Air Quality Monitoring System Using Machine Learning and IOT

Abstract

Air pollution is a significant and widespread environmental problem worldwide, with numerous researchers focusing on it in light of its impact on human health. One of the most effective ways to raise awareness about the issue and protect public health is through the use of air quality monitoring systems. In many urban areas, air pollution is a major environmental concern. By utilizing real-time monitoring of pollution data, local authorities can make informed decisions about traffic management and other relevant issues. Accurate prediction of air quality (AQ) is crucial for effective decision- making. IoT-based sensors are increasingly being used to dynamically improve AQ prediction, which has traditionally been limited by expensive and low-accuracy techniques. The advancement of machine learning (ML) algorithms has opened up new possibilities for AQ prediction, and recent research has explored the benefits and drawbacks of various methodologies for real-time monitoring and prediction of AQ, as well as the challenges involved.