徐燕飞,陈永春,李静,等. 煤电基地CO2和CH4遥感监测及时空特征分析[J]. 煤田地质与勘探,2024,52(6):79−90. DOI: 10.12363/issn.1001-1986.23.09.0537
引用本文: 徐燕飞,陈永春,李静,等. 煤电基地CO2和CH4遥感监测及时空特征分析[J]. 煤田地质与勘探,2024,52(6):79−90. DOI: 10.12363/issn.1001-1986.23.09.0537
XU Yanfei,CHEN Yongchun,LI Jing,et al. Remote sensing monitoring and spatiotemporal characteristics of CO2 and CH4 concentrations in coal-electricity production bases[J]. Coal Geology & Exploration,2024,52(6):79−90. DOI: 10.12363/issn.1001-1986.23.09.0537
Citation: XU Yanfei,CHEN Yongchun,LI Jing,et al. Remote sensing monitoring and spatiotemporal characteristics of CO2 and CH4 concentrations in coal-electricity production bases[J]. Coal Geology & Exploration,2024,52(6):79−90. DOI: 10.12363/issn.1001-1986.23.09.0537

煤电基地CO2和CH4遥感监测及时空特征分析

Remote sensing monitoring and spatiotemporal characteristics of CO2 and CH4 concentrations in coal-electricity production bases

  • 摘要: 【目的】CO2和CH4是煤电基地能源生产活动中的主要温室气体排放种类,其监测与时空分布是研究区碳监测体系建设的重要内容。【方法】以安徽淮南市为例,利用GOSAT、OCO-2和Sentinel-5P这3种卫星数据进行研究区CO2和CH4浓度监测,得到CO2、CH4柱浓度(XCO2和XCH4)变化和分布情况,采用源清单法分析CO2行业和区域排放特征,同时采用Pearson相关系数和多元回归方法分析影响研究区XCO2和XCH4浓度的主控因素。【结果和结论】结果表明:(1)基于GOSAT和OCO-2卫星融合数据分析显示,淮南市2016—2020年XCO2和XCH4浓度整体呈增长趋势,期间XCO2浓度增加12×10−6、XCH4浓度增加23×10−9;XCO2浓度和累计发电量的Pearson相关系数为0.98,XCH4浓度和累计煤炭产量的Pearson相关系数为0.99,均呈极强相关。(2)利用Sentinel-5P卫星搭载的对流层观测仪(TROPOMI)高分辨产品数据分析淮南市各区域XCH4浓度分布时空特征发现,研究区秋季XCH4浓度高于夏季,XCH4浓度受能源生产和农业生产两方面的影响。(3)源清单法得出淮南市一级源分类CO2排放最多的为化石燃料固定燃烧源,占全市CO2总排放量的89.59%,化石燃料固定燃烧源中电力供热占比99%以上;主要为淮南市潘集区和凤台县燃煤电厂CO2排放;源识别显示集中分布在淮南市北部的火力发电厂为研究区CO2最主要排放源。(4)影响研究区XCO2浓度的主控因素为地区生产总值、累计发电量和第二产业产值,影响XCH4浓度的主控因素为累计煤炭产量、第一产业产值、播种面积。研究结果对我国“双碳”目标下煤电基地碳监测体系构建与完善具有重要的参考意义。

     

    Abstract: Objective CO2 and CH4 are identified as the primary greenhouse gases emitted from energy production in coal-electricity production bases. Mointoring these emissions and analyzing their spatiotemporal distribution are essential components of building a carbon monitoring system in the study area. Methods This study investigated Huainan City, Anhui Province as an example to explore the spatiotemporal characteristics of CO2 and CH4 in coal-electricity production bases. Specifically, this study examined the CO2 and CH4 concentrations in the study area based on data from the GOSAT, OCO-2, and Sentinel-5P satellites, determining the column concentration changes and distribution of CO2 and CH4, which are XCO2 and XCH4. A sourcing method of inventory was employed to analyze the industrial and regional CO2 emission characteristics, and Pearson's correlation coefficient and multivariate regression were used to analyze the dominant factors affecting the XCO2 and XCH4 concentrations in the study area. Results and Conclusions Key findings are as follows: (1) The analysis of fusion data from the GOSAT and OCO-2 satellites indicate that the XCO2 and XCH4 concentrations in Huainan generally trended upward from 2016 to 2020, with the XCO2 and XCH4 concentrations increasing by 12×10−6 and 23×10−9, respectively. The Pearson's correlation coefficient between the XCO2 concentration and cumulative power generation was 0.98, and that between the XCH4 concentration and cumulative coal production was 0.99, both indicating extremely strong correlations. (2) As revealed by the analytical results of the spatiotemporal distribution characteristics of the XCH4 concentration in various zones of Huainan City derived based on data from the high-resolution Tropospheric Monitoring Instrument (TROPOMI) equipped in the Sentinel-5P satellite, the XCH4 concentration in the city is affected by both energy and agricultural production, being higher in autumn than in summer. (3) The results obtained using the sourcing method of inventory indicate that the maximum CO2 emissions of the primary sources originated from the stationary combustion sources of fossil fuels, accounting for 89.59% of the total CO2 emissions across the city. Furthermore, over 99% of the stationary combustion sources of fossil fuels were used for electric heating. The primary CO2 emissions sources include the coal-fired power plants in Panji District and Fengtai County of Huainan City. Source identification results indicate that the fossil-fired power plants concentrated in the northern Huainan proved to be predominant sources of CO2 emissions in the study area. (4) The dominant factors affecting the XCO2 concentration in the study area include regional GDP, cumulative power generation, and the output of the secondary industries, while those influencing the XCH4 concentration encompass cumulative coal production, the output of the primary industries, and the sown area. The results of this study provide a valuable reference for the construction and improvement of carbon monitoring systems for coal-electricity production bases under the context of reaching the goals of peak carbon dioxide emissions and carbon neutrality.

     

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