1,2,6,3,4,5,6,3- 1College of Environment and Ecology, Taiyuan University of Technology, Taiyuan, China
- 2Shanxi Key Laboratory of Complex Air Pollution Control and Carbon Reduction, Taiyuan University of Technology, Taiyuan, China
- 3School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou, China
- 4Centre for Climate Change and Environmental Health, Nanyang Technological University, Singapore, Singapore
- 5Earth Observatory of Singapore, Nanyang Technological University, Singapore, Singapore
- 6Asian School of the Environment, Nanyang Technological University, Singapore, Singapore
We present a two-step framework for estimating black carbon (BC) emissions by integrating satellite remote sensing, ground-based observations, and physically grounded algorithms. First, BC mass and number column densities are retrieved using OMI satellite and AERONET sun photometer data, based on a Mie theory–driven core–shell model that accounts for particle microphysics. This integration of complementary platforms improves the sensitivity and spatial coverage of retrievals. Second, BC emissions are estimated from the retrieved columns using both mass- and number-conservative methods, allowing comparison of results under different assumptions about particle behavior and size distribution.
Applied over South, Southeast, and East Asia in 2016, the framework reveals emissions in regions such as Myanmar, Laos, northern Thailand, and Vietnam that exceed reported values in current inventories (e.g., FINN and EDGAR-HTAP) by more than an order of magnitude during high-intensity events. These emissions, concentrated between March and May, suggest a longer biomass burning season than typically captured by satellite NO2 observations. Day-to-day estimates show substantial temporal variability, with emission uncertainties reaching up to 82% using the mass-conservative method and 75% using the number-based approach. Notably, the number-conservative method yields 20–43% higher emissions in biomass burning and urban areas, highlighting the limitations of mass-only assumptions that do not account for particle number sensitivity.
This framework also enables comparison between different emission estimation strategies, and reveals structural discrepancies linked to underlying particle representations. The number-based approach may offer a more complete picture of episodic BC emissions, especially in regions with high particle number concentrations or coarse assumptions in current inventories. While this study focuses on 2016 events, the methodology is flexible and compatible with upcoming satellite missions such as EarthCARE, supporting potential extensions to finer spatiotemporal scales. By embedding particle-level physics into a multi-instrument observational framework, this approach contributes to improved BC emission estimates in data-sparse and dynamic environments, providing a practical alternative to bottom-up inventories under high-impact conditions.
How to cite: Liu, J., Cohen, J., Yim, S., and Qin, K.: A Synergistic Framework for Black Carbon Emission Estimation via Satellite–Ground Retrievals and Particle-Conserving Methods, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16314, https://doi.org/10.5194/egusphere-egu26-16314, 2026.
