China Open Biomass Burning Emissions Inventory (COBBEI) from 2020 to 2023: A Fusion Approach with Fengyun-4A and Polar Orbit Satellites Data Based on Filtering Rules

Pollutants released from open biomass burning (OBB) considerably impact air quality, human health, and ecosystems. Only a few studies have used geostationary satellites to monitor OBB emissions in China. Therefore, we construct the China Open Biomass Burning Emissions Inventory (COBBEI) from 2020 to 2023. This dataset included eight pollutants with a temporal resolution of 1 hour and a spatial resolution of 2 km. The COBBEI integrated multi-satellite data, including MODIS, NPP, and Fengyun-4A (FY-4A). The Fire Radiation Power (FRP) data were reconstructed to the FRP cycle, and we integrated the curves to obtain the hourly biomass burned. We also developed five filtering rules based on FRP, considering fire point frequency, radiation values, timing, and variation. These rules were applied to correct the land cover maps, and their validity was verified. The annual average emissions of CO₂, CO, CH₄, NO_x, SO₂, PM_2.5, K, and LG were 46530, 2262, 132, 82, 25, 247, 11, and 12 Gg, respectively. The spatial distribution characteristics of all eight pollutants were generally consistent. Northeast China served as a major center of pollutant emissions. Different types of fires exhibited various spatial distributions. Emission peaks from cropland and grassland fires typically occurred between 11:00 and 13:00, with a smaller peak at midnight. The number of SFs significantly increased, indicating a rise in the extent and decentralization of OBB, particularly in Tibet, Qinghai, and Sichuan. By validating the method and comparing it with other databases, it was confirmed that COBBEI reduced uncertainty in the OBB emission inventory by providing more information on fire points and effectively screening out fires that were not from OBB. The dataset could offer essential data for air quality modeling, environmental policy development, and fire emergency response strategies.