Articles | Volume 15, issue 9
https://doi.org/10.5194/amt-15-2979-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-15-2979-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
13 May 2022
Research article |
| 13 May 2022
| 13 May 2022
Long-term behavior and stability of calibration models for NO and NO2 low-cost sensors
Horim Kim
Laboratory for Air Pollution and Environmental Technology, Empa, 8600 Dübendorf, Switzerland
Michael Müller
Laboratory for Air Pollution and Environmental Technology, Empa, 8600 Dübendorf, Switzerland
now at: Amt für Geoinformation, Kanton Basel-Landschaft, 4410 Liestal, Switzerland
Stephan Henne
Laboratory for Air Pollution and Environmental Technology, Empa, 8600 Dübendorf, Switzerland
Christoph Hüglin
CORRESPONDING AUTHOR
Laboratory for Air Pollution and Environmental Technology, Empa, 8600 Dübendorf, Switzerland
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Cited
16 citations as recorded by crossref.
- Low‐cost air quality monitoring networks for long‐term field campaigns: A review F. Carotenuto et al. 10.1002/met.2161
- Long-term evaluation of commercial air quality sensors: an overview from the QUANT (Quantification of Utility of Atmospheric Network Technologies) study S. Diez et al. 10.5194/amt-17-3809-2024
- Nitrogen Dioxide Monitoring by Means of a Low-Cost Autonomous Platform and Sensor Calibration via Machine Learning with Global Data Correlation Enhancement S. Koziel et al. 10.3390/s25082352
- Calibration and Inter-Unit Consistency Assessment of an Electrochemical Sensor System Using Machine Learning I. Apostolopoulos et al. 10.3390/s24134110
- Evaluation of low-cost gas sensors to quantify intra-urban variability of atmospheric pollutants A. Baruah et al. 10.1039/D2EA00165A
- Calibration and validation-based assessment of low-cost air quality sensors J. Dong et al. 10.1016/j.scitotenv.2025.179364
- On Memory-Based Precise Calibration of Cost-Efficient NO2 Sensor Using Artificial Intelligence and Global Response Correction S. Koziel et al. 10.1016/j.knosys.2024.111564
- A portable nitrogen dioxide instrument using cavity-enhanced absorption spectroscopy S. Bailey et al. 10.5194/amt-17-5903-2024
- Machine-learning-based precise cost-efficient NO2 sensor calibration by means of time series matching and global data pre-processing S. Koziel et al. 10.1016/j.jestch.2024.101729
- Calibration of NO, SO2, and PM using Airify: A low-cost sensor cluster for air quality monitoring M. Ionascu et al. 10.1016/j.atmosenv.2024.120841
- Review of low-cost sensors for indoor air quality: Features and applications M. Ródenas García et al. 10.1080/05704928.2022.2085734
- High-performance machine-learning-based calibration of low-cost nitrogen dioxide sensor using environmental parameter differentials and global data scaling S. Koziel et al. 10.1038/s41598-024-77214-y
- Cost-Efficient measurement platform and machine-learning-based sensor calibration for precise NO2 pollution monitoring A. Pietrenko-Dabrowska et al. 10.1016/j.measurement.2024.115168
- Two step calibration method for ozone low-cost sensor: Field experiences with the UrbanSense DCUs J. Sá et al. 10.1016/j.jenvman.2022.116910
- Air quality and transport behaviour: sensors, field, and survey data from Warsaw, Poland A. Hassani et al. 10.1038/s41597-024-04111-4
- Statistical data pre-processing and time series incorporation for high-efficacy calibration of low-cost NO2 sensor using machine learning S. Koziel et al. 10.1038/s41598-024-59993-6
16 citations as recorded by crossref.
- Low‐cost air quality monitoring networks for long‐term field campaigns: A review F. Carotenuto et al. 10.1002/met.2161
- Long-term evaluation of commercial air quality sensors: an overview from the QUANT (Quantification of Utility of Atmospheric Network Technologies) study S. Diez et al. 10.5194/amt-17-3809-2024
- Nitrogen Dioxide Monitoring by Means of a Low-Cost Autonomous Platform and Sensor Calibration via Machine Learning with Global Data Correlation Enhancement S. Koziel et al. 10.3390/s25082352
- Calibration and Inter-Unit Consistency Assessment of an Electrochemical Sensor System Using Machine Learning I. Apostolopoulos et al. 10.3390/s24134110
- Evaluation of low-cost gas sensors to quantify intra-urban variability of atmospheric pollutants A. Baruah et al. 10.1039/D2EA00165A
- Calibration and validation-based assessment of low-cost air quality sensors J. Dong et al. 10.1016/j.scitotenv.2025.179364
- On Memory-Based Precise Calibration of Cost-Efficient NO2 Sensor Using Artificial Intelligence and Global Response Correction S. Koziel et al. 10.1016/j.knosys.2024.111564
- A portable nitrogen dioxide instrument using cavity-enhanced absorption spectroscopy S. Bailey et al. 10.5194/amt-17-5903-2024
- Machine-learning-based precise cost-efficient NO2 sensor calibration by means of time series matching and global data pre-processing S. Koziel et al. 10.1016/j.jestch.2024.101729
- Calibration of NO, SO2, and PM using Airify: A low-cost sensor cluster for air quality monitoring M. Ionascu et al. 10.1016/j.atmosenv.2024.120841
- Review of low-cost sensors for indoor air quality: Features and applications M. Ródenas García et al. 10.1080/05704928.2022.2085734
- High-performance machine-learning-based calibration of low-cost nitrogen dioxide sensor using environmental parameter differentials and global data scaling S. Koziel et al. 10.1038/s41598-024-77214-y
- Cost-Efficient measurement platform and machine-learning-based sensor calibration for precise NO2 pollution monitoring A. Pietrenko-Dabrowska et al. 10.1016/j.measurement.2024.115168
- Two step calibration method for ozone low-cost sensor: Field experiences with the UrbanSense DCUs J. Sá et al. 10.1016/j.jenvman.2022.116910
- Air quality and transport behaviour: sensors, field, and survey data from Warsaw, Poland A. Hassani et al. 10.1038/s41597-024-04111-4
- Statistical data pre-processing and time series incorporation for high-efficacy calibration of low-cost NO2 sensor using machine learning S. Koziel et al. 10.1038/s41598-024-59993-6
Latest update: 06 May 2025
Short summary
In this study, the performance of electrochemical sensors for NO and NO2 for measuring air quality was determined over a longer operating period. The performance of NO sensors remained reliable for more than 18 months. However, the NO2 sensors showed decreasing performance over time. During deployment, we found that the NO2 sensors can distinguish general pollution levels, but they proved unsuitable for accurate measurements due to significant biases.
In this study, the performance of electrochemical sensors for NO and NO2 for measuring air...
