Impact of FY-4A Satellite-Based Surface Solar Irradiance on the Classification of Meteorology for Ozone Pollution

• Published in: Aerosol and Air Quality Research 2024-02, Vol.24 (2), p.1-18
• Main Authors: Cao, Yang, Zhao, Xiaoli, Su, Debin, Ren, Hong, Cheng, Xiang, Li, Yuchun, Wang, Chenxi
• Format: Article
• Language: English
• ISSN: 1680-8584; 2071-1409
• DOI: 10.4209/aaqr.230231

Meteorological conditions are important for ozone formation, among which solar radiation is a key factor affecting ozone concentrations through its direct influence on photochemical reactions and indirect influence on precursor emissions. Due to the limited number of ground observation stations, a meteorological classification method for ozone pollution levels, including solar radiation, has not been proposed. In this study, the surface solar irradiance (SSI) obtained from the Fengyun-4A (FY-4A) satellite with high temporal and spatial resolutions was used as one of the input physical parameters to train a classifier for meteorology of ozone pollution and compared with that classifier without SSI to analyze the impact of solar radiation on the classification performance of trained classifiers. By comparing the SSI of the FY-4A satellite with ground-based observations, it was verified that there was a significant difference in values between the two data sources, but their distribution trends were consistent. By analyzing the relationship between hourly ozone concentrations and SSI of the FY-4A satellite, it was found that there was a positive linear correlation between them, and the correlation was the highest when the lead time of SSI was 3 hr. After including SSI, among the 21 cities in the study area, the number of cities with a classification accuracy exceeding 80% increased from 7 to 14, with 20 cities having a positive accuracy growth rate and 8 cities having an accuracy growth rate exceeding 4%. In addition, the SSI of the FY-4A satellite induced a significant improvement in the classification accuracy of level 1 and level 2 samples. In general, the FY-4A SSI data are helpful for improving the classification performance of the trained classifier for the meteorology of ozone pollution in the study area.