Visualization of Correlation for Air Pollution Time Series using TAQMN Data
Harish Kumar K.S1, Doreswamy2
1Harishkumar K .S, Computer Science, Mangalore University, Mangalore, Karnataka, India.
2Doreswamy, Computer Science, Mangalore University, Mangalore, Karnataka, India.
Manuscript received on 01 August 2019. | Revised Manuscript received on 07 August 2019. | Manuscript published on 30 September 2019. | PP: 7922-7927 | Volume-8 Issue-3 September 2019 | Retrieval Number: C6620098319/19©BEIESP | DOI: 10.35940/ijrte.C6620.098319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Air pollution, Particulate Matter (PM2.5), Visualization, Framework, Wind Speed, Correlation, TAQMN.
Scope of the Article: Patterns and Frameworks