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