BEGIN:VCALENDAR VERSION:2.0 PRODID:-//jEvents 2.0 for Joomla//EN CALSCALE:GREGORIAN METHOD:PUBLISH BEGIN:VEVENT UID:a01d239d07d2aa4e3dfe4671dbd411c2 CATEGORIES:Polar Online Events CREATED:20210712T072915 SUMMARY:ACCAP Webinar: Using a random forest model to predict historical PM2.5 in Alaska LOCATION:Online DESCRIPTION:Tuesday, July 13 (10am AKDT)Using a random forest model to predict historic al PM2.5 in Alaska (https://uaf-accap.org/event/pm2-point-5-alaska/)Allison Baer | PhD Candidate\nUniversity of Maryland, Department of Geographical S ciences The spatiotemporal coverage of regulatory-grade, ground-based air q uality monitoring stations measuring PM2.5 concentrations is low across Ala ska. Recently, there has been an increase in the number of low-cost air qua lity 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 corre cted low-cost air quality monitoring data from the 2019 wildfire season (Ma y 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. ACCAP is partnering with NASA’s Arctic -Boreal Vulnerability Experiment (ABoVE) to highlight Alaska research r esults from this ongoing field campaign. ABoVE is a large-scale study of en vironmental change and its implications for social-ecological systems. ABoV E links field-based, process-level studies with geospatial data products de rived from airborne and satellite sensors, providing a foundation for impro ving the analysis, and modeling capabilities needed to understand and predi ct ecosystem responses and societal implications. ABoVE also has occasional webinar series focused on research in Yukon Territory and Northwest Territ ories. More information and registration: https://uaf-accap.org/event/pm2-p oint-5-alaska/ X-ALT-DESC;FMTTYPE=text/html:
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.
 
ACCAP is partnering with NASA’s Arctic-Boreal Vulnerability Experiment (ABoVE) to highlight Alaska research results from this ongoing field campaign. A BoVE is a large-scale study of environmental change and its implications fo r social-ecological systems. ABoVE links field-based, process-level studies with geospatial data products derived from airborne and satellite sensors, providing a foundation for improving the analysis, and modeling capabiliti es needed to understand and predict ecosystem responses and societal implic ations. ABoVE also has occasional webinar series focused on research in Yuk on Territory and Northwest Territories. 
More information and registration: http s://uaf-accap.org/event/pm2-point-5-alaska/
DTSTAMP:20260417T125717Z DTSTART;TZID=UTC;VALUE=DATE:20210713 DTEND;TZID=UTC;VALUE=DATE:20210714 SEQUENCE:0 TRANSP:OPAQUE END:VEVENT END:VCALENDAR