| Summary: | 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<sub>2.5</sub>) sensors with reference instruments near multiple fires in the western and eastern United States (maximum hourly PM<sub>2.5</sub> = 295 µg/m<sup>3</sup>). Sensors were moderately to strongly correlated with reference instruments (hourly averaged r<sup>2</sup> = 0.52–0.95), but overpredicted PM<sub>2.5</sub> 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<sub>2.5</sub> 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/m<sup>3</sup> in the hourly PM<sub>2.5</sub> concentrations when using a sensor-specific smoke correction equation.
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