Air Pollution Monitoring System using Machine Learning techniques for Smart cities

Abstract

Monitoring the environment is a complex task that is crucial for all living beings. Urban and industrial areas are particularly vulnerable to pollution, which can lead to various health issues. This system utilizes data analysis and machine learning to monitor and classify regions where gas emissions exceed permissible levels. Unlike existing systems, which can only predict air quality in small areas with limited efficiency, the proposed system can accurately predict air quality across larger regions. By factoring in the temperature of a specific location—since air quality is heavily influenced by global warming—the system can predict air grade. The DBSCAN algorithm proves to be particularly effective in identifying highly polluted areas, outperforming other algorithms in terms of accuracy and efficiency. Air Quality Operational Centres (AQOCs) are crucial in identifying regions with severe pollution. These centres analyse data gathered by various units to make final assessments. The predicting air pollution relied on complex physiochemical processes and dispersion models, which were both time-consuming and required large datasets, making them less efficient for practical use.