This dataset aims to develop a high-resolution daily OBB emissions inventory, including carbon (C), carbon dioxide (CO2), carbon monoxide (CO), methane (CH4), nitrogen oxides (NOx), sulfur dioxide (SO2), particulate organic carbon (OC), particulate black carbon (BC), ammonia (NH3), nitrogen dioxide (NO2), PM2.5, and PM10, and analyze various fire events and their emission patterns in 14 different regions. To estimate the OBB emissions of forests, savannas/shrublands, grasslands, and peatlands, we used an updated FY-3D GFR product based on the continuous spatiotemporal dynamics of AGB, spatiotemporal variable combustion efficiency, and emission factors specific to different land types. A comprehensive high-resolution OBB emission inventory is a valuable asset for air quality modeling, atmospheric transport simulation, and biogeochemical cycling research applications. This provides a strong framework for a deeper understanding and analysis of the environmental impact of brominated naphthalene on a global scale
In this dataset, a high-resolution (1 km x 1 km) daily inventory of polybrominated biphenyls emissions was compiled using global fire point monitoring data from China's Fengyun-3D satellite, satellite derived biomass data, spatiotemporal variable combustion efficiency derived from vegetation indices, and emission factors based on land types. The estimated annual emissions of OBB from 2020 to 2022 are: 2586.88 Tg C, 8841.45 Tg CO2, 382.96 Tg CO, 15.83 Tg CH4, 18.42 Tg NOX, 4.07 Tg SO2, 18.68 Tg OC, 3.77 Tg BC, 5.24 Tg NH3, 15.85 Tg NO2, 42.46 Tg PM2.5, and 56.03 Tg PM10. Specifically, taking carbon emissions as an example, the estimated annual OBBs for 2020-2022 are 72.71 (BONA in Northern North America), 165.73 (TENA in temperate North America), 34.11 (CEAM in Central America), 42.93 (NHSA in Northern South America), 520.55 (SHSA in Southern South America), 13.02 (EURO in Europe), 13.02 (PM2.5 in South America), and 56.03 (PM10), respectively. 02 (Europe, EURO), 8.37 (Middle East, MIDE), 394.25 (Northern Hemisphere Africa, NHAF), 847.03 (Southern Hemisphere Africa, SHAF), 167.35 (Northern Asia, BOAS), 27.93 (Central Asia, CEAS), 197.29 (Southeast Asia, SEAS), 13.20 (Equatorial Asia); EQAS and 82.38 (Australia and New Zealand; AUST) megatons of carbon per annual-1. A comprehensive high-resolution OBB emission inventory provides valuable information for improving the accuracy of air quality modeling, atmospheric transport, and biogeochemical cycling research
The processed fire event detection data comes from the Fengyun Satellite Remote Sensing Data Service Network of the National Satellite Meteorological Center（ http://satellite.nsmc.org.cn/PortalSite/Default.aspx ）；
NDVI data is obtained through the MODIS 16 d NDVI fusion product, which can be accessed on the Google Earth Engine platform
The TC data comes from the MOD44B product, which is based on MODIS generated on the Terra satellite（ https://lpdaac.usgs.gov/products/mod44bv061/ ）It provides a continuous global vegetation field with a resolution of 250 meters per year from 2000 to the present
AGB data comes from the 2010 global aboveground and underground biomass carbon density map product（ https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1763 The dataset was provided by Spawn and Gibbs (2020)
The location, time, and burning area of fire events used in GEIOBB were determined globally using FY-3D GFR products
The Global Open Air Biomass Burning Emissions Inventory (GEIOBB) (1 kilometer per day) is estimated using the burning area method based on the framework described by Wiedinmyer et al. (2006) and Shi et al. (2015)
GEIOBB includes combustion area retrieved based on FY-3D satellite activity fire data, available biomass measured by satellite and ground, CF scaled by tree cover (TC) and normalized difference vegetation index (NDVI), and OBB emissions based on land cover (LC) emission factors
This dataset utilizes thousands of satellite data points and ground measurement data to create a biomass map with a resolution of 1 kilometer
& emsp; Using 2118 other ground measurement data and LiDAR data to validate the observation results, the results showed that the root mean square error (RMSE) of the fused map was 15% -21% lower than the errors reported by Saatchi et al. (2011) and Baccini et al. (2012)
CSTR:11738.11.NCDC.FIGSHARE.DB6669.2024
10.6084/m9.figshare.24793623.v2
