首页|基于OCO-2/3卫星的中国超大型燃煤电厂CO2排放量的遥感反演

基于OCO-2/3卫星的中国超大型燃煤电厂CO2排放量的遥感反演

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基于轨道碳观测者2/3(OCO-2/3)卫星数据和高斯羽流模型对发电厂CO2排放量进行遥感反演。首先基于 OCO-2(2014-09-06-2021-10-01)和 OCO-3(2019-08-06-2021-10-01)数据检索中国超大型燃煤电厂(≥5000 MW)附近图像,共在托克托、嘉兴、外高桥电厂附近识别到7个CO2羽流。综合利用3种大气背景值确定方法,经过高斯羽流模型估算的CO2排放量范围为43~77 kt/d,模型拟合的相关系数0。50~0。87。单个羽流的不确定性变化为8%~32%(1σ),风速是最大的不确定性(6%~31%),其次是背景值(5%~18%)、增强值(1%~21%)和羽流上升(1%~8%)。经验证,估算结果与碳监测行动、碳简报、全球电厂排放数据库等排放清单一致性较高(托克托:(76。48±15。75)kt/d、外高桥:(55。98±6。90)kt/d、嘉兴:(64。55±15。89)kt/d)。这项研究有助于监测点源碳排放,这不仅是电力行业开展碳减排的前提,也有助于针对性制定区域碳减排政策,从而减少人为碳排放。
Remote sensing inversion of CO2 emissions from super-large coal-fired power plants in China based on OCO-2/3 satellite
Coal-fired power plants are important contributors to CO2 emissions in China.Due to the low timeliness of statistical data and inaccurate emission factors,the existing emission inventories gradually fail to reflect the CO2 emissions of power plants.This study provides a method to estimate CO2 emissions from power plants based on Orbiting Carbon Observatory 2/3(OCO-2/3)satellite data and Gaussian plume model,retrieving the images of super-large coal-fired power plants(≥5 000 MW)in China from the OCO-2(September 6,2014-October 1,2021)and OCO-3(August 6,2019-October 1,2021)dataset,and identifying a total of seven plumes near Tuoketuo,Waigaoqiao,and Jiaxing power plants.Using a combination of three atmospheric background value determination methods,the CO2 emissions estimated by the Gaussian plume model range from 43 to 77 kt/d,with correlation coefficients ranging from 0.50 to 0.87.The uncertainties of individual plumes varied from 8%to 32%(1σ),with wind speed being the largest uncertainty(6%-31%),followed by background values(5%-18%),enhanced values(1%-21%),and plume rise(1%-8%).The estimates are verified to be in high agreement with Carbon Monitoring for Action,Carbon Brief,and the Global Power Emissions Database(Tuoketuo:(76.48±15.75),Waigaoqiao:(55.98±6.90),Jiaxing:(64.55±15.89)kt/d).This study helps monitor and estimate important point source carbon emissions,which is not only a prerequisite for the power industry to carry out carbon reduction efforts but also helps develop specific regional carbon reduction policies,thereby reducing anthropogenic carbon emissions.

carbon dioxideGaussian plume modelOrbiting Carbon Observatory 2Orbiting Carbon Observatory 3super-large coal-fired power plant

郭文月、石玉胜

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中国科学院空天信息创新研究院,北京 100094

中国科学院大学,北京 100049

二氧化碳 高斯羽流模型 轨道碳观测者2号 轨道碳观测者3号 超大型燃煤电厂

国家卫星气象中心风云三号03批气象卫星工程地面应用系统生态监测评估应用项目(第1期)国家自然科学基金国家重点研发计划中国科学院人才项目

ZQC-R22227420713982021YFB3901000Y8YR2200QM

2024

中国科学院大学学报
中国科学院大学

中国科学院大学学报

CSTPCD北大核心
影响因子:0.614
ISSN:2095-6134
年,卷(期):2024.41(4)
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