Abstract: Coal-fired power plants are an important contributor to CO₂ emissions in the Chinese region. 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 (≥5000 MW) in the Chinese region from the OCO-2 (2014.09.06-2021.10.01) and OCO-3 (2019.08.06-2021.10.01) 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 43~77 (kt/d), with correlation coefficients ranging from 0.50 to 0.87. The uncertainties of individual plumes vary 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, which are important for 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