Systematic review of air quality modeling in digital twins for sustainable green cities

Abstract Urban climate change and air quality degradation are deeply interlinked challenges, demanding innovative technological interventions for effective management. Digital twin technology has emerged as a transformative tool, offering dynamic, data-driven virtual environments to simulate, evaluate, and optimize climate mitigation strategies before real-world implementation. This systematic review critically evaluates 100 peer-reviewed studies and 17 real-world case applications published between 2018 and 2024, focusing on the application of digital twins for decision-making in urban contexts. Practical applications span key sectors, including building energy management, transportation optimization, and climate-resilient urban planning. Notably, air quality management emerges as a central domain where digital twins enable real-time monitoring, pollution source attribution, and proactive policy simulation. This review further identifies core technical requirements—such as high-resolution geospatial data, interoperable platforms, and robust AI models—for developing effective city-scale digital twins. By synthesizing insights from both research and practice, this study highlights the pivotal role of digital twin technology in advancing urban sustainability, informing policy, and supporting data-driven, climate-resilient city planning.