Researchers build 'digital twin' to model Manhattan air quality
Cornell engineers have developed a "digital twin" framework that creates a real-time virtual representation of urban carbon dioxide conditions, providing a tool that could help city planners monitor emissions, identify hotspots and evaluate potential interventions before implementing them in the real world.
Researchers at Cornell University have developed a digital twin framework to create a real-time virtual representation of urban carbon dioxide conditions in Manhattan, providing a tool for city planners to monitor emissions, identify hotspots, and evaluate potential interventions before implementing them. The Sustainable Urban Digital Twin uses four layers of modular architecture that work together to collect data, model it, analyze it using machine learning, and provide actionable insights for decision-makers.
This prototype successfully integrated data from various sources in Manhattan, monitored carbon dioxide levels, quantified uncertainty, and generated visualizations of conditions across the city. The researchers chose Manhattan as their pilot site due to its dense and complex urban environment, which presented a significant challenge for the digital twin's capabilities.
Brief written by urgent.news from Phys.org's own syndicated text. Machine-written — may contain errors; check the original before relying on it.
