Preprints
https://doi.org/10.5194/amt-2021-154
https://doi.org/10.5194/amt-2021-154

  12 Jul 2021

12 Jul 2021

Review status: a revised version of this preprint is currently under review for the journal AMT.

Evaluation methods for low-cost particulate matter sensors

Jeffrey K. Bean Jeffrey K. Bean
  • Phillips 66, Bartlesville, OK 74003, United States

Abstract. Understanding and improving the quality of data generated from low-cost sensors is a crucial step in using these sensors to fill gaps in air quality measurement and understanding. This paper shows results from a 10-month long campaign that included side-by-side measurements and comparison between EPA-approved reference instruments and low-cost particulate matter sensors in Bartlesville, Oklahoma. At this rural site in the Midwestern United States the instruments typically encountered only low (under 20 µg/m3) concentrations of particulate matter, however higher concentrations (50–400 µg/m3) were observed on three different days during what were likely agricultural burning events. This study focused on methods for understanding and improving data quality for low-cost particulate matter sensors. The data offered insights on how averaging time, choice of reference instrument, and the observation of higher pollutant concentrations can all impact performance indicators (R2 and root mean square error) for an evaluation. The influence of these factors should be considered when comparing one sensor to another or when determining whether a sensor can produce data that fits a specific need. Though R2 and root mean square error remain the dominant metrics in sensor evaluations, an alternative approach using a prediction interval may offer more consistency between evaluations and a more direct interpretation of sensor data following an evaluation. Ongoing quality assurance for sensor data is needed to ensure data continues to meet expectations. Observations of trends in linear regression parameters and sensor bias were used to analyze calibration and other quality assurance techniques.

Jeffrey K. Bean

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on amt-2021-154', Anonymous Referee #1, 17 Jul 2021
    • AC1: 'Reply on RC1', Jeffrey Bean, 10 Sep 2021
  • RC2: 'Comment on amt-2021-154', Anonymous Referee #2, 30 Jul 2021
    • AC2: 'Reply on RC2', Jeffrey Bean, 10 Sep 2021
  • RC3: 'Comment on amt-2021-154', Anonymous Referee #3, 01 Aug 2021
    • AC3: 'Reply on RC3', Jeffrey Bean, 10 Sep 2021

Jeffrey K. Bean

Jeffrey K. Bean

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Short summary
Understanding and improving the quality of data generated from low-cost sensors is a crucial step in using these sensors. We show how averaging time, choice of reference instrument, and the observation of higher pollutant concentrations can all impact the perceived performance of low-cost sensors in an evaluation. The influence of these factors should be considered when comparing one sensor to another or when determining whether a sensor can produce data that fits a specific need.