Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 16, issue 5
Articles | Volume 16, issue 5
https://doi.org/10.5194/amt-16-1311-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-16-1311-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 16, issue 5
Research article
 | 
13 Mar 2023
Research article |  | 13 Mar 2023

An evaluation of the U.S. EPA's correction equation for PurpleAir sensor data in smoke, dust, and wintertime urban pollution events

Daniel A. Jaffe, Colleen Miller​​​​​​​, Katie Thompson, Brandon Finley, Manna Nelson, James Ouimette, and Elisabeth Andrews

Supplement

https://doi.org/10.5194/amt-16-1311-2023-supplement

Data sets

Air Data: Air Quality Data Collected at Outdoor Monitors Across the US U.S. Environmental Protection Agency (EPA) https://www.epa.gov/outdoor-air-quality-data

AirNow U.S. Environmental Protection Agency (EPA) https://www.airnowtech.org

Data for the Keeler, CA, site Great Basin Unified Control District (GBUCD) https://www.gbuapcd.org/cgi-bin/downloadData

Air quality Oregon Department of Environmental Quality https://www.oregon.gov/deq/aq

Real-Time Air Quality Map PurpleAir http://map.purpleair.com

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Short summary
PurpleAir sensors (PASs) are low-cost tools to measure fine particulate matter (PM) concentrations. However, the raw PAS data have significant biases, so the sensors must be corrected. We analyzed data from numerous sites and found that the standard correction to the PAS Purple Air data is accurate in urban pollution events and smoke events but leads to a 6-fold underestimate in the PM2.5 concentrations in dust events. We propose a new correction algorithm to address this problem.
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