Abstract: Air pollution is an important environmental risk factor in propagation diseases such as lung cancer, autism, asthma and low birth weight etc. Regulation of air quality is an important task of the government in developing countries for ensuring people’s health and welfare.
Air pollution differs from place to place and depends on multiple pollutant sources such as industrial emissions, heavy traffic congestions, temperature, pressure, wind, humidity and burning of fossil fuels etc.
Analyzing and protecting air quality has become one of the most required activities for the government in almost all the industrial and urban areas today. In this paper, machine learning algorithms are used to analyze the concentrations of air pollutants such as SO2, NO, PM2.5, O3 and PM10.
This model analyses the air quality based on various pollutant concentrations through visualizations for effective feature extraction and decision making. A machine learning model is built using linear regression and SARIMA model to predict the air quality index based on past air quality data. The experimental results show that the proposed model can be efficiently used to detect the quality of air and predict the level of air quality in the future. The model has scored 71.69% for the train data.
Keywords: Air Pollution, air pollutants, air quality index.
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