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<p>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>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月22日

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  • Lakshmi Babu Saheer,
  • Lorenzo Garbagna,
  • Manu Sasidharan

摘要

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.