scholarly journals Bootstrap model averaging in time series studies of particulate matter air pollution and mortality

2005 ◽  
Vol 16 (3) ◽  
pp. 242-250 ◽  
Author(s):  
Michael A Martin ◽  
Steven Roberts
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Particulate Matter ◽  
Model Averaging ◽  
Time Series Studies ◽  
Bootstrap Model

Bayesian Model Averaging in Time-Series Studies of Air Pollution and Mortality

2007 ◽  
Vol 70 (3-4) ◽  
pp. 311-315 ◽  
Author(s):  
Duncan C. Thomas ◽  
Michael Jerrett ◽  
Nino Kuenzli ◽  
Thomas A. Louis ◽  
Francesca Dominici ◽  
...  
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Time Series Studies

scholarly journals Generalized linear mixed models in time series studies of air pollution

2011 ◽  
Vol 2 (4) ◽  
pp. 428-435 ◽  
Author(s):  
Ya–Hsiu Chuang ◽  
Sati Mazumdar ◽  
Taeyoung Park ◽  
Gong Tang ◽  
Vincent. C. Arena ◽  
...  
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Mixed Models ◽  
Generalized Linear Mixed Models ◽  
Linear Mixed Models ◽  
Time Series Studies

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.

Time-series studies on air pollution and daily outpatient visits for allergic rhinitis in Beijing, China

2011 ◽  
Vol 409 (13) ◽  
pp. 2486-2492 ◽  
Author(s):  
Fengying Zhang ◽  
Wuyi Wang ◽  
Jinmei Lv ◽  
Thomas Krafft ◽  
Jin Xu
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Allergic Rhinitis ◽  
Outpatient Visits ◽  
Time Series Studies ◽  
Beijing China

Association of particulate matter air pollution and hospital visits for respiratory diseases: a time-series study from China

2019 ◽  
Vol 26 (12) ◽  
pp. 12280-12287 ◽  
Author(s):  
Zhenyu Zhang ◽  
Pengfei Chai ◽  
Jianbing Wang ◽  
Zhenhua Ye ◽  
Peng Shen ◽  
...  
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Particulate Matter ◽  
Respiratory Diseases ◽  
Time Series Study ◽  
Hospital Visits ◽  
Series Study
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