Evaluation of Low-Cost Particulate Matter Sensors for Measuring Wildfire Smoke
Presented by: Dena Vallano, US EPA
Summary: Until recently, most wildfire air quality impacts were determined by permanent stationary regulatory monitors that are used to calculate the Air Quality Index (AQI). Low-cost particulate matter (PM) sensors have found widespread use by the public in smoke impacted areas but 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. 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.