Predicting the Kinetics of Cu and Cd Release from Diverse Soil Dissolved Organic Matter: A Novel Hybrid Model Integrating Machine Learning with Mechanistic Kinetics Model

Kinetic release of trace metals from soil dissolved organic matter (DOM) to solution is the key process controlling the mobility and bioavailability of trace metals in soil environment. However, due to the complexity of soil DOM, predicting the reaction rates of trace metals with soil DOM from different sources remains challenging. In this study, we developed a novel hybrid model integrating machine learning with mechanistic kinetics model, which can quantitatively predict the release rates of Cu and Cd from diverse soil DOM based on their compositions and properties. Our model quantitatively demonstrated that the molecular compositions of DOM controlled metal release rates, which had more profound impact on Cu than Cd. Our modeling results also identified two key factors affecting metal release rates, in which high concentrations of Ca and Mg ions in DOM significantly decreased the release rates of Cu and Cd, and the reassociation reactions of metal ions with DOM became more significant with the release of metals from DOM. This work has provided a unified kinetic modeling framework combining both mechanistic and data-driven approaches, which offers a new perspective for developing predictive kinetics models and can be applied to different metals and DOM in dynamic environments.