Per- and polyfluoroalkyl substances (PFAS) are frequently used in the production of rubber and plastic, but little is known about the identity, concentration, or prevalence of PFAS in these products. In this study, a representative sample of plastic- and rubber-containing artificial turf (AT) fields from Stockholm, Sweden, was subjected to total fluorine (TF), extractable organic fluorine (EOF), and target PFAS analysis. TF was observed in all 51 AT samples (ranges of 16–313, 12–310, and 24–661 μg of F/g in backing, filling, and blades, respectively), while EOF and target PFAS occurred in <42% of all samples (<200 and <1 ng of F/g, respectively). A subset of samples extracted with water confirmed the absence of fluoride. Moreover, application of the total oxidizable precursor assay revealed negligible perfluoroalkyl acid (PFAA) formation across all three sample types, indicating that the fluorinated substances in AT are not low-molecular weight PFAA precursors. Collectively, these results point toward polymeric organofluorine (e.g., fluoroelastomer, polytetrafluoroethylene, and polyvinylidene fluoride), consistent with patent literature. The combination of poor extractability and recalcitrance toward advanced oxidation suggests that the fluorine in AT does not pose an imminent risk to users. However, concerns surrounding the production and end of life of AT, as well as the contribution of filling and blades to environmental microplastic contamination, remain. 

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.

Most known per- and polyfluoroalkyl substances (PFAS) bioaccumulate by binding to proteins or partitioning to phospholipids, leading to their prevalence in liver and blood. As a result, efforts to improve PFAS exposure assessments by identifying novel bioaccumulative substances have focused on these tissues. However, the recent discovery of high concentrations of unidentified extractable organofluorine (EOF) in the blubber of a killer whale (Orcinus orca) from Greenland suggests that some fluorinated substances bioaccumulate preferentially in storage lipids. The present work builds on this initial finding by characterizing EOF in an additional 3 killer whales (2 from Greenland, 1 from Sweden), and then analyzing extracts from all 4 whales via gas chromatography-atmospheric pressure chemical ionization-ion mobility mass spectrometry. Using collision cross sections, we prioritized features suspected to be highly fluorinated, and then selected 5 for manual annotation. Custom synthesised standards confirmed 10:2 and 12:2 fluorotelomer methylsulfone, 10:2 and 12:2 fluorotelomer chloromethylsulfone, and 6:2 bisfluorotelomer sulfone in all blubber samples from Greenland at concentrations ranging from <0.4-72.5 ng/g, explaining 34-75% of blubber EOF. None of these substances were observable in liver, suggesting preferential accumulation in storage lipids. To the best of our knowledge, this is the first report of fluorotelomer sulfones in wildlife and the first observation of lipophilic, highly fluorinated PFAS.

The present work applied gas chromatography - atmospheric pressure chemical ionization (GC-APCI) and liquid chromatography electrospray ionisation (LC-ESI) together with ion mobility - high resolution mass spectrometry (IM-HRMS) for comprehensive, nontarget characterization of organohalogen compounds (OHCs) in municipal waste water treatment plant (MWWTP) sludge and dust/sludge standard reference material (SRM) extracts. Three data processing strategies were applied to reduce the size of the dataset and prioritize OHCs: 1) isotope-based prioritization, which utilizes either bromine or chlorine isotope patterns, or in the case of fluorinated substances, the carbon isotope pattern together with a specific range of mass over number of carbon atoms (m/C); 2) collision cross section (CCS)-based prioritization, which takes advantage of the tendency for OHCs to have lower CCS values compared to non-halogenated compounds with similar m/z; and finally, 3) mass defect prioritization, which can be used as a filter given the characteristic negative mass defect of halogens. Following GC instrumental analysis, 3,890 out of a total of 17,982 features were prioritized as potential organo- bromines and -chorines, while following LC analysis, 7,440 out of 41,206 features were prioritized by one or more of the prioritization filters. Features matching ≥2 (GC) or 3 (LC) of the prioritizations were further inspected. Exact masses for these features were used to determine a putative molecular formula constrained by the number of halogens according to the isotope pattern. In sludge extracts and/or indoor dust SRM, GC analysis identified 54 legacy pollutants, including 32 polychlorinated biphenyls (PCBs), 10 polybrominated diphenyl ethers (PBDEs), 3 chlorinated organophosphate esters (Cl-OPEs), 2 dichlorodiphenyltrichloroethanes (DDTs) and 4 DDT metabolites at confidence levels (CLs) of 1-2b. In addition, 30 halogenated compounds were identified at confidence levels 4-5, including pesticides and a pharmaceutical metabolite. Using LC, analysis of MWWTP sludge and/or SRMs (dust/sludge), resulted in 36 PFAS identified at CL 1-2b, and 39 PFAS at CL 3-5, including linear and branched perfluoroalkyl acids (PFAAs) and PFAA-precursors, as well as unsaturated and H-substitute PFSAs. This work highlights the utility of universal OHC-based prioritization strategies which can be applied to any sample regardless of the experimental design.