Performance Evaluation of Purple Air Low-Cost Sensors under Atmospheric Conditions

Low-cost particulate matter (PM) sensors are promising tools for supplementing existing air quality monitoring networks. However, the performance of such devices when performing under atmospheric conditions has not been thoroughly evaluated. In this study, we evaluated the performance of Purple Air (PA) sensors (PA-II unit - Plantower model PMS5003) in comparison to Federal Equivalent Methods (FEMs) which are used by the Environmental Protection Agency (EPA). A total of 136 PA sensors and 58 EPA sensors were selected; each PA sensor is located 5km or less from one of the 58 EAP stations, all the sensors had been active during 2017 and/or 2018 and are located within the US. From the data gathered, we created 207 comparisons between different PA and EPA sensors, examining both hourly and daily average PM values. 193 comparisons were made for PM2.5 measurements and 14 comparisons were made for PM10 measurements. Using a linear regression method, we found that the correlation values (R²) for PM2.5 hourly measurements ranged from 0 up to 0.87 with an average value at 0.43± 0.26, and that 46% of the correlation values were above 0.5. Comparison base on daily average value improve the correlation. The average correlation value was 0.57 ±0.29, and 61% of the correlation values were above 0.5. The correlation values were not affected by the distance between the PA and EPA sites nor by the number of the measurement (hourly or daily average observations). In our presentation we will show the temporal and spatial comparisons that we made between the different PA and EPA sensors. We will explain our hypothesis for the causes of high correlation values in some cases and low correlation values in others. We will also present our findings on the effects of temperature and relative humidity on the PA sensor performance.