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Non-targeted analysis (NTA) has emerged as a valuable approach for comprehensive monitoring of chemicals of emerging concern (CECs) in the exposome. The NTA approach, theoretically, is able to identify compounds with diverse physicochemical properties and sources. Non-targeted analysis methods, even though generic and wide scoping, have been shown to have limitations in terms of their coverage of the chemical space, as the number of the identified chemicals in each sample is very low (e.g. < 5%). Investigating the chemical space covered by each NTA assay is crucial for understanding the limitations and challenges associated with the workflow from experimental methods to the data acquisition and data processing. In this review, we examined recent NTA studies published between 2017 and 2023 that employed liquid chromatography-high resolution mass spectrometry. The parameters used in each study were documented and reported chemicals at the confidence level 1 and 2 were retrieved. The chosen experimental setups and the quality of reporting were critically evaluated and discussed. The findings revealed that only around 2% of the estimated chemical space (i.e. Norman SusDat) was covered by the NTA studies investigated. Little to no trend was found between the experimental setup and the observed coverage, due to the generic and wide scope of NTA studies. The limited coverage of chemical space by the NTA studies highlights the necessity for a more comprehensive approach in experimental and data processing setups to enable the exploration of a broader range of chemical space, with the ultimate goal of protecting human and environmental health. Recommendations to further explore a wider range of the chemical space were given.

Non-target analysis (NTA) employing high-resolution mass spectrometry (HRMS) coupled with liquid chromatography is increasingly being used to identify chemicals of biological relevance. HRMS datasets are large and complex making the identification of potentially relevant chemicals extremely challenging. As they are recorded in vendor-specific formats, interpreting them is often reliant on vendor-specific software that may not accommodate the advancements in data processing. Here we present InSpectra, a vendor independent automated platform for the systematic detection of newly identified emerging chemical threats.InSpectra is web-based, open-source/access and modular providing highly flexible and extensible NTA and suspect screening workflows. As a cloud-based platform, InSpectra exploits parallel computing and big data archiving capabilities with a focus for sharing and community curation of HRMS data. InSpectra offers a reproducible and transparent approach for the identification, tracking and prioritisation of emerging chemical threats.

Fragment deconvolution is a crucial step during componentization of non-targeted analysis (NTA) high-resolution mass spectrometry (HRMS) data, aiming to filter out false positive (FP) signals that do not belong to the component. Moreover, inclusion of FP fragments could lead to, for example, wrong identification further down the workflow. Commonly used methods for deconvolution of fragment signals rely on the presence of a time domain (e.g., peak apex retention time difference and correlation analysis). However, when there is no or insufficient MS2 information in the time domain, these methods are unusable and only the mass domain remains. A probability based cumulative neutral loss (CNL) model for fragment deconvolution using the mass domain information was thus developed to allow deconvolution for such cases. The optimized model, with a mass tolerance of 0.005 Da and a CNL score threshold of -0.95, was able to achieve true positive rate (TPr) of 95.0%, a false discovery rate (FDr) of 25.6%, and a reduction rate of 39.9%. Additionally, the CNL model was extensively tested on real samples containing predominantly pesticides at different concentration levels and with matrix effects. Overall, the model was able to obtain a TPr above 95% with FD rates between 45% and 77% and reduction rates between 10% and 24%. Finally, the CNL model was compared with the retention time difference method and peak shape correlation analysis, showing that a combination of correlation analysis and the CNL model was the most effective for fragment deconvolution, obtaining a TPr of 93.1%, a FDr of 57.2%, and a reduction rate of 42.6%.

Plastic contamination is ubiquitous in the environment and has been related to increasing global plastic usage since the 1950s. Considering the omnipresence of additives in plastics, the risk posed by this contamination is related not only to the physical effects of plastic particles but also to their additive content. Until now, most routine environmental monitoring programs involving additives have not considered the presence of these additives still associated with the plastic they were added to during their production. Understanding environmental additive speciation is essential to address the risk they pose through their bioavailability and plastic-associated transport. Here, we present and apply a theoretical framework for sampling and analytical procedures to characterize the speciation of hydrophobic nonionized additives in environmental compartments. We show that this simple framework can help develop sampling and sample treatment procedures to quantify plastic-associated additives and understand additive distribution between plastics and organic matter. When applied to concrete cases, internal consistency checks with the model allowed for identifying plastic-associated additives in a sample. In other cases, the plastic-organic carbon ratio and additive concentration in the matrix are key factors affecting the ability to identify plastic-associated additives. The effect of additive dissipation through diffusion out of plastic particles is also considered.

In this research, we developed an online comprehensive two-dimensional liquid chromatographic (LC × LC) method hyphenated with high-resolution mass spectrometry (HRMS) for the non-targeted identification of poly- and perfluorinated compounds (PFASs) in fire-fighting aqueous-film forming foams (AFFFs). The method exploited the combination of mixed-mode weak anion exchange-reversed phase with a octadecyl stationary phase, separating PFASs according to ionic classes and chain length. To develop and optimize the LC × LC method we used a reference training set of twenty-four anionic PFASs, representing the main classes of compounds occurring in AFFFs and covering a wide range of physicochemical properties. In particular, we investigated different modulation approaches to reduce injection band broadening and breakthrough in the second dimension separation. Active solvent and stationary phase assisted modulations were compared, with the best results obtained with the last approach. In the optimal conditions, the predicted peak capacity corrected for undersampling was higher than three-hundred in a separation space of about 60 min. Subsequently, the developed method was applied to the non-targeted analysis of two AFFF samples for the identification of homologous series of PFASs, in which it was possible to identify up to thirty-nine potential compounds of interest utilizing Kendrick mass defect analysis. Even within the samples, the features considered potential PFAS by mass defect analysis elute in the chromatographic regions discriminating for the ionic group and/or the chain length, thus confirming the applicability of the method presented for the analysis of AFFF mixtures and, to a further extent, of environmental matrices affected by the AFFF.

Non-target analysis (NTA) employing high-resolution mass spectrometry (HRMS) coupled with liquid chromatography is increasingly being used to identify chemicals of biological relevance. HRMS datasets are large and complex making the identification of potentially relevant chemicals extremely challenging. As they are recorded in vendor-specific formats, interpreting them is often reliant on vendor-specific software that may not accommodate the advancements in data processing. Here we present InSpectra, a vendor independent automated platform for the systematic detection of newly identified emerging chemical threats. InSpectra is web-based, open-source/access and modular providing highly flexible and extensible NTA and suspect screening workflows. As a cloud-based platform, InSpectra exploits parallel computing and big data archiving capabilities with a focus for sharing and community curation of HRMS data. InSpectra offers a reproducible and transparent approach for the identification, tracking and prioritisation of emerging chemical threats.

The great potential gains in separation power and analysis time that can result from rigorously optimizing LC-MS and 2D-LC-MS methods for routine measurements has prompted many scientists to develop computer-aided method-development tools. The applicability of these has been proven in numerous applications, but their proliferation is still limited. Arguably, the majority of LC methods are still developed in a conventional manner, i.e. by analysts who rely on their knowledge and experience. In this work, a novel, open-source algorithm was developed for automated and interpretive method development of LC separations. A closed-loop workflow was constructed that interacted directly with the LC and ran unsupervised in an automated fashion. The algorithm was tested using two newly designed strategies. The first utilized retention modeling, whereas the second used the Bayesian-optimization machine-learning approach. In both cases, the algorithm could arrive within ten iterations at an optimum of the objective function, which included resolution and measurement time. The design of the algorithm was modular, so as to facilitate compatibility with previous works in literature and its performance thus hinged on each module (e.g., signal processing, choice of retention model, objective function). Key focus areas for further improvement were identified. Bayesian optimization did not require any peak tracking or retention modeling. Accurate prediction of elution profiles was found to be indispensable for the strategy using retention modeling. This is the first interpretive algorithm demonstrated with complex samples. Peak tracking was conducted using UV-Vis absorbance detection, but use of MS detection is expected to significantly broaden the applicability of the workflow.

The European Chemicals Agency (ECHA) and US Environmental Protection Agency (EPA) have listed approximately 800k chemicals that must be further investigated for their potential environmental and/or human health risk. A significant number of these chemicals have large enough global volumes of consumption (e.g. industrial and agro- chemical) to reach the limits of detection of our analytical chemistry methods in en- vironmental samples, but experimental data on their environmental fate and toxicity are largely missing. Filling these data gaps experimentally for such a large number of chemicals is practically impossible, making model approaches to predict chemical property data highly relevant. However, the currently available models suffer from limited training sets, linearity and continuity assumptions. In this study we present a supervised direct classification model that directly connects the molecular descriptors of chemicals to their toxicity. As a proof of concept we used 907 experimentally defined 96h LC50 values for acute fish toxicity. Classification was performed into two typesof toxicity categories: 1) categories derived via k-means clustering from the experimental dataset and 2) hazard categories defined by the Globally Harmonized System of Classification and Labelling of Chemicals (GHS), via machine learning. Our direct classification model explained ≈ 90% of variance in our data for the training set and ≈ 80% for the test set. Direct comparison of our classification model with the conventional strategy (i.e. QSAR regression model) resulted in a 5 fold decrease in the wrong chemical categorization for our model. The optimized model was employed to predict the toxicity categories of ≈ 32k chemicals (from the Norman SusDat). Finally, a comparison between the model based applicability domain (AD) vs the training set AD was performed, suggesting that the training set based AD is a more adequate way to avoid extrapolation when using such models. The better performance of our direct classification model compared to conventionally employed QSAR methods, makes this approach a viable tool for hazard identification and risk assessment of chemicals.

Isotopologue identification or removal is a necessary step to reduce the number of features that need to be identified in samples analyzed with non-targeted analysis. Currently available approaches rely on either predicted isotopic patterns or an arbitrary mass tolerance, requiring information on the molecular formula or instrumental error, respectively. Therefore, a Naive Bayes isotopologue classification model was developed that does not depend on any thresholds or molecular formula information. This classification model uses the elemental mass defects of six elemental ratios and successfully identified isotopologues for both theoretical isotopic patterns and wastewater influent samples, outperforming one of the most commonly used approaches (i.e., 1.0033 ​Da mass difference method - CAMERA). For the theoretical isotopologues, the classification model outperformed an “in-house” mass difference method with a true positive rate (TPr) of 99.0% and false positive rate (FPr) of 1.8% compared to a TPr of 16.2% and an FPr of 0.02%, assuming no error. As for the wastewater influent samples, the classification model, with a TPr of 99.8% and false detection rate (FDr) of 0.5%, again performed better than the mass difference method, with a TPr of 96.3% and FDr of 4.8%. Therefore, it can be concluded that the classification model can be used for isotopologue identification, requiring no thresholds or information on the molecular formula.

The differentiation of positional isomers is a well established analytical challenge for forensic laboratories. As more novel psychoactive substances (NPSs) are introduced to the illicit drug market, robust yet efficient methods of isomer identification are needed. Although current literature suggests that Direct Analysis in Real Time-Time-of-Flight mass spectrometry (DART-ToF) with in-source collision induced dissociation (is-CID) can be used to differentiate positional isomers, it is currently unclear whether this capability extends to positional isomers whose only structural difference is the precise location of a single substitution on an aromatic ring. The aim of this work was to determine whether chemometric analysis of DART-ToF data could offer forensic laboratories an alternative rapid and robust method of differentiating NPS positional ring isomers. To test the feasibility of this technique, three positional isomer sets (fluoroamphetamine, fluoromethamphetamine, and methylmethcathinone) were analyzed. Using a linear rail for consistent sample introduction, the three isomers of each type were analyzed 96 times over an eight-week timespan. The classification methods investigated included a univariate approach, the Welch t test at each included ion; a multivariate approach, linear discriminant analysis; and a machine learning approach, the Random Forest classifier. For each method, multiple validation techniques were used including restricting the classifier to data that was only generated on one day. Of these classification methods, the Random Forest algorithm was ultimately the most accurate and robust, consistently achieving out-of-bag error rates below 5%. At an inconclusive rate of approximately 5%, a success rate of 100% was obtained for isomer identification when applied to a randomly selected test set. The model was further tested with data acquired as a part of a different batch. The highest classification success rate was 93.9%, and error rates under 5% were consistently achieved.

Wastewater-based epidemiology is a potential complementary technique for monitoring the use of performance- and image-enhancing drugs (PIEDs), such as anabolic steroids and selective androgen receptor modulators (SARMs), within the general population. Assessing in-sewer transformation and degradation is critical for understanding uncertainties associated with wastewater analysis. An electrospray ionization liquid chromatography mass spectrometry method for the quantification of 59 anabolic agents in wastewater influent was developed. Limits of detection and limits of quantification ranged from 0.004 to 1.56 μg/L and 0.01 to 4.75 μg/L, respectively. Method performance was acceptable for linearity (R2 > 0.995, few exceptions), accuracy (68-119%), and precision (1-21%RSD), and applicability was successfully demonstrated. To assess the stability of the selected biomarkers in wastewater, we used laboratory-scale sewer reactors to subject the anabolic agents to simulated realistic sewer environments for 12 h. Anabolic agents, including parent compounds and metabolites, were spiked into freshly collected wastewater that was then fed into three sewer reactor types: control sewer (no biofilm), gravity sewer (aerobic conditions), and rising main sewer (anaerobic conditions). Our results revealed that while most glucuronide conjugates were completely transformed following 12 h in the sewer reactors, 50% of the investigated biomarkers had half-lives longer than 4 h (mean residence time) under gravity sewer conditions. Most anabolic agents were likely subject to biofilm sorption and desorption. These novel results lay the groundwork for any future wastewater-based epidemiology research involving anabolic steroids and SARMs.

The European and U.S. chemical agencies have listed approximately 800k chemicals about which knowledge of potential risks to human health and the environment is lacking. Filling these data gaps experimentally is impossible, so in silico approaches and prediction are essential. Many existing models are however limited by assumptions (e.g., linearity and continuity) and small training sets. In this study, we present a supervised direct classification model that connects molecular descriptors to toxicity. Categories can be driven by either data (using k-means clustering) or defined by regulation. This was tested via 907 experimentally defined 96 h LC50 values for acute fish toxicity. Our classification model explained ≈90% of the variance in our data for the training set and ≈80% for the test set. This strategy gave a 5-fold decrease in the frequency of incorrect categorization compared to a quantitative structure–activity relationship (QSAR) regression model. Our model was subsequently employed to predict the toxicity categories of ≈32k chemicals. A comparison between the model-based applicability domain (AD) and the training set AD was performed, suggesting that the training set-based AD is a more adequate way to avoid extrapolation when using such models. The better performance of our direct classification model compared to that of QSAR methods makes this approach a viable tool for assessing the hazards and risks of chemicals.

Isotopologue identification or removal is a necessary step to reduce the number of features that need to be identified in samples analyzed with non-targeted analysis. Currently available approaches rely on either predicted isotopic patterns or an arbitrary mass tolerance, requiring information on the molecular formula or instrumental error, respectively. Therefore, a Naive Bayes isotopologue classification model was developed that does not depend on any thresholds or molecular formula information. This classification model uses elemental mass defects of six elemental ratios and can successfully identify isotopologues in both theoretical isotopic patterns and wastewater influent samples, outperforming one of the most commonly used approaches (i.e., 1.0033 Da mass difference method - CAMERA).

Understanding the in-sewer stability of analgesic biomarkers is important for interpreting wastewater-based epidemiology (WBE) data to estimate community-wide analgesic drugs consumption. The in-sewer stability of a suite of 19 analgesics and their metabolites was assessed using lab-scale sewer reactors. Target biomarkers were spiked into wastewater circulating in simulated gravity, rising main and control (no biofilm) sewer reactors. In-sewer transformation was observed over a hydraulic retention time of 12 h. All investigated biomarkers were stable under control reactor conditions. In gravity sewer conditions, diclofenac, desmetramadol, ibuprofen carboxylic acid, ketoprofen, lidocaine and tapentadol were highly stable (0–20% transformation in 12 h). Valdecoxib, parecoxib, etoricoxib, indomethacin, naltrexone, naloxone, piroxicam, ketoprofen, lidocaine, tapentadol, oxymorphone, hydrocodone, meperidine, hydromorphone were considered as moderately stable biomarkers (20–50% transformation in 12 h). Celecoxib and sulindac were considered unstable biomarkers (>50% transformation in 12 h). Ketoprofen, lidocaine, tapentadol, meperidine, hydromorphone were transformed to 0–20% whereas diclofenac, desmetramadol, ibuprofen carboxylic acid, valdecoxib, parecoxib, etoricoxib, indomethacin, naltrexone, piroxicam were transformed up to 20–50% in 12 h in rising main reactor (RMR). These biomarkers were considered as highly stable and stable biomarkers in RMR, respectively. Sulindac, celecoxib, naloxone, oxymorphone and hydrocodone were transformed more than 50% in 12 h and considered as unstable biomarkers in RMR. This study provides the information for a better understanding of the in-sewer loss of the analgesics before using them in WBE biomarkers for estimating drug loads at the population level.

The majority of liquid chromatography (LC) methods are still developed in a conventional manner, that is, by analysts who rely on their knowledge and experience to make method development decisions. In this work, a novel, open-source algorithm was developed for automated and interpretive method development of LC(-mass spectrometry) separations ("AutoLC"). A closed-loop workflow was constructed that interacted directly with the LC system and ran unsupervised in an automated fashion. To achieve this, several challenges related to peak tracking, retention modeling, the automated design of candidate gradient profiles, and the simulation of chromatograms were investigated. The algorithm was tested using two newly designed method development strategies. The first utilized retention modeling, whereas the second used a Bayesian-optimization machine learning approach. In both cases, the algorithm could arrive within 4-10 iterations (i.e., sets of method parameters) at an optimum of the objective function, which included resolution and analysis time as measures of performance. Retention modeling was found to be more efficient while depending on peak tracking, whereas Bayesian optimization was more flexible but limited in scalability. We have deliberately designed the algorithm to be modular to facilitate compatibility with previous and future work (e.g., previously published data handling algorithms).

Non-target analysis (NTA) employing high-resolution mass spectrometry is a commonly applied approach for the detection of novel chemicals of emerging concern in complex environmental samples. NTA typically results in large and information-rich datasets that require computer aided (ideally automated) strategies for their processing and interpretation. Such strategies do however raise the challenge of reproducibility between and within different processing workflows. An effective strategy to mitigate such problems is the implementation of inter-laboratory studies (ILS) with the aim to evaluate different workflows and agree on harmonized/standardized quality control procedures. Here we present the data generated during such an ILS. This study was organized through the Norman Network and included 21 participants from 11 countries. A set of samples based on the passive sampling of drinking water pre and post treatment was shipped to all the participating laboratories for analysis, using one pre-defined method and one locally (i.e. in-house) developed method. The data generated represents a valuable resource (i.e. benchmark) for future developments of algorithms and workflows for NTA experiments., Measurement(s)chemical • drinking waterTechnology Type(s)high resolution mass spectrometry • non-target analysis • InterlaboratoryFactor Type(s)methodSample Characteristic - Environmentlaboratory environment Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.15028665

The purpose of this project is to dig deeper into the data material already generated in the Suspect screening in Nordic countries: Point sources in city areas (TemaNord: 2017:561) to further optimize the benefits of the major work that has already been done. Samples (effluent, sediment, and biota) from all of the Nordic countries were carefully selected, sampled and analysed by a consortium of some of the Nordic region’s most experienced scientific groups in analyses of emerging environmental contaminants. But where perhaps the full potential of the generated data is still to be realized. This project will try to further identify and describe the substances already detected, to be able to better understand what substances we in modern Nordic societies release into the sea via our wastewater.

Tire and road wear particles may constitute the largest source of microplastic particles into the environment. Quantification of these particles are associated with large uncertainties which are in part due to inadequate analytical methods. New methodology is presented in this work to improve the analysis of tire and road wear particles using pyrolysis gas chromatography mass spectrometry. Pyrolysis gas chromatography mass spectrometry of styrene butadiene styrene, a component of polymer-modified bitumen used on road asphalt, produces pyrolysis products identical to those of styrene butadiene rubber and butadiene rubber, which are used in tires. The proposed method uses multiple marker compounds to measure the combined mass of these rubbers in samples and includes an improved step of calculating the amount of tire and road based on the measured rubber content and site-specific traffic data. The method provides good recoveries of 83–92% for a simple matrix (tire) and 88–104% for a complex matrix (road sediment). The validated method was applied to urban snow, road-side soil and gully-pot sediment samples. Concentrations of tire particles in these samples ranged from 0.1 to 17.7 mg/mL (snow) to 0.6–68.3 mg/g (soil/sediment). The concentration of polymer-modified bitumen ranged from 0.03 to 0.42 mg/mL (snow) to 1.3–18.1 mg/g (soil/sediment).

An emission source of microplastics into the environment is laundering synthetic textiles and clothing. Mechanical drying as a pathway for emitting microplastics, however, is poorly understood. In this study, emissions of microplastic fibres were sampled from a domestic vented dryer to assess whether mechanical drying of synthetic textiles releases microplastic fibres into the surrounding air or are captured by the inbuilt filtration system. A blue polyester fleece blanket was repeatedly washed and dried using the 'Normal Dry' program of a common domestic dryer operated at temperatures between 56 and 59 °C for 20 min. Microfibres in the ambient air and during operation of the dryer were sampled and analysed using microscopy for particle quantification and characterisation followed by Fourier-Transform Infrared Spectroscopy (FTIR) and Pyrolysis Gas Chromatography-Mass Spectrometry (Pyr-GC/MS) for chemical characterisation. Blue fibres averaged 6.4 ± 9.2 fibres in the room blank (0.17 ± 0.27 fibres/m3 ), 8.8 ± 8.5 fibres (0.05 ± 0.05 fibres/m3 ) in the procedural blank and 58 ± 60 (1.6 ± 1.8 fibres/ m3 ) in the sample. This is the first study to measure airborne emissions of microplastic fibres from mechanical drying, confirming that it is an emission source of microplastic fibres into air – particularly indoor air. Also see: https://micro2022.sciencesconf.org/426554/document, In MICRO 2022, Online Atlas Edition: Plastic Pollution from MACRO to nano

The application of non-target analysis (NTA), a comprehensive approach to characterize unknown chemicals, including chemicals of emerging concern has seen a steady increase recently. Given the relative novelty of this type of analysis, robust quality assurance and quality control (QA/QC) measures are imperative to ensure quality and consistency of results obtained using different workflows. Due to fundamental differences to established targeted workflows, new or expanded approaches are necessary; for example to minimize the risk of losing potential substances of interest (i.e. false negatives, Type II error). We present an overview of QA/QC techniques for NTA workflows published to date, specifically focusing on the analysis of environmental samples using liquid chromatography coupled to HRMS. From a QA/QC perspective, we discuss methods used for each step of analysis: sample preparation, chromatography, mass spectrometry, and data processing. We then finish with a series of recommendations to improve the quality assurance of NTA workflows.

Naphthenic acids (NAs) constitute one of the toxic components of the produced water (PW) from offshore oil platforms discharged into the marine environment. We employed liquid chromatography (LC) coupled to high-resolution mass spectrometry with electrospray ionization (ESI) in negative mode for the comprehensive chemical characterization and quantification of NAs in PW samples from six different Norwegian offshore oil platforms. In total, we detected 55 unique NA isomer groups, out of the 181 screened homologous groups, across all tested samples. The frequency of detected NAs in the samples varied between 14 and 44 isomer groups. Principal component analysis (PCA) indicated a clear distinction of the PW from the tested platforms based on the distribution of NAs in these samples. The averaged total concentration of NAs varied between 6 and 56 mg L-1, among the tested platforms, whereas the concentrations of the individual NA isomer groups ranged between 0.2 and 44 mg L-1. Based on both the distribution and the concentration of NAs in the samples, the C8H14O2 isomer group appeared to be a reasonable indicator of the presence and the total concentration of NAs in the samples with a Pearson correlation coefficient of 0.89.

© The Author(s) 2020., The Partnership for Chemicals Risk Assessment (PARC) is currently under development as a joint research and innovation programme to strengthen the scientific basis for chemical risk assessment in the EU. The plan is to bring chemical risk assessors and managers together with scientists to accelerate method development and the production of necessary data and knowledge, and to facilitate the transition to next-generation evidence-based risk assessment, a non-toxic environment and the European Green Deal. The NORMAN Network is an independent, well-established and competent network of more than 80 organisations in the field of emerging substances and has enormous potential to contribute to the implementation of the PARC partnership. NORMAN stands ready to provide expert advice to PARC, drawing on its long experience in the development, harmonisation and testing of advanced tools in relation to chemicals of emerging concern and in support of a European Early Warning System to unravel the risks of contaminants of emerging concern (CECs) and close the gap between research and innovation and regulatory processes. In this commentary we highlight the tools developed by NORMAN that we consider most relevant to supporting the PARC initiative: (i) joint data space and cutting-edge research tools for risk assessment of contaminants of emerging concern; (ii) collaborative European framework to improve data quality and comparability; (iii) advanced data analysis tools for a European early warning system and (iv) support to national and European chemical risk assessment thanks to harnessing, combining and sharing evidence and expertise on CECs. By combining the extensive knowledge and experience of the NORMAN network with the financial and policy-related strengths of the PARC initiative, a large step towards the goal of a non-toxic environment can be taken.

Municipal wastewater typically contains many drugs and anthropogenic chemicals or biomarkers. The occurrence of these chemicals in wastewater is linked to the socioeconomic characteristics of the contributing population. Based on these relationships, we propose, execute and evaluate a novel model for predicting population socioeconomics. Specifically, we used biomarkers in wastewater to predict 37 socioeconomic characteristics of populations during the Australian Census. The resultant model was further tested on nine other populations separate from the training data set. Prediction performance in the test populations (defined as accuracy ± SD) fit within 75% and 125% for many features such as catchment median age, and specific measures of educational attainment (e.g., high school completion) and employment (e.g., managerial employment). Considering the relative ease, low cost and high frequency at which wastewater samples can be collected and analyzed, wastewater analysis could be used as a complementary technique for assessing population socioeconomics.

This is an open access article published by American Chemical Society in ACS Omega, available online: https://pubs.acs.org/ We quantified the concentrations of two little-studied brominated pollutants, 1,3,5-tribromobenzene (TBB) and 4-bromobiphenyl (4BBP), in the deep water column and sediments of Lake Geneva. We found aqueous concentrations of 625 ± 68 pg L−1 for TBB and 668 ± 86 pg L−1 for 4BBP over a depth range of 70−191.5 m (near-bottom depth), based on duplicate measurements taken at five depths during three separate 1 month sampling periods at our sampling site near Vidy Bay. These levels of TBB and 4BBP were 1 or 2 orders of magnitude higher than the quantified aqueous concentrations of the components of the pentabrominated biphenyl ether technical mixture, which is a flame retardant product that had a high production volume in Europe before 2001. We observed statistically significant vertical concentration trends for both TBB and 2,2′,4,4′,6-pentabromobiphenyl ether in the deep water column, which indicates that transport and/or degradation processes affect these compounds. These measurements were enabled by application of a comprehensive two-dimensional gas chromatograph coupled to an electron capture negative chemical ionization time-of-flight mass spectrometer (GC×GC-ENCI-TOFMS) and to a micro-electron capture detector (GC×GC-μECD). GC×GC-ENCI-TOFMS and GC×GC-μECD were found to be >10× more sensitive toward brominated pollutants than conventional GC×GC-EI-TOFMS (with an electron impact (EI) ionization source), the latter of which had insufficient sensitivity to detect these emerging brominated pollutants in the analyzed samples. GC×GC also enabled the estimation of several environmentally relevant partitioning properties of TBB and 4BBP, further confirming previous evidence that these pollutants are bioaccumulative and have long-range transport potential.

Comprehensive chemical characterization of naphthenic acids (NAs) in oilfield produced water is a challenging task due to sample complexity. The recovery of NAs from produced water, and the corresponding distribution of detectable NAs are strongly influenced by sample extraction methodologies. In this study, we evaluated the effect of the extraction method on chemical space (i.e. the total number of chemicals present in a sample), relative recovery, and the distribution of NAs in a produced water sample. Three generic and pre-established extraction methods (i.e. liquid-liquid extraction (Lq), and solid phase extraction using HLB cartridges (HLB), and the combination of ENV+ and C8 (ENV) cartridges) were employed for our evaluation. The ENV method produced the largest number of detected NAs (134 out of 181) whereas the HLB and Lq methods produced 108 and 91 positive detections, respectively, in the tested produced water sample. For the relative recoveries, the ENV performed better than the other two methods. The uni-variate and multi-variate statistical analysis of our results indicated that the ENV and Lq methods explained most of the variance observed in our data. When looking at the distribution of NAs in our sample the ENV method appeared to provide a more complete picture of the chemical diversity of NAs in that sample. Finally, the results are further discussed.

Significance To date, wastewater-based epidemiology has focused on reporting drug and pharmaceutical consumption patterns by analyzing domestic wastewater. Here we explore the relationships between chemicals in wastewater and social, demographic, and economic parameters of the respective populations. We show the extent to which consumption of chemicals such as opioids and illicit drugs are associated with sociodemographics. We also examine chemicals that reflect individuals’ consumption of food components in wastewater and show that disparities in diet are associated with educational level. Our study shows that chemicals in wastewater reflect the social, demographic, and economic properties of the respective populations and highlights the potential value of wastewater in studying the sociodemographic determinants of population health., Wastewater is a potential treasure trove of chemicals that reflects population behavior and health status. Wastewater-based epidemiology has been employed to determine population-scale consumption of chemicals, particularly illicit drugs, across different communities and over time. However, the sociodemographic or socioeconomic correlates of chemical consumption and exposure are unclear. This study explores the relationships between catchment specific sociodemographic parameters and biomarkers in wastewater generated by the respective catchments. Domestic wastewater influent samples taken during the 2016 Australian census week were analyzed for a range of diet, drug, pharmaceutical, and lifestyle biomarkers. We present both linear and rank-order (i.e., Pearson and Spearman) correlations between loads of 42 biomarkers and census-derived metrics, index of relative socioeconomic advantage and disadvantage (IRSAD), median age, and 40 socioeconomic index for area (SEIFA) descriptors. Biomarkers of caffeine, citrus, and dietary fiber consumption had strong positive correlations with IRSAD, while tramadol, atenolol, and pregabalin had strong negative correlation with IRSAD. As expected, atenolol and hydrochlorothiazide correlated positively with median age. We also found specific SEIFA descriptors such as occupation and educational attainment correlating with each biomarker. Our study demonstrates that wastewater-based epidemiology can be used to study sociodemographic influences and disparities in chemical consumption.

The Great Barrier Reef receives run-off from 424,000 km2 catchment area across coastal Queensland, incorporating diffuse agricultural run-off, and run-off point sources of land-based chemical pollutants from urban and industrial development. Marine biota, such as green turtles (Chelonia mydas), are exposed to these diverse chemical mixtures in their natural environments, and the long term effects on turtle and ecosystem health remain unknown. This study was part of a larger multi-disciplinary project characterising anthropogenic chemical exposures from the marine environment and turtle health. The aim of this study was to screen for a wide range of anthropogenic chemical pollutants present in the external and internal environment of green turtles, using a combination of traditional targeted chemical analyses, non-target suspect screening, and effect-based bioassay methods, while employing a case-control study design. A combination of passive (water) and grab (water, sediment) samples were investigated. Three known green turtle foraging sites were selected for sampling: two coastal ‘case’ sites influenced primarily by urban/industrial and agricultural activities, respectively; and a remote, offshore ‘control’ site. Water and sediment samples from each of the three sampling locations showed differences in chemical pollutant profiles that reflected the dominant land uses in the adjacent catchment. Targeted mass spectrometric analysis for a range of pesticides, industrial chemicals, pharmaceuticals and personal care products found the greatest detection frequency and highest concentrations in coastal samples, compared to the control. Non-target screening analysis of water showed clear differentiation in chemical profile of the urban/industrial site. In-vitro assays of sediment samples from the control site had lowest induction, compared to coastal locations, as expected. Here we present evidence that turtles foraging in coastal areas are exposed to a range of anthropogenic pollutants derived from the adjacent coastal catchment areas.