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[Spatial-temporal Variation and Driving Factors of Ozone in China from 2019 to 2021 Based on EOF Technique and KZ Filter].
Wang, Hao-Qi; Zhang, Yu-Fen; Luo, Zhong-Wei; Wang, Yan-Yang; Dai, Qi-Li; Bi, Xiao-Hui; Wu, Jian-Hui; Feng, Yin-Chang.
Afiliação
  • Wang HQ; State Environment Protection Key Laboratory of Urban Particulate Air Pollution Prevention, China Meteorological Administration-Nankai University Cooperative Laboratory for Atmospheric Environment-Health Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, Ch
  • Zhang YF; State Environment Protection Key Laboratory of Urban Particulate Air Pollution Prevention, China Meteorological Administration-Nankai University Cooperative Laboratory for Atmospheric Environment-Health Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, Ch
  • Luo ZW; State Environment Protection Key Laboratory of Urban Particulate Air Pollution Prevention, China Meteorological Administration-Nankai University Cooperative Laboratory for Atmospheric Environment-Health Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, Ch
  • Wang YY; State Environment Protection Key Laboratory of Urban Particulate Air Pollution Prevention, China Meteorological Administration-Nankai University Cooperative Laboratory for Atmospheric Environment-Health Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, Ch
  • Dai QL; State Environment Protection Key Laboratory of Urban Particulate Air Pollution Prevention, China Meteorological Administration-Nankai University Cooperative Laboratory for Atmospheric Environment-Health Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, Ch
  • Bi XH; State Environment Protection Key Laboratory of Urban Particulate Air Pollution Prevention, China Meteorological Administration-Nankai University Cooperative Laboratory for Atmospheric Environment-Health Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, Ch
  • Wu JH; State Environment Protection Key Laboratory of Urban Particulate Air Pollution Prevention, China Meteorological Administration-Nankai University Cooperative Laboratory for Atmospheric Environment-Health Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, Ch
  • Feng YC; State Environment Protection Key Laboratory of Urban Particulate Air Pollution Prevention, China Meteorological Administration-Nankai University Cooperative Laboratory for Atmospheric Environment-Health Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, Ch
Huan Jing Ke Xue ; 44(4): 1811-1820, 2023 Apr 08.
Article em Zh | MEDLINE | ID: mdl-37040932
ABSTRACT
Based on the hourly O3 concentration data of 337 prefectural-level divisions and simultaneous surface meteorological data in China, we applied empirical orthogonal function (EOF) analysis to analyze the main spatial patterns, variation trends, and main meteorological driving factors of O3 concentration in China from March to August in 2019-2021. In this study, a KZ (Kolmogorov-Zurbenko) filter was used to decompose the time series of O3 concentration and simultaneous meteorological factors into corresponding short-term, seasonal, and long-term components in 31 provincial capitals.Then, the stepwise regression was used to establish the relationship between O3 and meteorological factors. Ultimately, the long-term component of O3 concentration after "meteorological adjustment" was reconstructed. The results indicated that the first spatial patterns of O3 concentration showed a convergent change, that is, the volatility of O3 concentration was weakened in the high-value region of variability and enhanced in the low-value region.Before and after the meteorological adjustment, the variation trend of O3 concentration in different cities was different to some extent. The adjusted curve was "flatter" in most cities. Among them, Fuzhou, Haikou, Changsha, Taiyuan, Harbin, and Urumqi were greatly affected by emissions. Shijiazhuang, Jinan, and Guangzhou were greatly affected by meteorological conditions. Beijing, Tianjin, Changchun, and Kunming were greatly affected by emissions and meteorological conditions.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: Zh Revista: Huan Jing Ke Xue Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Suíça

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: Zh Revista: Huan Jing Ke Xue Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Suíça