The National Engineering Research Center for Remote Sensing Satellite Applications, Institute of Space and Space Information Innovation, Chinese Academy of Sciences, has made progress in the research of remote sensing inversion estimation of carbon dioxide (CO2) emissions from coal-fired power plants.
CO2 is mainly generated from fossil fuel combustion, and CO2 emissions from coal-fired power plants in China account for about 50% of the country’s total CO2 emissions. However, the existing greenhouse gas emission inventories of coal-fired power plants are no longer representative of the true emissions of power plants due to the lag in updating statistical data and inaccurate emission factors.
With the development of remote sensing technology, the gas emission information on the ground can be sensed by sensors in space through electromagnetic wave radiation, and the inversion of satellite identified emission information using atmospheric models provides a new method for estimating CO2 emissions from power plants. The method is based on actual satellite data, which is less affected by human factors and has higher temporal resolution, and provides a uniform standard for estimation in different regions.
Therefore, satellite remote sensing monitoring and inversion to accurately estimate CO2 emissions from coal-fired power plants in China is not only a prerequisite for carbon emission reduction in the power industry, but also provides independent and objective carbon emission monitoring data to help China’s carbon inventory and assess the effectiveness of carbon emission reduction in key industries.
The research team combines multi-source carbon satellite remote sensing data (Orbiting Carbon Observer 2 and 3) and optimized Gaussian plume model to carry out top-down remote inversion of CO2 emissions from coal-fired power plants in a long time series, and carries out CO2 emission inversion for different installed capacity power plants (very large (≥5000 MW), very large (4000-5000 MW), and large (≥3000 MW)). emission satellite identification for different installed capacities (very large (≥5000 MW), very large (4000-5000 MW), and large (≥3000 MW)), combined with Gaussian plume model, to invert the latest CO2 emission values of coal-fired power plants in the Chinese region, and improve the accuracy of the inversion results through optimization.
The results show that wind speed is the main influencing factor on the magnitude of CO2 column concentration observed by carbon satellite data. When the wind speed increases to near 10 m/s, the average dry air mixing ratio (XCO2) enhancement of the atmospheric CO2 column for all power plants in this study is less than 1 parts per million (ppm), implying that the accuracy of the satellite carbon emission inversion will be limited.
The CO2 emission values estimated in the study range from 63 kilotons/day for very large power plants to 37 kilotons/day for large power plants. It was verified to be in good agreement with the bottom-up emission inventory values for most coal-fired power plants, but in some plants the emission inventories show differences from the results of this study due to age, unit replacement, coal combustion type, etc.