Characterizing the Impact of Different Environments (Indoor and Outdoor) on the Performance of Low-cost Particulate Matter (PM) Sensors

SABRINA WESTGATE, Zahra Shivji, Nga Lee Ng, Georgia Institute of Technology

     Abstract Number: 131
     Working Group: Indoor Aerosols

Abstract
As the availability of low-cost sensors has progressively increased over the past years, so too has the importance of characterizing and quantifying low-cost sensor measurement capabilities. There is a particular need to assess how low-cost sensors perform during in-situ monitoring in different environments. Towards this end, in this study we compared the particulate matter (PM) data reported by two low-cost sensors (PurpleAir and QuantAQ MODULAIR-PM) in both outdoor and indoor environments on the Georgia Tech Campus. The PurpleAir sensor consists of a Plantower PMS nephelometer and reports PM₁, PM_2.5, and PM₁₀, while the QuantAQ sensor combines an AlphaSense optical particle counter (OPC) as well as a Plantower PMS nephelometer to report size-resolved particle mass concentration of PM₁, PM_2.5, and PM₁₀. To assess sensor performance, low-cost sensor data were analyzed for various environments including outdoors (urban Atlanta), in a classroom, in an academic common space, and in a cafeteria. Measurements both indoors and outdoors were also compared to measurements from a Scanning Mobility Particle Sizer (SMPS). To investigate the effects of aerosol composition on sensor performance, sensor data collected outdoors were compared to data from the Aerosol Chemical Speciation Monitor (ACSM). Both types of sensors generally agreed with research-grade instruments in the outdoor measurements. However, aerosol composition, as well as mass loading and aerosol size distribution, impacted sensor performance and agreement. Results also indicated that the Plantower sensor misses much of the PM mass in certain environments, such as when there are large particle events (particles >1 micron in diameter) and in a university classroom where PM mass concentrations are often below the limit of detection. Results from this study provide data-driven insights into what types of environments different sensor types are best suited for, and under what aerosol conditions they face the most limitations.