In Huan jing ke xue= Huanjing kexue
The problem of urban ozone (O3) pollution has become prominent in recent years. However, the meteorological factors associated with O3 pollution remain unclear. Analyzing the characteristics of O3 pollution in Suzhou, as a typical urban city, and exploring the high-impact meteorological factors with O3 pollution are crucial to the prevention and control of air pollution in this region. This study used correlation analysis and machine learning methods to analyze the variation in O3 concentration and the relationship between meteorological driving factors in Suzhou based on the O3 concentration data provided by Suzhou Environmental Monitoring Center and the contemporaneous meteorological observation data in Suzhou from April to September in 2015 to 2020. The results showed that: ① O3 pollution exceeding the standard rate was more than 20% in ozone seasons during the past six years; further, pollution days of O3 and the number of pollution days of O3 as the primary pollutant increased yearly. Evidently, the problem of O3 pollution has become increasingly prominent. ② The diurnal variations in O3 were unimodal with the valley point at 07:00 and the highest peak between 15:00 and 16:00. Similar trends were found in diurnal variations of both air temperature and solar radiation, but the daily highest peak came earlier than that of O3. The results also showed an apparent weekend effect of O3 concentration in 2017 and 2019 and a significant correlation between O3 concentration and solar irradiance during the week. In addition, the monthly variation in O3 concentration and pollution exceeding the standard rate was bimodal. ③The occurrence of ozone pollution was affected by various meteorological conditions. The maximum number of days appeared when daily sunshine hours lasted longer than 7 hours, with a daily maximum air temperature around 30℃, solar irradiance ranging from 350 to 440 kW·m-2, and relative humidity ranging from 50% to 75%, at which time the intensity of pollution was the strongest. When the wind speed of easterly wind was less than 1.5 m·s-1, or the wind speed of southwest wind was less than 3.5 m·s-1, moderate ozone pollution occurred. ④ An optimal prediction model of O3 concentration was established based on machine learning, which had good predictive ability for O3 concentration in April, May, July, and September but did not perform well when O3 concentration exceeded 200 μg·m-3. Meanwhile, it was found that solar radiation had the most obvious effect on O3 concentration, followed by relative humidity, whereas the temperature and wind were less important than the former two factors.
He Yan, Lin Hui-Juan, Cao Shu-Ya, Song Jing-Lu, Sun Wei, Xiong Yu
2023-Jan-08
O3 concentration, correlation analysis, machine learning, pollution characteristics, prediction model, solar radiation
