Articles | Volume 14, issue 8
https://doi.org/10.5194/amt-14-5637-2021
© Author(s) 2021. 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-14-5637-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
18 Aug 2021
Research article |
| 18 Aug 2021
| 18 Aug 2021
Machine learning calibration of low-cost NO2 and PM10 sensors: non-linear algorithms and their impact on site transferability
Grantham Institute – Climate Change and the Environment, Imperial College London, London SW7 2AZ, UK
Department of Physics, Imperial College London, London SW7 2AZ, UK
Data Science Institute, Imperial College London, London SW7 2AZ, UK
Climatic Research Unit, School of Environmental Sciences, University of East Anglia, Norwich NR4 7TJ, UK
Lev Konstantinovskiy
AirPublic Ltd, London, UK
Hannah Gardiner
AirPublic Ltd, London, UK
John Cant
AirPublic Ltd, London, UK
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Cited
29 citations as recorded by crossref.
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- Development of low-cost air quality stations for next-generation monitoring networks: calibration and validation of NO2 and O3 sensors A. Cavaliere et al. 10.5194/amt-16-4723-2023
- Bayesian Sensor Calibration M. Berger et al. 10.1109/JSEN.2022.3199485
- Calibration methodology of low-cost sensors for high-quality monitoring of fine particulate matter M. Aix et al. 10.1016/j.scitotenv.2023.164063
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- Unveiling the potential of a novel portable air quality platform for assessment of fine and coarse particulate matter: in-field testing, calibration, and machine learning insights D. Topalović et al. 10.1007/s10661-024-13069-0
- Applying machine learning for large scale field calibration of low‐cost PM2.5and PM10air pollution sensors P. Adong et al. 10.1002/ail2.76
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- 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
- Transferability of machine-learning-based global calibration models for NO2 and NO low-cost sensors A. Abu-Hani et al. 10.5194/amt-17-3917-2024
- Bayesian Sensor Calibration of a CMOS-Integrated Hall Sensor Against Thermomechanical Cross-Sensitivities M. Berger et al. 10.1109/JSEN.2023.3243783
- Response of stratospheric water vapour to warming constrained by satellite observations P. Nowack et al. 10.1038/s41561-023-01183-6
- Development and machine learning-based calibration of low-cost multiparametric stations for the measurement of CO2 and CH4 in air R. Biagi et al. 10.1016/j.heliyon.2024.e29772
- 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
- Application of Gaussian Mixture Regression for the Correction of Low Cost PM2.5 Monitoring Data in Accra, Ghana C. McFarlane et al. 10.1021/acsearthspacechem.1c00217
- 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
- Minimized Training of Machine Learning-Based Calibration Methods for Low-Cost O3 Sensors S. Tondini et al. 10.1109/JSEN.2023.3339202
- AirMLP: A Multilayer Perceptron Neural Network for Temporal Correction of PM2.5 Values in Turin M. Casari et al. 10.3390/s23239446
- Sampling Trade-Offs in Duty-Cycled Systems for Air Quality Low-Cost Sensors P. Ferrer-Cid et al. 10.3390/s22103964
- 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
- A systematic evaluation of high-cloud controlling factors S. Wilson Kemsley et al. 10.5194/acp-24-8295-2024
- 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 method of particulate matter sensor based on density peaks clustering combined with stacking algorithm J. Lu et al. 10.1016/j.atmosenv.2024.120460
- Temporal Pattern-Based Denoising and Calibration for Low-Cost Sensors in IoT Monitoring Platforms X. Allka et al. 10.1109/TIM.2023.3239626
- Enhancing the Reliability of NO2 Monitoring Using Low-Cost Sensors by Compensating for Temperature and Humidity Effects D. Alejo Sánchez et al. 10.3390/atmos15111365
- Low-Cost Sensor Performance Intercomparison, Correction Factor Development, and 2+ Years of Ambient PM2.5 Monitoring in Accra, Ghana G. Raheja et al. 10.1021/acs.est.2c09264
- A Network of Field-Calibrated Low-Cost Sensor Measurements of PM2.5 in Lomé, Togo, Over One to Two Years G. Raheja et al. 10.1021/acsearthspacechem.1c00391
26 citations as recorded by crossref.
- Detecting and quantifying PM2.5 and NO2 contributions from train and road traffic in the vicinity of a major railway terminal in Dublin, Ireland S. Priyan et al. 10.1016/j.envpol.2024.124903
- Development of low-cost air quality stations for next-generation monitoring networks: calibration and validation of NO2 and O3 sensors A. Cavaliere et al. 10.5194/amt-16-4723-2023
- Bayesian Sensor Calibration M. Berger et al. 10.1109/JSEN.2022.3199485
- Calibration methodology of low-cost sensors for high-quality monitoring of fine particulate matter M. Aix et al. 10.1016/j.scitotenv.2023.164063
- Low-Cost Formaldehyde Sensor Evaluation and Calibration in a Controlled Environment A. Chattopadhyay et al. 10.1109/JSEN.2022.3172864
- A machine learning approach to quantify meteorological drivers of ozone pollution in China from 2015 to 2019 X. Weng et al. 10.5194/acp-22-8385-2022
- Unveiling the potential of a novel portable air quality platform for assessment of fine and coarse particulate matter: in-field testing, calibration, and machine learning insights D. Topalović et al. 10.1007/s10661-024-13069-0
- Applying machine learning for large scale field calibration of low‐cost PM2.5and PM10air pollution sensors P. Adong et al. 10.1002/ail2.76
- A machine learning methodology for the generation of a parameterization of the hydroxyl radical D. Anderson et al. 10.5194/gmd-15-6341-2022
- 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
- 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
- Transferability of machine-learning-based global calibration models for NO2 and NO low-cost sensors A. Abu-Hani et al. 10.5194/amt-17-3917-2024
- Bayesian Sensor Calibration of a CMOS-Integrated Hall Sensor Against Thermomechanical Cross-Sensitivities M. Berger et al. 10.1109/JSEN.2023.3243783
- Response of stratospheric water vapour to warming constrained by satellite observations P. Nowack et al. 10.1038/s41561-023-01183-6
- Development and machine learning-based calibration of low-cost multiparametric stations for the measurement of CO2 and CH4 in air R. Biagi et al. 10.1016/j.heliyon.2024.e29772
- 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
- Application of Gaussian Mixture Regression for the Correction of Low Cost PM2.5 Monitoring Data in Accra, Ghana C. McFarlane et al. 10.1021/acsearthspacechem.1c00217
- 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
- Minimized Training of Machine Learning-Based Calibration Methods for Low-Cost O3 Sensors S. Tondini et al. 10.1109/JSEN.2023.3339202
- AirMLP: A Multilayer Perceptron Neural Network for Temporal Correction of PM2.5 Values in Turin M. Casari et al. 10.3390/s23239446
- Sampling Trade-Offs in Duty-Cycled Systems for Air Quality Low-Cost Sensors P. Ferrer-Cid et al. 10.3390/s22103964
- 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
- A systematic evaluation of high-cloud controlling factors S. Wilson Kemsley et al. 10.5194/acp-24-8295-2024
- 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 method of particulate matter sensor based on density peaks clustering combined with stacking algorithm J. Lu et al. 10.1016/j.atmosenv.2024.120460
- Temporal Pattern-Based Denoising and Calibration for Low-Cost Sensors in IoT Monitoring Platforms X. Allka et al. 10.1109/TIM.2023.3239626
3 citations as recorded by crossref.
- Enhancing the Reliability of NO2 Monitoring Using Low-Cost Sensors by Compensating for Temperature and Humidity Effects D. Alejo Sánchez et al. 10.3390/atmos15111365
- Low-Cost Sensor Performance Intercomparison, Correction Factor Development, and 2+ Years of Ambient PM2.5 Monitoring in Accra, Ghana G. Raheja et al. 10.1021/acs.est.2c09264
- A Network of Field-Calibrated Low-Cost Sensor Measurements of PM2.5 in Lomé, Togo, Over One to Two Years G. Raheja et al. 10.1021/acsearthspacechem.1c00391
Latest update: 20 Nov 2024
Short summary
Machine learning (ML) calibration techniques could be an effective way to improve the performance of low-cost air pollution sensors. Here we provide novel insights from case studies within the urban area of London, UK, where we compared the performance of three ML techniques to calibrate low-cost measurements of NO2 and PM10. In particular, we highlight the key issue of the method-dependent robustness in maintaining calibration skill after transferring sensors to different measurement sites.
Machine learning (ML) calibration techniques could be an effective way to improve the...
