Analyzing Local Carbon Dioxide and Nitrogen Oxide Emissions From Space Using the Divergence Method: An Application to the Synthetic SMARTCARB Dataset

Abstract

Since the Paris Agreement was adopted in 2015, the role of space-based observations for monitoring anthropogenic greenhouse gas (GHG) emissions has increased. To meet the requirements for monitoring carbon dioxide (CO 2 ) emissions, the European Copernicus programme is preparing a dedicated CO 2 Monitoring (CO2M) satellite constellation that will provide CO 2 and nitrogen dioxide (NO 2 ) observations at 4 km 2 resolution along a 250 km wide swath. In this paper, we adapt the recently developed divergence method to derive both CO 2 and nitrogen oxide (NO x ) emissions of cities and power plants from a CO2M satellite constellation by using synthetic observations from the COSMO-GHG model. Due to its long lifetime, the large CO 2 atmospheric background needs to be removed to highlight the anthropogenic enhancements before calculating the divergence. Since the CO 2 noise levels are large compared to the anthropogenic enhancements, we apply different denoising methods and compare the effect on the CO 2 emission estimates. The annual NO x and CO 2 emissions estimated from the divergence maps using the peak fitting approach are in agreement with the expected values, although with larger uncertainties for CO 2 . We also consider the possibility to use co-emitted NO x emission estimates for quantifying the CO 2 emissions, by using source-specific NO x -to-CO 2 emission ratios derived directly from satellite observations. In general, we find that the divergence method provides a promising tool for estimating CO 2 emissions, alternative to typical methods based on inverse modeling or on the analysis of individual CO 2 plumes.