A group-contribution model for predicting the physicochemical behavior of PFAS components for understanding environmental fate

The factors controlling per- and polyfluoroalkyl substances (PFAS) environmental fate remains the subject of considerable debate and study. As surfactants, PFAS readily partition to interfaces, a property that controls their transport and fate. A group contribution model is developed to predict the extent to which PFAS partitions to the air-water interface. Langmuir adsorption and Szyszkowski equation parameters were fitted to literature air-water surface tension data for a range of PFAS and conventional hydrocarbon surfactants. This approach enabled the prediction of the impact of the hydrophilic head group, and other molecular components, on PFAS interfacial partitioning in instances when PFAS data are unavailable but analogous hydrocarbon surfactant data are available. The model was extended to predict a range of parameters (i.e., solubility, critical micelle concentration (CMC), KD, Koc and Kow) that are used to predict PFAS environmental fate, including long-range PFAS transport and in multimedia models. Model predictions were consistent with laboratory and field derived parameters reported in the literature. Additionally, the proposed model can predict the impact of pH and speciation on the extent of PFAS interfacial partitioning, a potentially important feature for understanding the behaviors of some ionizable PFAS, such as fluorinated carboxylic acids. The proposed model provides a conceptually straightforward method to predict a wide range of environmental fate parameters for a wide range of PFAS. As such, the model is a powerful tool that can be used to determine parameters needed to predict PFAS environmental fate. [Display omitted] •Group contribution model predicts PFAS interfacial partitioning.•Contribution of individual PFAS molecule components to interfacial partitioning quantified.•Model predicts KD, Koc, Kow, solubility and CMC for PFAS.•Ability to predict PFAS speciation will help prediction of long range PFAS transport.