1. Collection of water samples
2. Sample extraction
NOTE: PFAS are ubiquitous and persistent. Ensure that all solvents are of the highest grade and have been analyzed for low level PFAS contamination. Thoroughly rinse all laboratory equipment used for preparing standards before preparing blanks and samples.
Quantitative LC-MS/MS results are in the form of ion-chromatograms for the total ion chromatogram (TIC) and the extracted ion chromatograms (EIC) of specific chemical transitions for measured chemicals (Figure 1). The integrated peak area of a chemical transition is related to the compound abundance and can be used to calculate the exact concentration using a calibration curve normalized to an internal standard (Figure 2). Low or flat response of individual analytes indicates that the calibration range is outside the linear range of the mass spectrometer, or that the instrument requires tuning/calibration. Poor precision of replicates indicates an issue with sample injection or inconsistent chromatography that requires modification of LC parameters.
Non-targeted analysis using a full MS1 scan yields a TIC for samples (Figure 3), which allows for ad hoc generation of EICs for individual ions (Figure 4). Any given chromatographic time point contains signals for chemical species, and when using a high-resolution mass spectrometer, the isotopic fingerprint of the compound. Identifying compounds from the MS1 scan is performed programmatically by a peak-picking algorithm using one of several approaches38,39,40. Peak picking yields chemical features with a measured accurate mass and chromatographic retention time, as well as the mass spectrum of the ion and the chromatographic peak area. This information is typically stored in a digital database format for further processing and filtering, but the nested and interconnected nature of the data can be understood conceptually (Figure 5).
The feature list is filtered for compounds meeting one of several criteria to be selected for further investigation. The first and most straightforward is filtering by mass defect (the difference between the exact mass of a feature and its nominal mass). PFAS compounds have negative mass defects (Figure 6) due to their preponderance of fluorine atoms, and polyfluorinated compounds have positive, but substantially smaller mass defects than homologous organic materials31,34. A second method filtering step is to identify homologous series containing repeating units common to PFAS species, such as CF2 or CF2O. Identifying these can be done using Kendrick Mass defect plots17,36, or software packages such as R's nontarget package35 (Figure 7).
Following filtering, assignment of chemical identity on the shortlist of highly differentially observed and/or tentatively per/polyfluorinated species can begin. Accurate mass provides a relatively small list of potential chemical formulas for matching but is insufficient for identification without the addition of spectral matching to isotope pattern of the mass spectrum41. From high resolution MS1 data, one or more putative chemical formulas are matched against the isotopic fingerprint of the mass spectrum and scored (Figure 8). Formulas for matching can be generated ab initio using a defined pool of atoms or can be sourced from a combination of literature reported compounds and the contents of one or more databases. The US EPA Chemistry Dashboard (https://comptox.epa.gov/dashboard/) hosts a constantly updated list of PFAS compounds identified by the agency, as well as lists compiled by other organizations such as the NORMAN Network42.
Chemical formulas can be further confirmed, and some structural information can be garnered from MS/MS spectra (Figure 9). Candidate structures are available from large chemical databases such as the EPA chemistry dashboard, Pubchem, the CAS registry, etc. Predicted spectra can be generated or acquired using a variety of fragmentation programs and assigned,43 or MS/MS spectra can be interpreted manually.
An example data matrix is available in the Supplemental Information containing a whole feature matrix from ten samples (5 upstream, 5 downstream) collected upstream and downstream of a fluorochemical point source. Each row represents a chemical feature with associated retention time, neutral mass, mass spectrum, and raw abundance for each sample. (Supplemental Table, Sheet 1). Initial filtering (Supplemental Table, Sheet 2) for negative mass defect and statistical significance in an unpaired t-test between upstream and downstream reduces the number of "interesting" chemical features to ~120. Predicted chemical formulae were obtained from Agilent IDBrowser and searched against the EPA Comptox Chemicals Dashboard, which returned possible matches (Supplemental Table, Sheet 3). The "top-hit" for each chemical formula based on data sources37 was assigned (Supplemental Table, Sheet 4). Note that more than half of the remaining features do not have high quality matches. Identified features with no matches can be the result of in-source fragmentation/adduct formation, poor formula assignment, or the identification of PFASs not found in the source database. Interpretation of the raw spectra in order to validate assignments is beyond the scope of this manuscript but more information can be found in the works cited15,30,31,44,45.
ID | Sample Name | Sample Type | Std Conc | Vial | LC Method | MS Method |
1 | DB_001 | Blank | 1:A,1 | PFAS grad 400uL/min – 9 min run | PFCMXA + HFPO-DA MS/MS – 9 min | |
2 | DB_002 | Blank | 1:A,1 | PFAS grad 400uL/min – 9 min run | PFCMXA + HFPO-DA MS/MS – 9 min | |
3 | DB_003 | Blank | 1:A,1 | PFAS grad 400uL/min – 9 min run | PFCMXA + HFPO-DA MS/MS – 9 min | |
4 | DB_004 | Blank | 1:A,1 | PFAS grad 400uL/min – 9 min run | PFCMXA + HFPO-DA MS/MS – 9 min | |
5 | DB_005 | Blank | 1:A,1 | PFAS grad 400uL/min – 9 min run | PFCMXA + HFPO-DA MS/MS – 9 min | |
6 | FB | Blank | 1:A,2 | PFAS grad 400uL/min – 9 min run | PFCMXA + HFPO-DA MS/MS – 9 min | |
7 | 10 std | Standard | 10 | 1:A,3 | PFAS grad 400uL/min – 9 min run | PFCMXA + HFPO-DA MS/MS – 9 min |
8 | 25 std | Standard | 25 | 1:A,4 | PFAS grad 400uL/min – 9 min run | PFCMXA + HFPO-DA MS/MS – 9 min |
9 | 50 std | Standard | 50 | 1:A,5 | PFAS grad 400uL/min – 9 min run | PFCMXA + HFPO-DA MS/MS – 9 min |
10 | 100 std | Standard | 100 | 1:A,6 | PFAS grad 400uL/min – 9 min run | PFCMXA + HFPO-DA MS/MS – 9 min |
11 | 250 std | Standard | 250 | 1:A,7 | PFAS grad 400uL/min – 9 min run | PFCMXA + HFPO-DA MS/MS – 9 min |
12 | 500 std | Standard | 500 | 1:A,8 | PFAS grad 400uL/min – 9 min run | PFCMXA + HFPO-DA MS/MS – 9 min |
13 | 750 std | Standard | 750 | 1:B,1 | PFAS grad 400uL/min – 9 min run | PFCMXA + HFPO-DA MS/MS – 9 min |
14 | 1000 std | Standard | 1000 | 1:B,2 | PFAS grad 400uL/min – 9 min run | PFCMXA + HFPO-DA MS/MS – 9 min |
15 | DB_006 | Blank | 1:B,3 | PFAS grad 400uL/min – 9 min run | PFCMXA + HFPO-DA MS/MS – 9 min | |
16 | SB_DUP1 | Analyte | 1:B,4 | PFAS grad 400uL/min – 9 min run | PFCMXA + HFPO-DA MS/MS – 9 min | |
17 | SB_DUP2 | Analyte | 1:B,5 | PFAS grad 400uL/min – 9 min run | PFCMXA + HFPO-DA MS/MS – 9 min | |
18 | SW Site 03 | Analyte | 1:B,6 | PFAS grad 400uL/min – 9 min run | PFCMXA + HFPO-DA MS/MS – 9 min | |
19 | SW Site 16 | Analyte | 1:B,7 | PFAS grad 400uL/min – 9 min run | PFCMXA + HFPO-DA MS/MS – 9 min | |
20 | SW Site 30 | Analyte | 1:B,8 | PFAS grad 400uL/min – 9 min run | PFCMXA + HFPO-DA MS/MS – 9 min | |
21 | DB_007 | Analyte | 1:C,1 | PFAS grad 400uL/min – 9 min run | PFCMXA + HFPO-DA MS/MS – 9 min | |
22 | SW Site 19 | Analyte | 1:C,2 | PFAS grad 400uL/min – 9 min run | PFCMXA + HFPO-DA MS/MS – 9 min | |
23 | SW Site 48 | Analyte | 1:C,3 | PFAS grad 400uL/min – 9 min run | PFCMXA + HFPO-DA MS/MS – 9 min | |
24 | SW Site 49 | Analyte | 1:C,4 | PFAS grad 400uL/min – 9 min run | PFCMXA + HFPO-DA MS/MS – 9 min | |
25 | SW Site 05 | Analyte | 1:C,5 | PFAS grad 400uL/min – 9 min run | PFCMXA + HFPO-DA MS/MS – 9 min | |
26 | SW Site 47 | Blank | 1:C,6 | PFAS grad 400uL/min – 9 min run | PFCMXA + HFPO-DA MS/MS – 9 min | |
27 | DB_008 | Analyte | 1:C,7 | PFAS grad 400uL/min – 9 min run | PFCMXA + HFPO-DA MS/MS – 9 min | |
28 | SW Site 19_DUP | Analyte | 1:C,8 | PFAS grad 400uL/min – 9 min run | PFCMXA + HFPO-DA MS/MS – 9 min | |
29 | SW Site 20 | Analyte | 1:D,1 | PFAS grad 400uL/min – 9 min run | PFCMXA + HFPO-DA MS/MS – 9 min | |
30 | SW Site 21 | Analyte | 1:D,2 | PFAS grad 400uL/min – 9 min run | PFCMXA + HFPO-DA MS/MS – 9 min | |
31 | SW Site 46 | Analyte | 1:D,3 | PFAS grad 400uL/min – 9 min run | PFCMXA + HFPO-DA MS/MS – 9 min | |
32 | SW Site 47 | Analyte | 1:D,4 | PFAS grad 400uL/min – 9 min run | PFCMXA + HFPO-DA MS/MS – 9 min | |
33 | DB_009 | Blank | 1:D,5 | PFAS grad 400uL/min – 9 min run | PFCMXA + HFPO-DA MS/MS – 9 min | |
28 | SW Site 32 | Analyte | 1:D,6 | PFAS grad 400uL/min – 9 min run | PFCMXA + HFPO-DA MS/MS – 9 min | |
29 | SW Site 50 | Analyte | 1:D,7 | PFAS grad 400uL/min – 9 min run | PFCMXA + HFPO-DA MS/MS – 9 min | |
30 | SW Site 25 | Analyte | 1:D,8 | PFAS grad 400uL/min – 9 min run | PFCMXA + HFPO-DA MS/MS – 9 min | |
31 | SW Site 21_DUP | Analyte | 1:E,1 | PFAS grad 400uL/min – 9 min run | PFCMXA + HFPO-DA MS/MS – 9 min | |
32 | SW Site 52 | Analyte | 1:E,2 | PFAS grad 400uL/min – 9 min run | PFCMXA + HFPO-DA MS/MS – 9 min | |
33 | DB_010 | Blank | 1:E,3 | PFAS grad 400uL/min – 9 min run | PFCMXA + HFPO-DA MS/MS – 9 min | |
34 | FB | Blank | 1:A,2 | PFAS grad 400uL/min – 9 min run | PFCMXA + HFPO-DA MS/MS – 9 min | |
35 | 10 std | Standard | 10 | 1:A,3 | PFAS grad 400uL/min – 9 min run | PFCMXA + HFPO-DA MS/MS – 9 min |
36 | 25 std | Standard | 25 | 1:A,4 | PFAS grad 400uL/min – 9 min run | PFCMXA + HFPO-DA MS/MS – 9 min |
37 | 50 std | Standard | 50 | 1:A,5 | PFAS grad 400uL/min – 9 min run | PFCMXA + HFPO-DA MS/MS – 9 min |
38 | 100 std | Standard | 100 | 1:A,6 | PFAS grad 400uL/min – 9 min run | PFCMXA + HFPO-DA MS/MS – 9 min |
39 | 250 std | Standard | 250 | 1:A,7 | PFAS grad 400uL/min – 9 min run | PFCMXA + HFPO-DA MS/MS – 9 min |
40 | 500 std | Standard | 500 | 1:A,8 | PFAS grad 400uL/min – 9 min run | PFCMXA + HFPO-DA MS/MS – 9 min |
41 | 750 std | Standard | 750 | 1:B,1 | PFAS grad 400uL/min – 9 min run | PFCMXA + HFPO-DA MS/MS – 9 min |
42 | 1000 std | Standard | 1000 | 1:B,2 | PFAS grad 400uL/min – 9 min run | PFCMXA + HFPO-DA MS/MS – 9 min |
43 | DB_011 | Blank | 1:B,2 | PFAS grad 400uL/min – 9 min run | PFCMXA + HFPO-DA MS/MS – 9 min | |
44 | DB_012 | Blank | 1:E,4 | PFAS grad 400uL/min – 9 min run | PFCMXA + HFPO-DA MS/MS – 9 min |
Table 1: Example worklist for Targeted Analysis and quantitation of PFAS using LC-MS/MS
Time (min) 0 |
% A (2.5mM Ammonium Acetate in 5% MeOH) 90 |
% B (2.5mM Ammonium Acetate in 95% MeOH) 10 |
5 | 15 | 85 |
5.1 | 0 | 100 |
7 | 0 | 100 |
7.1 | 90 | 10 |
9 | 90 | 10 |
Table 2: Example gradient for LC separation in targeted analysis
Capilary Voltage (kv) | 1.97 |
Cone Voltage (V) | 15 |
Extractor Voltage (V) | 3 |
RF Lens (V) | 0.3 |
Source Temp | 150 |
Desolvation Temp | 40 |
Desolvation Gas Flow (L/hr) | 300 |
Cone Gas Flow (L/hr) | 2 |
Table 3: Ionization source parameters for targeted analysis
Cmp | Precursor | Product | Dwell Time | Cone Voltage (V) | Collision Energy (eV) |
PFBA | 212.80 | 168.75 | 0.01 | 15 | 10 |
13C4-PFBA IS | 216.80 | 171.75 | 0.01 | 15 | 10 |
PFPeA | 262.85 | 218.75 | 0.01 | 15 | 9 |
PFBS °1 | 298.70 | 79.90 | 0.01 | 40 | 30 |
PFBS °2 | 298.70 | 98.80 | 0.01 | 40 | 28 |
PFHxA °1 | 312.70 | 118.70 | 0.01 | 13 | 21 |
PFHxA °2 | 312.70 | 268.70 | 0.01 | 13 | 10 |
13C2-PFHxA IS | 314.75 | 269.75 | 0.01 | 13 | 9 |
HFPO-DA 1° | 329.16 | 168.90 | 0.01 | 10 | 12 |
HFPO-DA 2° | 329.16 | 284.90 | 0.01 | 10 | 6 |
HFPO-DA IS 1° | 332.16 | 168.90 | 0.01 | 10 | 12 |
HFPO-DA IS 2° | 332.16 | 286.90 | 0.01 | 10 | 6 |
PFHpA °1 | 362.65 | 168.65 | 0.01 | 14 | 17 |
PFHpA °2 | 362.65 | 318.70 | 0.01 | 14 | 10 |
PFHxS °1 | 398.65 | 79.90 | 0.01 | 50 | 38 |
PFHxS °2 | 398.65 | 98.80 | 0.01 | 50 | 32 |
13C4-PFHxS IS | 402.65 | 83.90 | 0.01 | 50 | 38 |
PFOA °1 | 412.60 | 168.70 | 0.01 | 15 | 18 |
PFOA °2 | 412.60 | 368.65 | 0.01 | 15 | 11 |
13C4-PFOA IS | 416.75 | 371.70 | 0.01 | 15 | 11 |
PFNA °1 | 462.60 | 218.75 | 0.01 | 15 | 17 |
PFNA °2 | 462.60 | 418.60 | 0.01 | 15 | 11 |
PFNA IS | 467.60 | 422.60 | 0.01 | 15 | 11 |
PFOS °1 | 498.65 | 79.90 | 0.01 | 60 | 48 |
PFOS °2 | 498.65 | 98.80 | 0.01 | 60 | 38 |
13C4-PFOS IS | 502.60 | 79.70 | 0.01 | 60 | 48 |
PFDA °1 | 512.60 | 218.75 | 0.01 | 16 | 18 |
PFDA °2 | 512.60 | 468.55 | 0.01 | 16 | 12 |
13C2 – PFDA IS | 514.60 | 469.55 | 0.01 | 16 | 12 |
Table 4: Example transition table and MS/MS parameters for the contents of PFAC-MXA, along with HFPO-DA
Time (min) |
% A (2.5mM Ammonium Acetate in 5% MeOH) |
% B (2.5mM Ammonium Acetate in 95% MeOH) |
0 | 90 | 10 |
0.5 | 90 | 10 |
3 | 50 | 50 |
3.5 | 50 | 50 |
5.5 | 40 | 60 |
6 | 40 | 60 |
7 | 0 | 100 |
11 | 0 | 100 |
Table 5: Example gradient for LC separation in non-targeted analysis
Profinder Parameter | Setting Value |
Extraction Peak Height Filter | 800 counts |
Permitted Ion(s) | -H/+H |
Feature Extraction Isotope Model | Common organic molecules |
Allowed Charge States | 2-Jan |
Compound Ion Count Threshold | Two or more ions |
Alignment RT Tolerance | 0.40min + 0.0% |
Alignment Mass Tolerance | 20.00ppm + 2.0mDa |
Post-Processing Absolute Height Filter | >= 10000 counts in one sample |
Post-Processing MFE Score Filter | >= 75 in one sample |
Peak Integration Algorithm | Agile 2 |
Peak Integration Height Filter | >= 5000 counts |
Find by Ion Absolute Height Filter | >= 7500 counts in one sample |
Find by Ion Score Filter | >= 50.00 in one sample |
Table 6: Molecular feature extraction and alignment settings for Profinder software. All unlisted values retained their default settings for data processing.
Ion Abundance Threshold | Feature Thresholds | Replicate Threshold (n =5) | Run Time | Features | Pass Replicate Threshold | Pass CV Threshold | Features to 90% of TIC |
1x S/N | 2000 | None | 8.15 | 987 | 505 | 421 | 91 |
2x S/N | 5000 | None | 5.02 | 707 | 357 | 313 | 93 |
3x S/N | 10000 | None | 2.3 | 308 | 249 | 230 | 93 |
1x S/N | 2000 | 100% | 3.3 | 603 | 339 | 297 | 92 |
2x S/N | 35000 | 100% | 1.58 | 310 | 248 | 229 | 93 |
3x S/N | 10000 | 100% | 1.45 | 202 | 190 | 182 | 92 |
Table 7: Comparison of sample processing time and chemical feature identifications for different feature extraction thresholds.
Figure 1: Total ion chromatogram and extracted ion chromatograms for a subset of perfluorinated ether standards. Please click here to view a larger version of this figure.
Figure 2: Representative calibration curves for compounds demonstrating decreasing quality of analytical curve construction. Left-most panel indicates a high quality calibration; Middle panel indicates a compound with poor precision across preparation duplicates, particularly at the higher concentrations; Right Panel indicates a curve with poor precision and a low linear dynamic range, resulting in flat response at the high end of the calibration range, and no detectable signal at the lower end. Please click here to view a larger version of this figure.
Figure 3: Overlaid total ion chromatograms (TIC) for surface water extracts collected upstream and downstream of a fluorochemical production site. Please click here to view a larger version of this figure.
Figure 4: Extracted ion chromatograms (EIC) for all identified chemical features from a surface water sample containing multiple fluorochemical classes. Each chemical trace is a different color for differentiation. Please click here to view a larger version of this figure.
Figure 5: Conceptual diagram of raw and predicted information for a chemical feature identified as hexafluoropropylene oxide dimer acid (HFPO-DA). Chemical features are compiled from software extraction of raw data from MS measurements and contain chromatographic (e.g., retention time (RT)) and mass spectrometry information. Predicted formula, structures, and chemical identities are generated from raw measurement data for each feature. Please click here to view a larger version of this figure.
Figure 6: Mass defect plot for chemical features identified in a manufacturing outfall (red, left) and reference surface water (blue, right). Fluorinated compounds fall near and below the dashed zero line. Note the persistent PFOA/PFOS series in the background surface water sample (right). Please click here to view a larger version of this figure.
Figure 7: Mass vs mass defect plot for unidentified chemical features from a surface water sample with homologous series identified and labeled by the nontarget R package. Please click here to view a larger version of this figure.
Figure 8: Mass spectrum of an unknown chemical features with predicted isotopic intensities of three possible chemical formula with the same monoisotopic mass. Please click here to view a larger version of this figure.
Figure 9: Fragmentation spectrum of a perfluorinated ether compound with annotated fragment peaks. Please click here to view a larger version of this figure.
Figure 10: Graphical representation of filtering thresholds. From left to right, ion abundance threshold for chemical feature mass spectra, feature abundance threshold for extracted chromatographic features, and replicate threshold for feature detection frequency in a triplicate injection experiment. Please click here to view a larger version of this figure.
Acqity ultra-high performance liquid chromatography system | Waters Corporation | Modified with PFCs analysis kit (176001744); equivalent UPLC system is acceptible if PFAS background is checked and confirmed to be low | |
Ammonium acetate | Fluka | 17836 | Mass spectrometry grade >99% pure |
Ammonium Hydroxide | Sigma-Aldrich | 338818 | |
Balance | Mettler | AB204S | |
BEH C18 reverse phase UPLC column, 2.1×50 mm, 1.7 μm | Waters Corporation | 186002350 | |
Dual piston syringe pump | Waters Corporation | SPC10-C | |
Glacial Acetic Acid | Sigma-Aldrich | ARK2183 | |
Glass Microfiber Filters | Whatman | 1820-070 | |
High density polyethelye sample bottle | Nalgene | 2189-0032 | |
High Resolution Mass Spectrometer | Various | Mass Spectrometer should be capable of providing accurate mass to <10ppm and collecting MS/MS data. Agilent 6530 qTOF and Thermo Fisher Orbitrap Fusion were used in this work | |
Methanol | Sigma-Aldrich | ||
Nitric Acid (35% w/w) | Thermo Fisher Scientific | SVCN-5-1 | Can be prepared in house using concentrated nitric acid and reagent water |
Polypropylene Buchner funnel | ACE Glass | 12557-09 | |
Polypropylene cenitrfuge tube and cap | BD Falcon | 352096 | |
Polypropylene Vacuum Flask (1 L) | Nalgene | DS4101-1000 | |
Quattro Premier XE triple quadrupole mass spectrometer | Waters Corporation | Equivalent triple-quadrupole or better system can be used instead, should provide high sensitivity and stability for targeted analysis | |
Reagent Water | Any source determined to be PFAS free | ||
Sodium Acetate | Sigma-Aldrich | W302406 | |
TurboVap nitrogen evaporator | Caliper Life Sciences | 103198 | Equivalent systems or rotary vacuum evaporator may be used instead |
Weak anion exchange SPE cartridge (Oasis WAX Plus) | Waters Corporation | 186003519 | |
Standard Solutions | |||
2,3,3,3-Tetrafluoro-2-(1,1,2,2,3,3,3-heptafluoropropoxy)propanoic acid (HFPO-DA) | Wellington | HFPO-DA | |
Additional targeted compound standards of interest | to be determined based on preliminary analysis and standard availability | ||
Mass labeled HFPO-DA | Wellington | M2HFPO-DA | |
Native PFCA/PFAS Mixture (2 ug/mL) | Wellington | PFAC-MXA | or PFAC-MXB; or individually prepared mixture containing compounds of interest |
Stable Isotope Labeled PFCA/PFAS Mixture (2 ug/mL) | Wellington | MPFAC-MXA | or MPFAC-MXB; or individually prepared mixture containing compounds of interest as appropriate for Native PFASs |
Software | |||
Mass Profiler Professional | Agilent | Or open source software packages | |
Profinder | Agilent | Or open source software packages |
Historical and emerging per- and polyfluoroalkyl substances (PFASs) have garnered significant interest from the public and government agencies from the local to federal levels. The continuing evolution of PFAS chemistries presents a challenge to the environmental monitoring, where ongoing development of targeted methods necessarily lags the discovery of new chemical compounds. There is a need, therefore, to have forward-looking methodologies that can detect emerging and unexpected compounds, monitor these species over time, and resolve details of their chemical structure to enable future work in human health. To this end, non-targeted analysis by high-resolution mass spectrometry offers a broad base detection approach that can be combined with almost any sample preparation scheme and provides significant capabilities for compound identification after detection. Herein, we describe a solid-phase extraction (SPE) based sample concentration method tuned for shorter chain and more hydrophilic PFAS chemistries, such as per fluorinated ether acids and sulfonates, and describe analysis of samples prepared in this fashion in both targeted and non-targeted modes. Targeted methods provide superior quantification when reference standards are available but are intrinsically limited to expected compounds when performing analysis. In contrast, a non-targeted approach can identify the presence of unexpected compounds and provide some information about their chemical structure. Information about chemical features can be used to correlate compounds across sample locations and track abundance and occurrence over time.
Historical and emerging per- and polyfluoroalkyl substances (PFASs) have garnered significant interest from the public and government agencies from the local to federal levels. The continuing evolution of PFAS chemistries presents a challenge to the environmental monitoring, where ongoing development of targeted methods necessarily lags the discovery of new chemical compounds. There is a need, therefore, to have forward-looking methodologies that can detect emerging and unexpected compounds, monitor these species over time, and resolve details of their chemical structure to enable future work in human health. To this end, non-targeted analysis by high-resolution mass spectrometry offers a broad base detection approach that can be combined with almost any sample preparation scheme and provides significant capabilities for compound identification after detection. Herein, we describe a solid-phase extraction (SPE) based sample concentration method tuned for shorter chain and more hydrophilic PFAS chemistries, such as per fluorinated ether acids and sulfonates, and describe analysis of samples prepared in this fashion in both targeted and non-targeted modes. Targeted methods provide superior quantification when reference standards are available but are intrinsically limited to expected compounds when performing analysis. In contrast, a non-targeted approach can identify the presence of unexpected compounds and provide some information about their chemical structure. Information about chemical features can be used to correlate compounds across sample locations and track abundance and occurrence over time.
Historical and emerging per- and polyfluoroalkyl substances (PFASs) have garnered significant interest from the public and government agencies from the local to federal levels. The continuing evolution of PFAS chemistries presents a challenge to the environmental monitoring, where ongoing development of targeted methods necessarily lags the discovery of new chemical compounds. There is a need, therefore, to have forward-looking methodologies that can detect emerging and unexpected compounds, monitor these species over time, and resolve details of their chemical structure to enable future work in human health. To this end, non-targeted analysis by high-resolution mass spectrometry offers a broad base detection approach that can be combined with almost any sample preparation scheme and provides significant capabilities for compound identification after detection. Herein, we describe a solid-phase extraction (SPE) based sample concentration method tuned for shorter chain and more hydrophilic PFAS chemistries, such as per fluorinated ether acids and sulfonates, and describe analysis of samples prepared in this fashion in both targeted and non-targeted modes. Targeted methods provide superior quantification when reference standards are available but are intrinsically limited to expected compounds when performing analysis. In contrast, a non-targeted approach can identify the presence of unexpected compounds and provide some information about their chemical structure. Information about chemical features can be used to correlate compounds across sample locations and track abundance and occurrence over time.