High-Resolution Daily Emission Inventory of Biomass Burning in the Amur-Heilong River Basin Based on MODIS Fire Radiative Energy Data

被引:2
|
作者
Lv, Zhenghan [1 ,2 ,3 ]
Shi, Yusheng [1 ,3 ]
Guo, Dianfan [1 ,2 ]
Zhu, Yue [3 ]
Man, Haoran [1 ,2 ]
Zhang, Yang [1 ,2 ]
Zang, Shuying [1 ,2 ]
机构
[1] Harbin Normal Univ, Heilongjiang Prov Key Lab Geog Environm Monitorin, Harbin 150025, Peoples R China
[2] Harbin Normal Univ, Heilongjiang Prov Collaborat Innovat Ctr Cold Reg, Harbin 150025, Peoples R China
[3] Chinese Acad Sci, Aerosp Informat Res Inst, State Environm Protect Key Lab Satellite Remote S, Beijing 100101, Peoples R China
基金
中国国家自然科学基金; 黑龙江省自然科学基金;
关键词
long term; high-resolution; Amur-Heilong River Basin; biomass burning; fire radiative power; emission inventory; SOUTHEAST-ASIA; CARBON; CHINA; AEROSOL; RUSSIA; IMPACT; VIIRS; SMOKE;
D O I
10.3390/rs14164087
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Open biomass burning (OBB) is one of the major factors that influences the regional climate environment and surface vegetation landscape, and it significantly affects the regional carbon cycle process and atmospheric environment. The Amur-Heilong River Basin (ARB) is a fire-prone region in high-latitude boreal forests. In this study, we used fire radiative power (FRP) obtained from a Moderate-resolution Imaging Spectroradiometer (MODIS) to estimate OBB emissions from the ARB and established a long-term series (2003-2020) with a high spatiotemporal resolution and a daily 1 km emissions inventory. The results show that the annual average emissions of CO2, CO, CH4, NMHCs, NOx, NH3, SO2, BC, OC, PM2.5, and PM10 were estimated to be 153.57, 6.16, 0.21, 0.78, 0.28, 0.08, 0.06, 0.04, 0.39, 0.66, and 0.85 Tg/a, respectively. Taking CO2 as an example, grassland fire in the dry season (mainly in April and October) was the largest contributor (87.18 Tg/a), accounting for 56.77% of the total CO2 emissions from the ARB, followed by forest fire prone to occur in April-May (56.53 Tg/a, 36.81%) and crop fire during harvest season (9.86 Tg/a, 6.42%). Among the three countries in the ARB, Russia released the most total CO2 emissions (2227.04 Tg), much higher than those of China (338.41 Tg) and Mongolia (198.83 Tg). The major fire types were crop fires (40.73%) on the Chinese side and grass fires on the Russian (56.67%) and Mongolian (97.56%) sides. Over the past decade, OBB CO2 emissions have trended downward (-0.79 Tg/a) but crop burning has increased significantly (+0.81 Tg/a). Up to 83.7% of crop fires occurred in China (2010-2020), with a concentrated and southward trend. Comparisons with the Global Fire Emission Dataset (GFED4.1s), the Fire INventory from NCAR (FINNv2.2), and the Global Fire Assimilation System (GFASv1.2) showed that our newly established emission inventory was in good agreement with these three datasets in the ARB. However, this multi-year, daily 1 km high-resolution emission inventory has the advantages of detecting more small fire emissions that were overlooked by coarse-grid datasets. The methods described here can be used as an effective means of estimating greenhouse gas and aerosol emissions from biomass combustion.
引用
收藏
页数:21
相关论文
共 17 条
  • [1] A high-resolution emission inventory of crop burning in fields in China based on MODIS Thermal Anomalies/Fire products
    Huang, Xin
    Li, Mengmeng
    Li, Jianfeng
    Song, Yu
    [J]. ATMOSPHERIC ENVIRONMENT, 2012, 50 : 9 - 15
  • [2] Deriving High-Resolution Emission Inventory of Open Biomass Burning in China based on Satellite Observations
    Qiu, Xionghui
    Duan, Lei
    Chai, Fahe
    Wang, Shuxiao
    Yu, Qian
    Wang, Shulan
    [J]. ENVIRONMENTAL SCIENCE & TECHNOLOGY, 2016, 50 (21) : 11779 - 11786
  • [3] First High-Resolution Emission Inventory of Levoglucosan for Biomass Burning and Non-Biomass Burning Sources in China
    Wu, Jian
    Kong, Shaofei
    Zeng, Xin
    Cheng, Yi
    Yan, Qin
    Zheng, Huang
    Yan, Yingying
    Zheng, Shurui
    Liu, Dantong
    Zhang, Xiaoyang
    Fu, Pingqing
    Wang, Shuxiao
    Qi, Shihua
    [J]. ENVIRONMENTAL SCIENCE & TECHNOLOGY, 2021, 55 (03) : 1497 - 1507
  • [4] Estimation of emissions from biomass burning in China (2003-2017) based on MODIS fire radiative energy data
    Yin, Lifei
    Du, Pin
    Zhang, Minsi
    Liu, Mingxu
    Xu, Tingting
    Song, Yu
    [J]. BIOGEOSCIENCES, 2019, 16 (07) : 1629 - 1640
  • [5] Emissions from open biomass burning in India: Integrating the inventory approach with high-resolution Moderate Resolution Imaging Spectroradiometer (MODIS) active-fire and land cover data
    Venkataraman, C.
    Habib, G.
    Kadamba, D.
    Shrivastava, M.
    Leon, J. -F.
    Crouzille, B.
    Boucher, O.
    Streets, D. G.
    [J]. GLOBAL BIOGEOCHEMICAL CYCLES, 2006, 20 (02)
  • [6] A high-resolution nutrient emission inventory for hotspot identification in the Yangtze River Basin
    Li, Jincheng
    Chen, Yan
    Cai, Kaikui
    Fu, Jiaxing
    Ting, Tang
    Chen, Yihui
    Folberth, Christian
    Liu, Yong
    [J]. JOURNAL OF ENVIRONMENTAL MANAGEMENT, 2022, 321
  • [7] Wildfire combustion emission inventory in Southwest China (2001-2020) based on MODIS fire radiative energy data
    Ning, Xincen
    Li, Jianwei
    Zhuang, Pengkun
    Lai, Shifu
    Zheng, Xiaogan
    [J]. ATMOSPHERIC POLLUTION RESEARCH, 2024, 15 (11)
  • [8] Updated Glacial Lake Inventory of Indus River Basin based on High-Resolution Indian Remote Sensing Satellite Data
    Ankit Gupta
    Ruhi Maheshwari
    Nibedita Sweta
    B. Simhadri Guru
    P. Venkat Rao
    V. Venkateshwar Raju
    [J]. Journal of the Indian Society of Remote Sensing, 2022, 50 : 73 - 98
  • [9] Updated Glacial Lake Inventory of Indus River Basin based on High-Resolution Indian Remote Sensing Satellite Data
    Gupta, Ankit
    Maheshwari, Ruhi
    Sweta
    Guru, Nibedita
    Rao, B. Simhadri
    Raju, P. Venkat
    Rao, V. Venkateshwar
    [J]. JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING, 2022, 50 (01) : 73 - 98
  • [10] Method for High-resolution Emission Inventory for Road Vehicles in Chengdu Based on Traffic Flow Monitoring Data
    Pan, Yu-Jin
    Li, Yuan
    Chen, Jun-Hui
    Shi, Jia-Cheng
    Tian, Hong
    Zhang, Ji
    Zhou, Jing
    Chen, Xia
    Liu, Zheng
    Qian, Jun
    [J]. Huanjing Kexue/Environmental Science, 2020, 41 (08): : 3581 - 3590