Search

Per- and polyfluoroalkyl substances (PFAS) are under regulatory scrutiny since some of them are persistent, bioaccumulative, and toxic. The occurrence of 4777 PFAS was investigated in the Danube River Basin (DRB; 11 countries) using target and suspect screening. Target screening involved investigation of PFAS with 56 commercially available reference standards. Suspect screening covered 4777 PFAS retrieved from the NORMAN Substance Database, including all individual PFAS lists submitted to the NORMAN Suspect List Exchange Database. Mass spectrometry fragmentation patterns and retention time index predictions of the studied PFAS were established for their screening by liquid chromatography - high resolution mass spectrometry using NORMAN Digital Sample Freezing Platform (DSFP). In total, 82 PFAS were detected in the studied 95 samples of river water, wastewater, groundwater, biota and sediments. Suspect screening detected 72 PFAS that were missed by target screening. Predicted no effect concentrations (PNECs) were derived for each PFAS via a quantitative structure-toxicity relationship (QSTR)-based approach and used for assessment of their environmental risk. Risk characterization revealed 18 PFAS of environmental concern in at least one matrix. The presence of PFAS in all studied environmental compartments across the DRB indicates a potentially large-scale migration of PFAS in Europe, which might require their further systematic regulatory monitoring., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Peak prioritization is one of the key steps in non-target screening of environmental samples to direct the identification efforts to relevant and important features. Occurrence of chemicals is sometimes a function of time and their presence in consecutive days (trend) reveals important aspects such as discharges from agricultural, industrial or domestic activities. This study presents a validated computational framework based on deep learning conventional neural network to classify trends of chemicals over 30 consecutive days of sampling in two sampling sites (upstream and downstream of a river). From trend analysis and factor analysis, the chemicals could be classified into periodic, spill, increasing, decreasing and false trend. The developed method was validated with list of 42 reference standards (target screening) and applied to samples. 25 compounds were selected by the deep learning and identified via non-target screening. Three classes of surfactants were identified for the first time in river water and two of them were never reported in the literature. Overall, 21 new homologous series of the newly identified surfactants were tentatively identified. The aquatic toxicity of the identified compounds was estimated by in silico tools and a few compounds along with their homologous series showed potential risk to aquatic environment., (Copyright © 2022 Elsevier B.V. All rights reserved.)

During ozonation in wastewater treatment plants, ozone reacts with emerging pollutants, which are partially removed through the secondary treatment, as long as, with their biotransformation products, triggering the formation of ozonation transformation products (TPs). Although the transformation of parent compounds (PCs) and their metabolites has been reported in the literature, the probable transformation of biotransformation products has not been investigated so far. This study evaluates the fate of citalopram (CTR) and four of its biotransformation products (DESCTR, CTRAM, CTRAC and CTROXO) during ozonation experiments. A Gaussian curve-based trend analysis was performed for the first time for the automated detection of TPs in ozone concentrations ranging from 0.06 to 12 mg/L. In total 46 ozonation TPs were detected; 7 TPs of CTR, 10 of DESCTR, 9 of CTRAM, 12 of CTRAC and 8 of CTROXO and were structurally elucidated based on their high resolution tandem mass spectra interpretation and tandem mass spectra similarity with the respective PC. Results have demonstrated that the examined compounds follow common transformation pathways in reaction with ozone and that common TPs were formed through the ozonation of different structurally-alike compounds. Moreover, the toxicity of the identified TPs was predicted with an in-house risk assessment program., (Copyright © 2021 Elsevier B.V. All rights reserved.)

This study presents the development and validation of a comprehensive quantitative target methodology for the analysis of 2316 emerging pollutants in water based on Ultra-Performance Liquid Chromatography Quadrupole-Time-Of-Flight Mass Spectrometry (UPLC-Q-ToF-HRMS/MS). Target compounds include pesticides, pharmaceuticals, drugs of abuse, industrial chemicals, doping compounds, surfactants and transformation products, among others. The method was validated for 195 analytes, chosen to be representative of the chemical space of the target list, enabling the assessment of the performance of the method. The method involves a generic sample preparation based on mixed mode solid phase extraction, a UPLC-QTOF-MS/MS screening method using Data Independent Acquisition (DIA) mode, which provides MS and MS/MS spectra simultaneously and an elaborate strong post-acquisition evaluation of the data. The processing method was optimized to provide a successful identification rate >95 % and to minimize the number of false positive results (< 5 %). Decision limit (CCα) and detection capability (CCβ) were also introduced in the validation scheme to provide more realistic metrics on the performance of a HRMS-based wide-scope screening method. A new system of identification points (IPs) based on the one described in the Commission Decision 2002/657/EC was applied to communicate the confidence level in the identification of the analytes. This system considers retention time, mass accuracy, isotopic fit and fragmentation; taking full advantage of the capacities of the HRMS instruments. Finally, 398 contaminants were detected and quantified in real wastewater., Competing Interests: Declaration of Competing Interest All the authors declare no conflict of interest, (Copyright © 2019 Elsevier B.V. All rights reserved.)

Hydrophilic interaction liquid chromatography (HILIC) and reversed phase LC (RPLC) coupled to high resolution mass spectrometry (HRMS) are widely used for the identification of suspects and unknown compounds in the environment. For the identification of unknowns, apart from mass accuracy and isotopic fitting, retention time (t R ) and MS/MS spectra evaluation is required. In this context, a novel comprehensive workflow was developed to study the t R behavior of large groups of emerging contaminants using Quantitative Structure-Retention Relationships (QSRR). 682 compounds were analyzed by HILIC-HRMS in positive Electrospray Ionization mode (ESI). Moreover, an extensive dataset was built for RPLC-HRMS including 1830 and 308 compounds for positive and negative ESI, respectively. Support Vector Machines (SVM) was used to model the t R data. The applicability domains of the models were studied by Monte Carlo Sampling (MCS) methods. The MCS method was also used to calculate the acceptable error windows for the predicted t R from various LC conditions. This paper provides validated models for predicting t R in HILIC/RPLC-HRMS platforms to facilitate identification of new emerging contaminants by suspect and non-target HRMS screening, and were applied for the identification of transformation products (TPs) of emerging contaminants and biocides in wastewater and sludge., (Copyright © 2018 Elsevier B.V. All rights reserved.)