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Air Sensors: PurpleAir, AirNow Fire and Smoke Map, and their use Internationally
Citation:
Johnson, K., S. Frederick, A. Holder, AND A. Clements. Air Sensors: PurpleAir, AirNow Fire and Smoke Map, and their use Internationally. U.S. State Department Embassy Fellows Program Monthly Virtual Meeting, Durham, NC, December 03, 2020.
Impact/Purpose:
Lower cost air sensors are increasingly used to report air quality and smoke concentrations from wildfires, but the accuracy of these measurement under ambient and smoke conditions is largely unknown. This work briefly summarizes work to evaluate a popular low-cost PM2.5 sensor (PurpleAir) across the U.S and to develop a correction equation that can improve the accuracy of reported concentrations, even during smoke impacted times. This presentation seeks to inform users of the AirNow smoke and fire map how the PurpleAir correction was developed and some of the limitations and considerations one must make when using the map. The presentation will briefly discuss how the U.S.-wide correction works for a few international datasets and key considerations in extrapolating this work with other PM sensors.
Description:
Until recently air quality, including wildfire impacts, have been determined by permanent stationary regulatory monitors that are used to calculate the Air Quality Index (AQI). Low-cost particulate matter (PM) sensors, specifically PurpleAir sensors, have found widespread use by the public, especially in smoke impacted areas, but comparability to regulatory monitors is often a concern. The EPA Office of Research and Development has worked to develop a U.S.-wide correction equation for PurpleAir PM2.5 sensors and has validated the performance of the correction equation even during smoke impacted times. This presentation will review the process by which the U.S.-wide correction equation was developed, discuss additional work to validate the performance of the correction equation smoke impacted sites, and will discuss known/suspected issues and some of the commonly expressed QA/QC concerns of this crowdsourced data. The presentation will briefly discuss how the U.S.-wide correction works for a few international datasets and key considerations in extrapolating this work with other PM sensors.