Published Paper Details:

Deterioration of air quality is one of the major issues faced by various cities in our country. The meteriological factors,burning of fossil fuels,emissions from power plants etc interupts the flow of fresh air and results in the spread of sulphur dioxide,carbon monoxide,Nitrogen dioxide,particular pollutants which is harmfull for all living organisms. So it is necessary to understand the effectiveness of air quality monitoring programs for planning air pollution control strategies by analyzing the trends in air quality regularly. In this study,the varying trends of ambient air quality were analyzed and predicted in terms of Air Quality Index (AQI) based on the database of dangerous pollutants like NO2 and RSPM that are monitored at different monitoring stations in Thrissur District,Kerala,India. Here,we uses SARIMA (Seasonal Autoregressive Integrated Moving Average )model to predict the future month-wise concentration of pollutants and uses SVM (Support Vector Machine) algorithm to classify the pollution levels into different categories. This knowledge on air pollution levels of future months will help the public to take measures to reduce its level to a lesser than the harmful range.