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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 39.005&#x2013;41.405N and 115.005&#x2013;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.

Prediction of Vertical Profile of NO₂ Using Deep Multimodal Fusion Network Based on the Ground-Based 3-D Remote Sensing