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<p>Urban on-road air pollution remains a pervasive threat to public health, yet its management is fundamentally constrained by a lack of spatiotemporal precision in emission inventories. Conventional inventories rely on static, registration-based proxies that fail to capture the high-frequency fluctuations of traffic-derived pollutants. Here, we present an AI-driven, high-resolution digital twin framework that reconstructs hourly, road-segment-level emissions in Atlanta’s urban core as an example by integrating deep-vision vehicle classification with real-time roadside camera networks. Our findings reveal that traditional inventories systematically underestimate localized air pollution burdens because they neglect the significant impact of inter-city freight transportation. Validated through Monte Carlo uncertainty analysis, this system enables a transition from retrospective accounting to real-time operational monitoring. By pinpointing corridor-specific hotspots and resolving the complexities of fleet-driven pollutant burdens, this research provides a scalable architecture for hyper-local exposure forecasting and the targeted mitigation of environmental health disparities in global cities.</p>

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Digital twin for city traffic emissions using deep vision and real time monitoring

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年8月4日

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  • Tao Wang,
  • Bukola Bakare,
  • Joshua S. Fu,
  • Sara Hsu,
  • Yemisi Bolumole,
  • Zifa Wang,
  • Jia Xing

摘要

Urban on-road air pollution remains a pervasive threat to public health, yet its management is fundamentally constrained by a lack of spatiotemporal precision in emission inventories. Conventional inventories rely on static, registration-based proxies that fail to capture the high-frequency fluctuations of traffic-derived pollutants. Here, we present an AI-driven, high-resolution digital twin framework that reconstructs hourly, road-segment-level emissions in Atlanta’s urban core as an example by integrating deep-vision vehicle classification with real-time roadside camera networks. Our findings reveal that traditional inventories systematically underestimate localized air pollution burdens because they neglect the significant impact of inter-city freight transportation. Validated through Monte Carlo uncertainty analysis, this system enables a transition from retrospective accounting to real-time operational monitoring. By pinpointing corridor-specific hotspots and resolving the complexities of fleet-driven pollutant burdens, this research provides a scalable architecture for hyper-local exposure forecasting and the targeted mitigation of environmental health disparities in global cities.