煤电基地CO_(2)和CH_(4)遥感监测及时空特征分析  

Remote sensing monitoring and spatiotemporal characteristics of CO_(2)and CH_(4)concentrations in coal-electricity production bases

作　　者：徐燕飞 陈永春 李静 刘晓舟 苗伟 赵得荣 芮成奇 XU Yanfei;CHEN Yongchun;LI Jing;LIU Xiaozhou;MIAO Wei;ZHAO Derong;RUI Chengqi(State Key Laboratory for Safe Mining of Deep Coal Resources and Environment Protection,Huainan 232001,China;School of Environment and Spatial Informatics,China University of Mining and Technology,Xuzhou 221116,China;Anhui Province Engineering Research Center of Coal Mining Green Low-Carbon Development,Huainan 232001,China;Huainan Academy of Atmospheric Sciences,Huainan 232001,China)

机构地区：[1]深部煤炭安全开采与环境保护全国重点实验室,安徽淮南232001 [2]中国矿业大学环境与测绘学院,江苏徐州221116 [3]安徽省煤矿绿色低碳发展工程研究中心,安徽淮南232001 [4]淮南大气科学研究院,安徽淮南232001

出　　处：《煤田地质与勘探》2024年第6期79-90,共12页Coal Geology & Exploration

基　　金：安徽省自然科学基金项目(2208085ME123);淮南市科技计划项目(2021A261)。

摘　　要：【目的】CO_(2)和CH_(4)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CO_(2)和CH_(4)浓度监测,得到CO_(2)、CH_(4)柱浓度(XCO_(2)和XCH_(4))变化和分布情况,采用源清单法分析CO_(2)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XCO_(2)和XCH_(4)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XCO_(2)和XCH_(4)浓度整体呈增长趋势,期间XCO_(2)浓度增加12×10^(-6)、XCH_(4)浓度增加23×10^(-9);XCO_(2)浓度和累计发电量的Pearson相关系数为0.98,XCH_(4)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XCH_(4)浓度分布时空特征发现,研究区秋季XCH_(4)浓度高于夏季,XCH_(4)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CO_(2)排放最多的为化石燃料固定燃烧源,占全市CO_(2)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CO_(2)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CO_(2)最主要排放源。(4)影响研究区XCO_(2)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XCH_(4)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]CO_(2)and CH_(4)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 CO_(2)and CH_(4)in coal-electricity pro duction bases.Specifically,this study examined the CO_(2)and CH_(4)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 CO_(2)and CH_(4),which are XCO_(2)and XCH_(4).A sourcing method of inventory was employed to analyze the industrial and regional CO_(2)emission characteristics,and Pearson's correlation coefficient and multivariate regression were used to analyze the dominant factors affecting the XCO_(2)and XCH_(4)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 XCO_(2)and XCH_(4)concentrations in Huainan generally trended upward from 2016 to 2020,with the XCO_(2)and XCH_(4)concentrations increasing by 12×10^(-6)and 23×10^(-9),respectively.The Pearson's correlation coefficient between the XCO_(2)concentration and cumulative power generation was 0.98,and that between the XCH_(4)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 XCH_(4)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 XCH_(4)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

关 键 词：碳排放 遥感监测 XCO_(2) XCH_(4) 主控因素 多元回归分析 煤电基地 安徽淮南市 

分 类 号：X51[环境科学与工程—环境工程] TD167[矿业工程—矿山地质测量]