Neutral persistent, mobile, and toxic (PMT) chemicals are an emerging regulatory concern, because they can contaminate drinking water resources. The organic carbon sorption coefficient (KOC) is widely used as a mobility indicator for neutral organic compounds (NOCs), but global mobility assessments are hampered by the scarcity of KOC experimental data and the limited efficiency of predictive approaches. This study develops a descriptor-light stacking model (DL-SM) tailored for NOCs, which integrates 13 molecular descriptors and four tree-based learners into a multilayer perceptron. Trained on 1987 NOCs, DL-SM achieves high predictive performance (R2test = 0.825) and outperforms five commonly used mobility assessment tools on an independent validation test. Applying DL-SM to 129,875 NOCs from 12 national and regional inventories worldwide shows that 59.28%-67.79% of NOCs possess intrinsic mobility potential. Specially, the resulting mobility score distributions are remarkably similar across inventories despite around 80% of mobile substances being unique to a single inventory. Further analysis reveals that small molecular size (e.g., nC ≤ 15, Vabc ≤ 260 Å3) strongly drives mobility, while long methyl chains may attenuate the mobility-enhancing effects of hydrophilic groups. This work improves the mechanistic understanding of neutral chemical mobility and provides a robust and publicly available tool to support the identification and management of neutral PMT candidates.
Liu et al. (Wed,) studied this question.