Three-Dimensional Biomass Burning Emission Inventory for Southeast and East Asia Based on Multi-Source Data Fusion and Machine Learning

Abstract. Biomass burning (BB) is a major source of atmospheric pollutants in Southeast and East Asia (SEA), yet most existing emission inventories lack accurate diurnal cycles and vertical injection profiles, limiting the accuracy of air quality and climate simulations. This study develops the Southeast and East Asia Fire (SEAF) inventory, an hourly 3 km three-dimensional (3D) emission dataset for 2023, by fusing fire radiative power (FRP) from Himawari-8/9 AHI and VIIRS through cloud correction, cross-calibration, and a region–vegetation-specific Gaussian diurnal reconstruction with dynamic gap filling. Vertical profiles are further constrained using a random forest (RF) – Shapley Additive Explanations (SHAP) framework trained with Multi-angle Imaging SpectroRadiometer (MISR) smoke plume heights (SPH) and ERA5 meteorology. The SEAF inventory exhibited strong consistency with TROPOMI CO, showing a correlation of R = 0.97 in monthly columns and differing by only 7.81 % during a representative event on 9 March 2023. Annual PM_2.5 emissions in SEAF are approximately 2362 Gg y^-1, which is 67 % lower than the Fire INventory from NCAR (FINN) but aligns well with the Fire Energetics and Emissions Research (FEER) and the Quick Fire Emissions Dataset (QFED) estimates. The RF–SHAP framework successfully predicted SPH, with over 90 % of estimates within ± 500 m. This approach corrects the near-surface overweighting of conventional schemes by reducing emissions below 0.3 km and enhancing injection between 2.7–5.5 km during the spring burning peak, yielding vertical profiles that closely align with satellite observations. SHAP analysis identified temperature- and radiation-related factors, particularly the vertical integral of temperature (Vit) and terrain elevation, as the primary drivers of SPH, with additional contributions from FRP, planetary boundary layer height, and seasonal–meteorological interactions. These advances in both diurnal timing and vertical injection are anticipated to provide an observation-driven, hourly 3D BB emission dataset for SEA that can improve the reliability of air quality, climate, and policy assessment models.