An Abstract of the Dissertation Of

AN ABSTRACT OF THE DISSERTATION OF

Emerson C. Christie for the degree of Doctor of Philosophy in Toxicology presented on March 11, 2021.

Title: Per- and Polyfluoroalkyl Substances (PFAS): Protein Binding and Partitioning and Sorption in Light Non-Aqueous Phase Liquids

Abstract approved: ______Jennifer A. Field

Per- and polyfluoroalkyl substances (PFAS) are anthropogenic surfactants that have recently been identified as persistent organic pollutants. These so called “Forever

Chemicals” have been detected in drinking waters, ground waters, soils, and consumer and industrial products globally; with environmental impacts stretching into the artic, far from known PFAS sources. The increase in awareness regarding PFAS distribution in the environment has generated interest into how PFAS interact with humans, what

PFAS specific properties may be involved, and what additional environmental compartments may they be found in.

In Chapter 2 we discuss the use of molecular dynamics (MD) modeling to screen for protein – PFAS binding affinity to inform experimental measurements of binding affinity via equilibrium dialysis (Eq D). The equilibrium dissociation constants (KD) of six perfluoroalkyl carboxylates (PFCAs) and three perfluoroalkyl sulfonates (PFSAs) to liver and intestinal fatty acid binding proteins (L- and I-FABPs) and peroxisome proliferator activated nuclear receptors (PPAR-α, - δ and - γ) were determined via liquid

chromatography mass spectrometry. The MD models were found to predict relative and not absolute binding for all protein – PFAS combinations. This research was the first to identify sub micromolar binding between short chain PFAS (6 or less carbons) and

PPAR-α and δ, which may have implications for the assumed safety of shorter chain

PFAS due to rapid clearance. Chain length dependent binding was observed for L-

FABP but not observed for PPAR proteins which means that for these proteins binding affinity cannot be inferred by PFAS chain length. Additionally, a comparison was made between KDs derived from EqD and other in-vitro approaches, using these experimental results and results from literature. It was discovered that KDs derived from EqD were lower (i.e. higher binding affinity) than other in-vitro approaches which has implications for comparisons between methodologies and raises an important question regarding which KDs should be considered most relevant in-vivo.

Research discussed in Chapter 3 surrounds the development of an extraction and analytical method to quantify PFAS in environmental non-aqueous phase liquids

(NAPL). As mentioned above, PFAS are used in industrial products and one common group of industrial products that have been identified as the root cause for environmental PFAS contamination at U.S. military sites are aqueous film forming foams (AFFF). AFFF are complex mixtures known to contain high concentrations of many surfactants including PFAS. At U.S. military sites it is also common to encounter

NAPL in the subsurface. Co-disposal of PFAS (AFFF) with NAPL has happened historically through intentional use (e.g. firefighting) or unintentionally at waste sites.

In order to quantify PFAS within NAPL, a liquid-liquid extraction method was developed that could successfully extract anionic, cationic, and zwitterionic PFAS.

This research discovered the presence of PFAS in recovered NAPL at microgram per liter concentrations at non-source zone sites. Concentrations of PFAS in NAPL are likely much higher at source zone sites and could have implications for NAPL remediation.

Chapter 4 discusses the partitioning and interfacial adsorption of PFAS into NAPL at environmentally relevant concentrations (i.e. nano – microgram per liter). Given the discovery of low microgram per liter concentrations of PFAS in recovered NAPL discussed in Chapter 3, it is relevant to investigate what partitioning and sorption processes are occurring at these concentrations. Current research in this area has focused on the NAPL – water interface and has done so at high concentrations, milli – gram per liter. Here we performed batch equilibrium experiments at low concentrations

(2,000 – 100,000 ng/L) between jet fuel A (NAPL) and synthetic freshwater. Single point partition coefficients (Kn) were calculated for PFAS of carbon chain length 8-14 across the concentration range. Values for Kn decreased with increasing PFAS concentration indicating non-ideal partitioning, which become more evident with increasing chain length. Partitioning into jet fuel A was not observed for PFAS below eight carbons. Interfacial sorption (Knw) was estimated by mass difference and found to be orders of magnitude higher than previously reported literature values.

Per- and Polyfluoroalkyl Substances (PFAS): Protein Binding and Partitioning and Sorption in Light Non-Aqueous Phase Liquids

by Emerson C. Christie

A DISSERTATION

submitted to

Oregon State University

in partial fulfillment of the requirements for the degree of

Doctor of Philosophy

Presented March 11, 2021 Commencement June 2021

Doctor of Philosophy dissertation of Emerson C. Christie presented on March 11, 2021

APPROVED:

Major Professor, representing Toxicology

Head of the Department of Environmental and Molecular Toxicology

Dean of the Graduate School

I understand that my dissertation will become part of the permanent collection of Oregon State University libraries. My signature below authorizes release of my dissertation to any reader upon request.

Emerson C. Christie, Author

ACKNOWLEDGEMENTS I would like to thank my wife, Brittany Poirson, for her support during the intense ride that was finishing graduate school while parenting, buying a house, moving, and a double career change, all during a pandemic. My son, Obadiah, for mandating work life balance and providing an unconditional bright smile during acutely stressful times. My soon to be born daughter, Marigold, guiding me towards the world ahead. My parents, Kevin and Libby Christie, for continuous support throughout my life. My sister, Kara Trella, for teaching me creative vision. My brother, Jack Anson, for teaching me the strength of spirit.

Thank you to Dr. Alix Robel, Dr. Justin Rewerts, and Dr. Serhan Mermer for teaching me LC MS/MS and enough about lab and instrument health to keep things moving forward. Thank you to my Field Lab members who helped me develop ideas, accomplish objectives, and keep instruments running. Thank you to the many collaborators who worked very hard on the research here-in. Thank you to my committee Dr. Markus Kleber, Dr. Jeffrey Jenkins, Dr. Charles Schaefer, and Dr. Ramesh Sagili; for all their advice in making this research possible. And a big thank you to my adviser Dr. Jennifer Field for obviously seeing something in me, sharing her expertise, and guiding me on this quest.

CONTRIBUTION OF AUTHORS

In Chapter 2, Manoochehr Khazaee performed equilibrium dialysis experiments and contributed to manuscript write-up and edits. Weixiao Cheng performed molecular modeling and contributed to manuscript write-up and edits. Dr. Mandy Michalsen, Dr.

Jennifer Field, and Dr. Carla Ng provided expertise in experimental design and manuscript edits.

In Chapter 3, Dr. Bill Diguiseppi provided expertise in light non-aqueous phase liquids and manuscript edits. Dr. Konstantinos Kosterelos facilitated the collection of field samples and provided manuscript edits, and Dr. Jennifer A. Field provided experimental design expertise and manuscript edits.

In Chapter 4, Dr. Charles Schaefer provided PFAS interfacial modeling expertise and experimental design expertise. Dr. Konstantinos Kostarelos LNAPL and partitioning expertise. Dr. Jennifer A. Field provided experimental design expertise and manuscript edits.

TABLE OF CONTENTS

Page

Chapter 1- Introduction.....…………………………………………………………… 1

1.1 Per and Polyfluoroalkyl Substances ………...…………………………………… 1

1.2 PFAS – Protein Interactions……………………………………………………… 2

1.3 PFAS – NAPL Connection.....…………………………………………………… 3

1.4 Methods to Analyze PFAS in NAPL...... ………………………………………… 4

1.5 PFAS – NAPL Partitioning..…...………………………………………………… 4

1.6 PFAS – NAPL Interfacial Sorption..…………..………………………………… 5

1.7 Summary of Research Performed…...…………………………………………… 6

1.8 References………...... …………………………………………………………… 7

Chapter 2 - Perfluoroalkyl acid binding with peroxisome proliferator-activated receptors , , and  and fatty acid binding proteins by equilibrium dialysis with a comparison of methods.....…………………………………………………………...13

2.1 Abstract ….………………………………………………………………………14

2.2 Introduction ………...……………………………………………………………15

2.3 Materials and Methods ..…………………………………………………………17

2.4 Results and Discussion .…………………………………………………………12

2.5 Conclusion ………………………………………………………………………28 . 2.6 Acknowledgements …...…………………………………………………………29

2.7 References ……………………………………………………………………….30

Chapter 3 - Per and Polyfluorinated Alkyl Substances in Nonaqueous Phase Liquids……………………………………………………………………………….48

3.1 Abstract ….………………………………………………………………………49

3.2 Introduction ………...……………………………………………………………50

TABLE OF CONTENTS (Continued)

Page

3.3 Experimental ………….…………………………………………………………52

3.4 Results and Discussion…………………………………………………………..57

3.5 Implications ……..……………………………………………………………….59 . 3.6 Acknowledgements …...…………………………………………………………60

3.7 References …………………………………………………………………….…60

Chapter 4 - Interfacial uptake and partitioning of per and polyfluorinated alkyl substances in Jet Fuel A at environmental concentrations …..………………………65

4.1 Abstract ….………………………………………………………………………66

4.2 Introduction ………...……………………………………………………………66

4.3 Experimental ……….....…………………………………………………………69

4.4 Results and Discussion………………………………………………………...... 72

4.5 Implications ...……………………………………………………………………75 . 4.6 Acknowledgements …...…………………………………………………………77

4.7 References …………………………………………………………………….…77

Chapter 5 – Conclusion..……………………………………………………………..88

Bibliography …...……………………………………………………………………92

Appendicies .....…………………………………………………………………….103

LIST OF FIGURES

Figure Page

Figure 2.1. Decision tree for the inclusion of the equilibrium dialysate concentrations for the regression analysis …………………………………………………………...41

Figure 2.2. Predicted dissociation constant (KD) values (geometric mean ± 1 standard error) for different PPAR-PFAS complexes ………………………………………...42

Figure 2.3. Predicted dissociation constant (KD) values (geometric mean ± 1 standard error) for FABP and PFCAs …………………………………………………...... 43

Figure 2.4. Specific binding (μmol PFAS/μmol protein) vs free concentration of PFAS (μmol/L) (A) PFHxA and (B) PFNA with PPAR–α.…………………………44

Figure 2.5. Specific binding (μmol PFAS/μmol protein) vs free concentration of PFAS (μmol/L) (A) PFBA and (B) PFHxS with PPAR–δ…………………………..45

Figure 2.6. Specific binding (μmol PFAS/μmol protein) vs free concentration of PFAS (μmol/L) (A) PFOS with L-FABP and (B) PFNA with I-FABP …………….46

Figure 2.7. Comparison of KDs for PFAS with eight or fewer fluorinated carbons measured by equilibrium dialysis (EqD) in this study (red symbols) compared with other methods .……………………………………………………………………….47

Figure 4.1. PFOS Kn values over the concentration range …………………….……84

Figure 4.2. Kn values for PFCAs and PFSAs at 100,000 ng/L initial PFAS concentrations according to carbon chain length ……………………………………85

Figure 4.3. PFOS interfacial adsorption isotherm …………………………………..86

Figure 4.4. PFTeDA interfacial adsorption fitted to a Freundlich isotherm ………...87

LIST OF TABLES

Table Page

Table 2.1. Summary of 3-dimensional structure information for selected proteins…38

Table 2.2. Dialysis material extraction and sorption results ………………………...39

Table 2.3. Dissociation constant (KD) values ± SE measured by equilibrium dialysis. “ND”: no dissociation constant could be determined, indicating low to no binding...40

Table 3.1. Number of military installations and wells sampled along with the limited information available on fuel (LNAPL) type and year(s) released ………………….63

Table 3.2. Average concentrations ± standard deviation (ng/L) of PFAS field- collected LNAPL samples..………………………………………………………….64

Table 4.1. Single point Kn values calculated across the concentration range…………81

Table 4.2. Interfacial sorption coefficients from linear regressions.………………...82

Table 4.3. Interfacial sorption coefficients from Freundlich models………………...83

LIST OF APPENDICES

Appendix Page

A. Chapter 2 – Supplemental Information ………………………………...103

B. Chapter 3 – Supplemental Information ………………………………...122

C. Chapter 4 – Supplemental Information ………………………………...130

LIST OF APPENDIX FIGURES

Figure Page

Figure A1 Equilibrium dialysis setup with materials used (dialysis filters and vials) shown ………………………………………………………………..110

Figure A2 Equilibrium dialysis results for binding affinity of PFBA (A) and PFHpA (B) with PPAR–α ………………………………………………….111

Figure A3 Equilibrium dialysis results for binding affinity of PFOA (A) and PFOS (B) with PPAR– 훾 …………………………………………………..112

Figure A4 Equilibrium dialysis results for binding affinity of PFOS (A) and PFBS (B) with PPAR–δ ……………………………………………………113

Figure A5 Equilibrium dialysis results for binding affinity of PFOA (A), PFBS (B), PFHxA (C), and PFHxS (D) with L-FABP ……………………114

Figure A6. Equilibrium dialysis results for binding affinity of PFHpA with I- FABP ………………………………………………………………………115

Figure A7 Comparison of reported KD (± SE) values from literature for human serum albumin ……………………………………………………………...116

Figure A8 Comparison of reported KD (± SE) values from literature for bovine serum albumin ……………………………………………………………...117

Figure C1. PFOS equilibrium partitioning between Jet Fuel A and synthetic freshwater.……………………………………………………………...... 138

Figure C2. PFNS equilibrium partitioning between Jet Fuel A and synthetic freshwater. ……………………………………………………………….....139

Figure C3. PFOA equilibrium partitioning between Jet Fuel A and synthetic freshwater. ……………………………………………………………...... 140

Figure C4. PFNA equilibrium partitioning between Jet Fuel A and synthetic freshwater. ……………………………………………………………...... 141

Figure C5. PFDA equilibrium partitioning between Jet Fuel A and synthetic freshwater. ……………………………………………………………….....142

Figure C6. PFUnDA equilibrium partitioning between Jet Fuel A and synthetic freshwater………………………………………………………...143

LIST OF APPENDIX FIGURES (Continued)

Figure Page

Figure C7. PFDoDA equilibrium partitioning between Jet Fuel A and synthetic freshwater………………………………………………………...144

Figure C8. PFTrDA equilibrium partitioning between Jet Fuel A and synthetic freshwater…………………………………………………………………...145

Figure C9. PFTeDA equilibrium partitioning between Jet Fuel A and synthetic freshwater…………………………………………………………………...146

Figure C10. PFPrS mix and single solute interfacial sorption isotherms…..147

Figure C11. PFBS interfacial sorption isotherm……………………….…...148

Figure C12. PFHxS interfacial sorption isotherm...………………………...149

Figure C13. PFNS interfacial sorption isotherm………….………………...150

Figure C14. PFBA interfacial sorption isotherm…………………………...150

Figure C15. PFPeA interfacial sorption isotherm……………...…………...152

Figure C16. PFHxA interfacial sorption isotherm…..……………………...153

Figure C17. PFHpA interfacial sorption isotherm…..……………………...154

Figure C18. PFOA interfacial sorption isotherm…………………………...155

Figure C19. PFNA interfacial sorption isotherm…………………………...156

Figure C20. PFDA interfacial sorption isotherm…………………………...157

Figure C21. PFUdA interfacial sorption isotherm.………………………....158

Figure C22. PFDoDA interfacial sorption isotherm……….…………….…159

Figure C23. PFTrA interfacial sorption isotherm.…………………….……160

LIST OF APPENDIX TABLES

Table Page

Table A1. Matrix of Selected Protein-PFAS combinations for batch analysis……………………………………………………………………...107

Table A2. Comparison of methods L- & I-FABP and PPAR α, γ, δ ………108

Table A3. Comparison of methods HSA, BSA, RSA, and fish serum protein………………………………………………………………………109

Table B1. PFAS analytes names, acronyms, acquisition masses, parameters, calibration references, and data quality tiers…...…………………………...123

Table B2. Limit of detection, limit of quantification, accuracy, and precision for Qn analytes in Jet Fuel A……………...…...…………………………...126

Table B3. Accuracy and precision for direct analysis of Qn analytes via spiking into ethyl acetate and diluting 1:10………………………………...127

Table B4. PFAS (Qn) present in underlying aqueous phase samples.……...128

Table B5. PFAS (Sc) present in underlying aqueous phase samples.……...129

Table C1. Native and surrogate PFAS standards used……………………..132

Table C2. Synthetic tap water recipe……………………………………….133

Table C3. Accuracy and precision for direct analysis of Qn analytes in NAPL……………………………………………………………………….134

Table C4. Recoveries for select PFAS spiked in water from polypropylene tube………………………………………………………………………….135

Table C5. Recoveries for PFAS spiked in jet fuel A from polypropylene tube………………………………………………………………………….136

Table C6. Single solute PFOS equilibrium partitioning concentrations compared with PFAS mix equilibrium partitioning concentrations..………137

CHAPTER 1 – INTRODUCTION

1.1 Per and Polyfluoroalkyl Substances

Per and polyfluoroalkyl substances (PFAS) come in a variety of chemical structures but they all contain carbon chains where some or all the hydrogens have been substituted with fluorine attached to a polar headgroup1. These structural elements impart both hydrophobic and oleophobic properties, which makes PFAS useful as water and oil repellents and as surfactants1, 2. PFAS are anthropogenic compounds with manufacturing dating back to the 1940’s3 and thousands of unique PFAS are suspected to exist4. Two major manufacturing processes were historically used in the production of PFAS; electrochemical fluorination (ECF) and fluorotelomer synthesis. The former is attributed to a single manufacturer, 3M, and is characterized by the attachment of the polar headgroup directly to the perfluorinated carbon chain and the existence of branched and linear isomers5. Fluorotelomer synthesis has been used by a number of manufacturers and produces PFAS with a hydrocarbon spacer between the fluorinated chain and the polar headgroup2. Moreover, fluorotelomer PFAS can serve as precursors to shorter-chain PFCAs6, 7 and PFSAs8, 9 through degradation pathways. It is now recognized that PFAS are highly persistent1, 10 and mobile in the environment11 and have since been classified as persistent organic pollutants12.

Currently, PFAS are not subject to regulation in the U.S., however, the U.S. EPA in

2016 recommended a lifetime health advisory limit (HAL) of 70 ng/L of perfluorooctanoate (PFOA) and/or perfluorooctane sulfonate (PFOS)13. PFOA and

PFOS are the most researched PFAS and stand in the literature as the standard perfluorocarboxylic acid (PFCA) and perfluorosulfonate (PFSA). To date, PFAS have

been identified in nearly every environmental compartment they have been looked for including soil4, 14-16, groundwater15, 17, 18, and human serum19. Further, they are known to bioaccumulate4, 20 and have been shown to impact the immune system21, act as endocrine disruptors22, and cause cancer in test animals23. PFAS contamination of surface and groundwater on a global scale represents an unknown threat to human health as drinking water is the primary route of exposure24-26.

1.2 PFAS – Protein Interactions

For the reasons mentioned above, there is a desire to increase our understanding of

PFAS biological impacts through in vitro and in silico approaches. PFAS are typically found at high concentrations within the liver and blood plasma19, 27. Binding to and activation of proteins has been shown both in tissue distribution and in vitro studies using individual proteins or serum28-30. The most well studied of which are liver fatty acid binding protein (L-FABP)27, 30, which is central to the uptake and transport of fatty acids31; and human and bovine serum albumin28, 32.

Binding affinities have been determined for PFCAs and PFSAs with L-FABP via fluorescence displacement30, 33 and with serum albumin via several methods including equilibrium dialysis28, 34, which is the preferred method for the determination of binding affinity35. Methodology is important when comparing and determining PFAS binding affinities either through modeling or experimental means as order of magnitude differences can exist between methods36-38. For serum albumin and L-FABP a general trend for relative PFAS binding affinity has been observed with PFAS chain length, where longer chain PFAS having greater binding affinity compared to short chains.

However, less is known about relative binding based upon PFAS chain length in other

proteins. Due to the extensive research with L-FABP and serum albumin they are commonly used as positive controls to frame new results within the literature.

A family of related proteins, peroxisome proliferator-activated receptors (PPARs α, δ, and γ) are transcriptional sensors for fatty acids and are involved FABP expression39,

40. To date, only PPAR-α and -γ have been tested for binding with PFAS, with PPAR-

α assessed by fluorescence displacement41 and only the ligand binding domain of

PPAR-γ42. As mentioned above, little is known how PFAS chain length and relative binding affinity relate for other proteins, like PPARS, and specifically how short-chain

PFAS (≤ C6) interact with biologically relevant proteins. This last point is of particular interest as short-chain PFAS have been assumed safer based on rapid serum clearance, however, the rate of clearance does not account for any interactions the PFAS may have along the way.

1.3 PFAS – NAPL Connection

While a number of communities have noted concerns surrounding the use of PFAS12 they are still heavily used in commerce. One common industrial usage of PFAS is in aqueous film forming foams (AFFF). AFFFs are used globally by militaries and municipalities to fight hydrocarbon-based fuel fires and are known to contain gram per liter concentrations of PFAS43, 44. Use of AFFF at U.S. military locations has resulted extensive PFAS contamination of groundwater15, 17, 45, 46. It has been estimated that major U.S. military bases contain on average 10-12 individual sites where PFAS is known to impact the soil or groundwater47.

In addition to high AFFF usage, US military installations are known to have released non-aqueous phase liquids (NAPL) through training, waste dumping, or spills and/or

leaks45, 48. In many cases these contaminated PFAS and NAPL sites are co-located in fire training or waste areas45, 48. Early fire training practices dating back to the 1970s involved the extinguishing of diesel and waste oils in unlined pits with AFFF49.

1.4 Methods to Analyze PFAS in NAPL

Despite the co-location of PFAS and NAPL, determination of PFAS presence in recovered NAPL has not been performed and as such only one analytical method exists for quantifying PFAS in NAPL. In 2020, Zhu et. al. published a method for quantifying

PFCAs (C4-C12) and PFSAs (C4-C10) in automotive lubricants due to suspected

PFAS presence as additives50. The extraction as described is lengthy and requires mixing the sample in solvent, centrifuging, evaporation, and reconstitution in water before finally undergoing weak anion exchange solid phase extraction (WAX SPE).

This WAX SPE step is unable to extract cationic PFAS and may be inefficient at extracting zwitterionic compounds, which are common in AFFF43, 44, 51. A similar SPE extraction method was developed for naphthenic acids, representing the extraction of a polar compound from a non-polar matrix, and utilized strong anion exchange52, which again would not be effective for PFAS of AFFF origin due to the presence of cations and zwitterions.

1.5 PFAS – NAPL Partitioning

Partitioning of PFAS from an aqueous phase into NAPL has yet to be directly measured. Other contaminants known to be co-mingled with NAPL such as polychlorinated biphenyls (PCBs) and polycyclic aromatic hydrocarbons (PAHs) are known to sorb to oil phases in soil53-55. Batch partitioning experiments with PFOS and uncontaminated and oil contaminated sediment showed increased PFOS sorption to oil

contaminated sediment via loss in the aqueous phase, which indicated NAPL partitioning or sorption56. Similar research performed by Guelfo and Higgins showed trichloroethylene (TCE) acted as a synergist and potential sorbent for PFAS in soil45.

A few years later McKenzie et. al. performed TCE column and batch experiments that showed PFAS loss in the aqueous phase48. Decreases in PFAS concentration were associated with carbon chain length and they increased when TCE interfacial surface area was constant but the volume of TCE was increased indicating that bulk partitioning was occurring48. A complex mechanism of partitioning and adsorption was

48 hypothesized as the partition coefficient (KTCE) was not equal across volumes . To date, the NAPL partitioning documented in the above studies was by loss of PFAS from the system during batch experiments and this loss was attributed by mass balance to the NAPL phase.

1.6 PFAS – NAPL Interfacial Sorption

By design PFAS are surfactants, thus, most research into the PFAS – NAPL relationship is focused on the NAPL – water interface. Similar to PFAS – NAPL partitioning, interfacial sorption is a relatively understudied area of PFAS environmental interactions. Typically, interfacial sorption coefficients (Knw) are derived experimentally via interfacial tension measurements and use high (mg/L – g/L) surfactant concentrations57-59. Early reports of understanding PFAS at the NAPL-water interface were conducted by Handa and Mukerjee60 with perfluorohexane and by

61 Janczuk et. al. with PFOA, with Janczuk documenting PFOA Knw = 0.12E-3 cm.

More recently, Silva et. al. determined Knw for C5-C10 PFCAs by fitting interfacial tension with the Langmuir- Szykowski equation and using fitted parameters to calculate

Knw from a modified Gibbs equation, results for PFOA were on the same order of magnitude as Janczuk59. However, Schaefer et. al. (2019) has indicated that the

Langmuir model underpredicts interfacial uptake at the air-water interface at lower

58 PFAS concentrations and a similar trend may exist for Knw values . Air-water and

NAPL-water interfaces have been identified as the primary source of PFOA and PFOS retention in groundwater systems62. In batch experiments Kosterelos et. al. (2020) observed the formation of viscous, stable, microemulsions at the NAPL-water interface by mixing application strength AFFF (3% in water) and jet fuel A (NAPL)63. Constant flow and constant pressure column experiments also confirmed the creation of microemulsions and reported PFAS losses in the aqueous of up to 70%63. These results indicate that NAPL-water partitioning and interfacial adsorption can change substantially at high concentrations.

1.7 Summary of Research Performed

In Chapter 2 molecular dynamics (MD) was used as an initial screen for interactions between previously studied and relevant but untested proteins (L-FABP, I-FABP, and

PPARs α, δ, and γ) for their potential to bind with PFAS. In vitro evaluation of PFAS- protein pairs was conducted via equilibrium dialysis (EqD). The EqD results were then compared to both MD predictions and literature data for protein binding with PFAS.

Additionally, comparisons among the different approaches for quantifying protein binding affinity and how these results might be interpreted were also discussed.

In Chapter 3 a liquid-liquid extraction method was developed that could be used to quantify PFAS concentrations in recovered LNAPL from U.S. military sites. The extraction and subsequent analytical method was validated for 34 target PFAS and

showed the capability of extracting a variety of exotic zwitterionic PFAS. Method relevance was shown by the discovery and quantification of naturally incurred PFAS concentrations in 10 of the 18 LNAPL samples from U.S. military sites.

In Chapter 4 environmentally relevant PFAS partitioning into NAPL by quantification of PFAS concentrations in both synthetic freshwater and jet fuel A (NAPL) was performed. Considering the discovery made in Chapter 3, it became necessary to further investigate PFAS – NAPL interactions. Single point partition coefficients for each PFAS, when calculable, across the range of concentrations were made to determine chain length and head group contributions to partitioning. Interfacial sorption coefficients were determined by mass balance between the initial aqueous concentration and the equilibrated aqueous and LNAPL concentration.

1.8 References

1. Buck, R. C.; Franklin, J.; Berger, U.; Conder, J. M.; Cousins, I. T.; de Voogt, P.; Jensen, A. A.; Kannan, K.; Mabury, S. A.; van Leeuwen, S. P., Perfluoroalkyl and polyfluoroalkyl substances in the environment: terminology, classification, and origins. Integr Environ Assess Manag 2011, 7, (4), 513-41.

2. Prevedouros, K.; Cousins, I. T.; Buck, R. C.; Korzeniowski, S. H., Sources, fate and transport of perfluorocarboxylates. Environ Sci Technol 2006, 40, (1), 32-44.

3. Kissa, E., Fluorinated surfactants. Surfactant science series 1994, 50.

4. Sima, M. W.; Jaffe, P. R., A critical review of modeling Poly- and Perfluoroalkyl Substances (PFAS) in the soil-water environment. Sci Total Environ 2021, 757, 143793.

5. Paul, A. G.; Jones, K. C.; Sweetman, A. J., A first global production, emission, and environmental inventory for perfluorooctane sulfonate. Environmental science & technology 2009, 43, (2), 386-392.

6. Harding-Marjanovic, K. C.; Houtz, E. F.; Yi, S.; Field, J. A.; Sedlak, D. L.; Alvarez-Cohen, L., Aerobic Biotransformation of Fluorotelomer Thioether Amido Sulfonate (Lodyne) in AFFF-Amended Microcosms. Environ Sci Technol 2015, 49, (13), 7666-74.

7. Weiner, B.; Yeung, L. W.; Marchington, E. B.; D’Agostino, L. A.; Mabury, S. A., Organic fluorine content in aqueous film forming foams (AFFFs) and biodegradation of the foam component 6: 2 fluorotelomermercaptoalkylamido sulfonate (6: 2 FTSAS). Environmental Chemistry 2013, 10, (6), 486-493.

8. Liu, J.; Zhong, G.; Li, W.; Avendaño, S. M., Isomer-specific biotransformation of perfluoroalkyl sulfonamide compounds in aerobic soil. Science of The Total Environment 2019, 651, 766-774.

9. Wang, N.; Liu, J.; Buck, R. C.; Korzeniowski, S. H.; Wolstenholme, B. W.; Folsom, P. W.; Sulecki, L. M., 6: 2 Fluorotelomer sulfonate aerobic biotransformation in activated sludge of waste water treatment plants. Chemosphere 2011, 82, (6), 853- 858.

10. Giesy, J. P.; Kannan, K., Global distribution of perfluorooctane sulfonate in wildlife. Environ Sci Technol 2001, 35, (7), 1339-42.

11. Krafft, M. P.; Riess, J. G., Per-and polyfluorinated substances (PFASs): Environmental challenges. Current opinion in colloid & interface science 2015, 20, (3), 192-212.

12. Blum, A.; Balan, S. A.; Scheringer, M.; Trier, X.; Goldenman, G.; Cousins, I. T.; Diamond, M.; Fletcher, T.; Higgins, C.; Lindeman, A. E., The Madrid statement on poly-and perfluoroalkyl substances (PFASs). Environmental health perspectives 2015, 123, (5), A107-A111.

13. Water., U. S. E. P. A. O. o. Drinking Water Health Advisories for PFOA and PFOS. https://www.epa.gov/ground-water-and-drinking-water/drinking-water-health- advisories-pfoa-and-pfos

14. Barzen-Hanson, K. A.; Davis, S. E.; Kleber, M.; Field, J. A., Sorption of Fluorotelomer Sulfonates, Fluorotelomer Sulfonamido Betaines, and a Fluorotelomer Sulfonamido Amine in National Foam Aqueous Film-Forming Foam to Soil. Environ Sci Technol 2017, 51, (21), 12394-12404.

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16. Nickerson, A.; Maizel, A. C.; Kulkarni, P. R.; Adamson, D. T.; Kornuc, J. J.; Higgins, C. P., Enhanced Extraction of AFFF-Associated PFASs from Source Zone Soils. Environ Sci Technol 2020, 54, (8), 4952-4962.

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CHAPTER 2 – PERFLUOROALKYL ACID BINDING WITH PEROXISOME

PROLIFERATOR-ACTIVATED RECEPTORS , , AND  AND FATTY

ACID BINDING PROTEINS BY EQUILIBRIUM DIALYSIS WITH A

COMPARISON OF METHODS

Manoochehr Khazaee1†, Emerson Christie2†, Weixiao Cheng1, Mandy Michalsen3,

Jennifer Field2, and Carla Ng1,4*

1Department of Civil & Environmental Engineering, University of Pittsburgh,

Pittsburgh, PA 15261

2Department of Molecular and Environmental Toxicology, Oregon State University,

Corvallis, OR, 97330

3U.S. Army Engineer Research Development Center – Environmental Lab

4Secondary Appointment, Department of Environmental and Occupational Health,

Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15261

†Co-first authors.

Published - Toxics

St. Alban-Anlage 66

4052 Basel, Switzerland https://doi.org/10.3390/toxics9030045

2.1 Abstract

The biological impacts of per- and polyfluorinated alkyl substances (PFAS) are linked to their protein interactions. Existing research has largely focused on serum albumin and liver fatty acid binding protein, and binding affinities determined with a variety of methods show high variability. Moreover, few data exist for short-chain PFAS, though their prevalence in the environment is increasing. We used molecular dynamics (MD) to screen PFAS binding to liver and intestinal fatty acid binding proteins (L- and I-

FABPs) and peroxisome proliferator activated nuclear receptors (PPAR-α, - δ and - γ) with six perfluoroalkyl carboxylates (PFCAs) and three perfluoroalkyl sulfonates

(PFSAs). Equilibrium dissociation constants, KDs, were experimentally determined via equilibrium dialysis (EqD) with liquid chromatography tandem mass spectrometry for protein-PFAS pairs. A comparison was made between KDs derived from EqD, both here and in literature, and other in-vitro approaches (e.g. fluorescence) from literature.

EqD indicated strong binding between PPAR-δ and perfluorobutanoate (0.044 ± 0.013

µM) and perfluorohexane sulfonate (0.035 ± 0.0020 µM), and between PPAR-α and perfluorohexanoate (0.097 ± 0.070 µM). Unlike binding affinities for L-FABP, which increase with chain length, KDs for PPARs showed little chain length dependence by either MD simulation or EqD. Compared with other in vitro approaches, EqD-based

KDs consistently indicated higher affinity across different proteins. This is the first study to report PPARs binding with short-chain PFAS with KDs in the sub-micromolar range.

2.2. Introduction

Per- and polyfluoroalkyl substances (PFAS) are widely used in a variety of industrial and consumer applications as stain and water repellents, processing fluids, building blocks for fluoropolymers, and aqueous film-forming foams (AFFF) [1,2]. Various formulations of AFFFs containing short-chain PFAS continue to be used at military sites and airports to combat hydrocarbon-fueled fires, and their usage has resulted in persistent and widespread groundwater contamination [3–5]. AFFFs are complex mixtures containing high concentrations (g/L) of PFAS [6,7]. Polyfluorinated precursors in AFFF can degrade to form PFOS, perfluorooctanoic acid (PFOA) and shorter-chain PFCAs [8,9] and PFSAs [10,11]. It is now recognized that many of the anionic forms (e.g., perfluroalkane sulfonates or PFSAs and perfluoroalkyl carboxylates or PFCAs) are highly persistent and mobile in the environment [12–15].

Biomonitoring has indicated these perfluorinated acids are generally found in highest concentrations in the blood plasma and liver [16–18], and are bound to proteins, as evidenced by both tissue distributions observed in laboratory and field studies and by targeted in vitro studies with isolated proteins or serum [19–23]. Relevant to these compartments are liver- and intestinal- fatty acid binding proteins (L-FABP and I-

FABP), lipid-binding proteins highly expressed in the liver and intestine that play critical roles in binding, uptake, and transport of fatty acids[24]; and several subtypes

(, , and ) of peroxisome proliferator-activated receptors (PPARs), which serve as main transcriptional sensors of fatty acids and can control the expression of FABPs involved in fatty acid metabolism [25,26].

To date, only PPAR- and - have been tested for binding with PFAS, and studies with

FABPs have focused solely on the liver type [19,27,28]. Binding affinities for PFCAs

(C4-C18) and PFSAs (C4-C8) were previously determined by ﬂuorescence displacement methods with L-FABP [23,29,30] and PPAR-α [31]. There are no previously reported experimental data for PFAS binding to I-FABP or PPAR-δ and only one for the ligand-binding domain (not the entire protein) of PPAR-γ [32]. Such studies show that long-chain PFAS, such as perfluorooctane sulfonate (PFOS) and

PFCAs with chain lengths between 9 and 12, bioaccumulate and bind with high affinity to serum proteins and liver fatty acid binding proteins (L-FABP). Less is known about

PFAS binding to PPARs and how shorter-chain PFAS interact with biologically relevant proteins.

Because of the growing interest in the biological fate and effects of PFAS, experimental and modeling studies of PFAS-protein binding have proliferated. Yet large differences persist across studies and across in vitro methods to assess binding, as well as between in vitro and modeling results. To date, the majority of PFAS-protein binding studies have focused on serum proteins, particularly human and bovine serum albumin [22]. In vitro studies with albumin [33] used a variety of methods including equilibrium dialysis

[22,34–37], circular dichroism [38], NMR spectroscopy[22,32], ultrafiltration [39], surface tension [40], and electrophoresis [41]. Each technique has advantages and limitations, and lead to substantial differences in the binding affinities estimated. While ranking PFAS by chain length for relative protein binding affinity is well supported by both in vitro and in silico approaches for proteins such as serum albumin and L-FABP, there is little guidance on how to interpret the actual values obtained from the different

approaches, which can differ by orders of magnitude [18,42,43]. It is, therefore, challenging to compare existing data for PFAS-protein binding or place modeling predictions into the context of experimental data.

Here, we employed a model-guided framework as an initial screen for potentially strong interactions between previously studied and relevant but untested proteins (L-

FABP, I-FABP, and PPARs , , and ) for their potential to bind with PFAS, followed by in vitro evaluation of predicted high-affinity PFAS-protein pairs. Model simulations, using molecular docking followed by molecular dynamics (hereafter referred to as MD), predicted the free energies of binding. The approach was based on our previous study, which demonstrated that MD can successfully predict relative protein binding affinity for L-FABP and PFCAs (C4-C9) and PFSAs (C4, C6, and C8)

[42]. Here, our MD framework was used with new proteins to target potential high affinity binding to short-chain PFAS. Selected MD predictions were experimentally evaluated using equilibrium dialysis (EqD), which has been used previously to evaluate

PFAS interactions with serum albumin [22,34], and is considered the gold standard for quantifying binding affinities [44]. Our EqD results were then compared with both MD predictions and with other available experimental data for protein binding with short- chain PFAS. We discuss similarities and differences among the different approaches for quantifying protein binding affinity, how results might be interpreted, and needs for further cross-validation.

2.3 Materials and methods

2.3.1. Model-based PFAS-protein affinity screening

Initial selection of proteins for model-based screening was based on their known interactions with lipids and/or fatty acids, given the similarity between PFAS and these endogenous ligands [45–47]. The binding affinities between selected proteins and a total of five short-chain PFAS including perfluorobutanoic acid (PFBA), perfluoropentanoic acid (PFPeA), perfluorohexanoic acid (PFHxA), perfluoroheptanoic acid (PFHpA), and perfluorobutane sulfonate (PFBS) as well as four long-chain PFAS including PFOA, perfluorononanoic acid (PFNA), perfluorohexane sulfonate (PFHxS), and PFOS were estimated using the MD workflow developed by Cheng and Ng [42] with a goal to identify proteins that could have substantial binding affinity with short-chain PFAS. Briefly, three-dimensional (3D) structures were obtained from the Protein Data Bank (PDB, http://www.rcsb.org) for

L-FABP (PDB code: 3STM)[46], I-FABP (PDB code: 3AKM)[45], PPAR-α (PDB code: 4CI4)[48], PPAR-훾 (PDB code: 3U9Q)[47], and PPAR-δ (PDB code:

3TKM)[49]. These proteins and nuclear receptors (Table 1) were selected because of their high structural resolution (<3Å) and their completeness, which is indicated by the inclusion of all amino acid residues that could be important to the protein binding sites in the structural model. The 3D structures for the PFCAs and PFSAs were either extracted from PDB (if available) or constructed from scratch using the Avogadro molecular editor[50], as previously described [42].

2.3.2. Materials

Linear PFBS, PFHxS, PFOS, PFBA, PFHpA, PFHxA, PFOA, and PFNA (all > 98% purity) were purchased from Wellington Laboratories (Guelph, Ontario, Canada).

Purified human proteins L-FABP, I-FABP, PPAR-α, PPAR-훾, and PPAR-δ were

obtained from Novus Biologicals (Littleton, CO, USA). Slide-A-Lyzer mini dialysis devices (10K MWCO, 0.1 mL) were purchased from Thermo Scientific. Solvents and other reagents were of analytical grade. All buffers were prepared from 10X phosphate- buffered saline from GIBCO Invitrogen (Grand Island, USA). Dialysis materials were screened for PFCA and PFSA background and sorption prior to the onset of dialysis experiments. Material extraction analyses gave no concentrations of PFAS above the

LOD (Table 2) within the dialysis cups or the dialysis tubes. Additionally, spiked water and equilibration experiments (24-hour shake test) resulted in the recovery (75-235%) of PFAS analytes within the water, which indicated there was no level of detectable sorption of PFAS onto the dialysis cups or tubes. All other materials used in the processes were previously verified to have PFAS levels

2.3.3. Equilibrium Dialysis (EqD)

PFAS-protein binding affinities were evaluated by EqD. Experiments were conducted over a range of ligand : protein mole ratios (0.05, 0.1, 0.5, 1, and 5). These mole ratios represent concentrations ranging from 0.33 – 153.5 ng/mL depending on the PFAS. In general, the averages of PFSA and PFCA in plasma levels of people living in urban areas are 19.56 ng/ml and 10.23 ng/ml, respectively (51–57). It should be mentioned some studies reported high concentrations of PFAS (between ~ 60-110 ng/ml) in the plasma of people living near fluorochemical plants, airport, and/or military sites [e.g.,

58–60]. For all PFAS, 10 µM stock solutions were prepared by dissolving each chemical in 18.1 mS/cm phosphate-buffered saline, which was achieved by diluting the stock buffer tenfold with deionized water to give a solution that was pH 7.4. Stock solutions of different proteins were prepared fresh daily in phosphate buffered saline.

Specific PFAS and protein concentrations were selected to achieve a 1:1 PFAS to protein molar ratio at the midpoint of the range of selected PFAS concentrations.

Protein concentrations in prepared solutions were verified using the Qubit Protein assay kit (Thermo Fisher, Waltham, MA, USA).

EqD experiments were performed at room temperature by first adding 1.2 mL of the

18.1 mS/cm phosphate buffered saline (pH 7.4) spiked with PFAS to a 1.5 ml polypropylene microcentrifuge tube (Fig. S1). A Slide-a-Lyzer mini dialysis cup containing a semi-permeable membrane (molecular weight cutoff: 10kDa) was then inserted into the tube, through which PFAS could freely pass but which was impermeable to the proteins used (MW range 15.1 – 54.1 kDa). A known volume of protein in buffer (20 to 50 μL) was added to reach a 1 μM concentration for L-FABP,

I-FABP, and PPAR-훾, and 0.48 μM for PPAR-δ and PPAR-α. The lower concentration of PPAR-δ and PPAR-α was necessary due to the larger size of these proteins. Finally, the total volume in the dialysis cup was brought to 100 μL by adding the buffer spiked with PFAS.

Blanks were prepared using a protein solution with no PFAS. Non-binding controls

(containing PFAS but no protein) were prepared with the buffer spiked with different concentrations of PFAS. Finally, samples were placed on a rocker (Open-Air Rocker,

Fisher Scientific, Waltham, MA, USA) for 36 h to reach equilibrium at room temperature. All dialysis tests were performed in duplicate.

2.3.4 Analysis by LC-MS/MS.

All dialysate samples were analyzed without dilution or first diluted into water to reach concentrations of 100–2,000 ng/L prior to analysis. Final sample volumes (1.5 mL)

were spiked with 24 μL of isotopically labeled internal standards for quantification prior to injection. A modified Agilent 1100 series HPLC (Santa Clara, CA) was used for large volume (900 μL) injection of aqueous samples. A C18 (4.6x 50mm x 5μm

Zorbax Eclipse) delay column was used between the LC pump and autosampler to separate out instrumental background. Retention of analytes was achieved with a C18 analytical column (Eclipse 4.6 x 100mm x 3.5μm) and mobile phases were 20 mM ammonium acetate in HPLC-grade water (A) and HPLC-grade methanol (B). A ten min LC gradient was used as follows: mobile phase A at 0.5 mL/min for 3.5 min, mobile phase B at 1 mL/min for 1.5 min, and mobile phase A at 1.0 mL/min for 4.5 min reduced to 0.5 mL/min for the remaining 0.5 min.

Identification and quantification of analytes were previously described in Allred et al.

[61]. The analytical sequence consisted of a minimum 5-point calibration curve over the range of 20 -10,000 ng/L for all analytes. Accuracy was determined from the analysis of a second source of standards and were required to be 70–130% of the target value. Whole method precision, as indicated by relative standard deviation, was calculated from four replicate samples and ranged from 4-18%. The limit of detection

(LOD, 6 ng/L) was calculated by normalized-weighted regression (1/X), from which the limit of quantification (LOQ) (20 ng/L) was calculated as 3.3 x the LOD [7]. Each analytical sequence consisted of solvent blanks that were spiked with 24 μL of isotopically labeled standards; all blanks gave responses that fell below the LOQ.

Binding coefficients for protein-PFAS pairs were calculated from the difference in

PFAS concentrations (mole ratio) between the non-binding control and equilibrium dialysates. Data for all dialysis experiments were analyzed by nonlinear regression,

assuming a single-site binding model using GraphPad Prism V8.1.2 (GraphPad software, San Diego, CA, USA) to determine KD [62–65]. Some EqD concentrations, when subtracted from the non-binding control, produced a negative binding coefficient indicating a final equilibrated concentration greater than the initial dialysate concentration. As both the EqD experiment and non-binding control come from the same stock, the EqD concentration should, at most, equal that of the non-binding control. This may have been an artifact of dilution, at high initial concentrations, 15 to

3000-fold dilutions were required to bring PFAS on-scale for detection. In cases where large dilution factors were required, uncertainty about the calculated final concentrations in the dialysate may be magnified. In order to better address this, a decision tree was created to determine the handling of these incidents (Fig. 2.1).

2.3.5. Comparison to Existing PFAS-protein KDs and Methods.

In order to place our results in context with existing literature and provide insight into in vitro and modeling choices, we conducted a literature search for all available PFAS- protein binding data that used the same proteins as investigated here. In addition, we screened existing serum albumin studies that used equilibrium dialysis, where the results could be compared across different methods as done here for FABPs and

PPARs. The search spanned publication years between 1954 and 2020, and resulted in

37 studies used for comparison of methods.

2.4. Results and discussion

2.4.1. Screening protein-PFAS pairs by molecular dynamics

Molecular dynamics modeling predicted free energies of binding which, when converted to equilibrium dissociation constants (KD values), ranged between

approximately 10-5 to 106 μM and correspond to femtomolar to molar dissociation constants. Relevant interactions with and between biomolecules occur at a range of dissociation constants from low millimolar (the weakest) to femtomolar (the strongest)[66]. It is generally accepted that the most biologically relevant (moderate to strong) interactions correspond to KD values at micromolar levels and lower [67]. This suggests that predicted binding affinities, if assumed to be similar to in vivo binding affinities, are unlikely to be biologically relevant if they are substantially larger than

103 μM.

Based on the MD predictions, we selected fifteen PFAS-protein pairs for experimentally determine KD values using equilibrium dialysis (Table A1). We selected the short-chain PFCA PFBA for EqD testing with PPAR-α because of its strong predicted affinity (Figure 2.2A); PFHxA, PFHpA, and the long-chain PFNA were selected for EqD testing with PPAR-α as well. This range allowed us to evaluate both the surprising prediction of strong affinity for PFBA and the lack of chain length dependence for the PFCAs experimentally, particularly given the lack of other experimental data. For PPAR-훾, since no short-chain PFAS were predicted to bind strongly, we selected only PFOA and PFOS for EqD testing. For PPAR-δ/β, we selected the three sulfonates, PFBS, PFHxS, and PFOS. This allowed us to verify, first, the strong predicted binding with PFBS and, second, the counterintuitive chain length dependence predicted by MD for the sulfonates.

The relatively well studied L-FABP provides an opportunity to compare with multiple other studies, both modeling and in vitro. For L-FABP, PFOS was selected for EqD testing because it was predicted to have the strongest binding affinity (Figure 2.3B);

PFOA and PFHxS were selected as well to compare the effect of the head group

(carboxylate vs. sulfonate). For evaluating potential binding with short-chain PFAS, only PFBS has moderately strong predicted binding affinity (compared to carboxylates). For I-FABP, PFHpA and PFNA showed the strongest binding and were therefore selected. Further discussion regarding MD results can be found in the SI. Of the PFAS selected PFBA, PFHxA, PFOA, PFNA, PFBS, and PFOS have documented serum levels in humans living near industrial and urban areas and are 0.9 ng/ml, 0.1 ng/ml, 4.26 ng/ml, 0.81 ng/ml, 0.13 ng/ml, and 22.81 ng/ml, respectively (68–73).

2.4.2. PPAR-α.

Strong binding for PFHxA (Fig. 2.4A) and PFNA (Fig. 2.4B) and no binding for PFBA

(Fig. A2A) and PFHpA (Fig. A2B) were observed via EqD experiments. The lack of chain length dependence observed was in agreement with the MD predictions.

However, MD simulations suggested only PFBA would have strong binding for PPAR-

α, which was not borne out by dialysis. The relatively strong dissociation constant of

0.097 μM for PFHxA could have implications for short-chain PFAS safety.

2.4.3. PPAR– 훾.

Strong binding was found between PFOA and PPAR-훾 (Figure A3) which agrees with previous experimental evidence that PFOA is a PPAR-훾 activator [74].

Additionally, PFOS binds to PPAR-훾, albeit with substantially lower affinity. These

EqD-derived KD values are the first reported for PPAR-훾 with PFOA and PFOS. MD binding predictions were in agreement with observed KD values for both PFOA and

PFOS (Figure 2.2C & D).

2.4.4. PPAR–δ.

Strong binding to PFBA, PFHxS, and PFOS (Figure 2.5 and A4) was observed for the first time with this protein. Like PPAR-α, PPAR-δ also had binding of a short chain

PFCA (PFBA) and did not adhere to the increased binding affinity with increasing chain length trend observed for L-FABP. Again this indicates that short-chain PFAS safety on the basis of body clearance time may not be that simple and more research into the interactions that may occur during clearance time is warranted. Additionally, chain length, while generally a good indicator of PFAS retention in a system may not be an indicator of binding affinity to any given protein. The detectable binding affinities for PPAR-δ were in the range of 10-2 to 10-1 μM. MD simulations were in agreement for PFHxS and PFOS, however, predicted binding to PFBS was not detected experimentally, whereas experimental binding to PFBA was observed but not predicted

(Figure 2.2E & F). Overall, PPAR MD simulations were effective in identifying relative binding affinity and provided confidence in the selection of PFAS – protein combinations but are not currently able to predict absolute affinity.

2.4.5. L-FABP.

Our EqD results for L-FABP generally agreed with previous observations in terms of relative affinities. That is, binding was strongest for the long-chain PFAS tested, PFOA and PFOS (0.099 and 0.18 μM, respectively, see Figure 2.6A for PFOS and Figure

A5A for PFOA), weaker for PFHxS (1.7 μM, Figure A5D), and not detected for the shortest PFAS tested, PFHxA and PFBS. Experimentally derived KD values for PFOS,

PFHxS, and PFOA fell within the range of model predictions (Figure 2.3A & B).

2.4.6. I-FABP.

These are the first experimental data for PFCAs binding to I-FABP. Molecular dynamics results for I-FABP indicated PFHpA and PFNA should both demonstrate relatively strong binding (Figure 2.3C). However, no binding was detected by EqD for either PFHpA or PFNA (Figure 2.6B and A6) and therefore no KD values could be determined (Table 2.3).

Since these are the first experimentally determined KDs for I-FABP, there are no other studies to aid in evaluating whether the MD simulations or dialysis results are more problematic. The MD results of PFSAs indicated very weak interactions for all chain lengths, which is more in line with the dialysis observations for the PFCAs tested.

2.4.7. Comparison Across in Vitro Methods to Evaluate Binding

Comparison of experimentally derived KD values from this and previous studies suggest that EqD consistently generates lower KD values (stronger binding affinities) than other approaches. Fluorescence displacement has recently emerged as a widely applied method to measure protein binding affinity [75]. Fluorescence displacement is a convenient and relatively high-throughput approach but, as shown here, will consistently indicate lower affinity binding that EqD (Figure 2.7 and SI Tables A2 and

A3). For L-FABP, observed KD values from this study were substantially lower than previously published values (Figure 2.7A)[23,76]. Experimentally derived KD values for PFOA and PFOS with PPAR-훾 were lower than those reported by Zhang et al. [32], three to four orders of magnitude in the case of PFOA and one order of magnitude for

PFOS (Figure 2.7B). KD values for PFHxA and PFNA with PPAR-α measured by

equilibrium dialysis are lower than those reported by Ishibashi et al. [31] by several orders of magnitude (Figure 2.7C). Although Ishibashi et al. [31] report 50% inhibitory concentrations (IC50) rather than KD, the magnitude of the differences between results is unlikely to be attributable to this. The IC50 in the case of the Ishibashi et al. [31] study describes the concentration of the competitor (i.e., PFAS) at which 50% of the fluorescent molecule was displaced, and is thus an indirect measure of binding affinity.

IC50 may vary according to the competition regime and experimental conditions, but for competitive inhibition (i.e., displacement by PFAS from the same binding site) should be of similar magnitude, as these values are linked by ligand and substrate concentrations. Similar to results for PPAR-α, Li et al. [77] reported competitive binding based IC50 for PPAR-δ with PFBA, PFHxS, and PFOS, wherein only PFOS showed detectable binding (Figure 2.7D). EqD-determined binding coefficients in this study for PFBA, PFHxS, and PFOS with PPAR-δ were lower than those reported IC50 values, with PFBA and PFHxS in particular showing strong binding.

Similar observations have been made before, for example between EqD and 19F-NMR and micro-size exclusion chromatography for serum albumins [22]. A literature search comparing methods to determine binding for human serum albumin (HSA) and bovine serum albumin (BSA) also showed EqD to consistently produce lower KD values than other methods (Fig. A7-A8 and Table A3). This indicates that the low KD values measured here are not an artifact of this study but rather a consistent outcome of the

EqD approach.

2.5 Conclusion

This is the first study to report short chain PFAS – PPAR binding with KDs in the sub- micromolar range, raising the possibility that short-chain replacements for long-chain

PFAS may still be bioactive, despite the assumed “safety” of short-chain PFAS on the basis of rapid serum clearance [67]. PPARs are nuclear receptors that play critical roles in the regulation of many biological processes, including cell growth, lipid metabolism, differentiation, and inflammation [78]. Previous in vitro and in vivo studies have reported that both PFCAs and PFSAs can activate PPAR-α and PPAR-훾 [15,32,79] , but have not found activation of PPAR-δ [28]. This is the first study to report strong interactions with PPAR-δ and PFCAs having fewer than six perfluorinated carbons.

The lack of chain length dependence we observed with PPAR-α and PPAR-δ by both

MD simulations and EqD indicates that PFAS binding affinity to proteins should not be inferred by PFAS carbon chain length for all proteins but is rather specific to the protein being considered.

Despite the accumulating data, there is a persistent lack of clarity on how either modeling or in vitro studies relate to the behavior of PFAS in vivo, within natural biological and environmental contexts—that is, in competition with native ligands and other environmental contaminants. EqD may indicate higher binding affinity because it measures binding in a highly controlled system independent of other factors. In vivo, competitive interactions are more likely to be the dominant mode. It is still unclear whether typically used fluorophores are at all representative of native ligands and other xenobiotics that make up the real-world competitors of PFAS for protein sites. Thus a competitor-agnostic approach, such as equilibrium dialysis, may still be preferable.

Moreover, consistently lower KD values across many different proteins raises an important question that is yet to be answered and will be key for making reliable in vitro to in vivo extrapolations: do the lower KDs indicate the EqD approach is capable of quantify binding that other approaches do not? If so, this could suggest that binding affinities of PFAS to proteins considered here, and possibly other proteins, have been historically underestimated, and subsequent research using data from different approaches should recognize that EqD generates lower KD values.

In some cases, it is possible that MD simulations could be improved by longer simulation times. However, increasing the simulation time from 24 ns to 45 ns for all the PPAR – PFAS combinations presented here would require months of additional computation time. Therefore, when undertaking and interpreting these modeling approaches it is important to acknowledge the time resource component. The comparison of modeled and experimentally determined values in this study further confirms our previous observation [42] that MD simulations are best for predicting relative rather than absolute KD values. The extent of agreement between measured and modeled values varied substantially among proteins, but chain length dependencies or lack thereof were generally consistent. Additionally, MD simulations predict stronger binding than is experimentally observed through fluorescence displacement but weaker binding than may be observed via equilibrium dialysis. Future research is needed to understand how different binding values relate to in-vivo consequences and if any particular method should be used for in-vitro to in-vivo extrapolation.

2.6 Acknowledgments: This study was supported by a Strategic Environmental

Research and Defense Program (SERDP) Grant ER-181417.

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72. Worley, R.R.; Moore, S.M.; Tierney, B.C.; Ye, X.; Calafat, A.M.; Campbell, S.; Woudneh, M.B.; Fisher, J. Per- and Polyfluoroalkyl Substances in Human Serum and Urine Samples from a Residentially Exposed Community. Environ Int 2017, 106, 135–143, doi:10.1016/j.envint.2017.06.007.

73. Weihe, P.; Kato, K.; Calafat, A.M.; Nielsen, F.; Wanigatunga, A.A.; Needham, L.L.; Grandjean, P. Serum Concentrations of Polyfluoroalkyl Compounds

in Faroese Whale Meat Consumers. Environ Sci Technol 2008, 42, 6291–6295, doi:10.1021/es800695m.

74. Riu Anne; Grimaldi Marina; le Maire Albane; Bey Gilbert; Phillips Kevin; Boulahtouf Abdelhay; Perdu Elisabeth; Zalko Daniel; Bourguet William; Balaguer Patrick Peroxisome Proliferator-Activated Receptor γ Is a Target for Halogenated Analogs of Bisphenol A. Environmental Health Perspectives 2011, 119, 1227–1232, doi:10.1289/ehp.1003328.

75. Hall, M.D.; Yasgar, A.; Peryea, T.; Braisted, J.C.; Jadhav, A.; Simeonov, A.; Coussens, N.P. Fluorescence Polarization Assays in High-Throughput Screening and Drug Discovery: A Review. Methods Appl Fluoresc 2016, 4, 022001, doi:10.1088/2050-6120/4/2/022001.

76. Sheng, N.; Cui, R.; Wang, J.; Guo, Y.; Wang, J.; Dai, J. Cytotoxicity of Novel Fluorinated Alternatives to Long-Chain Perfluoroalkyl Substances to Human Liver Cell Line and Their Binding Capacity to Human Liver Fatty Acid Binding Protein. Arch Toxicol 2018, 92, 359–369, doi:10.1007/s00204-017-2055-1.

77. Li, C.-H.; Ren, X.-M.; Cao, L.-Y.; Qin, W.-P.; Guo, L.-H. Investigation of Binding and Activity of Perfluoroalkyl Substances to the Human Peroxisome Proliferator-Activated Receptor β/δ. Environ. Sci.: Processes Impacts 2019, 21, 1908–1914, doi:10.1039/C9EM00218A.

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79. Lee, S.S.; Pineau, T.; Drago, J.; Lee, E.J.; Owens, J.W.; Kroetz, D.L.; Fernandez-Salguero, P.M.; Westphal, H.; Gonzalez, F.J. Targeted Disruption of the Alpha Isoform of the Peroxisome Proliferator-Activated Receptor Gene in Mice Results in Abolishment of the Pleiotropic Effects of Peroxisome Proliferators. Mol Cell Biol 1995, 15, 3012–3022.

Table 2.1. Summary of 3-dimensional structure information for selected proteins

Chain Known Protein PDB Code Resolution Length Ligands L-FABP 3STM 2.22 Å 132 palmitic acid

11- (Dansylamino) I-FABP 3AKM 1.9 Å 131 undecanoic acid

PPAR-α 4CI4 2.3 Å 274 propanoic acid

PPAR- 3U9Q 1.5 Å 269 decanoic acid

PPAR-δ 3TKM 1.95 Å 275 GW0742

Table 2.2. Dialysis material extraction and sorption results.

Material Surrogate Extracts PFBA PFHxA PFHpA PFOA PFNA PFBS PFHxS PFOS Recovery Collection tube

Sorption to Materials PFBA PFHxA PFHpA PFOA PFNA PFBS PFHxS PFOS 2000 ng/L Spike 1 5700 2700 3200 3300 2600 2600 2030 2700 2000 ng/L Spike 2 2500 1500 2100 2600 1900 1700 2600 2400

% Recovery 1 285% 135% 160% 165% 130% 130% 101% 135% % Recovery 2 125% 75% 105% 130% 95% 85% 130% 120%

Table 2.3. Dissociation constant (KD) values ± SE measured by equilibrium dialysis.

“ND”: no dissociation constant could be determined, indicating low to no binding.

Protein PFAS KD (µM) PFHxA ND PFOA 0.099 ± 0.015 L-FABP PFBS ND PFHxS 1.7 ± 0.031 PFOS 0.18 ± 0.032

PFHpA ND I-FABP PFNA ND

PFBA ND PFHxA 0.097 ± 0.070 PPAR- α PFHpA ND PFNA 0.083 ± 0.028

PFOA 0.057 ± 0.027 PPAR- γ PFOS 8.5 ± 0.46

PFBA 0.044 ± 0.013 PFBS ND PPAR- δ PFHxS 0.035 ± 0.0020 PFOS 0.69 ± 0.33

Figure 2.1. Decision tree for the inclusion of the equilibrium dialysate concentrations for the regression analysis.

A B

6 6 PPAR-α )

M 3 3

, D 0

K 0

(

o -3 -3 l

-6 -6

PFBA PFPeA PFHxA PFHpA PFOA PFNA PFBS PFHxS PFOS

C D PPAR-γ 6 6

) 3

M 3

, D 0

K 0

(

o -3

l -3

-6 -6

PFBA PFPeA PFHxA PFHpA PFOA PFNA PFBS PFHxS PFOS E F 6 6 PPAR-δ

) 3 3

D 0 0

(

o -3 -3 l

-6 -6

PFBA PFPeA PFHxA PFHpA PFOA PFNA PFBS PFHxS PFOS

Figure 2.2. Predicted dissociation constant (KD) values (geometric mean ± 1 standard error) for different PPAR-PFAS complexes. (A) PPAR-α and PFCAs (B) PPAR-α and PFSAs (C) PPAR-훾 and PFCAs (D) PPAR-훾 and PFSAs (E) PPAR-δ and PFCAs

(F) PPAR-δ and PFSAs. Values of log KD >3 correspond to millimolar or weaker binding, between -3 and 3 are moderate (in the micromolar range) and < -3 correspond to strong, nanomolar or lower binding.

Figure 2.3. Predicted dissociation constant (KD) values (geometric mean ± 1 standard error) for (A) L-FABP and PFCAs, (B) L-FABP and PFSAs, (C) I-FABP and PFCAs, and (D) I-FABP and PFSAs. Values of log KD > 3 correspond to millimolar or weaker binding, between -3 and 3 are moderate (in the micromolar range) and < -3 correspond to strong, nanomolar or lower binding.

Figure 2.4. Specific binding (μmol PFAS/μmol protein) vs free concentration of

PFAS (μmol/L), used for nonlinear fit of KD (in μM, ± S.E.) for (A) PFHxA and (B)

PFNA with PPAR–α.

Figure 2.5. Specific binding (μmol PFAS/μmol protein) vs free concentration of

PFAS (μmol/L), used for nonlinear fit of KD (in μM, ± S.E.) for binding affinity of

(A) PFBA and (B) PFHxS with PPAR–δ.

Figure 2.6. Specific binding (μmol PFAS/μmol protein) vs free concentration of

PFAS (μmol/L), used for nonlinear fit of dissociation constant KD (in μM, ± S.E.) for

(A) PFOS with L-FABP and (B) PFNA with I-FABP. The latter had a non-detectable

(ND) KD, indicating low to no binding affinity.

4 4 A) L-FABP B) PPAR-γ EqD ITC

3 3 FD

) ) M

M 2 2

µ µ

( (

D D

K K

1 0

0 1

1 1

g g

o o

l l 0 0

-1 -1

-2 -2 P P P P P P F P P P F F F F F H F F F H O H O O H O p B p A x S A A S xS S A S

4 4 C) PPAR-α D) PPAR-δ KD, EqD IC , FD

3 3 50

)

(

0 5

2 5 2

1 D 1

g o

0 o 0

l l

-1 -1

-2 -2 P P P P P P P P P P P F F F F F F F F F F F B P H H O B H O B H O A e x p A S x S A x S A A A S S

Figure 2.7. Comparison of KDs for PFAS with eight or fewer fluorinated carbons measured by equilibrium dialysis (EqD) in this study (red symbols) compared with (A)

KD measured by fluorescence displacement (FD) and isothermal titration calorimetry

(ITC) for L-FABP, (B) KD measured by FD for PPAR-훾, and (C) IC50 (right axis) measured by FD for PPAR-⍺ and (D) PPAR-δ.

Chapter 3 - Per and Polyfluorinated Alkyl Substances in Nonaqueous Phase

Liquids

Emerson C. Christie1, Bill Diguiseppi2, Konstantinos Kosterelos3, and Jennifer A.

Field1

1 Department of Environmental and Molecular Toxicology, Oregon State University,

Corvallis, OR, USA

2 Jacobs, Denver, Colorado 80112, United States

3 Department of Petroleum Engineering, University of Houston, Houston, TX, USA

In preparation for submission

3.1 Abstract

Per- and polyfluoroalkyl substances (PFAS) were co-disposed with fuels and solvents historically at waste sites, during fire fighter training exercises, and in response to emergencies. Subsurface light non-aqueous phase liquids (LNAPLs) remain a number of U.S. military sites and some are likely to have contact with PFAS-contaminated groundwater. A micro liquid-liquid extraction method was developed for the analysis of anionic and zwitterionic PFAS in LNAPL recovered from groundwater wells. The method was then demonstrated on 10 LNAPL field samples obtained from four military installations for which the last known spills of LNAPLs included petroleum, Jet Fuel

5, diesel, naval special fuel oil, and diesel fuel marine and ranged from 10 to 70 years ago. Concentrations of seven anionic and nine zwitterionic PFAS found in LNAPL ranged from

6:2 fluorotelomer sulfonate. Only one sample gave perfluorooctanoic acid (PFOA) at

430 ng/L. Members of the perfluoroalkyl sulfonamide (C4, C6, and C8) were found in all 10 samples at concentrations up to 27,000 ng/L (C6), and nine zwitterionic PFAS.

The presence of PFAS in recovered LNAPL indicates that LNAPL may act as long- term source of PFAS in the environment. In addition, LNAPL with PFAS requires additional ex-situ treatment. Moreover, as these concentrations were derived from samples collected from locations that were not identified as PFAS source areas, it is possible that recovered LNAPL from source areas could have greater PFAS concentrations.

3.2 Introduction

Per and polyfluorinated alkyl substances (PFAS) are a group of anthropogenic chemicals that are used in numerous industrial and consumer applications1, 2. It is now recognized that they are highly persistent1, 3 and mobile in the environment4 and some have since been classified internationally as persistent organic pollutants5. PFAS are detected in soil6, groundwater6, 7, and in human serum8 and some bioaccumulate9, impact the immune systems10, act as endocrine disruptors11 and cause cancer in test animals12.

Despite these known endpoints, PFAS continue to be used globally, particularly for applications where suitable compounds have not yet been identified, such as MilSpec aqueous film forming foams (AFFF). Highly concentrated PFAS-containing products, such as AFFFs, that contain gram per liter levels of PFAS, are used by militaries and municipalities to combat hydrocarbon-fueled fires13, 14. As such, repeated use of AFFF for fire-fighter training and equipment testing has resulted in persistent and widespread groundwater contamination at U.S. military sites6, 7, 15, 16. Contaminated areas are classified as source zones where PFAS were released and milligram per liter concentrations of PFAS can be found in the groundwater, even though the use of ‘live’

AFFF by the military sites has decline significantly over the last 20-30 years. Non- source zones are not associated with a specific PFAS release and concentrations are typically lower. On average, major DOD bases have 10-12 sites with PFAS impacts to soil or groundwater, based on site inspections17.

Another common occurrence throughout US military installations is the release of nonaqueous phase liquids (NAPL), either intentionally through training and former waste

disposal practices or unintentionally due to spills or leaks15, 18. In some cases, AFFF

(PFAS) were co-disposed with LNAPL through waste disposal sites and fire training areas15, 18. Early fire training practices dating back to the 1970s involved the extinguishing of diesel and waste oils in unlined pits with AFFF19, 20. Additionally,

PFAS that are mobile in groundwater plumes can come in contact with residual

LNAPL. As demonstrated by Zhu et al., PFAS occur in fluids like lubricants but concentrations indicated that PFAS were not deliberately added21. While PFASs are well studied in groundwater, nothing is known about PFAS concentrations in environmental LNAPL.

Laboratory studies indicate that PFAS partition from water into NAPL.

Trichloroethylene (TCE) was indicated as a potential PFAS sorbent low organic carbon content soil15. Laboratory batch and column experiments demonstrate the impact of

PFAS fluorinated carbon chain length and TCE volume on PFAS partitioning into the

TCE phase as well as absorption occurring at the interface18. Chen et al. reported increased loss of PFOS from solution on to oil-contaminated soil compared to no oil controls22. In these laboratory experiments, partitioning is only measured by loss from the aqueous phase. Brusseau et. al. acknowledged partitioning into LNAPL could be another mechanism of PFAS retention23 and Kosterelos et. al. upon mixing application- strength AFFF (3% in water) and Jet Fuel observed the formation of viscous, stable, microemulsions (three-dimensional)24. Microemulsions that formed in situ served as a sink for up to 70% of the injected PFAS24. Results from these studies indicate that

PFAS concentrations in subsurface LNAPL may act as a long-term source of PFAS.

Furthermore, LNAPL containing PFAS recovered from the subsurface may require advanced treatment.

Despite these concerns, there are few data that describe the concentration and composition of PFAS in recovered LNAPL and only one analytical method is reported for analysis of PFAS in NAPL. Zhu et. al. 2019 quantified PFCAs (C4-C12) and PFSAs

(C4-C10) in purchased automotive lubricants21. However, they employed multiple steps from mixing in solvent, centrifuging, evaporation, reconstitution in water, followed by weak anion exchange solid phase extraction (WAX SPE)21. Because WAX

SPE is designed to concentrate anions, the performance of this method is unknown for zwitterionic PFAS, which are found in 3M and fluorotelomer-based AFFF13, 14, 25.

Analytical methods for quantification of a diverse array of PFAS in recovered LNAPL are needed in order to assess the environmental relevance of this issue and generate a more complete picture of PFAS contamination at LNAPL sites.

The objective of this research was to develop an analytical method to quantify anionic and zwitterionic PFAS in LNAPL recovered from groundwater wells at U.S. military sites and to demonstrate the methodology on field-collected LNAPL samples. Micro liquid-liquid extraction coupled with large volume (900 L) injection liquid chromatography tandem mass spectrometry (LC-MS/MS) to quantify 34 target anionic and zwitterionic PFAS. The composition and concentration of PFAS in LNAPL is presented for 10 field samples.

3.3 Experimental

Materials. Methanol, water, and ethyl acetate used were HPLC grade from Fisher

Scientific (Hampton, NH). All native and mass-labeled surrogate and internal standards

(Table B1) were purchased from Wellington Laboratories (Ontario, Canada).

Commercially available Jet Fuel A, purchased from Corvallis Municipal Airport

(Corvallis, OR), was used for method development. Glass bottles (120 mL, VWR,

Radnor, PA) were used for the collection, transport, and storage of LNAPL samples.

Polypropylene tubes (15 mL and 50 mL) from VWR (Radnor, PA) were used for any aqueous phase separated from LNAPL.

Sample Collection. Individual sampling locations at four military installations were selected by U.S. Department of Defense site managers based upon PFAS monitoring data and records of LNAPL recovery. At these sites, free-product thicknesses ranged from 0.07 – 0.8 m and depth to free product from the surface was < 3 m. Site information indicates that PFAS and LNAPL were not intentionally co-disposed at any site and it is unknown how LNAPL and contaminated groundwater were introduced.

The LNAPL composition included jet fuel, diesel, and other liquids and dates of last

LNAPL release (1950 – 2010) were provided by site managers (Table 3.1). The wells selected for sampling were those that previously gave g/L PFAS concentrations in groundwater. None of the sites were former fire-fighter training areas and the years of

PFAS release are not known.

LNAPL was collected in 60 mL glass amber bottles without lids. Trip and field blanks consisted of 50:50 water and Jet Fuel A that were shipped to field sites. The field blank was opened on-site during the time of sampling. It is important to note that sample quantities were required to be no more than 30 mL LNAPL to meet U.S. shipping regulations.

For any sample that consisted of two phases, the LNAPL and aqueous phases were separated via pipette and placed in glass and 50 mL HDPE containers, respectively.

LNAPL samples were stored in glass at room temperature in a flammable cabinet and aqueous samples were frozen until analysis.

LNAPL Sample Preparation and Extraction. A 10 mL aliquot of LNAPL was centrifuged at approximately 2000 g for 5 min to ensure that the LNAPL and residual groundwater and/or particulate matter in the sample were separated. A 1.5 mL aliquot of centrifuged LNAPL was placed in a 15 mL polypropylene centrifuge tube and spiked with 0.75 ng of 21 isotopically labeled surrogate standards (Table B1) in ethyl acetate.

For the methanol-based standards, spikes first had to be made into a small volume of ethyl acetate to ensure miscibility with NAPL, a description and accuracy of this procedure can be found in the supplemental information (SI). Three rounds of extraction were performed by adding 500 L methanol, vortexing for 30 s; and then transferring 333 L to an autosampler vial. The extraction was repeated two more times with additions of 375 L and 325 L of methanol for a total extracted volume of

999 L. The autosampler vial was brought to a final extract volume of 1,500 L. Direct analysis of LNAPL was performed via 1:10 dilution into ethyl acetate.

Aqueous Sample Preparation. Groundwater samples (one analysis per sample) were diluted 1:10 into methanol, to bring PFAS on scale of the instrument calibration, spiked with 0.75 ng of isotopically labeled surrogate and internal standards, and analyzed directly.

LC MS/MS. Analyses were performed with an Agilent 1100 (Santa Clara, CA) for separation and a Waters TQ Detector (Milford, MA) triple quadrupole mass

spectrometer for acquisition. The Agilent 1100 was modified with a 900 L injection loop to accommodate 900 L sample injections. Two Agilent ZORBAX amino propyl and one Agilent ZORBAX silica guard columns (4.6mm x 12.5mm x 5 um) were connected in line with an Agilent Eclipse 4.6mm x 75mm x 3.5 um C18 analytical column. The initial solvent A (3% methanol in water) was held for two min at a flow rate of 0.6 mL/min, during which column effluent was diverted to waste. The gradient was then transitioned to 50: 50 A and 10 mM ammonium acetate in methanol (B). The percent organic was increased linearly over 13.5 min until reaching 99% B and then held for an additional 4.5 min. The flow rate was increase to 1.0 mL/min for two min before switching to a 100% A, which was held for 11 min, for a total run time of 33 min.

The MS/MS method was composed of multiple reaction monitoring (MRM) acquisition windows with parent and transition ions monitored based on retention time and was set to pole switch between positive and negative electrospray ionization mode based upon the PFAS monitored. The MS parameters were as follows: capillary potential 2800V, extractor potential 2V, source temp 150ºC, desolvation temp 450ºC, desolvation gas 1100 L/hr, and cone gas 75 L/h.

Analyte concentrations were determined by internal standard calibration and 1/x weighted linear regression over a 7-point calibration curve. The calibration curve spanned from 20-10,000 ng/L for all analytes and R2 values were typically 0.99 or greater. A 20 ng/L standard was analyzed every 10 samples and was required to fall within 70-130% to ensure sensitivity and calibration were maintained throughout analysis. Continuing calibration verification was used at the start of each analysis and

was also required to fall between 70-130%, otherwise a new calibration curve was made.

All PFAS names, acronyms, acquisition masses and parameters, calibration references, and data quality tiers are listed in Table B1. Quantitative (Qn) refers to an analyte with a commercially available native standard and a mass labeled standard, semi quantitative

(Sq) analytes have a native standard but not a matched mass-labeled standard, and screen (Sc) analytes are those without a native and matched mass-labeled standard.

Method Performance. Spike and recovery experiments were used to determine method accuracy, as percent recovery, and precision, as relative standard deviation, and were obtained from four replicate samples of Jet Fuel A that were overspiked with Qn native

PFAS (Table B1) to give a final concentration of 100 ng/L. The limit of detection

(LOD) and limit of quantification (LOQ) were determined in accordance to the methods described in Vial and Jardy26. Briefly, Jet Fuel A was overspiked to provide a final concentration range spanning from 1 – 100 ng/L, each sample was then extracted, and a 1/x weighted regression was used to calculate the LOD and the LOQ was defined as 3.3 x LOD. Direct LNAPL accuracy and precision were obtained from four replicate samples of Jet Fuel A that were overspiked with Qn native PFAS (Table B1) to give a final concentration of 5,000 ng/L, which were then diluted 1:10.

Quality Control. All LNAPL samples analyzed for the method demonstration were analyzed in triplicate. Method blanks were analyzed at the start of each analysis.

Method blanks consisted of Jet Fuel A (previously determined to be blank) and were extracted in accordance with the method described above. All blanks (whole analytical method, field blanks, and trip blanks) gave PFAS below the instrumental LOD.

3.4 Results and Discussion

Methanol Liquid-Liquid Extraction. Extracting non-volatile PFAS from LNAPL consisting of jet fuel, diesel, and other LNAPL (Table 3.1) required a solvent that forms an immiscible phase with a variety of LNAPL21. The accuracy of the whole method for

Qn PFAS ranged from 77 – 130%, as indicated by the average recovery, while precision ranged from 2-30% for most analytes (Table B2). Although FOSA gave 100% recovery, shorter-chain sulfonamides FBSA and FHxSA gave recoveries of 47 and

33% respectively (Table B2). The whole method LOQ ranged from 5 – 70 ng/L for most analytes, except for the sulfonamides, which ranged from 60 – 500 ng/L (Table

B2) and are comparable to those obtained by Zhu et. al.21. Accuracy of direct LNAPL analysis via dilution for Qn PFAS ranged from 89-150%, average recovery, and precision ranged from 3-9% (Table B3).

This extraction methodology only required 1.5 mL of LNAPL for analysis and the only waste generated during the extraction was two polypropylene tubes and pipette tips.

Each extraction takes approximately 10 min and multiple extractions can be performed in tandem. The ZORBAX amino propyl and silica guard columns were used for 50 sample injections and were treated as disposables.

Method Demonstration. Both electrochemical fluorination- (ECF) and telomer-derived

PFAS (n:2 FtS) were observed in the 10 demonstration samples where the LNAPLs included petroleum, diesel, Jet Fuel 5 along with unknown liquids. Of the 10 LNAPL samples, all had quantifiable PFAS concentrations with PFOS found at the highest frequency followed by FHxSA, 6:2 FtS, and FOSA (Table 3.2). Concentrations and a frequency of detection for sulfonamides (FBSA, FHxSA, and FOSA) at or near that of

PFOS is unusual. The sulfonamides have a higher pKa (6.2)27, 28 and may partition into

LNAPL to a greater extent since a much greater fraction of this class of PFAS are in the neutral form at the pH of groundwater (e.g., pH 5-9). Notably are the low concentrations of PFOA and absence of PFHxS, which are often found at concentrations similar to that of PFOS29 in groundwater, as well as the absence of other perfluoroalkyl carboxylates. Perfluoroalkyl carboxylates are 1/100th the concentration of PFOS in ECF-based AFFF13. Although to the best of our knowledge there are no partition coefficients for PFAS into LNAPLs including diesel and jet fuel, Guelfo et al. reported a general trend in PFAS partitioning with chain length for perfluoroalkyl carboxylates and sulfonates into TCE and dodecane15.

Except the 6:2 and 8:2 FtS, all PFAS including the zwitterionics, were branched and linear, which indicates that the majority of PFAS associated with LNAPL at these sites were ECF-based. The zwitterionic PFAS occurred primarily in samples 9 and 10. The

ECF- and telomer-based PFAS associated with NAPL are similar to those found at

AFFF-impacted sites25, 30-32. Although the sites sampled were not associated with fire- fighter training areas, PFAS occur in soil and water at military sites due to handling of

AFFFs in and around hangars and other buildings19.

A limited number of aqueous phase analyses were conducted. Each of the 10 aqueous samples associated with the LNAPL samples had one or more detectable Qn PFAS

(Table 3.2 Table B4, Table B5). Only PFOS (5/10), 6-2 FtS (3/10) and PFOA (3/10) were detected in the aqueous phase associated with the LNAPL samples. In three cases,

PFAS were detected in the aqueous phase when none were > LOQ in the LNAPL phase

(e.g., PFOA in samples 2 and 6 and 6:2 FtS in sample 6). When examined as a ratio

(LNAPL/aqueous), four pairs gave ratios that were greater than one. For example,

PFOS had ratios of 2.05 in sample 1 and a ratio of 1.8 (sample 10). The ratios of 6:2

FtS were 1.8 and 1.4 in sample 3 and 4, respectively. In contrast, ratios of less than one were obtained for PFOS in samples 2, 6, and 8 and for PFOA in sample 1. There is a lack of a clear relation between the PFAS ratios computed from this limited set of

LNAPL-aqueous sample pairs. Assuming the aqueous and LNAPL phases are at local equilibrium prior to sampling cannot be evaluated from this limited sample and data set.

3.5 Implications

The concentration and composition of PFAS determined for recovered LNAPL indicates that LNAPL may serve as a long-term source of PFAS. It is not known if

PFAS were co-released or if co-mingling occurred after release. The sampling locations from four military installations are from area not known to be directly impacted by fire- training activities. As such, fire-fighter training areas may have higher concentrations of PFAS in LNAPL as a result of microemulsions that may have formed with LNAPL and AFFF mixed during fire-fighter training exercises24. Characterization of sites that remain impacted by LNAPL should consider the PFAS mass associated with LNAPL to better understand total PFAS mass at sites. Further, LNAPL containing PFAS recovered from groundwater may require special treatment prior to disposal, as LNAPL treatment and recycling technologies are not suited to addressing PFAS. Further investigation into the partitioning of PFAS into LNAPL are currently being performed.

Determination of partition coefficients and a better understanding of which PFAS structural properties including chain length and headgroup impact partitioning will aid

in understanding why PFAS associated with LNAPL and how LNAPL acts as a long- term source of PFAS to groundwater.

3. 6 Acknowledgements

This work is funded by the Strategic Environmental Research and Development

Program grants ER-2104, ER-2720, and ER-1259. Meadows CMPG conducted the field sampling and we thank Bethany Parker for valuable discussion.

3.7 References 1. Buck, R. C.; Franklin, J.; Berger, U.; Conder, J. M.; Cousins, I. T.; de Voogt, P.; Jensen, A. A.; Kannan, K.; Mabury, S. A.; van Leeuwen, S. P., Perfluoroalkyl and polyfluoroalkyl substances in the environment: terminology, classification, and origins. Integr Environ Assess Manag 2011, 7, (4), 513-41. 2. Prevedouros, K.; Cousins, I. T.; Buck, R. C.; Korzeniowski, S. H., Sources, fate and transport of perfluorocarboxylates. Environ Sci Technol 2006, 40, (1), 32-44. 3. Giesy, J. P.; Kannan, K., Global distribution of perfluorooctane sulfonate in wildlife. Environ Sci Technol 2001, 35, (7), 1339-42. 4. Krafft, M. P.; Riess, J. G., Per-and polyfluorinated substances (PFASs): Environmental challenges. Current opinion in colloid & interface science 2015, 20, (3), 192-212. 5. Blum, A.; Balan, S. A.; Scheringer, M.; Trier, X.; Goldenman, G.; Cousins, I. T.; Diamond, M.; Fletcher, T.; Higgins, C.; Lindeman, A. E., The Madrid statement on poly-and perfluoroalkyl substances (PFASs). Environmental health perspectives 2015, 123, (5), A107-A111. 6. Houtz, E. F.; Higgins, C. P.; Field, J. A.; Sedlak, D. L., Persistence of perfluoroalkyl acid precursors in AFFF-impacted groundwater and soil. Environ Sci Technol 2013, 47, (15), 8187-95. 7. Kärrman, A.; Elgh-Dalgren, K.; Lafossas, C.; Møskeland, T., Environmental levels and distribution of structural isomers of perfluoroalkyl acids after aqueous firefighting foam (AFFF) contamination. Environmental Chemistry 2011, 8, (4). 8. Karrman, A.; Langlois, I.; van Bavel, B.; Lindstrom, G.; Oehme, M., Identification and pattern of perfluorooctane sulfonate (PFOS) isomers in human serum and plasma. Environ Int 2007, 33, (6), 782-8. 9. Conder, J. M.; Hoke, R. A.; De Wolf, W.; Russell, M. H.; Buck, R. C., Are PFCAs bioaccumulative? A critical review and comparison with regulatory criteria and persistent lipophilic compounds. Environ Sci Technol 2008, 42, (4), 995-1003.

10. Lau, C.; Anitole, K.; Hodes, C.; Lai, D.; Pfahles-Hutchens, A.; Seed, J., Perfluoroalkyl acids: a review of monitoring and toxicological findings. Toxicol Sci 2007, 99, (2), 366-94. 11. Jensen, A. A.; Leffers, H., Emerging endocrine disrupters: perfluoroalkylated substances. Int J Androl 2008, 31, (2), 161-9. 12. Woskie, S. R.; Gore, R.; Steenland, K., Retrospective exposure assessment of perfluorooctanoic acid serum concentrations at a fluoropolymer manufacturing plant. Ann Occup Hyg 2012, 56, (9), 1025-37. 13. Backe, W. J.; Day, T. C.; Field, J. A., Zwitterionic, cationic, and anionic fluorinated chemicals in aqueous film forming foam formulations and groundwater from U.S. military bases by nonaqueous large-volume injection HPLC-MS/MS. Environ Sci Technol 2013, 47, (10), 5226-34. 14. Place, B. J.; Field, J. A., Identification of novel fluorochemicals in aqueous film-forming foams used by the US military. Environ Sci Technol 2012, 46, (13), 7120- 7. 15. Guelfo, J. L.; Higgins, C. P., Subsurface transport potential of perfluoroalkyl acids at aqueous film-forming foam (AFFF)-impacted sites. Environ Sci Technol 2013, 47, (9), 4164-71. 16. Houtz, E. F.; Sutton, R.; Park, J. S.; Sedlak, M., Poly- and perfluoroalkyl substances in wastewater: Significance of unknown precursors, manufacturing shifts, and likely AFFF impacts. Water Res 2016, 95, 142-9. 17. SCF In Site Investigations of Fire Fighting Foam Usage at Various Air Force Bases in the United States, Presentation to AFCEC, 2015; 2015. 18. McKenzie, E. R.; Siegrist, R. L.; McCray, J. E.; Higgins, C. P., The influence of a non-aqueous phase liquid (NAPL) and chemical oxidant application on perfluoroalkyl acid (PFAA) fate and transport. Water Res 2016, 92, 199-207. 19. Anderson, R. H.; Long, G. C.; Porter, R. C.; Anderson, J. K., Occurrence of select perfluoroalkyl substances at US Air Force aqueous film-forming foam release sites other than fire-training areas: Field-validation of critical fate and transport properties. Chemosphere 2016, 150, 678-685. 20. Coats, G., A History of USAF Fire Protection Training at Chanute Air Force Base, 1964-1976. History Office, Chanute Technical Training Center: 1977. 21. Zhu, H.; Kannan, K., A pilot study of per- and polyfluoroalkyl substances in automotive lubricant oils from the United States. Environmental Technology & Innovation 2020, 19. 22. Chen, H.; Chen, S.; Quan, X.; Zhao, Y.; Zhao, H., Sorption of perfluorooctane sulfonate (PFOS) on oil and oil-derived black carbon: influence of solution pH and [Ca2+]. Chemosphere 2009, 77, (10), 1406-11.

23. Brusseau, M. L., The influence of molecular structure on the adsorption of PFAS to fluid-fluid interfaces: Using QSPR to predict interfacial adsorption coefficients. Water Res 2019, 152, 148-158. 24. Kostarelos, K.; Sharma, P.; Christie, E.; Wanzek, T.; Field, J., Viscous Microemulsions of Aqueous Film-Forming Foam (AFFF) and Jet Fuel A Inhibit Infiltration and Subsurface Transport. Environmental Science & Technology Letters 2020. 25. Barzen-Hanson, K. A.; Roberts, S. C.; Choyke, S.; Oetjen, K.; McAlees, A.; Riddell, N.; McCrindle, R.; Ferguson, P. L.; Higgins, C. P.; Field, J. A., Discovery of 40 Classes of Per- and Polyfluoroalkyl Substances in Historical Aqueous Film-Forming Foams (AFFFs) and AFFF-Impacted Groundwater. Environ Sci Technol 2017, 51, (4), 2047-2057. 26. Vial, J.; Jardy, A., Experimental Comparison of the Different Approaches To Estimate LOD and LOQ of an HPLC Method. Anal Chem 1999, 71, (14), 2672-2677. 27. Rayne, S.; Forest, K., Perfluoroalkyl sulfonic and carboxylic acids: a critical review of physicochemical properties, levels and patterns in waters and wastewaters, and treatment methods. J Environ Sci Health A Tox Hazard Subst Environ Eng 2009, 44, (12), 1145-99. 28. Rayne, S.; Forest, K., Comment on "Indirect photolysis of perfluorochemicals: hydroxyl radical-initiated oxidation of N-ethyl perflurooctane sulfonamido acetate (N- EtFOSAA) and other perfluoroalkanesulfonamides". Environ Sci Technol 2009, 43, (20), 7995-6; author reply 7997. 29. Rodowa, A. E.; Knappe, D. R.; Chiang, S.-Y. D.; Pohlmann, D.; Varley, C.; Bodour, A.; Field, J. A., Pilot scale removal of per-and polyfluoroalkyl substances and precursors from AFFF-impacted groundwater by granular activated carbon. Environmental Science: Water Research & Technology 2020, 6, (4), 1083-1094. 30. Barzen-Hanson, K. A.; Davis, S. E.; Kleber, M.; Field, J. A., Sorption of Fluorotelomer Sulfonates, Fluorotelomer Sulfonamido Betaines, and a Fluorotelomer Sulfonamido Amine in National Foam Aqueous Film-Forming Foam to Soil. Environ Sci Technol 2017, 51, (21), 12394-12404. 31. Nickerson, A.; Maizel, A. C.; Kulkarni, P. R.; Adamson, D. T.; Kornuc, J. J.; Higgins, C. P., Enhanced Extraction of AFFF-Associated PFASs from Source Zone Soils. Environ Sci Technol 2020, 54, (8), 4952-4962. 32. Nickerson, A.; Rodowa, A. E.; Adamson, D. T.; Field, J. A.; Kulkarni, P. R.; Kornuc, J. J.; Higgins, C. P., Spatial Trends of Anionic, Zwitterionic, and Cationic PFASs at an AFFF-Impacted Site. Environ Sci Technol 2021, 55, (1), 313-323.

Table 3.1. – Number of military installations and wells sampled along with the limited information available on fuel (LNAPL) type and year(s) released. Military Installation Sample Fuel Type Estimated Release Year 1 1 Various POL 1950 -1960 1 2 Various POL 1950 -1960 1 3 Diesel 1984 1 4 Diesel 1984 1960s, 1979, 1981, 1 5 JP-5 2010 1 6 NA NA 2 7 JP-5 1982, 1996 3 8 No. 2 Fuel Oil 1996 Diesel, No.5/No.6 4 9 Oil 2012 Diesel, No.5/No.6 4 10 Oil 2012 POL – Petroleum, oil, lubricants

1 Table 3.2. Average concentrations ± standard deviation (ng/L) of PFAS field-collected LNAPL samples. Associated aqueous sample 2 concentration (n=1) in ( ) when >LOQ. 3 Sample No. Frequency PFAS (>LOQ) 1 2 3 4 5 6 7 8 9 10 430±39 < LOQ

Chapter 4 - Interfacial Uptake and Partitioning of Per and Polyfluorinated Alkyl

Substances in Jet Fuel A at Environmental Concentrations

Emerson C. Christie1, Charles Schaefer2, Konstantinos Kostarelos3, and Jennifer A.

Field1

1 Department of Molecular and Environmental Toxicology, Oregon State University,

1007 Agricultural and Life Science Building, Corvallis, Oregon 97331, United States

2 CDM Smith, 110 Fieldcrest Avenue, #8, 6th Floor, Edison, New Jersey 08837,

United States

3 Department of Petroleum Engineering, University of Houston, Houston, TX, USA

In preparation for submission

4.1 Abstract Per and polyfluoroalkyl substances (PFAS) have historically been disposed with nonaqueous phase liquids (NAPL) both during proper usage or due to waste dumping or spills. Still little is known about the partitioning and adsorption of PFAS between water and NAPL. Here we performed batch equilibrium experiments between a synthetic tap water and municipal jet fuel A at relatively low PFAS concentrations (2,000 – 100,000 ng/L). By quantifying PFAS in both the water and jet fuel A we were able to, by difference, determine the jet fuel A – water interfacial sorption coefficients (Knw) and make inferences about NAPL – water partitioning (Kn). For PFAS with chain lengths of less than 10 carbons data were best fit to a linear model and Knw ranged from 0.06 –

0.26 cm, which is orders of magnitude higher than previously reported literature values.

Perfluorocarboxylic acids (PFCAs) with 11 – 14 carbons were better fit to the

Freundlich model and showed greater accumulation at the interface, with PFTeDA

(C14) Knw = 20 cm. Partitioning into bulk jet fuel A was not observed for PFAS below eight carbons and increased with increasing carbon chain length. Single point Kn values decreased with increasing PFAS concentration indicating non-ideal partitioning for these PFAS and this relationship became more pronounced with increasing carbon chain length.

4.2 Introduction

Per and polyfluoroalkyl substances (PFAS) are a group of anthropogenic surfactants that see ubiquitous use due to their water and oil repelling properties1-3. However,

PFAS have been identified as persistent organic pollutants4 due to their resistance towards degradation5 and mobility in the environment6. Additionally, PFAS are known to contaminate soils3, 7-9 and groundwater8, 10, 11 and bioaccumulate3, 12.

Aqueous film forming foams (AFFF) are used globally to suppress hydrocarbon-based fuel fires and contain PFAS at high concentrations (i.e. g/L)13, 14. The use of AFFFs at

U.S. military sites has produced widespread groundwater contamination of PFAS8, 10,

15, 16 and making drinking water in these areas the primary route of human exposure17-

19. As a direct result of their usage AFFFs and non-aqueous phase liquids (NAPL) are also commonly co-disposed through training and actual firefighting practices or at waste sites15, 20, 21. Despite the relationship between NAPL and PFAS contaminated groundwater research into this topic is still emerging.

Currently efforts investigating the aqueous and NAPL relationship has focused primarily on their interface. Recent work with kerosene to measure PFAS interfacial adsorption indicated NAPL-water interfacial sorption coefficients (Knw) where approximately an order of magnitude lower than the corresponding air-water sorption

22 coefficients (Kaw) . Fitting for both the air-water and NAPL-water interface was done with the Langmuir-Szykowski equation with sorption coefficients calculated via the

Gibbs equation22.

1 훿훾 퐾푖 = − ( ) 푅푇 훿퐶푤

However, recent work by Schaefer et. al. has indicated that the Langmuir model underpredicts interfacial uptake at the air-water interface at lower PFAS concentrations23. Brusseau et. al. recently identified that the air-water and NAPL-water interfaces are the primary source of PFOA and PFOS retention in groundwater systems24. Brusseau and VanGlubt investigated a variety of parameters on PFAS adsorption to fluid-fluid interfaces (i.e. PFAS headgroup, PFAS size, solution ionic strength, solution pH, mixtures) using experimental and literature data and concluded

that aqueous composition likely has a low impact on PFAS interfacial sorption at low concentrations25.

Little is known about the partitioning of PFAS into bulk NAPL. Presumed partitioning has been documented by loss of PFAS from the system during batch experiments. Other co-disposed contaminants such as polychlorinated biphenyls (PCBs) and polycyclic aromatic hydrocarbons (PAHs) are known to sorb to oil phases in soil26-28. Batch partitioning with oil contaminated sediment showed strong PFOS sorption via loss in the aqueous phase29. Trichloroethylene (TCE), a model dense nonaqueous phase liquid

(DNAPL), was shown to act as a synergist and potential sorbent for PFAS in soil with

15 low organic carbon content (Foc) . TCE column and isolated TCE batch experiments indirectly demonstrated PFAS sorption to TCE by measuring loss in the aqueous phase21. The losses were associated with fluorinated carbon chain length and they increased with increasing volume of TCE indicating that absorption as well as

21 interfacial adsorption was occurring . The partition coefficient (KTCE) was not equal across volumes, therefore, a complex absorption and interfacial mechanism must exist21. Partition coefficients between water and NAPL for perfluorocarboxylic acids

(PFCAs) and perfluorosulfonates (PFSAs) calculated by Guelfo and Higgins and

McKenzie et. al. were determined by difference and not as a result of direct measurement in the NAPL phase.

More recently, Kosterelos et. al. discovered the formation of viscous, stable, 3- dimensional microemulsions at the NAPL-water interface by mixing application strength AFFF (3% in water) and Jet Fuel A (NAPL)30. The same study confirmed microemulsion presence in constant flow and constant pressure column experiments

and reported PFAS losses in the aqueous of up to 70%30. These results indicate that

NAPL-water partitioning and interfacial adsorption change substantially when concentrations exceed the critical micelle concentration (CMC).

The objective of this research was to directly measure PFAS partitioning into jet fuel

A by quantification of both the aqueous and NAPL PFAS concentrations at environmentally relevant PFAS concentrations. Water-NAPL interfacial sorption coefficients were determined by mass balance between the initial aqueous concentration and the equilibrated aqueous and NAPL concentration. Partition coefficients were determined for each PFAS, when calculable, across the range of concentrations.

4.3 Experimental

Materials. HPLC grade methanol, water, and ethyl acetate from Fisher Scientific

(Hampton, NH) were used. Native and mass labeled PFAS standards were from

Wellington Laboratories (Ontario, Canada) and can be found in Table C1 of the supplemental information (SI). The synthetic freshwater recipe used can be found in

Table C2 and produced a water with a conductivity of approximately 180 uS/cm. Jet

Fuel A was purchased from Corvallis Municipal Airport (Corvallis, OR). As noted in the literature petroleum based NAPLs can change throughout their time in the subsurface31-33. The major variables that influence these changes are the initial product chemistry31, local soil microbial community composition33, soil properties32, and groundwater flow32. Due to the number of variables included in this process no two weathered LNAPLs have identical endpoints and for reproducibility fresh municipal

Jet Fuel A was used.

Partitioning Experiments. Eight-point partition isotherms were made in triplicate at initial PFAS concentration ranges from 0 – 100,000 ng/L. A 1:1 synthetic fresh water and Jet Fuel A ratio was used in a 15 mL polypropylene tube (VWR, Radnor, PA). All

PFAS were spiked into 1.5 mL of water and vortexed for 30 seconds. After vortexing,

1.5 mL of Jet Fuel A was added to the tube. Tubes were set to shake on an orbital shaker table for 72 hours15, 22 to thoroughly mix and then let stand for 48 hours to finish equilibrating prior to sampling.

Sample Preparation and Analysis. Sampling of the bulk Jet Fuel A and the underlying bulk aqueous phase was done via pipette. All samples were diluted 1:10 or 1:100 to bring their concentrations within detectable range of the instrument (20-10,000 ng/L) and analyzed directly. For aqueous samples dilutions were into MeOH. For LNAPL samples dilutions were into ethyl acetate, see supplemental information (SI) for Jet Fuel

A spiking. Accuracy of direct LNAPL analysis via dilution for Qn PFAS ranged from

89-142%, average recovery, and precision ranged from 3-9% (Table C3). Samples were spiked with 0.75 ng of isotopically labeled internal standard before analysis for quantification and final sample volumes were 1.5 mL. Separation was done with an

Agilent 1100 (Santa Clara, CA) and detection with a Waters TQ Detector (Milford,

MA) triple quadrupole mass spectrometer for acquisition. Methodology is similar to what has been described previously13, 34, specific details for the LC method and gradient and MS parameters are described in the SI.

Quantification QA/QC. Internal standard calibration and 1/x weighted linear regression over a 7-point calibration curve was used to determine analyte concentrations.

Calibration standards ranged from 20-10,000 ng/L for all analytes. Every 10 samples

throughout the analysis a low concentration standard was analyzed to verify calibration through recovery (70-130%). At the start of each analysis a continuing calibration verification (CCV) was acquired and required to recover 70-130%, in the event the

CCV did not fall within the acceptable range, a new calibration curve was created.

The limit of detection (LOD) was determined from a 1/x weighted regression and the limit of quantification (LOQ) was defined as 3.3 x LOD. All partitioning experiments included a method blank which consisted of unspiked water and Jet Fuel A subjected to the partition experiment described above. Method and solvent blanks were analyzed at the start of each analytical sequence and fell below ½ of the limit of quantification.

Adsorptive losses of PFAS to polypropylene tubes in both NAPL and water are negligible. Spike and recovery experiments in polypropylene tubes have been previously performed with recoveries of select PFAS in water ranging from 75-130%

(Table C4) and recoveries for PFAS in NAPL ranging 79 – 125% (Table C5) depending on the individual PFAS used in this study.

Determination of Partition and Interfacial Coefficients. Jet fuel A – water partition coefficients (Kn) were calculated as single points between equilibrated water and jet fuel A concentrations across the range of initial water concentrations.

퐶푒푞 푗푒푡 퐾푛 = 퐶푒푞 푤푎푡푒푟

Jet Fuel A – water interfacial mass was determined by mass balance within the system by subtracting the total initial mass in the aqueous (i.e. total mass in system) by the equilibrated aqueous mass and the equilibrate jet fuel A mass.

푚푖푛푡푒푟푓푎푐푒 = 푚표 − 푚푒푞 푤푎푡푒푟 − 푚푒푞 푗푒푡

Values for Knw were determined for PFAS between equilibrated aqueous concentration

(units of ng/cm3) and calculated interfacial mass per unit area (ng/cm2) through linear regression and Freundlich curve fitting. The Freundlich model has been shown to better

23 represent PFAS Kaw values at lower PFAS concentrations .

4.4 Results and Discussion

Jet Fuel A – Synthetic Freshwater Partitioning. Schaefer et. al. showed that simple mixes of low concentration of PFAS (i.e. below CMC) could be represented by single solute Kaw and that Kaw results from a simple mixture were not statistically different

23 from a single solute Kaw . A similar approach was taken for these batch experiments.

PFOS as single solute was partitioned in triplicate according to the methodology described above. Table C6 shows the average concentrations of PFOS between the single solute and PFAS mixture in both the synthetic fresh water and jet fuel A and the associated T-statistic which indicated that over the range of concentrations equilibrated concentrations between the single solute and the mix were not different. It was therefore concluded that a simple mixture of PFAS could be used for batch partitioning experiments, which greatly reduced the total number of LC MS/MS analyses.

Carbon chain length was the determining factor for presence of PFAS in jet fuel A.

However, polyfluorinated PFAS (i.e. telomer chemistry) showed decreased partitioning compared to perfluorinated PFAS. No detectable Jet Fuel A concentrations were observed for PFBA, PFPeA, PFPrS, and PFBS. Short chain PFAS are less hydrophobic due to their decreased carbon chain length and are more mobile in the environment35, this combined with the hydrophilic headgroup and oleophobic

properties of the fluorinated tail could explain why partitioning into the Jet Fuel A was not observed.

Detectable PFAS concentrations, defined as greater than the limit of detection (LOD) and less than the limit of quantification (LOQ), in Jet Fuel A were observed for PFHxA,

PFHpA, and PFHxS. Perfluorinated PFAS of six and seven carbon chain lengths represent the transition point where the PFAS are detectable in the Jet Fuel A but under the limit of quantification and this has been noted in PFAS bioconcentration research as well36.

Quantifiable PFAS concentrations in jet fuel A were observed for PFOA, PFNA,

PFDA, PFUdA, PFDoDA, PFTrDA, PFTeDA, PFOS, and PFNS. Carbon chain length was the determining factor for PFAS partitioning to Jet Fuel A in these experiments.

Single point Kn values were calculated for analytes across the concentration range and are reported in Table 4.1. Kn values at the highest initial concentration of 100,000 ng/L ranged from 0.003 – 16 for C8-C14 PFCAs and 0.021 and 0.013 for PFOS and PFNS respectively, the associated figures are in the SI (Figures C1-C9). For all analytes Kn

(i.e. partitioning into bulk Jet Fuel A) decreased with increasing initial PFAS concentration indicating non-ideal partitioning of PFAS between synthetic freshwater and Jet Fuel A, example PFOS (Figure 4.1). Non-ideal partitioning has been observed in surfactants between water and NAPL both above37 and below CMC21, 38. Belhaj et. al. related the decrease of alkylpolyglucoside, a nonionic surfactant, partitioning with increasing concentration to interfacial tension38. Here we observed a similar trend, where partitioning decreased as the mass at the interface increased.

Figure 4.2 shows Kn values for 100,000 ng/L initial PFAS concentrations according to carbon chain length for the PFCAs and PFSAs and shows increasing partitioning with increasing carbon chain length. The eight-carbon inflection point for quantification in

Jet Fuel A is consistent with PFAS bioaccumulation research12, 36, 39. Additionally, the decrease in synthetic freshwater concentrations for longer chain PFAS is consistent with literature material sorption studies which have indicated that long chain PFAS partitioning and adsorption increases with increasing chain length and is driven by hydrophobicity40.

Jet Fuel A – Synthetic Freshwater Interfacial Sorption. Interfacial sorption regardless of the interface type are typically derived experimentally via interfacial tension measurements and typically use high (mg/L) surfactant concentrations22, 23. However, when measured by mass difference, as was performed here, interfacial sorption can be determined at lower PFAS concentrations. By determining no sorptive losses to the polypropylene tube in water and Jet Fuel A systems, described above in the methods, and assuming that the Jet Fuel A – air interface is negligible we are able to consider that the interfacial mass measured by difference is associated with the synthetic freshwater – Jet Fuel A interfacial area.

For C4 – C10 PFCAs and C6, C8, and C9 PFSAs showed that mass attributed to the interface was approximately 10% and best fit by linear regression, example PFOS in

Figure 4.2. Short chain PFSAs, PFPrS (Figure C10) and PFBS (Figure C11) both showed enhanced interfacial adsorption. In order to ensure that this was not due to the

PFAS mixture, an additional PFPrS single solute partitioning experiment was performed and showed interfacial uptake within the 95% confidence interval to the

PFAS mix although the trend was of a lower slope (Figure C10). A similar observation was made by Guelfo and Higgins with short chain PFCA sorption to soil where they

15 saw increased Kd values for PFPeA and PFBA . As these findings departed from the hydrophobic mechanism typically tied to soil sorption the authors concluded that ion exchange or some other mechanism must be dominant for soil sorption of these PFAS.

The results here, although not observed for PFBA and PFPeA, indicate that a mechanism other than hydrophobicity is determining short chain sulfonate interfacial adsorption with Jet Fuel A.

For C11 – C14 PFCAs, mass attributed to the interface increased with increasing chain length and decreased with increasing PFAS concentration. This relationship generated curved isotherms that were fitted to the Freundlich model, example PFTeDA in Figure

4.3. Increased interfacial sorption with increasing PFAS chain length at the water –

NAPL interface has been observed previously22. Interfacial sorption coefficients from linear regression are available in Table 4.2 and from the Freundlich model in Table 4.3 with the associated isotherms available in the SI (Figures C10 – C23). Calculated Knw values here are orders of magnitude higher than previously reported measurements22,

41, 42, the most recent and similar of which by Silva et. al. using kerosene and synthetic groundwater included in Table 4.2. These findings are similar to air – water interfacial adsorption results described by Schaefer et. al23.

4.5 Implications

In this research, through direct measurement, we verified PFAS partitioning into bulk

Jet Fuel A from synthetic freshwater. No PFAS below a carbon chain length of eight could be quantified in the Jet Fuel A, which is consistent with bioaccumulation

literature. Mass of PFAS partitioned varied greatly with chain length. Thus, we confirmed that PFAS partitioning into Jet Fuel A is determined by PFAS chain length and that the partitioning is non-ideal, with Kn decreasing as PFAS concentrations increase.

We have determined, by mass balance, Knw that are orders of magnitude higher than previously reported values in the literature. Determination of Knw by mass difference via LC MS/MS is possible, however, the indirect measurement lends itself to larger confidence intervals and limited sample sets as data are acquired through a lengthier analytical process. However, the larger confidence intervals still do not account for the magnitude of difference between what was measured here and literature values.

The work done here was performed with a synthetic freshwater. Ionic strength is known to impact partitioning of PFAS in sediments7 and produced changes to interfacial adsorption between PFAS and kerosene22 and is therefore presumed to be a factor in

PFAS partitioning to NAPL. Additional research will need to be performed to evaluate

PFAS partitioning, interfacial adsorption, and the linear relationships between initial and equilibrated concentrations at ionic strengths similar to what may be found in brackish groundwaters.

For longer chain PFAS it may be necessary to determine the NAPL – air interface. This research identified considerable mass partitioning into bulk Jet Fuel A for PFAS with chain lengths longer than 10 carbons that may break the common assumption of negligible NAPL – air interfacial sorption.

4.6 Acknowledgements

This work is funded by the Strategic Environmental Research and Development

Program grants ER-2104, ER-2720, and ER-1259.

4.7 References

2. Prevedouros, K.; Cousins, I. T.; Buck, R. C.; Korzeniowski, S. H., Sources, fate and transport of perfluorocarboxylates. Environ Sci Technol 2006, 40, (1), 32-44.

3. Sima, M. W.; Jaffe, P. R., A critical review of modeling Poly- and Perfluoroalkyl Substances (PFAS) in the soil-water environment. Sci Total Environ 2021, 757, 143793.

4. Blum, A.; Balan, S. A.; Scheringer, M.; Trier, X.; Goldenman, G.; Cousins, I. T.; Diamond, M.; Fletcher, T.; Higgins, C.; Lindeman, A. E., The Madrid statement on poly-and perfluoroalkyl substances (PFASs). Environmental health perspectives 2015, 123, (5), A107-A111.

5. Giesy, J. P.; Kannan, K., Global distribution of perfluorooctane sulfonate in wildlife. Environ Sci Technol 2001, 35, (7), 1339-42.

6. Krafft, M. P.; Riess, J. G., Per-and polyfluorinated substances (PFASs): Environmental challenges. Current opinion in colloid & interface science 2015, 20, (3), 192-212.

7. Barzen-Hanson, K. A.; Davis, S. E.; Kleber, M.; Field, J. A., Sorption of Fluorotelomer Sulfonates, Fluorotelomer Sulfonamido Betaines, and a Fluorotelomer Sulfonamido Amine in National Foam Aqueous Film-Forming Foam to Soil. Environ Sci Technol 2017, 51, (21), 12394-12404.

8. Houtz, E. F.; Higgins, C. P.; Field, J. A.; Sedlak, D. L., Persistence of perfluoroalkyl acid precursors in AFFF-impacted groundwater and soil. Environ Sci Technol 2013, 47, (15), 8187-95.

9. Nickerson, A.; Maizel, A. C.; Kulkarni, P. R.; Adamson, D. T.; Kornuc, J. J.; Higgins, C. P., Enhanced Extraction of AFFF-Associated PFASs from Source Zone Soils. Environ Sci Technol 2020, 54, (8), 4952-4962.

10. Kärrman, A.; Elgh-Dalgren, K.; Lafossas, C.; Møskeland, T., Environmental levels and distribution of structural isomers of perfluoroalkyl acids after aqueous firefighting foam (AFFF) contamination. Environmental Chemistry 2011, 8, (4).

11. Nickerson, A.; Rodowa, A. E.; Adamson, D. T.; Field, J. A.; Kulkarni, P. R.; Kornuc, J. J.; Higgins, C. P., Spatial Trends of Anionic, Zwitterionic, and Cationic PFASs at an AFFF-Impacted Site. Environ Sci Technol 2021, 55, (1), 313-323.

12. Conder, J. M.; Hoke, R. A.; De Wolf, W.; Russell, M. H.; Buck, R. C., Are PFCAs bioaccumulative? A critical review and comparison with regulatory criteria and persistent lipophilic compounds. Environ Sci Technol 2008, 42, (4), 995-1003.

13. Backe, W. J.; Day, T. C.; Field, J. A., Zwitterionic, cationic, and anionic fluorinated chemicals in aqueous film forming foam formulations and groundwater from U.S. military bases by nonaqueous large-volume injection HPLC-MS/MS. Environ Sci Technol 2013, 47, (10), 5226-34.

14. Place, B. J.; Field, J. A., Identification of novel fluorochemicals in aqueous film-forming foams used by the US military. Environ Sci Technol 2012, 46, (13), 7120- 7.

15. Guelfo, J. L.; Higgins, C. P., Subsurface transport potential of perfluoroalkyl acids at aqueous film-forming foam (AFFF)-impacted sites. Environ Sci Technol 2013, 47, (9), 4164-71.

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Table 4.1. Single point Kn values calculated across the concentration range. PFOA PFNA PFDA PFUnDA PFDoDA PFTrDA PFTeDA PFOS PFNS 95% 95% 95% 95% 95% 95% 95% 95% 95% Kn CI Kn CI Kn CI Kn CI Kn CI Kn CI Kn CI Kn CI Kn CI 0 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 2000 0.000 NA 0.000 NA 0.18 NA 1.7 0.06 NA NA NA NA NA NA 0.062 0.020 0.29 0.080 5000 0.009 NA 0.013 0.015 0.19 0.01 1.4 0.06 9.8 0.92 NA NA NA NA 0.056 0.006 0.31 0.036 10000 0.004 NA 0.007 0.008 0.15 0.02 1.3 0.05 7.3 0.51 NA NA NA NA 0.054 0.003 0.25 0.006 20000 0.008 0.004 0.005 0.006 0.13 0.03 1.0 0.04 6.4 0.31 21 2.4 NA NA 0.033 0.002 0.25 0.012 50000 0.001 0.009 0.001 0.001 0.11 0.01 0.9 0.02 6.3 0.22 16 3.5 60 8.1 0.026 0.009 0.16 0.021 75000 0.003 0.001 0.000 0.001 0.10 0.01 0.7 0.00 5.2 0.22 15 3.4 39 0.8 0.024 0.001 0.16 0.009 100000 0.003 0.001 0.001 0.001 0.09 0.01 0.6 0.03 3.5 0.21 10 1.3 16 2.4 0.021 0.001 0.11 0.013

Table 4.2. Interfacial sorption coefficients from linear regressions reported as cm. 22 Knw ± SE Knw Silva et. al. PFBA 0.12 0.01 PFPeA 0.11 0.003 1.25E-05 PFHxA 0.08 0.01 3.64E-05 PFHpA 0.11 0.01 1.23E-04 PFOA 0.10 0.01 3.86E-04 PFNA 0.06 0.01 1.71E-03 PFDA 0.09 0.01 7.27E-03

PFPrS 0.36 0.02 PFBS 0.26 0.02 PFHxS 0.06 0.01 PFOS 0.15 0.01 PFNS 0.11 0.01

Table 4.3. Interfacial sorption coefficients from Freundlich models.

2 Knw ± SE n ± SE R PFUdA 0.62 0.20 0.84 0.12 0.88 PFDoDA 2.6 0.16 0.83 0.06 0.95 PFTrDA 12 2.0 0.60 0.14 0.90 PFTeDA 20 2.2 0.72 0.11 0.93

PFOS 0.07

0.06

0.05

0.04 Kn 0.03

0.02

0.01

0.00 0.00 20000.00 40000.00 60000.00 80000.00 100000.00 120000.00 Initial aqueous (ng/L)

Figure 4.1. PFOS Kn values over the concentration range.

18.00

16.00

14.00

12.00

10.00

8.00

at 100,000 100,000 at ng/L 6.00 n K 4.00

2.00

0.00 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 n Carbons

Figure 4.2. Kn values for PFCAs and PFSAs at 100,000 ng/L initial PFAS concentrations according to carbon chain length.

PFOS 18 Equation y = a + b*x 16 Plot Ceq Interface Weight No Weighting 14 Intercept 0 ± -- Slope 0.14929 ± 0.00543 2.3565 12 Residual Sum of Squares Pearson's r 0.9954 R-Square (COD) 0.99082 10 Adj. R-Square 0.98951

Ceq Interface (ng/cm^2) Interface Ceq 2

-2 0 20 40 60 80 Ceq Aqueous (ng/cm^3)

Figure 4.3. PFOS interfacial adsorption isotherm.

PFTeDA 50

Model Freundlich (User) Equation qe = K*Ce^(n) 20 Plot Ceq Interface K 20.46369 ± 2.22014 n 0.72322 ± 0.10887 Ceq Interface (ng/cm^2) Interface Ceq 10 Reduced Chi-Sqr 0.51824 R-Square (COD) 0.93325 Adj. R-Square 0.91991

0 0 1 2 3 Ceq Aqueous (ng/cm^3)

Figure 4.4. PFTeDA interfacial adsorption fitted to a Freundlich isotherm.

Chapter 5 – Conclusion

Understanding PFAS binding, partitioning, and sorption into various compartments will be critical in determining the scope of the PFAS problem and how to best develop a response to these persistent pollutants.

In Chapter 2 PFAS protein binding affinity (KD) was assessed for C4-C9 PFCAs and

C4, C6, and C8 PFSAs between liver and intestinal fatty acid binding proteins (L- and

I-FABPs) and peroxisome proliferator activated nuclear receptors (PPAR-α, - δ and -

γ) via molecular dynamics modeling and equilibrium dialysis. Additionally a comparison was made between KDs derived from EqD, both here and in literature, and other in-vitro approaches (e.g. fluorescence) from literature. This was the first study to report strong (sub micromolar) binding between PFAS and PPAR-δ and the lack of binding between PFAS and I-FABP. This was also the first study to report strong short chain PFAS binding to PPARs; PPAR-δ and perfluorobutanoate (0.044 ± 0.013 µM) and perfluorohexane sulfonate (0.035 ± 0.0020 µM), and between PPAR-α and perfluorohexanoate (0.097 ± 0.070 µM), which may have implications regarding short chain PFAS use and assumed safety. The short chain binding results also showed that

PFAS binding affinity cannot be inferred by PFAS carbon chain length for all proteins.

The MD results in this study support previous results that MD predicted binding affinities should be considered relative rather than absolute. Agreement between the model predictions and observed KD varied based on protein but in general was more consistent at predicting FABP binding affinity, although chain length dependence or lack thereof was consistent across all proteins. Comparison of EqD obtained KDs, both here and in the literature, to those of other methods from the literature indicate that EqD

KDs are consistently lower. This raises two important questions. The first and most

impactful is, KDs indicate the EqD approach is capable of quantify binding that other approaches do not? If so than PFAS binding to proteins has historically been underestimated. The second, which method should be used to better translate to what is happening in vivo? In vivo, competitive interactions are more likely to occur and it may therefore follow that a competitive binding approach might be more realistic, however, it is unclear if the fluorophores used are at all representative of the native ligands or xenobiotics that make up real world competition. Future research may want to focus on how to best answer these questions as they are fundamental in setting a standard by which PFAS – protein binding affinity should be measured.

In Chapter 3 a micro liquid-liquid extraction method was developed for the analysis of

34 PFAS in non-aqueous phase liquids (NAPL). The extraction method takes less time and uses less sample (1.5 mL), solvent (1.5 mL), and consumables (one 15 mL and one

2 mL polypropylene tube) then the only other known extraction method for PFAS from

NAPL. A method demonstration on 18 NAPL field samples obtained from six coastal sites with NAPL release dates ranging from the 1950’s - 2012 resulted in the discovery of PFAS-impacted NAPL. Microgram per liter concentrations of PFAS were documented at ten of the sites and included perfluorooctane sulfonate (PFOS), perfluorooctanoic acid (PFOA), 6:2 and 8:2 fluorotelomer sulfonates, perfluorohexane sulfonamide (FHxSA), and several zwitterionic compounds. The NAPL samples in this work were from non AFFF source zones and it is reasonable to assume that NAPL at

AFFF source zones would contain far greater PFAS concentrations. Additionally,

NAPL could serve as a long term source of PFAS and potentially protect it from degradation. The primary conclusions from the research discussed in Chapter 3 are: 1)

at complex sites where NAPL is present, consideration of the PFAS mass partitioned into the bulk NAPL phase will need to be considered to understand total PFAS mass at a site and 2) the remediation of NAPL from areas with known PFAS contamination may required special consideration before removal and/or treatment.

Chapter 4, based on the findings of Chapter 3, investigated PFAS partitioning and sorption to NAPL in equilibrium batch experiments. Given the knowledge that PFAS could be directly measured in NAPL via LC MS/MS it became possible to perform equilibrium experiments at low and environmentally relevant PFAS concentrations. By quantifying PFAS in both the water and NAPL we were able to calculate NAPL – water partition coefficients (Kn) across the entire concentration range and by mass balance determine the NAPL – water interfacial sorption coefficients (Knw). Partitioning into bulk NAPL was not observed for PFAS below eight carbons, however, partitioning increased with increasing carbon chain length for PFAS eight carbons and above, which is consistent with bioaccumulation research. Non-ideal partitioning was observed as single point Kn values decreased with increasing PFAS concentration. Knw values were several orders of magnitude higher than previously reported values in the literature which would suggest that PFAS mass at the NAPL – water interface has been underestimated at environmentally relevant concentrations. The determination of Knw by mass difference via LC MS/MS was possible, however, the indirect measurement lends itself to larger confidence intervals, as it inherits the error from both the aqueous and NAPL measurements, and limited sample sets, as data are acquired through a lengthier analytical process.

It is important to note that the work done here was performed with a synthetic freshwater. Ionic strength is known to impact PFAS partitioning in other systems and is therefore presumed to be a factor in PFAS partitioning to NAPL. Additional research at other environmentally relevant ionic strengths will need to be done to evaluate how

PFAS – NAPL partitioning, interfacial adsorption, and the linear relationships change with aqueous composition. It is also important to note that longer chain PFAS partition into bulk NAPL at concentrations far greater than were expected and it may be necessary to determine the NAPL – air interface for these PFAS, as it may be unreasonable to assume that this interface is negligible for these concentrations.

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Appendix A – Chapter 2 Supplemental Information

Molecular dynamics method for PFAS-protein affinity screening

A previously developed molecular dynamics (MD) workflow [7] was used to estimate protein binding affinities (free energy of binding, ΔGbind), which were subsequently translated to dissociation constants. Briefly, the workflow consists of three major steps: molecular docking, MD simulation, and molecular mechanics combined with Poisson-

Boltzmann surface area (MM-PBSA) energy calculation [7]. The MM-PBSA method

[8] was used to calculate ΔGbind as follows:

Complex Protein PFAS ΔGbind = G − G − G where GComplex, GProtein, and GPFAS are the free energies of the protein-PFAS complex, the protein, and the PFAS ligand, respectively. The energy terms were calculated using the MMPBSA.py program in AMBER 14. The calculated ΔGbind values were then translated into equilibrium dissociation constants (KD, with units of μM) using the following equation: [9,10]

ΔGbind = RTln (KD / C0) where R is the gas constant (1.987 cal K-1 mol-1), T is temperature (which is assumed to be 300 K), and C0 is the standard state concentration (1 M). All simulations were carried out on an AMBER GPU Certified molecular dynamics workstation (Exxact

Corporation, CA).

Material Extractions for Sorption Quality Control

Dialysis filters and vials (Figure S1) were extracted according to Robel et. al. (2020).

Briefly, items were cut into 4.0 ± 0.5 cm2 pieces with methanol rinsed scissors.

Materials were extracted by submerging with 3.3 mL of heated methanol (60−65°C),

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shaking on a wrist-action shaker for 10 min, centrifuging at 2808 g for 10 min, and then collecting the supernatant a secondary centrifuge tube. This process was repeated two additional times with each round’s supernatant collected in the same secondary tube, yielding a 9.9 mL extract. Extracts were brought to a final volume of 10 mL with additional methanol.

Material extracts were prepared for analysis as follows: 1) 60 μL aliquots of extract were placed in 1.5 mL HDPE autosampler vials, 2) each vial was spiked with 0.72 ng of isotopically labeled standards, 3) vials were diluted with methanol to a final volume of 1.2 mL. In order to assess sorption to the dialysis filters and vials, a spike and recovery experiment was performed. Filters and vials were equilibrated on a shaker for

24 h with 1.5 mL of 500 ng/L of native PFASs (Table S1) in water. The spiked water was removed and extracted utilizing the micro liquid-liquid extraction technique described by Backe et. al. [11] and modified by Barzen-Hanson et al. [12].

Molecular dynamics results for PFAS-protein affinity screening

After the serum albumins, L-FABP is probably the most-studied protein for binding with PFAS, both experimentally and using molecular modeling tools.[13–16] The focus on this particular fatty acid binding protein is driven in large part by observations of high accumulation of long-chain PFAS in liver tissue [17]. Existing literature shows a strong increase of binding affinity between PFAS and L-FABP up to a carbon chain length for PFCAs of 11, after which it levels off. In our previous modeling study [7], which established the MD framework used here, PFHxA was the only short-chain

PFCA predicted to bind strongly with L-FABP, but was a clear outlier in the chain length relationship. Here we increased the simulation time in order to sample a greater

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number of conformations, thus improving our predictions. The updated predictions for all PFCAs now fall in line with the expected chain length trend (Figure 3). The strongest binding was predicted for PFOA, PFNA, and PFOS. Among the short-chain PFAS, binding was strongest for PFBS.

There are no published experimental or modeling studies for PFAS binding with other fatty acid binding proteins, precluding comparisons with our evaluation of I-FABP.

Our MD results indicated strongest I-FABP binding affinities for PFHpA and PFNA among the carboxylates (Figure 3C), while binding between I-FABP and all sulfonates was predicted to be weak, with no chain length trend and little difference in KD among them (Figure 3D). This emphasizes the point that PFAS-protein binding affinity is not determined exclusively by PFAS chain length; protein- and PFAS-specific attributes determine binding affinity and should be considered individually.

The relationship between binding affinity predicted by MD and chain length is even weaker for the PPARs (Figure 2). In some cases, simulations predict similar or stronger binding for short-chain PFAS than for long-chain PFAS. For example, among the

PFCAs, PPAR-α (Figure 2A) is surprisingly predicted to bind most strongly with

PFBA. For the remaining PFCAs all binding affinities are relatively weak and overlapping, with KD values higher than those considered biologically relevant. In comparison, binding with PFSAs is predicted to be relatively stronger, though without a chain length dependence; PPAR-α is predicted to bind equally well with PFBS and

PFOS and less strongly with PFHxS (Figure 2B).

106

Previous studies found mixed evidence of PPAR-훾 activation by PFOA and PFOS.

Takacs and Abbott [18] found no evidence of PPAR-훾 activation by either PFOA or

PFOS (in contrast with PPAR- α), while Vanden Heuvel et al.[19] found that PFOA and PFOS were at least partial activators of PPAR- 훾, but with lower activity than PPAR alpha. Finally, Buhrke et al. [20] found PFOA activated PPAR- 훾 in primary human hepatocytes. The predicted binding affinities for PPAR-훾 with both PFCAs and PFSAs

(Figure 2C and D) were all relatively weak and about the same except for PFNA and

PFOS, which were the only ones predicted to have moderate to strong binding

(geometric mean KD ≤ 1μM). Finally, the binding affinities predicted for PPAR-δ were strongest for PFPeA among the PFCAs, but all were in the micromolar and larger range

(Figure 2E). For PFSAs, binding was predicted to be only slightly stronger, with essentially no difference in predicted binding affinities among PFSA chain lengths

(Figure 2F).

107

Table A1. Matrix of Selected Protein-PFAS combinations for batch analysis PFAS L-FABP I-FABP PPAR-α PPAR- PPAR-δ PFBA X X PFHxA X X PFHpA X X PFOA X X PFNA X X PFBS X X PFHxS X X PFOS X X X

108

Table A2. Comparison of methods L- & I-FABP and PPAR α,   [14,15,34–38]

109

Table A3. Comparison of methods HSA, BSA, RSA, and fish serum protein. [21–26,28–33,39–41]

110

2 Figure A1 Equilibrium dialysis setup with materials used (dialysis filters and vials) shown. 3

111

4 5 Figure (A) PFBA - PPAR-α pH=7.4 (B) PFHpA - PPAR-α pH=7.4 6 A2

0.3 KD = ND 0.5 KD = ND

g 0.0

n g

i 0.2

n d

i 0.2 0.4 0.6

n i

B [Ligand]free µM

B -0.5

i c

0.1 c

e p

p -1.0

S S

0.0 -1.5 0.0 0.1 0.2 0.3 [Ligand]free µM 7 Equilibrium dialysis results for binding affinity of PFBA (A) and PFHpA (B) with PPAR–α at normal experiment 8 with pH = 7.4 and ionic strength = 18.1 mS/cm. The negative result for PFHpA indicates no KD could be ascertained 9 from these data. PFHpA may have been lost from the system due to non-specific interactions that were not due to the 10 protein or there was a problem with the analysis of PFAS in the dialysate. 11

112

(A) PFOA - PPAR-훾 pH=7.4 (B) PFOS - PPAR-훾 pH=7.4

0.020 KD = 0.057 ± 0.027 μM 1.5 KD = 8.477 ± 0.459 μM

0.015 g

i n

i 1.0

i B

B 0.010

i c

c 0.5

e p

0.005 p

S S

0.000 0.0 0.0 0.1 0.2 0.3 0.4 0 1 2 3 [Ligand]free µM [Ligand]free µM 13 14 Figure A3 Equilibrium dialysis results for binding affinity of PFOA (A) and PFOS (B) with PPAR– 훾 at normal 15 experiment with pH = 7.4 and ionic strength = 18.1 mS/cm. 16

113

(A) PFOS - PPAR-δ pH=7.4 (B) PFBS - PPAR-δ pH=7.4

0.10 KD = 0.686 ± 0.329 μM 0.3 KD = ND

0.08

n i

i 0.2

d d

0.06 n

f f

i 0.04

i c

c 0.1

p S 0.02 S

0.00 0.0 0.0 0.1 0.2 0.3 0.4 0.5 0.00 0.02 0.04 0.06 0.08 [Ligand]free µM [Ligand]free µM 18 Figure A4 Equilibrium dialysis results for binding affinity of PFOS (A) and PFBS (B) with PPAR–δ at normal 19 experiment with pH = 7.4 and ionic strength = 18.1 mS/cm 20

114

21 22

(A) PFOA-LFABP pH=7.4 (B) PFBS - LFABP pH=7.4

0.04 KD = 0.099 ± 0.015 μM 0.025 KD = ND

0.020

0.03

i d

d 0.015

i B

0.02 B

f f

i 0.010

e p

0.01 p S S 0.005

0.00 0.000 0 1 2 3 4 0.00 0.05 0.10 0.15 [Ligand]free µM [Ligand]free µM

0.15 KD = ND 1.0 KD = 1.695 ± 0.031 μM

0.8

i n

0.10 i

n n

i 0.6

f i

i 0.4 c

0.05 c

p S S 0.2

0.00 0.0 0 1 2 3 4 0 1 2 3 4 [Ligand]free µM [Ligand]free µM 23 Figure A5 Equilibrium dialysis results for binding affinity of PFOA (A), PFBS (B), PFHxA (C), and PFHxS (D) with 24 L-FABP at normal experiment with pH = 7.4 and ionic strength = 18.1 mS/cm 25

115

26 PFHpA- IFABP pH=7.4

1.0 KD = ND

0.8

i d

n 0.6

i f

i 0.4

e p

S 0.2

0.0 0 1 2 3 4 [Ligand]free µM 27 Figure A6. Equilibrium dialysis results for binding affinity of PFHpA with I-FABP at normal experiment with pH = 28 7.4 and ionic strength = 18.1 mS/cm. 29

116

30 31

32 Figure A7 Comparison of reported KD (± SE) values from literature for human serum albumin [21–30] 33

117

34 35

36 Figure A8 Comparison of reported KD (± SE) values from literature for bovine serum albumin. 37 [21,26,30–33] 38

118

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179

180 Appendix B – Chapter 3 Supplemental Information 181 182 Preparation of Methanol Based Standard Spikes into Ethyl Acetate 183 184 As methanol is immiscible with LNAPL, a technique was needed in order to successfully spike

185 methanol based standards into LNAPL. This was achieved by spiking methanol based standards

186 into ethyl acetate first at a 1:1 ratio (e.g. 25 L methanol standard into 25 L of ethyl acetate)

187 before spiking into the LNAPL. Accuracy of the preparation technique was assessed through a

188 replicated (n=4) recovery of LNAPL spiked with 5,000 ng/L that was further diluted 1:10 in

189 ethyl acetate to give a final concentration of 500 ng/L (Table B5).

190

123

191 Table B1: PFAS analytes names, acronyms, acquisition masses, parameters, calibration references, and data quality tiers. Analytes in 192 data quality tiers that are in bold are Qn analytes.

Analyte Acronym PI* (m/z) FI-1* FI-2* Internal Std Calibration Reference Data Quality* (m/z) (m/z) 13 Perfluorobutanoic acid PFBA 213 169 n/a [ C4] PFBA PFBA Qn

13 Perfluoropentanoic acid PFPeA 263 219 n/a [ C3] PFPeA PFPeA Qn

13 Perfluorohexanoic acis PFHxA 313 269 119 [ C2] PFHxA PFHxA Qn

13 Perfluoroheptanoic acid PFHpA 363 319 169 [ C4] PFOA PFHpA Qn

13 Perfluorooctanoic acid PFOA 413 369 169 [ C4] PFOA PFOA Qn

13 Perfluorononaoic acid PFNA 463 419 169 [ C5] PFNA PFNA Qn

13 Perfluorodecanoic acid PFDA 513 469 269 [ C2] PFDA PFDA Qn

13 Perfluoroundecanoic acid PFUnDA 563 519 169 [ C2] PFUnDA PFUnDA Qn

13 Perfluorododecanoic acid PFDoDA 613 569 169 [ C2] PFDoDA PFDoDA Qn

13 Perfluorortridecanoic acid PFTriDA 663 619 169 [ C2] PFDoDA PFTriDA Ql

13 Perfluorotetradecanoic acid PFTeDA 713 669 169 [ C2] PFDoDA PFTeDA Ql

13 Perfluoropropane sulfonate PFPrS 249 80 99 [ C3] PFPrS PFPrS Qn

13 Perfluorobutane sulfonate PFBS 299 80 99 [ C3] PFBS PFBS Qn

13 Perfluoropentane sulfonate PFPeS 349 80 99 [ C3] PFHxS PFHxS Ql

13 Perfluorohexane sulfonate PFHxS 399 80 99 [ C3] PFHxS PFHxS Qn

13 Perfluorheptane sulfonate PFHpS 449 80 99 [ C2] PFOS PFOS Ql

13 Perfluorooctanesulfonic acid PFOS 499 80 99 [ C2] PFOS PFOS Qn

13 Perfluorononane sulfonate PFNS 549 80 99 [ C2] PFOS PFNS Ql

13 Perfluorodecane sulfonate PFDS 599 80 99 [ C2] PFOS PFDS Ql 13 Perfluorododecane sulfonate PFDoS 699 80 99 [ C2] PFOS PFDoS Ql

13 4:2 fluorotemomer sulfonate 4:2 FTSA 327 307 81 [ C2] FTSA 4:2 FTSA Qn

13 6:2 fluorotemomer sulfonate 6:2 FTSA 427 407 81 [ C2] FTSA 6:2 FTSA Qn

13 8:2 fluorotemomer sulfonate 8:2 FTSA 527 507 81 [ C2] FTSA 8:2 FTSA Qn

13 2H-perfluoro-2-octenoic acid 6:2 FTUCA 357 293 243 [ C2] 6:2 FTUCA FHUEA Qn

13 2H-perfluoro-2-decenoic acid 8:2 FTUCA 457 393 343 [ C2] 8:2 FTUCA FOUEA Qn

13 3-Perfluoropentyl propanoic acid (5:3) 5:3 FTCA 341 237 217 [ C2] 6:2 FTCA FPePA Ql

13 298 78 319 [ C8]FOSA FBSA Ql Perfluoro-butanesulfonamide FBSA 13 398 78 319 [ C8]FOSA FHxSA Ql Perfluorohexanesulfonamide FHxSA 13 Perfluoro-1-octanesulfonamide FOSA 498 78 319 [ C8]FOSA FOSA Qn

124

2 Perfluorooctane sulfonamido acetic acid FOSAA 556 498 78 [ H3] MeFOSAA FOSAA Sq

2 Ethylperfluorooctane sulfonamido acetic acid EtFOSAA 584 419 526 [ H5] EtFOSAA EtFOSAA Qn

13 Chlorinated Perfluorooctanesulfonic acid 8Cl-PFOS 515 80 99 [ C2] PFOS 8Cl-PFOS Ql

13 N-TrimethylAmmonio perFluoroAlkaneSulfonamide TAmPFHxSa 499 60 73 [ C8]PFOS PFOS Sc

13 N-dimethyl ammonio propyl perfluoroalkane sulfonamide AmPr-FPrSA 335 85 58 [ C8]PFOS PFOS Sc

13 AmPr-FBSA 385 85 58 [ C8]PFOS PFOS Sc

13 AmPr-FPeSA 435 85 58 [ C8]PFOS PFOS Sc

13 AmPr-FHxSA 485 85 58 [ C8]PFOS PFOS Sc

13 AmPr-FHpSA 535 85 58 [ C8]PFOS PFOS Sc

13 N-dimethyl ammonio propyl perfluoralkane sulfonamido propanoic acid AmPr-FEtSA-PrA 357 85 129 [ C8]PFOS PFOS Sc

13 AmPr-FPrSA-PrA 407 85 129 [ C8]PFOS PFOS Sc

13 AmPr-FBSA-PrA 457 85 129 [ C8]PFOS PFOS Sc

13 AmPr-FPeSA-PrA 507 85 129 [ C8]PFOS PFOS Sc

13 AmPr-FHxSA-PrA 557 85 129 [ C8]PFOS PFOS Sc

13 AmPr-FHpSA-PrA 607 85 129 [ C8]PFOS PFOS Sc

13 N-CarboxyMethyldimethylAmmonioPropyl- perFluoroAlkaneSulfonamide CmeAmPr-FPrSA 393 58 333 [ C8]PFOS PFOS Sc

13 CmeAmPr-FBSA 443 58 333 [ C8]PFOS PFOS Sc

13 CmeAmPr-FPeSA 493 58 333 [ C8]PFOS PFOS Sc

13 CmeAmPr-FHxSA 543 58 333 [ C8]PFOS PFOS Sc

13 N-carboxy ethyl dimethyl ammonio propyl perfluoralkane sulfonamide CEtAmPr-FEtSA 357 85 70 [ C8]PFOS PFOS Sc

13 CEtAmPr-FPrSA 407 85 70 [ C8]PFOS PFOS Sc

13 CEtAmPr-FBSA 457 85 70 [ C8]PFOS PFOS Sc

13 CEtAmPr-FPeSA 507 85 70 [ C8]PFOS PFOS Sc

13 CEtAmPr-FHxSA 557 85 70 [ C8]PFOS PFOS Sc

13 CEtAmPr-FHpSA 607 85 70 [ C8]PFOS PFOS Sc

13 CEtAmPr-FOSA 657 85 70 [ C8]PFOS PFOS Sc

13 N-Sulfo Propyl dimethyl Ammonio Propyl perFluoroAlkaneSulfonamide SrPrAmPr-FEtSA 407 58 168 [ C8]PFOS PFOS Sc

13 SrPrAmPr-FPrSA 457 58 168 [ C8]PFOS PFOS Sc

13 SrPrAmPr-FBSA 507 58 168 [ C8]PFOS PFOS Sc

13 SrPrAmPr-FPeSA 557 58 168 [ C8]PFOS PFOS Sc

13 SrPrAmPr-FHxSA 607 58 168 [ C8]PFOS PFOS Sc

13 N-TrimethylAmmonioPropyl perFluoroAlkaneSulfonamide TAmPr-FBSA 399 60 [ C8]PFOS PFOS Sc

13 TAmPr-FPeSA 449 60 [ C8]PFOS PFOS Sc

125

13 TAmPr-FHxSA 499 60 [ C8]PFOS PFOS Sc

13 13 Perfluoro[1,2,3,4- C4]butanoic acid [ C4] PFBA 217 172 n/a n/a n/a n/a

13 13 Perfluoro[3,4,5- C3]pentanoic acid [ C3] PFPeA 266 222 n/a n/a n/a n/a

13 13 Perfluoro[1,2- C2]hexanoic acid [ C2] PFHxA 315 270 n/a n/a n/a n/a

13 13 Perfluoro[1,2,3,4- C4]heptanoic acid [ C4] PFHpA 367 322 n/a n/a n/a n/a

13 13 Perfluoro[1,2,3,4- C4]octanoic acid [ C4] PFOA 417 372 n/a n/a n/a n/a

13 13 Perfluoro[1,2,3,4,5- C5]nonanoic acid [ C5] PFNA 468 423 n/a n/a n/a n/a

13 13 Perfluoro[1,2- C2]decanoic acid [ C2] PFDA 515 470 n/a n/a n/a n/a

13 13 Perfluoro[1,2- C2]undecanoic acid [ C2] PFUnDA 565 520 n/a n/a n/a n/a

13 13 Perfluoro[1,2- C2]dodecanoic acid [ C2] PFDoDA 615 570 n/a n/a n/a n/a

13 13 2-perfluorohexyl-[ C2]-ethanoic acid [ C2] 6:2 FTCA 379 294 n/a n/a n/a n/a

13 13 2H-Perfluoro-[1,2- C2]-2-octenoic acid [ C2] 6:2 FTUCA 359 294 n/a n/a n/a n/a

13 13 2H-Perfluoro-[1,2- C2]-2-decenoic acid [ C2] 8:2 FTUCA 459 294 n/a n/a n/a n/a

13 13 Perfluoropropane[ C3]sulfonate [ C3] PFPrS 252 81 n/a n/a n/a n/a

13 13 Perfluorobutane[ C3]sulfonate [ C3] PFBS 302 299 n/a n/a n/a n/a

13 13 Perfluorohexane[ C3]sulfonate [ C3] PFHxS 402 99 n/a n/a n/a n/a

13 13 Perfluorooctane[1,2,3,4- C4] sulfonate [ C2] PFOS 503 99 n/a n/a n/a n/a

13 13 4:2 [ C2] fluorotelomer sulfonate [ C2] 4:2 FTSA 329 81 n/a n/a n/a n/a

13 13 6:2 [ C2] fluorotelomer sulfonate [ C2] 6:2 FTSA 429 81 n/a n/a n/a n/a

13 13 8:2 [ C8] fluorotelomer sulfonate [ C2] 8:2 FTSA 529 81 n/a n/a n/a n/a

13 13 Perfluoro-1-[ C8]octanesulfonamide [ C8] FOSA 506 81 n/a n/a n/a n/a

2 Ethyl-d9-perfluorooctane sulfonamido acetic acid [ H9] EtFOSAA 589 419 n/a n/a n/a n/a 193 *PI (precursor ion), FI (fragmentation ion), Qn (quantitative), Sq (semiquantitative), Ql (qualitative), Sc (screen)

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194 195 Table B2 – Limit of detection, limit of quantification, accuracy, and precision for Qn analytes in 196 Jet Fuel A. Accuracy LOD LOQ Analyte (Recovery, %) Precision (% RSD) (ng/L) (ng/L) PFBA 100 6 14 45 PFPeA 100 10 7.9 26 PFHxA 93 2 7.2 24 PFHpA 105 7 7.7 25 PFOA 93 14 6.3 21 PFNA 110 20 5.1 17 PFDA 95 11 9.1 30 PFUdA 95 7 11 35 PFDoDA 130 3 7.8 25 PFTrDA 98 18 7.1 23 PFTeDA 79 14 8.7 29 PFPrS 81 10 7.1 23 PFBS 100 4 4.4 14 PFPeS 97 8 7.4 25 PFHxS 99 9 6.0 20 PFHpS 88 8 6.0 20 PFOS 94 14 3.9 13 PFNS 83 12 4.7 16 PFDS 100 16 6.6 22 PFDoS 130 30 15 50 FBSA 47 9 18 60 FHxSA 33 12 150 500 FOSA 100 3 64 210 FOSAA 96 21 13 43 EtFOSAA 80 15 12 40 PFEtCHxS 100 7 6.4 21 8Cl-PFOS 90 5 5.1 17 4:2 FtS 77 5 2.1 7 6:2 FtS 81 18 14 46 8:2 FtS 110 30 20 67 5:3 FTCA 100 24 4.1 14 6:2 UFTCA 100 3 1.0 5 8:2 UFTCA 96 7 5.3 17

197

127

198 Table B3. Accuracy and precision for direct analysis of Qn analytes via spiking into ethyl acetate

199 and diluting 1:10.

Accuracy Precision Analyte (Recovery, %) (% RSD) PFBA 105% 9% PFHxA 112% 5% PFHpA 139% 7% PFOA 107% 3% PFNA 108% 7% PFDA 101% 5% PFUdA 115% 5% PFDoA 112% 3% PFTrDA 142% 8% PFTeDA 139% 7% PFPrS 114% 4% PFBS 121% 5% PFPeS 132% 4% PFHxS 98% 6% PFHpS 89% 6% PFOS 102% 8% PFNS 116% 9% PFDS 130% 9% Cl-PFOS 114% 8% 4:2 FTS 132% 7% 6:2 FTS 150% 4% 8:2 FTS 120% 4% 200

201

128

202 Table B4. PFAS (Qn) present in underlying aqueous phase samples. Analyte list is restricted to

203 those that had quantifiable PFAS in at least one LNAPL sample.

ng/L Site NAPL PFOA PFOS FBSA FHxSA FOSA 6:2 FtS 8:2 FtS 1 NAPL 1 1400 4300

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205

206 Table B5. PFAS (Sc) present in underlying aqueous phase samples. Analyte list is restricted to those that had quantifiable PFAS in at

207 least one LNAPL sample.

ng/L

N-TAmP- TAmPr- AmPr- AmPr- CEtAmPr- SPrAmPr- AmPr- AmPr- CEtAmPr- Installation Sample FHxSA FHxSA FPeSA FHxSA-PrA FHxSA FPeSA FHxSA FHpSA FOSA 1 1

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Appendix C – Chapter 4 Supplemental Information

Preparation of Methanol Based Standard Spikes into Ethyl Acetate

In order to successfully spike methanol based standards into LNAPL the methanol standards were first be spiked into ethyl acetate at a 1:1 ratio (e.g. 25 L methanol standard into 25 L of ethyl acetate) before spiking into the LNAPL. Accuracy of the preparation technique was assessed through a replicated (n=4) recovery of LNAPL spiked with 5,000 ng/L that was further diluted 1:10 in ethyl acetate to give a final concentration of 500 ng/L (Table C3).

LC MS/MS Method Details.

An Agilent 1100 (Santa Clara, CA) was modified with a 900 uL injection loop and 900 uL sample injections were used. Two Agilent ZORBAX amino propyl and one Agilent

ZORBAX silica guard columns (4.6mm x 12.5mm x 5 um, Agilent, Santa Clara, CA) separate PFAS from the matrix. Mobile phase A was 3% methanol in water and B was

10mM ammonium acetate in methanol. The sample was loaded during a 100% A gradient for two minutes at 0.6 mL per minute. The gradient was diverted to an Agilent

4.6mm x 75mm x 3.5µm C18 analytical column and changed to 50:50 A and B. Percent

B was increased linearly over 13.5 minutes until reaching 99% in order to separate

PFAS and held for an additional 4.5 minutes to ensure all PFAS were eluted off the column. The flow rate was increase to 1.0 mL/min for two minutes before switching to

100% A for 11 minutes to restore starting conditions. Total run time was 33 minutes.

The MS/MS method was composed of multiple reaction monitoring (MRM) acquisition windows with parent and transition ions monitored based on retention time and was operating in negative electrospray ionization mode. The MS parameters were

131

as follows: capillary potential = 2800V, extractor potential = 2V, source temp = 150ºC, desolvation temp = 450ºC, desolvation gas flow rate = 1100 L/hr, and cone gas flow rate = 75 L/h.

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Table C1. Native and surrogate PFAS standards used. Class Acronym Analyte Acronym Matched Surrogate PFBA MPFBA PFPeA M5PFPeA PFHxA M2PFHxA PFHpA M4PFHpA PFOA M4PFOA PFCA PFNA M5PFNA PFDA MPFDA PFUdA MPFUdA Native Standards PFDoA MPFDoA PFTrDA MPFDoA PFTeDA MPFDoA PFPrS M3PFBS PFBS M3PFBS PFSA PFHxS MPFHxS PFOS MPFOS PFNS MPFOS

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Table C2. Synthetic tap water recipe. Concentration Amount of Concentratio Component Formula of Stock Stock n (mg/liter) (mg/liter) Added (ml) Sodium Bicarbonate NaHCO3 10000 10 100 Magnesium sulfate heptahydrate* MgSO4•7H2O 1000 13.4 13.4 Potassium phosphate dibasic K2HPO4 1000 0.7 0.7 Potassium phosphate monobaisc KH2PO4 1000 0.3 0.3 Ammonium Sulfate (NH4)2SO4 100 0.1 0.01 Sodium Chloride NaCl 100 0.1 0.01 Iron (II) sulfate heptahydrate* FeSO4•7H2O 10 0.1 0.001 Sodium nitrate NaNO3 1000 1 1 Calcium sulfate dihydrate* CaSO4•2H2O 1265 21.3 27

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Table C3. Accuracy and precision for direct analysis of Qn analytes in NAPL.

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Table C4. Recoveries for select PFAS spiked in water from polypropylene tube Analyte Recovery PFBA 125% PFHxA 75% PFHpA 105% PFOA 130% PFNA 95% PFBS 85% PFHxS 130% PFOS 120%

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Table C5. Recoveries for PFAS spiked in jet fuel A from polypropylene tube Analyte Recovery % PFBA 104 PFPeA 100 PFHxA 93 PFHpA 105 PFOA 93 PFNA 112 PFDA 95 PFUdA 95 PFDoDA 125 PFTrDA 98 PFTeDA 79 PFPrS 81 PFBS 100 PFHxS 99 PFOS 94 PFNS 83

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Table C6. Single solute PFOS equilibrium partitioning concentrations compared with PFAS mix equilibrium partitioning concentrations. One concentration for 10,000 ng/L showed a significant difference between single solute and the mixture, however, as this was bounded by concentrations that showed no difference it was considered an outlier. Individual Mixture Co Aqueous Ceq Aqueous Ceq Aqueous T (ng/L) (ng/L) 95% CI (ng/L) 95% CI Statistic 0 0 NA 0 NA NA 2000 1569 217 1631 267 0.74 5000 4198 349 3986 303 0.42 10000 6118 161 7036 348 0.02 20000 15291 1204 15331 706 0.96 50000 40953 4290 40289 4363 0.84 75000 52693 9940 61422 4267 0.19 100000 80624 2911 77490 6171 0.42

Individual Mixture Co Aqueous Ceq Jet Fuel T (ng/L) (ng/L) 95% CI Ceq Jet Fuel (ng/L) 95% CI Statistic 0 0 NA 0 NA NA 2000 133 131 72 142 0.57 5000 133 131 152 149 0.86 10000 139 137 378 28 0.03 20000 225 224 526 168 0.11 50000 835 NA 1039 NA NA 75000 1561 587 1392 325 0.73 100000 1926 406 1581 802 0.62

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PFOS 2500

2000

1500

1000

Ceq Jet Fuel A (ng/L) A Fuel Jet Ceq 500

0 0 20000 40000 60000 80000 100000 Ceq Aqueous (ng/L)

Figure C1. PFOS equilibrium partitioning between Jet Fuel A and synthetic freshwater.

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PFNS

14000

12000

10000

8000

6000

4000 Ceq Jet Fuel A (ng/L) A Fuel Jet Ceq 2000

0 0 20000 40000 60000 80000 100000 Ceq Aqueous (ng/L)

Figure C2. PFNS equilibrium partitioning between Jet Fuel A and synthetic freshwater.

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PFOA 450

400

350

300

250

200

150

100 Ceq Jet Fuel A (ng/L) A Fuel Jet Ceq

0 0 20000 40000 60000 80000 100000 Ceq Aqueous (ng/L)

Figure C3. PFOA equilibrium partitioning between Jet Fuel A and synthetic freshwater.

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PFNA 2500

2000

1500

1000

Ceq Jet Fuel A (ng/L) A Fuel Jet Ceq 500

0 0 20000 40000 60000 80000 100000 120000 Ceq Aqueous (ng/L)

Figure C4. PFNA equilibrium partitioning between Jet Fuel A and synthetic freshwater.

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PFDA 12000

10000

8000

6000

4000

Ceq Jet Fuel A (ng/L) A Fuel Jet Ceq 2000

0 0 20000 40000 60000 80000 100000 Ceq Aqueous (ng/L)

Figure C5. PFDA equilibrium partitioning between Jet Fuel A and synthetic freshwater.

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PFUnDA 35000

30000

25000

20000

15000

10000 Ceq Jet Fuel A (ng/L) A Fuel Jet Ceq 5000

0 0 10000 20000 30000 40000 50000 60000 70000 Ceq Aqueous (ng/L)

Figure C6. PFUnDA equilibrium partitioning between Jet Fuel A and synthetic freshwater.

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PFDoDA 70000

60000

50000

40000

30000

20000 Ceq Jet Fuel A (ng/L) A Fuel Jet Ceq 10000

0 0 5000 10000 15000 20000 Ceq Aqueous (ng/L)

Figure C7. PFDoDA equilibrium partitioning between Jet Fuel A and synthetic freshwater.

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PFTrDA 60000

50000

40000

30000

20000

Ceq Jet Fuel A (ng/L) A Fuel Jet Ceq 10000

0 -1000 0 1000 2000 3000 4000 5000 6000 Ceq Aqueous (ng/L)

Figure C8. PFTrDA equilibrium partitioning between Jet Fuel A and synthetic freshwater.

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PFTeDA 60000

50000

40000

30000

20000

Ceq Jet Fuel A (ng/L) A Fuel Jet Ceq 10000

0 0 500 1000 1500 2000 2500 3000 3500 Ceq Aqueous (ng/L)

Figure C9. PFTeDA equilibrium partitioning between Jet Fuel A and synthetic freshwater.

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Single Solute Mix Solutes

25 y = 0.3597x R² = 0.9788

0 0 10 20 30 40 50 60 70

Figure C10. PFPrS mix and single solute interfacial sorption isotherms.

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PFBS 34 32 Equation y = a + b*x 30 Plot Ceq Interface 28 Weight No Weighting 26 Intercept 0 ± -- 24 Slope 0.26171 ± 0.02159 22 Residual Sum of Squares 28.92016 20 Pearson's r 0.97699 18 R-Square (COD) 0.95451 16 Adj. R-Square 0.94801 14 12 10 8

6 Ceq Interface (ng/cm^2) Interface Ceq 4 2 0 -2 0 20 40 60 80 Ceq Aqueous (ng/cm^3)

Figure C11. PFBS interfacial sorption isotherm.

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PFHxS 16

14 Equation y = a + b*x 12 Plot Ceq Interface Weight No Weighting Intercept 0 ± -- 10 Slope 0.06405 ± 0.00559 Residual Sum of Squares 2.04936 8 Pearson's r 0.98147 R-Square (COD) 0.96328 6 Adj. R-Square 0.95594

Ceq Interface (ng/cm^2) Interface Ceq 2

-2 0 20 40 60 80 100 Ceq Aqueous (ng/cm^3)

Figure C12. PFHxS interfacial sorption isotherm.

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PFNS 12

Equation y = a + b*x Plot Ceq Interface 10 Weight No Weighting Intercept 0 ± -- Slope 0.10668 ± 0.0085 8 Residual Sum of Squares 5.43559 Pearson's r 0.97849 R-Square (COD) 0.95744 Adj. R-Square 0.95136 6

2 Ceq Interface (ng/cm^2) Interface Ceq

-2 0 20 40 60 80 Ceq Aqueous (ng/cm^3)

Figure C13. PFNS interfacial sorption isotherm.

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PFBA

Equation y = a + b*x 15 Plot Ceq Interface Weight No Weighting Intercept 0 ± -- Slope 0.11778 ± 0.00734 Residual Sum of Squares 4.85995 Pearson's r 0.98669 10 R-Square (COD) 0.97356 Adj. R-Square 0.96978

5 Ceq Interface (ng/cm^2) Interface Ceq

0 20 40 60 80 Ceq Aqueous (ng/cm^3)

Figure C14. PFBA interfacial sorption isotherm.

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PFPeA

15 Equation y = a + b*x Plot Ceq Interface Weight No Weighting Intercept 0 ± -- Slope 0.10503 ± 0.00352 Residual Sum of Squares 1.16424 10 Pearson's r 0.99608 R-Square (COD) 0.99218 Adj. R-Square 0.99106

5 Ceq Interface (ng/cm^2) Interface Ceq

0 20 40 60 80 100 Ceq Aqueous (ng/cm^3)

Figure C15. PFPeA interfacial sorption isotherm.

153

PFHxA 14

Equation y = a + b*x 12 Plot Ceq Interface Weight No Weighting 10 Intercept 0 ± -- Slope 0.0814 ± 0.00589 Residual Sum of Squares 3.48453 8 Pearson's r 0.98218 R-Square (COD) 0.96467 Adj. R-Square 0.95962 6

2 Ceq Interface (ng/cm^2) Interface Ceq 0

-2

0 20 40 60 80 100 Ceq Aqueous (ng/cm^3)

Figure C16. PFHxA interfacial sorption isotherm.

154

PFHpA

20 Equation y = a + b*x Plot Ceq Interface Weight No Weighting Intercept 0 ± -- Slope 0.1115 ± 0.00668 15 Residual Sum of Squares 4.10956 Pearson's r 0.98765 R-Square (COD) 0.97546 Adj. R-Square 0.97195 10

5 Ceq Interface (ng/cm^2) Interface Ceq

0 20 40 60 80 Ceq Aqueous (ng/cm^3)

Figure C17. PFHpA interfacial sorption isotherm.

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PFOA 20

Equation y = a + b*x Plot Ceq Interface Weight No Weighting 15 Intercept 0 ± -- Slope 0.10447 ± 0.00934 Residual Sum of Squares 8.15851 Pearson's r 0.97312 10 R-Square (COD) 0.94697 Adj. R-Square 0.9394

5 Ceq Interface (ng/cm^2) Interface Ceq

0 20 40 60 80 Ceq Aqueous (ng/cm^3)

Figure C18. PFOA interfacial sorption isotherm.

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PFNA 14 Equation y = a + b*x Plot Ceq Interface Weight No Weighting 12 Intercept 0 ± -- Slope 0.05632 ± 0.00772 10 Residual Sum of Squares 6.28971 Pearson's r 0.94011 8 R-Square (COD) 0.8838 Adj. R-Square 0.8672 6

Ceq Interface (ng/cm^2) Interface Ceq -2

-4

-6

0 50 100 Ceq Aqueous (ng/cm^3)

Figure C19. PFNA interfacial sorption isotherm.

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PFDA 18 Equation y = a + b*x Plot Ceq Interface Weight No Weighting 16 Intercept 0 ± -- Slope 0.09374 ± 0.00855 14 Residual Sum of Squares 4.95462 Pearson's r 0.97593 R-Square (COD) 0.95244 12 Adj. R-Square 0.94451 10 8 6 4 2

0 Ceq Interface (ng/cm^2) Interface Ceq -2 -4 -6 0 20 40 60 80 Ceq Aqueous (ng/cm^3)

Figure C20. PFDA interfacial sorption isotherm.

158

PFUdA 20

Model Freundlich (User) Equation qe = K*Ce^(n) Plot Ceq Interface K 0.62121 ± 0.19804 15 n 0.84056 ± 0.11545 Reduced Chi-Sqr 0.78295 R-Square (COD) 0.88877 Adj. R-Square 0.86653

5 Ceq Interface (ng/cm^2) Interface Ceq

0 0 20 40 60 Ceq Aqueous (ng/cm^3)

Figure C21. PFUdA interfacial sorption isotherm.

159

PFDoDA 35 Model Freundlich (User) Equation qe = K*Ce^(n) 30 Plot Ceq Interface K 2.62986 ± 0.16097 n 0.82508 ± 0.05994 25 Reduced Chi-Sqr 0.28522 R-Square (COD) 0.94727 Adj. R-Square 0.93672 20

10 Ceq Interface (ng/cm^2) Interface Ceq

0 0 2 4 6 8 10 12 14 16 18 Ceq Aqueous (ng/cm^3)

Figure C22. PFDoDA interfacial sorption isotherm.

160

PFTrDA 35

20 Model Freundlich (User) qe = K*Ce^(n) 15 Equation Plot Ceq Interface K 11.77638 ± 1.99772 10 n 0.60237 ± 0.13958 Ceq Interface (ng/cm^2) Interface Ceq Reduced Chi-Sqr 1.24242 5 R-Square (COD) 0.89942 Adj. R-Square 0.87931 0 0 2 4 6 Ceq Aqueous (ng/cm^3)

Figure C23. PFTrA interfacial sorption isotherm.