Innovative analytical methodologies for characterizing chemical exposure with a view to next-generation risk assessment.

The chemical burden on the environment and human population is increasing. Consequently, regulatory risk assessment must keep pace to manage, reduce, and prevent adverse impacts on human and environmental health associated with hazardous chemicals. Surveillance of chemicals of known, emerging, or potential future concern, entering the environment-food-human continuum is needed to document the reality of risks posed by chemicals on ecosystem and human health from a one health perspective, feed into early warning systems and support public policies for exposure mitigation provisions and safe and sustainable by design strategies. The use of less-conventional sampling strategies and integration of full-scan, high-resolution mass spectrometry and effect-directed analysis in environmental and human monitoring programmes have the potential to enhance the screening and identification of a wider range of chemicals of known, emerging or potential future concern. Here, we outline the key needs and recommendations identified within the European Partnership for Assessment of Risks from Chemicals (PARC) project for leveraging these innovative methodologies to support the development of next-generation chemical risk assessment.

Influent wastewater analysis to investigate emerging trends of new psychoactive substances use in Europe.

Wastewater-based epidemiology (WBE) can provide objective and timely information on the use of new psychoactive substances (NPS), originally designed as legal alternatives of internationally controlled drugs. NPS have rapidly emerged on the global drug market, posing a challenge to drug policy and constituting a risk to public health. In this study, a WBE approach was applied to monitor the use of more than 300 NPS, together with fentanyl and its main metabolite norfentanyl, in influent wastewater collected from 12 European cities during March-June 2021. Quantitative and qualitative analysis of NPS in composite 24 h influent wastewater samples were based on solid phase extraction and liquid chromatography-mass spectrometry. In-sample stability tests demonstrated the suitability of most investigated biomarkers, except for a few synthetic opioids, synthetic cannabinoids and phenetylamines. Fentanyl, norfentanyl and eight NPS were quantified in influent wastewater and at least three substances were found in each city, demonstrating their use in Europe. N,N-dimethyltryptamine and 3-methylmethcathinone (3-MMC) were the most common NPS found, with the latter having the highest mass loads (up to 24.8 mg/day/1000 inhabitants). Seven additional substances, belonging to five categories of NPS, were identified in different cities. Spatial trends of NPS use were observed between cities and countries, and a changing weekly profile of use was observed for 3-MMC. WBE is a useful tool to rapidly evaluate emerging trends of NPS use, complementing common indicators (i.e. population surveys, seizures) and helping to establish measures for public health protection.

Machine Learning for Detecting Virus Infection Hotspots Via Wastewater-Based Epidemiology: The Case of SARS-CoV-2 RNA.

Wastewater-based epidemiology (WBE) has been proven to be a useful tool in monitoring public health-related issues such as drug use, and disease. By sampling wastewater and applying WBE methods, wastewater-detectable pathogens such as viruses can be cheaply and effectively monitored, tracking people who might be missed or under-represented in traditional disease surveillance. There is a gap in current knowledge in combining hydraulic modeling with WBE. Recent literature has also identified a gap in combining machine learning with WBE for the detection of viral outbreaks. In this study, we loosely coupled a physically-based hydraulic model of pathogen introduction and transport with a machine learning model to track and trace the source of a pathogen within a sewer network and to evaluate its usefulness under various conditions. The methodology developed was applied to a hypothetical sewer network for the rapid detection of disease hotspots of the disease caused by the SARS-CoV-2 virus. Results showed that the machine learning model's ability to recognize hotspots is promising, but requires a high time-resolution of monitoring data and is highly sensitive to the sewer system's physical layout and properties such as flow velocity, the pathogen sampling procedure, and the model's boundary conditions. The methodology proposed and developed in this paper opens new possibilities for WBE, suggesting a rapid back-tracing of human-excreted biomarkers based on only sampling at the outlet or other key points, but would require high-frequency, contaminant-specific sensor systems that are not available currently.

Capturing the SARS-CoV-2 infection pyramid within the municipality of Rotterdam using longitudinal sewage surveillance.

Despite high vaccination rates in the Netherlands, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to circulate. Longitudinal sewage surveillance was implemented along with the notification of cases as two parts of the surveillance pyramid to validate the use of sewage for surveillance, as an early warning tool, and to measure the effect of interventions. Sewage samples were collected from nine neighborhoods between September 2020 and November 2021. Comparative analysis and modeling were performed to understand the correlation between wastewater and case trends. Using high resolution sampling, normalization of wastewater SARS-CoV-2 concentrations, and 'normalization' of reported positive tests for testing delay and intensity, the incidence of reported positive tests could be modeled based on sewage data, and trends in both surveillance systems coincided. The high collinearity implied that high levels of viral shedding around the onset of disease largely determined SARS-CoV-2 levels in wastewater, and that the observed relationship was independent of variants of concern and vaccination levels. Sewage surveillance alongside a large-scale testing effort where 58 % of a municipality was tested, indicated a five-fold difference in the number of SARS-CoV-2-positive individuals and reported cases through standard testing. Where trends in reported positive cases were biased due to testing delay and testing behavior, wastewater surveillance can objectively display SARS-CoV-2 dynamics for both small and large locations and is sensitive enough to measure small variations in the number of infected individuals within or between neighborhoods. With the transition to a post-acute phase of the pandemic, sewage surveillance can help to keep track of re-emergence, but continued validation studies are needed to assess the predictive value of sewage surveillance with new variants. Our findings and model aid in interpreting SARS-CoV-2 surveillance data for public health decision-making and show its potential as one of the pillars of future surveillance of (re)emerging viruses.

Wastewater-based epidemiology for the assessment of population exposure to chemicals: The need for integration with human biomonitoring for global One Health actions.

WBE has now become a complimentary tool in SARS-CoV-2 surveillance. This was preceded by the established application of WBE to assess the consumption of illicit drugs in communities. It is now timely to build on this and take the opportunity to expand WBE to enable comprehensive assessment of community exposure to chemical stressors and their mixtures. The goal of WBE is to quantify community exposure, discover exposure-outcome associations, and trigger policy, technological or societal intervention strategies with the overarching aim of exposure prevention and public health promotion. To achieve WBE's full potential, the following key aspects require further action: (1) Integration of WBE-HBM (human biomonitoring) initiatives that provide comprehensive community-individual multichemical exposure assessment. (2) Global WBE monitoring campaigns to provide much needed data on exposure in low- and middle-income countries (LMICs) and fill in the gaps in knowledge especially in the underrepresented highly urbanised as well as rural settings in LMICs. (3) Combining WBE with One Health actions to enable effective interventions. (4) Advancements in new analytical tools and methodologies for WBE progression to enable biomarker selection for exposure studies, and to provide sensitive and selective multiresidue analysis for trace multi-biomarker quantification in a complex wastewater matrix. Most of all, further developments of WBE needs to be undertaken by co-design with key stakeholder groups: government organisations, health authorities and private sector.

Occurrence, Fate, and Related Health Risks of PFAS in Raw and Produced Drinking Water.

This study investigates human exposure to per- and polyfluoroalkyl substances (PFAS) via drinking water and evaluates human health risks. An analytical method for 56 target PFAS, including ultrashort-chain (C2-C3) and branched isomers, was developed. The limit of detection (LOD) ranged from 0.009 to 0.1 ng/L, except for trifluoroacetic-acid and perfluoropropanoic-acid with higher LODs of 35 and 0.24 ng/L, respectively. The method was applied to raw and produced drinking water from 18 Dutch locations, including groundwater or surface water as source, and applied various treatment processes. Ultrashort-chain (300 to 1100 ng/L) followed by the group of perfluoroalkyl-carboxylic-acids (PFCA, ≥C4) (0.4 to 95.1 ng/L) were dominant. PFCA and perfluoroalkyl-sulfonic-acid (≥C4), including precursors, showed significantly higher levels in drinking water produced from surface water. However, no significant difference was found for ultrashort PFAS, indicating the need for groundwater protection. Negative removal of PFAS occasionally observed for advanced treatment indicates desorption and/or degradation of precursors. The proportion of branched isomers was higher in raw and produced drinking water as compared to industrial production. Drinking water produced from surface water, except for a few locations, exceed non-binding provisional guideline values proposed; however, all produced drinking waters met the recent soon-to-be binding drinking-water-directive requirements.

Identifying antimicrobials and their metabolites in wastewater and surface water with effect-directed analysis.

This study aimed to identify antimicrobial contaminants in the aquatic environment with effect-directed analysis. Wastewater influent, effluent, and surface water (up- and downstream of the discharge location) were sampled at two study sites. The samples were enriched, subjected to high-resolution fractionation, and the resulting 80 fractions were tested in an antibiotics bioassay. The resulting bioactive fractions guided the suspect and nontargeted identification strategy in the high-resolution mass spectrometry data that was recorded in parallel. Chemical features were annotated with reference databases, assessed on annotation quality, and assigned identification confidence levels. To identify antibiotic metabolites, Phase I metabolites were predicted in silico for over 500 antibiotics and included as a suspect list. Predicted retention times and fragmentation patterns reduced the number of annotations to consider for confirmation testing. Overall, the bioactivity of three fractions could be explained by the identified antibiotics (clarithromycin and azithromycin) and an antibiotic metabolite (14-OH(R) clarithromycin), explaining 78% of the bioactivity measured at one study site. The applied identification strategy successfully identified antibiotic metabolites in the aquatic environment, emphasizing the need to include the toxic effects of bioactive metabolites in environmental risk assessments.

Rise and fall of SARS-CoV-2 variants in Rotterdam: Comparison of wastewater and clinical surveillance.

Monitoring of SARS-CoV-2 in wastewater (WW) is a promising tool for epidemiological surveillance, correlating not only viral RNA levels with the infection dynamics within the population, but also to viral diversity. However, the complex mixture of viral lineages in WW samples makes tracking of specific variants or lineages circulating in the population a challenging task. We sequenced sewage samples of 9 WW-catchment areas within the city of Rotterdam, used specific signature mutations from individual SARS-CoV-2 lineages to estimate their relative abundances in WW and compared them against those observed in clinical genomic surveillance of infected individuals between September 2020 and December 2021. We showed that especially for dominant lineages, the median of the frequencies of signature mutations coincides with the occurrence of those lineages in Rotterdam's clinical genomic surveillance. This, along with digital droplet RT-PCR targeting signature mutations of specific variants of concern (VOCs), showed that several VOCs emerged, became dominant and were replaced by the next VOC in Rotterdam at different time points during the study. In addition, single nucleotide variant (SNV) analysis provided evidence that spatio-temporal clusters can also be discerned from WW samples. We were able to detect specific SNVs in sewage, including one resulting in the Q183H amino acid change in the Spike gene, that was not captured by clinical genomic surveillance. Our results highlight the potential use of WW samples for genomic surveillance, increasing the set of epidemiological tools to monitor SARS-CoV-2 diversity.

Triangulating Amsterdam's illicit stimulant use trends by wastewater analysis and recreational drug use monitoring.

Drug consumption estimates are of relevance because of public health effects as well as associated criminal activities. Wastewater analysis of drug residues enables the estimation of drug consumption and drug markets. Short-term and long-term trends of cocaine, MDMA (ecstasy), amphetamine (speed) and methamphetamine (crystal meth), were studied for the city of Amsterdam. MDMA (+41%) and cocaine (+26%) showed significantly higher weekend vs. week consumption, while no differences were observed for the other drugs. The consumption of MDMA, cocaine, amphetamine and methamphetamine significantly increased between 2011 and 2019. Weekly trends emerging from wastewater analyses were supported by qualitative and quantitative data from a recreational drug use monitoring scheme. However, information collected in panel interviews within nightlife networks and surveys among visitors of pubs, clubs and festivals only partially reflected the long term increase in consumption as registered from wastewater analysis. Furthermore, methamphetamine use was not well presented in survey data, panel studies and test service samples, but could be monitored trough wastewater analysis. This illustrates that wastewater analysis can function as an early warning if use and user groups are small or difficult to reach trough other forms of research. All in all, this study illustrates that wastewater-based epidemiology is complementary to research among user groups, and vice versa. These different types of information enable to connect observed trends in total drug consumption to behaviour of users and the social context in which the use takes place as well as validate qualitative signals about (increased) consumption of psychoactive substances. Such a multi angular approach to map the illicit drug situation on local or regional scale can provide valuable information for public health.

Analytical investigation of cannabis biomarkers in raw urban wastewater to refine consumption estimates.

Wastewater analysis of Δ9-tetrahydrocannabinol (THC) biomarkers can provide essential information on trends in cannabis consumption. Although analysis is mostly focused on the aqueous phase, previous studies have illustrated the need of improving the measurements of raw influent wastewater (IWW) considering also suspended solids. This is important for cannabis biomarkers, because a substantial part of them is expected to be found in the suspended solids due to their more lipophilic character compared with other metabolites/drugs included in these types of studies. However, it remains open to which extent trend estimates might be affected by solely analysing the liquid phase. To investigate this aspect, robust analytical methodologies are required to measure both the liquid and solid phases of IWW. In this work, we firstly tested liquid-liquid extraction (LLE) for THC and its major metabolites (THCOH, and THCCOOH). Using LLE, no filtration or centrifugation step was required for raw IWW analysis, and the three analytes were extracted from both the liquid and the solid phase simultaneously. In parallel, the raw IWW was centrifuged and the obtained solid and liquid phases were analyzed separately: the liquid phase by both LLE and solid phase extraction (SPE) for comparison of data, and the suspended solids by solid-liquid extraction (SLE). The separate analysis of both phases in a number of samples revealed that a significant amount of cannabis biomarkers (ranging from 42 to 90%) was found in the suspended solids. In addition, the total amount of cannabis biomarkers obtained by analysing raw IWW on the one hand, and by separate analysis of the liquid and the solid phases, on the other hand, was in good agreement. Data from this study show that the sole analysis of the liquid phase would lead to a notable underestimation of cannabis biomarkers concentrations in IWW.

Monitoring the exposure and emissions of antibiotic resistance: Co-occurrence of antibiotics and resistance genes in wastewater treatment plants.

The COVID-19 pandemic has brought new momentum to 'wastewater-based epidemiology' (WBE). This approach can be applied to monitor the levels of antibiotic-resistant genes (ARGs), which in terms are used to make inferences about the burden of antimicrobial resistance (AMR) in human settlements. However, there is still little information about temporal variability in ARG levels measured in wastewater streams and how these influence the inferences made about the occurrence of AMR in communities. The goal of this study was hence to gain insights into the variability in ARG levels measured in the influent and effluent of two wastewater treatment plants in The Netherlands and link these to levels of antibiotic residues measured in the same samples. Eleven antibiotics were detected, together with all selected ARGs, except for VanB. Among the measured antibiotics, significant positive correlations (p > 0.70) with the corresponding resistance genes and some non-corresponding ARGs were found. Mass loads varied up to a factor of 35 between days and in concomitance with rainfall. Adequate sampling schemes need to be designed to ensure that conclusions are drawn from valid and representative data. Additionally, we advocate for the use of mass loads to interpret levels of AMR measured in wastewater.

Spatial and temporal assessment of crack cocaine use in 13 European cities through wastewater-based epidemiology.

Already in early 2000s, concerns have been growing in the EU about increasing use of cocaine and it is estimated that below 1 % of the population administer the drug by smoking crack cocaine. New available data suggests an increase in the use of crack cocaine and an increase in the number of crack cocaine users entering treatment has been reported in several European countries. Robust estimations of crack cocaine use are however not available yet. The use of crack cocaine has long been associated with severe adverse socio-economic conditions as well as mental health problems, such as suicide ideation and depression. The aim of this study was to assess spatial trends in population-normalized mass loads of crack cocaine biomarkers (i.e., anhydroecgonine and anhydroecgonine methyl ester) in 13 European cities in six countries (the Netherlands, Belgium, Ireland, Portugal, Spain and Italy). Furthermore, temporal trends over a five-year period were evaluated through the analysis of historic samples collected in the Netherlands. Finally, the stability of the crack cocaine biomarkers in wastewater was investigated through batch experiments. The samples were analyzed with a new developed and validated hydrophilic interaction liquid chromatography coupled to mass spectrometry method. Targeted crack cocaine biomarkers were found in all cities. Also, crack cocaine biomarker was detected in wastewater from 2017 to 2021 in the Netherlands, but no significance between the years were found. With respect to biomarker in-sample stability, AEME was found to be stable in wastewater. This study assessed crack cocaine use for the first time on a broad scale, both temporal and in cities across Europe, with wastewater-based epidemiology and it shows the importance of wastewater analysis to monitor community loads of crack cocaine use.

Quantum cascade laser imaging (LDIR) and machine learning for the identification of environmentally exposed microplastics and polymers.

Monitoring of microplastics in environmental samples is relevant to the scientific world, as well as to environmental agencies and water authorities, in particular considering increasing efforts to decrease emissions and the growing concern of governments and the public. Therefore, rapid accurate detection and identification of microplastics including polymers, despite their degradation in the environment, is crucial. The degradation has a significant impact on the infrared spectra of the microplastics and can impede the identification process. This work presents a novel approach to addressing the problem of identification of weathered microplastics. A quantum cascade laser (LDIR) was used to record the infrared spectra of various polymeric particles (81,291 individual particles). Using a combination of pristine and weathered particles, two supervised machine learning (ML) models, namely Subspace k-Nearest Neighbor (Sub-kNN) and Boosted Decision Tree (BDT), were trained to recognize the spectrum characteristics of labeled particles and then used to identify unlabeled samples, with an identification accuracy of 89.7% and 77.1% using 10-fold cross validation. About 90% of the samples could be identified via the Sub-kNN or BDT models. Subsequently, a non-supervised ML model, namely, Density-based Spatial Clustering of Applications with Noise (DBSCAN), was used to cluster samples which could not be labeled from the supervised ML model. This enabled the detection of additional subgroups of microplastics. Manual labelling can then be carried out on a selection of spectra per group (e.g., centroids of each cluster), hence accelerating the identification process and allowing to add new labeled samples to the initial supervised ML. Although expert efforts are still needed, the proposed method greatly lowers labeling efforts by using the combined supervised and unsupervised learning models. In the future, the use of deep neural networks could further boost the implementation of these kinds of approaches for polymer and microplastic identification in environmental settings.

Retrospective suspect and non-target screening combined with similarity measures to prioritize MDMA and amphetamine synthesis markers in wastewater.

3,4-Methylenedioxymethamphetamine (MDMA) and amphetamine are commonly used psychoactive stimulants. Illegal manufacture of these substances, mainly located in the Netherlands and Belgium, generates large amounts of chemical waste which is disposed in the environment or released in sewer systems. Retrospective analysis of high-resolution mass spectrometry (HRMS) data was implemented to detect synthesis markers of MDMA and amphetamine production in wastewater samples. Specifically, suspect and non-target screening, combined with a prioritization approach based on similarity measures between detected features and mass loads of MDMA and amphetamine was implemented. Two hundred and thirty-five 24 h-composite wastewater samples collected from a treatment plant in the Netherlands between 2016 and 2018 were analyzed by liquid chromatography coupled to high-resolution mass spectrometry. Samples were initially separated into two groups (i.e., baseline consumption versus dumping) based on daily loads of MDMA and amphetamine. Significance testing and fold-changes were used to find differences between features in the two groups. Then, associations between peak areas of all features and MDMA or amphetamine loads were investigated across the whole time series using various measures (Euclidian distance, Pearson's correlation coefficient, Spearman's rank correlation coefficient, distance correlation and maximum information coefficient). This unsupervised and unbiased approach was used for prioritization of features and allowed the selection of 28 presumed markers of production of MDMA and amphetamine. These markers could potentially be used to detect dumps in sewer systems, help in determining the synthesis route and track down the waste in the environment.

An Analysis of SARS-CoV-2 in Wastewater to Evaluate the Effectiveness of Nonpharmaceutical Interventions against COVID-19 in The Netherlands.

Wastewater-based epidemiology (WBE) is increasingly being recognized as a powerful tool for detecting and monitoring SARS-CoV-2 trends at a population level. This study looked to extend the use of WBE to explore the effectiveness of nonpharmaceutical interventions (NPIs) that have been used in response to COVID-19 and compare the results to the effect of such interventions on COVID-19 hospitalizations. A data-driven approach demonstrated that trends of SARS-CoV-2 RNA in wastewater, from Amsterdam and Utrecht (The Netherlands), precede hospitalizations by at least 3-9 days. Additionally, the effect of NPIs can be seen in wastewater and hospitalizations after 20 and 24 days, respectively. Changepoint analysis indicated that the closure of schools and universities significantly reduced the level of SARS-CoV-2 RNA in wastewater and COVID-19 hospitalizations. Regression modeling suggested the stay-at-home policy is an effective intervention for reducing the level of SARS-CoV-2 RNA in wastewater, whereas the closure of workplaces significantly reduced hospitalizations in both Dutch cities. This study demonstrates how WBE can be used to inform public health decisions and anticipate future strain on healthcare facilities in major cities but also indicates a need for higher temporal resolution of wastewater sampling.

Risk-based prioritization of suspects detected in riverine water using complementary chromatographic techniques.

Surface waters are widely used as drinking water sources and hence their quality needs to be continuously monitored. However, current routine monitoring programs are not comprehensive as they generally cover only a limited number of known pollutants and emerging contaminants. This study presents a risk-based approach combining suspect and non-target screening (NTS) to help extend the coverage of current monitoring schemes. In particular, the coverage of NTS was widened by combining three complementary separations modes: Reverse phase (RP), Hydrophilic interaction liquid chromatography (HILIC) and Mixed-mode chromatography (MMC). Suspect lists used were compiled from databases of relevant substances of very high concern (e.g., SVHCs) and the concentration of detected suspects was evaluated based on ionization efficiency prediction. Results show that suspect candidates can be prioritized based on their potential risk (i.e., hazard and exposure) by combining ionization efficiency-based concentration estimation, in vitro toxicity data or, if not available, structural alerts and QSAR.based toxicity predictions. The acquired information shows that NTS analyses have the potential to complement target analyses, allowing to update and adapt current monitoring programs, ultimately leading to improved monitoring of drinking water sources.

Making Waves: Collaboration in the time of SARS-CoV-2 - rapid development of an international co-operation and wastewater surveillance database to support public health decision-making.

The presence of SARS-CoV-2 RNA in wastewater was first reported in March 2020. Over the subsequent months, the potential for wastewater surveillance to contribute to COVID-19 mitigation programmes has been the focus of intense national and international research activities, gaining the attention of policy makers and the public. As a new application of an established methodology, focused collaboration between public health practitioners and wastewater researchers is essential to developing a common understanding on how, when and where the outputs of this non-invasive community-level approach can deliver actionable outcomes for public health authorities. Within this context, the NORMAN SCORE "SARS-CoV-2 in sewage" database provides a platform for rapid, open access data sharing, validated by the uploading of 276 data sets from nine countries to-date. Through offering direct access to underpinning meta-data sets (and describing its use in data interpretation), the NORMAN SCORE database is a resource for the development of recommendations on minimum data requirements for wastewater pathogen surveillance. It is also a tool to engage public health practitioners in discussions on use of the approach, providing an opportunity to build mutual understanding of the demand and supply for data and facilitate the translation of this promising research application into public health practice.

Changes in drug use in European cities during early COVID-19 lockdowns - A snapshot from wastewater analysis.

The COVID-19 outbreak has forced countries to introduce severe restrictive measures to contain its spread. In particular, physical distancing and restriction of movement have had important consequences on human behaviour and potentially also on illicit drug use and supply. These changes can be associated with additional risks for users, in particular due to reduced access to prevention and harm reduction activities. Furthermore, there have been limitations in the amount of data about drug use which can be collected due to restrictions. To goal of this study was to obtain information about potential changes in illicit drug use impacted by COVID-19 restrictions. Wastewater samples were collected in seven cities in the Netherlands, Belgium, Spain and Italy at the beginning of lockdowns (March-May 2020). Using previously established and validated methods, levels of amphetamine (AMP), methamphetamine (METH), MDMA, benzoylecgonine (BE, the main metabolite of cocaine) and 11-nor-9-carboxy-Δ9-tetrahydrocannabinol (THC-COOH, main metabolite of tetrahydrocannabinol (THC)) were measured and compared with findings from previous years. Important differences in levels of consumed drugs were observed across the considered countries. Whilst for some substances and locations, marked decreases in consumption could be observed (e.g., 50% decrease in MDMA levels compared to previous years). In some cases, similar or even higher levels compared to previous years could be found. Changes in weekly patterns were also observed, however these were not clearly defined for all locations and/or substances. Findings confirm that the current situation is highly heterogeneous and that it remains very difficult to explain and/or predict the effect that the present pandemic has on illicit drug use and availability. However, given the current difficulty in obtaining data due to restrictions, wastewater analysis can provide relevant information about the situation at the local level, which would be hard to obtain otherwise.

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

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

Online Prioritization of Toxic Compounds in Water Samples through Intelligent HRMS Data Acquisition.

LC-HRMS-based nontarget screening (NTS) has become the method of choice to monitor organic micropollutants (OMPs) in drinking water and its sources. OMPs are identified by matching experimental fragmentation (MS2) spectra with library or in silico-predicted spectra. This requires informative experimental spectra and prioritization to reduce feature numbers, currently performed post data acquisition. Here, we propose a different prioritization strategy to ensure high-quality MS2 spectra for OMPs that pose an environmental or human health risk. This online prioritization triggers MS2 events based on detection of suspect list entries or isotopic patterns in the full scan or an additional MS2 event based on fragment ion(s)/patterns detected in a first MS2 spectrum. Triggers were determined using cheminformatics; potentially toxic compounds were selected based on the presence of structural alerts, in silico-fragmented, and recurring fragments and mass shifts characteristic for a given structural alert identified. After MS acquisition parameter optimization, performance of the online prioritization was experimentally examined. Triggered methods led to increased percentages of MS2 spectra and additional MS2 spectra for compounds with a structural alert. Application to surface water samples resulted in additional MS2 spectra of potentially toxic compounds, facilitating more confident identification and emphasizing the method's potential to improve monitoring studies.