Air Quality Monitoring with IoT and Prediction Model using Data Analytics

Abstract

In India, with the advancing urbanization and rapid developments in the transportation has led to a serious concern called Air Pollution. It is becoming an Invisible Killer. Air pollution levels, particularly in cities, are the most alarming threats posed to humanity. However, the existing air quality monitoring systems do not measure the pollutants at the ground level. Although the actual exposure to human beings happens at the ground level, as the emissions from the vehicles are directly inhaled. So, there is a deep mismatch between the ambient levels of air quality measured and the actual pollutants that people inhale at the ground level. This paper focuses to monitor the real-time pollutants using the sensors for the pollutants PM2.5, NO2 and CO as these are the major pollutants from the vehicular emissions and pose serious impacts on human health. Our proposed system uses deep learning-based Long Short-Term Memory (LSTM) algorithm for forecasting the pollutants as this will influence the decision making to improve the city’s quality of air and helps the people plan their day accordingly and take precautions when the pollution levels are unsatisfactory. Finally, our work gives the comparison between prediction of the pollutants at the ground level and ambient air quality levels.