Predicting apparent adsorption capacity of sediment-amended activated carbon for hydrophobic organic contaminants using machine learning

In-situ stabilization of hydrophobic organic compounds (HOCs) using activated carbon (AC) is a promising sediment remediation approach. However, predicting HOC adsorption capacity of sediment-amended AC remains a challenge because a prediction model is currently unavailable. Thus, the objective of this study was to develop machine learning models that could predict the apparent adsorption capacity of sediment-amended AC (KAC,apparent) for HOCs. These models were trained using 186 sets of experimental data obtained from the literature. The best-performing model among those employing various model frameworks, machine learning algorithms, and combination of candidate input features excellently predicted logKAC,apparent with a coefficient of determination of 0.94 on the test dataset. Its prediction results and experimental data for KAC,apparent agreed within 0.5 log units with few exceptions. Analysis of feature importance for the machine learning model revealed that KAC,apparent was strongly correlated with the hydrophobicity of HOCs and the particle size of AC, which agreed well with the current knowledge obtained from experimental and mechanistic assessments. On the other hand, correlation of KAC,apparent to sediment characteristics, duration of AC-sediment contact, and AC dose identified in the model disagreed with relevant arguments made in the literature, calling for further assessment in this subject. This study highlights the promising capability of machine learning in predicting adsorption capacity of AC in complex systems. It offers unique insights into the influence of model parameters on KAC,apparent. [Display omitted] •Developed machine learning models to predict KAC,apparent for HOCs.•The best-performing model predicted logKAC,apparent with an R2 of 0.94•The model confirmed that KAC,apparent depends on HOC hydrophobicity and AC size.•For dependence of KAC,apparent on other features the model challenged former claims.•The machine learning approach provided unique insight for future research needs.