Zhongting Wang’s research while affiliated with Ecology & Environment and other places

Global warming, driven by greenhouse gas emissions from human activities, poses significant environmental challenges. Accurate greenhouse gas measurement data are crucial for effective emission reduction policies and international cooperation. The spaceborne integrated path differential absorption lidar offers high precision for monitoring global atmospheric carbon dioxide (CO 2 ) concentrations on both days and nights. However, its accuracy can be compromised by water vapor interference. We evaluated the impact of water vapor on CO 2 detection, focusing on measurements from an aerosol and carbon dioxide detection lidar onboard the atmospheric environment monitoring satellite. The bias due to water vapor absorption was negligible. However, water vapor broadened the absorption spectrum, causing molecular interference, which could introduce considerable CO 2 column concentrations (XCO 2 ) bias. In areas with high water vapor, the bias could exceed 1 ppm. Globally, the annual average bias of XCO 2 due to water vapor broadening effects was 0.42 ppm. The analysis highlights the importance to account for water vapor spectrum broadening effects, not only in spaceborne lidar measurements such as ACDL but also in other atmospheric measurement techniques to improve CO 2 measurement accuracy and enhance our understanding of global climate change and the carbon cycles.

Plain Language Summary For global greenhouse gas monitoring, passive remote sensing technology has consistently struggled to balance the reliability and usability of monitoring data in cloudy regions. The AEMS employs 1,572 nm IPDA lidar technology for active remote sensing of global XCO2, enabling effective processing and utilization of cloud echo data. In this study, we focused on the concentrations of CO2 columns using cloud top echoes and performed a preliminary comparison of cloud top XCO2 results with related data products from the passive satellite OCO‐2 and CarbonTracker. By quantifying the difference in CO2 concentration between two altitude layers above the sea surface, we assessed ocean carbon absorption capacity, and the results demonstrated high reliability. This work highlights the significant advantages of spaceborne IPDA lidar in global CO2 measurement, cloud echo data processing, and ocean carbon flux assessment, providing valuable data support for climate change research.

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 and seasonal variations in CO2 emissions from the power plants, finding that emission variations align with changes in electricity consumption in the surrounding regions. This method is effective for monitoring the diurnal variations of strong emission sources like power plants.