Published Paper Details:

Examining air quality has become one of the most essential activities for the peoples in many industrial and urban areas today. The quality of air is adversely affected due to various forms of pollution caused by electricity, transportation, fuel uses etc. The deposition of harmful gases is creating a serious problem for the quality of life in smart cities. With this increasing air pollution, we need to implement efficient air quality monitoring and prdiction models which collect information about the concentration of air pollutants and provide assessment of air pollution in area. Particulate matter contains microscopic solids or liquid droplets that are so small that they can be inhaled and cause serious health problems. Of these, particles less than 2.5 micrometres in diameter, also known as fine particles or PM2.5, pose the greatest risk to health. Hence, air quality evaluation and prediction has become an important research area. The focus of this project is to study about the data mining and machine learning techniques used for prediction of air pollution and the mainly focus is on the prediction of PM2.5 on the basis of all the other air pollutants and temperature and humidity.