A Digital Twin (DT) framework to enhance carbon-based gas plume monitoring is critical for supporting timely and effective mitigation responses to environmental hazards such as industrial gas leaks, or wildfire outbreaks carrying large carbon emissions. We present AIMNET, a one-of-a-kind DT framework that integrates a built-in-house Internet of Things (IoT)-based continuous sensing network with a physics-based multi-scale weather-gas transport model, that enables high-resolution and real-time simulation and detection of carbon gas emissions. AIMNET features a three-layer system architecture: (i) physical world: custom-built devices for continuous monitoring; (ii) bidirectional information feedback links: intelligent data transmission and reverse control; and (iii) digital twin world: AI-driven analytics for prediction, anomaly detection, and dynamic weather-gas coupled molecule transport modeling. Designed for scalable, energy-efficient deployment in remote environments, AIMNET architecture is realized through a small-scale distributed sensing network over an oil and gas production basin. To demonstrate the high-resolution, fast-responding concept, an equivalent mobile-based emission monitoring network was deployed around a wastewater treatment plant that constantly emits methane plumes. Our preliminary results through which, have successfully captured the methane emission events whose dynamics have been further resolved by the tiered model simulations. This work supports our position that AIMNET provides a promising DT framework for reliable, real-time monitoring and predictive risk assessment. In the end, we also discuss key implementation challenges and outline future directions for advancing such a new DT framework for translation deployment.

翻译：用于增强碳基气体羽流监测的数字孪生（DT）框架，对于支持针对工业气体泄漏或携带大量碳排放的野火爆发等环境危害的及时有效缓解响应至关重要。我们提出了AIMNET，这是一种独特的DT框架，它将内置的基于物联网（IoT）的连续传感网络与基于物理的多尺度天气-气体传输模型相结合，能够实现高分辨率、实时的碳气体排放模拟与检测。AIMNET采用三层系统架构：（i）物理世界：用于连续监测的定制设备；（ii）双向信息反馈链路：智能数据传输与反向控制；（iii）数字孪生世界：用于预测、异常检测以及动态天气-气体耦合分子传输建模的AI驱动分析。AIMNET专为在偏远环境中可扩展、高能效部署而设计，其架构通过在一个油气生产盆地上部署的小型分布式传感网络得以实现。为验证其高分辨率、快速响应的概念，我们在一个持续排放甲烷羽流的污水处理厂周围部署了等效的基于移动设备的排放监测网络。我们的初步结果已成功捕获了甲烷排放事件，其动态过程通过分层模型模拟得到了进一步解析。这项工作支持我们的观点，即AIMNET为可靠、实时的监测和预测性风险评估提供了一个有前景的DT框架。最后，我们还讨论了关键的实施挑战，并概述了推进此类新型DT框架向实际部署转化的未来方向。