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Performance Evaluations of Multiple Commercial PM Sensors in Research Triangle Park, NC
Citation:
Frederick, S., K. Johnson, C. Johnson, R. Yaga, I. VonWald, AND A. Clements. Performance Evaluations of Multiple Commercial PM Sensors in Research Triangle Park, NC. To be Presented at Annual Conference of the American Association for Aerosol Research, Raleigh, NC, October 05 - 09, 2020.
Impact/Purpose:
Interest and concern about air quality has grown in recent years. Simultaneously, growth in the popularity and use of air sensors across the US has also occurred. However, adoption of this technology is limited due to uncertainty and variation in the quality of the data provided. This work evaluates the performance of a multiple commercially available air sensors models in Research Triangle Park, NC. It seeks to inform sensor users of data accuracy and precision relative to regulatory-grade instruments measuring PM1, PM2.5, and PM10. This abstract is for a presentation that will be made to the community of aerosol researchers attending the AAAR conference in Raleigh, NC (Oct 2020).
Description:
Recent developments in particulate air sensor technology have led to increasing access to air quality measurements and have facilitated individual and community-led actions for mitigating exposure. Central to enabling these discussions is determining sensor data accuracy relative to reference instruments and precision among sensors of the same model. This work summarizes evaluations of several air sensors measuring particulate matter (PM) at an urban background site in Research Triangle Park, North Carolina. Sensor models assessed include the APT Maxima, Aeroqual AQY, PurpleAir PA-II, SENSIT RAMP, Clarity Node-S, and AirVisual Pro. At least three of each sensor model were deployed simultaneously for at least 30 days with evaluation periods spanning March 2019- February 2020. One-hour and 24-hour averaged PM10, PM2.5, and PM1 sensor data were compared to collocated Grimm EDM180 and Teledyne T640x Federal Equivalent Methods, as available. Preliminary results suggest wide variability in precision among sensor models. The impact of meteorological effects, including temperature and relative humidity (RH), have been explored. For some models, higher RH did not lead to overestimation, stressing the importance of understanding manufacturers correction algorithms which may already include RH corrections. These results provide valuable information regarding the reliability of particulate air sensor data when compared to regulatory methods and provide context for EPA’s sensor performance targets under development. These results highlight the importance of evaluating sensors in order to understand their limitations, have confidence in their data, and acknowledge the effort required to manage sensors marketed to those outside traditional air monitoring agencies. Although this abstract was reviewed by EPA and approved for publication, it may not necessarily reflect official Agency policy. Mention of trade names or commercial products does not constitute endorsement or recommendation for use.