The School of Electrical and Computer Engineering has developed a solution for estimating air quality without the need to install new air quality monitoring stations
Researchers at the School of Electrical and Computer Engineering have developed an AI-based model based on graph neural networks for accurately estimating air pollutant levels in areas lacking air quality monitoring stations. The proposed approach can support more informed decision-making by city managers and improve access to reliable air pollution information for citizens.
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