Environment

Scientists Turn to Deep Learning to Improve Air Quality Forecasts

Air pollution from the burning of fossil fuels impacts human health but predicting pollution levels at a given time and place remains challenging, according to a team of scientists who are turning to deep learning to improve air quality estimates.

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Results of the team’s study could be helpful for modelers examining how economic factors like industrial productivity and health factors like hospitalizations change with pollution levels.

“Air quality is one of the major issues within an urban area that affects people’s lives,” said Manzhu Yu, assistant professor of geography at Penn State. “Yet existing observations are not adequate to provide comprehensive information that may help vulnerable populations to plan ahead.”

Satellite and ground-based observations each measure air pollution, but they are limited, the scientists said. Satellites, for instance, may pass a given location at the same time each day and miss how emissions vary at different hours. Ground-based weather stations continuously collect data but only in a limited number of locations.

To address this, the scientists used deep learning, a type of machine learning, to analyze the relationship between satellite and ground-based observations of nitrogen dioxide in the greater Los Angeles area. Nitrogen dioxide is largely associated with emissions from traffic and power plants, the scientists said.

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