Statistics as a Technology to Predict the Seasonal Variation of Air Pollution
Tanushree Bhattacharya1, Tripta Narayan2, Soubhik Chakraborty3, Swapan Konar4, Shilpi Singh5
1Tripta Narayan *, Department of Physics, Birla Institute of Technology, Mesra, Ranchi, Jharkhand, India.
2Tanushree Bhattacharya, Department of Civil and Environment Engineering, Birla Institute of Technology, Mesra, Ranchi, Jharkhand, India.
3Soubhik Chakraborty, Department of Mathematics , Birla Institute of Technology, Mesra, Ranchi, Jharkhand, India.
4Swapan Konar, Department of Physics, Birla Institute of Technology, Mesra, Ranchi, Jharkhand, India.
5Shilpi Singh, School of Engineering and Technology, Amity University Patna, Patna, India.
Manuscript received on December 13, 2019. | Revised Manuscript received on December 22, 2019. | Manuscript published on January 10, 2020. | PP: 1426-1431 | Volume-9 Issue-3, January 2020. | Retrieval Number: B7599129219/2020©BEIESP | DOI: 10.35940/ijitee.B7599.019320
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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: The present study focuses on the analysis and prediction of the seasonal air quality over an industrial city of eastern India. It investigates the seasonal characteristics of three air pollutants nitrogen dioxide, PM10, and sulphur dioxide (SO2 ) between 2005 and 2015. The data has been obtained from the ground monitoring station of the Jharkhand State Pollution Control Board. The study concentrated on the seasons’ based findings of RSPM, SO2 and NOX. SPSS 22 software was used to find meteorological influences on the conditions of particular matters. The study shows the strength of statistics as a technology to analyse and to make a prediction even when the available information includes only one variable.
Keywords: Seasonal Variation, RSPM, Probability Density Function, NAAQ
Scope of the Article: Bio-science and Bio-technology