Acute and chronic effects of ambient fine particulate matter on preterm births in Beijing, China: A time-series model

2019 ◽  
Vol 650 ◽  
pp. 1671-1677 ◽  
Author(s):  
Tianjia Guan ◽  
Tao Xue ◽  
Suhong Gao ◽  
Min Hu ◽  
Xin Liu ◽  
...  
Keyword(s):  
Time Series ◽  
Particulate Matter ◽  
Fine Particulate Matter ◽  
Preterm Births ◽  
Time Series Model ◽  
Chronic Effects ◽  
Fine Particulate ◽  
Beijing China ◽  
Acute And Chronic Effects

scholarly journals Visualization of Correlation for Air Pollution Time Series using TAQMN Data

2019 ◽  
Vol 8 (3) ◽  
pp. 7922-7927
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Particulate Matter ◽  
Pearson Correlation ◽  
Fine Particulate Matter ◽  
Correlation Method ◽  
Well Being ◽  
Pollution Levels ◽  
Fine Particulate ◽  
Pm2.5 Concentration

In Taiwan country Annan, Chiayi, Giran, and Puzi cities are facing a serious fine particulate matter (PM2.5) issue. To date the impressive advance has been made toward understanding the PM2.5 issue, counting special temporal characterization, driving variables and well-being impacted. However, notable research as has been done on the interaction of the content between the selected cities of Taiwan country for particulate matter (PM2.5) concentration. In this paper, we purposed a visualization technique based on this principle of the visualization, cross-correlation method and also the time-series concentration with particulate matter (PM2.5) for different cities in Taiwan. The visualization also shows that the correlation between the different meteorological factors as well as the different air pollution pollutants for particular cities in Taiwan. This visualization approach helps to determine the concentration of the air pollution levels in different cities and also determine the Pearson correlation, r values of selected cities are Annan, Puzi, Giran, and Wugu.

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