Tuesday, July 13 (10am AKDT)
Allison Baer | PhD Candidate
University of Maryland, Department of Ge
ographical Sciences
The spatiotemporal coverage of regulator
y-grade, ground-based air quality monitoring stations measuring PM2.5 conce
ntrations is low across Alaska. Recently, there has been an increase in the
number of low-cost air quality monitoring stations for PM2.5 that expand t
he spatiotemporal coverage of PM2.5 monitoring in Alaska and globally. This
study uses a random forest model to predict PM2.5 concentrations from regu
latory-grade data and corrected low-cost air quality monitoring data from t
he 2019 wildfire season (May through September) in Alaska. Results show tha
t the model predicts a high amount of the variance at over 0.75. These resu
lts will inform mapping of PM2.5 continuous concentrations across Alaska.
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