A Systematic Review of Greenhouse Gas Emissions Derived From Combined Sewer Overflows and Synergistic Control Strategies Toward Carbon Neutrality - Yilin Xu; Cheng Ye; Zuxin Xu; Wenhai Chu (Engineering, 2025)
Climate change is accelerating globally, raising significant concerns regarding the environmental risks associated with combined sewer overflows (CSOs). These rainfall events lead to the excessive discharge of multiple pollutants into natural waters. However, greenhouse gas (GHG) emissions from CSOs, which are crucial for carbon neutrality in urban water systems, remain fragmented. Using the life-cycle assessment method expansion approach, this study breaks down the formation and discharge processes of CSOs and uncovers the underlying mechanisms driving GHG emissions during each period. Given the complexity and uncertainty in the spatial distribution of GHG emissions from CSOs, the development of standard monitoring and estimation methods is vital. This study identifies the factors influencing GHG emissions within the urban drainage system (UDS) and defines the interactive GHG emission boundaries and accounting framework related to CSOs. This framework is expanded to consider the hybrid nature of urban engineering and hydraulic interactions during the CSO events. Advanced modeling technologies have emerged as essential tools for predicting and managing GHG emissions from CSOs. This review promotes comprehensive data-driven methods for predicting GHG emissions from CSOs, fully considering the inherent heterogeneity of CSOs and the impact of multi-source contaminants discharged into aquatic environments. It emphasizes refining emission boundary definitions, novel accounting practices adapting data-driven methods, and comprehensive management strategies in line with the move toward carbon neutrality in the UDS. It advocates the adoption of solutions including advanced technologies and artificial intelligent methods to mitigate CSO-related GHG emissions, stressing the significance of integrating low-carbon solutions and a comprehensive data-driven management framework in future research directions.