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Unraveling the Context-Dependent Effects of Microplastics on Soil Enzymatic Activities via Interpretable Machine Learning
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Researchers used computer models to analyze existing published data on how microplastics affect soil enzyme activity. The analysis found that particle size, especially 200 to 600 micrometers, was most strongly linked to reduced enzyme activity, while the amount of microplastic and exposure time showed more complex patterns.
Microplastics (MPs) increasingly accumulate in agricultural soils, yet their effects on soil enzymatic activity remain heterogeneous. Here, a literature-derived dataset was analyzed using an interpretable machine learning framework. eXtreme Gradient Boosting (XGBoost) and Random Forest (RF) captured non-linear patterns in enzymatic responses to MP characteristics. Particle size showed the strongest model-based contribution, with a dataset-derived size-sensitive range of 200–600 μm associated with more pronounced predicted inhibition. Dosage showed a plateau-like pattern above approximately 100 g/kg, while exposure time exhibited non-linear variation across the observed duration. SHapley Additive exPlanations (SHAP) analysis further characterized the relative contributions of individual features to model predictions. Overall, the results provide quantitative, data-driven insights into heterogeneous MP-soil enzyme associations and highlight particle size as an important factor for further experimental investigation and risk assessment.
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