A novel method of retrieving low visibility during heavily polluted episodes in the North China plain. (January 2021)

• Record Type: Journal Article
• Authors: Shen, Xiaojing; Sun, Junying; Zhang, Xiaoye; Wang, Hong; Zhou, Chunhong; Zhang, Yangmei; Zhong, Junting; Liu, Zhaodong; Xia, Can; Hu, Xinyao; Zhang, Sinan
• Is Part Of: Atmospheric environment. Volume 9(2021)
• Publication Date: 2021-01
• Subjects: Low visibility prediction -- Particle hygroscopicity -- Particle number size distribution -- Light extinction parameterization
• DOI: 10.1016/j.aeaoa.2021.100101
• Languages: English
• ISSNs: 2590-1621

Abstract

Abstract: The prediction of visibility is an ongoing problem in air quality models, particularly that of low visibility during heavily polluted episodes. In this study, a new method of calculating visibility based on the particle mass concentration of PM2.5 (particles with diameter ≤ 2.5 μm) and relative humidity (RH), data for which are generally available in most regions of China, is developed. The method also considers the particle number size distribution (PNSD) and hygroscopic parameter ( κ ), and focuses on visibility below 10 km. First, the PNSD was re-constructed under dry condition (PNSDdry, rec ) based on the relationship between PM2.5 and the particle volume size distribution modal parameters obtained in a previous study conducted in the North China Plain. Then, the ambient PNSD (PNSDamb, rec ) was retrieved based on the PNSDdry, rec and κ, and the light extinction was calculated by applying the Mie code ( σ ext, amb ). Finally, the visibility was calculated based on the Koschmieder experimental equation, and denoted as Vis cal . A parameterization scheme was proposed based on the σ ext, amb, PM2.5, and RH to simulate the visibility ( Vis simu ), which is more applicable than the theoretical calculation described above. This method was validated at different locations in different regions in China. The values calculated by the scheme showed agreed well with the observed data in general, especially for low visibility of ≤5 km, associated with severe haze. Although a large bias occurred at some sites, both the hourly and daily averages for almost every event with visibility lower than 5 km were captured. The method reported in this work exhibited smaller bias below 2 km than the other visibility parameterization scheme, and will be available for improving the prediction of visibility in air quality models. Highlights: A new method of retrieving visibility based on the PM2.5 mass concentration and RH was developed. The method considered the particle number size distribution and hygroscopicity. It was validated in several locations in China and performed well. It improved the prediction accuracy of the low visibility below 5 km during aerosol pollution period.