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Low-Cost Particulate Matter Sensors for Measuring Wildfire Smoke
Citation:
Holder, A., L. Maghran, A. Mebust, D. Vallano, M. McGown, AND R. Elleman. Low-Cost Particulate Matter Sensors for Measuring Wildfire Smoke. A&WMA Visibility Conference, Bryce Canyon, Utah, October 20 - 23, 2021.
Impact/Purpose:
Low-cost portable PM monitors are increasingly popular for measuring air quality impacts from wildfires. We evaluated their performance compared to reference instruments under smoky conditions near wildfires in the Western United States. The sensors were highly correlated with the reference instruments, but required calibration to provide accurate PM2.5 concentration readings.
Description:
Wildfire smoke is a major source of ambient air pollution that can result in degraded visibility across large portions of the US as well as localized severe visibility impacts. Low-cost particulate matter (PM) sensors are increasingly being used by local air quality agencies and the public to monitor wildfire smoke impacts. However, many of these sensors have not been evaluated at the high smoke concentrations frequently encountered near wildfires. We collocated three low-cost PM/air quality sensor systems (Aeroqual – AQY1, PurpleAir - PAII-SD, Sensevere - RAMP) with reference PM monitors near three wildfires in the western U.S. and one prescribed fire in the eastern U.S. (max PM = 295 µg/m3). The sensors were moderately - highly correlated with the reference monitor (hourly averaged r2 = 0.52-0.95). All sensors overpredicted PM2.5 concentrations, with an average normalized mean bias of 41%, 62%, and 40% for AQY1, PAII-SD, and the RAMP respectively. Calibration factors for individual fires varied, likely due to the different concentration ranges observed at each fire, not due to changing smoke optical properties. By combining all datasets, a smoke specific calibration factor was developed that reduced the normalized root mean square error to less than 35%. The calibration factors varied among the sensors, demonstrating the impact of the physical configuration of the sensor and the algorithm used to translate the size and count information into PM concentrations. These results suggest the low-cost sensors tested here can fill in the large spatial gaps in monitoring networks near wildfires with errors of less than 10 µg/m3 in the hourly PM2.5 concentrations when using a sensor specific smoke calibration factor. This abstract was reviewed by EPA and approved for publication, it may not necessarily reflect official Agency policy. Mention of trade names or commercial products does not constitute endorsement or recommendation for use.