A generative physics-informed machine learning model for soil microplastic accumulation dynamics

Microplastic pollution is one of the challenges facing humanity, and the transport of microplastics in soils is a major limitation of traditional methods due to heterogeneity, complex particle-organic matter interactions, and inconsistent sampling protocols. To overcome these limitations, we present an integrated, mechanistically informed approach to soil Microplastics dynamics that combines experimental data with a machine learning model based on mixed physics, advanced statistical dependency analysis, and interpretability techniques. The framework, which uses TabNet for predictive modeling, is calibrated against an experimental dataset and reinforced with first-principles PDEs to ensure physical consistency. Statistical methods using Spearman's rho, Kendall's tau,distance correlation, HSIC, copula-based modeling, and Granger causality are employed, while interpretability is enhanced through SHAP, partial dependence plots, symbolic metamodeling, Double ML, and TCAV. The results show that density solution is one of the most influential parameters because it effectively acts as a latent and composite variable that integrates the interactions of all other inputs into a single, dominant indicator. Secondary factors, including land use (≈0.9-0.93), size range (≈0.77-0.86), sampling depth (≈0.73-0.81), and SOM operations (≈0.64-0.72), exert significant but context-dependent influence. Statistical dependency analyses further demonstrate nonlinear interactions, with Granger causality emphasizing the temporal and causal importance of density solution, land use, and size range.