ACCAP Webinar - Using a random forest model to predict historical PM2.5 in Alaska
Presented by Allison Baer, University of Maryland
Tuesday, July 13
The spatiotemporal coverage of regulatory-grade, ground-based air quality monitoring stations measuring PM2.5 concentrations 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 the spatiotemporal coverage of PM2.5 monitoring in Alaska and globally. This study uses a random forest model to predict PM2.5 concentrations from regulatory-grade data and corrected low-cost air quality monitoring data from the 2019 wildfire season (May through September) in Alaska. Results show that the model predicts a high amount of the variance at over 0.75. These results will inform mapping of PM2.5 continuous concentrations across Alaska.
This is the first webinar in a new series from the Alaska Center for Climate Assessment and Policy (ACCAP) in partnership with NASA’s Arctic-Boreal Vulnerability Experiment (ABoVE) highlighting Alaska research results from ABoVE's ongoing field campaign.