基于TanSat的中国大陆CO<sub>2</sub>浓度监测及驱动因子分析

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摘要基于2017年3月至2018年3月TanSat卫星观测的CO₂柱平均干空气混合比（XCO₂），利用美国航空航天局（NASA）的轨道碳观测2号（OCO-2）卫星数据与碳同化系统（CT）和总碳柱观测网络（TCCON）地面监测数据，验证了TanSat-XCO₂的数据精度和可靠性，分析了中国大陆五大区域XCO₂的区域特征，采用皮尔逊相关性分析和地理探测器的方法探讨了自然和社会经济因子对大气CO₂浓度的驱动作用.结果表明，TanSat卫星监测结果与OCO-2卫星监测结果基本一致，差异在（-3~3）×10^-6之间.XCO₂浓度整体随季节呈规律性波动.夏季我国大气XCO₂浓度区域差异明显，南部地区因人为影响较强，XCO₂浓度处于较高水平（>403×10^-6），北部地区植被的强光合作用使其XCO₂浓度（<401×10^-6）低于其他地区，西部地区XCO₂趋势较其他区域相对平缓.归一化差值植被指数（NDVI）是影响XCO₂浓度变化的主要自然因子，NDVI与TanSat监测结果呈显著线性负相关（r=-0.658，P<0.05）.化石燃料燃烧对XCO₂空间异质性的解释力最强，其与各自然因子的交互作用远大于单因子的驱动作用，且与降雨量的交互作用对大气XCO₂的影响最为显著（q=0.495）.

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	李凯旋
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关键词 ： TanSat, 精度验证, 相关性分析, 地理探测器, 浓度监测, 驱动因子分析

Abstract：Based on XCO₂, the column-averaged dry-air mole fraction of CO₂, observed by TanSat from March 2017 to March 2018, the accuracy and reliability of this dataset were comprehensively verified and validated against NASA's OCO-2 satellite observations and CT and TCCON ground measurements in China's mainland. XCO₂ characteristics in five main regions were analyzed, and driving effects of natural and socioeconomic factors on atmospheric CO₂ concentration were investigated by using Pearson correlation analysis and Geodetector method. The results demonstrate a good consistency between TanSat and OCO-2 observations only with a minor difference between (-3~3)×10^-6. In general, XCO₂ concentration fluctuates regularly with seasons. In summer obvious regional distinctions were detected. XCO₂ concentration in south China was at a higher level and greater than 403×10^-6 due to intense human influence. Compared with other regions, in north China, a lower average XCO₂ concentration (<401×10^-6) was observed due to intense vegetation photosynthesis herein. Whereas XCO₂ trend in west China was relatively flat. NDVI is the major natural factor affecting XCO₂ concentration and presents a significant linear negative correlation with TanSat observations (r=-0.658, P<0.05). While fossil fuel burning had the strongest explanatory power for XCO₂ spatial heterogeneity, and its interaction with various natural factors had much greater effect than each single factor. However, interaction between fossil fuel burning and rainfall would be the most influential one with a q value of 0.495.

Key words： TanSat accuracy verification correlation analysis geodetector concentration monitoring driving factor analysis

收稿日期: 2023-03-28

PACS:

X511

基金资助:陕西省科技厅基金项目（2020ZDLSF06-07）

通讯作者: 杨丽萍,副教授,zylpyang@chd.edu.cn E-mail: zylpyang@chd.edu.cn

作者简介: 李凯旋(1998-),男,陕西咸阳人,长安大学硕士研究生,主要研究方向为大气环境遥感.2021126050@chd.edu.cn.

引用本文:

李凯旋, 杨丽萍, 张静, 任杰, 王宇. 基于TanSat的中国大陆CO₂浓度监测及驱动因子分析[J]. 中国环境科学, 2023, 43(11): 5645-5654. LI Kai-xuan, YANG Li-ping, ZHANG Jing, REN Jie, WANG Yu. Monitoring and driving factors analysis of CO₂ concentration in China's mainland based on TanSat. CHINA ENVIRONMENTAL SCIENCECE, 2023, 43(11): 5645-5654.

链接本文:

http://www.zghjkx.com.cn/CN/ 或 http://www.zghjkx.com.cn/CN/Y2023/V43/I11/5645

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