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This study demonstrates how generative downscaling models like NVIDIA Earth-2 can drastically reduce the computational cost and energy footprint of high-resolution environmental forecasting. By enabling rapid training on accessible hardware, this approach lowers barriers for localized pollution modeling and allows researchers to efficiently simulate future scenarios for policy development.

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Manchester physicists use AI to predict UK air quality

Quantum ZeitgeistSeptember 18, 20265 min read

David Topping and Hao Zhang, physicists at the University of Manchester, applied NVIDIA Earth-2 AI to forecast UK air quality—a factor in 30,000 annual deaths.

An estimated 30,000 deaths in the U.K. each year are linked to air pollution, and researchers at the University of Manchester are applying artificial intelligence to address this critical public health issue. Physicists David Topping and Hao Zhang successfully trained NVIDIA’s Earth-2 CorrDiff, a generative downscaling model, to forecast air quality on its first attempt, using data from the U.K.’s Isambard-AI supercomputer. “The biggest challenge is the compute required to forecast air quality,” said Topping, adding that this new approach allows them to model potential future scenarios and inform policy changes. The team demonstrated the entire workflow,…

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