Articles | Volume 12, issue 2
https://doi.org/10.5194/amt-12-1325-2019
© Author(s) 2019. 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-12-1325-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
28 Feb 2019
Research article |
| 28 Feb 2019
| 28 Feb 2019
An improved low-power measurement of ambient NO2 and O3 combining electrochemical sensor clusters and machine learning
Kate R. Smith
Wolfson Atmospheric Chemistry Laboratories, University of York, York,
YO10 5DD, UK
Wolfson Atmospheric Chemistry Laboratories, University of York, York,
YO10 5DD, UK
Peter D. Ivatt
Wolfson Atmospheric Chemistry Laboratories, University of York, York,
YO10 5DD, UK
James D. Lee
Wolfson Atmospheric Chemistry Laboratories, University of York, York,
YO10 5DD, UK
National Centre for Atmospheric Science, University of York, York, YO10
5DD, UK
Freya Squires
Wolfson Atmospheric Chemistry Laboratories, University of York, York,
YO10 5DD, UK
Chengliang Dai
Wolfson Atmospheric Chemistry Laboratories, University of York, York,
YO10 5DD, UK
Richard E. Peltier
Environmental Health Science, University of Massachusetts, 686 North
Pleasant Street, Amherst, MA 01003, USA
Mat J. Evans
Wolfson Atmospheric Chemistry Laboratories, University of York, York,
YO10 5DD, UK
National Centre for Atmospheric Science, University of York, York, YO10
5DD, UK
State Key Laboratory of Atmospheric Boundary Layer Physics and
Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of
Sciences, Beijing, China
Alastair C. Lewis
Wolfson Atmospheric Chemistry Laboratories, University of York, York,
YO10 5DD, UK
National Centre for Atmospheric Science, University of York, York, YO10
5DD, UK
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- First-Principles Algorithm for Air Quality Electrochemical Gas Sensors B. Ouyang 10.1021/acssensors.0c01129
- Corrigendum to “An improved sensor calibration with anomaly detection and removal” [Sens. Actuators, B 307 15 March (2020) 127428] X. Fang & I. Bate 10.1016/j.snb.2020.128239
- Reliability of Lower-Cost Sensors in the Analysis of Indoor Air Quality on Board Ships O. Schalm et al. 10.3390/atmos13101579
- Field comparison of electrochemical gas sensor data correction algorithms for ambient air measurements Y. Liang et al. 10.1016/j.snb.2020.128897
- Corrigendum to “An Improved Sensor Calibration with Anomaly Detection and Removal” [Sens. Actuators B: Chem. 307 (15 March) (2020) 127428] X. Fang & I. Bate 10.1016/j.snb.2020.128207
- Evaluation of low-cost gas sensors to quantify intra-urban variability of atmospheric pollutants A. Baruah et al. 10.1039/D2EA00165A
- 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
- Concept Drift Mitigation in Low-Cost Air Quality Monitoring Networks G. D’Elia et al. 10.3390/s24092786
- Time Series Reconstruction With Feature-Driven Imputation: A Comparison of Base Learning Algorithms N. Bashir et al. 10.1109/ACCESS.2024.3416321
- Measurements of the Limit of Detection for Electrochemical Gas Sensors J. Saffell & N. Martin 10.1520/JTE20230675
- A Global Multiunit Calibration as a Method for Large-Scale IoT Particulate Matter Monitoring Systems Deployments S. De Vito et al. 10.1109/TIM.2023.3331428
- Assessment of the applicability of a low-cost sensor–based methane monitoring system for continuous multi-channel sampling I. Nagahage et al. 10.1007/s10661-021-09290-w
- In situ drift correction for a low-cost NO2 sensor network J. Miech et al. 10.1039/D2EA00145D
- Development of a Portable and Sensitive CO2 Measurement Device with NDIR Sensor Clusters and Minimizing Water Vapor Impact Z. Wu et al. 10.3390/su15021533
- Machine learning combined with electrochemical sensor for rapid detection of Sudan Red I in food X. Sun et al. 10.1007/s11694-023-02150-w
- Introduction to the special issue “In-depth study of air pollution sources and processes within Beijing and its surrounding region (APHH-Beijing)” Z. Shi et al. 10.5194/acp-19-7519-2019
- Unravelling a black box: an open-source methodology for the field calibration of small air quality sensors S. Schmitz et al. 10.5194/amt-14-7221-2021
- A lightweight low-cost and multipollutant sensor package for aerial observations of air pollutants in atmospheric boundary layer X. Pang et al. 10.1016/j.scitotenv.2020.142828
- Characterizing the Aging of Alphasense NO2 Sensors in Long-Term Field Deployments J. Li et al. 10.1021/acssensors.1c00729
- Rational design of hybrid sensor arrays combined synergistically with machine learning for rapid response to a hazardous gas leak environment in chemical plants W. Ku et al. 10.1016/j.jhazmat.2024.133649
- Assessing the sources of particles at an urban background site using both regulatory instruments and low-cost sensors – a comparative study D. Bousiotis et al. 10.5194/amt-14-4139-2021
- A low-cost air quality monitoring system based on Internet of Things for smart homes M. Taştan 10.3233/AIS-210458
- Factors affecting variability in infiltration of ambient particle and gaseous pollutants into home at urban environment M. Hossain et al. 10.1016/j.buildenv.2021.108351
- An Improved Sensor Calibration with Anomaly Detection and Removal X. Fang & I. Bate 10.1016/j.snb.2019.127428
- Using Lidar technology to assess regional air pollution and improve estimates of PM2.5 transport in the North China Plain Y. Xiang et al. 10.1088/1748-9326/ab9cfd
- Long-term behavior and stability of calibration models for NO and NO<sub>2</sub> low-cost sensors H. Kim et al. 10.5194/amt-15-2979-2022
- A study on the performance of low-cost sensors for source apportionment at an urban background site D. Bousiotis et al. 10.5194/amt-15-4047-2022
- Investigating the Sources of Urban Air Pollution Using Low-Cost Air Quality Sensors at an Urban Atlanta Site L. Yang et al. 10.1021/acs.est.1c07005
- Future Low-Cost Urban Air Quality Monitoring Networks: Insights from the EU’s AirHeritage Project S. De Vito et al. 10.3390/atmos15111351
- Development of a reliable measurement station for air quality monitoring based on low-cost sensors and active redundancy S. Poupry et al. 10.1016/j.ifacol.2022.07.631
31 citations as recorded by crossref.
- Calibration methodology of low-cost sensors for high-quality monitoring of fine particulate matter M. Aix et al. 10.1016/j.scitotenv.2023.164063
- A Low-Cost Calibration Method for Temperature, Relative Humidity, and Carbon Dioxide Sensors Used in Air Quality Monitoring Systems R. González Rivero et al. 10.3390/atmos14020191
- First-Principles Algorithm for Air Quality Electrochemical Gas Sensors B. Ouyang 10.1021/acssensors.0c01129
- Corrigendum to “An improved sensor calibration with anomaly detection and removal” [Sens. Actuators, B 307 15 March (2020) 127428] X. Fang & I. Bate 10.1016/j.snb.2020.128239
- Reliability of Lower-Cost Sensors in the Analysis of Indoor Air Quality on Board Ships O. Schalm et al. 10.3390/atmos13101579
- Field comparison of electrochemical gas sensor data correction algorithms for ambient air measurements Y. Liang et al. 10.1016/j.snb.2020.128897
- Corrigendum to “An Improved Sensor Calibration with Anomaly Detection and Removal” [Sens. Actuators B: Chem. 307 (15 March) (2020) 127428] X. Fang & I. Bate 10.1016/j.snb.2020.128207
- Evaluation of low-cost gas sensors to quantify intra-urban variability of atmospheric pollutants A. Baruah et al. 10.1039/D2EA00165A
- 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
- Concept Drift Mitigation in Low-Cost Air Quality Monitoring Networks G. D’Elia et al. 10.3390/s24092786
- Time Series Reconstruction With Feature-Driven Imputation: A Comparison of Base Learning Algorithms N. Bashir et al. 10.1109/ACCESS.2024.3416321
- Measurements of the Limit of Detection for Electrochemical Gas Sensors J. Saffell & N. Martin 10.1520/JTE20230675
- A Global Multiunit Calibration as a Method for Large-Scale IoT Particulate Matter Monitoring Systems Deployments S. De Vito et al. 10.1109/TIM.2023.3331428
- Assessment of the applicability of a low-cost sensor–based methane monitoring system for continuous multi-channel sampling I. Nagahage et al. 10.1007/s10661-021-09290-w
- In situ drift correction for a low-cost NO2 sensor network J. Miech et al. 10.1039/D2EA00145D
- Development of a Portable and Sensitive CO2 Measurement Device with NDIR Sensor Clusters and Minimizing Water Vapor Impact Z. Wu et al. 10.3390/su15021533
- Machine learning combined with electrochemical sensor for rapid detection of Sudan Red I in food X. Sun et al. 10.1007/s11694-023-02150-w
- Introduction to the special issue “In-depth study of air pollution sources and processes within Beijing and its surrounding region (APHH-Beijing)” Z. Shi et al. 10.5194/acp-19-7519-2019
- Unravelling a black box: an open-source methodology for the field calibration of small air quality sensors S. Schmitz et al. 10.5194/amt-14-7221-2021
- A lightweight low-cost and multipollutant sensor package for aerial observations of air pollutants in atmospheric boundary layer X. Pang et al. 10.1016/j.scitotenv.2020.142828
- Characterizing the Aging of Alphasense NO2 Sensors in Long-Term Field Deployments J. Li et al. 10.1021/acssensors.1c00729
- Rational design of hybrid sensor arrays combined synergistically with machine learning for rapid response to a hazardous gas leak environment in chemical plants W. Ku et al. 10.1016/j.jhazmat.2024.133649
- Assessing the sources of particles at an urban background site using both regulatory instruments and low-cost sensors – a comparative study D. Bousiotis et al. 10.5194/amt-14-4139-2021
- A low-cost air quality monitoring system based on Internet of Things for smart homes M. Taştan 10.3233/AIS-210458
- Factors affecting variability in infiltration of ambient particle and gaseous pollutants into home at urban environment M. Hossain et al. 10.1016/j.buildenv.2021.108351
- An Improved Sensor Calibration with Anomaly Detection and Removal X. Fang & I. Bate 10.1016/j.snb.2019.127428
- Using Lidar technology to assess regional air pollution and improve estimates of PM2.5 transport in the North China Plain Y. Xiang et al. 10.1088/1748-9326/ab9cfd
- Long-term behavior and stability of calibration models for NO and NO<sub>2</sub> low-cost sensors H. Kim et al. 10.5194/amt-15-2979-2022
- A study on the performance of low-cost sensors for source apportionment at an urban background site D. Bousiotis et al. 10.5194/amt-15-4047-2022
- Investigating the Sources of Urban Air Pollution Using Low-Cost Air Quality Sensors at an Urban Atlanta Site L. Yang et al. 10.1021/acs.est.1c07005
- Future Low-Cost Urban Air Quality Monitoring Networks: Insights from the EU’s AirHeritage Project S. De Vito et al. 10.3390/atmos15111351
Latest update: 14 Dec 2024
Short summary
Clusters of low-cost, low-power atmospheric gas sensors were built into a sensor instrument to monitor NO2 and O3 in Beijing, alongside reference instruments, aiming to improve the reliability of sensor measurements. Clustering identical sensors and using the median sensor signal was used to minimize drift over short and medium timescales. Three different machine learning techniques were used for all the sensor data in an attempt to correct for cross-interferences, which worked to some degree.
Clusters of low-cost, low-power atmospheric gas sensors were built into a sensor instrument to...
