학술논문

IoT Based: Air Quality Index and Traffic Volume Correlation
Document Type
Conference
Source
2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), 2020 11th IEEE Annual. :0143-0147 Oct, 2020
Subject
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Fields, Waves and Electromagnetics
General Topics for Engineers
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Air pollution
Monitoring
Sensors
Indexes
Pollution measurement
Atmospheric measurements
Mathematical model
Internet of Things (IoT)
Air Quality Index (AQI)
Multiple Linear Regression (MLR)
Air Pollutants
and Traffic Volume
Language
Abstract
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.