Air Quality Monitoring and Predicting System for Sustainable Health Management using Multi-Linear Regression in IoT

Abstract

Fresh Air is the preeminent requirement of each and every human being for a healthy living. With the increase in urbanization and the number of vehicles on road, large amount of various poisonous gases and Particulate Matter is released into the environment, causing global warming, rise in sea level, change in climatic condition, rainfall pattern, droughts and floods, etc. along with different types of endemic and epidemic diseases. Air pollution has become non-trivial phenomena in the world and has a diverse effect on every living being. In this paper, a model is built to provide a solution, to monitor the pollution level in air in any location and a warning message is sent against the exposure of living beings to hazardous gases. System is built using Machine Learning technique, the real time data collected from different locations is used as test data, and the model is trained with the current values to predict the future gaseous values. A graphical representation of the air quality is presented to the user to display the current and predicted values. If values exceed a certain predefined threshold, then possible symptoms are displayed to the user.