New PFASs Identified in AFFF Impacted Groundwater by Passive Sampling and Nontarget Analysis.

Monitoring contamination from per- and polyfluoroalkyl substances (PFASs) in water systems impacted by aqueous film-forming foams (AFFFs) typically addresses a few known PFAS groups. Given the diversity of PFASs present in AFFFs, current analytical approaches do not comprehensively address the range of PFASs present in these systems. A suspect-screening and nontarget analysis (NTA) approach was developed and applied to identify novel PFASs in groundwater samples contaminated from historic AFFF use. A total of 88 PFASs were identified in both passive samplers and grab samples, and these were dominated by sulfonate derivatives and sulfonamide-derived precursors. Several ultrashort-chain (USC) PFASs (≤C3) were detected, 11 reported for the first time in Australian groundwater. Several transformation products were identified, including perfluoroalkane sulfonamides (FASAs) and perfluoroalkane sulfinates (PFASis). Two new PFASs were reported (((perfluorohexyl)sulfonyl)sulfamic acid; m/z 477.9068 and (E)-1,1,2,2,3,3,4,5,6,7,8,8,8-tridecafluorooct-6-ene-1-sulfonic acid; m/z 424.9482). This study highlights that several PFASs are overlooked using standard target analysis, and therefore, the potential risk from all PFASs present is likely to be underestimated.

Influence of extraction windows for data-independent acquisition on feature annotation during suspect screening.

Non-target analysis (NTA) using high-resolution mass spectrometry is becoming a useful approach to screen for suspect and unknown chemicals. For comprehensive analyses, data-independent acquisition (DIA), like Sequential Windowed Acquisition of all THeoretical Mass Spectra (SWATH-MS) on Sciex instruments, is necessary, usually followed by library matching for feature annotation. The choice of parameters, such as acquisition window number and size, may influence the comprehensiveness of the suspect features detected. The goal of this study was to assess how mass spectrometric DIA settings may influence the ability to obtain confident annotations and identifications of features in environmental (river water, passive sample extract (PSE)), wastewater (unpreserved and acidified) and biological (urine) sample matrices. Each matrix was analysed using 11 different MS methods, with 5-15 variable size acquisition windows. True positive (TP) annotation (i.e., matching experimental and library spectra) rates were constant for PSE (40%) and highest for urine (18%), wastewater (34% and 36%, unpreserved and acidified, respectively) and river water (8%) when using higher numbers of windows (15). The number of annotated features was highest for PSE (12%) and urine (8.5%) when using more acquisition windows (9 and 14, respectively). Less complex matrices (based on average total ion chromatogram intensities) like river water, unpreserved and acidified wastewater have higher annotation rates (7.5%, 8% and 13.2%, respectively) when using less acquisition windows (5-6), indicating matrix dependency of optimum settings. Library scores varied widely for correct (scores between 6 and 100) as well as incorrect annotations (scores between 2 and 100), making it hard to define specific ideal cut-off values. Results highlight the need for properly curated libraries and careful optimization of SWATH-MS and other DIA methods for each individual matrix, finding the best ratio of total annotations to true positive, (i.e., correct) annotations to achieve best NTA results.

Is Nontarget Analysis Ready for Regulatory Application? Influence of Peak-Picking Algorithms on Data Analysis.

The use of peak-picking algorithms is an essential step in all nontarget analysis (NTA) workflows. However, algorithm choice may influence reliability and reproducibility of results. Using a real-world data set, the aim of this study was to investigate how different peak-picking algorithms influence NTA results when exploring temporal and/or spatial trends. For this, drinking water catchment monitoring data, using passive samplers collected twice per year across Southeast Queensland, Australia (n = 18 sites) between 2014 and 2019, was investigated. Data were acquired using liquid chromatography coupled to high-resolution mass spectrometry. Peak picking was performed using five different programs/algorithms (SCIEX OS, MSDial, self-adjusting-feature-detection, two algorithms within MarkerView), keeping parameters identical whenever possible. The resulting feature lists revealed low overlap: 7.2% of features were picked by >3 algorithms, while 74% of features were only picked by a single algorithm. Trend evaluation of the data, using principal component analysis, showed significant variability between the approaches, with only one temporal and no spatial trend being identified by all algorithms. Manual evaluation of features of interest (p-value <0.01, log fold change >2) for one sampling site revealed high rates of incorrectly picked peaks (>70%) for three algorithms. Lower rates (<30%) were observed for the other algorithms, but with the caveat of not successfully picking all internal standards used as quality control. The choice is therefore currently between comprehensive and strict peak picking, either resulting in increased noise or missed peaks, respectively. Reproducibility of NTA results remains challenging when applied for regulatory frameworks.

Estimation of per- and poly-fluoroalkyl substances mass loads in the Danube River using passive sampling.

As Europe's second longest river, the Danube is an important water source for drinking water and irrigation for many countries, before discharging into the Black Sea in the East. Per- and poly-fluoroalkyl substances (PFAS) have been observed over the last two decades in concentrations exceeding the European Union's drinking water guidelines for total sum of 20 select PFAS of 0.1 µg L-1. Their presence is a result of current and historical use and high environmental persistence, necessitating their monitoring for human risk assessments. The aim of this study is to use recently developed passive sampling technology to calculate time-integrated water concentrations and mass loads of 11 select PFAS at 9 sites along the Danube River. Results indicate ∑11 PFAS concentrations in the range of 9.3-29.6 ng L-1 were not in exceedance of EU drinking water guidelines, but perfluorooctanesulfonic acid (PFOS) was in exceedance of the environmental quality standard (0.65 ng L-1) at all sampling locations. The highest ∑11 PFAS mass loads were observed at Ruse (9.5 kg day-1) and Budapest (6.3 kg day-1), believed to be driven by proximity to industrial facilities and large populations (urban runoff). Finally, we estimate 4.9 kg of total PFAS (∑11 PFAS) were delivered to the Black Sea daily over Summer 2019.

Inter-laboratory mass spectrometry dataset based on passive sampling of drinking water for non-target analysis.

Non-target analysis (NTA) employing high-resolution mass spectrometry is a commonly applied approach for the detection of novel chemicals of emerging concern in complex environmental samples. NTA typically results in large and information-rich datasets that require computer aided (ideally automated) strategies for their processing and interpretation. Such strategies do however raise the challenge of reproducibility between and within different processing workflows. An effective strategy to mitigate such problems is the implementation of inter-laboratory studies (ILS) with the aim to evaluate different workflows and agree on harmonized/standardized quality control procedures. Here we present the data generated during such an ILS. This study was organized through the Norman Network and included 21 participants from 11 countries. A set of samples based on the passive sampling of drinking water pre and post treatment was shipped to all the participating laboratories for analysis, using one pre-defined method and one locally (i.e. in-house) developed method. The data generated represents a valuable resource (i.e. benchmark) for future developments of algorithms and workflows for NTA experiments.

Column bleed in the analysis of highly polar substances: an overlooked aspect in HRMS.

To close the "analytical gap" in the liquid chromatographic (LC) analysis of highly polar substances, two techniques which have been suggested earlier were tested in terms of retention factors and detection limits: hydrophilic interaction liquid chromatography (HILIC) and mixed-mode chromatography (MMC). A substance mix of 55 analytes ranging from logD - 8.2 to 3.4 and 17 different LC columns, also comprising additional reversed-phase columns were used. Contrary to most reversed-phase columns, column bleed has been identified as an important factor, which may cause serious restrictions during high-resolution mass spectrometric detection (HRMS). We found that highly abundant background masses continuously eluting from the columns heavily influence ion transmission to the detector. As a result, the linear dynamic range as well as the sensitivity decreases and thus limits the HRMS applicability of some columns. We therefore recommend a thorough investigation of ion transmission during HRMS method development. This will help to maintain the high potential of HRMS in terms of qualitative and quantitative screening analysis.