Prediction of Vertical Profile of NOx2082; Using Deep Multimodal Fusion Network Based on the Ground-Based 3-D Remote Sensing

doi:10.1109/TGRS.2021.3061476

	Prediction of Vertical Profile of NOx2082; Using Deep Multimodal Fusion Network Based on the Ground-Based 3-D Remote Sensing
	Zhang, Shulin 2; Li, Bo 1; Liu, Lei 2; Hu, Qihou 3; Liu, Haoran 4; Zheng, Rui 2; Zhu, Yizhi 1; Liu, Ting 1; Sun, Mingzhai 2; Liu, Cheng 2
刊名	IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
	2022
卷号	60
关键词	Pollution measurement Atmospheric modeling Atmospheric measurements Predictive models Solid modeling Data models Instruments 3-D prediction of NO2 deep learning neural network multiaxis differential optical absorption spectroscopy (MAX-DOAS) multimodal information fusion remote sensing
ISSN号	0196-2892
DOI	10.1109/TGRS.2021.3061476
通讯作者	Sun, Mingzhai(mingzhai@ustc.edu.cn) ; Liu, Cheng(chliu81@ustc.edu.cn)
英文摘要	The vertical distribution profiles of NO₂ are essential for understanding the mechanisms, detecting near-surface emissions, and tracking pollutant transportation at high altitude. However, most of the published NO₂ studies are based on the surface 2-D measurements. The ground-based 3-D remote-sensing stations were recently built to measure vertical distribution profiles of NO₂. However, the stations were spatially sparse due to the high cost and could not make the measurements without sunlight. In this study, we first developed a multimodel fusion network (MF-net) based on the sparse vertical observations from the Jing-Jin-Ji region. We achieved the 3-D profile prediction of NO₂ in the range of 39.005x2013;41.405N and 115.005x2013;117.905E with 24-h coverage. The MF-net significantly surpassed the conventional WRF-CHEM model and provided a more accurate evaluation of the NO₂ transmission between Beijing and the neighboring cities. Besides, the MF-net covers the monitoring of NO₂ to the whole study area and extends the monitoring time to the entire day (24 h), making it serviceable for continuous spatial-temporal estimation of NO₂ and its transmission in pollution events. The MF-net provides more robust data support to formulate reasonable and effective pollution prevention and control measures.
资助项目	National Natural Science Foundation of China[41722501] ; National Key Research and Development Program of China[2017YFC0210002] ; National High-Resolution Earth Observation Project of China[05-Y30B01-9001-19/20-3] ; Key Research Program of Frontier Sciences, CAS[ZDBSLY-DQC008]
WOS关键词	ABSORPTION CROSS-SECTIONS ; MAX-DOAS OBSERVATIONS ; TEMPERATURE-DEPENDENCE ; AEROSOL EXTINCTION ; TROPOSPHERIC NO2 ; 000 CM(-1) ; EMISSIONS ; MODEL ; FORMALDEHYDE ; CHINA
WOS研究方向	Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000728266600118
资助机构	National Natural Science Foundation of China ; National Key Research and Development Program of China ; National High-Resolution Earth Observation Project of China ; Key Research Program of Frontier Sciences, CAS
内容类型	期刊论文
源URL	[http://ir.hfcas.ac.cn:8080/handle/334002/126408]
专题	中国科学院合肥物质科学研究院
通讯作者	Sun, Mingzhai; Liu, Cheng
作者单位	1.Univ Sci & Technol China, Sch Earth & Space Sci, Hefei 230026, Peoples R China 2.Univ Sci & Technol China, Dept Precis Machinery & Instrumentat, Hefei 230026, Peoples R China 3.Chinese Acad Sci, Anhui Inst Opt & Fine Mech, Key Lab Environm Opt & Technol, Hefei 230031, Peoples R China 4.Anhui Univ, Inst Phys Sci & Informat Technol, Hefei 230601, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Shulin,Li, Bo,Liu, Lei,et al. Prediction of Vertical Profile of NOx2082; Using Deep Multimodal Fusion Network Based on the Ground-Based 3-D Remote Sensing[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2022,60.
APA	Zhang, Shulin.,Li, Bo.,Liu, Lei.,Hu, Qihou.,Liu, Haoran.,...&Liu, Cheng.(2022).Prediction of Vertical Profile of NOx2082; Using Deep Multimodal Fusion Network Based on the Ground-Based 3-D Remote Sensing.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,60.
MLA	Zhang, Shulin,et al."Prediction of Vertical Profile of NOx2082; Using Deep Multimodal Fusion Network Based on the Ground-Based 3-D Remote Sensing".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 60(2022).