Predicting the transport of perfluoroalkyl acids (PFAAs) in the vadose zone is critically important for PFAA site cleanup and risk mitigation. PFAAs exhibit several unusual and poorly understood transport behaviors, including partitioning to the air−water interface, which is currently the subject of debate. This study develops a novel use of quasi-saturated (residual air saturation) column experiments to estimate chemical partitioning parameters of both linear and branched perfluorooctane sulfonate (PFOS) in unsaturated soils. The ratio of linear-to-branched air− water interfacial partitioning constants for all six experiments was 1.62 ± 0.24, indicating significantly greater partitioning of linear PFOS isomers at the air−water interface. Standard breakthrough curve analysis and numerical inversion of HYDRUS models support the application of a Freundlich isotherm for PFOS air−water interfacial partitioning below a critical reference concentration (CRC). Data from this study and previously reported unsaturated column data on perfluorooctanoate (PFOA) were reevaluated to examine unsaturated systems for transport nonidealities. This reanalysis suggests both transport nonidealities and Freundlich isotherm behavior for PFOA below the CRC using drainage-based column methods, contrary to the assertions of the original authors. Finally, a combined Freundlich−Langmuir isotherm was proposed to describe PFAA air−water interfacial partitioning across the full range of relevant PFAA concentrations.
A comprehensive, generalized approach to predict the
retention
of per- and polyfluoroalkyl substances (PFAS) from aqueous film-forming
foam (AFFF) by a soil matrix as a function of PFAS molecular and soil
physiochemical properties was developed. An AFFF with 34 major PFAS
(12 anions and 22 zwitterions) was added to uncontaminated soil in
one-dimensional saturated column experiments and PFAS mass retained
was measured. PFAS mass retention was described using an exhaustive
statistical approach to generate a poly-parameter quantitative structure–property
relationship (ppQSPR). The relevant predictive properties were PFAS
molar mass, mass fluorine, number of nitrogens in the PFAS molecule,
poorly crystalline Fe oxides, organic carbon, and specific (BET-N2) surface area. The retention of anionic PFAS was nearly independent
of soil properties and largely a function of molecular hydrophobicity,
with the size of the fluorinated side chain as the main predictor.
Retention of nitrogen-containing zwitterionic PFAS was related to
poorly crystalline metal oxides and organic carbon content. Knowledge
of the extent to which a suite of PFAS may respond to variations in
soil matrix properties, as developed here, paves the way for the development
of reactive transport algorithms with the ability to capture PFAS
dynamics in source zones over extended time frames.
Per- and polyfluoroalkyl substances (PFASs) are a class
of environmental
contaminants that originate from various sources. The unique chemical
fingerprints associated with many commercial products and industrial
applications make PFASs ideal candidates for machine learning (ML)-assisted
environmental forensics. Here, we propose a novel use of PFAS fingerprints
in fish tissue from surface water systems to classify exposure from
multiple sources of PFASs using a proof-of-concept demonstration.
Three supervised ML classification techniques (k-nearest neighbors
(KNN), decision trees, support vector machines) implementing two predictive
features are used to classify literature-reported PFAS fingerprints
in fish (n = 1057). The importance of additional
predictive features was explored using brute force optimization of
a multifeature KNN algorithm. The multiclass classification considered
exposure to aqueous film-forming foam-impacted water, paper industry
wastewater, diffuse sources, or PFASs undergoing long-range transport.
The optimized classifiers demonstrated 85%–94% classification
accuracy for this first known multiclass classification of PFASs for
environmental forensics. The optimized classifiers also demonstrated
79%–92% classification accuracy with a set of independent external
validation data (n = 192). Our results demonstrate
that PFAS fingerprints in fish tissue may be an effective means of
PFAS source tracking in surface water systems. The source code is
provided for guidance on best practices for ML-assisted environmental
forensics.
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