In this article, a dataset from a collaborative non-target screening trial organized by the NORMAN Association is used to review the state-of-the-art and discuss future perspectives of non-target screening using high resolution mass spectrometry in water analysis. A total of 18 institutes from 12 European countries analysed an extract of the same water sample collected from the River Danube with either one or both of liquid and gas chromatography coupled with mass spectrometric detection. This article focuses mainly on the use of high resolution screening techniques with target, suspect and non-target workflows to identify substances in environmental samples. Specific examples are given to highlight major challenges such as isobaric and co-eluting substances, dependence on target and suspect lists, formula assignment, the use of retention information and the confidence of identification. Approaches and methods applicable to unit resolution data are also discussed. While most substances were identified using high resolution data with target and suspect screening approaches, some participants proposed tentative non-target identifications. This comprehensive dataset revealed that non-target analytical techniques are already considerably harmonized between the participants, but the data processing remains time-consuming. Although the dream of a "fully-automated identification workflow" remains elusive in the short-term, important steps in this direction have been taken, exemplified in the growing popularity of suspect screening approaches. Major recommendations to improve non-target screening include better integration and connection of desired features into software packages, the exchange of target and suspect lists and the contribution of more spectra from standard substances into (openly accessible) databases.

An integrated workflow based on liquid chromatography coupled to a quadrupole-time-of-flight mass spectrometer (LC-QTOF-MS) was developed and applied to detect and identify suspect and unknown contaminants in Greek wastewater. Tentative identifications were initially based on mass accuracy, isotopic pattern, plausibility of the chromatographic retention time and MS/MS spectral interpretation (comparison with spectral libraries, in silico fragmentation). Moreover, new specific strategies for the identification of metabolites were applied to obtain extra confidence including the comparison of diurnal and/or weekly concentration trends of the metabolite and parent compounds and the complementary use of HILIC. Thirteen of 284 predicted and literature metabolites of selected pharmaceuticals and nicotine were tentatively identified in influent samples from Athens and seven were finally confirmed with reference standards. Thirty four nontarget compounds were tentatively identified, four were also confirmed. The sulfonated surfactant diglycol ether sulfate was identified along with others in the homologous series (SO4C2H4(OC2H4)xOH), which have not been previously reported in wastewater. As many surfactants were originally found as nontargets, these compounds were studied in detail through retrospective analysis.

A B S T R A C TIdentification of transformation products (TPs) of emerging pollutants is challenging, due to the vast number of compounds, mostly unknown, the complexity of the matrices and their often low concentrations, requiring highly selective, highly sensitive techniques. We compile background information on biotic and abiotic formation of TPs and analytical developments over the past five years. We present a database of biotic or abiotic TPs compiled from those identified in recent years. We discuss mass spectrometric (MS) techniques and workflows for target, suspect and non-target screening of TPs with emphasis on liquid chromatography coupled to MS (LC-MS). Both low-and high-resolution (HR) mass analyzers have been applied, but HR-MS is the technique of choice, due to its high confirmatory capabilities, derived from the high resolving power and the mass accuracy in MS and MS/MS modes, and the sophisticated software developed.

Over the past decade, the application of liquid chromatography-high resolution mass spectroscopy (LC-HRMS) has been growing extensively due to its ability to analyze a wide range of suspected and unknown compounds in environmental samples. However, various criteria, such as mass accuracy and isotopic pattern of the precursor ion, MS/MS spectra evaluation, and retention time plausibility, should be met to reach a certain identification confidence. In this context, a comprehensive workflow based on computational tools was developed to understand the retention time behavior of a large number of compounds belonging to emerging contaminants. Two extensive data sets were built for two chromatographic systems, one for positive and one for negative electrospray ionization mode, containing information for the retention time of 528 and 298 compounds, respectively, to expand the applicability domain of the developed models. Then, the data sets were split into training and test set, employing k-nearest neighborhood clustering, to build and validate the models’ internal and external prediction ability. The best subset of molecular descriptors was selected using genetic algorithms. Multiple linear regression, artificial neural networks, and support vector machines were used to correlate the selected descriptors with the experimental retention times. Several validation techniques were used, including Golbraikh–Tropsha acceptable model criteria, Euclidean based applicability domain, modified correlation coefficient (r m 2), and concordance correlation coefficient values, to measure the accuracy and precision of the models. The best linear and nonlinear models for each data set were derived and used to predict the retention time of suspect compounds of a wide-scope survey, as the evaluation data set. For the efficient outlier detection and interpretation of the origin of the prediction error, a novel procedure and tool was developed and applied, enabling us to identify if the suspect compound was in the applicability domain or not.

The occurrence and fate of 5 cyclic (D3 to D7) and 12 linear (L3 to L14) siloxanes were investigated in raw and treated wastewater (both particulate and dissolved phases) as well as in sludge from a wastewater treatment plant (WWTP) in Athens, Greece. Cyclic and linear siloxanes (except for L3) were detected in all influent wastewater and sludge samples at mean concentrations of (sum of 17 siloxanes) 20 μg L(-1) and 75 mg kg(-1), respectively. The predominant compounds in wastewater were L11 (24% of the total siloxane concentration), L10 (16%), and D5 (13%), and in sludge were D5 (20%) and L10 (15%). The distribution of siloxanes between particulate and dissolved phases in influents differed significantly for linear and cyclic siloxanes. Linear siloxanes showed higher solid-liquid distribution coefficients (log K(d)) than did cyclic compounds. For 10 of the 16 compounds detected in influents, the removal efficiency was higher than 80%. Sorption to sludge and biodegradation and/or volatilization losses are important factors that affect the fate of siloxanes in WWTPs. The mean total mass of siloxanes that enter into the WWTP via influent was 15.1 kg per day(-1), and the mean total mass released into the environment via effluent was 2.67 kg per day(-1).