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Meta Analysis ? AI-assigned paper type based on the abstract. Classification may not be perfect — flag errors using the feedback button. Tier 1 ? Systematic review or meta-analysis. Synthesizes findings across many studies. Strongest evidence. Environmental Sources Human Health Effects Marine & Wildlife Sign in to save

Machine learning-enhanced meta-analysis unravels the global patterns of microplastic-heavy metal co-toxicity in terrestrial ecosystems

Environmental Pollution 2025 5 citations ? Citation count from OpenAlex, updated daily. May differ slightly from the publisher's own count.
Hongmei Jiang, Tao Che, Qing Su, Bin Jin, Yan Lu, Hongyu Zhou, Min Fu, Jingya Zhou

Summary

This meta-analysis of 1,820 datasets found that combined microplastic and heavy metal exposure significantly inhibits plant growth (22% height decrease), reduces microbial diversity, and increases animal intestinal damage and mortality. Nanoscale microplastics amplified heavy metal toxicity the most, suggesting that smaller plastic particles in soil pose the greatest combined pollution risk to ecosystems and food safety.

Study Type Review

The combined pollution of microplastics (MPs) and heavy metals (HMs) poses a significant threat to global food security and ecological health. However, the combined toxic effects of this co-contamination on terrestrial organisms and the underlying mechanisms remain unclear. This study synthesizes 1,820 control-observation datasets from 113 toxicity experiments worldwide. By integrating meta-analysis with machine learning models, we systematically evaluated the synergistic toxic effects of MPs and HMs on terrestrial organisms. The results showed that the co exposure of MPs and HMs significantly inhibited plant growth (such as a 22.14% decrease in plant height and a 17.80% decrease in biomass) and microbial diversity (such as a 2.65% decrease in Shannon index), and exacerbated animal toxicity (such as a 7.00% decrease in survival rate and a 46.61% increase in intestinal damage). The excellent predictive performance of the XGBoost model (R = 0.71) indicates that it can be applied to the risk prediction of MPs and HMs combined pollution. Shapley additive explanations (SHAP) analysis and partial least squares path modeling revealed that HM concentration, exposure time, and MP particle size significantly modulated the magnitude of the combined toxicity, with nanoscale MPs exhibiting the most pronounced amplification effect on the combined pollution toxicity. This study elucidates the ecological risks associated with combined MP and HM pollution, providing a scientific basis for global soil pollution monitoring and remediation, and it underscores the importance of standardizing experimental protocols and promoting international collaboration in pollution management.

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