Improvement of Space-Observation of Aerosol Chemical Composition by Synergizing a Chemical Transport Model and Ground-Based Network Data

Aerosol chemical components are critical parameters that influence the atmospheric environment, climate effects, and human health. Retrieving global columnar atmospheric aerosol components from satellite observations provides foundational data and practical value. This study develops a method for retrieving aerosol component composition from polarized satellite data by synergizing a chemical transport model with ground-based remote sensing data. The method enables the rapid acquisition of columnar mass concentrations for seven aerosol components on a global scale, including black carbon (BC), brown carbon (BrC), organic carbon (OC), ammonium sulfate (AS), aerosol water (AW), dust (DU), and sea salt (SS). We first establish a remote sensing model based on the multiple solution mixing mechanism (MSM2) to obtain aerosol chemical components using AERONET ground-based measurements. We then employ a cross-layer adaptive fusion (CAF)-Transformer model to learn the spatial distribution characteristics of aerosol components from the MERRA-2 model. Furthermore, we optimize the retrieval model by transfer learning from the ground-based composition data to achieve satellite remote sensing of aerosol components. Residual analysis indicates that the retrieval model exhibits robust generalization capabilities for components such as BC, OC, AS, and DU, achieving a coefficient of determination of 0.7. Moreover, transfer learning effectively enhances the consistency between satellite retrievals and ground-based remote sensing results, with an average improvement of 0.23 in the correlation coefficient. We present annual and seasonal means of global distributions of the retrieved aerosol component concentrations, with a major focus on the spatial and temporal variations of BC and DU. Additionally, we analyze three typical atmospheric environmental cases, wildfire, dust storm, and particulate pollution, by comparing our retrievals with model data and other datasets. This demonstrates the ability of satellite remote sensing to identify the location, intensity, and impact range of environmental pollution events. Satellite-retrieved aerosol component data offers high spatial resolution and efficiency, particularly providing significant advantages for near-real-time monitoring of regional atmospheric environmental events.