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
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