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 ›› 2021, Vol. 37 ›› Issue (3): 40-46.doi: 10.3969/j.issn.1673-503X.2021.03.006

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Analysis of the characteristics of O3 concentration and its influencing factors in summer from 2017 to 2019 in the southern urban area of Taiyuan

Sheng-dong LU1(),Jun-xia LI1,Fen LI1,Jun-jie ZHAO1,Ze-hui JIN2,Ying LI3,Xiao LIU1   

  1. 1. Shanxi Meteorological Disaster Prevention Technology Center, Taiyuan 030012, China
    2. Wutai Mountain Meteorological station, Shanxi 035515, China
    3. Shanxi Meteorological Science Research Institute, Taiyuan 030002, China
  • Received:2020-07-28 Online:2021-06-30 Published:1900-01-01

Abstract:

Based on the hourly data of pollutant concentration and relative meteorological factors during summer (June to August) from 2017-2019, the distribution characteristics of O3 concentration and its influencing factors in Taiyuan were analyzed using the neural network method.The results show that, during the summer from 2017-2019, the numbers of days in which the O3 concentration exceeds the limit in Taiyuan are 55 d, 39 d, 59 d, respectively.The cases that the O3 concentration exceeds the limit mostly occur in June and July.The diurnal variation of O3 concentration is unimodal, with the lowest around 06:00, and the peak around 15:00.The conditions such as high temperature, strong radiation, low humidity, low pressure, and southwest wind can easily lead to an increase of O3 concentration in Taiyuan urban areas.The NW wind is beneficial to the diffusion of O3 concentration.The relationships of NO2 and CO with O3 concentration are negative, and the influence of NO2 is more significant.The selected cases show that the O3 concentration fluctuates with the influencing factors.The relationship between O3 concentration and the influencing factors is constructed using the neural network method, with the correlation coefficient of 0.96, the mean square root and average absolute error of 8 μg·m-3 and 6%, respectively, and TS score of 0.95.The neural network model is valuable for the O3 concentration prediction and ozone pollution control in the Taiyuan area.

Key words: O3 concentration, Meteorological factors, Neural network

CLC Number: