气候变化领域集成服务门户(CCPortal): Inverse modelling of Chinese NOx emissions using deep learning: integrating in situ observations with a satellite-based chemical reanalysis

CCPortal

DOI	10.5194/acp-22-14059-2022
	Inverse modelling of Chinese NOx emissions using deep learning: integrating in situ observations with a satellite-based chemical reanalysis
	He, Tai-Long; Jones, Dylan B. A.; Miyazaki, Kazuyuki; Bowman, Kevin W.; Jiang, Zhe; Chen, Xiaokang; Li, Rui; Zhang, Yuxiang; Li, Kunna
发表日期	2022
ISSN	1680-7316
EISSN	1680-7324
起始页码	14059
结束页码	14074
卷号	22 期号:21 页码:16
英文摘要	Nitrogen dioxide (NO2) column density measurements from satellites have been widely used in constraining emissions of nitrogen oxides (NOx = NO + NO2). However, the utility of these measurements is impacted by reduced observational coverage due to cloud cover and their reduced sensitivity toward the surface. Combining the information from satellites with surface observations of NO2 will provide greater constraints on emission estimates of NOx. We have developed a deep-learning (DL) model to integrate satellite data and in situ observations of surface NO2 to estimate NO, emissions in China. A priori information for the DL model was obtained from satellite-derived emissions from the Tropospheric Chemistry Reanalysis (TCR-2). A two-stage training strategy was used to integrate in situ measurements from the China Ministry of Ecology and Environment (MEE) observation network with the TCR-2 data. The DL model is trained from 2005 to 2018 and evaluated for 2019 and 2020. The DL model estimated a source of 19.4 Tg NO for total Chinese NOx emissions in 2019, which is consistent with the TCR-2 estimate of 18.5 +/- 3.9 Tg NO and the 20.9 Tg NO suggested by the Multi-resolution Emission Inventory for China (MEIC). Combining the MEE data with TCR-2, the DL model suggested higher NOx emissions in some of the less-densely populated provinces, such as Shaanxi and Sichuan, where the MEE data indicated higher surface NO2 concentrations than TCR-2. The DL model also suggested a faster recovery of NOx emissions than TCR-2 after the Chinese New Year (CNY) holiday in 2019, with a recovery time scale that is consistent with Baidu Qianxi mobility data. In 2020, the DL-based analysis estimated about a 30 % reduction in NOx emissions in eastern China during the COVID-19 lockdown period, relative to pre-lockdown levels. In particular, the maximum emission reductions were 42 % and 30 % for the Jing-Jin-Ji (JJJ) and the Yangtze River Delta (YRD) mega-regions, respectively. Our results illustrate the potential utility of the DL model as a complementary tool for conventional data-assimilation approaches for air quality applications.
学科领域	Environmental Sciences; Meteorology & Atmospheric Sciences
语种	英语
WOS研究方向	Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences
WOS记录号	WOS:000878123100001
来源期刊	ATMOSPHERIC CHEMISTRY AND PHYSICS
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/273914
作者单位	University of Toronto; California Institute of Technology; National Aeronautics & Space Administration (NASA); NASA Jet Propulsion Laboratory (JPL); Chinese Academy of Sciences; University of Science & Technology of China, CAS; University of Washington; University of Washington Seattle
推荐引用方式 GB/T 7714	He, Tai-Long,Jones, Dylan B. A.,Miyazaki, Kazuyuki,et al. Inverse modelling of Chinese NOx emissions using deep learning: integrating in situ observations with a satellite-based chemical reanalysis[J],2022,22(21):16.
APA	He, Tai-Long.,Jones, Dylan B. A..,Miyazaki, Kazuyuki.,Bowman, Kevin W..,Jiang, Zhe.,...&Li, Kunna.(2022).Inverse modelling of Chinese NOx emissions using deep learning: integrating in situ observations with a satellite-based chemical reanalysis.ATMOSPHERIC CHEMISTRY AND PHYSICS,22(21),16.
MLA	He, Tai-Long,et al."Inverse modelling of Chinese NOx emissions using deep learning: integrating in situ observations with a satellite-based chemical reanalysis".ATMOSPHERIC CHEMISTRY AND PHYSICS 22.21(2022):16.