Analysis of visibility and temperature patterns of Indian cities and it´s clustering to identify the effect of presence of aerosol particles in the atmosphere

Variations in ambient air quality data are caused by changes in the pollutant emission rate, and meteorological and topographical conditions of the place. Mass concentration of aerosol is a measure of air quality and aerosol source strength at a particular location. It has been shown that clear sky visibility over land has decreased globally over the past 30 years, indicative of an increase in aerosols, or airborne particulates, over the world´s continents during that time. The change in climatic conditions is of great concern in environment, industry and agriculture. The disturbance of temperature and other climate factors due to presence of aerosol particles in air, results in global climate changes. The aim of this research is to develop artificial neural network based clustering method for ambient atmospheric condition prediction in Indian city. Self-Organizing Map (SOM) Neural Network to divide data into four clusters which represents association in between atmospheric conditions belonging to cities of one cluster due to the amount of aerosol particles present in the atmosphere of those cities. The experimental results determined climate changes due to concentration of aerosol particles in the atmosphere of different cities in India and the correlation in between change in visibility and change in the temperature during the months of March to June.