水库规模对中国甲烷排放的影响

IF 12.8 1区 环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL Water Research Pub Date : 2025-07-01 Epub Date: 2025-03-05 DOI:10.1016/j.watres.2025.123441
Zilin Wang , Meili Feng , Matthew F. Johnson , Aldo Lipani , Faith Chan
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引用次数: 0

摘要

水库作为甲烷(CH₄)排放源起着至关重要的作用,由于表面积、水深、使用、操作方法和空间分布等因素,水库的排放率和排放量差异很大。深入了解不同油藏规模的排放特征可以帮助设计和管理油藏,有效地减少甲烷的排放。在这项研究中,应用机器学习模型来估计中国97,435个水库的漫漫性和沸腾性氯化物排放,涵盖五类存储容量。这项综合评估几乎涵盖了全国所有的水库,揭示了大约5,414 Gg的总甲烷排放量。其中约90%的排放是由0.01 km3造成的,主要是由于高扩散通量率和广泛的表面积。nh4在储层中的扩散0.01 km3主要受其热分层和有机质聚集能力的影响。此外,这些水库特别容易受到气候变暖的影响,与较小的水库(小于0.01 km³)相比,较大的水库可能会更快地加速CH₄排放率。因此,在储层中优先进行CH - 4管理;0.01 km3是必要的。然而,储层中CH _4的高沸腾通量<;0.01 km3,与它们的浅深度有关,突出了从较小的水生系统中产生大量氯化氢的可能性。考虑到大、小范围水库的空间分布格局不同,建议采取有针对性的管理策略:大型水库采用项目级管理,小型水库采用流域级管理。
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The role of reservoir size in driving methane emissions in China
Reservoirs play a crucial role as sources of methane (CH₄) emissions, with emission rates and quantities varying widely depending on reservoir size due to factors such as surface area, water depth, usage, operational methods, and spatial distribution. Gaining insights into emission characteristics across different reservoir sizes can aid in designing and managing reservoirs to mitigate CH₄ emissions effectively. In this study, machine learning models were applied to estimate both diffusive and ebullitive CH₄ emissions across 97,435 reservoirs in China, spanning five categories of storage capacity. This comprehensive assessment covers nearly all reservoirs within the country, revealing total CH₄ emissions of approximately 5,414 Gg. Reservoirs > 0.01 km3 are responsible for about 90 % of these emissions, primarily due to high diffusive flux rates and extensive surface areas. Elevated CH₄ diffusion in reservoirs > 0.01 km3 is largely influenced by their thermal stratification and capacity for organic matter accumulation. Furthermore, these reservoirs are particularly vulnerable to climate warming, which could accelerate CH₄ emission rates more rapidly in larger reservoirs than in smaller ones (below 0.01 km³). Consequently, prioritising CH₄ management in reservoirs > 0.01 km3 is imperative. Nevertheless, the high ebullitive flux of CH₄ in reservoirs < 0.01 km3, linked to their shallow depth, highlighting the potential for significant CH₄ ebullition from smaller aquatic systems. Given large and small-ranged reservoirs' distinct spatial distribution patterns, targeted management strategies are recommended: project-level management for large reservoirs and basin-level approaches for smaller reservoirs.
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来源期刊
Water Research
Water Research 环境科学-工程:环境
CiteScore
20.80
自引率
9.40%
发文量
1307
审稿时长
38 days
期刊介绍: Water Research, along with its open access companion journal Water Research X, serves as a platform for publishing original research papers covering various aspects of the science and technology related to the anthropogenic water cycle, water quality, and its management worldwide. The audience targeted by the journal comprises biologists, chemical engineers, chemists, civil engineers, environmental engineers, limnologists, and microbiologists. The scope of the journal include: •Treatment processes for water and wastewaters (municipal, agricultural, industrial, and on-site treatment), including resource recovery and residuals management; •Urban hydrology including sewer systems, stormwater management, and green infrastructure; •Drinking water treatment and distribution; •Potable and non-potable water reuse; •Sanitation, public health, and risk assessment; •Anaerobic digestion, solid and hazardous waste management, including source characterization and the effects and control of leachates and gaseous emissions; •Contaminants (chemical, microbial, anthropogenic particles such as nanoparticles or microplastics) and related water quality sensing, monitoring, fate, and assessment; •Anthropogenic impacts on inland, tidal, coastal and urban waters, focusing on surface and ground waters, and point and non-point sources of pollution; •Environmental restoration, linked to surface water, groundwater and groundwater remediation; •Analysis of the interfaces between sediments and water, and between water and atmosphere, focusing specifically on anthropogenic impacts; •Mathematical modelling, systems analysis, machine learning, and beneficial use of big data related to the anthropogenic water cycle; •Socio-economic, policy, and regulations studies.
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