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Calibration Assessment of Low-Cost Carbon Dioxide Sensors Using the Extremely Randomized Trees Algorithm.

Tiago Araújo1,2, Lígia Silva3, Ana Aguiar4

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This study enhances low-cost carbon dioxide (CO2) sensor accuracy using an extremely randomized trees algorithm. Machine learning significantly improves data quality from CO2 sensors for indoor and outdoor air quality monitoring.

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carbon dioxide sensorsenvironmental monitoringmachine learningsensor calibration

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Area of Science:

  • Environmental Science
  • Sensor Technology
  • Data Science

Background:

  • Accurate carbon dioxide (CO2) monitoring is crucial for assessing indoor and outdoor air quality.
  • Low-cost CO2 sensors often suffer from reduced accuracy, impacting data reliability.
  • Existing calibration methods may not fully address the limitations of low-cost sensor technologies.

Purpose of the Study:

  • To develop and validate a machine learning approach for enhancing the accuracy of low-cost CO2 sensors.
  • To evaluate the proposed method's effectiveness across different sensor types (MOS, NDIR) and environments (indoor, outdoor).
  • To investigate factors influencing calibration performance, such as sensor exposure time and data variety.

Main Methods:

  • Utilized an extremely randomized trees algorithm for sensor calibration.
  • Collected experimental data from metal oxide semiconductor (MOS) and non-dispersive infrared (NDIR) CO2 sensors in an indoor setting.
  • Analyzed a third-party dataset comprising geographically distributed low-cost NDIR sensors for outdoor validation.

Main Results:

  • Achieved significant reductions in mean absolute error (MAE) for NDIR sensors, up to 90% indoors and 98% outdoors.
  • Demonstrated excellent linearity (r^2 value of 0.994) with MOS sensors in the indoor experiment.
  • Identified sensor exposure time and data variety as important factors for effective calibration.

Conclusions:

  • Machine learning, specifically the extremely randomized trees algorithm, effectively enhances the accuracy of low-cost CO2 sensors.
  • The proposed calibration method improves data quality for both indoor and outdoor air quality monitoring applications.
  • The approach is versatile, applicable to various low-cost gas sensor technologies and environmental conditions.