Estimation of diurnal emissions of CO₂ from thermal power plants using spaceborne integrated path differential absorption (IPDA) lidar

Abstract

Coal-fired power plants are a major source of global carbon emissions, and accurately accounting for these significant emission sources is crucial in addressing global warming. Many previous studies have used Gaussian plume models to estimate power plant emissions, but there is a gap in observation capabilities for high-latitude regions and nighttime emissions. However, large emitting power plants exist in high-latitude areas. The DQ-1 satellite is equipped with the world's first active remote sensing lidar for detecting CO2 column concentrations, which, compared to passive remote sensing satellites, enables observations in these regions. This paper applies a two-dimensional Gaussian plume model to the XCO2 results from the DQ-1 satellite and analyses the instantaneous CO2 emissions of 10 power plants globally. Among these, 15 cases of data are from nighttime observations, and 3 cases are from power plants located above 60° N latitude. The estimation results show good consistency when compared with emission inventories such as Climate TRACE and Carbon Brief, with a correlation coefficient R = 0.97. The correlation coefficient between the model fits and satellite observations ranges from 0.49 to 0.88, and the overall relative random error in the estimates is 15.11 %. This paper also analyses the diurnal differences in CO2 emissions from power plants and finds emission fluctuations directly correlated with regional electricity demand dynamics. This method is very effective for monitoring emissions from strong point sources such as power plants.