Development of an Internet of Things Based Air Quality Monitoring System Using Machine Learning

Abstract

Air pollution and its negative impacts on human health have become serious concerns in many places throughout the world. The traditional methods of monitoring air quality, such as manual sampling and laboratory analysis, are time-consuming, expensive, and may not provide real-time information. In this study, an IoT-based Air Quality Monitoring System that uses Machine Learning to provide accurate and timely analysis of air quality data is presented. The system collects data from a network of sensors measuring various air quality parameters, processes the data using ML algorithms to identify patterns and predict future conditions, and provides insights into the current state of the environment. The findings showed that the emissions had an inversely proportional impact on air quality in the study region and the system achieved an accuracy of 0.978. This study has the potential to provide accurate and timely analysis of air quality data and regulate air quality in real-time.