Towards digital filter methods for CO2 sink and source identification

Greenhouse gases (GHG) are considered major environmental pollutants and the dominant cause of the global increase in the average temperature on our planet [1,2]. Atmospheric carbon dioxide (CO₂) takes 75% of all the GHG [3], so our research focuses on its monitoring and further reduction based on open-source datasets.

The Copernicus Atmosphere Monitoring Service (CAMS) is one of the leading open-source institutions monitoring GHG. CAMS monitors and records levels of CO₂ in the atmosphere using instruments on the ground, in the air, and onboard satellites [4]. Modern tasks in this sphere require a near real-time mode for the GHG sinks, source identification, and balance monitoring. Image processing techniques help to solve them, especially for non-periodic or single-time GHG emission and fixation processes.

Here we present an approach to improve the information content in CO₂ concentration (CDC) maps by applying digital filters. The purpose is to detect the edges of CO₂ sink and source areas. Considering that the presence of sinks and sources leads to changes in the spatial concentration of GHG in the atmosphere, similar to changes in intensity or colour on images, the task of their detection can be solved using edge detection – primarily high-pass filters. Taking the atmospheric CDC as the result of carbon flux balance at the specific spatial cell and the difference of CDC between neighbouring cells, we can assess the relative effectiveness of CO₂ fixation in these cells as long as transport can be neglected. The arithmetical signs of the differences define cells with higher and lower CO₂ concentrations, which can be interpreted as CO₂ sinks or sources when they persist over time. The magnitude of difference identifies the relative intensity of the GHG flux. We plan to further investigate the time dependence of the Laplacian in the future.

We check the effectiveness of this approach by comparing our results with events from the NASA EARTHDATA fire datasets [5,6] and show that for several significant CO₂ concentration differences – a big fire and areas with different types of CO₂ sinks and sources could be identified in the near real-time.

References:

1. IPCC, 2023: Synthesis Report. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, H. Lee and J. Romero (eds.)]. IPCC, Geneva, Switzerland, pp. 1-34, doi:10.59327/IPCC/AR6-9789291691647.001

2. NASA climate portal (https://climate.nasa.gov/vital-signs/carbon-dioxide/)

3. Friedlingstein, P. et. al: Global Carbon Budget 2023, Earth Syst. Sci. Data, 15, 5301–5369, doi:10.5194/essd-15-5301-2023

4. NASA climate portal (https://climate.nasa.gov/news/423/carbon-dioxide-controls-earths-temperature/)

5. Lesley Ott (2020), GEOS-Carb CASA-GFED Daily Fire and Fuel Emissions 0.5 degree x 0.5 degree V2, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC)

6. Global carbon dioxide and methane monitoring (https://atmosphere.copernicus.eu/GHG-services)