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Scaling, performance, and quality: Machine-Learning evidence for how WWTP and compost processes shape microplastics
Summary
Researchers applied machine learning with SHAP analysis to 635 records from 99 wastewater treatment studies and identified a narrow operational window — total plastic removal near 93–96% and daily capacity below 300,000 m³/day — that minimizes downstream microplastic burdens, while meta-analysis confirmed that membrane bioreactors concentrate the highest microplastic loads in sludge.
Composting is a key pathway for microplastics (MPs) to enter soils via organic waste. Based on 635 records from 99 studies, MP abundance was higher in sludge-based and mixed composts than in plant-derived materials. Meta-analysis of wastewater treatment plants (WWTPs) found that primary clarification removed the most MPs. Among secondary treatments, MBR sludge had the highest MP levels - 68.53% higher than SBR and 5.6% higher than CAS. The machine-learning models (random forest with SHAP and partial dependence) were used to resolve actionable process levers with R > 0.72 for WWTP process technologies and >0.77 for scenario prediction. Two parameters dominated: total plastic removal rate (TPR) and daily treatment capacity (DTC), with an operational window (TPR ≈ 92.5-96% and DTC <300,000 m/d) minimizing downstream MP burdens. Finally, Scenario projections suggest two mitigation pathways: source control to reduce influent loads and performance-based upgrades to sludge handling.