Name
Title | ||
|---|---|---|
Prediction of Vertical Profile of NO₂ Using Deep Multimodal Fusion Network Based on the Ground-Based 3-D Remote Sensing |
Abstract | ||
|---|---|---|
The vertical distribution profiles of NO
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are essential for understanding the mechanisms, detecting near-surface emissions, and tracking pollutant transportation at high altitude. However, most of the published NO
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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 NO
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. 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 NO
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in the range of 39.005–41.405N and 115.005–117.905E with 24-h coverage. The MF-net significantly surpassed the conventional WRF-CHEM model and provided a more accurate evaluation of the NO
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transmission between Beijing and the neighboring cities. Besides, the MF-net covers the monitoring of NO
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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 NO
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and its transmission in pollution events. The MF-net provides more robust data support to formulate reasonable and effective pollution prevention and control measures. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TGRS.2021.3061476 | IEEE Transactions on Geoscience and Remote Sensing |
Keywords | DocType | Volume |
3-D prediction of NO₂,deep learning neural network,multiaxis differential optical absorption spectroscopy (MAX-DOAS),multimodal information fusion,remote sensing | Journal | 60 |
ISSN | Citations | PageRank |
0196-2892 | 0 | 0.34 |
References | Authors | |
0 | 10 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shulin Zhang | 1 | 0 | 0.68 |
| Bo Li | 2 | 0 | 0.34 |
| Lei Liu | 3 | 0 | 1.01 |
| Qihou Hu | 4 | 0 | 0.34 |
| Haoran Liu | 5 | 0 | 0.34 |
| Rui Zheng | 6 | 0 | 0.68 |
| Yizhi Zhu | 7 | 0 | 0.34 |
| Ting Liu | 8 | 0 | 0.34 |
| Mingzhai Sun | 9 | 18 | 2.02 |
| Cheng Liu | 10 | 0 | 6.42 |