Preprints
https://doi.org/10.5194/egusphere-2024-1142
https://doi.org/10.5194/egusphere-2024-1142
02 May 2024
 | 02 May 2024
Status: this preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).

Calibration of Low-Cost Particulate Matter Sensors PurpleAir: Model Development for Air Quality under High Relative Humidity Conditions

Martine E. Mathieu-Campbell, Chuqi Guo, Andrew P. Grieshop, and Jennifer Richmond-Bryant

Abstract. The primary source of measurement error from the widely-used particulate matter (PM) PurpleAir sensors is ambient relative humidity (RH). Recently, the U.S. EPA developed a national correction model for PM2.5 concentrations measured by PurpleAir sensors (Barkjohn model). However, their study included few sites in the Southeastern U.S., the most humid region of the country. To provide high-quality spatial and temporal data and inform community exposure risks in this area, our study developed and evaluated PurpleAir correction models for use in the warm-humid climate zones of the U.S. We used hourly PurpleAir data and hourly reference grade PM2.5 data from the EPA Air Quality System database from January 2021 to August 2023. Compared with the Barkjohn model, we found improved performance metrics with error metrics decreasing by 16–23 % when applying a multi linear regression (MLR) model with RH and temperature as predictive variables. We also tested a novel semi-supervised clustering (SSC) method and found that a nonlinear effect between PM2.5 and RH emerges around a RH of 50 % with slightly greater accuracy. Therefore, our results suggested that a clustering approach might be more accurate in high humidity conditions to capture the non-linearity associated with PM particle hygroscopic growth.

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Martine E. Mathieu-Campbell, Chuqi Guo, Andrew P. Grieshop, and Jennifer Richmond-Bryant

Status: open (until 07 Jun 2024)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on egusphere-2024-1142', JAMES OUIMETTE, 09 May 2024 reply
  • RC1: 'Comment on egusphere-2024-1142', Anonymous Referee #1, 11 May 2024 reply
Martine E. Mathieu-Campbell, Chuqi Guo, Andrew P. Grieshop, and Jennifer Richmond-Bryant
Martine E. Mathieu-Campbell, Chuqi Guo, Andrew P. Grieshop, and Jennifer Richmond-Bryant

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Short summary
PurpleAir samples are widely used by scientists and members of the general public to monitor PM2.5. However, the accuracy of those measurements is very sensitive to relative humidity. Recently, the EPA developed a national low-cost sensor error correction model, but that model did not include much data from the humid Southeastern portion of the United States. Hence, this article aims to present a data correction model that was trained and validated with data from the Southeastern United States.