Environmental Data Analysis for Air-Quality Monitoring and Control

Air pollution has become one of the most serious environmental challenges affecting human health and ecological balance worldwide. Rapid industrialization, urban expansion, and increasing vehicular emissions have significantly contributed to the deterioration of air quality. Conventional air quality monitoring systems mainly focus on real-time measurement of pollutants and lack the capability to predict future conditions, which is essential for taking preventive actions. In this study, a machine learning-based air quality prediction system is proposed to estimate the Air Quality Index (AQI) using historical environmental data. The system utilizes algorithms such as Random Forest to analyze parameters including benzene concentration (C6H6), nitrogen oxides (NOx), nitrogen dioxide (NO2), relative humidity (RH), and absolute humidity (AH). Furthermore, a web-based interface is developed to allow users to input data and visualize prediction results through graphical representations. The experimental results demonstrate that the proposed model achieves reliable accuracy and can be effectively used for environmental monitoring and decision-making.