Observing air pollution from satellite: EUMETSAT contribution to air quality monitoring at the global scale

EUMETSATs (European Organisation for the Exploitation of Meteorological Satellites) contribution to global air quality monitoring is multifaceted, encompassing technological advancements, long-term commitments, and a collaborative approach to address environmental challenges. The organization has been actively involved in satellite observations since 1990 through programs like Meteosat and Metop, and since 2015, it has been contributing to the Copernicus EU program. This effort is particularly significant for supporting air quality monitoring in developing countries, where reliable in-situ observatories are limited, and there is high vulnerability to pollutants and climate change impacts. The data provision is set to continue for the next two decades thanks to next-generation missions such as Meteosat Third Generation (MTG). These missions, along with instruments like Sentinel-4 and Sentinel-5 under the Copernicus EU program, are dedicated to air quality monitoring in specific regions, including North Africa and globally. Here, we will showcase how the atmospheric composition data obtained from EUMETSAT's satellites can be utilized for air quality analysis at the continental and local scale. Recent scientific applications based on datasets from Infrared Atmospheric Sounding Interferometer (IASI) and Sentinel instruments will be reviewed. In addition, examples on how EUMETSAT's satellite data is used to monitor phenomena that have direct implications for health and security, such as desert dust storms and wildfire emissions. A critical part of the discussion will focus on the advantages and drawbacks of satellite data due to observational configurations. This may involve addressing challenges and limitations while highlighting the strengths of satellite observations for air quality monitoring. Finally, it will be shown the importance of data access and training for effective utilization of satellite data. Additional value can be derived from satellite information through techniques like data assimilation and the application of artificial intelligence and machine learning (AI-ML methods).