Articles | Volume 25, issue 17
https://doi.org/10.5194/acp-25-10379-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-25-10379-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
11 Sep 2025
Research article |
| 11 Sep 2025
| 11 Sep 2025
Spatiotemporal patterns and drivers of wildfire CO2 emissions in China from 2001 to 2022
Xuehong Gong
State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
State Key Laboratory of Loess Science, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an 710061, China
State Key Laboratory of Loess Science, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an 710061, China
National Observation and Research Station of Regional Ecological Environment Change and Comprehensive Management in the Guanzhong Plain, Xi'an 710061, China
Jie Tian
State Key Laboratory of Loess Science, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an 710061, China
National Observation and Research Station of Regional Ecological Environment Change and Comprehensive Management in the Guanzhong Plain, Xi'an 710061, China
Qiyuan Wang
State Key Laboratory of Loess Science, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an 710061, China
National Observation and Research Station of Regional Ecological Environment Change and Comprehensive Management in the Guanzhong Plain, Xi'an 710061, China
Guohui Li
State Key Laboratory of Loess Science, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an 710061, China
National Observation and Research Station of Regional Ecological Environment Change and Comprehensive Management in the Guanzhong Plain, Xi'an 710061, China
Zhisheng An
State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
State Key Laboratory of Loess Science, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an 710061, China
State Key Laboratory of Loess Science, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an 710061, China
National Observation and Research Station of Regional Ecological Environment Change and Comprehensive Management in the Guanzhong Plain, Xi'an 710061, China
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Atmos. Chem. Phys., 25, 11703–11718, https://doi.org/10.5194/acp-25-11703-2025, https://doi.org/10.5194/acp-25-11703-2025, 2025
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The impacts of agricultural fertilization on nitrogen oxide and air quality are becoming more pronounced with continuous reductions in fossil fuel sources in China. We report that atmospheric nitrogen dioxide pulses driven by agricultural fertilization largely complicate air pollution in the North China Plain, highlighting the necessity of agricultural emission control.
Zheng Yang, Qiaoqiao Wang, Qiyuan Wang, Nan Ma, Jie Tian, Yaqing Zhou, Ge Xu, Miao Gao, Xiaoxian Zhou, Yang Zhang, Weikang Ran, Ning Yang, Jiangchuan Tao, Juan Hong, Yunfei Wu, Junji Cao, Hang Su, and Yafang Cheng
Atmos. Chem. Phys., 25, 11051–11065, https://doi.org/10.5194/acp-25-11051-2025, https://doi.org/10.5194/acp-25-11051-2025, 2025
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Our results demonstrate that the reduction in mass absorption efficiency from biomass burning is mainly driven by the decline in the imaginary part, with particle size playing a minor role. And light absorption of oxygenated brown carbon (BrC) increases significantly with aging, but hydrocarbon-like BrC decreases over time. These results emphasize the necessity to classify BrC into different groups based on their mass absorption efficiency and atmospheric behavior in climate models.
Naifang Bei, Bo Xiao, Ruonan Wang, Yuning Yang, Lang Liu, Yongming Han, and Guohui Li
Atmos. Chem. Phys., 25, 10931–10948, https://doi.org/10.5194/acp-25-10931-2025, https://doi.org/10.5194/acp-25-10931-2025, 2025
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This study uses a cloud-resolving model to examine how aerosols influence a mesoscale convective system (MCS) in central China via aerosol–radiation interaction (ARI) and aerosol–cloud interaction (ACI). Without ARIs, added aerosols do not significantly affect precipitation due to cloud competition for moisture. ARIs can stabilize or enhance convection. High aerosol levels lead to a combined ARI and ACI effect that greatly reduces precipitation.
Minxia Shen, Weining Qi, Yali Liu, Yifan Zhang, Wenting Dai, Lu Li, Xiao Guo, Yue Cao, Yingkun Jiang, Qian Wang, Shicong Li, Qiyuan Wang, and Jianjun Li
EGUsphere, https://doi.org/10.5194/egusphere-2025-3094, https://doi.org/10.5194/egusphere-2025-3094, 2025
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This study examines how dust transport modulates oxalic acid formation pathways in aerosols across different elevations of Mount Hua. During dust events, oxalic acid and precursors shift from fine to coarse particles, with concurrent heterogeneous reactions on coarse particle surfaces. The characteristic isotopic fractionation signatures accompanying these transformations yield novel theoretical frameworks for elucidating aerosol aging mechanisms in mountainous environments.
Jiamao Zhou, Jiarui Wu, Xiaoli Su, Ruonan Wang, Imad EI Haddad, Xia Li, Qian Jiang, Ting Zhang, Wenting Dai, Junji Cao, Andre S. H. Prevot, Xuexi Tie, and Guohui Li
Atmos. Chem. Phys., 25, 7563–7580, https://doi.org/10.5194/acp-25-7563-2025, https://doi.org/10.5194/acp-25-7563-2025, 2025
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Brown carbon (BrC) is a type of airborne particle produced from various combustion sources which is light absorption. Historically, climate models have categorizing organic particles as either non-absorbing or purely reflective. Our study shows that BrC can reduce the usual cooling effect of organic particles. While BrC is often linked to biomass burning, however, BrC from fossil fuels contributes significantly to atmospheric heating.
Binyu Xiao, Fan Zhang, Zeyu Liu, Yan Zhang, Rui Li, Can Wu, Xinyi Wan, Yi Wang, Yubao Chen, Yong Han, Min Cui, Libo Zhang, Yingjun Chen, and Gehui Wang
Atmos. Chem. Phys., 25, 7053–7069, https://doi.org/10.5194/acp-25-7053-2025, https://doi.org/10.5194/acp-25-7053-2025, 2025
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Jingnan Shi, Zhisheng Zhang, Li Li, Li Liu, Yaqing Zhou, Shuang Han, Shaobin Zhang, Minghua Liang, Linhong Xie, Weikang Ran, Shaowen Zhu, Hanbing Xu, Jiangchuan Tao, Alfred Wiedensohler, Qiaoqiao Wang, Qiyuan Wang, Nan Ma, and Juan Hong
EGUsphere, https://doi.org/10.5194/egusphere-2025-2643, https://doi.org/10.5194/egusphere-2025-2643, 2025
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Meng Wang, Qiyuan Wang, Steven Sai Hang Ho, Jie Tian, Yong Zhang, Shun-cheng Lee, and Junji Cao
Atmos. Chem. Phys., 24, 11175–11189, https://doi.org/10.5194/acp-24-11175-2024, https://doi.org/10.5194/acp-24-11175-2024, 2024
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We studied nitrogen-containing organic compounds (NOCs) in particulate matter <2.5 µm particles on the southeastern Tibetan Plateau. We found that biomass burning and transboundary transport are the main sources of NOCs in the high-altitude area. Understanding these aerosol sources informs how they add to regional and potentially global climate changes. Our findings could help shape effective environmental policies to enhance air quality and address climate impacts in this sensitive region.
Fan Zhang, Binyu Xiao, Zeyu Liu, Yan Zhang, Chongguo Tian, Rui Li, Can Wu, Yali Lei, Si Zhang, Xinyi Wan, Yubao Chen, Yong Han, Min Cui, Cheng Huang, Hongli Wang, Yingjun Chen, and Gehui Wang
Atmos. Chem. Phys., 24, 8999–9017, https://doi.org/10.5194/acp-24-8999-2024, https://doi.org/10.5194/acp-24-8999-2024, 2024
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Mandatory use of low-sulfur fuel due to global sulfur limit regulations means large uncertainties in volatile organic compound (VOC) emissions. On-board tests of VOCs from nine cargo ships in China were carried out. Results showed that switching from heavy-fuel oil to diesel increased emission factor VOCs by 48 % on average, enhancing O3 and the secondary organic aerosol formation potential. Thus, implementing a global ultra-low-sulfur oil policy needs to be optimized in the near future.
Zeyu Sun, Zheng Zong, Yang Tan, Chongguo Tian, Zeyu Liu, Fan Zhang, Rong Sun, Yingjun Chen, Jun Li, and Gan Zhang
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This is the first report of ship-emitted nitrogen stable isotope composition (δ15N) of nitrogen oxides (NOx). The results showed that δ15N–NOx from ships was −18.5 ± 10.9 ‰ and increased monotonically with tightening emission regulations. The selective catalytic reduction system was the most vital factor. The temporal variation in δ15N–NOx was evaluated and can be used to select suitable δ15N–NOx for a more accurate assessment of the contribution of ship-emitted exhaust to atmospheric NOx.
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Yong Zhang, Jie Tian, Qiyuan Wang, Lu Qi, Manousos Ioannis Manousakas, Yuemei Han, Weikang Ran, Yele Sun, Huikun Liu, Renjian Zhang, Yunfei Wu, Tianqu Cui, Kaspar Rudolf Daellenbach, Jay Gates Slowik, André S. H. Prévôt, and Junji Cao
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PM2.5 pollution still frequently occurs in northern China during winter, and it is necessary to figure out the causes of air pollution based on intensive real-time measurement. The findings elaborate the chemical characteristics and source contributions of PM2.5 in three pilot cities, reveal potential formation mechanisms of secondary aerosols, and highlight the importance of controlling biomass burning and inhibiting generation of secondary aerosol for air quality improvement.
Jie Tian, Qiyuan Wang, Yongyong Ma, Jin Wang, Yongming Han, and Junji Cao
Atmos. Chem. Phys., 23, 1879–1892, https://doi.org/10.5194/acp-23-1879-2023, https://doi.org/10.5194/acp-23-1879-2023, 2023
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We investigated the light absorption properties of brown carbon (BrC) in the Tibetan Plateau (TP). BrC made a substantial contribution to the submicron aerosol absorption, which is related to the cross-border transport of biomass burning emission and secondary aerosol from Southeast Asia. The radiative effect of BrC was half that of black carbon, which can remarkably affect the radiative balance of the TP.
Qian Zhang, Yujie Zhang, Zhichun Wu, Bin Zhang, Yaling Zeng, Jian Sun, Hongmei Xu, Qiyuan Wang, Zhihua Li, Junji Cao, and Zhenxing Shen
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2022-801, https://doi.org/10.5194/acp-2022-801, 2022
Revised manuscript not accepted
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We identified the brown carbon (BrC) molecules and their absorbing abilities on a molecular level from animal dung fuel combustion over the Tibetan Plateau region in China. The ultra-high performance liquid chromatography quadrupole time-of-flight mass spectrometer coupled with the partial least squares regression were precisely applied to characterize the molecular absorptions, key molecular markers, and radiative effects of BrC from household combustion scenarios at the high-altitude area.
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Atmos. Chem. Phys., 22, 12789–12802, https://doi.org/10.5194/acp-22-12789-2022, https://doi.org/10.5194/acp-22-12789-2022, 2022
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In this study, we report the long-term measurement of organic carbon (OC) and elementary carbon (EC) in PM2.5 with hourly time resolution conducted at a regional site in Shanghai from 2016 to 2020. The results from this study provide critical information about the long-term trend of carbonaceous aerosol, in particular secondary OC, in one of the largest megacities in the world and are helpful for developing pollution control measures from a long-term planning perspective.
Huikun Liu, Qiyuan Wang, Suixin Liu, Bianhong Zhou, Yao Qu, Jie Tian, Ting Zhang, Yongming Han, and Junji Cao
Atmos. Chem. Phys., 22, 11739–11757, https://doi.org/10.5194/acp-22-11739-2022, https://doi.org/10.5194/acp-22-11739-2022, 2022
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Atmospheric motions play an important role in the mass concentration and the direct radiative effect (DRE) of black carbon (BC). The finding from this study elaborated the impacts of different scales of atmospheric motion on source-specific BC and its DREs, which revealed the nonlinear change between BC mass concentration and its DREs and emphasizes the importance of regionally transported BC for potential climatic effects.
Jie Tian, Qiyuan Wang, Huikun Liu, Yongyong Ma, Suixin Liu, Yong Zhang, Weikang Ran, Yongming Han, and Junji Cao
Atmos. Chem. Phys., 22, 8369–8384, https://doi.org/10.5194/acp-22-8369-2022, https://doi.org/10.5194/acp-22-8369-2022, 2022
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We investigated aerosol optical properties and the direct radiative effect (DRE) at an urban site in China before and during the COVID-19 lockdown. The total light extinction coefficient (bext) decreased under emission control measures; however, bext from biomass burning increased due to the undiminished need for residential cooking and heating. Biomass burning, rather than traffic-related emissions, became the largest positive effect contributor to aerosol DRE in the lockdown.
Minxia Shen, Kin Fai Ho, Wenting Dai, Suixin Liu, Ting Zhang, Qiyuan Wang, Jingjing Meng, Judith C. Chow, John G. Watson, Junji Cao, and Jianjun Li
Atmos. Chem. Phys., 22, 7489–7504, https://doi.org/10.5194/acp-22-7489-2022, https://doi.org/10.5194/acp-22-7489-2022, 2022
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Looking at characteristics and δ13C compositions of dicarboxylic acids and related compounds in BB aerosols, we used a combined combustion and aging system to generate fresh and aged aerosols from burning straw. The results showed the emission factors (EFaged) of total diacids of aging experiments were around an order of magnitude higher than EFfresh. This meant that dicarboxylic acids are involved with secondary photochemical processes in the atmosphere rather than primary emissions from BB.
Jiarui Wu, Naifang Bei, Yuan Wang, Xia Li, Suixin Liu, Lang Liu, Ruonan Wang, Jiaoyang Yu, Tianhao Le, Min Zuo, Zhenxing Shen, Junji Cao, Xuexi Tie, and Guohui Li
Atmos. Chem. Phys., 21, 2229–2249, https://doi.org/10.5194/acp-21-2229-2021, https://doi.org/10.5194/acp-21-2229-2021, 2021
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A source-oriented version of the WRF-Chem model is developed to conduct source identification of wintertime PM2.5 in the North China Plain. Trans-boundary transport of air pollutants generally dominates the haze pollution in Beijing and Tianjin. The air quality in Hebei, Shandong, and Shanxi is generally controlled by local emissions. Primary aerosol species, such as EC and POA, are generally controlled by local emissions, while secondary aerosol shows evident regional characteristics.
Huikun Liu, Qiyuan Wang, Li Xing, Yong Zhang, Ting Zhang, Weikang Ran, and Junji Cao
Atmos. Chem. Phys., 21, 973–987, https://doi.org/10.5194/acp-21-973-2021, https://doi.org/10.5194/acp-21-973-2021, 2021
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We conducted black carbon (BC) source apportionment on the southeastern Tibetan Plateau (TP) by an improved aethalometer model with the site-dependent Ångström exponent and BC mass absorption cross section (MAC). The result shows that the biomass-burning BC on the TP is slightly higher than fossil fuel BC, mainly from cross-border transportation instead of the local region, and the BC radiative effect is lower than that in the southwestern Himalaya but higher than that on the northeastern TP.
Qiyuan Wang, Huikun Liu, Ping Wang, Wenting Dai, Ting Zhang, Youzhi Zhao, Jie Tian, Wenyan Zhang, Yongming Han, and Junji Cao
Atmos. Chem. Phys., 20, 15537–15549, https://doi.org/10.5194/acp-20-15537-2020, https://doi.org/10.5194/acp-20-15537-2020, 2020
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Light-absorbing carbonaceous (LAC) aerosol is an important influencing factor for global climate forcing. In this study, we used a receptor model coupling multi-wavelength absorption with chemical species to explore the source-specific LAC optical properties at a tropical marine monsoon climate zone. The results can improve our understanding of the LAC radiative effects caused by ship emissions.
Qiyuan Wang, Li Li, Jiamao Zhou, Jianhuai Ye, Wenting Dai, Huikun Liu, Yong Zhang, Renjian Zhang, Jie Tian, Yang Chen, Yunfei Wu, Weikang Ran, and Junji Cao
Atmos. Chem. Phys., 20, 15427–15442, https://doi.org/10.5194/acp-20-15427-2020, https://doi.org/10.5194/acp-20-15427-2020, 2020
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Recently, China has promulgated a series of regulations to reduce air pollutants. The decreased black carbon (BC) and co-emitted pollutants could affect the interactions between BC and other aerosols, which in turn results in changes in BC. Herein, we re-assessed the characteristics of BC of a representative pollution site in northern China in the final year of the Chinese
Action Plan for the Prevention and Control of Air Pollution.
Zhisheng An, Peizhen Zhang, Hendrik Vogel, Yougui Song, John Dodson, Thomas Wiersberg, Xijie Feng, Huayu Lu, Li Ai, and Youbin Sun
Sci. Dril., 28, 63–73, https://doi.org/10.5194/sd-28-63-2020, https://doi.org/10.5194/sd-28-63-2020, 2020
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Earth has experienced remarkable climate–environmental changes in the last 65 million years. The Weihe Basin with its 6000–8000 m infill of a continuous sedimentary sequence gives a unique continental archive for the study of the Cenozoic environment and exploration of deep biospheres. This workshop report concludes key objectives of the two-phase Weihe Basin Drilling Project and the global significance of reconstructing Cenozoic climate evolution and tectonic–monsoon interaction in East Asia.
Cited articles
Abatzoglou, J. T., Williams, A. P., Boschetti, L., Zubkova, M., and Kolden, C. A.: Global patterns of interannual climate-fire relationships, Glob. Change Biol., 24, 5164–5175, https://doi.org/10.1111/gcb.14405, 2018.
Andreae, M. O. and Merlet, P.: Emission of trace gases and aerosols from biomass burning, Global Biogeochem. Cy., 15, 955–966, https://doi.org/10.1029/2000GB001382, 2001.
Anselin, L.: Local Indicators of Spatial Association – LISA, Geogr. Anal., 27, 93–115, https://doi.org/10.1111/j.1538-4632.1995.tb00338.x, 1995.
Cao, G., Zhang, X., Wang, D., and Zhang, F.: Inventory of atmospheric pollutants discharged from biomass burning in China continent, Chin. Environ. Sci., 25, 389–393, 2005 (in Chinese).
Chang, C., Chang, Y., Xiong, Z., Ping, X., Zhang, H., Guo, M., and Hu, Y.: Predicting Grassland Fire-Occurrence Probability in Inner Mongolia Autonomous Region, China, Remote Sens., 15, 2999, https://doi.org/10.3390/rs15122999, 2023.
Chen, J., Gao, M., Cheng, S., Hou, W., Song, M., Liu, X., and Liu, Y.: Global 1 km × 1 km gridded revised real gross domestic product and electricity consumption during 1992–2019 based on calibrated nighttime light data, Sci. Data, 9, 202, https://doi.org/10.1038/s41597-022-01322-5, 2022.
Chen, Y., Hall, J., van Wees, D., Andela, N., Hantson, S., Giglio, L., van der Werf, G. R., Morton, D. C., and Randerson, J. T.: Multi-decadal trends and variability in burned area from the fifth version of the Global Fire Emissions Database (GFED5), Earth Syst. Sci. Data, 15, 5227–5259, https://doi.org/10.5194/essd-15-5227-2023, 2023.
Chuvieco, E., Cocero, D., Riaño, D., Martin, P., Martıìnez-Vega, J., De La Riva, J., and Pérez, F.: Combining NDVI and surface temperature for the estimation of live fuel moisture content in forest fire danger rating, Remote Sens. Environ., 92, 322–331, https://doi.org/10.1016/j.rse.2004.01.019, 2004.
Chuvieco, E., Mouillot, F., van der Werf, G. R., San Miguel, J., Tanase, M., Koutsias, N., García, M., Yebra, M., Padilla, M., Gitas, I., Heil, A., Hawbaker, T. J., and Giglio, L.: Historical background and current developments for mapping burned area from satellite Earth observation, Remote Sens. Environ., 225, 45–64, https://doi.org/10.1016/j.rse.2019.02.013, 2019.
CMA (China Meteorological Administration): Daily Meteorological Dataset of Essential Meteorological Elements of China National Surface Weather Station (V3.0), China Meteorological Data Service Center, Beijing, China, [data set], http://data.cma.cn, last access: 25 May 2025.
Fang, J., Liu, G., and Xu, S.: Biomass and net production of forest vegetation in China, Acta. Eco. Sin., 16, 497–508, 1996 (in Chinese).
Fang, K., Yao, Q., Guo, Z., Zheng, B., Du, J., Qi, F., Yan, P., Li, J., Ou, T., Liu, J., He, M., and Trouet, V.: ENSO modulates wildfire activity in China, Nat. Commun., 12, 1764, https://doi.org/10.1038/s41467-021-21988-6, 2021.
Gao, J., Chen, Y., Lü, S., Feng, C., Chang, X., Ye, S., and Liu, J.: A ground spectral model for estimating biomass at the peak of the growing season in Hulunbeier grassland, Inner Mongolia, China, Int. J. Remote Sens., 33, 4029–4043, https://doi.org/10.1080/01431161.2011.639401, 2012.
Gao, J., Yang, Y., Wang, H., Wang, P., Li, B., Li, J., Wei, J., Gao, M., and Liao, H.: Climate responses in China to domestic and foreign aerosol changes due to clean air actions during 2013–2019, npj Clim. Atmos. Sci., 6, 160, https://doi.org/10.1038/s41612-023-00488-y, 2023.
Gao, J., Zhang, H., Zhang, W., Chen, X., Shen, W., Xiao, T., Zhang, Y., and Shi, Y.: China regional 250 m fractional vegetation cover data set (2000–2023), TPDC [data set], https://doi.org/10.11888/Terre.tpdc.300330, 2024a.
Gao, J., Zhang, H., Zhang, W., Chen, X., Shen, W., Xiao, T., Zhang, Y., and Shi, Y.: China regional 250m normalized difference vegetation index data set (2000–2023), TPDC [data set], https://doi.org/10.11888/Terre.tpdc.300328, 2024b.
Gao, X., Ma, W., Ma, C., Zhang, F., and Wang, Y.: Analysis on the Current Status of Utilization of Crop Straw in China, J. Huazhong Agric. Univ., 21, 242–247, https://doi.org/10.13300/j.cnki.hnlkxb.2002.03.012, 2002.
Getis, A. and Ord, J. K.: The Analysis of Spatial Association by Use of Distance Statistics, Geogr. Anal., 24, 189–206, https://doi.org/10.1111/j.1538-4632.1992.tb00261.x, 1992.
Giglio, L., Randerson, J. T., van der Werf, G. R., Kasibhatla, P. S., Collatz, G. J., Morton, D. C., and DeFries, R. S.: Assessing variability and long-term trends in burned area by merging multiple satellite fire products, Biogeosciences, 7, 1171–1186, https://doi.org/10.5194/bg-7-1171-2010, 2010.
Giglio, L., Schroeder, W., and Justice, C. O.: The collection 6 MODIS active fire detection algorithm and fire products, Remote Sens. Environ., 178, 31–41, https://doi.org/10.1016/j.rse.2016.02.054, 2016.
Giglio, L., Boschetti, L., Roy, D. P., Humber, M. L., and Justice, C. O.: The Collection 6 MODIS burned area mapping algorithm and product, Remote Sens. Environ., 217, 72–85, https://doi.org/10.1016/j.rse.2018.08.005, 2018.
Han, J., Shen, Z., Li, Y., Luo, C., Xu, Q., Yang, K., and Zhang, Z.: Beta Diversity Patterns of Post-fire Forests in Central Yunnan Plateau, Southwest China: Disturbances Intensify the Priority Effect in the Community Assembly, Front. Plant Sci., 9, 1000, https://doi.org/10.3389/fpls.2018.01000, 2018.
He, M., Wang, X., Han, L., Feng, X., and Mao, X.: Emission Inventory of Crop £Òesidues Field Burning and Its Temporal and Spatial Distribution in Sichuan Province, Environm. Sci., 36, 1208–1215, 2015 (in Chinese).
He, X., Huang, Q., Yang, D., Yang, Y., Xie, G., Yang, S., Liang, C., and Qin, Z.: Spatiotemporal Analysis of Open Biomass Burning in Guangxi Province, China, from 2012 to 2023 Based on VIIRS, Fire, 7, 370, https://doi.org/10.3390/fire7100370, 2024.
Hély, C., Caylor, K., Alleaume, S., Swap, R. J., and Shugart, H. H.: Release of gaseous and particulate carbonaceous compounds from biomass burning during the SAFARI 2000 dry season field campaign, J. Geophys. Res., 108, 8470, https://doi.org/10.1029/2002JD002482, 2003.
Hoelzemann, J. J., Schultz, M. G., Brasseur, G. P., Granier, C., and Simon, M.: Global Wildland Fire Emission Model (GWEM): Evaluating the use of global area burnt satellite data, J. Geophys. Res., 109, 2003JD003666, https://doi.org/10.1029/2003JD003666, 2004.
Hu, H. F., Zhi Heng, W., Guo Hua, L., and Bo Jie, F.: Vegetation carbon storage of major shrublands in China, Chin. J. Plant Ecol., 30, 539–544, https://doi.org/10.17521/cjpe.2006.0071, 2006.
Hu, R., Chen, X., Chen, J., Zhang, S., Kuang, Y., Yu, H., Ji, H., Zhao, X., Yi, S., Meng, B., and Li, M.: MODIS NDVI saturation assessment of alpine meadow grassland biomass estimation using remote sensing: a case study in the eastern edge of the Qinghai-Tibet Plateau, Acta. Eco. Sin., 44, 6357–6372, https://doi.org/10.20103/j.stxb.202310172262, 2024.
Hu, T. and Zhou, G.: Drivers of lightning- and human-caused fire regimes in the Great Xing'an Mountains, Forest Ecol. Manag., 329, 49–58, https://doi.org/10.1016/j.foreco.2014.05.047, 2014.
Huang, B., Wu, B., and Barry, M.: Geographically and temporally weighted regression for modeling spatio-temporal variation in house prices, Int. J. Geogr. Inf. Sci., 24, 383–401, https://doi.org/10.1080/13658810802672469, 2010.
Huang, X., Li, M., Li, J., and Song, Y.: A high-resolution emission inventory of crop burning in fields in China based on MODIS Thermal Anomalies/Fire products, Atmos. Environ., 50, 9–15, https://doi.org/10.1016/j.atmosenv.2012.01.017, 2012.
Hurvich, C. M. and Tsai, C.-L.: Regression and time series model selection in small samples, Biometrika, 76, 297–307, 1989.
Hurvich, C. M., Simonoff, J. S., and Tsai, C.-L.: Smoothing Parameter Selection in Nonparametric Regression Using an Improved Akaike Information Criterion, J. R. Stat. Soc. B., 60, 271–293, https://doi.org/10.1111/1467-9868.00125, 1998.
IPCC: 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Volume 4: Agriculture, Forestry and Other Land Use, Chapter 2 and Chapter 6, Intergovernmental Panel on Climate Change, https://www.ipcc-nggip.iges.or.jp/public/2019rf/index.html (last access: 21 May 2025), 2019.
Jin, Q., Wang, W., Zheng, W., Innes, J. L., Wang, G., and Guo, F.: Dynamics of pollutant emissions from wildfires in Mainland China, J. Environ. Manage., 318, 115499, https://doi.org/10.1016/j.jenvman.2022.115499, 2022.
Junpen, A., Roemmontri, J., Boonman, A., Cheewaphongphan, P., Thao, P. T. B., and Garivait, S.: Spatial and Temporal Distribution of Biomass Open Burning Emissions in the Greater Mekong Subregion, Climate, 8, 90, https://doi.org/10.3390/cli8080090, 2020.
Kaiser, J. W., Heil, A., Andreae, M. O., Benedetti, A., Chubarova, N., Jones, L., Morcrette, J.-J., Razinger, M., Schultz, M. G., Suttie, M., and van der Werf, G. R.: Biomass burning emissions estimated with a global fire assimilation system based on observed fire radiative power, Biogeosciences, 9, 527–554, https://doi.org/10.5194/bg-9-527-2012, 2012.
Kasischke, E. S., Christensen, N. L., and Stocks, B. J.: Fire, Global Warming, and the Carbon Balance of Boreal Forests, Ecol. Appl., 5, 437–451, https://doi.org/10.2307/1942034, 1995.
Kelly, R., Chipman, M. L., Higuera, P. E., Stefanova, I., Brubaker, L. B., and Hu, F. S.: Recent burning of boreal forests exceeds fire regime limits of the past 10 000 years, P. Natl. Acad. Sci. USA., 110, 13055–13060, https://doi.org/10.1073/pnas.1305069110, 2013.
Koster, R. D., Darmenov, A. S., and da Silva, A. M.: The Quick Fire Emissions Dataset (QFED): Documentation of Versions 2.1, 2.2 and 2.4, NASA/TM–2015–104606, NASA, 38, https://ntrs.nasa.gov/citations/20180005253 (last access: 3 September 2025), 2015.
Lan, Z., Su, Z., Guo, M., C. Alvarado, E., Guo, F., Hu, H., and Wang, G.: Are Climate Factors Driving the Contemporary Wildfire Occurrence in China?, Forests, 12, 392, https://doi.org/10.3390/f12040392, 2021.
Langenfelds, R. L., Francey, R. J., Pak, B. C., Steele, L. P., Lloyd, J., Trudinger, C. M., and Allison, C. E.: Interannual growth rate variations of atmospheric CO2 and its δ13C, H2, CH4, and CO between 1992 and 1999 linked to biomass burning, Global Biogeochem. Cy., 16, https://doi.org/10.1029/2001GB001466, 2002.
Lasslop, G., Hantson, S., Harrison, S. P., Bachelet, D., Burton, C., Forkel, M., Forrest, M., Li, F., Melton, J. R., Yue, C., Archibald, S., Scheiter, S., Arneth, A., Hickler, T., and Sitch, S.: Global ecosystems and fire: Multi-model assessment of fire-induced tree-cover and carbon storage reduction, Glob. Change Biol., 26, 5027–5041, https://doi.org/10.1111/gcb.15160, 2020.
Lebakula, V., Sims, K., Reith, A., Rose, A., McKee, J., Coleman, P., Kaufman, J., Urban, M., Jochem, C., Whitlock, C., Ogden, M., Pyle, J., Roddy, D., Epting, J., and Bright, E.: LandScan Global 30 Arcsecond Annual Global Gridded Population Datasets from 2000 to 2022, Sci Data, 12, 495, https://doi.org/10.1038/s41597-025-04817-z, 2025.
Li, B., Xu, Z., Liu, B., Zhang, Z., Qiu, W., and Wang, W.: Development of a finer-resolution multi-year emission inventory for open biomass burning in Heilongjiang Province, China, Sci. Rep., 14, 29969, https://doi.org/10.1038/s41598-024-81092-9, 2024a.
Li, M., Wu, Y., Liu, Y., Zhang, Y., and Yu, Q.: Study on the Driving Factors of the Spatiotemporal Pattern in Forest Lightning Fires and 3D Fire Simulation Based on Cellular Automata, Forests, 15, 1857, https://doi.org/10.3390/f15111857, 2024b.
Li, Y., Zhao, J., Guo, X., Zhang, Z., Tan, G., and Yang, J.: The Influence of Land Use on the Grassland Fire Occurrence in the Northeastern Inner Mongolia Autonomous Region, China, Sensors, 17, 437, https://doi.org/10.3390/s17030437, 2017.
Lian, C., Xiao, C., Feng, Z., and Ma, Q.: Accelerating decline of wildfires in China in the 21st century, Front. For. Glob. Change, 6, 1252587, https://doi.org/10.3389/ffgc.2023.1252587, 2024a.
Lian, C., Feng, Z., Gu, H., and Gao, B.: Disentangling the Roles of Climate Variables in Forest Fire Occurrences in China, Remote Sens., 17, 88, https://doi.org/10.3390/rs17010088, 2024b.
Lin, Z., Huang, L., Tian, H., Chen, A., and Wang, X.: China Wildfire Emission Dataset (ChinaWED v1) for the period 2012–2022, Geosci. Model Dev., 18, 2509–2520, https://doi.org/10.5194/gmd-18-2509-2025, 2025.
Liu, Y., Chen, J., Shi, Y., Zheng, W., Shan, T., and Wang, G.: Global Emissions Inventory from Open Biomass Burning (GEIOBB): utilizing Fengyun-3D global fire spot monitoring data, Earth Syst. Sci. Data, 16, 3495–3515, https://doi.org/10.5194/essd-16-3495-2024, 2024.
Lizundia-Loiola, J., Otón, G., Ramo, R., and Chuvieco, E.: A spatio-temporal active-fire clustering approach for global burned area mapping at 250 m from MODIS data, Remote Sens. Environ., 236, 111493, https://doi.org/10.1016/j.rse.2019.111493, 2020.
Long, T., Zhang, Z., He, G., Jiao, W., Tang, C., Wu, B., Zhang, X., Wang, G., and Yin, R.: 30 m Resolution Global Annual Burned Area Mapping Based on Landsat Images and Google Earth Engine, Remote Sens., 11, 489, https://doi.org/10.3390/rs11050489, 2019.
Lü, A., Tian, H., Liu, M., Liu, J., and Melillo, J. M.: Spatial and temporal patterns of carbon emissions from forest fires in China from 1950 to 2000, J. Geophys. Res., 111, D05313, https://doi.org/10.1029/2005JD006198, 2006.
Luo, Y., Zhang, Z., Li, Z., Chen, Y., Zhang, L., Cao, J., and Tao, F.: Identifying the spatiotemporal changes of annual harvesting areas for three staple crops in China by integrating multi-data sources, Environ. Res. Lett., 15, 074003, https://doi.org/10.1088/1748-9326/ab80f0, 2020a.
Luo, Y., Zhang, Z., Li, Z., Chen, Y., Zhang, L., Cao, J., and Tao, F.: Data for: Identifying the spatiotemporal changes of annual harvesting areas for three staple crops in China by integrating multi-data sources, V2, Mendeley Data [data set], https://doi.org/10.17632/jbs44b2hrk.2, 2020b.
Ma, W., Feng, Z., Cheng, Z., Chen, S., and Wang, F.: Identifying Forest Fire Driving Factors and Related Impacts in China Using Random Forest Algorithm, Forests, 11, 507, https://doi.org/10.3390/f11050507, 2020.
McGuire, A. D., Sitch, S., Clein, J. S., Dargaville, R., Esser, G., Foley, J., Heimann, M., Joos, F., Kaplan, J., Kicklighter, D. W., Meier, R. A., Melillo, J. M., Moore, B., Prentice, I. C., Ramankutty, N., Reichenau, T., Schloss, A., Tian, H., Williams, L. J., and Wittenberg, U.: Carbon balance of the terrestrial biosphere in the Twentieth Century: Analyses of CO2, climate, and land use effects with four process-based ecosystem models, Global Biogeochem. Cy., 15, 183–206, https://doi.org/10.1029/2000GB001298, 2001.
Mieville, A., Granier, C., Liousse, C., Guillaume, B., Mouillot, F., Lamarque, J.-F., Grégoire, J.-M., and Pétron, G.: Emissions of gases and particles from biomass burning during the 20th century using satellite data and an historical reconstruction, Atmos. Environ., 44, 1469–1477, https://doi.org/10.1016/j.atmosenv.2010.01.011, 2010.
Moran, P. A. P.: The Interpretation of Statistical Maps, J. Roy. Stat. Soc. B Met., 10, 243–251, https://doi.org/10.1111/j.2517-6161.1948.tb00012.x, 1948.
NBSC (National Bureau of Statistics of China): China Statistical Yearbook 2001–2022, China Statistics Press, Beijing, https://www.stats.gov.cn/sj/ndsj/ (last access: 21 May 2025), 2001–2022 (in Chinese).
Our World in Data: CO2 emissions from wildfires, Our World in Data, https://ourworldindata.org/wildfires, last access: 21 May 2025.
Phillips, C. A., Rogers, B. M., Elder, M., Cooperdock, S., Moubarak, M., Randerson, J. T., and Frumhoff, P. C.: Escalating carbon emissions from North American boreal forest wildfires and the climate mitigation potential of fire management, Sci. Adv., 8, eabl7161, https://doi.org/10.1126/sciadv.abl7161, 2022.
Ping, X., Chang, Y., Liu, M., Hu, Y., Yuan, Z., Shi, S., Jia, Y., Li, D., and Yu, L.: Fuel burning efficiency under various fire severities of a boreal forest landscape in north-east China, Int. J. Wildland Fire, 30, 691–701, https://doi.org/10.1071/WF20143, 2021.
Qin, X., Yan, H., Zhan, Z., and Li, Z.: Characterising vegetative biomass burning in China using MODIS data, Int. J. Wildland Fire, 23, 69, https://doi.org/10.1071/WF12163, 2014.
Qiu, X., Duan, L., Chai, F., Wang, S., Yu, Q., and Wang, S.: Deriving High-Resolution Emission Inventory of Open Biomass Burning in China based on Satellite Observations, Environ. Sci. Technol., 50, 11779–11786, https://doi.org/10.1021/acs.est.6b02705, 2016.
Quan, D., Quan, H., Zhu, W., Lin, Z., and Jin, R.: A Comparative Study on the Drivers of Forest Fires in Different Countries in the Cross-Border Area between China, North Korea and Russia, Forests, 13, 1939, https://doi.org/10.3390/f13111939, 2022.
Ren, J., Yu, P., and Xu, X.: Straw Utilization in China-Status and Recommendations, Sustainability, 11, 1762, https://doi.org/10.3390/su11061762, 2019.
Rogelj, J., Popp, A., Calvin, K. V., Luderer, G., Emmerling, J., Gernaat, D., Fujimori, S., Strefler, J., Hasegawa, T., Marangoni, G., Krey, V., Kriegler, E., Riahi, K., Van Vuuren, D. P., Doelman, J., Drouet, L., Edmonds, J., Fricko, O., Harmsen, M., Havlík, P., Humpenöder, F., Stehfest, E., and Tavoni, M.: Scenarios towards limiting global mean temperature increase below 1.5 °C, Nat. Clim. Change, 8, 325–332, https://doi.org/10.1038/s41558-018-0091-3, 2018.
Shiraishi, T., Hirata, R., and Hirano, T.: New Inventories of Global Carbon Dioxide Emissions through Biomass Burning in 2001–2020, Remote Sensing, 13, 1914, https://doi.org/10.3390/rs13101914, 2021.
Streets, D. G., Yarber, K. F., Woo, J.-H., and Carmichael, G. R.: Biomass burning in Asia: Annual and seasonal estimates and atmospheric emissions, Global Biogeochem. Cy., 17, 1099, https://doi.org/10.1029/2003GB002040, 2003.
Su, Y., Guo, Q., Xue, B., Hu, T., Alvarez, O., Tao, S., and Fang, J.: Spatial distribution of forest aboveground biomass in China: Estimation through combination of spaceborne lidar, optical imagery, and forest inventory data, Remote Sens. Environ., 173, 187–199, https://doi.org/10.1016/j.rse.2015.12.002, 2016.
Tian, H., Zhao, D., and Wang, Y.: Emission inventories of atmospheric pollutants discharged from biomass burning in China, Acta Sci. Circumst., 31, 349–357, 2011 (in Chinese).
van der Werf, G. R., Randerson, J. T., Collatz, G. J., Giglio, L., Kasibhatla, P. S., Arellano, A. F., Olsen, S. C., and Kasischke, E. S.: Continental-Scale Partitioning of Fire Emissions During the 1997 to 2001 El Niño/La Niña Period, Science, 303, 73–76, https://doi.org/10.1126/science.1090753, 2004.
van der Werf, G. R., Randerson, J. T., Giglio, L., van Leeuwen, T. T., Chen, Y., Rogers, B. M., Mu, M., van Marle, M. J. E., Morton, D. C., Collatz, G. J., Yokelson, R. J., and Kasibhatla, P. S.: Global fire emissions estimates during 1997–2016, Earth Syst. Sci. Data, 9, 697–720, https://doi.org/10.5194/essd-9-697-2017, 2017.
van Leeuwen, T. T., van der Werf, G. R., Hoffmann, A. A., Detmers, R. G., Rücker, G., French, N. H. F., Archibald, S., Carvalho Jr., J. A., Cook, G. D., de Groot, W. J., Hély, C., Kasischke, E. S., Kloster, S., McCarty, J. L., Pettinari, M. L., Savadogo, P., Alvarado, E. C., Boschetti, L., Manuri, S., Meyer, C. P., Siegert, F., Trollope, L. A., and Trollope, W. S. W.: Biomass burning fuel consumption rates: a field measurement database, Biogeosciences, 11, 7305–7329, https://doi.org/10.5194/bg-11-7305-2014, 2014.
Van Wees, D., van der Werf, G. R., Randerson, J. T., Andela, N., Chen, Y., and Morton, D. C.: The role of fire in global forest loss dynamics, Glob. Change Biol., 27, 2377–2391, https://doi.org/10.1111/gcb.15591, 2021.
Van Wees, D., van der Werf, G. R., Randerson, J. T., Rogers, B. M., Chen, Y., Veraverbeke, S., Giglio, L., and Morton, D. C.: Global biomass burning fuel consumption and emissions at 500 m spatial resolution based on the Global Fire Emissions Database (GFED), Geosci. Model Dev., 15, 8411–8437, https://doi.org/10.5194/gmd-15-8411-2022, 2022.
Wang, S. X. and Zhang, C. Y.: Spatial and Temporal Distribution of Air Pollutant Emissions from Open Burning of Crop Residues in China, Sciencepaper Online, 3, 329–333, 2008 (in Chinese).
Wang, Z., Wang, Z., Zou, Z., Chen, X., Wu, H., Wang, W., Su, H., Li, F., Xu, W., Liu, Z., and Zhu, J.: Severe Global Environmental Issues Caused by Canada's Record-Breaking Wildfires in 2023, Adv. Atmos. Sci., 41, 565–571, https://doi.org/10.1007/s00376-023-3241-0, 2023.
Wiedinmyer, C., Akagi, S. K., Yokelson, R. J., Emmons, L. K., Al-Saadi, J. A., Orlando, J. J., and Soja, A. J.: The Fire INventory from NCAR (FINN): a high resolution global model to estimate the emissions from open burning, Geosci. Model Dev., 4, 625–641, https://doi.org/10.5194/gmd-4-625-2011, 2011.
Wiedinmyer, C., Kimura, Y., McDonald-Buller, E. C., Emmons, L. K., Buchholz, R. R., Tang, W., Seto, K., Joseph, M. B., Barsanti, K. C., Carlton, A. G., and Yokelson, R.: The Fire Inventory from NCAR version 2.5: an updated global fire emissions model for climate and chemistry applications, Geosci. Model Dev., 16, 3873–3891, https://doi.org/10.5194/gmd-16-3873-2023, 2023.
Wotawa, G. and Trainer, M.: The Influence of Canadian Forest Fires on Pollutant Concentrations in the United States, Science, 288, 324–328, https://doi.org/10.1126/science.288.5464.324, 2000.
Wu, J., Kong, S., Wu, F., Cheng, Y., Zheng, S., Yan, Q., Zheng, H., Yang, G., Zheng, M., Liu, D., Zhao, D., and Qi, S.: Estimating the open biomass burning emissions in central and eastern China from 2003 to 2015 based on satellite observation, Atmos. Chem. Phys., 18, 11623–11646, https://doi.org/10.5194/acp-18-11623-2018, 2018.
Xie, B., Jia, X., Qin, Z., Zhao, C., and Shao, M.: Comparison of interpolation methods for soil moisture prediction on China's Loess Plateau, Vadose Zone J., 19, e20025, https://doi.org/10.1002/vzj2.20025, 2020.
Xie, X., Zhang, Y., Liang, R., Chen, W., Zhang, P., Wang, X., Zhou, Y., Cheng, Y., and Liu, J.: Wintertime Heavy Haze Episodes in Northeast China Driven by Agricultural Fire Emissions, Environ. Sci. Tech. Let., 11, 150–157, https://doi.org/10.1021/acs.estlett.3c00940, 2024.
Xu, X., Liu, J., Zhang, S., Li, R., Yan, C., and Wu, S.: China's multi-period land use land cover remote sensing monitoring dataset (CNLUCC), Data Registration and Publishing System of the Resource and Environm., Sci. Data Center of the Chinese Academy of Sciences [data set], https://doi.org/10.12078/2018070201, 2018.
Yan, S., He G., and Zhang X.: Forest aboveground biomass products in China, 2013–2021 [DS/OL]. V1, Science Data Bank [data set], https://cstr.cn/31253.11.sciencedb.07122 (last access: 3 September 2025), 2023.
Yan, X., Ohara, T., and Akimoto, H.: Bottom-up estimate of biomass burning in mainland China, Atmos. Environ., 40, 5262–5273, https://doi.org/10.1016/j.atmosenv.2006.04.040, 2006.
Yang, S., He, H., Lu, S., Chen, D., and Zhu, J.: Quantification of crop residue burning in the field and its influence on ambient air quality in Suqian, China, Atmos. Environ., 42, 1961–1969, https://doi.org/10.1016/j.atmosenv.2007.12.007, 2008.
Yang, W. and Jiang, X.: High-resolution estimation of air pollutant emissions from vegetation burning in China (2000-2018), Front. Environ. Sci., 10, 896373, https://doi.org/10.3389/fenvs.2022.896373, 2022.
Yin, L., Du, P., Zhang, M., Liu, M., Xu, T., and Song, Y.: Estimation of emissions from biomass burning in China (2003–2017) based on MODIS fire radiative energy data, Biogeosciences, 16, 1629–1640, https://doi.org/10.5194/bg-16-1629-2019, 2019.
Ying, L., Cheng, H., Shen, Z., Guan, P., Luo, C., and Peng, X.: Relative humidity and agricultural activities dominate wildfire ignitions in Yunnan, Southwest China: Patterns, thresholds, and implications, Agr. Forest Meteorol., 307, 108540, https://doi.org/10.1016/j.agrformet.2021.108540, 2021.
Zeng, Y., Liu, S., Huang, S., Patil, S. D., Gao, W., and Li, H.: Exploring Spatiotemporal Characteristics and Driving Forces of Straw Burning in Hunan Province, China, from 2010 to 2020, Remote Sens., 16, 1438, https://doi.org/10.3390/rs16081438, 2024.
Zhang, W., Yang, Y., Hu, C., Zhang, L., Hou, B., Wang, W., Li, Q., and Li, Y.: NPP and Carbon Emissions under Forest Fire Disturbance in Southwest and Northeast China from 2001 to 2020, Forests, 14, 999, https://doi.org/10.3390/f14050999, 2023a.
Zhang, W., Shao, H., Sun, H., Zhang, W., and Yan, Q.: Optimizing Carbon Sequestration in Forest Management Plans Using Advanced Algorithms: A Case Study of Greater Khingan Mountains, Forests, 14, 1785, https://doi.org/10.3390/f14091785, 2023b.
Zhang, Y., Shao, M., Lin, Y., Luan, S., Mao, N., Chen, W., and Wang, M.: Emission inventory of carbonaceous pollutants from biomass burning in the Pearl River Delta Region, China, Atmos. Environ., 76, 189–199, https://doi.org/10.1016/j.atmosenv.2012.05.055, 2013.
Zhao, Y., Nielsen, C. P., Lei, Y., McElroy, M. B., and Hao, J.: Quantifying the uncertainties of a bottom-up emission inventory of anthropogenic atmospheric pollutants in China, Atmos. Chem. Phys., 11, 2295–2308, https://doi.org/10.5194/acp-11-2295-2011, 2011.
Zhou, Y., Xing, X., Lang, J., Chen, D., Cheng, S., Wei, L., Wei, X., and Liu, C.: A comprehensive biomass burning emission inventory with high spatial and temporal resolution in China, Atmos. Chem. Phys., 17, 2839–2864, https://doi.org/10.5194/acp-17-2839-2017, 2017.
Short summary
We studied wildfire carbon dioxide emissions across China from 2001 to 2022 and found that cropland and forest fires are the main contributors. While forest and shrub fires have decreased, cropland fires are rising, especially in northeastern China. Our findings suggest that climate and local policies affect wildfire emissions and that better fire management is needed to reduce future carbon impacts.
We studied wildfire carbon dioxide emissions across China from 2001 to 2022 and found that...
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