Reconstructing top-down global black carbon emissions using remote sensing and models

Black Carbon (BC) is both an absorbing component and air pollutant that significantly impacts environment, climate, and human health. Currently, the monitoring of BC emissions relies primarily on bottom-up inventories, which often lack spatial and temporal validation or verification from satellite-based observational platforms. This gap limits our understanding of BC's concentration and variability over time and space. This study reconstructs a BC emission inventory based on separate bottom-up and top-down Kalman Filter estimations from 2002 to 2009 yielding a variable enhancement factor in different areas. EOF (Empirical Orthogonal Function) is employed to identify 9 unique BC source regions contributing over 77 % of the variance, in alignment with climatological patterns of NO2 and UVAI (Ultraviolet Aerosol Index) observations during this period. Simplified inversion emission estimation provides a medium to high confidence inventory that effectively captures both geographic and temporal variations of BC across different regions and percentiles. The emission difference between our inversion and a priori estimation is not uniform, with BC emissions globally underestimated by a factor of 1.8-4.0. Urban and rapidly developing regions including Europe, China, United States, and India are highly underestimated in the a priori inventory.