Regression Model to Analyse Air Pollutants Over a Coastal Industrial Station Visakhapatnam ( India ) - Prasad, N.V. Krishna; Sarma, M.S.S.R.K.N.; Sasikala, P.; Raju M, Naga; Madhavi, N. (International Journal of Data Science, 2020)
Particulate matter concentration and its study has gained tremendous significance in view of increase in air pollution. Since air pollution has many adverse effects on mankind, measures may be taken by observing the trends in PM2.5 (particulate matter) and concentrations of pollutants like NO2, SO2, NO2, NO, NOx, CO, NH3 and RH(Relative Humidity) as well as temperature. Even though continuous monitoring of air pollution in urban locations has been increasing in view of its huge impact on the sustainable development and ecological balance a regression model is essential always to analyse large sets of data. These regression models also play vital role in some cases where data was not observed due to unavoidable circumstances and during times when the measuring instruments do not work. In this context an attempt was made to develop a regression model exclusively for Visakhapatnam(India) a coastal, urban and industrial station and to analyse the trends in particulate matter concentration at this staion. A regression model was developed with PM2.5 as dependent variable and SO2, NOx, NO2, CO, NH3, temperature(Temp) and relative humidity(RH) as independent variables. The efficiency of the model was tested with known independent variables and PM2.5 was estimated. It is found that observed and estimated PM2.5 values are highly correlated.