Regression modeling of gaseous air pollutants and meteorological parameters in a steel city, Rourkela

Abstract

Traditional algorithms such as diffusion model employed for estimating the distribution of pollutants in ambient air are complicated involving the solution of complex differential equations. Employing multivariate statistical models which attempt to find the underlying relationships between a set of inputs and outputs may give an easy way to predict these gaseous pollutants. A multiple linear regression model has been developed for predicting sulphur dioxide, oxides of nitrogen, ammonia and carbon monoxide in a steel city using the meteorological parameters like temperature, relative humidity, wind speed and wind direction. Results have shown a good correlation between predictors and predicted values (R2≈0.7). A uniform effect of the meteorological parameters in distributing these gaseous pollutants has been observed.