Machine learning prediction of DOC–water partitioning coefficients for organic pollutants from diverse DOM origins

in natural aquatic DOM, while its negative correlation in all DOM may reflect the great diversity of DOM properties in that group. Partial dependence plots revealed that when CrippenLogP > 6, Mor29m between 0.45 and 0.52, MATS2m < -0.015, and SlogP_VSA1 < 7, organic pollutants tended to partition more into DOM. These findings support the application of machine learning models for assessing pollutant interactions with DOM, contributing to improved environmental risk predictions.