Elucidating the impacts of microplastics on soil greenhouse gas emissions through automatic machine learning frameworks

With the rise in global plastic production and agricultural demand, the released microplastics (MPs) have increasingly influenced the elemental cycles of soils, leading to notable effects on greenhouse gas emissions. Despite initial research, there remains a gap in establishing a detailed modeling approach that comprehensively explores the impacts of MPs on GHG emissions. Herein, we utilized literature mining to assemble a comprehensive dataset examining the interplays between MPs and emissions of CO, CH, and NO. Five automated machine learning frameworks were employed for predictive modeling. The GAMA framework was particularly effective in predicting CO (Q = 0.946) and CH (Q = 0.991) emissions. The Autogluon framework provided the most accurate prediction for NO emission, though it exhibited signs of overfitting. Interpretability analysis indicated that the type of MPs significantly influenced CO emission. Degradable MPs (i.e., polyamide) inherently led to elevated CO emission, and the environmental aging further exacerbated this effect. Although both linear and nonlinear correlations between MPs and CH₄ emission were not identified, the incorporation of specific MPs that elevate soil pH, augment soil water retention, and cultivate anaerobic conditions may potentially elevate soil CH₄ emission. This research underscores the profound influence of MPs on soil GHG emissions, providing vital insights for shaping agricultural policies and soil management practices in the context of escalating plastic use.