【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。【方法】为研究煤电基地CO₂和CH₄时空特征，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。【结果与结论】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柱浓度增加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。

[Objective] CO₂ and CH₄ are identified as the primary greenhouse gases emitted from energy production in coal-electricity production bases. [Method] 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 concentration changes and distribution of 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.