BREATHE: A GeoAI-Powered Air Quality Monitoring and Forecasting System for Urban Sustainability

Abstract. Air pollution is one of the most important environmental and public health challenges of our time, and it uniquely impacts urban areas, especially in the rapidly developing countries of the UAE. Traditional air quality monitoring systems lack the predictive capabilities needed for proactive intervention and sustainable urban planning. The research proposes BREATHE—a system of integrated real-time monitoring and machine learning-based forecasting to address air pollution challenges in urban environments by using GeoAI. The research implements deep learning algorithms alongside geographical data to establish a scalable system for air quality management. BREATHE system features four essential aspects that include (1) real-time AQI checks at 12 locations across the UAE territory (2) predictive models for AQI forecasting through climatic and historical data analyses (3) interactive dashboards with mapping visuals and alert features and (4) AI-powered chatbot assistance along with non-specialist user-friendly accessibility. The data processing together with model deployment operates without interruptions through Python-based automation. By bridging the gap between monitoring and predictive analytics, this study presents a replicable framework for large-scale air quality management in urban environments worldwide.