07 Aug 2023
 | 07 Aug 2023
Status: this preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).

Towards a Universal Hygroscopic Growth Calibration for Low-Cost PM2.5 Sensors

Milan Y. Patel, Pietro F. Vannucci, Jinsol Kim, William M. Berelson, and Ronald C. Cohen

Abstract. Low-cost particulate matter (PM) sensors continue to grow in popularity, but issues such as aerosol size-dependent sensitivity drive the need for effective calibration schemes. Here we devise a time-evolving calibration method for the Plantower PMS5003 PM2.5 mass concentration measurements. We use 2 years of measurements from the Berkeley Environmental Air-quality and CO2 Network sensors deployed in San Francisco and Los Angeles in our analysis. The calibration uses a hygroscopic growth correction factor derived from k-Köhler Theory, where the calibration parameters are determined empirically using EPA AQS reference data at co-location sites during the period from 2021–2022. The parameters are found to vary cyclically through the seasons, and the seasonal cycles match changes in sulfate and elemental carbon PM composition fractions throughout the year. In both regions, the seasonal RH dependence calibration performs better than the uncalibrated data and data calibrated with the EPA’s national Plantower calibration algorithm. In the San Francisco Bay Area, the seasonal RH dependence calibration reduces the RMSE by ~40 % from the uncalibrated data and maintains a mean bias much smaller than the EPA National Calibration scheme (–0.90 vs –2.73 µg/m3). We also find that calibration parameters forecasted beyond those fit with the EPA reference data continue to outperform the uncalibrated data and EPA calibration data, enabling real-time application of the calibration scheme even in the absence of reference data. While the correction greatly improves the data accuracy, non-Gaussian distribution of the residuals suggests that other processes besides hygroscopic growth can be parameterized for future improvement of this calibration.

Milan Y. Patel et al.

Status: open (extended)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on egusphere-2023-1701', JAMES OUIMETTE, 10 Aug 2023 reply
    • AC1: 'Reply on CC1', Milan Patel, 11 Aug 2023 reply
  • CC2: 'Comment on egusphere-2023-1701', JAMES OUIMETTE, 15 Aug 2023 reply
  • CC3: 'Comment on egusphere-2023-1701', JAMES OUIMETTE, 18 Aug 2023 reply
  • RC1: 'Comment on egusphere-2023-1701', Anonymous Referee #1, 25 Sep 2023 reply

Milan Y. Patel et al.

Data sets

Datasets Used in this Work Milan Y. Patel

Milan Y. Patel et al.


Total article views: 334 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
217 97 20 334 29 5 6
  • HTML: 217
  • PDF: 97
  • XML: 20
  • Total: 334
  • Supplement: 29
  • BibTeX: 5
  • EndNote: 6
Views and downloads (calculated since 07 Aug 2023)
Cumulative views and downloads (calculated since 07 Aug 2023)

Viewed (geographical distribution)

Total article views: 327 (including HTML, PDF, and XML) Thereof 327 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
Latest update: 01 Oct 2023
Short summary
Low-cost particulate matter (PM) sensors are becoming increasingly common in community monitoring and atmospheric research, but these sensors require proper calibration to provide accurate reporting. Here, we propose a hygroscopic growth calibration scheme that evolves in time to account for seasonal changes in hygroscopic growth. In San Francisco and Los Angeles, CA, applying a seasonal hygroscopic growth calibration can account for sensor biases driven by the seasonal cycles in PM composition.