基于卫星遥感等多源数据的发电厂CO<sub>2</sub>排放分析

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摘要应用烟羽分割、背景插值和高斯烟羽模型,使用轨道碳监测传感器(OCO-3)SAM模式在1年内的4次有效观测数据,估算了托克托电厂CO₂排放量,并利用年度核算报告结果、月度发电量与连续排放监测系统(CEMS)监测数据对卫星遥感估算结果进行验证.结果表明,2021年4次卫星过境时CO₂排放速率分别为1900.82、3353.96、2941.07和3701.71tCO₂/h,其不确定性分别为25.10%、20.27%、19.59%和29.52%,不确定性主要来源于气象数据、背景和其它排放源影响.CO₂小时排放速率卫星遥感反演结果和核算分配结果验证具有较好的一致性,相关系数R为0.822.电厂月度发电量及小时级在线监测数据分析结果表明,电厂活动水平具有明显的季节性变化和小时变化特征,其中与CO₂排放密切相关的月度发电量相对标准偏差可达14.55%,NO_x小时排放量相对标准偏差可达12.35%.

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	付金杯
	李梦南
	徐炜达
	王宇萌
	李怀瑞
	尹捷
	杨昱锟
	卓俊玲

关键词 ：大型燃煤电厂, 卫星遥感, 碳排放, SAM模式

Abstract：This study had estimated CO₂ emissions from the Tuoketuo Power Plant using observations from the Orbital Carbon Monitoring Satellite (OCO-3) SAM mode with segmentation, background-interpolation and Gaussian plume model. To verify the estimation results from satellite remote sensing, annual accounting report CO₂ data, monthly power generation, and continuous emission monitoring system (CEMS) data were used. The results showed that the hourly CO₂ emission rates of the four estimated results were 1900.82, 3353.96, 2941.07, and 3701.71tCO₂/h, respectively, with uncertainties of 25.10%, 20.27%, 19.59%, and 29.52%, and the uncertainty mainly came from meteorological data, background, and secondary sources. The satellite remote sensing inversion results of hourly CO₂ emission rate were very consistent with the accounting allocation value, with a correlation coefficient of 0.822. The activity data of power plants showed an obvious seasonal and hourly variations, with a relative standard deviation of 14.55% for monthly power generation and 12.35% for NO_x hourly emissions.

Key words： large-scale power plant satellite remote sensing carbon emission SAM mode

收稿日期: 2023-09-15

PACS:

X511

基金资助:生态环境部生态环境执法局立项课题“生态环境执法”(144024000000200024)

通讯作者: 卓俊玲,正高级工程师,zhuojl@acee.org.cn E-mail: zhuojl@acee.org.cn

引用本文:

付金杯, 李梦南, 徐炜达, 王宇萌, 李怀瑞, 尹捷, 杨昱锟, 卓俊玲. 基于卫星遥感等多源数据的发电厂CO₂排放分析[J]. 中国环境科学, 2024, 44(4): 1805-1815. FU Jin-Bei, LI Meng-Nan, XU Wei-Da, WANG Yu-Meng, LI Huai-Rui, YIN Jie, YANG Yu-Kun, ZHUO Jun-Ling. CO₂ emissions from power plants based on multi-source data such as satellite remote sensing. CHINA ENVIRONMENTAL SCIENCECE, 2024, 44(4): 1805-1815.

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http://www.zghjkx.com.cn/CN/ 或 http://www.zghjkx.com.cn/CN/Y2024/V44/I4/1805

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