Explore the vast range of titles available.

Non-target screening (NTS) including suspect screening with high resolution mass spectrometry has already shown its feasibility in detecting and identifying emerging contaminants, which subsequently triggered exposure mitigating measures. NTS has a large potential for tasks such as effective evaluation of regulations for safe marketing of substances and products, prioritization of substances for monitoring programmes and assessment of environmental quality. To achieve this, a further development of NTS methodology is required, including: (i) harmonized protocols and quality requirements, (ii) infrastructures for efficient data management, data evaluation and data sharing and (iii) sufficient resources and appropriately trained personnel in the research and regulatory communities in Europe. Recommendations for achieving these three requirements are outlined in the following discussion paper. In particular, in order to facilitate compound identification it is recommended that the relevant information for interpretation of mass spectra, as well as about the compounds usage and production tonnages, should be made accessible to the scientific community (via open-access databases). For many purposes, NTS should be implemented in combination with effect-based methods to focus on toxic chemicals.

The occurrence of antibiotics in the environment could result in the development of antibiotic-resistant bacteria, which could result in a public health crisis. The occurrence of 676 antibiotics and the main transformation products (TPs) was investigated in the 48 wastewater treatment plants (WWTPs) from 11 countries (Germany, Romania, Serbia, Croatia, Slovenia, Hungary, Slovakia, Czechia, Austria, Cyprus, and Greece) by target and suspect screening. Target screening involved the investigation of antibiotics with reference standards (40 antibiotics). Suspect screening covered 676 antibiotics retrieved from the NORMAN Substance Database (antibiotic list on NORMAN network). Forty-seven antibiotics were detected in effluent wastewater samples: thirty-two by target screening and fifteen additional ones by suspect screening. An ecotoxicological risk assessment was performed based on occurrence data and predicted no effect concentration (PNEC), which involved the derivation of frequency of appearance (FoA), frequency of PNEC exceedance (FoE), and extent of PNEC exceedance (EoE). Azithromycin, erythromycin, clarithromycin, ofloxacin, and ciprofloxacin were prioritized as the calculated risk score was above 1. The median of antibiotics’ load to freshwater ecosystems was 0.59 g/day/WWTP. The detection of antibiotics across countries indicates the presence of antibiotics in the ecosystems of Europe, which may trigger unwanted responses from the ecosystem, including antibiotic resistance.

The Black Sea is the largest semi‐closed permanently anoxic basin on our planet with long‐term stratification. The study aimed at describing the Black Sea microbial community taxonomic and functional composition within the range of depths spanning across oxic/anoxic interface, and to uncover the factors behind both their vertical and regional differentiation. 16S rRNA gene MiSeq sequencing was applied to get the data on microbial community taxonomy, and the PICRUSt pipeline was used to infer their functional profile. The normoxic zone was mainly inhabited by primary producers and heterotrophic prokaryotes (e.g., Flavobacteriaceae, Rhodobacteraceae, Synechococcaceae) whereas the euxinic zone—by heterotrophic and chemoautotrophic taxa (e.g., MSBL2, Piscirickettsiaceae, and Desulfarculaceae). Assimilatory sulfate reduction and oxygenic photosynthesis were prevailing within the normoxic zone, while the role of nitrification, dissimilatory sulfate reduction, and anoxygenic photosynthesis increased in the oxygen‐depleted water column part. Regional differentiation of microbial communities between the Ukrainian shelf and offshore zone was detected as well, yet it was significantly less pronounced than the vertical one. It is suggested that regional differentiation within a well‐oxygenated zone is driven by the difference in phytoplankton communities providing various substrates for the prokaryotes, whereas redox stratification is the main driving force behind microbial community vertical structure. The study aimed at describing the Black Sea microbial community taxonomic and functional composition within the range of depths spanning across oxic/anoxic interface, and to uncover the factors behind both their vertical and regional differentiation. It is suggested that regional differentiation within a well‐oxygenated zone is driven by the difference in phytoplankton communities providing various substrates for the bacteria, whereas redox stratification is the main driving force behind microbial community vertical structure.

Background: Antibiotic resistance genes (ARGs) are considered as pollutants and are found in natural and anthropogenically impacted environments. Distribution of ARGs in marine environment poses a threat to human health turning the water body into a pool for the ARGs’ transmission. Objectives: A large-scale study of antibiotic resistance in microbial communities has been performed in the Black Sea, both in the coastal and offshore regions. Methods: The quantitative distribution of the genes responsible for the inactivation of the beta-lactam ( bla CMY , bla SHV ), vancomycin ( vanA , vanB ), macrolides ( ermB ) and colistin ( mcr-1 ) was assessed with real-time quantitative PCR. Concentrations of the antibiotics belonging to the classes of beta-lactam/cephalosporin/carbapenem, macrolides and glycopeptides were determined by LC-ESI-QTOF-MS. Results: The present study revealed the distribution of antibiotic resistance genes targeting the response to all antibiotics included in our analysis at various locations across the Black Sea. According to the ARGs copy number normalized to the 16S rRNA, vanB (2 × 10 −1 ± 1 × 10 −1 ) and bla SHV (4 × 10 −2 ± 1 × 10 −2 ) were the most numerous genes, followed by bla CMY (1 × 10 −2 ± 3 × 10 −3 ) and mcr-1 (3 × 10 −2 ± 2 × 10 −2 ). The less abundant gene was ermB (1 × 10 −3 ± 5 × 10 −4 ) and vanA (1 × 10 −5 ± 5 × 10 −4 ). The mcr-1 , bla CMY and bla SHV had moderate positive correlation with markers of ruminant faecal pollution. The concentration of antibiotics in seawater was below the detection limit. The abundance of all ARGs included in the study was significantly higher ( p -value<0.05) within the northwest coastal area when compared to the offshore stations. The results clearly indicate an alarming antibiotic resistance problem in the region and call for a regular monitoring of ARGs abundance in the Black Sea and its major freshwater tributaries.

Liquid chromatography-high resolution mass spectrometry (LC-HRMS) and gas chromatography-high resolution mass spectrometry (GC-HRMS) have revolutionized analytical chemistry among many other disciplines. These advanced instrumentations allow to theoretically capture the whole chemical universe that is contained in samples, giving unimaginable opportunities to the scientific community. Laboratories equipped with these instruments produce a lot of data daily that can be digitally archived. Digital storage of data opens up the opportunity for retrospective suspect screening investigations for the occurrence of chemicals in the stored chromatograms. The first step of this approach involves the prediction of which data is more appropriate to be searched. In this study, we built an optimized multi-label classifier for predicting the most appropriate instrumental method (LC-HRMS or GC-HRMS or both) for the analysis of chemicals in digital specimens. The approach involved the generation of a baseline model based on the knowledge that an expert would use and the generation of an optimized machine learning model. A multi-step feature selection approach, a model selection strategy, and optimization of the classifier’s hyperparameters led to a model with accuracy that outperformed the baseline implementation. The models were used to predict the most appropriate instrumental technique for new substances. The scripts are available at GitHub and the dataset at Zenodo.

Wastewater treatment plants (WWTPs) represent an important input of contaminants in the environment. Therefore, it is critical to continuously monitor the performance of WWTPs to take appropriate action and avoid an influx of contaminants in the environment. In this study, a battery of seven in vitro bioassays covering a selected spectrum of toxicity effects is proposed for quality control of wastewater effluents. The bioassays address mixture toxicity, which is the combined adverse effect of multiple contaminants and can act as an early warning system. The proposed battery was applied to samples from 11 WWTPs of representative technology from the Danube River Basin (DRB). The order of toxic effects in terms of extent of exceedance of effect-based trigger values (EBTs) was PAH (PAH activity) > PXR (xenobiotic metabolism) > ERα (estrogenic activity) > PPARγ > Nrf2 (oxidative stress) > anti-AR > GR. A mitigation plan for WWTP operators based on EBT exceedance is proposed. This study demonstrates that the proposed effect-based monitoring battery is a complementary tool to the chemical analysis approach. A regular application of such time- and cost-effective bioanalytical tools in the WWTPs of the DRB is proposed to provide a ‘safety net’ for aquatic ecosystems.

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

This study was carried out to identify the relations between macroinvertebrate communities and river basin specific (RBS) pollutants in the Danube River. The investigation was performed at 68 sites along 2,500 km of the Danube. Forward selection (FS), canonical correspondence analyses (CCA), the Spearman correlation coefficient (SC) and BIO-ENV analysis (to detect synergistic effects) were used to identify the relations between the macroinvertebrate dataset and selected biological metrics with RBS pollutants. Of the 20 analysed pollutants (preselected based on NORMAN network methodology), seven (2,4-dinitrophenol, chloroxuron, bromacil, dimefuron, amoxicillin, bentazon and fluoranthene) were found to significantly correlate with macroinvertebrate communities. BIO-ENV analysis revealed 3 subsets of environmental variables that were in high correlation with the biota resemblance matrix, consisting mainly of a combination of the above-mentioned pollutants. Our results indicate that there are significant correlations between chemical determinants and aquatic biota. Moreover, this study contributes to the validation of the methodology used for prioritization of RBS pollutants proposed by the NORMAN network.

Currently, chemical monitoring based on priority substances fails to consider the majority of known environmental micropollutants not to mention the unexpected and unknown chemicals that may contribute to the toxic risk of complex mixtures present in the environment. Complementing component- and effect-based monitoring with wide-scope target, suspect, and non-target screening (NTS) based on high-resolution mass spectrometry (HRMS) data is recommended to support environmental impact and risk assessment. This will allow for detection of newly emerging compounds and transformation products, retrospective monitoring efforts, and the identification of possible drivers of toxicity by correlation with effects or modelling of expected effects for future and abatement scenarios. HRMS is becoming increasingly available in many laboratories. Thus, the time is right to establish and harmonize screening methods, train staff, and record HRMS data for samples from regular monitoring events and surveys. This will strongly enhance the value of chemical monitoring data for evaluating complex chemical pollution problems, at limited additional costs. Collaboration and data exchange on a European-to-global scale is essential to maximize the benefit of chemical screening. Freely accessible data platforms, inter-laboratory trials, and the involvement of international partners and networks are recommended.

Untargeted analysis of a composite house dust sample has been performed as part of a collaborative effort to evaluate the progress in the field of suspect and nontarget screening and build an extensive database of organic indoor environment contaminants. Twenty-one participants reported results that were curated by the organizers of the collaborative trial. In total, nearly 2350 compounds were identified (18%) or tentatively identified (25% at confidence level 2 and 58% at confidence level 3), making the collaborative trial a success. However, a relatively small share (37%) of all compounds were reported by more than one participant, which shows that there is plenty of room for improvement in the field of suspect and nontarget screening. An even a smaller share (5%) of the total number of compounds were detected using both liquid chromatography–mass spectrometry (LC-MS) and gas chromatography–mass spectrometry (GC-MS). Thus, the two MS techniques are highly complementary. Most of the compounds were detected using LC with electrospray ionization (ESI) MS and comprehensive 2D GC (GC×GC) with atmospheric pressure chemical ionization (APCI) and electron ionization (EI), respectively. Collectively, the three techniques accounted for more than 75% of the reported compounds. Glycols, pharmaceuticals, pesticides, and various biogenic compounds dominated among the compounds reported by LC-MS participants, while hydrocarbons, hydrocarbon derivatives, and chlorinated paraffins and chlorinated biphenyls were primarily reported by GC-MS participants. Plastics additives, flavor and fragrances, and personal care products were reported by both LC-MS and GC-MS participants. It was concluded that the use of multiple analytical techniques was required for a comprehensive characterization of house dust contaminants. Further, several recommendations are given for improved suspect and nontarget screening of house dust and other indoor environment samples, including the use of open-source data processing tools. One of the tools allowed provisional identification of almost 500 compounds that had not been reported by participants.