Login / Signup

Predicting Concentration- and Ionic-Strength-Dependent Air-Water Interfacial Partitioning Parameters of PFASs Using Quantitative Structure-Property Relationships (QSPRs).

John F StultsYoun-Jeong ChoiCooper RockwellCharles E SchaeferDung D NguyenDetlef R U KnappeTissa H IllangasekareChristopher P Higgins
Published in: Environmental science & technology (2023)
Air-water interfacial retention of poly- and perfluoroalkyl substances (PFASs) is increasingly recognized as an important environmental process. Herein, column transport experiments were used to measure air-water interfacial partitioning values for several perfluoroalkyl ethers and for PFASs derived from aqueous film-forming foam, while batch experiments were used to determine equilibrium K ia data for compounds exhibiting evidence of rate-limited partitioning. Experimental results suggest a Freundlich isotherm best describes PFAS air-water partitioning at environmentally relevant concentrations (10 1 -10 6 ng/L). A multiparameter regression analysis for K ia prediction was performed for the 15 PFASs for which equilibrium K ia values were determined, assessing 246 possible combinations of 8 physicochemical and system properties. Quantitative structure-property relationships (QSPRs) based on three to four parameters provided predictions of high accuracy without model overparameterization. Two QSPRs ( R 2 values of 0.92 and 0.83) were developed using an assumed average Freundlich n value of 0.65 and validated across a range of relevant concentrations for perfluorooctane sulfonate (PFOS), perfluorooctanoate (PFOA), and hexafluoropropylene oxide-dimer acid (i.e., GenX). A mass action model was further modified to account for the changing ionic strength on PFAS air-water interfacial sorption. The final result was two distinct QSPRs for estimating PFAS air-water interfacial partitioning across a range of aqueous concentrations and ionic strengths.
Keyphrases
  • ionic liquid
  • molecular dynamics simulations
  • electron transfer
  • room temperature
  • drinking water
  • risk assessment
  • gold nanoparticles
  • deep learning
  • data analysis
  • life cycle