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 (K_oc) 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 K_oc 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 K_oc 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 (E_LUMO) and maximum average local ionization energy on the molecular surface (ALIE_max), 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 K_oc of conventional and emerging PFASs effectively, filling the data gap of the log K_oc 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.

The water resources carrying capacity (WRCC) is a crucial indicator for assessing the sustainability of regional development. This study integrates the gray water footprint (GWF) into the WRCC evaluation, constructing a comprehensive framework that encompasses five subsystems: water resources, society, economy, ecology, and climate. Using the CRITIC-TOPSIS model, the WRCC of Yunnan Province from 2012 to 2022 is analyzed, and a dynamic assessment is conducted through spatiotemporal hotspot and obstacle factor coupling analyses. The results show that the comprehensive WRCC of Yunnan decreased from 0.489 in 2012 to 0.477 in 2022, displaying an overall fluctuating downward trend with uneven spatial distribution. The per capita GWF and GWF load significantly impacted the WRCC within the social and ecological subsystems, respectively, highlighting the importance of water quality in the WRCC evaluation. The results reveal differing development trends in the dynamic changes of WRCC cold- and hotspots across various regions in Yunnan. Through coupling the obstacle factors of each regional subsystem, the main challenges and key measures for sustainable water resource development in each area are identified. This study optimizes the traditional evaluation framework by addressing shortcomings in water quality considerations, enriching the WRCC assessment, and providing a more comprehensive and accurate decision-support tool for regional development.