Global Emissions Inventory from Open Biomass Burning (GEIOBB): utilizing Fengyun-3D global fire spot monitoring data
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Published:2024-08-02
Issue:8
Volume:16
Page:3495-3515
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ISSN:1866-3516
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Container-title:Earth System Science Data
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language:en
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Short-container-title:Earth Syst. Sci. Data
Author:
Liu YangORCID, Chen Jie, Shi YushengORCID, Zheng Wei, Shan Tianchan, Wang Gang
Abstract
Abstract. Open biomass burning (OBB) significantly affects regional and global air quality, the climate, and human health. The burning of forests, shrublands, grasslands, peatlands, and croplands influences OBB. A global emissions inventory based on satellite fire detection enables an accurate estimation of OBB emissions. In this study, we developed a global high-resolution (1 km×1 km) daily OBB emission inventory using the Chinese Fengyun-3D satellite's global fire spot monitoring data, satellite-derived biomass data, vegetation-index-derived spatiotemporally variable combustion efficiencies, and land-type-based emission factors. The average annual estimated OBB emissions for 2020–2022 were 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 particulate organic carbon (OC), 3.77 Tg particulate black carbon (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 average annual estimated OBBs for 2020–2022 were 72.71 (Boreal North America, BONA), 165.73 (Temperate North America, TENA), 34.11 (Central America, CEAM), 42.93 (Northern Hemisphere South America, NHSA), 520.55 (Southern Hemisphere South America, SHSA), 13.02 (Europe, EURO), 8.37 (Middle East, MIDE), 394.25 (Northern Hemisphere Africa, NHAF), 847.03 (Southern Hemisphere Africa, SHAF), 167.35 (Boreal 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) Tg C yr−1. Overall, savanna grassland burning contributed the largest proportion of the annual total carbon emissions (1209.12 Tg C yr−1; 46.74 %), followed by woody savanna/shrubs (33.04 %) and tropical forests (12.11 %). SHAF was found to produce the most carbon emissions globally (847.04 Tg C yr−1), followed by SHSA (525.56 Tg C yr−1), NHAF (394.26 Tg C yr−1), and SEAS (197.30 Tg C yr−1). More specifically, savanna grassland burning was predominant in SHAF (55.00 %, 465.86 Tg C yr−1), SHSA (43.39 %, 225.86 Tg C yr−1), and NHAF (76.14 %, 300.21 Tg C yr−1), while woody savanna/shrub fires were dominant in SEAS (51.48 %, 101.57 Tg C yr−1). Furthermore, carbon emissions exhibited significant seasonal variability, peaking in September 2020 and August of 2021 and 2022, with an average of 441.32 Tg C month−1, which is substantially higher than the monthly average of 215.57 Tg C month−1. Our comprehensive high-resolution inventory of OBB emissions provides valuable insights for enhancing the accuracy of air quality modeling, atmospheric transport, and biogeochemical cycle studies. The GEIOBB dataset can be downloaded at http://figshare.com (last access: 30 July 2024) with the following DOI: https://doi.org/10.6084/m9.figshare.24793623.v2 (Liu et al., 2023).
Funder
National Key Research and Development Program of China National Natural Science Foundation of China
Publisher
Copernicus GmbH
Reference121 articles.
1. Akagi, S. K., Yokelson, R. J., Wiedinmyer, C., Alvarado, M. J., Reid, J. S., Karl, T., Crounse, J. D., and Wennberg, P. O.: Emission factors for open and domestic biomass burning for use in atmospheric models, Atmos. Chem. Phys., 11, 4039–4072, https://doi.org/10.5194/acp-11-4039-2011, 2011. 2. Anderson, G. K., Sandberg, D. V., and Norheim, R. A.: Fire emission production simulator (FEPS) user's guide, USDA Forest Service, https://www.frames.gov/catalog/6921 (last access: 31 July 2024), 2004. 3. Avitabile, V., Herold, M., Heuvelink, G. B. M., Lewis, S. L., Phillips, O. L., Asner, G. P., Armston, J., Ashton, P. S., Banin, L., Bayol, N., Berry, N. J., Boeckx, P., de Jong, B. H. J., DeVries, B., Girardin, C. A. J., Kearsley, E., Lindsell, J. A., Lopez-Gonzalez, G., Lucas, R., Malhi, Y., Morel, A., Mitchard, E. T. A., Nagy, L., Qie, L., Quinones, M. J., Ryan, C. M., Ferry, S. J. W., Sunderland, T., Laurin, G. V., Gatti, R. C., Valentini, R., Verbeeck, H., Wijaya, A., and Willcock, S.: An integrated pan-tropical biomass map using multiple reference datasets, Global Change Biol., 22, 1406–1420, https://doi.org/10.1111/gcb.13139, 2016. 4. Baccini, A., Goetz, S. J., Walker, W. S., Laporte, N. T., Sun, M., Sulla-Menashe, D., Hackler, J., Beck, P. S. A., Dubayah, R., Friedl, M. A., Samanta, S., and Houghton, R. A.: Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps, Nat. Clim. Change, 2, 182–185, https://doi.org/10.1038/nclimate1354, 2012. 5. Bowman, D. M. J. S., Williamson, G. J., Abatzoglou, J. T., Kolden, C. A., Cochrane, M. A., and Smith, A. M. S.: Human exposure and sensitivity to globally extreme wildfire events, Nat. Ecol. Evol., 1, 1–6, https://doi.org/10.1038/s41559-016-0058, 2017.
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