Analyzing Algorithm and Thermodynamic Influences on Purple Air Sensors in Baltimore, Maryland

Purple Air sensors are low-cost air quality sensors, widely used by community scientists for local air quality monitoring. However, Purple Air data has been shown to overestimate PM2.5 concentrations when compared to the EPA’s Air Quality Monitoring Network. PM2.5 is particulate matter with a diameter of 2.5 micrometers or smaller which can pose significant health risks. This research investigates the environmental conditions contributing to measurement errors in Purple Air sensors, specifically focusing on how humidity, temperature and dewpoint temperature affects sensor accuracy in the Chesapeake Bay area, particularly in Baltimore, Maryland. Using data collected from a two and a half year period, a nationwide correction model based on Clements et al 2020 was applied to the Purple Air data. This research compared the CF_1 and ALT_CF3 algorithms used by Purple Air to process PM concentrations. The goal of this research is to better understand the underlying cause of PM2.5 measurement errors in Purple Air sensors, which can inform future improvements in calibration and data interpretation. The findings confirm previous research claims that the CF_1 algorithm significantly overestimates PM 2.5 concentrations while the ALT_CF3 is less susceptible to environmental biases. The nationwide correction factor is more effective when applied to the ALT_CF3 data.