Validation of PurpleAir PM2.5 Sensors in Temperate Urban Environments

Abstract

Low-cost sensors (LCS), such as PurpleAir (PA), are widely used for measuring fine particulate matter (PM_2.5) because they are inexpensive, easy to deploy, and provide publicly accessible data. However, PA sensors are known to overestimate PM_2.5, and thus, several correction methods have been developed to improve their accuracy. In this study, we evaluated accuracy by comparing nationwide and locally derived correction methods during summer and spring seasons, examined PA output data types, assessed the influence of relative humidity using both PA and weather station data, and analyzed the performance after one year of field deployment. In the moderate climate of Philadelphia, the nationwide correction of Barkjohn et al. (2021) combined with using the mean value of two PA_atm data outputs (RMSE = 1.640 μg/m³, MBE = 0.103 μg/m³, R² = 0.683) for both 1 h and 24 h measurements provided the highest accuracy. Relative humidity measurements, whether obtained from a weather station or the PA sensor, had minimal effect on PM_2.5 accuracy (difference in R² = 0.04). PA sensor accuracy declined after one year of use (R² = 0.567), showing requiring extra sensor calibration in long-term deployment. This methodology enhances the performance of PA sensors and establishes greater trust in the LCS.