A new flux-gradient method of CO2 flux estimation based on drone monitoring

To address the issue of globally increasing greenhouse gases, it is crucial to understand the emissions and absorption of carbon dioxide (CO2), one of the key greenhouse gases. The vertical CO2 flux near surfaces, which represents the addition or exception of CO2 in the atmosphere, is influenced by near-surface turbulent mixing processes. Therefore, turbulence measurement is essential for accurately quantifying CO2 flux. The eddy covariance (EC) technique, a commonly used method for this purpose, has spatial and temporal limitations due to instrumental installation on a fixed tower in a homogeneous area. In contrast, a drone offers a cost-effective and reliable alternative tool for measuring wind speed and turbulence at various altitudes and locations. This study investigated a newly developed methodology of estimating CO2 flux by calculating turbulent diffusion coefficients using drone monitoring in complex urban areas. Drone monitoring was conducted in Chuncheon-si, Gangwon Province during 3 hours after sunrise and 3 hours before sunset, while hovering at 30 m intervals ranging from 0 m to 250 m. A sonic anemometer (FT742), a CO2 sensor (GMP252), and a temperature-pressure sensor (Imet-X4) were equipped with meteorological and air quality drones. Results showed differences in CO2 flux in the morning and afternoon, suitably capturing CO2 emissions at surfaces in the urban area. These findings highlight the importance of accurate turbulent diffusion coefficients and CO2 fluxes in understanding relevant CO2 emission processes. This work was funded by the Korea Meteorological Administration Research and Development Program under Grant RS-2024-00404042. And supported by Korea Environment Industry & Technology Institute (KEITI) through Project for developing an observation-based GHG emissions geospatial information map, funded by Korea Ministry of Environment (MOE) (RS-2023-00232066).