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 2 are essential for understanding the mechanisms, detecting near-surface emissions, and tracking pollutant transportation at high altitude. However, most of the published NO 2 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 2. 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 2 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 2 transmission between Beijing and the neighboring cities. Besides, the MF-net covers the monitoring of NO 2 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 2 and its transmission in pollution events. The MF-net provides more robust data support to formulate reasonable and effective pollution prevention and control measures. Index Terms-3-D prediction of NO 2 , deep learning neural network, multiaxis differential optical absorption spectroscopy (MAX-DOAS), multimodal information fusion, remote sensing. I. INTRODUCTION I N RECENT years, people have increasingly been concerned about air pollution with the development of urban Manuscript