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Updated: Jul 9, 2026

Mesocosm-Scale Constructed Wetland Design for Wastewater Treatment
Published on: May 2, 2025
Pollution removal efficiency enhancement by agricultural biomass additions in constructed wetlands: A framework
Wenjie Li1, Jian Wang1, Xinya Liu2
1Fujian Key Laboratory of Coastal Pollution Prevention and Control, College of the Environment and Ecology, Xiamen University, Xiamen, China.
Abstract:
The agricultural biomass addition on constructed wetlands (CWs) is a sustainable strategy that integrates pollution control with waste valorization. However, their widespread applications remain constrained by variations in biomass types, pretreatments, and system designs. This study combined meta-analysis and explainable machine learning to assess how agricultural biomass addition influences Chemical Oxygen Demand (COD) and Total Nitrogen (TN) removal in CWs, particularly under low C/N conditions, using 272 and 1,283 independent observations for meta-analysis and machine learning, respectively. Results showed that bamboo biochar significantly enhances COD removal efficiency in CWs by an average of 54.8% (SMD = 2.50), while lotus leaf biochar improves TN removal efficiency by 32.5% (SMD = 1.20) compared with controls. Additionally, multiple machine learning models were tested and the results showed that the XGBoost model demonstrated the robustperformance in simulating TN removal (R2 = 0.83), whereas the Random Forest model is effective for COD removal (R2 = 0.76). SHAP analysis further indicatedthat increasing wetland volume can simultaneously enhance both COD and TN removal efficiencies. This study underscores the power of combining meta-analysis and explainable machine learning to optimize CW design and management, offering a robust framework for improving pollution removal in CWs.
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