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 created a digital twin framework designed to model real-time urban carbon dioxide conditions, with Manhattan serving as the first test case. This digital twin, developed by H. Oliver Gao and his team, combines physical data collection, digital data modeling, data analysis, and decision support tools into a single platform.
Using carbon dioxide, temperature, and humidity data recorded every five minutes across the city, the researchers were able to map emission patterns and identify potential hotspots in Manhattan. While the current prototype focuses on carbon dioxide monitoring, the researchers aim to expand the platform to include other pollutants and provide decision support for air quality management.
They envision that the digital twin could eventually help city officials and urban planners make informed decisions about air quality and its health impacts.
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