Quantifying CO2 emissions of power plants with Aerosols and Carbon Dioxide Lidar onboard DQ-1

Abstract

Transparent, accurate, and affordable monitoring of CO2 emissions from power plants is crucial for realization of transition away from fossil fuels. Satellite remote sensing has emerged as a recent research focus, enabling emission estimation without the cooperation of emitters. In 2022, China launched the world's first CO2 monitoring satellite based on laser detection, DQ-1. Equipped with ACDL, it enables accurate detection even under nighttime and high aerosol load conditions, serving as a significant complement to existing optical satellite systems. However, the feasibility of laser detection with narrow swath width for monitoring power plant emissions remains uncertain. This work presents, for the first time, the practical capability of using spaceborne LIDAR to estimate CO2 emissions from power plants. We employ the Gaussian dispersion model as the transport model and quantify emission rates using genetic and trust-region algorithms. Through satellite observations from June to December 2022, we conducted emission estimation experiments for 15 globally distributed power plants. Results indicate that using ACDL's XCO2 yields approximately unbiased estimates (∼2%) of power plant emissions overall, despite a single-overpass uncertainty of 18%. This performance is comparable to OCO-2/3, suggesting consistent benchmarks for different satellite mechanisms in reporting power plant emissions. Consequently, combining their results enhances monitoring of global power plants. We advocate the joint use of diverse CO2 monitoring satellites employing various detection mechanisms to gather a more comprehensive XCO2 dataset, aiming to achieve routine satellite-based verification of power plant emissions.