IoT Based: Air Quality Index and Traffic Volume Correlation

Major problem facing urban areas today is air pollution. Gas emissions from cars are considered the most important source of this kind of pollution. Pollutant gases emitted as parts of car exhaust consist of chemicals such as carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3), particulate matter (PM), and Sulphur dioxide (SO2). The environmental Protection Agency (EPA) guides to measure these chemicals by several methods to calculate the gases’ concentration. An Internet of Things (IoT) device is used to monitor air quality in real-time is also described in this paper. It uses a set of sensors that measure air quality at the street level. This paper determined the relationship between traffic volume and the Air Quality Index (AQI) as defined by EPA guidelines. Multiple Linear Regression (MLR) is used to create a mathematical model for the relationship between traffic volume and the Air Quality Index (AQI). This model has been tested on one of the streets in the city of Melbourne, Florida.