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Updated: Jun 13, 2025

O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression

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Low-Cost CO₂ NDIR Sensors: Performance Evaluation and Calibration Using Machine Learning Techniques.

Ravish Dubey¹, Arina Telles², James Nikkel²

¹School of the Environment, Yale University, New Haven, CT 06511, USA.

Sensors (Basel, Switzerland)

|September 14, 2024

Summary

This study compared low-cost carbon dioxide (CO2) sensors, finding that higher-priced models offered better accuracy. Machine learning calibration significantly improved sensor performance, especially for affordable options.

Keywords:

collocated measurements low-cost CO2 sensors machine learning calibration performance evaluation

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Area of Science:

Environmental monitoring
Sensor technology
Data science

Background:

Accurate carbon dioxide (CO2) monitoring is crucial for environmental and climate studies.
Low-cost CO2 sensors offer potential for widespread deployment but require performance validation.
Existing research often lacks comprehensive intercomparison across diverse sensor price points.

Purpose of the Study:

To evaluate the performance of various low-cost CO2 sensors against a reference instrument.
To investigate the efficacy of machine learning techniques for calibrating these sensors.
To assess the potential of machine learning to enhance the accuracy of affordable CO2 sensing technologies.

Main Methods:

Comparative analysis of three CO2 sensors (Senseair Sunrise AB, Senseair K30, Vaisala GMP 343) against a Los Gatos precision greenhouse gas analyzer.
Application of machine learning models, including linear regression, gradient boosting regression, and random forest regression, for sensor calibration.
Development and evaluation of a stack ensemble model combining multiple machine learning approaches.

Main Results:

Significant performance variations were observed among the tested CO2 sensors, with higher-cost Vaisala sensors showing superior accuracy.
Lower-cost Senseair Sunrise sensors provided reasonable accuracy, while the K30 model exhibited higher noise and variability.
Machine learning calibration, particularly a stack ensemble model, improved sensor accuracy by approximately 65%.

Conclusions:

Machine learning offers a powerful tool for enhancing the accuracy of low-cost CO2 sensors, bridging the gap between affordability and reliability.
The study provides valuable data for selecting and calibrating CO2 sensors across different price tiers for environmental applications.
Further research into advanced machine learning algorithms can unlock greater potential for low-cost sensor networks.