Statistics as a Technology to Predict the Seasonal Variation of Air Pollution
Tripta Narayan*, Tanushree Bhattacharya, Soubhik Chakraborty, Swapan Konar, Shilpi Singh
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Source: Crossref
Published: Jan 30, 2020
DOI: 10.35940/ijitee.b7599.019320
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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.
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