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Field evaluation of Low-Cost Particulate Matter Sensors for Measuring Wildfire Smoke
Citation:
Holder, A., A. Mebust, L. Maghran, M. McGown, K. Stewart, D. Vallano, R. Elleman, AND K. Baker. Field evaluation of Low-Cost Particulate Matter Sensors for Measuring Wildfire Smoke. Sensors. MDPI, Basel, Switzerland, 20(17):4796, (2020). https://doi.org/10.3390/s20174796
Impact/Purpose:
In the past few years there has been a rapid increase in commercially available low-cost air quality sensors. The increasing popularity of these sensors is demonstrated by widespread use in many parts of the U.S. Due to the large gaps in the ambient monitoring networks, low-cost sensors are also being turned to for information on wildfire smoke. The objective of this study was to identify commonly used low-cost PM sensors and evaluate their performance to wildfire smoke. Three different types of sensors were collocated with reference monitors near wildfires and prescribed fires in the U.S. All the sensors showed a linear response but required a correction to provide accurate PM2.5 concentrations, even during times of heavy smoke. These results show that low-cost sensor data with appropriate correction may be used to provide information on wildfire smoke air quality impacts.
Description:
Until recently, air quality impacts from wildfires were predominantly determined using the Air Quality Index (AQI), calculated from data from permanent stationary regulatory air pollution monitors. However, low-cost particulate matter (PM) sensors are now being widely used by the public as a source of air quality information during wildfires. The performance of these low-cost sensors has not been evaluated at the high smoke concentrations frequently encountered near wildfires. We collocated three low-cost PM sensor systems with reference PM instruments near three wildfires in the western United States and one prescribed fire in the eastern United States (maximum hourly fine PM, PM2.5 = 295 µg/m3). The sensors were moderately to highly correlated with reference instruments (hourly averaged r2 = 0.52 – 0.95). All sensors overpredicted PM2.5 concentrations, with average normalized root mean square errors ranging from 80% to 167%. Correction equations were estimated to quantitatively relate each of the sensors with collocated reference instruments. The factors for individual fires varied, likely due to the different concentration ranges observed at each fire. By combining all datasets, we developed a correction equation for wildfire smoke that reduced the normalized root mean square error to less than 27%. Correction equations also varied among the sensors, demonstrating the impact of the physical configuration of the package and the algorithm used to translate the size and count information into PM2.5 concentrations. These results suggest the low-cost sensors tested here can fill in the large spatial gaps in monitoring networks near wildfires with mean absolute errors of less than 10 µg/m3 in the hourly PM2.5 concentrations when using a sensor-specific smoke correction equation.
URLs/Downloads:
DOI: Field evaluation of Low-Cost Particulate Matter Sensors for Measuring Wildfire Smoke
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