Field Evaluation of Low-Cost Particulate Matter Sensors for Measuring Wildfire Smoke

Until recently, air quality impacts from wildfires were predominantly determined based on data from permanent stationary regulatory air pollution monitors. However, low-cost particulate matter (PM) sensors are now widely used by the public as a source of air quality information during wildfires, although their performance during smoke impacted conditions has not been thoroughly evaluated. We collocated three types of low-cost fine PM (PM_2.5) sensors with reference instruments near multiple fires in the western and eastern United States (maximum hourly PM_2.5 = 295 µg/m³). Sensors were moderately to strongly correlated with reference instruments (hourly averaged r² = 0.52-0.95), but overpredicted PM_2.5 concentrations (normalized root mean square errors, NRMSE = 80-167%). We developed a correction equation for wildfire smoke that reduced the NRMSE to less than 27%. Correction equations were specific to each sensor package, demonstrating the impact of the physical configuration and the algorithm used to translate the size and count information into PM_2.5 concentrations. These results suggest the low-cost sensors 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 PM_2.5 concentrations when using a sensor-specific smoke correction equation.