2013-2022年中国大陆各省月甲烷排放量。

IF 6.9 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Duo Cui, Siyao Yang, Xuanren Song, Xiaoting Huang, Cuncun Duan, Mingrui Ji, Zhongyan Li, Siqi Yu, Zhu Deng, Piyu Ke, Xinyu Dou, Taochun Sun, Zhu Liu
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引用次数: 0

摘要

作为世界上最大的甲烷排放国,准确测量和跟踪中国各行业的甲烷排放量对全球应对气候变化的努力至关重要。甲烷是一种强效温室气体,由多种人为和自然来源排放,其中许多来源表现出明显的时间变异性。特别是,水稻种植、能源利用和牲畜管理的排放表现出强烈的季节性特征,但缺乏高频和空间详细的甲烷排放清单。本研究介绍了中国各省甲烷排放月度清查(MMCP),这是一个涵盖2013年1月至2022年12月的综合数据集。该数据集包括八个关键部门的排放数据:煤炭开采、石油和天然气系统、能源燃烧、水稻种植、牲畜、固体废物、废水和湿地。通过提供特定部门和暂时解决的排放估算,MMCP为科学研究、政策评估和减排规划提供了宝贵资源。该清单有助于提高对排放趋势的了解,并支持更准确地模拟大气甲烷浓度和气候反馈。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Monthly methane emissions in Chinese mainland provinces from 2013-2022.

As the world's largest source of methane emissions, accurately measuring and tracking China's emissions across various sectors is essential for global climate change efforts. Methane, a potent greenhouse gas, is emitted from diverse anthropogenic and natural sources, many of which exhibit pronounced temporal variability. In particular, emissions from rice cultivation, energy use, and livestock management show strong seasonal patterns, yet high-frequency and spatially detailed methane emission inventories have been lacking. This study introduces the Monthly Methane Emission Inventory for China's Provinces (MMCP), a comprehensive dataset covering the period from January 2013 to December 2022. The dataset includes emissions data from eight key sectors: coal mining, oil and gas systems, energy combustion, rice cultivation, livestock, solid waste, wastewater, and wetlands. By offering sector-specific and temporally resolved emission estimates, MMCP serves as a valuable resource for scientific research, policy evaluation, and emission mitigation planning. This inventory facilitates improved understanding of emission trends and supports more accurate modeling of atmospheric methane concentrations and climate feedbacks.

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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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