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Machine Learning-Driven Prediction of Organic Compound Adsorption onto Microplastics in Freshwater
Summary
Seven machine learning algorithms were trained on 173 published measurements to predict how strongly organic contaminants adsorb onto different types of microplastics in freshwater. Accurate adsorption predictions are essential for assessing environmental risk, because microplastics that strongly bind pollutants become vectors that concentrate and transport toxic chemicals through aquatic food webs.
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 logKd 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 logKd 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.