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

郭文月; 石玉胜

doi:10.7523/j.ucas.2023.050

中国科学院大学学报 >

2024 , Vol. 41 >Issue 4: 490 - 502

DOI: https://doi.org/10.7523/j.ucas.2023.050

环境科学与地理学

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

郭文月 ,
石玉胜

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1. 中国科学院空天信息创新研究院, 北京 100094;
2. 中国科学院大学, 北京 100049

收稿日期: 2022-12-06

修回日期: 2023-05-08

网络出版日期: 2023-05-08

基金资助

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)(ZQC-R22227)、国家自然科学基金(42071398)、国家重点研发计划(2021YFB3901000)和中国科学院人才项目(Y8YR2200QM)资助

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Remote sensing inversion of CO₂ emissions from super-large coal-fired power plants in China based on OCO-2/3 satellite

GUO Wenyue ,
SHI Yusheng

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1. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China;
2. University of Chinese Academy of Sciences, Beijing 100049, China

Received date: 2022-12-06

Revised date: 2023-05-08

Online published: 2023-05-08

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摘要

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(≥5 000 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。

关键词： 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

本文引用格式

郭文月 , 石玉胜 . 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[J]. 中国科学院大学学报, 2024 , 41(4) : 490 -502 . DOI: 10.7523/j.ucas.2023.050

Abstract

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.

Key words： carbon dioxide; Gaussian plume model; Orbiting Carbon Observatory 2; Orbiting Carbon Observatory 3; super-large coal-fired power plant

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