Main Factors Influencing Winter Visibility at the Xinjin Flight College of the Civil Aviation Flight University of China. (20th October 2020)

• Record Type: Journal Article
• Authors: Zhang, Jing; Zhao, Pengguo; Wang, Xiuting; Zhang, Jie; Liu, Jia; Li, Bolan; Zhou, Yunjun; Wang, Hao
• Other Names: Levy Ilan Academic Editor.
• Is Part Of: Advances in meteorology. Volume 2020(2020)
• Publication Date: 2020-10-20
• Subjects: Meteorology -- Periodicals; Meteorology; Periodicals; 551.505
• DOI: 10.1155/2020/8899750
• Languages: English
• ISSNs: 1687-9309

Abstract

Abstract : Utilizing routine hourly meteorological data of Xinjin Airport and daily average PM2.5 concentration data for Chengdu, winter visibility characteristics at Xinjin Airport between 2013 and 2017 and their relationship with meteorological conditions and particulate matter were analyzed. Between 2013 and 2017, the average winter visibility in Xinjin Airport was lowest in January, followed by that in December. The occurrence frequency of haze days in winter was much higher than that of nonhaze (clean) days, being 90.2% and 9.8%, respectively. These were mainly mild haze days, with an occurrence frequency of 44.4%, while severe haze days occurred the least, with a frequency of 7.7%. The linear and nonlinear relationships between winter visibility, meteorological factors, and PM2.5 were measured using daily data in winter from 2013 to 2016. The linear correlation between PM2.5 concentration and visibility was the most evident, followed by that of relative humidity. Visibility had a higher nonlinear correlation with PM2.5 concentration, relative humidity, and dew point depression. When relative humidity was between 70% and 80%, the negative correlation between visibility and PM2.5 concentration was the most significant and could be described by a power function. The multivariate linear regression equation of PM2.5 concentration and relative humidity could account for 65.9% of the variation in winter visibility, and the multivariate nonlinear regression equation of PM2.5 concentration, relative humidity, and wind speed could account for 68.1% of the variation in winter visibility. These two equations reasonably represented the variation in winter visibility in 2017.