主办单位:中国气象局沈阳大气环境研究所
国际刊号:ISSN 1673-503X
国内刊号:CN 21-1531/P

Journal of Meteorology and Environment ›› 2023, Vol. 39 ›› Issue (2): 44-52.doi: 10.3969/j.issn.1673-503X.2023.02.006

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Variations of ozone concentration and its influence factors in Changsha from 2016 to 2019

Rui-yang HUA1(),Wei LI2,Cheng-zhi YE2,Chang-jian NI1,*()   

  1. 1. Plateau Atmosphere and Environment Key Laboratory of Sichuan Province, College of Atmospheric Sciences, Chengdu University of Information Technology, Chengdu 610225, China
    2. Hu'nan Meteorological Observatory, Changsha 410000, China
  • Received:2022-06-13 Online:2023-04-28 Published:2023-05-20
  • Contact: Chang-jian NI E-mail:850412268@qq.com;ncj1970@163.com

Abstract:

Based on the hourly monitoring data of ozone, NO2, and CO in Changsha from 2016 to 2019 and the corresponding meteorological observation data, the temporal and spatial variations of ozone concentration and the meteorological influence factors were analyzed.The results show that the annual mean maximum 8-h moving average concentration of ozone in Changsha during 2016 and 2019 were (52.81±26.61) μg·m-3, (55.89±25.42) μg·m-3, (61.74±29.56) μg·m-3 and (60.77±31.68) μg·m-3, respectively, with an increasing trend.Ozone level is the highest in summer (mean 74.56 μg·m-3) and the lowest in winter (mean 33.64 μg·m-3).The diurnal variation in ozone presents a significant single-peak pattern with the peak level appearing at around 16:00 and the exceeding standard period between 12:00 and 18:00.The spatial heterogeneity in ozone shows that the levels appear larger at the urban periphery than at the urban center sites, with the largest heterogeneity in winter (0.12) and the smallest heterogeneity in summer (0.04).The concentrations of ozone in Changsha are positively correlated with temperature and solar radiation with the coefficients of 0.61 and 0.65, respectively, and negatively correlated with relative humidity, NO2, and CO with coefficients of -0.42, -0.35, and -0.27, respectively.

Key words: Ozone level, Meteorological factors, Generalized additive models

CLC Number: