Abstract
The escalating challenges of urban air pollution necessitate advanced monitoring and predictive capabilities. This paper proposes the development of robust digital twin frameworks tailored for real-time urban air quality monitoring and predictive modeling. We explore the integration of Internet of Things (IoT) sensors, remote sensing data, and spatial computing techniques to create dynamic, high-fidelity digital replicas of urban environments. The framework aims to enable continuous, near real-time assessment of air quality parameters, facilitating timely interventions and policy-making. Predictive modeling, powered by artificial intelligence and machine learning algorithms, will be incorporated to forecast air quality trends, identify pollution hotspots, and simulate the impact of various mitigation strategies. The proposed digital twin architecture will leverage advanced data assimilation techniques to ensure accuracy and responsiveness. This research contributes to the growing body of work on smart city technologies, offering a comprehensive approach to urban air quality management by bridging the gap between real-time data, sophisticated modeling, and actionable insights. The ultimate goal is to enhance public health, environmental sustainability, and the overall livability of urban areas through intelligent, data-driven decision-making.
Keywords
Digital Twin, Urban Air Quality, Real-time Monitoring, Predictive Modeling, Smart Cities, IoT, Remote Sensing, Spatial Computing