Americans in the northeast paid greater attention to air quality alerts this summer as wildfire smoke thickened skies with an orange-tinted haze. Smoke and other sources of air pollution contain tiny particles, called fine particulate matter (PM 2.5). Smaller than the width of a human hair, PM 2.5 pose health dangers when inhaled, especially to people with pre-existing heart and lung conditions. To assess exposure to PM 2.5 and help public health officials develop strategies, a Penn State-led research team designed improved models using artificial intelligence and mobility data.
“Our research shows that incorporating artificial intelligence and mobility data into air quality models can improve the models and help decision makers and public health officials prioritize areas that need extra monitoring or safety alerts because of unhealthy air quality or a combination of unhealthy air quality and high pedestrian traffic,” said Manzhu Yu, assistant professor of geography at Penn State and first author of the study.
Reported in the journal Frontiers in Environmental Science, the researchers examined PM 2.5 measurements across eight large metropolitan areas in the continental United States. Air quality data came from Environmental Protection Agency (EPA) monitoring stations and low-cost sensors usually purchased and distributed by local community organizations. They used the data to find hourly PM 2.5 averages in each region.
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Beaver Stadium in June 2023 when smoke from Canadian wildfires blanketed skies across the northeastern United States. A Penn State-led research team used data from low-cost sensors, artificial intelligence and mobility data to improve models that assess human exposure to fine particulate matter (PM 2.5), tiny particles in smoke and other forms of air pollution that can pose health dangers. Public health officials can use the models to develop strategies to reduce exposure to unhealthy air quality, according to the researchers. (Photo Credit: Patrick Mansell / Penn State)