Article impact statement: Sorption coefficients for eight anionic PFAS spanned three to five log units indicating limited value for estimating impacts to groundwater.

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Abstract

Published literature for reported sorption coefficients (K_d) of eight anionic per- and polyfluoroalkyl substances (PFAS) in soil was reviewed. K_d values spanned three to five log units indicating that no single value would be appropriate for use in estimating PFAS impacts to groundwater using existing soil-water partition equations. Regression analysis was used to determine if the soil or solution parameters might be used to predict K_d values. None of the 15 experimental parameters collected could individually explain variability in reported K_d values. Significant associations between K_d and soil calcium and sodium content were found for many of the selected PFAS, suggesting that soil cation content may be critical to PFAS sorption, as previously noted in sources like Higgins and Luthy (2006), while organic carbon content was significant only at elevated levels (>5%). Unexplained discrepancies between the results from studies where PFAS were introduced to soil and desorbed in the laboratory and those that used material from PFAS-impacted sites suggest that laboratory experiments may be overlooking some aspects critical to PFAS sorption. Future studies would benefit from the development and use of standardized analytical methods to improve data quality and the establishment of soil parameters appropriate for collection to produce more complete data sets for predictive analysis.

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Table S1. PFAS vapor pressure and Henry's Law constants.

Table S2. Range of K_d values from included studies.

Table S3. Methods for PFAS extraction and quantitation, by study.

Table S4. Results from simple linear regression of variables versus log K_d by chemical.

Table S5. Summary of ANOVA results—laboratory vs. field measurements.

Table S6. Summary of ANOVA results—adsorption vs. desorption measurements.

Table S7. Results from multiple linear regression of three models for PFOA.

Table S8. Results from multiple linear regression of three models for PFOS.

Table S9. Results from multiple linear regression of three models for PFBS.

Table S10. Results from multiple linear regression of three models for PFBA.

Table S11. Results from multiple linear regression of three models for PFNA.

Table S12. Results from multiple linear regression of three models for PFDA.

Table S13. Results from multiple linear regression of three models for PFHxS.

Figure S1. Linear regression of log K_d vs. percent sand content of soil.

Figure S2. Linear regression of log K_d vs. percent clay content of soil.

Figure S3. Linear regression of log K_d vs. percent silt content of soil.

Figure S4. Linear regression of log K_d vs. soil pH.

Figure S5. Linear regression of log K_d vs. aqueous pH.

Figure S6. Linear regression of log K_d vs. calcium concentration of soil (mg/kg).

Figure S7. Linear regression of log K_d vs. potassium concentration of soil (mg/kg).

Figure S8. Linear regression of log K_d vs. sodium concentration of soil (mg/kg).

Figure S9. Linear regression of log K_d vs. iron concentration of soil (g/kg).

Figure S10. Linear regression of log K_d vs. aluminum concentration of soil (g/kg).

Figure S11. Linear regression of log K_d vs. magnesium concentration of soil (mg/kg).

Figure S12. Linear regression of log K_d vs. cation exchange capacity of soil (mmol/kg).

Figure S13. Linear regression of log K_d vs. anion exchange capacity of soil (mmol/kg).

Figure S14. Linear regression of log K_d vs. aqueous concentration of PFAS (μg/L) over entire data range. Two additional figures (S15 and S16) are provided at different scales.

Figure S15. Linear regression of log K_d vs. aqueous concentration of PFAS (μg/L) up to a concentration of 600 μg/L.

Figure S16. Linear regression of log K_d vs. aqueous concentration of PFAS (μg/L) up to a concentration of 25 μg/L.

Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

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Volume41, Issue4

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Limitations of Current Approaches for Predicting Groundwater Vulnerability from PFAS Contamination in the Vadose Zone

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Limitations of Current Approaches for Predicting Groundwater Vulnerability from PFAS Contamination in the Vadose Zone

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