Machine learning prediction of organic contaminant partitioning to microplastics: A volume-normalized cross-polymer...

Microplastics (MPs) can act as vectors for hydrophobic organic contaminants, altering their environmental fate and exposure pathways in aquatic systems. However, current understanding of contaminant-microplastic partitioning is still largely derived from fragmented batch sorption experiments, where heterogeneity in polymer types, contaminant structures, and test conditions limits cross-system comparison and predictive generalization. Here, we develop a machine learning framework to predict microplastic - water partition coefficients across diverse polymer materials and environmental conditions. The model integrates contaminant molecular descriptors (logK_ow and Abraham parameters), microplastic properties (polymer type, density, particle size, surface area, and aging status), and environmental variables (pH, salinity, and temperature). To enable physically consistent comparison among polymers, a volume-normalized partition coefficient (logK_MP/W) was introduced to correct for density-related bias. Under leave-one-polymer-out validation, the XGBoost model achieved R² values of 0.60-0.78 for the major hydrophobic polymers, indicating reasonable predictive performance under a stringent cross-polymer extrapolation setting, and showed promising generalization to contaminants not included in the training dataset (R² = 0.766). Model interpretation revealed that contaminant molecular properties dominated predictions (≈64 %), while microplastic characteristics and environmental conditions showed secondary nonlinear effects, including threshold-like responses and pH-dependent sign reversal. Application of the framework to 66 regulatory-relevant contaminants across three environmental scenarios identified 37 compounds (56 %) with consistent classification outcomes, including 25 high-priority and 12 low-priority substances. An interactive web-based platform (https://mp-insight.streamlit.app/) was developed to support rapid screening of emerging contaminants. Overall, this framework reduces reliance on repetitive experiments and provides a practical tool for data-limited assessments and evidence-based environmental management.