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Cracking the code-Matching a proprietary algorithm for a low-cost sensor measuring PM1 and PM2.5.

Lance Wallace1

  • 1US EPA (retired), 428 Woodley Way, Santa Rosa, CA 95409, United States.

The Science of the Total Environment
|June 19, 2023
PubMed
Summary

This study developed new models to accurately estimate particulate matter (PM) mass concentrations from low-cost sensors. These models offer an alternative to proprietary algorithms, improving data reliability for researchers studying air quality.

Keywords:
ALT-CF3CF_1PM(2.5)PlantowerPurpleAirSensors

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Area of Science:

  • Environmental Science
  • Atmospheric Chemistry
  • Sensor Technology

Background:

  • Low-cost particle sensors often use proprietary algorithms for estimating particulate matter (PM) mass concentrations, with limited transparency regarding their methodology.
  • Proprietary algorithms can present challenges for researchers seeking to fundamentally correct or adjust sensor data due to a lack of information on calibration and development.
  • Existing methods for adjusting sensor data may be insufficient to fully correct inherent flaws in proprietary algorithms.

Purpose of the Study:

  • To develop and validate alternative models for estimating PM mass concentrations from low-cost Plantower PMS 5003 sensors, bypassing the manufacturer's proprietary CF_1 algorithm.
  • To provide researchers with a more transparent and fundamentally correctable method for utilizing data from low-cost air quality monitors.
  • To investigate the potential reasons behind the CF_1 algorithm's tendency to report zero values for certain PM estimates.

Main Methods:

  • Collected six months of data from four collocated PurpleAir PA-II monitors, each equipped with two Plantower PMS 5003 sensors.
  • Utilized data from two monitors previously calibrated against research-grade instruments for model development.
  • Developed linear regression models relating particle number counts in different size bins (N1, N2, N3) to PM1 and PM2.5 mass concentrations.

Main Results:

  • Established best-fitting models for PM1 (PM1 = a*(N1+N2)+d) and PM2.5 (PM2.5 = a*(N1+N2)+b*N3+d) based on particle number concentrations.
  • Achieved high correlation (R² > 0.99), near-zero intercepts, and slopes of 0.99-1.01 when comparing proposed models to manufacturer-reported CF_1 values for PM1 and PM2.5.
  • Developed general models with a mean absolute error (MAE) < 1 μg/m³ applicable to other datasets, potentially explaining the CF_1 algorithm's zero-value reporting.

Conclusions:

  • The developed models provide a robust and transparent alternative to proprietary algorithms for estimating PM mass concentrations from Plantower PMS 5003 sensors.
  • These new models enhance the reliability and usability of data from low-cost air quality monitoring networks.
  • The findings offer insights into the limitations of current proprietary algorithms and suggest pathways for improved sensor data interpretation.