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

<p dir="ltr">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.</p>