International Comparison, Risk Assessment, and Prioritisation of 26 Endocrine Disrupting Compounds in Three European River Catchments in the UK, Ireland, and Spain.

Endocrine-disrupting compounds (EDCs) constitute a wide variety of chemistries with diverse properties that may/can pose risks to both humans and the environment. Herein, a total of 26 compounds, including steroids, flame retardants, and plasticizers, were monitored in three major and heavily urbanized river catchments: the R. Liffey (Ireland), the R. Thames (UK), and the R. Ter (Spain), by using a single solid-phase extraction liquid chromatography-mass spectrometry (SPE-LC-MS/MS) method. Occurrence and frequency rates were investigated across all locations over a 10-week period, with the highest concentration obtained for the flame retardant tris(2-chloroethyl) phosphate (TCEP) at 4767 ng∙L-1 in the R. Thames in Central London. Geographical variations were observed between sites and were partially explained using principal component analysis (PCA) and hierarchical cluster analysis (HCA). In particular, discrimination between the R. Ter and the R. Thames was observed based on the presence and concentration of flame retardants, benzotriazole, and steroids. Environmental risk assessment (ERA) across sites showed that caffeine, a chemical marker, and bisphenol A (BPA), a plasticizer, were classified as high-risk for the R. Liffey and R. Thames, based on relative risk quotients (rRQs), and that caffeine was classified as high-risk for the R. Ter, based on RQs. The total risks at each location, namely ΣRQriver, and ΣrRQriver, were: 361, 455, and 723 for the rivers Liffey, Thames, and Ter, respectively. Caffeine, as expected, was ubiquitous in all 3 urban areas, though with the highest RQ observed in the R. Ter. High contributions of BPA were also observed across the three matrices. Therefore, these two compounds should be prioritized independently of location. This study represents a comprehensive EDC monitoring comparison between different European cities based on a single analytical method, which allowed for a geographically independent ERA prioritization to be performed.

Quantitative Determination and Environmental Risk Assessment of 102 Chemicals of Emerging Concern in Wastewater-Impacted Rivers Using Rapid Direct-Injection Liquid Chromatography-Tandem Mass Spectrometry.

The rapid source identification and environmental risk assessment (ERA) of hundreds of chemicals of emerging concern (CECs) in river water represent a significant analytical challenge. Herein, a potential solution involving a rapid direct-injection liquid chromatography-tandem mass spectrometry method for the quantitative determination of 102 CECs (151 qualitatively) in river water is presented and applied across six rivers in Germany and Switzerland at high spatial resolution. The method required an injection volume of only 10 µL of filtered sample, with a runtime of 5.5 min including re-equilibration with >10 datapoints per peak per transition (mostly 2 per compound), and 36 stable isotope-labelled standards. Performance was excellent from the low ng/L to µg/L concentration level, with 260 injections possible in any 24 h period. The method was applied in three separate campaigns focusing on the ERA of rivers impacted by wastewater effluent discharges (1 urban area in the Basel city region with 4 rivers, as well as 1 semi-rural and 1 rural area, each focusing on 1 river). Between 25 and 40 compounds were quantified directly in each campaign, and in all cases small tributary rivers showed higher CEC concentrations (e.g., up to ~4000 ng/L in total in the R. Schwarzach, Bavaria, Germany). The source of selected CECs could also be identified and differentiated from other sources at pre- and post- wastewater treatment plant effluent discharge points, as well as the effect of dilution downstream, which occurred over very short distances in all cases. Lastly, ERA for 41 CECs was performed at specific impacted sites, with risk quotients (RQs) at 1 or more sites estimated as high risk (RQ > 10) for 1 pharmaceutical (diclofenac), medium risk (RQ of 1-10) for 3 CECs (carbamazepine, venlafaxine, and sulfamethoxazole), and low risk (RQ = 0.1-1.0) for 7 CECs (i.e., RQ > 0.1 for 11 CECs in total). The application of high-throughput methods like this could enable a better understanding of the risks of CECs, especially in low flow/volume tributary rivers at scale and with high resolution.

Quantitative profile-profile relationship (QPPR) modelling: a novel machine learning approach to predict and associate chemical characteristics of unspent ammunition from gunshot residue (GSR).

Evidence association in forensic cases involving gunshot residue (GSR) remains very challenging. Herein, a new in silico approach, called quantitative profile-profile relationship (QPPR) modelling, is reported. This is based on the application of modern machine learning techniques to predict the pre-discharge chemical profiles of selected ammunition components from those of the respective post-discharge GSR. The obtained profiles can then be compared with one another and/or with other measured profiles to make evidential links during forensic investigations. In particular, the approach was optimised and successfully tested for the prediction of GC-MS profiles of smokeless powders (SLPs) from organic GSR in spent cases, for nine ammunition types. Results showed a high degree of similarity between predicted and experimentally measured profiles, after adequate combination and evaluation of fourteen machine learning techniques (median correlation of 0.982). Areas under the curve (AUCs) of 0.976 and 0.824 were observed after receiver operating characteristic (ROC) analysis of the results obtained in the comparisons between predicted-predicted and predicted-measured profiles, respectively, in the specific case that the ammunition types of interest were excluded from the training dataset (i.e., extrapolation). Furthermore, AUCs of 0.962 and 0.894 were observed in interpolation mode. These values were close to those of the comparison of the measured SLP profiles between themselves (AUC = 0.998), demonstrating excellent potential to correctly associate evidence in a number of different forensic scenarios. This work represents the first time that a quantitative approach has successfully been applied to associate a GSR to a specific ammunition.

The First Attempt at Non-Linear in Silico Prediction of Sampling Rates for Polar Organic Chemical Integrative Samplers (POCIS).

Modeling and prediction of polar organic chemical integrative sampler (POCIS) sampling rates (Rs) for 73 compounds using artificial neural networks (ANNs) is presented for the first time. Two models were constructed: the first was developed ab initio using a genetic algorithm (GSD-model) to shortlist 24 descriptors covering constitutional, topological, geometrical and physicochemical properties and the second model was adapted for Rs prediction from a previous chromatographic retention model (RTD-model). Mechanistic evaluation of descriptors showed that models did not require comprehensive a priori information to predict Rs. Average predicted errors for the verification and blind test sets were 0.03 ± 0.02 L d(-1) (RTD-model) and 0.03 ± 0.03 L d(-1) (GSD-model) relative to experimentally determined Rs. Prediction variability in replicated models was the same or less than for measured Rs. Networks were externally validated using a measured Rs data set of six benzodiazepines. The RTD-model performed best in comparison to the GSD-model for these compounds (average absolute errors of 0.0145 ± 0.008 L d(-1) and 0.0437 ± 0.02 L d(-1), respectively). Improvements to generalizability of modeling approaches will be reliant on the need for standardized guidelines for Rs measurement. The use of in silico tools for Rs determination represents a more economical approach than laboratory calibrations.

The retrieval of fingerprint friction ridge detail from elephant ivory using reduced-scale magnetic and non-magnetic powdering materials.

An evaluation of reduced-size particle powdering methods for the recovery of usable fingermark ridge detail from elephant ivory is presented herein for the first time as a practical and cost-effective tool in forensic analysis. Of two reduced-size powder material types tested, powders with particle sizes ≤ 40 µm offered better chances of recovering ridge detail from unpolished ivory in comparison to a conventional powder material. The quality of developed ridge detail of these powders was also assessed for comparison and automated search suitability. Powder materials and the enhanced ridge detail on ivory were analysed by scanning electron microscopy and energy dispersive X-ray spectroscopy and interactions between their constituents and the ivory discussed. The effect of ageing on the quality of ridge detail recovered showed that the best quality was obtained within 1 week. However, some ridge detail could still be developed up to 28 days after deposition. Cyanoacrylate and fluorescently-labelled cyanoacrylate fuming of ridge detail on ivory was explored and was less effective than reduced-scale powdering in general. This research contributes to the understanding and potential application of smaller scale powdering materials for the development of ridge detail on hard, semi-porous biological material typically seized in wildlife-related crimes.

Environmental monitoring of urban streams using a primary fish gill cell culture system (FIGCS).

The primary fish gill cell culture system (FIGCS) is an in vitro technique which has the potential to replace animals in whole effluent toxicity tests. In the current study FIGCS were transported into the field and exposed to filtered (0.2µm) river water for 24h from 4 sites, on 2 different sampling dates. Sites 1 and 2 are situated in an urban catchment (River Wandle, London, UK) with site 1 downstream of a sewage treatment work; site 3 is located in a suburban park (River Cray, Kent, UK), and site 4 is more rural (River Darent, Kent, UK). The change in transepithelial electrical resistance (TER), the expression of the metal responsive genes metallothionein A (mta) and B (mtb), cytochrome P450 1A1 (cyp1a1) and 3A27 (cyp3a27), involved in phase 1 metabolism, were assessed following exposure to sample water for 24h. TER was comparable between FIGCS exposed to 0.2µm filtered river water and those exposed to synthetic moderately soft water for 24h. During the first sampling time, there was an increase in mta, cyp1a1 and cyp3a27 gene expression in epithelium exposed to water from sites 1 and 2, and during the second sampling period an increase in cyp3a27 gene expression at sites 1 and 4. Urban river water is a complex mixture of contaminants (e.g., metals, pesticides, pharmaceuticals and polyaromatic hydrocarbons) and the increase in the expression of genes encoding mta, cyp1a1 and cyp3a27 in FIGCS is indicative of the presence of biologically active pollutants.

Apportioning sources of chemicals of emerging concern along an urban river with inverse modelling.

Concentrations of chemicals in river water provide crucial information for assessing environmental exposure and risks from fertilisers, pesticides, heavy metals, illicit drugs, pathogens, pharmaceuticals, plastics and perfluorinated substances, among others. However, using concentrations measured along waterways (e.g., from grab samples) to identify sources of contaminants and understand their fate is complicated by mixing of chemicals downstream from diverse diffuse and point sources (e.g., agricultural runoff, wastewater treatment plants). To address this challenge, a novel inverse modelling approach is presented. Using waterway network topology, it quantifies locations and concentrations of contaminant sources upstream by inverting concentrations measured in water samples. It is computationally efficient and quantifies uncertainty. The approach is demonstrated for 13 contaminants of emerging concern (CECs) in an urban stream, the R. Wandle (London, UK). Mixing (the forward problem) was assumed to be conservative, and the location of sources and their concentrations were treated as unknowns to be identified. Calculated CEC source concentrations, which ranged from below detection limit (a few ng/L) up to 1µg/L, were used to predict concentrations of chemicals downstream. Using this approach, >90% of data were predicted within observational uncertainty. Principal component analysis of calculated source concentrations revealed signatures of two distinct chemical sources. First, pharmaceuticals and insecticides were associated with a subcatchment containing a known point source of treated effluent from a wastewater treatment plant. Second, illicit drugs and salicylic acid were associated with multiple sources, interpreted as input from untreated sewage including Combined Sewer Overflows (CSOs), misconnections, runoff and direct disposal throughout the catchment. Finally, a simple algorithmic approach that incorporates network topology was developed to design sampling campaigns to improve resolution of source apportionment. Inverse modelling of contaminant measurements can provide objective means to apportion sources in waterways from spot samples in catchments on a large scale.

Prediction of chromatographic retention time in high-resolution anti-doping screening data using artificial neural networks.

The computational generation of gradient retention time data for retrospective detection of suspected sports doping species in postanalysis human urine sample data is presented herein. Retention data for a selection of 86 compounds included in the London 2012 Olympic and Paralympic Games drug testing schedule were used to train, verify, and test a range of computational models for this purpose. Spiked urine samples were analyzed using solid phase extraction followed by ultrahigh-pressure gradient liquid chromatography coupled to electrospray ionization high-resolution mass spectrometry. Most analyte retention times varied ≤0.2 min over the relatively short runtime of 10 min. Predicted retention times were within 0.5 min of experimental values for 12 out of 15 blind test compounds (largest error: 0.97 min). Minimizing the variance in predictive ability across replicate networks of identical architecture is presented for the first time along with a quantitative discussion of the contribution of each selected molecular descriptor toward the overall predicted value. The performance of neural computing predictions for isobaric compound retention time is also discussed. This work presents the application of neural networks to the prediction of gradient retention time in archived high-resolution urine analysis sample data for the first time in the field of anti-doping.

A year-long study of the occurrence and risk of over 140 contaminants of emerging concern in wastewater influent, effluent and receiving waters in the Republic of Ireland.

Despite being a developed country in the European Union (EU), knowledge of the nature and extent of contamination of water bodies with contaminants of emerging concern (CECs) in Ireland is limited. In this study, >140 CECs including pharmaceuticals, pesticides and personal care products were monitored in monthly samples of wastewater treatment plant (WWTP) influent, effluent and receiving surface waters at both an urban and a rural location (72 samples in total) in Ireland over a 12-month period in 2018-2019. In total, 58 CECs were detected, including several EU Water Framework Directive Watch List compounds. Of all classes, the highest concentrations were measured for pharmaceuticals across all media, i.e., propranolol in surface waters (134 ng·L-1), hydrochlorothiazide in effluent (1067 ng·L-1) and venlafaxine in influent wastewater (8273 ng·L-1). Overall, high wastewater treatment removal was observed and a further reduction in CEC occurrence and concentration was measured via dilution in the receiving river environment. Lastly, an environmental risk assessment (ERA) was performed using risk quotients (RQ), which revealed that in surface waters, total RQ for all CECs was an order of magnitude lower than in effluents. The majority of CECs in surface waters posed a lower risk except E2 and EE2 which presented a medium risk (RQs of 3.5 and 1.1, respectively) in the rural area. This work represents the most comprehensive CEC monitoring dataset to date for Ireland which allowed for an ERA prioritisation to be performed for the first time.

A One-Health environmental risk assessment of contaminants of emerging concern in London's waterways throughout the SARS-CoV-2 pandemic.

The SARS-CoV-2 pandemic had huge impacts on global urban populations, activity and health, yet little is known about attendant consequences for urban river ecosystems. We detected significant changes in occurrence and risks from contaminants of emerging concern (CECs) in waterways across Greater London (UK) during the pandemic. We were able to rapidly identify and monitor large numbers of CECs in n = 390 samples across 2019-2021 using novel direct-injection liquid chromatography-mass spectrometry methods for scalable targeted analysis, suspect screening and prioritisation of CEC risks. A total of 10,029 measured environmental concentrations (MECs) were obtained for 66 unique CECs. Pharmaceutical MECs decreased during lockdown in 2020 in the R. Thames (p ≤ 0.001), but then increased significantly in 2021 (p ≤ 0.01). For the tributary rivers, the R. Lee, Beverley Brook, R. Wandle and R. Hogsmill were the most impacted, primarily via wastewater treatment plant effluent and combined sewer overflows. In the R. Hogsmill in particular, pharmaceutical MEC trends were generally correlated with NHS prescription statistics, likely reflecting limited wastewater dilution. Suspect screening of âˆ¼ 1,200 compounds tentatively identified 25 additional CECs at the five most impacted sites, including metabolites such as O-desmethylvenlafaxine, an EU Watch List compound. Lastly, risk quotients (RQs) ≥ 0.1 were calculated for 21 compounds across the whole Greater London freshwater catchment, of which seven were of medium risk (RQ ≥ 1.0) and three were in the high-risk category (RQ ≥ 10), including imidacloprid (RQ = 19.6), azithromycin (15.7) and diclofenac (10.5). This is the largest spatiotemporal dataset of its kind for any major capital city globally and the first for Greater London, representing âˆ¼ 16 % of the population of England, and delivering a foundational One-Health case study in the third largest city in Europe across a global pandemic.

Enhanced selectivity for acidic contaminants in drinking water: From suspect screening to toxicity prediction.

A novel analytical workflow for suspect screening of organic acidic contaminants in drinking water is presented, featuring selective extraction by silica-based strong anion-exchange solid-phase extraction, mixed-mode liquid chromatography-high resolution accurate mass spectrometry (LC-HRMS), peak detection, feature reduction and compound identification. The novel use of an ammonium bicarbonate-based elution solvent extended strong anion-exchange solid-phase extraction applicability to LC-HRMS of strong acids. This approach performed with consistently higher recovery and repeatability (88 ± 7 % at 500 ng L-1), improved selectivity and lower matrix interference (mean = 12 %) over a generic mixed-mode weak anion exchange SPE method. In addition, a novel filter for reducing full-scan features from fulvic and humic acids was successfully introduced, reducing workload and potential for false positives. The workflow was then applied to 10 London municipal drinking water samples, revealing the presence of 22 confirmed and 37 tentatively identified substances. Several poorly investigated and potentially harmful compounds were found which included halogenated hydroxy-cyclopentene-diones and dibromomethanesulfonic acid. Some of these compounds have been reported as mutagenic in test systems and thus their presence here requires further investigation. Overall, this approach demonstrated that employing selective extraction improved detection and helped shortlist suspects and potentially toxic chemical contaminants with higher confidence.

Occurrence of Z-drugs, benzodiazepines, and ketamine in wastewater in the United States and Mexico during the Covid-19 pandemic.

Z-drugs, benzodiazepines and ketamine are classes of psychotropic drugs prescribed for treating anxiety, sleep disorders and depression with known side effects including an elevated risk of addiction and substance misuse. These drugs have a strong potential for misuse, which has escalated over the years and was hypothesized here to have been exacerbated during the COVID-19 pandemic. Wastewater-based epidemiology (WBE) constitutes a fast, easy, and relatively inexpensive approach to epidemiological surveys for understanding the incidence and frequency of uses of these drugs. In this study, we analyzed wastewater (n = 376) from 50 cities across the United States and Mexico from July to October 2020 to estimate drug use rates during a pandemic event. Both time and flow proportional composite and grab samples of untreated municipal wastewater were analyzed using solid-phase extraction followed by liquid chromatography-tandem mass spectrometry to determine loadings of alprazolam, clonazepam, diazepam, ketamine, lorazepam, nordiazepam, temazepam, zolpidem, and zaleplon in raw wastewater. Simultaneously, prescription data of the aforementioned drugs were extracted from the Medicaid database from 2019 to 2021. Results showed high detection frequencies of ketamine (90 %), lorazepam (87 %), clonazepam (76 %) and temazepam (73 %) across both Mexico and United States and comparatively lower detection frequencies for zaleplon (22 %), zolpidem (9 %), nordiazepam (<1 %), diazepam (<1 %), and alprazolam (<1 %) during the pandemic. Average mass consumption rates, estimated using WBE and reported in units of mg/day/1000 persons, ranged between 62 (temazepam) and 1100 (clonazepam) in the United States. Results obtained from the Medicaid database also showed a significant change (p < 0.05) in the prescription volume between the first quarter of 2019 (before the pandemic) and the first quarter of 2021 (pandemic event) for alprazolam, clonazepam and lorazepam. Study results include the first detections of zaleplon and zolpidem in wastewater from North America.

A miniaturized passive sampling-based workflow for monitoring chemicals of emerging concern in water.

The miniaturization of a full workflow for identification and monitoring of contaminants of emerging concern (CECs) is presented. Firstly, successful development of a low-cost small 3D-printed passive sampler device (3D-PSD), based on a two-piece methacrylate housing that held up to five separate 9 mm disk sorbents, is discussed. Secondly, a highly sensitive liquid chromatography-tandem mass spectrometry (LC-MS/MS) method reduced the need for large scale in-laboratory apparatus, solvent, reagents and reference material quantities for in-laboratory passive sampler device (PSD) calibration and extraction. Using hydrophilic-lipophilic balanced sorbents, sampling rates (Rs) were determined after a low 50 ng L-1 exposure over seven days for 39 pesticides, pharmaceuticals, drug metabolites and illicit drugs over the range 0.3 to 12.3 mL day-1. The high sensitivity LC-MS/MS method enabled rapid analysis of river water using only 10 µL of directly injected sample filtrate to measure occurrence of 164 CECs and sources along 19 sites on the River Wandle, (London, UK). The new 3D-PSD was then field-tested over seven days at the site with the highest number and concentration of CECs, which was down-river from a wastewater treatment plant. Almost double the number of CECs were identified in 3D-PSD extracts across sites in comparison to water samples (80 versus 42 CECs, respectively). Time-weighted average CEC concentrations ranged from 8.2 to 845 ng L-1, which were generally comparable to measured concentrations in grab samples. Lastly, high resolution mass spectrometry-based suspect screening of 3D-PSD extracts enabled 113 additional compounds to be tentatively identified via library matching, many of which are currently or are under consideration for the EU Watch List. This miniaturized workflow represents a new, cost-effective, and more practically efficient means to perform passive sampling chemical monitoring at a large scale. SYNOPSIS: Miniaturized, low cost, multi-disk passive samplers enabled more efficient multi-residue chemical contaminant characterization, potentially for large-scale monitoring programs.

Multicompartment and cross-species monitoring of contaminants of emerging concern in an estuarine habitat.

The fate of many chemicals in the environment, particularly contaminants of emerging concern (CEC), have been characterised to a limited extent with a major focus on occurrence in water. This study presents the characterisation, distribution and fate of multiple chemicals including pharmaceuticals, recreational drugs and pesticides in surface water, sediment and fauna representing different food web endpoints in a typical UK estuary (River Colne, Essex, UK). A comparison of contaminant occurrence across different benthic macroinvertebrates was made at three sites and included two amphipods (Gammarus pulex &Crangon crangon), a polychaete worm (Hediste diversicolor) and a gastropod (Peringia ulvae). Overall, multiple contaminants were determined in all compartments and ranged from;

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

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

Trace multi-class organic explosives analysis in complex matrices enabled using LEGO®-inspired clickable 3D-printed solid phase extraction block arrays.

The development of a new, lower cost method for trace explosives recovery from complex samples is presented using miniaturised, click-together and leak-free 3D-printed solid phase extraction (SPE) blocks. For the first time, a large selection of ten commercially available 3D printing materials were comprehensively evaluated for practical, flexible and multiplexed SPE using stereolithography (SLA), PolyJet and fused deposition modelling (FDM) technologies. Miniaturised single-piece, connectable and leak-free block housings inspired by Lego® were 3D-printed in a methacrylate-based resin, which was found to be most stable under different aqueous/organic solvent and pH conditions, using a cost-effective benchtop SLA printer. Using a tapered SPE bed format, frit-free packing of multiple different commercially available sorbent particles was also possible. Coupled SPE blocks were then shown to offer efficient analyte enrichment and a potentially new approach to improve the stability of recovered analytes in the field when stored on the sorbent, rather than in wet swabs. Performance was measured using liquid chromatography-high resolution mass spectrometry and was better, or similar, to commercially available coupled SPE cartridges, with respect to recovery, precision, matrix effects, linearity and range, for a selection of 13 peroxides, nitramines, nitrate esters and nitroaromatics. Mean % recoveries from dried blood, oil residue and soil matrices were 79 ± 24%, 71 ± 16% and 76 ± 24%, respectively. Excellent detection limits between 60 fg for 3,5-dinitroaniline to 154 pg for nitroglycerin were also achieved across all matrices. To our knowledge, this represents the first application of 3D printing to SPE of so many organic compounds in complex samples. Its introduction into this forensic method offered a low-cost, 'on-demand' solution for selective extraction of explosives, enhanced flexibility for multiplexing/design alteration and potential application at-scene.

Untargeted metabolomics changes on Gammarus pulex induced by propranolol, triclosan, and nimesulide pharmaceutical drugs.

The presence of pharmaceuticals and personal care products (PPCPs) in natural water resources due to incomplete removal in Wastewater Treatment Plants (WWTPs) is a serious environmental concern at present. In this work, the effects of three pharmaceuticals (propranolol, triclosan, and nimesulide) on Gammarus pulex metabolic profiles at different doses and times of exposure have been investigated by liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS). The complex data sets generated in the different exposure experiments were analyzed with the ROIMCR procedure, based on the selection of the MS regions of interest (ROI) data and on their analysis by the Multivariate Curve-Resolution Alternating Least Squares (MCR-ALS) chemometrics method. This approach, allowed the resolution and identification of the metabolites present in the analyzed samples, as well as the estimation of their concentration changes due to the exposure experiments. ANOVA Simultaneous Component Analysis (ASCA) and Partial Least Squares Discriminant Analysis (PLS-DA) were then conducted to assess the changes in the concentration of the metabolites for the three pharmaceuticals at the different conditions of exposure. The three tested pharmaceuticals changed the concentrations of metabolites, which were related to different KEGG functional classes. These changes summarize the biochemical response of Gammarus pulex to the exposure by the three investigated pharmaceuticals. Possible pathway alterations related to protein synthesis and oxidative stress were observed in the concentration of identified metabolites.

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

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

Spatio-temporal assessment of illicit drug use at large scale: evidence from 7 years of international wastewater monitoring.

BACKGROUND AND AIMS: Wastewater-based epidemiology is an additional indicator of drug use that is gaining reliability to complement the current established panel of indicators. The aims of this study were to: (i) assess spatial and temporal trends of population-normalized mass loads of benzoylecgonine, amphetamine, methamphetamine and 3,4-methylenedioxymethamphetamine (MDMA) in raw wastewater over 7 years (2011-17); (ii) address overall drug use by estimating the average number of combined doses consumed per day in each city; and (iii) compare these with existing prevalence and seizure data. DESIGN: Analysis of daily raw wastewater composite samples collected over 1 week per year from 2011 to 2017. SETTING AND PARTICIPANTS: Catchment areas of 143 wastewater treatment plants in 120 cities in 37 countries. MEASUREMENTS: Parent substances (amphetamine, methamphetamine and MDMA) and the metabolites of cocaine (benzoylecgonine) and of Δ9 -tetrahydrocannabinol (11-nor-9-carboxy-Δ9 -tetrahydrocannabinol) were measured in wastewater using liquid chromatography-tandem mass spectrometry. Daily mass loads (mg/day) were normalized to catchment population (mg/1000 people/day) and converted to the number of combined doses consumed per day. Spatial differences were assessed world-wide, and temporal trends were discerned at European level by comparing 2011-13 drug loads versus 2014-17 loads. FINDINGS: Benzoylecgonine was the stimulant metabolite detected at higher loads in southern and western Europe, and amphetamine, MDMA and methamphetamine in East and North-Central Europe. In other continents, methamphetamine showed the highest levels in the United States and Australia and benzoylecgonine in South America. During the reporting period, benzoylecgonine loads increased in general across Europe, amphetamine and methamphetamine levels fluctuated and MDMA underwent an intermittent upsurge. CONCLUSIONS: The analysis of wastewater to quantify drug loads provides near real-time drug use estimates that globally correspond to prevalence and seizure data.