A 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. The models may help public health officials develop strategies for reducing exposure to unhealthy air quality.
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 . 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.
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 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.
Yu and her colleagues took a nonlinear approach to better account for these changing or complex factors by incorporating automated machine learning -- a type of artificial intelligence that automatically performs time-consuming tasks such as data preparation, parameter selection, and model selection and deployment -- into the land use regression model.
"Many areas may have consistently high air pollution levels, like those near factories and major transportation hubs, but that is not enough information to make a prioritized list of places needing extra monitoring or health alerts," she said."Our mobility-based exposure maps show public health officials and decision makers hotspots that have unhealthy air quality levels plus high visitor traffic.
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