An Internet of Things System for Sensing, Analysis & Forecasting Urban Air Quality

Constant monitoring of air quality is required in a smart city to improve human health and quality of life. Major cities of the world measure and analyze air quality and pollutant concentration with the help of few static air quality monitoring stations. Roads are arteries of a city and used by majority of the population for commuting and transportation. A low cost air quality sensing system installed in a vehicle that commutes through different routes of the city gives a fine-grained real time information about the state of pollutants and air quality in different parts of the city. In this work, we have developed an environment sensing, location aware, Internet of Things system to monitor, collect and analyze the presence of different environmental parameters in real time. Pollution route map of the routes traversed by the vehicle with sensor-setup has been created which can be accessed by mobile users in other vehicles. As air pollution is highly location dependent, there is a need to predict air quality at places for which air quality information is not known. Multiple Linear Regression has been used to used to predict AQI levels from historic data for such locations.