Interpretable machine learning reveals dominant controls and operational windows for humic-associated phosphorus formation during aerobic composting.

Authors

Shibo Han, Boya He, Baichun Wang, Xianchao Ma, Xiaoxuan Li, Fengge Xi, Zirui Huang, Ziqi Zhou, Aijiao Zhou

Published in

Bioresource technology. Pages 136152. Oct 28, 2026. Epub Oct 28, 2026.

Abstract

Humic-associated phosphorus (NaOH-P) is an important stabilized phosphorus fraction formed during aerobic composting, yet its dominant controls, threshold responses, and interaction-defined regulation windows remain poorly quantified across heterogeneous composting systems. In this study, an interpretable machine-learning framework was developed to predict NaOH-P formation and identify the key factors governing its accumulation. A literature-derived dataset was analyzed using nine machine-learning algorithms before and after preprocessing. After preprocessing, CatBoost showed the best balance between predictive accuracy and train-test stability, with a test-set R² of 0.913, RMSE of 0.179, and MAE of 0.129. SHAP analysis revealed a highly concentrated attribution structure, with TP, N/P, MC, and pH showing the largest contributions to model prediction. TP showed predominantly positive associations with predicted NaOH-P, N/P showed predominantly negative associations, and MC and pH exhibited nonlinear response patterns. Partial dependence analysis further showed that NaOH-P regulation was threshold-controlled and interaction-dependent rather than driven by independent single-factor effects. Favorable NaOH-P formation was generally associated with TP > 20 g·kg^-1, N/P below approximately 1.0, MC > 40 %, and pH 6.0-7.5, particularly after the initial 15-20 d of composting. Laboratory-scale validation showed close agreement between predicted and measured NaOH-P values under the tested conditions, yielding an R² of 0.968, RMSE of 0.037, and MAE of 0.028. Overall, this study shows that interpretable machine learning can convert heterogeneous composting data into quantitative regulation insights for targeted phosphorus stabilization.

PMID:
42951149
Bibliographic data and abstract were imported from PubMed on 29 Oct 2026.

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