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
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;
Conference_Titel :
Earth Observation and Remote Sensing Applications (EORSA), 2014 3rd International Workshop on
Conference_Location :
Changsha
Print_ISBN :
978-1-4799-5757-6
DOI :
10.1109/EORSA.2014.6927860