Geo-AQI: A Real-Time Air Quality Monitoring and Forecasting System using ARIMA Model

This study introduces Geo-AQI, an advanced air quality monitoring system designed to perform real-time assessments and accurate forecasts of air quality indices (AQIs). The system features a responsive user interface built with HTML5, CSS3, and JavaScript, enriched by interactive data visualizations using Leaflet. Its robust backend, powered by Python and Django, integrates the Django Rest Framework for efficient API communication. The key to the system's predictive capabilities are ARIMA-based models, which achieve high accuracy in forecasting AQIs for pollutants such as PM10, NO2, and SO2, as evidenced by low mean squared error (MSE) and root mean square error (RMSE) metrics. Real-time data collection, caching with Redis, and historical data management using SQLite ensure reliability and performance. Geo-AQI also provides precise 5-day AQI forecasts, supporting public health and environmental decision-making. Future enhancements include expanding sensor integration, implementing advanced machine learning models, and developing a mobile application to improve accessibility. By combining cutting-edge technologies with statistical rigor, Geo-AQi offers a scalable and effective solution for addressing air pollution challenges.