中国池塘甲烷排放加剧:变暖、富营养化和深度变化的相互作用

IF 12.4 1区 环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL Water Research Pub Date : 2025-03-31 DOI:10.1016/j.watres.2025.123576
Mingquan Lv, Ping Huang, Xin Gao, Jilong Chen, Shengjun Wu
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引用次数: 0

摘要

池塘是全球甲烷(CH4)排放的重要贡献者。然而,准确估计它们历史或未来的CH4排放量仍然具有挑战性,特别是在富营养化、沉积驱动的浅化和全球变暖等动态环境变化的情况下。我们综合了674个观测值,以确定关键驱动因素,并建立了一个基于过程的预测模型。在考虑温度依赖性、深度、养分水平和池塘面积等因素的基础上,提出了1960 - 2020年中国池塘氯化甲烷排放的空间显式估算框架。研究结果表明,池塘CH₄排放物具有强烈的温度依赖性,其特征是平均活化能高(0.834 eV)。值得注意的是,沸腾发射比扩散发射表现出更大的温度敏感性。氮浓度和水柱深度是总CH₄通量的关键预测因子。在过去的60年里,中国池塘的四氯化甲烷排放量增加了大约9倍,从1960年的0.16 Tg四氯化甲烷- 1增加到2020年的1.53 Tg四氯化甲烷- 1,这表明它们在全球甲烷排放中的作用越来越大。值得注意的是,这些排放的一半发生在夏季,沸腾占总硫酸ch通量的66%。这种增加主要是由变暖、营养物富集、水深下降和池塘扩张的相互作用驱动的。我们的研究结果强调了池塘在四氯甲烷排放中日益重要的作用,并强调了迫切需要采取缓解措施,如减少养分负荷和实施定期疏浚管理。该研究为在全球环境变化的背景下改善甲烷排放估算和制定可持续的池塘管理实践提供了坚实的基础。
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Intensifying methane emissions in Chinese Ponds: The interplay of warming, eutrophication, and depth changes
Ponds are significant contributors to global methane (CH4) emissions. However, accurately estimating their historical or future CH4 emissions remains challenging, particularly under dynamic environmental changes such as eutrophication, sedimentation-driven shallowing, and global warming. We synthesized 674 observations of CH₄ emission rates to identify key drivers and develop a process-based predictive model. We present a framework for spatially explicit estimation of pond CH₄ emissions in China from 1960 to 2020, accounting for factors such as temperature dependence, depth, nutrient levels, and pond area. Our findings show that pond CH₄ emissions are strongly temperature-dependent, characterized by a high average activation energy (0.834 eV). Notably, ebullitive emissions exhibit greater temperature sensitivity than diffusive emissions. Nitrogen concentrations and water column depth emerged as critical predictors of total CH₄ fluxes. Over the past six decades, CH₄ emissions from Chinese ponds increased approximately 9-fold, from 0.16 Tg CH₄ yr−1 in 1960 to 1.53 Tg CH₄ yr⁻¹ by 2020, emphasizing their growing role in global methane emissions. Notably, half of these emissions occur during summer, with ebullition accounting for 66 % of the total CH₄ flux. This increase was primarily driven by the interactions of warming, nutrient enrichment, declining water depth, and pond expansion. Our results underscore the growing role of ponds in CH₄ emissions and highlight the urgent need for mitigation measures, such as reducing nutrient loading and implementing periodic dredging management. This study provides a robust foundation for improving CH₄ emission estimates and developing sustainable management practices for ponds in the context of global environmental change.
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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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