Development and Application of Liquid Chromatographic Retention Time Indices in HRMS-Based Suspect and Nontarget Screening

Advanced throughput analytical strategies for the comprehensive HRMS screening of organic micropollutants in eggs of different bird species

Raptors are ideal indicators for biomonitoring studies using wildlife in order to assess the environmental pollution in the terrestrial ecosystem, since they are placed in the highest trophic position in the food webs and their life expectancy is relatively long. In this study, 26 eggs of 4 bird species (Peregrine falcon, Eurasian curlew, Little owl and Eagle owl) collected in Germany, were investigated for the presence of persistent organic pollutants (POPs) and thousands of contaminants of emerging concern (CECs). Generic sample preparation protocols were followed for the extraction of the analytes and the purification of the extracts, and the samples were analyzed both by liquid (LC) and gas chromatography (GC) coupled to high resolution mass spectrometry (HRMS), for capturing a wide range of organic micropollutants with different physicochemical properties. State-of-the-art screening methodologies were applied in the acquired HRMS data, including wide-scope target analysis of 2448 known pollutants and suspect screening of over 65,000 environmentally relevant compounds. Overall, 58 pollutants from different chemical classes, such as plant protection products, per- and polyfluoroalkyl substances and medicinal products, as well as their transformation products, were determined through target analysis. Most of the detected compounds were lipophilic (logP>2), although the presence of (semi)polar contaminants should not be overlooked, underlying the need for holistic analytical approaches in environmental monitoring studies. p,p'-DDE, PCB 153 and PCB138, PFOS and methylparaben were the most frequently detected compounds. 50 additional substances were identified and semi-quantified through suspect screening workflows, including mainly compounds of industrial use with high production volume.

Making use of apex predator sample collections: an integrated workflow for quality assured sample processing, analysis and digital sample freezing of archived samples

Using monitoring data from apex predators for chemicals risk assessment can provide important information on bioaccumulating as well as biomagnifying chemicals in food webs. A survey among European institutions involved in chemical risk assessment on their experiences with apex predator data in chemical risk assessment revealed great interest in using such data. However, the respondents indicated that constraints were related to expected high costs, lack of standardisation and harmonised quality criteria for exposure assessment, data access, and regulatory acceptance/application. During the Life APEX project, we demonstrated that European sample collections (i.e. environmental specimen banks (ESBs), research collection (RCs), natural history museums (NHMs)) archive a large variety of biological samples that can be readily used for chemical analysis once appropriate quality assurance/control (QA/QC) measures have been developed and implemented. We therefore issued a second survey on sampling, processing and archiving procedures in European sample collections to derive key quality QA/QC criteria for chemical analysis. The survey revealed great differences in QA/QC measures between ESBs, NHMs and RCs. Whereas basic information such as sampling location, date and biometric data were mostly available across institutions, protocols to accompany the sampling strategy with respect to chemical analysis were only available for ESBs. For RCs, the applied QA/QC measures vary with the respective research question, whereas NHMs are generally less aware of e.g. chemical cross-contamination issues. Based on the survey we derived key indicators for assessing the quality of biota samples that can be easily implemented in online databases. Furthermore, we provide a QA/QC workflow not only for sampling and processing but also for the chemical analysis of biota samples. We focussed on comprehensive analytical techniques such as non-target screening and provided insights into subsequent storage of high-resolution chromatograms in online databases (i.e. digital sample freezing platform) to ultimately support chemicals risk assessment.

Assessment of contaminants of emerging concern in European apex predators and their prey by LC-QToF MS wide-scope target analysis

Apex predators are good indicators of environmental pollution since they are relatively long-lived and their high trophic position and spatiotemporal exposure to chemicals provides insights into the persistent, bioaccumulative and toxic (PBT) properties of chemicals. Although monitoring data from apex predators can considerably support chemicals' management, there is a lack of pan-European studies, and longer-term monitoring of chemicals in organisms from higher trophic levels. The present study investigated the occurrence of contaminants of emerging concern (CECs) in 67 freshwater, marine and terrestrial apex predators and in freshwater and marine prey, gathered from four European countries. Generic sample preparation protocols for the extraction of CECs with a broad range of physicochemical properties and the purification of the extracts were used. The analysis was performed utilizing liquid (LC) chromatography coupled to high resolution mass spectrometry (HRMS), while the acquired chromatograms were screened for the presence of more than 2,200 CECs through wide-scope target analysis. In total, 145 CECs were determined in the apex predator and their prey samples belonging in different categories, such as pharmaceuticals, plant protection products, per- and polyfluoroalkyl substances, their metabolites and transformation products. Higher concentration levels were measured in predators compared to prey, suggesting that biomagnification of chemicals through the food chain occurs. The compounds were prioritized for further regulatory risk assessment based on their frequency of detection and their concentration levels. The majority of the prioritized CECs were lipophilic, although the presence of more polar contaminants should not be neglected. This indicates that holistic analytical approaches are required to fully characterize the chemical universe of biota samples. Therefore, the present survey is an attempt to systematically investigate the presence of thousands of chemicals at a European level, aiming to use these data for better chemicals management and contribute to EU Zero Pollution Ambition.

Effect-directed analysis of a hospital effluent sample using A-YES for the identification of endocrine disrupting compounds

An effect-directed analysis (EDA) approach was used to identify the compounds responsible for endocrine disruption in a hospital effluent (Basque Country). In order to facilitate the identification of the potentially toxic substances, a sample was collected using an automated onsite large volume solid phase extraction (LV-SPE) system. Then, it was fractionated with a two-step orthogonal chromatographic separation and tested for estrogenic effects with a recombinant yeast (A-YES) in-vitro bioassay. The fractionation method was optimized and validated for 184 compounds, and its application to the hospital effluent sample allowed reducing the number of unknowns from 292 in the raw sample to 35 after suspect analysis of the bioactive fractions. Among those, 7 of them were confirmed with chemical standards. In addition, target analysis of the raw sample confirmed the presence of mestranol, estrone and dodemorph in the fractions showing estrogenic activity. Predictive estrogenic activity modelling using quantitative structure-activity relationships indicated that the hormones mestranol (5840 ng/L) and estrone (128 ng/L), the plasticiser bisphenol A (9219 ng/L) and the preservative butylparaben (1224 ng/L) were the main contributors of the potential toxicity. Derived bioanalytical equivalents (BEQs) pointed mestranol and estrone as the main contributors (56 % and 43 %, respectively) of the 50 % of the sample's explained total estrogenic activity.

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.

Detecting the sources of chemicals in the Black Sea using non-target screening and deep learning convolutional neural networks

The Black Sea is an important ecosystem, which is affected by various anthropogenic pressures, such as shipping activities and wastewater inputs from large coastal cities. Significant loads of chemical pollutants are being continuously brought in by major European rivers. This study investigated the spatial distribution of chemicals in the Ukrainian shelf (the northwestern part of the Black Sea) and their main sources. Chemical occurrence data used in the study was generated within the Joint Black Sea Surveys (JBSS), which took place in 2016 and 2017 as a part of the EU/UNDP EMBLAS II project (www.emblasproject.org). During the JBSS, seawater samples were analyzed by a non-target screening workflow using liquid chromatography high-resolution mass spectrometry (LC-HRMS). Open-source algorithms were applied to generate a combined dataset of 30,489 detected chemical signals and their intensities. Out of these, 35 compounds were tentatively identified by the application of a non-target screening identification workflow based on automated matching of their mass spectra against those in available mass spectral libraries. The dataset was used to generate images, representing spatial distribution of each of the signals. These images were then used as an input to a deep learning convolutional neural network classification model. The study resulted in the development of an open-source end-to-end workflow for the estimation of the pollution load by chemicals contributed by the two major inflowing rivers (Danube and Dnieper) and other, so far unidentified, sources. A dedicated dashboard was built to facilitate data visualization per detected signal/compound. The presented model proved to be especially useful at the prioritization of signals of unknown compounds, which is of key importance for the follow up structure elucidation efforts of bulky non-target screening data. The deep learning approach for peak prioritization of unknown chemicals in the environment has been used for the first time.

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.

Target and suspect screening of 4777 per- and polyfluoroalkyl substances (PFAS) in river water, wastewater, groundwater and biota samples in the Danube River Basin

Per- and polyfluoroalkyl substances (PFAS) are under regulatory scrutiny since some of them are persistent, bioaccumulative, and toxic. The occurrence of 4777 PFAS was investigated in the Danube River Basin (DRB; 11 countries) using target and suspect screening. Target screening involved investigation of PFAS with 56 commercially available reference standards. Suspect screening covered 4777 PFAS retrieved from the NORMAN Substance Database, including all individual PFAS lists submitted to the NORMAN Suspect List Exchange Database. Mass spectrometry fragmentation patterns and retention time index predictions of the studied PFAS were established for their screening by liquid chromatography - high resolution mass spectrometry using NORMAN Digital Sample Freezing Platform (DSFP). In total, 82 PFAS were detected in the studied 95 samples of river water, wastewater, groundwater, biota and sediments. Suspect screening detected 72 PFAS that were missed by target screening. Predicted no effect concentrations (PNECs) were derived for each PFAS via a quantitative structure-toxicity relationship (QSTR)-based approach and used for assessment of their environmental risk. Risk characterization revealed 18 PFAS of environmental concern in at least one matrix. The presence of PFAS in all studied environmental compartments across the DRB indicates a potentially large-scale migration of PFAS in Europe, which might require their further systematic regulatory monitoring.

In Silico Structure Predictions for Non-targeted Analysis: From Physicochemical Properties to Molecular Structures

While important advances have been made in high-resolution mass spectrometry (HRMS) and its applications in non-targeted analysis (NTA), the number of identified compounds in biological and environmental samples often does not exceed 5% of the detected chemical features. Our aim was to develop a computational pipeline that leverages data from HRMS but also incorporates physicochemical properties (equilibrium partition ratios between organic solvents and water; Ksolvent-water) and can propose molecular structures for detected chemical features. As these physicochemical properties are often sufficiently different across isomers, when put together, they can form a unique profile for each isomer, which we describe as the "physicochemical fingerprint". In our study, we used a comprehensive database of compounds that have been previously reported in human blood and collected their Ksolvent-water values for 129 partitioning systems. We used RDKit to calculate the number of RDKit fragments and the number of RDKit bits per molecule. We then developed and trained an artificial neural network, which used as an input the physicochemical fingerprint of a chemical feature and predicted the number and types of RDKit fragments and RDKit bits present in that structure. These were then used to search the database and propose chemical structures. The average success rate of predicting the right chemical structure ranged from 60 to 86% for the training set and from 48 to 81% for the testing set. These observations suggest that physicochemical fingerprints can assist in the identification of compounds with NTA and substantially improve the number of identified compounds.

First Novel Workflow for Semiquantification of Emerging Contaminants in Environmental Samples Analyzed by Gas Chromatography-Atmospheric Pressure Chemical Ionization-Quadrupole Time of Flight-Mass Spectrometry

The ionization efficiency of emerging contaminants was modeled for the first time in gas chromatography-high-resolution mass spectrometry (GC-HRMS) which is coupled to an atmospheric pressure chemical ionization source (APCI). The recent chemical space has been expanded in environmental samples such as soil, indoor dust, and sediments thanks to recent use of high-resolution mass spectrometric techniques; however, many of these chemicals have remained unquantified. Chemical exposure in dust can pose potential risk to human health, and semiquantitative analysis is potentially of need to semiquantify these newly identified substances and assist with their risk assessment and environmental fate. In this study, a rigorously tested semiquantification workflow was proposed based on GC-APCI-HRMS ionization efficiency measurements of 78 emerging contaminants. The mechanism of ionization of compounds in the APCI source was discussed via a simple connectivity index and topological structure. The quantitative structure–property relationship (QSPR)-based model was also built to predict the APCI ionization efficiencies of unknowns and later use it for their quantification analyses. The proposed semiquantification method could be transferred into the household indoor dust sample matrix, and it could include the effect of recovery and matrix in the predictions of actual concentrations of analytes. A suspect compound, which falls inside the application domain of the tool, can be semiquantified by an online web application, free of access at http://trams.chem.uoa.gr/semiquantification/.

Suspect screening of wastewaters to trace anti-COVID-19 drugs: Potential adverse effects on aquatic environment

During the first period of the SARS-CoV-2 pandemic, the lack of specific therapeutic treatments led to the provisional use of a number of drugs, with a continuous review of health protocols when new scientific evidence emerged. The management of this emergency sanitary situation could not take care of the possible indirect adverse effects on the environment, such as the release of a large amount of pharmaceuticals from wastewater treatment plants. The massive use of drugs, which were never used so widely until then, implied new risks for the aquatic environment. In this study, a suspect screening approach using Liquid Chromatography-High Resolution Mass Spectrometry techniques, allowed us to survey the presence of pharmaceuticals used for COVID-19 treatment in three WWTPs of Lombardy region, where the first European cluster of SARS-CoV-2 cases was detected. Starting from a list of sixty-three suspect compounds used against COVID-19 (including some metabolites and transformation products), six compounds were fully identified and monitored together with other target analytes, mainly pharmaceuticals of common use. A monthly monitoring campaign was conducted in a WWTP from April to December 2020 and the temporal trends of some anti-COVID-19 drugs were positively correlated with those of COVID-19 cases and deaths. The comparison of the average emission loads among the three WWTPs evidenced that the highest loads of hydroxychloroquine, azithromycin and ciprofloxacin were measured in the WWTP which received the sewages from a hospital specializing in the treatment of COVID-19 patients. The monitoring of the receiving water bodies evidenced the presence of eight compounds of high ecological concern, whose risk was assessed in terms of toxicity and the possibility of inducing antibiotic and viral resistance. The results clearly showed that the enhanced, but not completely justified, use of ciprofloxacin and azithromycin represented a risk for antibiotic resistance in the aquatic ecosystems.

Harmonized Quality Assurance/Quality Control Provisions for Nontargeted Measurement of Urinary Pesticide Biomarkers in the HBM4EU Multisite SPECIMEn Study

A set of quality assurance/quality control (QA/QC) criteria for nontargeted measurement of pesticide exposure markers in a large-scale study of human urine has been proposed and applied across five laboratories within the HBM4EU project. Quality control material, including reference standards and fortified pooled urine samples (QC urine) were prepared in a centralized way and distributed across participants to monitor analytical performance and consistency of the liquid chromatography coupled to high-resolution mass spectrometry data generated with a harmonized workflow. Signal intensities, mass accuracy, and retention times of selected QA/QC markers covering a broad range of physicochemical properties were monitored across QC solvent standards, QC urine samples, study urine samples, and procedural blanks, setting acceptance thresholds for repeatability and accuracy. Overall, results showed high repeatability of the collected data. The RSDs of the signal intensities were typically below 20-30% in QC and study samples, with good stability of the chromatographic separation (retention time drift within 2-4 s intrabatch and 5 s interbatch) and excellent mass accuracy (average error < 2 ppm). The use of the proposed criteria allowed for the identification of handling errors, instrumental issues, and potential batch effects. This is the first elaboration of harmonized QA/QC criteria applied across multiple laboratories to assess the quality of data generated by nontargeted analysis of human samples.

A novel workflow for semi-quantification of emerging contaminants in environmental samples analyzed by LC-HRMS

There is an increasing need for developing a strategy to quantify the newly identified substances in environmental samples, where there are not always reference standards available. The semi-quantitative analysis can assist risk assessment of chemicals and their environmental fate. In this study, a rigorously tested and system-independent semi-quantification workflow is proposed based on ionization efficiency measurement of emerging contaminants analyzed in liquid chromatography-high-resolution mass spectrometry. The quantitative structure-property relationship (QSPR)-based model was built to predict the ionization efficiency of unknown compounds which can be later used for their semi-quantification. The proposed semi-quantification method was applied and tested in real environmental seawater samples. All semi-quantification-related calculations can be performed online and free of access at http://trams.chem.uoa.gr/semiquantification/ .

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.

Equivalent Carbon Number and Interclass Retention Time Conversion Enhance Lipid Identification in Untargeted Clinical Lipidomics

Chromatography is often used as a method for reducing sample complexity prior to analysis by mass spectrometry, and the use of retention time (RT) is becoming increasingly popular to add valuable supporting information in lipid identification. The RT of lipids with the same headgroup in reversed-phase separation can be predicted using the equivalent carbon number (ECN) model. This model describes the effects of acyl chain length and degree of saturation on lipid RT. For the first time, we have found a robust correlation in the chromatographic separation of lipids with different headgroups that share the same fatty acid motive. This relationship can be exploited to perform interclass RT conversion (IC-RTC) by building a model from RT measurements from lipid standards that allows the prediction of RT of one lipid subclass based on another. Here, we utilize ECN modeling and IC-RTC to build a glycerophospholipid RT library with 517 entries based on 136 tandem mass spectrometry-characterized lipid RTs from NIST SRM-1950 plasma and lipid standards. The library was tested on a patient cohort undergoing coronary artery bypass grafting surgery (n = 37). A total of 156 unique circulating glycerophospholipids were identified, of which 52 (1 LPG, 24 PE, 5 PG, 18 PI, and 9 PS) were detected with IC-RTC, thereby demonstrating the utility of this technique for the identification of lipid species not found in commercial standards.

A Modular and Expandable Ecosystem for Metabolomics Data Annotation in R

Liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics experiments have become increasingly popular because of the wide range of metabolites that can be analyzed and the possibility to measure novel compounds. LC-MS instrumentation and analysis conditions can differ substantially among laboratories and experiments, thus resulting in non-standardized datasets demanding customized annotation workflows. We present an ecosystem of R packages, centered around the MetaboCoreUtils, MetaboAnnotation and CompoundDb packages that together provide a modular infrastructure for the annotation of untargeted metabolomics data. Initial annotation can be performed based on MS¹ properties such as m/z and retention times, followed by an MS²-based annotation in which experimental fragment spectra are compared against a reference library. Such reference databases can be created and managed with the CompoundDb package. The ecosystem supports data from a variety of formats, including, but not limited to, MSP, MGF, mzML, mzXML, netCDF as well as MassBank text files and SQL databases. Through its highly customizable functionality, the presented infrastructure allows to build reproducible annotation workflows tailored for and adapted to most untargeted LC-MS-based datasets. All core functionality, which supports base R data types, is exported, also facilitating its re-use in other R packages. Finally, all packages are thoroughly unit-tested and documented and are available on GitHub and through Bioconductor.

High-Performance Data Processing Workflow Incorporating Effect-Directed Analysis for Feature Prioritization in Suspect and Nontarget Screening

Effect-directed analysis (EDA) aims at the detection of bioactive chemicals of emerging concern (CECs) by combining toxicity testing and high-resolution mass spectrometry (HRMS). However, consolidation of toxicological and chemical analysis techniques to identify bioactive CECs remains challenging and laborious. In this study, we incorporate state-of-the-art identification approaches in EDA and propose a robust workflow for the high-throughput screening of CECs in environmental and human samples. Three different sample types were extracted and chemically analyzed using a single high-performance liquid chromatography HRMS method. Chemical features were annotated by suspect screening with several reference databases. Annotation quality was assessed using an automated scoring system. In parallel, the extracts were fractionated into 80 micro-fractions each covering a couple of seconds from the chromatogram run and tested for bioactivity in two bioassays. The EDA workflow prioritized and identified chemical features related to bioactive fractions with varying levels of confidence. Confidence levels were improved with the in silico software tools MetFrag and the retention time indices platform. The toxicological and chemical data quality was comparable between the use of single and multiple technical replicates. The proposed workflow incorporating EDA for feature prioritization in suspect and nontarget screening paves the way for the routine identification of CECs in a high-throughput manner.

Characterization of the contamination fingerprint of wastewater treatment plant effluents in the Henares River Basin (central Spain) based on target and suspect screening analysis

The interest in contaminants of emerging concern (CECs) has increased lately due to their continued emission and potential ecotoxicological hazards. Wastewater treatment plants (WWTPs) are generally not capable of eliminating them and are considered the main pathway for CECs to the aquatic environment. The number of CECs in WWTPs effluents is often so large that complementary approaches to the conventional target analysis need to be implemented. Within this context, multitarget quantitative analysis (162 compounds) and a suspect screening (>40,000 suspects) approaches were applied to characterize the CEC fingerprint in effluents of five WWTPs in the Henares River basin (central Spain) during two sampling campaigns (summer and autumn). The results indicated that 76% of the compounds quantified corresponded to pharmaceuticals, 21% to pesticides and 3% to industrial chemicals. Apart from the 82 compounds quantified, suspect screening increased the list to 297 annotated compounds. Significant differences in the CEC fingerprint were observed between summer and autumn campaigns and between the WWTPs, being those serving the city of Alcalá de Henares the ones with the largest number of compounds and concentrations. Finally, a risk prioritization approach was applied based on risk quotients (RQs) for algae, invertebrates, and fish. Azithromycin, diuron, chlortoluron, clarithromycin, sertraline and sulfamethoxazole were identified as having the largest risks to algae. As for invertebrates, the compounds having the largest RQs were carbendazim, fenoxycarb and eprosartan, and for fish acetaminophen, DEET, carbendazim, caffeine, fluconazole, and azithromycin. The two WWTPs showing higher calculated Risk Indexes had tertiary treatments, which points towards the need of increasing the removal efficiency in urban WWTPs. Furthermore, considering the complex mixtures emitted into the environment and the low dilution capacity of Mediterranean rivers, we recommend the development of detailed monitoring plans and stricter regulations to control the chemical burden created to freshwater ecosystems.

Screening of legacy and emerging substances in surface water, sediment, biota and groundwater samples collected in the Siverskyi Donets River Basin employing wide-scope target and suspect screening

Siverskyi Donets is the fourth longest river in Ukraine and its ecosystem is heavily affected by numerous agricultural and industrial activities. An impact of the on-going armed military conflicts in the Eastern Ukraine to the overall pollution by the chemicals has been studied. Considering the uncontrolled activities in the catchment due to the conflict, there is a high demand to assess the contamination status of the Siverskyi Donets basin. In this study, the occurrence of the EU Water Framework Directive priority substances, selected physicochemical parameters and wide-range emerging contaminants were investigated in surface water, groundwater, biota and river sediments samples from 13 sampling sites in the river basin. The study included metals, inorganic, non-polar and polar organic contaminants. The wide-scope target screening of 2316 substances and suspect screening of 2219 substances revealed occurrence of 83 compounds in the studied samples. A few industrial chemicals such as plasticizers bisphenol A and DEHP, as well as flame retardant brominated diphenylethers were found to be potentially hazardous to the ecosystem, exceeding the established legacy environmental quality standards (EQS) or the provisional no-effect concentration (PNEC) values. River sediment samples contained traces of long-term banned chemicals such as polychlorinated biphenyls (PCBs) and degradation products of DDT (p,p'-DDD and p,p'-DDE). A simplified risk assessment based on comparison of measured concentration of the detected compounds against their (eco)toxicity threshold values from the NORMAN Ecotoxicology Database has been performed to aid their prioritization in future monitoring and, eventually, establishing the list of Siverskyi Donets River Basin Specific Pollutants. A comparison with the recent similar studies in the Dniester and Dnieper river basins in Ukraine has shown that the overall pollution by chemicals in the Siverskyi Donets basin is significantly lower.

An actionable annotation scoring framework for gas chromatography-high-resolution mass spectrometry

Omics-based technologies have enabled comprehensive characterization of our exposure to environmental chemicals (chemical exposome) as well as assessment of the corresponding biological responses at the molecular level (eg, metabolome, lipidome, proteome, and genome). By systematically measuring personal exposures and linking these stimuli to biological perturbations, researchers can determine specific chemical exposures of concern, identify mechanisms and biomarkers of toxicity, and design interventions to reduce exposures. However, further advancement of metabolomics and exposomics approaches is limited by a lack of standardization and approaches for assigning confidence to chemical annotations. While a wealth of chemical data is generated by gas chromatography high-resolution mass spectrometry (GC-HRMS), incorporating GC-HRMS data into an annotation framework and communicating confidence in these assignments is challenging. It is essential to be able to compare chemical data for exposomics studies across platforms to build upon prior knowledge and advance the technology. Here, we discuss the major pieces of evidence provided by common GC-HRMS workflows, including retention time and retention index, electron ionization, positive chemical ionization, electron capture negative ionization, and atmospheric pressure chemical ionization spectral matching, molecular ion, accurate mass, isotopic patterns, database occurrence, and occurrence in blanks. We then provide a qualitative framework for incorporating these various lines of evidence for communicating confidence in GC-HRMS data by adapting the Schymanski scoring schema developed for reporting confidence levels by liquid chromatography HRMS (LC-HRMS). Validation of our framework is presented using standards spiked in plasma, and confident annotations in outdoor and indoor air samples, showing a false-positive rate of 12% for suspect screening for chemical identifications assigned as Level 2 (when structurally similar isomers are not considered false positives). This framework is easily adaptable to various workflows and provides a concise means to communicate confidence in annotations. Further validation, refinements, and adoption of this framework will ideally lead to harmonization across the field, helping to improve the quality and interpretability of compound annotations obtained in GC-HRMS.

Applying an instrument-agnostizing methodology for the standardization of pesticide quantitation using different liquid chromatography-mass spectrometry platforms: A case study

Liquid chromatography coupled to mass spectrometry (LC-MS) is a powerful technique commonly used for pesticide residue analysis in agri-food matrices. Despite the fact it has several advantages, one of the main problems is the transferability of the data from one analytical equipment to another for identification and quantitation purposes. In this study, instrument-agnostizing methodology was used to set standard retention scores (SRSs), which was utilized as a parameter for the identification of 74 targeted compounds when different instruments are used. The SRS variation was lower than 5% for most of the compounds included in this study, which is much lower than those obtained when retention times were compared, correcting the elution shift between LC instruments. Additionally, this methodology was also tested for quantitation purposes, and normalized areas were used as analytical responses, allowing for the determination of the concentrations of the targeted compounds in samples injected in one equipment using the analytical responses of standards from another one. The applicability of this approach was tested at two concentrations, 0.06 and 0.15 mg/kg, and less than 10 out of 74 compounds were quantified with an error higher than 40% at 0.06 mg/kg and 0.15 mg/kg, showing that this methodology could be useful to minimize differences between LC-MS systems.