Per- and polyfluoroalkyl substances (PFASs) are highly mobile, persistent, and toxic chemicals which are known to adsorb to air-water interfaces in unsaturated soil systems. Air-water interfacial partitioning has been demonstrated to be an important factor which controls the long-term leaching of PFASs from the vadose zone into saturated groundwater systems. There are well correlated relationships between physicochemical properties of PFASs (i.e. functional group, chain length, C18 retention time) and air-water interfacial partitioning parameters for PFASs with experimentally data. These relationships, referred to as Linear Free Energy Relationships (LEFRs) and Quantitative Structural Property Relationships (QSPRs), have been successfully demonstrated for predicting solid phase partitioning parameters of PFASs. Currently, LFERs and QSPRs for the prediction of air-water interfacial partitioning parameters are limited to a small subset of key PFASs and only consider equilibrium partitioning parameters for linear isotherms. Unfortunately, many of the parameters used to predict equilibrium partitioning coefficients all co-vary with one another. Furthermore, experimental evidence presented and used in this study suggests that concentration, background ionic strength, functional group, and rate limitation may also be important factors to consider for air-water interfacial partitioning. Column transport experiments are used to measure air-water interfacial partitioning values for several classes of PFASs, many of which had no experimental data available prior to this study. This newly developed dataset is combined with all available equilibrium air-water interfacial partitioning data to validate hundreds of multivariate regression based LEFR and QSPR models of equilibrium air-water interfacial partitioning parameters. The most accurate multivariate regression models are used to predict concentration dependent equilibrium partitioning of PFASs at relevant field concentrations. A semi-quantitative relationship between physiochemical descriptors and rate-limited air-water interfacial partitioning parameters is evaluated using advanced statistical methods.

Predicting concentration dependent air-water interfacial partitioning parameters of PFASs with multiparametric computational models

ACS Spring 2022

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