Obtaining the adsorption equilibrium coefficient (
Kd) of organic compounds on microplastics (MPs) is critical for understanding their environmental behaviors. Given the limited availability of these
Kd values, it is imperative to develop predictive models for rapid acquisition of
K
[...] Read more.
Obtaining the adsorption equilibrium coefficient (
Kd) of organic compounds on microplastics (MPs) is critical for understanding their environmental behaviors. Given the limited availability of these
Kd values, it is imperative to develop predictive models for rapid acquisition of
Kd values for different MPs. Herein, seven machine learning-based algorithms, i.e., MLR, RF, GBDT, XGBoost, CatBoost, LightGBM and SVM, were used to establish predictive models on the basis of 173 log
Kd values in freshwater. The evaluation parameters, including
R2t,
RMSEt,
Q2v,
RMSEv and
Q2, indicate that the developed models have a satisfactory predictive capability. The developed MLR models can predict the log
Kd values for chlorinated polyethylene (CPE), polybutylene succinate (PBS), polycaprolactone (PCL) and low-density polyethylene (LDPE) MPs. Given the limited performance of MLR in predicting adsorption on PE MPs, RF, GBDT, XGBoost, CatBoost, LightGBM and SVM were employed to develop predictive models, which significantly enhanced the predictive accuracy. The predictive models for PE MPs have a wider AD, covering organic compounds with different functional groups than previous models. Hydrogen bonding, hydrophobic, electrostatic and dispersion interactions may be involved in adsorption. The developed models can serve as efficient tools for estimating the
Kd values for different MPs in freshwater, thereby providing the necessary data for evaluating the environmental risks of organic compounds and MPs.
Full article