Water-Energy-Carbon Nexus: Greenhouse Gas Emissions from Integrated Urban Drainage Systems in China

Deciphering carbon emissions in urban sewer networks: Bridging urban sewer networks with city-wide environmental dynamics

As urbanization accelerates, understanding and managing carbon emissions from urban sewer networks have become crucial for sustainable urban water cycles. This review examines the factors influencing greenhouse gas (GHG) emissions within urban sewage systems, analyzing the complex effects between water quality, hydrodynamics, and sewer infrastructure on GHG production and emission processes. It reveals significant spatiotemporal heterogeneity in GHG emissions, particularly under long-term scenarios where flow rates and temperatures exhibit strong impacts and correlations. Given the presence of fugitive and dissolved potential GHGs, standardized monitoring and accounting methods are deemed essential. Advanced modeling techniques emerge as crucial tools for large-scale carbon emission prediction and management. The review identifies that traditional definitions and computational frameworks for carbon emission boundaries fail to fully consider the inherent heterogeneity of sewers and the dynamic changes and impacts of multi-source pollution within the sewer system during the urban water cycle. This includes irregular fugitive emissions, the influence of stormwater systems, climate change, geographical features, sewer design, and the impacts of food waste and antibiotics. Key strategies for emission management are discussed, focusing on the need for careful consideration of approaches that might inadvertently increase global emissions, such as ventilation, chemical treatments, and water management practices. The review advocates for an overarching strategy that encompasses a holistic view of carbon emissions, stressing the importance of refined emission boundary definitions, novel accounting practices, and comprehensive management schemes in line with the water treatment sector's move towards carbon neutrality. It champions the adoption of interdisciplinary, technologically advanced solutions to mitigate pollution and reduce carbon emissions, emphasizing the importance of integrating cross-scale issues and other environmentally friendly measures in future research directions.

A simulation and risk assessment framework for water-energy-environment nexus: A case study in the city cluster along the middle reach of the Yangtze River, China

In the Anthropocene, there is a strong interlinkage among water, energy, and the environment. The water-energy-environment nexus (WEEN) has been vigorously advocated as an emerging development paradigm and a global research agenda. Based on the nexus concept, a framework for the WEEN complex system simulation and risk assessment is developed. The three metropolitan areas of the city cluster along the middle reaches of the Yangtze River (CCMRYR) are taken as the objects. Regional policies are combined with generic shared socio-economic pathways (SSPs) to form a localized SSPs suitable for the research region. The dynamic simulation of the WEEN complex system and the risk analysis are carried out with the combination of system dynamics models and copula functions. Results show that: There are obvious differences in water utilization, energy consumption, air pollutant emissions, and water pollutant emissions among the three metropolitan areas. The issue of high carbon intensity in the Wuhan Metropolitan Coordinating Region needs to be emphasized and solved from the perspective of optimizing the industrial structure. Adhering to current development patterns, there will be successive peaks in water utilization, energy consumption, and carbon emissions in Wuhan, Dongting Lake, and Poyang Lake Metropolitan Coordinating Region by 2030, leading to high synergy risks at the systemic level, with maximum values of 0.84, 0.85, 0.62, respectively. A development path based on conservation priorities indicates that future policymaking needs to prioritize a resource-saving and pollution-control development pattern directed by technological upgrading against the backdrop of scarce natural resource endowments. The localized SSPs are a beneficial extension that enriches the narrative of regional-scale SSPs. The evolutionary trajectories and risk assessments of WEEN complex systems under different localized SSPs provide a sweeping insight into the consequences of policy decisions, thus enabling policymakers to appraise policy rationality and implement appropriate corrective measures.

Modeling Indirect Greenhouse Gas Emissions Sources from Urban Wastewater Treatment Plants: Integrating Machine Learning Models to Compensate for Sparse Parameters with Abundant Observations

Electricity consumption and sludge yield (SY) are important indirect greenhouse gas (GHG) emission sources in wastewater treatment plants (WWTPs). Predicting these byproducts is crucial for tailoring technology-related policy decisions. However, it challenges balancing mass balance models and mechanistic models that respectively have limited intervariable nexus representation and excessive requirements on operational parameters. Herein, we propose integrating two machine learning models, namely, gradient boosting tree (GBT) and deep learning (DL), to precisely pointwise model electricity consumption intensity (ECI) and SY for WWTPs in China. Results indicate that GBT and DL are capable of mining massive data to compensate for the lack of available parameters, providing a comprehensive modeling focusing on operation conditions and designed parameters, respectively. The proposed model reveals that lower ECI and SY were associated with higher treated wastewater volumes, more lenient effluent standards, and newer equipment. Moreover, ECI and SY showed different patterns when influent biochemical oxygen demand is above or below 100 mg/L in the anaerobic-anoxic-oxic process. Therefore, managing ECI and SY requires quantifying the coupling relationships between biochemical reactions instead of isolating each variable. Furthermore, the proposed models demonstrate potential economic-related inequalities resulting from synergizing water pollution and GHG emissions management.