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.

Studying Venom Toxin Variation Using Accurate Masses from Liquid Chromatography-Mass Spectrometry Coupled with Bioinformatic Tools

This study provides a new methodology for the rapid analysis of numerous venom samples in an automated fashion. Here, we use LC-MS (Liquid Chromatography-Mass Spectrometry) for venom separation and toxin analysis at the accurate mass level combined with new in-house written bioinformatic scripts to obtain high-throughput results. This analytical methodology was validated using 31 venoms from all members of a monophyletic clade of Australian elapids: brown snakes (Pseudonaja spp.) and taipans (Oxyuranus spp.). In a previous study, we revealed extensive venom variation within this clade, but the data was manually processed and MS peaks were integrated into a time-consuming and labour-intensive approach. By comparing the manual approach to our new automated approach, we now present a faster and more efficient pipeline for analysing venom variation. Pooled venom separations with post-column toxin fractionations were performed for subsequent high-throughput venomics to obtain toxin IDs correlating to accurate masses for all fractionated toxins. This workflow adds another dimension to the field of venom analysis by providing opportunities to rapidly perform in-depth studies on venom variation. Our pipeline opens new possibilities for studying animal venoms as evolutionary model systems and investigating venom variation to aid in the development of better antivenoms.

Is Nontarget Analysis Ready for Regulatory Application? Influence of Peak-Picking Algorithms on Data Analysis

The use of peak-picking algorithms is an essential step in all nontarget analysis (NTA) workflows. However, algorithm choice may influence reliability and reproducibility of results. Using a real-world data set, the aim of this study was to investigate how different peak-picking algorithms influence NTA results when exploring temporal and/or spatial trends. For this, drinking water catchment monitoring data, using passive samplers collected twice per year across Southeast Queensland, Australia (n = 18 sites) between 2014 and 2019, was investigated. Data were acquired using liquid chromatography coupled to high-resolution mass spectrometry. Peak picking was performed using five different programs/algorithms (SCIEX OS, MSDial, self-adjusting-feature-detection, two algorithms within MarkerView), keeping parameters identical whenever possible. The resulting feature lists revealed low overlap: 7.2% of features were picked by >3 algorithms, while 74% of features were only picked by a single algorithm. Trend evaluation of the data, using principal component analysis, showed significant variability between the approaches, with only one temporal and no spatial trend being identified by all algorithms. Manual evaluation of features of interest (p-value <0.01, log fold change >2) for one sampling site revealed high rates of incorrectly picked peaks (>70%) for three algorithms. Lower rates (<30%) were observed for the other algorithms, but with the caveat of not successfully picking all internal standards used as quality control. The choice is therefore currently between comprehensive and strict peak picking, either resulting in increased noise or missed peaks, respectively. Reproducibility of NTA results remains challenging when applied for regulatory frameworks.

From Molecular Descriptors to Intrinsic Fish Toxicity of Chemicals: An Alternative Approach to Chemical Prioritization

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.

Critical Assessment of the Chemical Space Covered by LC-HRMS Non-Targeted Analysis

Non-targeted analysis (NTA) has emerged as a valuable approach for the comprehensive monitoring of chemicals of emerging concern (CECs) in the exposome. The NTA approach can theoretically identify compounds with diverse physicochemical properties and sources. Even though they are generic and have a wide scope, non-targeted analysis methods have been shown to have limitations in terms of their coverage of the chemical space, as the number of identified chemicals in each sample is very low (e.g., ≤5%). Investigating the chemical space that is covered by each NTA assay is crucial for understanding the limitations and challenges associated with the workflow, from the experimental methods to the data acquisition and data processing techniques. 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 the reported chemicals at confidence levels 1 and 2 were retrieved. The chosen experimental setups and the quality of the reporting were critically evaluated and discussed. Our findings reveal that only around 2% of the estimated chemical space was covered by the NTA studies investigated for this review. Little to no trend was found between the experimental setup and the observed coverage due to the generic and wide scope of the NTA studies. The limited coverage of the chemical space by the reviewed NTA studies highlights the necessity for a more comprehensive approach in the experimental and data processing setups in order to enable the exploration of a broader range of chemical space, with the ultimate goal of protecting human and environmental health. Recommendations for further exploring a wider range of the chemical space are given.

Novel Per- and Polyfluoroalkyl Substances Discovered in Cattle Exposed to AFFF-Impacted Groundwater

The leaching of per- and polyfluoroalkyl substances (PFASs) from Australian firefighting training grounds has resulted in extensive contamination of groundwater and nearby farmlands. Humans, farm animals, and wildlife in these areas may have been exposed to complex mixtures of PFASs from aqueous film-forming foams (AFFFs). This study aimed to identify PFAS classes in pooled whole blood (n = 4) and serum (n = 4) from cattle exposed to AFFF-impacted groundwater and potentially discover new PFASs in blood. Thirty PFASs were identified at various levels of confidence (levels 1a-5a), including three novel compounds: (i) perfluorohexanesulfonamido 2-hydroxypropanoic acid (FHxSA-HOPrA), (ii) methyl((perfluorohexyl)sulfonyl)sulfuramidous acid, and (iii) methyl((perfluorooctyl)sulfonyl)sulfuramidous acid, belonging to two different classes. Biotransformation intermediate, perfluorohexanesulfonamido propanoic acid (FHxSA-PrA), hitherto unreported in biological samples, was detected in both whole blood and serum. Furthermore, perfluoroalkyl sulfonamides, including perfluoropropane sulfonamide (FPrSA), perfluorobutane sulfonamide (FBSA), and perfluorohexane sulfonamide (FHxSA) were predominantly detected in whole blood, suggesting that these accumulate in the cell fraction of blood. The suspect screening revealed several fluoroalkyl chain-substituted PFAS. The results suggest that targeting only the major PFASs in the plasma or serum of AFFF-exposed mammals likely underestimates the toxicological risks associated with exposure. Future studies of AFFF-exposed populations should include whole-blood analysis with high-resolution mass spectrometry to understand the true extent of PFAS exposure.

Cumulative Neutral Loss Model for Fragment Deconvolution in Electrospray Ionization High-Resolution Mass Spectrometry Data

Clean high-resolution mass spectra (HRMS) are essential to a successful structural elucidation of an unknown feature during nontarget analysis (NTA) workflows. This is a crucial step, particularly for the spectra generated during data-independent acquisition or during direct infusion experiments. The most commonly available tools only take advantage of the time domain for spectral cleanup. Here, we present an algorithm that combines the time domain and mass domain information to perform spectral deconvolution. The algorithm employs a probability-based cumulative neutral loss (CNL) model for fragment deconvolution. The optimized model, with a mass tolerance of 0.005 Da and a scoreCNL threshold of 0.00, was able to achieve a true positive rate (TPr) of 95.0%, a false discovery rate (FDr) of 20.6%, and a reduction rate of 35.4%. 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 88.8% with FD rates between 33 and 79% and reduction rates between 9 and 45%. 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 84.7%, an FDr of 54.4%, and a reduction rate of 51.0%.

InSpectra - A platform for identifying emerging chemical threats

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 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.

Instrument-independent chemometric models for rapid, calibration-free NPS isomer differentiation from mass spectral GC-MS data

Chemometric analysis of mass spectral data for the purpose of differentiating positional isomers of novel psychoactive substances has seen a substantial increase in popularity in recent years. However, the process of generating a large and robust dataset for chemometric isomer identification is time consuming and impractical for forensic laboratories. To begin to address this problem, three sets of ortho/meta/para positional ring isomers (fluoroamphetamine (FA), fluoromethamphetamine (FMA), and methylmethcathinone (MMC)) were analyzed using multiple GC-MS instruments at three distinct laboratories. A diverse assortment of instrument manufacturers, model types, and parameters was utilized in order to incorporate substantial instrumental variation. The dataset was randomly split into 70% training and 30% validation sets, stratified by instrument. Following an approach based on Design of Experiments, the validation set was used to optimize the preprocessing steps performed prior to Linear Discriminant Analysis. Using the optimized model, a minimum m/z fragment threshold was determined to allow analysts to assess whether an unknown spectrum is of sufficient abundance and quality to be compared to the model. To assess the robustness of the models, a test set was developed utilizing two instruments from a fourth laboratory that was not involved in the generation of the primary dataset in addition to spectra from widely used mass spectral libraries. Of the spectra that reached the threshold, the classification accuracy was 100% for all three isomer types. Only two of the test and validation spectra that did not reach the threshold were misclassified. The results indicate that forensic illicit drug experts world-wide can use these models for robust NPS isomer identification on the basis of preprocessed mass spectral data without the need for acquiring reference drug standards and creating instrument specific GC-MS reference datasets. The continued robustness of the models could be ensured through international collaboration to collect data that captures all potential GC-MS instrumental variation encountered in forensic illicit drug analysis laboratories. This would allow every forensic institute to confidently assign isomeric structures without the need for additional chemical analysis.

Predicting RP-LC retention indices of structurally unknown chemicals from mass spectrometry data

Non-target analysis combined with liquid chromatography high resolution mass spectrometry is considered one of the most comprehensive strategies for the detection and identification of known and unknown chemicals in complex samples. However, many compounds remain unidentified due to data complexity and limited number structures in chemical databases. In this work, we have developed and validated a novel machine learning algorithm to predict the retention index (r[Formula: see text]) values for structurally (un)known chemicals based on their measured fragmentation pattern. The developed model, for the first time, enabled the predication of r[Formula: see text] values without the need for the exact structure of the chemicals, with an [Formula: see text] of 0.91 and 0.77 and root mean squared error (RMSE) of 47 and 67 r[Formula: see text] units for the NORMAN ([Formula: see text]) and amide ([Formula: see text]) test sets, respectively. This fragment based model showed comparable accuracy in r[Formula: see text] prediction compared to conventional descriptor-based models that rely on known chemical structure, which obtained an [Formula: see text] of 0.85 with an RMSE of 67.

Metabolome-Based Classification of Snake Venoms by Bioinformatic Tools

Snakebite is considered a neglected tropical disease, and it is one of the most intricate ones. The variability found in snake venom is what makes it immensely complex to study. These variations are present both in the big and the small molecules found in snake venom. This study focused on examining the variability found in the venom's small molecules (i.e., mass range of 100-1000 Da) between two main families of venomous snakes-Elapidae and Viperidae-managing to create a model able to classify unknown samples by means of specific features, which can be extracted from their LC-MS data and output in a comprehensive list. The developed model also allowed further insight into the composition of snake venom by highlighting the most relevant metabolites of each group by clustering similarly composed venoms. The model was created by means of support vector machines and used 20 features, which were merged into 10 principal components. All samples from the first and second validation data subsets were correctly classified. Biological hypotheses relevant to the variation regarding the metabolites that were identified are also given.

National Wastewater Reconnaissance of Analgesic Consumption in Australia

A wastewater-based epidemiology (WBE) method is presented to estimate analgesic consumption and assess the burden of treated pain in Australian communities. Wastewater influent samples from 60 communities, representing ∼52% of Australia's population, were analyzed to quantify the concentration of analgesics used to treat pain and converted to estimates of the amount of drug consumed per day per 1000 inhabitants using pharmacokinetics and WBE data. Consumption was standardized to the defined daily dose per day per 1000 people. The population burden of pain treatment was classified as mild to moderate pain (for non-opioid analgesics) and strong to severe pain (for opioid analgesics). The mean per capita weighted total DDD of non-opioid analgesics was 0.029 DDD/day/person, and that of opioid-based analgesics was 0.037 DDD/day/person across Australia. A greater burden of pain (mild to moderate or strong to severe pain index) was observed at regional and remote sites. The correlation analysis of pain indices with different socioeconomic descriptors revealed that pain affects populations from high to low socioeconomic groups. Australians spent an estimated US $3.5 (AU $5) per day on analgesics. Our findings suggest that WBE could be an effective surveillance tool for estimating the consumption of analgesics at a population scale and assessing the total treated pain burden in communities.

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.

Socioeconomic status and public health in Australia: A wastewater-based study

Analysis of untreated municipal wastewater is recognized as an innovative approach to assess population exposure to or consumption of various substances. Currently, there are no published wastewater-based studies investigating the relationships between catchment social, demographic, and economic characteristics with chemicals using advanced non-targeted techniques. In this study, fifteen wastewater samples covering 27% of the Australian population were collected during a population Census. The samples were analysed with a workflow employing liquid chromatography high-resolution mass spectrometry and chemometric tools for non-target analysis. Socioeconomic characteristics of catchment areas were generated using Geospatial Information Systems software. Potential correlations were explored between pseudo-mass loads of the identified compounds and socioeconomic and demographic descriptors of the wastewater catchments derived from Census data. Markers of public health (e.g., cardiac arrhythmia, cardiovascular disease, anxiety disorder and type 2 diabetes) were identified in the wastewater samples by the proposed workflow. They were positively correlated with descriptors of disadvantage in education, occupation, marital status and income, and negatively correlated with descriptors of advantage in education and occupation. In addition, markers of polypropylene glycol (PPG) and polyethylene glycol (PEG) related compounds were positively correlated with housing and occupation disadvantage. High positive correlations were found between separated and divorced people and specific drugs used to treat cardiac arrhythmia, cardiovascular disease, and depression. Our robust non-targeted methodology in combination with Census data can identify relationships between biomarkers of public health, human behaviour and lifestyle and socio-demographics of whole populations. Furthermore, it can identify specific areas and socioeconomic groups that may need more assistance than others for public health issues. This approach complements important public health information and enables large-scale national coverage with a relatively small number of samples.

Automated Feature Mining for Two-Dimensional Liquid Chromatography Applied to Polymers Enabled by Mass Remainder Analysis

A fast algorithm for automated feature mining of synthetic (industrial) homopolymers or perfectly alternating copolymers was developed. Comprehensive two-dimensional liquid chromatography–mass spectrometry data (LC × LC–MS) was utilized, undergoing four distinct parts within the algorithm. Initially, the data is reduced by selecting regions of interest within the data. Then, all regions of interest are clustered on the time and mass-to-charge domain to obtain isotopic distributions. Afterward, single-value clusters and background signals are removed from the data structure. In the second part of the algorithm, the isotopic distributions are employed to define the charge state of the polymeric units and the charge-state reduced masses of the units are calculated. In the third part, the mass of the repeating unit (i.e., the monomer) is automatically selected by comparing all mass differences within the data structure. Using the mass of the repeating unit, mass remainder analysis can be performed on the data. This results in groups sharing the same end-group compositions. Lastly, combining information from the clustering step in the first part and the mass remainder analysis results in the creation of compositional series, which are mapped on the chromatogram. Series with similar chromatographic behavior are separated in the mass-remainder domain, whereas series with an overlapping mass remainder are separated in the chromatographic domain. These series were extracted within a calculation time of 3 min. The false positives were then assessed within a reasonable time. The algorithm is verified with LC × LC–MS data of an industrial hexahydrophthalic anhydride-derivatized propylene glycol-terephthalic acid copolyester. Afterward, a chemical structure proposal has been made for each compositional series found within the data.

Utilization of Machine Learning for the Differentiation of Positional NPS Isomers with Direct Analysis in Real Time Mass Spectrometry

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.

A novel method for the quantification of tire and polymer-modified bitumen particles in environmental samples by pyrolysis gas chromatography mass spectroscopy

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).

In-Sewer Stability Assessment of Anabolic Steroids and Selective Androgen Receptor Modulators

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.

From Centroided to Profile Mode: Machine Learning for Prediction of Peak Width in HRMS Data

Centroiding is one of the major approaches used for size reduction of the data generated by high-resolution mass spectrometry. During centroiding, performed either during acquisition or as a pre-processing step, the mass profiles are represented by a single value (i.e., the centroid). While being effective in reducing the data size, centroiding also reduces the level of information density present in the mass peak profile. Moreover, each step of the centroiding process and their consequences on the final results may not be completely clear. Here, we present Cent2Prof, a package containing two algorithms that enables the conversion of the centroided data to mass peak profile data and vice versa. The centroiding algorithm uses the resolution-based mass peak width parameter as the first guess and self-adjusts to fit the data. In addition to the m/z values, the centroiding algorithm also generates the measured mass peak widths at half-height, which can be used during the feature detection and identification. The mass peak profile prediction algorithm employs a random-forest model for the prediction of mass peak widths, which is consequently used for mass profile reconstruction. The centroiding results were compared to the outputs of the MZmine-implemented centroiding algorithm. Our algorithm resulted in rates of false detection ≤5% while the MZmine algorithm resulted in 30% rate of false positive and 3% rate of false negative. The error in profile prediction was ≤56% independent of the mass, ionization mode, and intensity, which was 6 times more accurate than the resolution-based estimated values.

In-sewer stability of selected analgesics and their metabolites

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.

Plastics contamination of store-bought rice

This study investigated mass concentrations of selected plastics in store-bought rice, the staple of more than half the world's population. Polyethylene, polyethylene terephthalate, poly-(methyl methacrylate), polypropylene, polystyrene and polyvinyl chloride were quantified using pressurized liquid extraction coupled to double-shot pyrolysis gas chromatography/mass spectrometry. Polyethylene, polypropylene and polyethylene terephthalate were quantifiable in the rice samples with polyethylene the most frequently detected (95%). There was no statistical difference between total plastic concentration in paper and plastic packaged rice. Shaking the rice in its packaging had no significant difference on the concentration of plastics. Washing the rice with water significantly reduced plastic contamination. Instant (pre-cooked) rice contained fourfold higher levels of plastics, suggesting that industrial processing potentially increases contamination. A preliminary estimate of the intake of plastic through rice consumption for Australians established 3.7 mg per serve (100 g) if not washed and 2.8 mg if washed. Annual consumption was estimated around 1 g/person.

Plastics in biosolids from 1950 to 2016: A function of global plastic production and consumption

Plastics are ubiquitous contaminants that leak into the environment from multiple pathways including the use of treated sewage sludge (biosolids). Seven common plastics (polymers) were quantified in the solid fraction of archived biosolids samples from Australia and the United Kingdom from between 1950 and 2016. Six plastics were detected, with increasing concentrations observed over time for each plastic. Biosolids plastic concentrations correlated with plastic production estimates, implying a potential link between plastics production, consumption and leakage into the environment. Prior to the 1990s, the leakage of plastics into biosolids was limited except for polystyrene. Increased leakage was observed from the 1990s onwards; potentially driven by increased consumption of polyethylene, polyethylene terephthalate and polyvinyl chloride. We show that looking back in time along specific plastic pollution pathways may help unravel the potential sources of plastics leakage into the environment and provide quantitative evidence to support the development of source control interventions or regulations.

Airborne emissions of microplastic fibres from domestic laundry dryers

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.

Quantitative Analysis of Selected Plastics in High-Commercial-Value Australian Seafood by Pyrolysis Gas Chromatography Mass Spectrometry

Microplastic contamination of the marine environment is widespread, but the extent to which the marine food web is contaminated is not yet known. The aims of this study were to go beyond visual identification techniques and develop and apply a simple seafood sample cleanup, extraction, and quantitative analysis method using pyrolysis gas chromatography mass spectrometry to improve the detection of plastic contamination. This method allows the identification and quantification of polystyrene, polyethylene, polyvinyl chloride, polypropylene, and poly(methyl methacrylate) in the edible portion of five different seafood organisms: oysters, prawns, squid, crabs, and sardines. Polyvinyl chloride was detected in all samples and polyethylene at the highest total concentration of between 0.04 and 2.4 mg g^-1 of tissue. Sardines contained the highest total plastic mass concentration (0.3 mg g^-1 tissue) and squid the lowest (0.04 mg g^-1 tissue). Our findings show that the total concentration of plastics is highly variable among species and that microplastic concentration differs between organisms of the same species. The sources of microplastic exposure, such as packaging and handling with consequent transference and adherence to the tissues, are discussed. This method is a major development in the standardization of plastic quantification techniques used in seafood.

Identification and quantification of selected plastics in biosolids by pressurized liquid extraction combined with double-shot pyrolysis gas chromatography-mass spectrometry

The identification and quantification of selected plastics (polystyrene (PS), polycarbonate (PC), poly-(methyl methacrylate) (PMMA), polypropylene (PP), polyethylene terephthalate (PET), polyethylene (PE) and polyvinyl chloride (PVC)) in biosolids (treated sewage sludge) was performed by pressurized liquid extraction (PLE) combined with double-shot pyrolysis gas chromatography-mass spectrometry. Validation of the method yielded recoveries of between 85 and 128% (mean RSD 11%) at a linear range of between 0.01 and 2 μg. The distribution of plastics within 25 biosolid samples from a single wastewater treatment plant in Australia was assessed. The mass concentration of PE, PVC, PP, PS and PMMA was between 0.1 and 4.1 mg/g dry weight (dw) across all samples, with a total plastic concentration Ʃ_Plastics of between 2.8 and 6.6 mg/g dw (median = 4.1 mg/g dw). PE was the predominant plastic detected (mean concentration of 2.2 mg/g dw), contributing to 50% of the total of all plastics. Overall, this study demonstrates that pressurized liquid extraction (PLE) combined with double-shot pyrolysis gas chromatography-mass spectrometry can be used to identify and quantify PE, PP, PVC, PS, and PMMA in biosolids.

Wastewater-based estimation of the prevalence of gout in Australia

Allopurinol, a first-line gout treatment drug in Australia, was assessed as a wastewater-based epidemiology biomarker of gout via quantification of the urinary metabolite, oxypurinol in wastewater. The in-sewer stability of oxypurinol was examined using laboratory-scale sewer reactors. Wastewater from 75 wastewater treatment plants across Australia, covering approximately 52% (12.2 million) of the country's population, was collected on the 2016 census day. Oxypurinol was quantified in the wastewater samples and population-weighted mass loads calculated. Pearson and Spearman rank-order correlations were applied to investigate any link between allopurinol, other selected wastewater biomarkers, and socio-economic indicators. Oxypurinol was shown to be stable in sewer conditions and suitable as a WBE biomarker. Oxypurinol was detected in all wastewater samples. The estimated consumption of allopurinol ranged from 1.9 to 32 g/day/1000 people equating to 4.8 to 80 DDD/day/1000 people. The prevalence of gout across all tested sewer catchments was between 0.5% to 8%, with a median of 2.9% nationally. No significant positive correlation was observed between allopurinol consumption and alcohol consumption, mean age of catchment population, remoteness or higher socioeconomic status. There was a significant positive correlation with selective analgesic drug use. Wastewater analysis can be used to study gout prevalence and can provide additional insights on population level risk factors when triangulated with other biomarkers.

Concentration and Distribution of Naphthenic Acids in the Produced Water from Offshore Norwegian North Sea Oilfields

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.

Social, demographic, and economic correlates of food and chemical consumption measured by wastewater-based epidemiology

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.

Integrated chemical exposure assessment of coastal green turtle foraging grounds on the Great Barrier Reef

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.

The effect of extraction methodology on the recovery and distribution of naphthenic acids of oilfield produced water

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.

Exploring the Potential of a Global Emerging Contaminant Early Warning Network through the Use of Retrospective Suspect Screening with High-Resolution Mass Spectrometry

A key challenge in the environmental and exposure sciences is to establish experimental evidence of the role of chemical exposure in human and environmental systems. High resolution and accurate tandem mass spectrometry (HRMS) is increasingly being used for the analysis of environmental samples. One lauded benefit of HRMS is the possibility to retrospectively process data for (previously omitted) compounds that has led to the archiving of HRMS data. Archived HRMS data affords the possibility of exploiting historical data to rapidly and effectively establish the temporal and spatial occurrence of newly identified contaminants through retrospective suspect screening. We propose to establish a global emerging contaminant early warning network to rapidly assess the spatial and temporal distribution of contaminants of emerging concern in environmental samples through performing retrospective analysis on HRMS data. The effectiveness of such a network is demonstrated through a pilot study, where eight reference laboratories with available archived HRMS data retrospectively screened data acquired from aqueous environmental samples collected in 14 countries on 3 different continents. The widespread spatial occurrence of several surfactants (e.g., polyethylene glycols ( PEGs ) and C12AEO-PEGs ), transformation products of selected drugs (e.g., gabapentin-lactam, metoprolol-acid, carbamazepine-10-hydroxy, omeprazole-4-hydroxy-sulfide, and 2-benzothiazole-sulfonic-acid), and industrial chemicals (3-nitrobenzenesulfonate and bisphenol-S) was revealed. Obtaining identifications of increased reliability through retrospective suspect screening is challenging, and recommendations for dealing with issues such as broad chromatographic peaks, data acquisition, and sensitivity are provided.

Combining a Deconvolution and a Universal Library Search Algorithm for the Nontarget Analysis of Data-Independent Acquisition Mode Liquid Chromatography-High-Resolution Mass Spectrometry Results

Nontarget analysis is considered one of the most comprehensive tools for the identification of unknown compounds in a complex sample analyzed via liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS). Due to the complexity of the data generated via LC-HRMS, the data-dependent acquisition mode, which produces the MS² spectra of a limited number of the precursor ions, has been one of the most common approaches used during nontarget screening. However, data-independent acquisition mode produces highly complex spectra that require proper deconvolution and library search algorithms. We have developed a deconvolution algorithm and a universal library search algorithm (ULSA) for the analysis of complex spectra generated via data-independent acquisition. These algorithms were validated and tested using both semisynthetic and real environmental data. A total of 6000 randomly selected spectra from MassBank were introduced across the total ion chromatograms of 15 sludge extracts at three levels of background complexity for the validation of the algorithms via semisynthetic data. The deconvolution algorithm successfully extracted more than 60% of the added ions in the analytical signal for 95% of processed spectra (i.e., 3 complexity levels multiplied by 6000 spectra). The ULSA ranked the correct spectra among the top three for more than 95% of cases. We further tested the algorithms with 5 wastewater effluent extracts for 59 artificial unknown analytes (i.e., their presence or absence was confirmed via target analysis). These algorithms did not produce any cases of false identifications while correctly identifying ∼70% of the total inquiries. The implications, capabilities, and the limitations of both algorithms are further discussed.

Elevated Concentrations of 4-Bromobiphenyl and 1,3,5-Tribromobenzene Found in Deep Water of Lake Geneva Based on GC×GC-ENCI-TOFMS and GC×GC-μECD

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.

GC×GC Quantification of Priority and Emerging Nonpolar Halogenated Micropollutants in All Types of Wastewater Matrices: Analysis Methodology, Chemical Occurrence, and Partitioning

We report the development and validation of a method to detect and quantify diverse nonpolar halogenated micropollutants in wastewater treatment plant (WWTP) influent, effluent, primary sludge, and secondary sludge matrices (including both the liquid and particle phases) by comprehensive two-dimensional gas chromatography (GC×GC) coupled to micro- electron capture detector (μECD). The 59 target analytes included toxaphenes, polychlorinated naphthalenes, organochlorine pesticides, polychlorinated biphenyls, polybrominated diphenyl ethers, and emerging persistent and bioaccumulative chemicals. The method is robust for a wide range of nonpolar halogenated micropollutants in all matrices. For most analytes, recoveries fell between 70% and 130% in all matrix types. GC×GC-μECD detections of several target analytes were confirmed qualitatively by further analysis with GC×GC coupled to electron capture negative chemical ionization-time-of-flight mass spectrometry (ENCI-TOFMS). We then quantified the concentrations and apparent organic solid-water partition coefficients (Kp) of target micropollutants in samples from a municipal WWTP in Switzerland. Several analyzed pollutants exhibited a high frequency of occurrence in WWTP stream samples, including octachloronaphthalene, PCB-44, PCB-52, PCB-153, PCB-180, several organochlorine pesticides, PBDE-10, PBDE-28, PBDE-116, musk tibetene, and pentachloronitrobenzene. Our results suggest that sorption to dissolved organic carbon (DOC) can contribute substantially to the apparent solids-liquid distribution of hydrophobic micropollutants in WWTP streams.

Structure investigations of binary azeotrope of diethyl amine-acetone by FT-IR and (1)H NMR spectroscopy

The article shows results of FT-IR and (1)H NMR study for azeotrope diethyl amine-acetone. Changes in chemical shifts and vibrational frequencies for pure diethyl amine, pure acetone and their azeotrope were obtained. The unit-structure of cluster has been suggested on the basis of mole ratio, boiling point depression for azeotrope of diethyl amine and acetone, and comparison between the spectra obtained by FT-IR and (1)H NMR techniques.