Air Quality Prediction Using Machine Learning Techniques: A Case Study On Sulphur Dioxide Forecasting

Abstract

Air pollution is one of the greatest problems being faced by mankind. Millions of people die each year because of reasons directly or indirectly related to air pollution. Effective strategies to counter the harmful effects of air pollution are an imperative need of the times. The responses to the air pollution problems are usually knee-jerk reactions, which don’t help in the long run. For developing an effective counter-strategy for combating air pollution, it is necessary to focus the efforts on the pollutants that are most responsible for the air pollution. Examining and protecting air quality has become one of the most essential activities for the government in many industrial and urban areas today. The meteorological and traffic factors, burning of fossil fuels, and industrial parameters play significant roles in air pollution. With this increasing air pollution, We are in need of implementing models which will record information about concentrations of air pollutants(so2,no2,etc).The deposition of this harmful gases in the air is affecting the quality of people’s lives, especially in urban areas. Lately, many researchers began to use Big Data Analytics approach as there are environmental sensing networks and sensor data available. In this project, machine learning techniques are used to predict the concentration of so2 in the environment. Sulphur dioxide irritates the skin and mucous membranes of the eyes, nose, throat, and lungs. Models in time series are employed to predict the so2 readings in nearing years or months.