The water environmental recalcitrance and ecotoxicity caused by polychlorinated biphenyls (PCBs) are international issues of common concern. The partition coefficients with PCBs between low-density polyethylene (LDPE) and water (KPE-w) are significant to assess their environmental transport and/or fate in aquatic environment. Even moderately hydrophobic PCBs, however, possess large KPE-w values, which makes directly experimental measurement labored. Here, based on the combination of quantitative structure-property relationships (QSPRs) and machine-learning algorithms, 10 in-silico models are developed to provide a quick estimate of KPE-w. These models exhibit good goodness-of-fit (R2adj: 0.919-0.975), robustness (Q2LOO: 0.870-0.954) and external prediction performances (Q2ext: 0.880-0.971), providing a speedy feasibility to close data gaps for limited or absent experimental information, especially the RF-2 model. Particularly, an additional experimental verification is performed for models by a rapid and accurate three-phase system (aqueous phase, surfactant micelles and LDPE). The results of the experiments for 16 PCBs show the modeling results agree well with experimental values, within or approaching the residuals of ± 0.3 log unit. Mechanism interpretations imply that the number of chlorine atoms and ortho-substituted chlorines are the great effect parameters for KPE-w. This result also heightens interest in measuring and predicting the KPE-w values of chemicals containing halogen atoms in water.
Keywords: Passive sampling; Polychlorinated biphenyls; Polyethylene-water partition coefficients; QSPR; Surfactant micelles.
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