{"title":"[Effect of Meteorological Elements and Air Pollutants on Ozone in Yinchuan City Park].","authors":"Cong-Hui Wang, Guang-Yao Shi, Si-Qi Yang, Xi-Lu Ni, Li-Rong Yang, Li-Ping Ji","doi":"10.13227/j.hjkx.202309171","DOIUrl":null,"url":null,"abstract":"<p><p>To examine the underlying determinants of ozone (O<sub>3</sub>) in Yinchuan's urban park during varying seasons and to ascertain the role played by meteorological events and air contaminants in influencing O<sub>3</sub> concentrations at high altitudes, data on O<sub>3</sub>, meteorological factors, and air pollutants were collected through prolonged positional observations carried out at the Ningxia Yinchuan National Urban Ecosystem Research Station. Pearson correlation analysis and a structural equation model were utilized to investigate the spatio-temporal distribution patterns, trends, and the primary factors influencing O<sub>3</sub>. The findings demonstrated a notable seasonal variability in O<sub>3</sub> levels in Yinchuan's urban park, displaying an \"unimodal type\" with the O<sub>3</sub> concentration peaking in summer (131.18 μg·m<sup>-3</sup>) and bottoming out in winter (71.45 μg·m<sup>-3</sup>). Among the meteorological factors, the highest impact on O<sub>3</sub> was attributed to temperature and wind speed (temperature mainly through direct effects and wind speed mainly through indirect effects). Conversely, air pollutants such as NO<i><sub>x</sub></i> and SO<sub>2</sub> greatly affected O<sub>3</sub> primarily through direct effects. Wind speed was identified as the primary influencing factor on O<sub>3</sub> during spring and summer, potentially contributing 29% and 24.7%, respectively. Conversely, NO<sub>2</sub> was implicated as the primary factor during autumn and winter, with an estimated contribution of 26.6% and 29.7%, respectively. Thus, a structural equation model can efficiently reveal the primary determinants behind O<sub>3</sub> variations throughout various seasons, which could furnish a scientifically rigorous foundation and technical aid for mitigating and managing O<sub>3</sub> levels in high-altitude regions.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"45 9","pages":"5149-5156"},"PeriodicalIF":0.0000,"publicationDate":"2024-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"环境科学","FirstCategoryId":"1087","ListUrlMain":"https://doi.org/10.13227/j.hjkx.202309171","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Environmental Science","Score":null,"Total":0}
引用次数: 0
Abstract
To examine the underlying determinants of ozone (O3) in Yinchuan's urban park during varying seasons and to ascertain the role played by meteorological events and air contaminants in influencing O3 concentrations at high altitudes, data on O3, meteorological factors, and air pollutants were collected through prolonged positional observations carried out at the Ningxia Yinchuan National Urban Ecosystem Research Station. Pearson correlation analysis and a structural equation model were utilized to investigate the spatio-temporal distribution patterns, trends, and the primary factors influencing O3. The findings demonstrated a notable seasonal variability in O3 levels in Yinchuan's urban park, displaying an "unimodal type" with the O3 concentration peaking in summer (131.18 μg·m-3) and bottoming out in winter (71.45 μg·m-3). Among the meteorological factors, the highest impact on O3 was attributed to temperature and wind speed (temperature mainly through direct effects and wind speed mainly through indirect effects). Conversely, air pollutants such as NOx and SO2 greatly affected O3 primarily through direct effects. Wind speed was identified as the primary influencing factor on O3 during spring and summer, potentially contributing 29% and 24.7%, respectively. Conversely, NO2 was implicated as the primary factor during autumn and winter, with an estimated contribution of 26.6% and 29.7%, respectively. Thus, a structural equation model can efficiently reveal the primary determinants behind O3 variations throughout various seasons, which could furnish a scientifically rigorous foundation and technical aid for mitigating and managing O3 levels in high-altitude regions.