[Analysis of Characteristics and Meteorological Influence Factors of Ozone Pollution in Henan Province].

Huan Jing Ke Xue

Research Institute of Environmental Science, College of Chemistry and Molecular Engineering, Zhengzhou University, Zhengzhou 450001, China.

Published: February 2020

AI Article Synopsis

  • The study analyzed ozone pollution in Henan Province in 2017, finding an average maximum daily 8-hour ozone concentration (MDA8) of 108 μg·m, with levels peaking in summer and varying significantly between cities.
  • The worst pollution was noted during late spring and early summer, particularly in June, with Anyang experiencing 88 exceeding days compared to just 17 in Xinyang.
  • A negative correlation was found between ozone concentration and particulate matter, CO, and NO, while meteorological factors influenced ozone levels differently across seasons and locations.

Article Abstract

The spatio-temporal characteristics of ozone (O) pollution in the Henan Province in 2017 and its relationship with particulate matter, precursors, and meteorological factors were studied using the data obtained from the air quality monitoring station and national baseline ground climate station. Results showed that the annual mean O concentration of a maximum daily 8-h average (MDA8) was 108 μg·m in the Henan Province with the trend of summer > spring > autumn > winter. Different levels of O were observed in various cities. The number of days exceeding the standard was up to 88 days in Anyang, while the lowest was found in Xinyang with 17 days. The most severe of O pollution occurred during late spring and early summer. During this period, the average monthly concentration of O MDA8 was above 140 μg·m, and peaked in June. The qualitative and quantitative analysis showed that the monthly average concentration of O MDA8 was negatively correlated with particulate matter, and the O hourly concentration was also negatively correlated with CO and NO. The O MDA8 concentration and meteorological factors (sunshine duration, temperature, rainfall, visibility, humidity, and wind speed) had different correlations during different seasons and various cities.

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http://dx.doi.org/10.13227/j.hjkx.201908122DOI Listing

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