Shuang Liu , Fuyuan Mai , Meiqi Huang , Qing Yang , Xiaodong Li , Guangli Mu , Bingya Zhang , Linyuan Lu , Yu Pang , Ding He , Qianggong Zhang , Yiwen Liu , Yindong Tong
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引用次数: 0
Abstract
Glacier melting, a direct consequence of global climate change, significantly influences lake ecosystem structures and greenhouse gases (GHGs) emission in the glacier-fed lake. As glaciers release substantial meltwater containing nitrogen and carbon into lakes, microbial communities and their GHGs emissions could also evolve accordingly. So far, studies on seasonal and diel GHGs emission characteristics and their driving mechanism at high-altitude (> 5000 m) glacier-fed lakes remains relatively constrained. This study has focused on the Lhasa Valley Glacier, a typical continental-type glacier on the Tibetan Plateau, to explore the GHGs characteristics in the three newly formed glacier-fed lakes during distinct periods of glacier melting (i.e., initial ablation, peak ablation and end of ablation stage). A combination of techniques including multi-point continuous sampling, physicochemical characteristic analysis, 16S rRNA sequencing, and machine learning models had been utilized. Our results indicated that the annual average CH4, N2O, and CO2 emission rates were 0.76±1.00, 0.02±0.08, and -5.19±50.16 mmol·m-2·d-1, respectively, demonstrating that glacier-fed lakes were significant CH4 and N2O source to the atmosphere. We found substantial seasonal variation of GHGs emissions from lakes, particularly for CH4, with the maximum fluxes 104 times as high as the minimum value. Diurnal monitoring showed that GHGs emission were primarily concentrated during the daytime. Based on the 16S RNA sequencing results, we also observed seasonal variation of the microbial communities and their roles in driving GHGs emissions. Using Partial Least Squares Path Modeling, we further quantified complex relations among GHGs emissions, microorganism communities, and environmental factors. We found that the impact of microorganisms on GHGs emission could be further regulated by environmental factors such as water temperature and NO3--N. The Tibetan Plateau plays a critical role in global climate system. This study characterizes the GHGs emissions in glacier-fed lakes that has been less considered. Characterizing the GHGs emissions from this region could provide insights into how emissions contribute to global warming and climate change.
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.