Calibration and validation-based assessment of low-cost air quality sensors
Year: 2025
Type: article
Abstract: Air pollution poses a significant threat to public health. Low-cost air quality sensors (LCSs) can provide a data foundation for air quality monitoring, particularly supplementing the regulatory monitoring network and identifying local air quality issues. However, the performance varies considerably, and questions remain regarding reliability and accuracy of LCS data. We evaluated the accuracy, stability and precision of six LCSs over a three-month period of collocation with reference instruments at two locations. A mathematical workflow including calibration and validation was developed for accuracy and stability, incorporating a combination of environmental factors (e.g., temperature, relative humidity), linear and nonlinear regression, followed by precision evaluation by Bland-Altman plots. For particulate matter, data from LCSs was found to be reliable after simple linear regression (R 2 > 0.9 for both calibration and validation). For gas sensors including nitrogen dioxide, carbon monoxide, and Ozone, nonlinear methods that met the validation requirements also performed well using simple linear regression models (R 2 > 0.7 for both calibration and validation), whereas machine learning models, such as random forest, could not pass the validation, and require cautious application. In non-laboratory environments, incorporating environmental factors into the calibration function may lead to subsequent performance instability. Regarding precision between LCSs, unstable measurement biases among devices have been observed. Linear regression method is recommended as the preferred method for onsite calibration of LCSs, with caution advised when incorporating environmental factors due to increased uncertainty. Furthermore, when deploying LCSs, it is important to consider their varying responses to high or low pollutant concentrations. • Calibration-validation process is proposed for accuracy, stability and precision. • Linear/nonlinear regression and machine learning algorithms are explored. • Incorporation of environmental factors may not improve model performance. • Bland-Altman plots emphasize the precision between low-cost air quality sensors. • Machine learning models such as random forest require cautious application.
Institutions: RMIT University, University of Canberra
Language: English
Subfield: Environmental Engineering
Field: Environmental Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,250
OpenAlex ID: W4409441405