Long-Term Statistical Characteristics of Air Pollutants in a Traffic-Congested Area of Ranchi, India

Abstract

In this paper, we present an analysis of the air quality in a traffic-congested area in Ranchi, the proposed smart city as identified by the government of India. The main purpose of this study is to analyze the concentration of pollutants over a long period and to find the best possible way for its prediction. We have selected four air pollutants, particularly RSPM, SPM, SO₂ and NO _X, analyzed their distribution and compared with the National Ambient Air Quality standards over the period 2005–2015. The obtained data have been processed with two different methods and probability model as well as multiple regression models has been established for the prediction purpose. Since pollutants data are in continuous form, we have employed Easyfit software to find out the distribution pattern. Johnson SB, Error, Burr (4P) and Cauchy distributions were found to be the appropriate representatives of the RSPM, SPM, SO₂ and NO _X concentration patterns, respectively. Inverse cumulative density function has been used to predict the future concentration of particulate matters. With the help of SPSS 17 software, the impacts of the meteorological conditions on the variation of major pollutants have been examined by identifying the correlation between each pollutant and meteorological parameters and among the pollutants themselves.

Keywords

Probability density function / Probability of exceedance / MLE / Inverse CDF / ANOVA