To accurately predict the environmental fate of per‐ and polyfluoroalkyl substances (PFAS), high‐quality physicochemical property data are required. Because such data are often not available from experiments, assessment of the accuracy of existing property estimation models is essential. The quality of predicted physicochemical property data for a set of 25 PFAS was examined using COSMOtherm, EPI Suite, the estimation models accessible through the US Environmental Protection Agency's CompTox Chemicals Dashboard, and Linear Solvation Energy Relationships (LSERs) available through the UFZ‐LSER Database. The results showed that COSMOtherm made the most accurate acid dissociation constant and air–water partition ratio estimates compared with literature data. The OPEn structure‐activity/property Relationship App (OPERA; developed through the CompTox Chemicals Dashboard) estimates of vapor pressure and dry octanol–air partition ratios were the most accurate compared with other models of interest. Wet octanol–water partition ratios were comparably predicted by OPERA and EPI Suite, and the organic carbon soil coefficient and solubility were well predicted by OPERA and COSMOtherm. Acid dissociation of the perfluoroalkyl acids has a significant impact on their physicochemical properties, and corrections for ionization were included where applicable. Environ Toxicol Chem 2020;39:775–786. © 2020 SETAC

Linear quantitative structure-property relationships (QSPRs) for the prediction of diffusion coefficients (log D p ) were developed for organic contaminants in two common passive sampler materials, polydimethylsiloxane (PDMS) and low-density polyethylene (LDPE). Literature data was compiled for both PDMS and LDPE resulting in final data sets of 196 and 79 compounds, respectively. Data sets contained compounds with log D p values that ranged over about 5 log units and 3 log units for PDMS and LDPE, respectively. The quality of log D p prediction using either simple molecular descriptors or quantum-chemical based COSMO-RS sigma moment descriptors was compared for both materials. For PDMS, the sigma moment descriptor QSPR had the best predictivity with a correlation coefficient of R 2 = 0.85 and root mean square error (RMSE) of 0.36 for log D p . The molecular descriptor QSPR resulted in a correlation coefficient of R 2 = 0.78 and RMSE of 0.45 for log D p . For LDPE, the molecular descriptor QSPR had the best predictivity, with the final correlation coefficient of R 2 = 0.86 and RMSE of 0.21 for log D p . The sigma moment descriptor QSPR resulted in a correlation coefficient of R 2 = 0.66 and RMSE of 0.33 for log D p . The purely electronic structure-based sigma moments are therefore shown to be a viable option for descriptors compared to the more commonly used molecular descriptors for organic contaminants in PDMS. The significance of the descriptors in each QSPR is discussed.