Prediction of Vertical Profile of NOx2082; Using Deep Multimodal Fusion Network Based on the Ground-Based 3-D Remote Sensing
文献类型：期刊论文
| 作者 | Zhang, Shulin2; Li, Bo3 ; Liu, Lei2; Hu, Qihou1; Liu, Haoran4; Zheng, Rui2; Zhu, Yizhi3; Liu, Ting3; Sun, Mingzhai2; Liu, Cheng2
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| 刊名 | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
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| 出版日期 | 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. |
| WOS关键词 | ABSORPTION CROSS-SECTIONS ; MAX-DOAS OBSERVATIONS ; TEMPERATURE-DEPENDENCE ; AEROSOL EXTINCTION ; TROPOSPHERIC NO2 ; 000 CM(-1) ; EMISSIONS ; MODEL ; FORMALDEHYDE ; CHINA |
| 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研究方向 | Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:000728266600118 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 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 |
| 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.Chinese Acad Sci, Anhui Inst Opt & Fine Mech, Key Lab Environm Opt & Technol, Hefei 230031, Peoples R China 2.Univ Sci & Technol China, Dept Precis Machinery & Instrumentat, Hefei 230026, Peoples R China 3.Univ Sci & Technol China, Sch Earth & Space Sci, Hefei 230026, 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). |
入库方式： OAI收割
来源：合肥物质科学研究院
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