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Spatiotemporal visualization and exposure-priority assessment of global soil microplastic abundance based on machine learning
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Scientists used artificial intelligence to map microplastic pollution in soils worldwide, revealing hotspots that are likely to grow through 2030, especially in parts of Africa and South and Southeast Asia. Since these microplastics can enter our food and water, this mapping helps identify where people face the greatest exposure risk, guiding where monitoring and cleanup efforts are needed most.
Global soil microplastic (MP) pollution is an emerging environmental concern, yet its large-scale distribution remains difficult to characterize because observations are spatially clustered and environmental relationships are highly nonlinear. We compiled a global soil MP dataset and developed an interpretable ensemble machine learning framework using 500-km spatial blocking and nested spatial cross-validation. Among four tree-based algorithms, CatBoost showed the strongest spatial generalization (mean spatial-CV R = 0.587 ± 0.115; pooled out-of-fold R = 0.623). SHAP analysis identified soil organic carbon, land cover, PM, and pH as the most influential predictors, while soil, meteorological, and socioeconomic variables contributed 38.2%, 34.0%, and 27.8% of total mean absolute SHAP attribution, respectively. Nonlinear SHAP dependence analysis revealed model-derived breakpoints for several predictors, indicating transitions in model contributions rather than universal environmental thresholds. Predictions for 2022, 2025, and 2030 showed pronounced spatial heterogeneity and region-specific temporal redistribution, with persistent and potentially developing high-abundance areas under the assumed predictor trajectories. Spatial block bootstrap analysis revealed geographic variation in prediction stability. EPI and ADD captured complementary patterns of population-weighted exposure priority and scenario-based potential intake, with the conditional 2030 projection indicating greater exposure priority in parts of Africa and South and Southeast Asia. Overall, the framework supports global soil MP mapping, uncertainty-informed monitoring, exposure-priority assessment, and region-specific management.
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