Globally, wildfires are becoming more frequent and destructive, generating a significant amount of smoke that can be transported thousands of miles, driving the need for more accurate air pollution forecasts.
Globally, wildfires are becoming more frequent and destructive, generating a significant amount of smoke that can be transported thousands of miles, driving the need for more accurate air pollution forecasts. A team of Penn State researchers developed a deep learning model that provides improved predictions of air quality in wildfire-prone areas and can differentiate between wildfires and non-wildfires.
“As climate change continues to cause ecological changes and challenges, it is likely that wildfire activities will continue to rise,” said Manzhu Yu, assistant professor of geography at Penn State and lead investigator on the project. “Because of this, it is an urgent research priority to accurately predict the concentration of air pollutants induced by wildfire smoke, especially in wildfire-prone areas.”
Wildfire smoke contains a combination of particulate matter and many gaseous pollutants. Fine particulate matter, referred to as PM2.5, has been associated with significant risks to human health and is regulated by the U.S. EPA.
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