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Characteristics of Atmospheric Pollution in a Chinese Megacity: Insights from Three Different Functional Areas

Author

Listed:
  • Jie Yang

    (Yangtze River Delta Urban Wetland Ecosystem National Field Scientific Observation and Research Station, School of Environmental and Geological Sciences, Shanghai Normal University, Shanghai 200234, China)

  • Xinran Fu

    (Yangtze River Delta Urban Wetland Ecosystem National Field Scientific Observation and Research Station, School of Environmental and Geological Sciences, Shanghai Normal University, Shanghai 200234, China)

  • Liping Qiao

    (State Environmental Protection Key Laboratory of Formation and Prevention of Urban Air Pollution Complex, Shanghai Academy of Environment Sciences, Shanghai 200233, China)

  • Lan Yao

    (Yangtze River Delta Urban Wetland Ecosystem National Field Scientific Observation and Research Station, School of Environmental and Geological Sciences, Shanghai Normal University, Shanghai 200234, China
    State Environmental Protection Key Laboratory of Formation and Prevention of Urban Air Pollution Complex, Shanghai Academy of Environment Sciences, Shanghai 200233, China
    Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), Shanghai 200438, China)

  • Fei Zhang

    (Zhejiang Provincial Key Laboratory of Organic Pollution Process and Control, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China)

  • Weiyue Li

    (Yangtze River Delta Urban Wetland Ecosystem National Field Scientific Observation and Research Station, School of Environmental and Geological Sciences, Shanghai Normal University, Shanghai 200234, China)

Abstract

The most important atmospheric pollutants include PM 2.5 , PM 10 , SO 2 , NO 2 , CO and O 3 . Characteristics of atmospheric pollution were investigated by analyzing daily and hourly concentrations of the six key pollutants in three different functional areas (urban, suburban, and rural) of Shanghai during 2019–2021. Results show that O 3 , exceeding PM 2.5 , has become the primary pollutant determining air quality in Shanghai. The frequency of O 3 as a primary pollutant ranged from 40% in an urban area to 71% in a rural area, which was much higher than that of PM 2.5 (14–21%). NO 2 and SO 2 , precursors of PM 2.5 , presented a clear weekend effect, whereas PM 2.5 at weekends seems higher than that on weekdays. In the warm season, O 3 at weekends was higher than that on weekdays in the three different functional areas, whereas no significant difference was observed between O 3 on weekdays and at weekends in the cold season. Potential source contribution function analysis indicated that air pollution in Shanghai was impacted by inter-regional and intra-regional transport. The potential source areas of PM 2.5 and O 3 were different, which brought challenges to the coordinated control of PM 2.5 and O 3 in Shanghai. This study emphasizes the prominent O 3 pollution in Shanghai, and argues that the prevention and control of O 3 pollution requires regional joint prevention and control strategy.

Suggested Citation

  • Jie Yang & Xinran Fu & Liping Qiao & Lan Yao & Fei Zhang & Weiyue Li, 2023. "Characteristics of Atmospheric Pollution in a Chinese Megacity: Insights from Three Different Functional Areas," Sustainability, MDPI, vol. 15(3), pages 1-14, January.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:3:p:2429-:d:1050787
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    Citations

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    Cited by:

    1. Francesco D’Amico & Ivano Ammoscato & Daniel Gullì & Elenio Avolio & Teresa Lo Feudo & Mariafrancesca De Pino & Paolo Cristofanelli & Luana Malacaria & Domenico Parise & Salvatore Sinopoli & Giorgia D, 2024. "Anthropic-Induced Variability of Greenhouse Gasses and Aerosols at the WMO/GAW Coastal Site of Lamezia Terme (Calabria, Southern Italy): Towards a New Method to Assess the Weekly Distribution of Gathe," Sustainability, MDPI, vol. 16(18), pages 1-31, September.
    2. Hui Chen & Jingjing Liu & Peizhi Wang & Xiao Lin & Jingjin Ma & Chunying Wang, 2024. "Characteristics of PM 2.5 Chemical Species in 23 Chinese Cities Identified Using a Vehicular Platform," Sustainability, MDPI, vol. 16(6), pages 1-16, March.

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