AI-Based Visual Air Quality Assessment Using Image and Video Processing Techniques

Air pollution has become a major environmental concern due to its impact on human health, the environment, and overall quality of life. Monitoring air quality is important for understanding pollution levels and identifying conditions that may affect the surrounding environment. Traditional air-quality monitoring systems generally depend on physical sensors and monitoring stations, which may not always be readily available in every location. To address this limitation, this research presents an AI-Based Visual Air Quality Assessment System Using Image and Video Processing Techniques. The proposed system is a software-based application that analyses uploaded images and videos to identify visible indicators associated with air pollution, such as smoke, haze, dust, and reduced atmospheric clarity. The system processes the uploaded media and examines relevant visual characteristics to estimate the overall visual air-quality condition. For video inputs, selected frames are analysed to observe changes in visible pollution indicators over time. The results are presented through a user-friendly dashboard that displays a visual air-quality score, pollution indicators, prediction confidence, and analysis summaries. The application also provides a separate section for external air-quality data, allowing users to distinguish visual estimates from pollutant measurements obtained through external sources. Unlike conventional monitoring equipment, the proposed approach does not directly measure pollutant concentrations from images or videos. Instead, it provides a visual assessment that may help users understand visible atmospheric conditions. The system can be further extended by integrating reliable air-quality APIs, physical sensors, and validated machine-learning models to improve its analytical capabilities. Overall, the project explores the potential of AI-assisted visual analysis as a complementary approach to environmental monitoring.