Feature importance analysis was performed for the ML and hybrid models to quantify the relative contribution of each independent variable to the prediction of Kd. For ensemble models such as RF, GB, and XGB, the feature importance scores were derived from the mean decrease in impurity (Gini index) or gain, depending on the algorithm. These scores provided valuable insights into the factors most strongly influencing PFAS adsorption, guiding the development of targeted remediation strategies. Results for feature importance are showcased in SI with Figure , S7, and S8.