non-target analysis (NTA) have been successfully applied for retrospective screening of FRS and its TPs in real samples. The findings demonstrated that SS allows the proper identification of TPs of FRS in complex matrices proving its outstanding importance compared to NTA. In total, six TPs were identified by SS with potential ecotoxicological implications for two of them according to in silico risk assessment.

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.

Enhanced database creation with in silico workflows for suspect screening of unknown tebuconazole transformation products in environmental samples by UHPLC-HRMS

The search and identification of organic contaminants in agricultural watersheds has become a crucial effort to better characterize watershed contamination by pesticides. The past decade has brought a more holistic view of watershed contamination via the deployment of powerful analytical strategies such as non-target and suspect screening analysis that can search more contaminants and their transformation products. However, suspect screening analysis remains broadly confined to known molecules, primarily due to the lack of analytical standards and suspect databases for unknowns such as pesticide transformation products. Here we developed a novel workflow by cross-comparing the results of various in silico prediction tools against literature data to create an enhanced database for suspect screening of pesticide transformation products. This workflow was applied on tebuconazole, used here as a model pesticide, and resulted in a suspect screening database counting 291 transformation products. The chromatographic retention times and tandem mass spectra were predicted for each of these compounds using 6 models based on multilinear regression and more complex machine-learning algorithms. This comprehensive approach to the investigation and identification of tebuconazole transformation products was retrospectively applied on environmental samples and found 6 transformation products identified for the first time in river water samples.

Are preserved coastal water bodies in Spanish Mediterranean basin impacted by human activity? Water quality evaluation using chemical and biological analyses

The Spanish Mediterranean basin is particularly susceptible to climate change and human activities, making it vulnerable to the influence of anthropogenic contaminants. Therefore, conducting comprehensive and exhaustive water quality assessment in relevant water bodies of this basin is pivotal. In this work, surface water samples from coastal lagoons or estuaries were collected across the Spanish Mediterranean coastline and subjected to target and suspect screening of 1,585 organic micropollutants by liquid chromatography coupled to ion mobility separation and high resolution mass spectrometry. In total, 91 organic micropollutants could be confirmed and 5 were tentatively identified, with pharmaceuticals and pesticides being the most prevalent groups of chemicals. Chemical analysis data was compared with data on bioanalysis of those samples (recurrent aryl hydrocarbon receptor (AhR) activation, and estrogenic receptor (ER) inhibition in wetland samples affected by wastewater streams). The number of identified organic contaminants containing aromatic rings could explain the AhR activation observed. For the ER antagonistic effects, predictions on estrogenic inhibition potency for the detected compounds were used to explain the activities observed. The integration of chemical analysis with bioanalytical observations allowed a comprehensive overview of the quality of the water bodies under study.

Assessment of artificial neural network to identify compositional differences in ultrahigh-resolution mass spectra acquired from coal mine affected soils

This study assessed the applicability of artificial neural networks (ANNs) as a tool to identify compounds contributing to compositional differences in coal-contaminated soils. An artificial neural network model was constructed from laser desorption ionization ultrahigh-resolution mass spectra obtained from coal contaminated soils. A good correlation (R² = 1.00 for model and R² = 0.99 for test) was observed between the measured and predicted values, thus validating the constructed model. To identify chemicals contributing to the coal contents of the soils, the weight values of the constructed model were evaluated. Condensed hydrocarbon and low oxygen containing compounds were found to have larger weight values and hence they were the main contributors to the coal contents of soils. In contrast, compounds identified as lignin did not contribute to the coal contents of soils. These findings were consistent with the conventional knowledge on coal and results from the conventional partial least square method. Therefore, we concluded that the weight interpretation following ANN analysis presented herein can be used to identify compounds that contribute to the compositional differences of natural organic matter (NOM) samples.

Use of Passive and Grab Sampling and High-Resolution Mass Spectrometry for Non-Targeted Analysis of Emerging Contaminants and Their Semi-Quantification in Water

Different groups of organic micropollutants including pharmaceuticals and pesticides have emerged in the environment in the last years, resulting in a rise in environmental and human health risks. In order to face up and evaluate these risks, there is an increasing need to assess their occurrence in the environment. Therefore, many studies in the past couple of decades were focused on the improvements in organic micropollutants’ extraction efficiency from the different environmental matrices, as well as their mass spectrometry detection parameters and acquisition modes. This paper presents different sampling methodologies and high-resolution mass spectrometry-based non-target screening workflows for the identification of pharmaceuticals, pesticides, and their transformation products in different kinds of water (domestic wastewater and river water). Identification confidence was increased including retention time prediction in the workflow. The applied methodology, using a passive sampling technique, allowed for the identification of 85 and 47 contaminants in the wastewater effluent and river water, respectively. Finally, contaminants’ prioritization was performed through semi-quantification in grab samples as a fundamental step for monitoring schemes.

Developments in high-resolution mass spectrometric analyses of new psychoactive substances

The proliferation of new psychoactive substances (NPS) has necessitated the development and improvement of current practices for the detection and identification of known NPS and newly emerging derivatives. High-resolution mass spectrometry (HRMS) is quickly becoming the industry standard for these analyses due to its ability to be operated in data-independent acquisition (DIA) modes, allowing for the collection of large amounts of data and enabling retrospective data interrogation as new information becomes available. The increasing popularity of HRMS has also prompted the exploration of new ways to screen for NPS, including broad-spectrum wastewater analysis to identify usage trends in the community and metabolomic-based approaches to examine the effects of drugs of abuse on endogenous compounds. In this paper, the novel applications of HRMS techniques to the analysis of NPS is reviewed. In particular, the development of innovative data analysis and interpretation approaches is discussed, including the application of machine learning and molecular networking to toxicological analyses.

Prediction of tempo-spatial patterns and exceedance probabilities of atmospheric corrosion of Q235 carbon steel across China

To reduce the losses caused by the atmospheric corrosion of carbon steels, it is important to establish a prediction model to determine the corrosion rate of carbon steels in natural environments. In this study, a prediction model of atmospheric corrosion of Q235 carbon steel (PMACC-Q235) in China was established by coupling the mean impact value algorithm and back propagation artificial neural network. Tempo-spatial patterns of corrosion rates in five long-exposure time categories across China were analyzed. Ten main factors affecting the atmospheric corrosion of Q235 were identified. The corrosion rates in a single year were similar (approximately 30 μm/a) and larger than those for 2 (25.30 μm/a) and 3 years (21.66 μm/a). The spatial corrosion rates in the northwestern areas were primarily lower than those in southeastern coastal areas. This could be influenced by climatic factors, such as temperature, humidity, and precipitation. All corrosion rates reached the C2 level (>1.3 μm/a), and there was some possibility that they reached higher corrosion levels. The largest probability for the C3 level in all periods was an average of 0.91, and that for the C4 level was 0.83. Spatially, higher probabilities were mainly located in the southern area, especially in Hainan, located in the south and surrounded by sea. Corrosion rates largely varied among climatic zones, and mean corrosion rates in the tropical monsoon climate zone were the largest (average of three periods 33.39 μm/a). SO₂ and soluble-dust fall had the largest impact on the variations in the corrosion rates among different climatic zones.

Spotlight on mass spectrometric non-target screening analysis: Advanced data processing methods recently communicated for extracting, prioritizing and quantifying features

Non-target screening of trace organic compounds complements routine monitoring of water bodies. So-called features need to be extracted from the raw data that preferably represent a chemical compound. Relevant features need to be prioritized and further be interpreted, for instance by identifying them. Finally, quantitative data is required to assess the risks of a detected compound. This review presents recent and noteworthy contributions to the processing of non-target screening (NTS) data, prioritization of features as well as (semi-) quantitative methods that do not require analytical standards. The focus lies on environmental water samples measured by liquid chromatography, electrospray ionization and high-resolution mass spectrometry. Examples for fully-integrated data processing workflows are given with options for parameter optimization and choosing between different feature extraction algorithms to increase feature coverage. The regions of interest-multivariate curve resolution method is reviewed which combines a data compression alternative with chemometric feature extraction. Furthermore, prioritization strategies based on a confined chemical space for annotation, guidance by targeted analysis and signal intensity are presented. Exploiting the retention time (RT) as diagnostic evidence for NTS investigations is highlighted by discussing RT indexing and prediction using quantitative structure-retention relationship models. Finally, a seminal technology for quantitative NTS is discussed without the need for analytical standards based on predicting ionization efficiencies.

Predicting reversed-phase liquid chromatographic retention times of pesticides by deep neural networks

To be able to predict reversed phase liquid chromatographic (RPLC) retention times of contaminants is an asset in order to solve food contamination issues. The development of quantitative structure-retention relationship models (QSRR) requires selection of the best molecular descriptors and machine-learning algorithms. In the present work, two main approaches have been tested and compared, one based on an extensive literature review to select the best set of molecular descriptors (16), and a second with diverse strategies in order to select among 1545 molecular descriptors (MD), 16 MD. In both cases, a deep neural network (DNN) were optimized through a gridsearch.

Development of a single retention time prediction model integrating multiple liquid chromatography systems: Application to new psychoactive substances

Database-driven suspect screening has proven to be a useful tool to detect new psychoactive substances (NPS) outside the scope of targeted screening; however, the lack of retention times specific to a liquid chromatography (LC) system can result in a large number of false positives. A singular stream-lined, quantitative structure-retention relationship (QSRR)-based retention time prediction model integrating multiple LC systems with different elution conditions is presented using retention time data (n = 1281) from the online crowd-sourced database, HighResNPS. Modelling was performed using an artificial neural network (ANN), specifically a multi-layer perceptron (MLP), using four molecular descriptors and one-hot encoding of categorical labels. Evaluation of test set predictions (n = 193) yielded coefficient of determination (R²) and mean absolute error (MAE) values of 0.942 and 0.583 min, respectively. The model successfully differentiated between LC systems, predicting 54%, 81% and 97% of the test set within ±0.5, ±1 and ±2 min, respectively. Additionally, retention times for an analyte not previously observed by the model were predicted within ±1 min for each LC system. The developed model can be used to predict retention times for all analytes on HighResNPS for each participating laboratory's LC system to further support suspect screening.

The relevant role of ion mobility separation in LC-HRMS based screening strategies for contaminants of emerging concern in the aquatic environment

Ion mobility separation (IMS) coupled to high resolution mass spectrometry (IMS-HRMS) is a promising technique for (non-)target/suspect analysis of micropollutants in complex matrices. IMS separates ionized compounds based on their charge, shape and size facilitating the removal of co-eluting isomeric/isobaric species. Additionally, IMS data can be translated into collision cross-section (CCS) values, which can be used to increase the identification reliability. However, IMS-HRMS for the screening of contaminants of emerging concern (CECs) have been scarcely explored. In this study, the role of IMS-HRMS for the identification of CECs in complex matrices is highlighted, with emphasis on when and with which purpose is of use. The utilization of IMS can result in much cleaner mass spectra, which considerably facilitates data interpretation and the obtaining of reliable identifications. Furthermore, the robustness of IMS measurements across matrices permits the use of CCS as an additional relevant parameter during the identification step even when reference standards are not available. Moreover, an effect on the number of true and false identifications could be demonstrated by including IMS restrictions within the identification workflow. Data shown in this work is of special interest for environmental researchers dealing with the detection of CECs with state-of-the-art IMS-HRMS instruments.

Wide-scope screening of pharmaceuticals, illicit drugs and their metabolites in the Amazon River

Only a limited number of households in the Amazon are served by sewage collection or treatment facilities, suggesting that there might be a significant emission of pharmaceuticals and other wastewater contaminants into freshwater ecosystems. In this work, we performed a wide-scope screening to assess the occurrence of pharmaceuticals, illicit drugs and their metabolites in freshwater ecosystems of the Brazilian Amazon. Our study included 40 samples taken along the Amazon River, in three of its major tributaries, and in small tributaries crossing four important urban areas (Manaus, Santarém, Macapá, Belém). More than 900 compounds were investigated making use of target and suspect screening approaches, based on liquid chromatography coupled to high-resolution mass spectrometry with ion mobility separation. Empirical collision-cross section (CCS) values were used to help and confirm identifications in target screening, while in the suspect screening approach CCS values were predicted using Artificial Neural Networks to increase the confidence of the tentative identification. In this way, 51 compounds and metabolites were identified. The highest prevalence was found in streams crossing the urban areas of Manaus, Macapá and Belém, with some samples containing up to 30 - 40 compounds, while samples taken in Santarém showed a lower number (8 - 11), and the samples taken in the main course of the Amazon River and its tributaries contained between 1 and 7 compounds. Most compounds identified in areas with significant urban impact belonged to the analgesics and antihypertensive categories, followed by stimulants and antibiotics. Compounds such as caffeine, cocaine and its metabolite benzoylecgonine, and cotinine (the metabolite of nicotine), were also detected in areas with relatively low anthropogenic impact and showed the highest total prevalence. This study supports the need to improve the sanitation system of urban areas in the Brazilian Amazon and the development of follow-up studies aimed at quantifying exposure levels and risks for Amazonian freshwater biodiversity.

An annotation database for chemicals of emerging concern in exposome research

BACKGROUND

Chemicals of Emerging Concern (CECs) include a very wide group of chemicals that are suspected to be responsible for adverse effects on health, but for which very limited information is available. Chromatographic techniques coupled with high-resolution mass spectrometry (HRMS) can be used for non-targeted screening and detection of CECs, by using comprehensive annotation databases. Establishing a database focused on the annotation of CECs in human samples will provide new insight into the distribution and extent of exposures to a wide range of CECs in humans.

OBJECTIVES

This study describes an approach for the aggregation and curation of an annotation database (CECscreen) for the identification of CECs in human biological samples.

METHODS

The approach consists of three main parts. First, CECs compound lists from various sources were aggregated and duplications and inorganic compounds were removed. Subsequently, the list was curated by standardization of structures to create "MS-ready" and "QSAR-ready" SMILES, as well as calculation of exact masses (monoisotopic and adducts) and molecular formulas. The second step included the simulation of Phase I metabolites. The third and final step included the calculation of QSAR predictions related to physicochemical properties, environmental fate, toxicity and Absorption, Distribution, Metabolism, Excretion (ADME) processes and the retrieval of information from the US EPA CompTox Chemicals Dashboard.

RESULTS

All CECscreen database and property files are publicly available (DOI: https://doi.org/10.5281/zenodo.3956586). In total, 145,284 entries were aggregated from various CECs data sources. After elimination of duplicates and curation, the pipeline produced 70,397 unique "MS-ready" structures and 66,071 unique QSAR-ready structures, corresponding with 69,526 CAS numbers. Simulation of Phase I metabolites resulted in 306,279 unique metabolites. QSAR predictions could be performed for 64,684 of the QSAR-ready structures, whereas information was retrieved from the CompTox Chemicals Dashboard for 59,739 CAS numbers out of 69,526 inquiries. CECscreen is incorporated in the in silico fragmentation approach MetFrag.

DISCUSSION

The CECscreen database can be used to prioritize annotation of CECs measured in non-targeted HRMS, facilitating the large-scale detection of CECs in human samples for exposome research. Large-scale detection of CECs can be further improved by integrating the present database with resources that contain CECs (metabolites) and meta-data measurements, further expansion towards in silico and experimental (e.g., MassBank) generation of MS/MS spectra, and development of bioinformatics approaches capable of using correlation patterns in the measured chemical features.

Prediction of pesticide retention time in reversed-phase liquid chromatography using quantitative-structure retention relationship models: A comparative study of seven molecular descriptors datasets

Predicting chromatographic retention times of pesticides has become more and more important for suspect and non-target screening. Indeed, high-resolution mass spectrometry hyphenated (HRMS) to liquid chromatography (LC) are of growing interest for research and monitoring of pesticides, their metabolites and transformation products. The development of quantitative structure-retention relationship models require selecting the most adequate and best set of molecular descriptors and the best machine-learning algorithm. Here, we used seven molecular descriptor sets extracted from four well-known studies and applied them to roughly 800 pesticides and their chromatographic reversed-phase retention times. We used and optimized five different machine-learning algorithms with these descriptor sets to carry out predictions. Our results show that a support-vector machine regression algorithm with only eight molecular descriptors gave the best compromise between the number of molecular descriptors, processing time and model complexity to optimize prediction performance for this specific gradient LC method.

Targeted and suspect screening of contaminants in coastal water and sediment samples in Qatar

In recent years, high resolution mass spectrometry (HRMS) combined with separation techniques has allowed comprehensive analysis of contaminants of emerging concern (CECs) as well as their metabolites and transformation products in various environmental samples via retrospective screening. However, to date, only a few suspect or non-targeted studies on the occurrence of CECs in marine aquatic system are reported. In this study, two methods, based on direct injection for seawater, or ultrasound-assisted extraction for sediments, followed by LC-Q-TOF-MS analysis were developed and applied for the simultaneous targeted and screening of contaminants in coastal samples (seawater, particulates and sediment) from Qatar collected in 2017-2018. Among the twenty-one target analytes (pesticides, PPCPs and a plasticizer), two compounds only were detected in seawater. Caffeine was detected in seawater samples at all sampling sites, and cotinine was detected in seawater samples collected in Umm Bab in 2018 and seawaters receiving stormwater. Traces of trimethoprim and carbamazepine were detected in sediment samples collected at four sites in 2017. These results suggest some inputs of domestic wastewater in the coastal waters in Qatar. In total, twelve molecular features were tentatively identified from suspect screening at concentration levels significantly higher than that in procedure blanks. The presence of four plasticizers and one pesticide were further confirmed using reference standards: diethyl phthalate (DEP), dibutyl phthalate (DBP), and tributyl phosphate (TBP) in seawater samples; bis(2-ethylhexyl) phthalate (DEHP) in sediment and particulate samples; and dinoterb in seawater after storm event and particulate samples. Overall, this study demonstrated the potential of high resolution LC-Q-TOF-MS/MS for combined targeted and non-targeted analyses of trace contaminants in marine systems over a broad range of log P values.

What's in the water? - Target and suspect screening of contaminants of emerging concern in raw water and drinking water from Europe and Asia

There is growing worry that drinking water can be affected by contaminants of emerging concern (CECs), potentially threatening human health. In this study, a wide range of CECs (n = 177), including pharmaceuticals, pesticides, perfluoroalkyl substances (PFASs) and other compounds, were analysed in raw water and in drinking water collected from drinking water treatment plants (DWTPs) in Europe and Asia (n = 13). The impact of human activities was reflected in large numbers of compounds detected (n = 115) and high variation in concentrations in the raw water (range 15-7995 ng L^-1 for ∑₁₇₇CECs). The variation was less pronounced in drinking water, with total concentration ranging from 35 to 919 ng L^-1. Treatment efficiency was on average 65 ± 28%, with wide variation between different DWTPs. The DWTP with the highest ∑CEC concentrations in raw water had the most efficient treatment procedure (average treatment efficiency 89%), whereas the DWTP with the lowest ∑₁₇₇CEC concentration in the raw water had the lowest average treatment efficiency (2.3%). Suspect screening was performed for 500 compounds ranked high as chemicals of concern for drinking water, using a prioritisation tool (SusTool). Overall, 208 features of interest were discovered and three were confirmed with reference standards. There was co-variation between removal efficiency in DWTPs for the target compounds and the suspected features detected using suspect screening, implying that removal of known contaminants can be used to predict overall removal of potential CECs for drinking water production. Our results can be of high value for DWTPs around the globe in their planning for future treatment strategies to meet the increasing concern about human exposure to unknown CECs present in their drinking water.

An integrated approach to MS-based identification and risk assessment of pharmaceutical biotransformation in wastewater

The omnipresence of pharmaceuticals at relatively high concentrations (μg/L) in environmental compartments indicated their inadequate removal by wastewater treatment plants. As such, batch reactors seeded with activated sludge were set up to assess the biotransformation of metformin, ranitidine, lidocaine and atorvastatin. The main objective was to identify transformation products (TPs) through the establishment of an integrated workflow for suspect and non-target screening based on reversed phase liquid chromatography quadrupole-time-of-flight mass spectrometry. To support the identification, hydrophilic interaction liquid chromatography (HILIC) was used as a complementary tool, in order to enhance the completeness of the developed workflow by identifying the more polar TPs. The structure assignment/elucidation of the candidate TPs was mainly based on interpretation of MS/MS spectra. Twenty-two TPs were identified, with fourteen of them reaching high identification confidence levels (level 1: confirmed structure by reference standards and level 2: probable structure by library spectrum match and diagnostic evidence). Finally, retrospective analysis in influent and effluent wastewater was performed for the TPs for four consecutive years in wastewater sampled in Athens, Greece. The potential toxicological threat of the compounds to the aquatic environment was assessed and atorvastatin with two of its TPs showed a potential risk to the aquatic organisms.

Using Estrogenic Activity and Nontargeted Chemical Analysis to Identify Contaminants in Sewage Sludge

Diverse organic compounds, many derived from consumer products, are found in sewage sludge worldwide. Understanding which of these poses the most significant environmental threat following land application can be investigated through a variety of predictive and cell-based toxicological techniques. Nontargeted analysis using high-resolution mass spectrometry with predictive estrogenic activity modeling was performed on sewage sludge samples from 12 wastewater treatment plants in California. Diisobutyl phthalate and dextrorphan were predicted to exhibit estrogenic activity and identified in >75% of sludge samples, signifying their universal presence and persistence. Additionally, the application of an estrogen-responsive cell bioassay revealed reductions in agonistic activity during mesophilic and thermophilic treatment but significant increases in antagonism during thermophilic treatment, which warrants further research. Ten nontarget features were identified (metoprolol, fenofibric acid, erythrohydrobupropion, oleic acid, mestranol, 4'-chlorobiphenyl-2,3-diol, medrysone, scillarenin, sudan I, and N,O-didesmethyltramadol) in treatment set samples and are considered to have influenced the in vitro estrogenic activity observed. The combination of predictive and in vitro estrogenicity with nontargeted analysis has led to confirmation of 12 estrogen-active contaminants in California sewage sludge and has highlighted the importance of evaluating both agonistic and antagonistic responses when evaluating the bioactivity of complex samples.

Suspect screening of environmental contaminants by UHPLC-HRMS and transposable Quantitative Structure-Retention Relationship modelling

A Quantitative Structure-Retention Relationship (QSRR) model is proposed and aims at increasing the confidence level associated to the identification of organic contaminants by Ultra-High Performance Liquid Chromatography hyphenated to High Resolution Mass Spectrometry (UHPLC-HRMS) in environmental samples under a suspect screening approach. The model was built from a selection of 8 easily accessible physicochemical descriptors, and was validated from a set of 274 organic compounds commonly found in environmental samples. The proposed predictive figure approach is based on the mobile phase composition at solute elution (expressed as % acetonitrile), that has the major advantage of making the model reusable by other laboratories, since the elution composition is independent of both the column geometry and the UHPLC-system. The model quality was assessed and was altered neither by the columns from different lots, nor by the complex matrices of environmental water samples. Then, the solute retention of any organic compound present in water samples is expected to be predicted within ± 14.3% acetonitrile by our model. Solute retention can therefore be used as a supplementary tool for the identification of environmental contaminants by UHPLC-HRMS, in addition to mass spectrometry data already used in the suspect screening approach.

Data sharing in PredRet for accurate prediction of retention time: Application to plant food bioactive compounds

Prediction of retention times (RTs) is increasingly considered in untargeted metabolomics to complement MS/MS matching for annotation of unidentified peaks. We tested the performance of PredRet (http://predret.org/) to predict RTs for plant food bioactive metabolites in a data sharing initiative containing entry sets of 29-103 compounds (totalling 467 compounds, >30 families) across 24 chromatographic systems (CSs). Between 27 and 667 predictions were obtained with a median prediction error of 0.03-0.76 min and interval width of 0.33-8.78 min. An external validation test of eight CSs showed high prediction accuracy. RT prediction was dependent on shape and type of LC gradient, and number of commonly measured compounds. Our study highlights PredRet's accuracy and ability to transpose RT data acquired from one CS to another CS. We recommend extensive RT data sharing in PredRet by the community interested in plant food bioactive metabolites to achieve a powerful community-driven open-access tool for metabolomics annotation.

Use of ion mobility-high resolution mass spectrometry in metabolomics studies to provide near MS/MS quality data in a single injection

The use of ion mobility separations (IMSs) in metabolomics approaches has started to be deeply explored in the last years. In this work, the use of liquid chromatography (LC) coupled to IMS-quadrupole time-of-flight mass spectrometry (QTOF MS) has been evaluated in a metabolomics experiments using single injection of the samples. IMS has allowed obtaining cleaner fragmentation spectra, of nearly tandem MS quality, in data-independent acquisition mode. This is much useful in this research area as a second injection, generally applied in LC-QTOF MS workflows to obtain tandem mass spectra, is not necessary, saving time and evading possible compound degradation. As a case study, the smoke produced after combustion of herbal blends used to spray synthetic cannabinoids has been selected as study matrix. The smoke components were trapped in carbon cartridges, desorbed and analyzed by LC-IMS-QTOF MS using different separation mechanisms (reversed phase and HILIC) and acquiring in both positive and negative mode to widen the chemical domain. Partial Least Squares-Discriminant Analysis highlighted several compounds, and ratio between N-Isopropyl-3-(isoquinolinyl)-2-propen-1-amine and quinoline allowed differentiating between tobacco and herbal products. These two compounds were tentatively identified using the cleaner fragmentation spectra from a single injection in the IMS-QTOF MS, with additional confidence obtained by retention time (Rt) and collisional cross section (CCS) prediction using artificial neural networks. Data from this work show that LC-IMS-QTOF is an efficient technique in untargeted metabolomics, avoiding re-injection of the samples for elucidation purposes. In addition, the prediction models for Rt and CCS resulted of help in the elucidation process of potential biomarkers.

Benchmarking of the quantification approaches for the non-targeted screening of micropollutants and their transformation products in groundwater

A wide range of micropollutants can be monitored with non-targeted screening; however, the quantification of the newly discovered compounds is challenging. Transformation products (TPs) are especially problematic because analytical standards are rarely available. Here, we compared three quantification approaches for non-target compounds that do not require the availability of analytical standards. The comparison is based on a unique set of concentration data for 341 compounds, mainly pesticides, pharmaceuticals, and their TPs in 31 groundwater samples from Switzerland. The best accuracy was observed with the predicted ionization efficiency-based quantification, the mean error of concentration prediction for the groundwater samples was a factor of 1.8, and all of the 74 micropollutants detected in the groundwater were quantified with an error less than a factor of 10. The quantification of TPs with the parent compounds had significantly lower accuracy (mean error of a factor of 3.8) and could only be applied to a fraction of the detected compounds, while the mean performance (mean error of a factor of 3.2) of the closest eluting standard approach was similar to the parent compound approach.

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

A novel and rapid approach to characterise the occurrence of contaminants of emerging concern (CECs) in river water is presented using multi-residue targeted analysis and machine learning-assisted in silico suspect screening of passive sampler extracts. Passive samplers (Chemcatcher®) configured with hydrophilic-lipophilic balanced (HLB) sorbents were deployed in the Central London region of the tidal River Thames (UK) catchment in winter and summer campaigns in 2018 and 2019. Extracts were analysed by; (a) a rapid 5.5 min direct injection targeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) method for 164 CECs and (b) a full-scan LC coupled to quadrupole time of flight mass spectrometry (QTOF-MS) method using data-independent acquisition over 15 min. From targeted analysis of grab water samples, a total of 33 pharmaceuticals, illicit drugs, drug metabolites, personal care products and pesticides (including several EU Watch-List chemicals) were identified, and mean concentrations determined at 40 ± 37 ng L^-1. For targeted analysis of passive sampler extracts, 65 unique compounds were detected with differences observed between summer and winter campaigns. For suspect screening, 59 additional compounds were shortlisted based on mass spectral database matching, followed by machine learning-assisted retention time prediction. Many of these included additional pharmaceuticals and pesticides, but also new metabolites and industrial chemicals. The novelty in this approach lies in the convenience of using passive samplers together with machine learning-assisted chemical analysis methods for rapid, time-integrated catchment monitoring of CECs.

Prediction of Liquid Chromatographic Retention Time with Graph Neural Networks to Assist in Small Molecule Identification

The predicted liquid chromatographic retention times (RTs) of small molecules are not accurate enough for wide adoption in structural identification. In this study, we used the graph neural network to predict the retention time (GNN-RT) from structures of small molecules directly without the requirement of molecular descriptors. The predicted accuracy of GNN-RT was compared with random forests (RFs), Bayesian ridge regression, convolutional neural network (CNN), and a deep-learning regression model (DLM) on a METLIN small molecule retention time (SMRT) dataset. GNN-RT achieved the highest predicting accuracy with a mean relative error of 4.9% and a median relative error of 3.2%. Furthermore, the SMRT-trained GNN-RT model can be transferred to the same type of chromatographic systems easily. The predicted RT is valuable for structural identification in complementary to tandem mass spectra and can be used to assist in the identification of compounds. The results indicate that GNN-RT is a promising method to predict the RT for liquid chromatography and improve the accuracy of structural identification for small molecules.

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

Currently, the most powerful approach to monitor organic micropollutants (OMPs) in environmental samples is the combination of target, suspect, and nontarget screening strategies using high-resolution mass spectrometry (HRMS). However, the high complexity of sample matrices and the huge number of OMPs potentially present in samples at low concentrations pose an analytical challenge. Ion mobility separation (IMS) combined with HRMS instruments (IMS-HRMS) introduces an additional analytical dimension, providing extra information, which facilitates the identification of OMPs. The collision cross-section (CCS) value provided by IMS is unaffected by the matrix or chromatographic separation. Consequently, the creation of CCS databases and the inclusion of ion mobility within identification criteria are of high interest for an enhanced and robust screening strategy. In this work, a CCS library for IMS-HRMS, which is online and freely available, was developed for 556 OMPs in both positive and negative ionization modes using electrospray ionization. The inclusion of ion mobility data in widely adopted confidence levels for identification in environmental reporting is discussed. Illustrative examples of OMPs found in environmental samples are presented to highlight the potential of IMS-HRMS and to demonstrate the additional value of CCS data in various screening strategies.

High-throughput multi-residue quantification of contaminants of emerging concern in wastewaters enabled using direct injection liquid chromatography-tandem mass spectrometry

A rapid quantitative method for 135 contaminants of emerging concern (CECs) in untreated wastewater enabled with direct injection liquid chromatography-tandem mass spectrometry is presented. All compounds were analysed within 5 min on a short biphenyl cartridge using only 10 μL of filtered sample per injection. Up to 76 compounds were monitored simultaneously during the gradient (including mostly two transitions per compound and stable isotope-labelled analogues) while yielding >10 data points per peak. Evaluation of seven solid phase extraction sorbents showed no advantage for wastewater matrix removal. Excellent linearity, range, accuracy and precision was achieved for most compounds. Matrix effects were <11 % and detection limits were <30 ng L^-1 on average. Application to untreated wastewater samples from three wastewater treatment works in the UK, USA and Mexico, enabled quantification of 56 compounds. Banned and EU 'watch-list' substances are critically discussed, including pesticides, macrolide antibiotics, diclofenac, illicit drugs as well as multiple pharmaceuticals and biocides. This high-throughput method sets a new standard for the speedy and confident determination of over a hundred CECs in wastewater at the part-per-trillion level, as demonstrated by performing over 260 injections per day.

Perspectives and challenges associated with the determination of new psychoactive substances in urine and wastewater - A tutorial

New psychoactive substances (NPS), often designed as (legal) substitutes to conventional illicit drugs, are constantly emerging in the drug market and being commercialized in different ways and forms. Their use continues to cause public health problems and is therefore of major concern in many countries. Monitoring NPS use, however, is arduous and different sources of information are required to get more insight of the prevalence and diffusion of NPS use. The determination of NPS in pooled urine and wastewater has shown great potential, adding a different and complementary light on this issue. However, it also presents analytical challenges and limitations that must be taken into account such as the complexity of the matrices, the high sensitivity and selectivity required in the analytical methods as a consequence of the low analyte concentrations as well as the rapid transience of NPS on the drug market creating a scenario with constantly moving analytical targets. Analytical investigation of NPS in pooled urine and wastewater is based on liquid chromatography hyphenated to mass spectrometry and can follow different strategies: target, suspect and non-target analysis. This work aims to discuss the advantages and disadvantages of the different data acquisition workflows and data exploration approaches in mass spectrometry, but also pays attention to new developments such as ion mobility and the use of in-silico prediction tools to improve the identification capabilities in high-complex samples. This tutorial gives an insight into this emerging topic of current concern, and describes the experience gathered within different collaborations and projects supported by key research articles and illustrative practical examples.

Machine learning to predict retention time of small molecules in nano-HPLC

Retention time is an important parameter for identification in untargeted LC-MS screening. Precise retention time prediction facilitates the annotation process and is well known for proteomics. However, the lack of available experimental information for a long time has limited the prediction accuracy for small molecules. Recently introduced large databases for small-molecule retention times make possible reliable machine learning-based predictions for the whole diversity of compounds. Applying simple projections may expand these predictions on various LC systems and conditions. In our work, we describe a complex approach to predict retention times for nano-HPLC that includes the consequent deployment of binary and regression gradient boosting models trained on the METLIN small-molecule dataset and simple projection of the results with a small number of easily available compounds onto nano-HPLC separations. The proposed model outperforms previous attempts to use machine learning for predictions with a 46-s mean absolute error. The overall performance after transfer to nano-LC conditions is less than 155 s (10.8%) in terms of the median absolute (relative) error. To illustrate the applicability of the described approach, we successfully managed to eliminate averagely 25 to 42% of false-positives with a filter threshold derived from ROC curves. Thus, the proposed approach should be used in addition to other well-established in silico methods and their integration may broaden the range of correctly identified molecules.

The complexities associated with new psychoactive substances in influent wastewater: The case of 4-ethylmethcathinone

Consumption of new psychoactive substances (NPS) is an international problem for health, policing, forensic, and analytical laboratories. The transience of these substances in the community, combined with continual slight structural changes to evade legislation makes the elucidation of NPS an analytical challenge. This is amplified in a matrix as complex as wastewater. For that reason, suspect and non-target methodologies, employing high resolution mass spectrometry are the most appropriate current tool to facilitate the identification of new and existing compounds. In the current work, a qualitative screening method of influent wastewater using liquid chromatography-high resolution mass spectrometry showed a strong signal at m/z 192.1382 - identical to that of two NPS standards that were in our method (pentedrone and 4-methylethcathinone), and with identical fragment ions, but the retention times did not match. This work shows the methodology followed to identify this compound, highlighting the challenges of the identifying "new" compounds in influent wastewater.

Investigation of pesticides and their transformation products in the Júcar River Hydrographical Basin (Spain) by wide-scope high-resolution mass spectrometry screening

The Water Framework Directive 2000/60/EC implemented by the European Union established as the main objectives to achieve a "good ecological and chemical status" of the surface water and a "good quantitative and chemical status" of groundwater bodies. One of the major pressures affecting water bodies comes from the use of pesticides and their potential presence in the water ecosystems. For this purpose, the reliable determination of pesticides and their transformation products (TPs) in natural waters (both surface and groundwater) is required. The high number of compounds potentially reaching the aquatic environment makes extraordinary difficult, if not impossible, to investigate all these compounds even using the most powerful analytical techniques. Among these, liquid chromatography coupled to high-resolution mass spectrometry is emphasized due to its strong potential for detection and identification of many organic contaminants thanks to the accurate-mass full spectrum acquisition data. This work focuses on wide-scope screening of many pesticides and their TPs in surface water and groundwater samples, collected between March and May 2017, in the Júcar River Hydrographical Basin, Spain. For this purpose, a home-made database containing more than 500 pesticides and TPs was employed. Analyses performed by liquid chromatography coupled to quadrupole-time of flight mass spectrometry (LC-QTOF MS) allowed the identification of up to 27 pesticides and 6 TPs. The most detected compounds in groundwater were the herbicides atrazine, simazine, terbuthylazine, and their TPs (atrazine-desethyl, terbumeton-desethyl and terbuthylazine-desethyl). Regarding surface water, the fungicides carbendazim, thiabendazole and imazalil, the herbicide terbutryn and the TP terbumeton-desethyl were also detected. These results illustrate the wide use of these compounds (in the present or in the recent past) in the area under study and the vulnerability of the water bodies, and are in accordance with previous findings in other water bodies of the different Spanish Hydrographic systems.

Using LC Retention Times in Organic Structure Determination: Drug Metabolite Identification

Background:

There is a continued need for improvements in the efficiency of metabolite structure elucidation.

Objective:

We propose to take LC Retention Time (RT) into consideration during the process of structure determination.

Methods:

Herein, we develop a simple methodology that employs a Chromatographic Hydrophobicity Index (CHI) framework for standardizing LC conditions and introduce and utilize the concept of a pre-dictable CHI change upon Phase 1 biotransformation (CHIbt). Through the analysis of literature exam-ples, we offer a Quantitative Structure-Retention Relationship (QSRR) for several types of biotransfor-mation (especially hydroxylation) using physicochemical properties (clogP, hydrogen bonding).

Results:

The CHI system for retention indexing is shown to be practical and simple to implement. A da-tabase of CHIbt values has been created from re-incubation of 3 compounds and from analysis of an addi-tional 17 datasets from the literature. Application of this database is illustrated.

Conclusion:

In our experience, this simple methodology allows complementing the discovery efforts that saves resources for in-depth characterization using NMR.

The role of analytical chemistry in exposure science: Focus on the aquatic environment

Exposure science, in its broadest sense, studies the interactions between stressors (chemical, biological, and physical agents) and receptors (e.g. humans and other living organisms, and non-living items like buildings), together with the associated pathways and processes potentially leading to negative effects on human health and the environment. The aquatic environment may contain thousands of compounds, many of them still unknown, that can pose a risk to ecosystems and human health. Due to the unquestionable importance of the aquatic environment, one of the main challenges in the field of exposure science is the comprehensive characterization and evaluation of complex environmental mixtures beyond the classical/priority contaminants to new emerging contaminants. The role of advanced analytical chemistry to identify and quantify potential chemical risks, that might cause adverse effects to the aquatic environment, is essential. In this paper, we present the strategies and tools that analytical chemistry has nowadays, focused on chromatography hyphenated to (high-resolution) mass spectrometry because of its relevance in this field. Key issues, such as the application of effect direct analysis to reduce the complexity of the sample, the investigation of the huge number of transformation/degradation products that may be present in the aquatic environment, the analysis of urban wastewater as a source of valuable information on our lifestyle and substances we consumed and/or are exposed to, or the monitoring of drinking water, are discussed in this article. The trends and perspectives for the next few years are also highlighted, when it is expected that new developments and tools will allow a better knowledge of chemical composition in the aquatic environment. This will help regulatory authorities to protect water bodies and to advance towards improved regulations that enable practical and efficient abatements for environmental and public health protection.

Non-targeted GC/MS analysis of exhaled breath samples: Exploring human biomarkers of exogenous exposure and endogenous response from professional firefighting activity

A non-targeted analysis workflow was applied to analyze exhaled breath samples collected from firefighters pre- and post-structural fire suppression. Breath samples from firefighters functioning in attack and search positions were examined for target and non-target compounds in automated thermal desorption-GC/MS (ATD-GC/MS) selected ion monitoring (SIM)/scan mode and reviewed for prominent chemicals. Targeted chemicals included products of combustion such as benzene, toluene, xylenes, and polycyclic aromatic hydrocarbons (PAH) that serve as a standard assessment of exposure. Sixty unique chemical features representative of exogenous chemicals and endogenous compounds, including single-ring aromatics, polynuclear aromatic hydrocarbons, volatile sulfur-containing compounds, aldehydes, alkanes, and alkenes were identified using the non-targeted analysis workflow. Fifty-seven out of 60 non-targeted features changed by at least 50% from pre- to post-fire suppression activity in at least one subject, and 7 non-targeted features were found to exhibit significantly increased or decreased concentrations for all subjects as a group. This study is important for (1) alerting the firefighter community to potential new exposures, (2) expanding the current targeted list of toxicants, and (3) finding biomarkers of response to firefighting activity as reflected by changes in endogenous compounds. Data demonstrate that there are non-targeted compounds in firefighters' breath that are indicative of environmental exposure despite the use of protective gear, and this information may be further utilized to improve the effectiveness of personal protective equipment.