An Integrated Digital Twin Architecture for Real-Time Urban Air Quality Management

The growing demand for smart cities necessitates advanced solutions to promote urban sustainability. Digital Twin (DT) technology offers transformative potential by synchronizing virtual and physical environments in real time and enabling capabilities such as monitoring, forecasting, simulation, and prescription. However, existing smart city approaches often emphasize virtual representation while lacking core DT functionalities and a unified, extensible framework. This paper introduces a comprehensive DT framework designed to support a broad range of urban applications with enhanced operational capabilities. Validated using real-world air quality data, the proposed system demonstrates significant improvements in emissions reduction, air quality monitoring, and pollutant management. Key contributions of this work include a function-rich and generalizable DT frame-work, AI-driven adaptability, and extensive validation through predictive analytics. This study establishes a replicable blueprint for metropolitan-scale DTs, balancing comprehensive functionality with responsive, data-driven urban analytics.