IoT-Based Air Quality Monitoring System with Machine Learning for Accurate and Real-time Data Analysis

Air quality plays a crucial role in human health and the well-being of the environment. Unfortunately, air pollution has been on the rise due to various sources such as vehicle emissions, industrial activities, energy production, and natural disasters like wildfires. Understanding and assessing the quality of the air we breathe is of utmost importance. Air Quality Monitoring (AQM) systems, integrated with sensors and advanced technologies, are utilized to measure particulate matter and air pollutants like ozone, nitrogen oxides, and sulfur dioxide. The data collected by these systems helps formulate policies, monitor pollution reduction efforts, and empower the public to make informed decisions regarding their health and well-being. Currently, AQM stations are primarily used for calculating the Air Quality Index (AQI) and monitoring pollution. However, the infrastructure requirements, operational complexities, and ongoing expenses associated with these stations limit the expansion of AQM networks and the availability of air pollution data. To overcome these limitations, it is imperative to develop low-cost, efficient, and real-time data-sensing devices.