Combining Advanced Analytical Methodologies to Uncover Suspect PFAS and Fluorinated Pharmaceutical Contributions to Extractable Organic Fluorine in Human Serum (Tromsø Study).

A growing number of studies have reported that routinely monitored per- and polyfluoroalkyl substances (PFAS) are not sufficient to explain the extractable organic fluorine (EOF) measured in human blood. In this study, we address this gap by screening pooled human serum collected over 3 decades (1986-2015) in Tromsø (Norway) for >5000 PFAS and >300 fluorinated pharmaceuticals. We combined multiple analytical techniques (direct infusion Fourier transform ion cyclotron resonance mass spectrometry, liquid chromatography-Orbitrap-high-resolution mass spectrometry, and total oxidizable precursors assay) in a three-step suspect screening process which aimed at unequivocal suspect identification. This approach uncovered the presence of one PFAS and eight fluorinated pharmaceuticals (including some metabolites) in human serum. While the PFAS suspect only accounted for 2-4% of the EOF, fluorinated pharmaceuticals accounted for 0-63% of the EOF, and their contribution increased in recent years. Although fluorinated pharmaceuticals often contain only 1-3 fluorine atoms, our results indicate that they can contribute significantly to the EOF. Indeed, the contribution from fluorinated pharmaceuticals allowed us to close the organofluorine mass balance in pooled serum from 2015, indicating a good understanding of organofluorine compounds in humans. However, a portion of the EOF in human serum from 1986 and 2007 still remained unexplained.

A multi-platform approach for the comprehensive analysis of per- and polyfluoroalkyl substances (PFAS) and fluorine mass balance in commercial ski wax products.

The unique properties of per- and polyfluoroalkyl substances (PFAS) have led to their extensive use in consumer products, including ski wax. Based on the risks associated with PFAS, and to align with PFAS regulations, the international ski federation (FIS) implemented a ban on products containing "C8 fluorocarbons/perfluorooctanoate (PFOA)" at all FIS events from the 2021/2022 season, leading manufactures to shift their formulations towards short-chain PFAS chemistries. To date, most studies characterising PFAS in ski waxes have measured a suite of individual substances using targeted analytical approaches. However, the fraction of total fluorine (TF) in the wax accounted for by these substances remains unclear. In this study, we sought to address this question by applying a multi-platform, fluorine mass balance approach to a total of 10 commercially available ski wax products. Analysis of TF by combustion ion chromatography (CIC) revealed concentrations of 1040-51700 µg F g-1 for the different fluorinated waxes. In comparison, extractable organic fluorine (EOF) determined in methanol extracts by CIC (and later confirmed by inductively-coupled plasma-mass spectrometry and 19F- nuclear magnetic resonance spectroscopy) ranged from 92 to 3160 µg g-1, accounting for only 3-8.8 % of total fluorine (TF). Further characterisation of extracts by cyclic ion mobility-mass spectrometry (IMS) revealed 15 individual PFAS with perfluoroalkyl carboxylic acid concentrations up to 33 µg F g-1, and 3 products exceeding the regulatory limit for PFOA (0.025 µg g-1) by a factor of up to 100. The sum of all PFAS accounted for only 0.01-1.0 % of EOF, implying a high percentage of unidentified PFAS, thus, pyrolysis gas chromatography-mass spectrometry was used to provide evidence of the nature of the non-extractable fluorine present in the ski wax products.

Closing the Organofluorine Mass Balance in Marine Mammals Using Suspect Screening and Machine Learning-Based Quantification.

High-resolution mass spectrometry (HRMS)-based suspect and nontarget screening has identified a growing number of novel per- and polyfluoroalkyl substances (PFASs) in the environment. However, without analytical standards, the fraction of overall PFAS exposure accounted for by these suspects remains ambiguous. Fortunately, recent developments in ionization efficiency (IE) prediction using machine learning offer the possibility to quantify suspects lacking analytical standards. In the present work, a gradient boosted tree-based model for predicting log IE in negative mode was trained and then validated using 33 PFAS standards. The root-mean-square errors were 0.79 (for the entire test set) and 0.29 (for the 7 PFASs in the test set) log IE units. Thereafter, the model was applied to samples of liver from pilot whales (n = 5; East Greenland) and white beaked dolphins (n = 5, West Greenland; n = 3, Sweden) which contained a significant fraction (up to 70%) of unidentified organofluorine and 35 unquantified suspect PFASs (confidence level 2-4). IE-based quantification reduced the fraction of unidentified extractable organofluorine to 0-27%, demonstrating the utility of the method for closing the fluorine mass balance in the absence of analytical standards.