Perfluorinated and polyfluoroalkyl substances (PFASs) are known for their long-distance migration, bioaccumulation, and toxicity. The transport of PFASs in the environment has been a source of increasing concerned. The organic carbon normalized sorption coefficient (Koc) is an important parameter from which to understand the distribution behavior of organic matter between solid and liquid phases. Currently, the theoretical prediction research on log Koc of PFASs is extremely limited. The existing models have limitations such as restricted application fields and unsatisfactory prediction results for some substances. In this study, a quantitative structure-property relationship (QSPR) model was established to predict the log Koc of PFASs, and the potential mechanism affecting the distribution of PFASs between two phases from the perspective of molecular structure was analyzed. The developed model had sufficient goodness of fit and robustness, satisfying the model application requirements. The molecular weight (MW) related to the hydrophobicity of the compound; lowest unoccupied molecular orbital energy (ELUMO) and maximum average local ionization energy on the molecular surface (ALIEmax), both related to electrostatic properties; and the dipole moment (μ), related to the polarity of the compound; are the key structural variables that affect the distribution behavior of PFASs. This study carried out a standardized modeling process, and the model dataset covered a comprehensive variety of PFASs. The model can be used to predict the log Koc of conventional and emerging PFASs effectively, filling the data gap of the log Koc of uncommon PFASs. The explanation of the mechanism of the model has proven to be of great value for understanding the distribution behavior and migration trends of PFASs between sediment/soil and water, and for estimating the potential environmental risks generated by PFASs.
Keywords: distribution behavior; organic carbon normalized sorption coefficient; perfluorinated and polyfluoroalkyl substances; quantitative structure–property relationship.