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A region-specific multi-task data-driven framework for deciphering and mitigating indirect carbon emissions in
Jinqi Jiang1, Yichao Lyu2, Boyi Cheng2
1Hubei Key Laboratory of Multi-media Pollution Cooperative Control in Yangtze Basin, School of Environmental Science & Engineering, Huazhong University of Science and Technology (HUST), 1037 Luoyu Road, Wuhan, Hubei 430074, China; Institute of Artificial Intelligence, Huazhong University of Science and Technology, Wuhan, China.
Bioresource Technology
|July 6, 2026
Summary
This study developed a data-driven framework to accurately quantify indirect carbon emissions from wastewater treatment plants (WWTPs), identifying key operational factors for reducing energy and chemical consumption. The models achieved significant emission reduction potentials, offering a scalable solution for precise carbon accounting.
Area of Science:
- Environmental Science
- Environmental Engineering
- Data Science
Background:
- Indirect carbon (C) emissions from wastewater treatment plants (WWTPs) are significant, driven by energy consumption (EC) and chemical consumption (CC).
- Accurate quantification of these emissions is challenging due to data constraints and incomplete carbon accounting.
- Data-driven models offer a promising approach but require robust frameworks for regional application.
Purpose of the Study:
- To develop and apply a regional data-driven framework for precise carbon accounting in WWTPs.
- To identify key operational parameters influencing energy and chemical consumption for emission mitigation.
- To evaluate the potential for emission reductions through optimized WWTP operations.
Main Methods:
- A regional data-driven framework was developed using monthly data from 177 WWTPs in Hubei Province.
- Gradient Boosted Decision Trees (GBDT) and MultiCCNet models were employed for energy and chemical consumption prediction, respectively.
- Post-hoc interpretability analysis and scenario analysis were conducted to identify optimal operational ranges and emission reduction potentials.
Main Results:
- The GBDT model achieved high accuracy in predicting EC (R²test = 0.86), outperforming other machine learning models.
- MultiCCNet demonstrated superior performance in CC prediction (R²test = 0.72 ± 0.04).
- Optimal operational ranges for low EC and key levers for mitigating CC were identified, with potential emission reductions of 59.0% for EC and 97.4% for CC.
Conclusions:
- The developed framework enables precise carbon accounting and mitigation strategies for WWTPs.
- Optimizing operational parameters significantly reduces energy and chemical consumption, leading to substantial emission reductions.
- This approach provides a scalable scientific framework applicable to other regions for enhanced environmental management.
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