• DocumentCode
    124500
  • Title

    Mass concentration variations characteristics of PM10 and PM2.5 in Guangzhou (China)

  • Author

    Runping Liu ; Fenglei Fan

  • Author_Institution
    Sch. of Geogr., South China Normal Univ., Guangzhou, China
  • fYear
    2014
  • fDate
    11-14 June 2014
  • Firstpage
    111
  • Lastpage
    115
  • Abstract
    As the main pollutants in the atmosphere, PM10 and PM2.5 get much attention and become primary focus recently due to their significant effect on human health. In this paper, the paralleled 24-hour average concentrations of PM10 and PM2.5 during June 2012 to May 2013 are obtained from 13 monitoring stations which spread all over the Guangzhou (China). The characteristics variations of PM10 and PM2.5 are analyzed using SPSS software. According to the curves of PM10 and PM2.5, it can be found that these two curves (PM10 and PM2.5) are considerable volatility but quite similar trend with high correlation. The regression analysis between PM10 and PM2.5 are finished, the equation is PM10=1.26*PM2.5+3.28(R2=0.94). Meanwhile, the ratio (PM2.5/PM10) is analyzed to explore which one is the main pollution type in Guangzhou. Based on our work, we find that:(i) the ratio is range from 0.42 to 0.98 with the average value of 0.76, which suggests that PM2.5 is the main pollution type and greater than PM2.5-10 in Guangzhou; (ii) seasonal variation of the ratios are shown as followed: Winter (0.80) = Autumn (0.80) > Spring (0.76) > Summer (0.62). (iii) Spatially, the maximum value of the ratio (0.85) occurs in South (Panyu) of Guangzhou, followed by Center (0.76), North (Conghua, 0.75) and Northwest (Huadu, 0.72) of Guangzhou orderly. Lastly, the spatial concentration map of PM10 and PM2.5 is drawn using GIS.
  • Keywords
    aerosols; air pollution; regression analysis; AD 2012 06 to 2013 05; China; Conghua; GIS; Guangzhou; Huadu; PM2.5 mass concentration; PM10 mass concentration; PM2.5 mass concentration; Panyu; SPSS software; air pollutants; human health; main pollution type; mass concentration variation characteristics; monitoring stations; regression analysis; seasonal variation; spatial concentration map; volatility; Air pollution; Correlation coefficient; Distribution functions; Graphical models; Monitoring; Remote sensing; Guangzhou; PM10; PM2.5; seasonal variation; spatial variation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Earth Observation and Remote Sensing Applications (EORSA), 2014 3rd International Workshop on
  • Conference_Location
    Changsha
  • Print_ISBN
    978-1-4799-5757-6
  • Type

    conf

  • DOI
    10.1109/EORSA.2014.6927860
  • Filename
    6927860