摘要
准确评估大气CO_(2)浓度和人为CO_(2)排放时空变化对于减缓温室气体排放导致的气候变化至关重要,因此,本文基于GOSAT和OCO-2卫星数据融合生成的全球长时间序列、时空连续的Mapping-XCO_(2)产品,研究2010~2020年中国大气CO_(2)柱浓度(XCO_(2))时空变化特征以及卫星监测人为CO_(2)排放能力.结果表明:Mapping-XCO_(2)与中国大气本底站观测存在较高的一致性,具有良好的适用性;2010~2020年中国XCO_(2)呈现东高西低的空间分布,年均XCO_(2)为400.4×10^(-6),年增长速率为2.47×10^(-6);非生长季XCO_(2)异常可刻画人为CO_(2)排放时空变化,各省级行政区非生长季XCO_(2)异常与人为排放清单EDGAR和ODIAC的相关系数分别为0.71、0.67;2010~2020年京津冀、长三角、珠三角等三个城市群人为CO_(2)排放增长率分别为0.12×10^(-6)/a、0.08×10^(-6)/a、0.08×10^(-6)/a.研究结果表明卫星遥感在监测大气CO_(2)浓度和人为CO_(2)排放时空变化方面的有效性.
Accurately assess the spatiotemporal changes of atmospheric CO_(2)concentration and anthropogenic CO_(2)emission,is critical to mitigate greenhouse gas emissions contributing to climate change.The spatial distribution and interannual change of column-averaged dry air mole fraction of CO_(2)(XCO_(2))and anthropogenic CO_(2)emission in China from 2010 to 2020 were evaluated using the continuous XCO_(2)dataset(Mapping-XCO_(2))fused from the Greenhouse Gases Observing Satellite(GOSAT)and Orbiting Carbon Observatory(OCO-2).Results showed that Mapping-XCO_(2)had a high consistency with in-situ observations from atmospheric background stations,indicating the potential of Mapping-XCO_(2)to apply for regional analysis.From 2010 to 2020,the annual average XCO_(2)in China was 400.4×10^(-6),with a high value in the East and a low value in the West.The annual increase rate of XCO_(2)was 2.47×10^(-6).The XCO_(2)anomalies in the non-growing season were well consistent with anthropogenic CO_(2)emissions collected from EDGAR and ODIAC,with a correlation coefficient of 0.71 and 0.67 at provincial-level,respectively.The increase rate of anthropogenic CO_(2)emissions in Beijing-Tianjin-Hebei,Yangtze River Delta and Pearl River Delta was 0.12×10^(-6)/a,0.08×10^(-6)/a and 0.08×10^(-6)/a respectively.The results demonstrate the reliability and effectiveness of the satellite retrieved XCO_(2)data in evaluating atmospheric CO_(2)concentration and anthropogenic CO_(2)emissions.
作者
王震山
绳梦雅
肖薇
杨凤珠
林彬
徐兴祝
柳艺博
WANG Zhen-shan;SHENG Meng-ya;XIAO Wei;YANG Feng-zhu;LIN Bin;XU Xing-zhu;LIU Yi-bo(Collaborative Innovation Center on Forecast and Evaluation Meteorological Disasters(CIC-FEMD),Nanjing University of Information Science&Technology,Nanjing 210044,China;School of Applied Meteorology/Jiangsu Key Laboratory of Agricultural Meteorology/Institute of Ecology,Nanjing University of Information Science&Technology,Nanjing 210044,China;Key Laboratory of Digital Earth Science/Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing 100094,China;The 7th Institute of Geology&Mineral Exploration of Shandong Province,Linyi 276006,China;Natural Resources and Planning Bureau of Linyi City,Linyi 276006,China)
出处
《中国环境科学》
EI
CAS
CSCD
北大核心
2023年第3期1053-1063,共11页
China Environmental Science
基金
国家重点研发计划(2020YFA0607503)
国家自然科学基金资助项目(42130506)
江苏省杰出青年基金资助项目(BK20220055)。
