Articles | Volume 15, issue 10
https://doi.org/10.5194/amt-15-3261-2022
© 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-15-3261-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
01 Jun 2022
Research article |
| 01 Jun 2022
| 01 Jun 2022
Machine learning techniques to improve the field performance of low-cost air quality sensors
Tony Bush
Department of Engineering Science, University of Oxford, Parks Road,
Oxford, OX1 3PJ, UK
Apertum Consulting, Harwell, Oxfordshire, UK
Nick Papaioannou
Department of Engineering Science, University of Oxford, Parks Road,
Oxford, OX1 3PJ, UK
Felix Leach
CORRESPONDING AUTHOR
Department of Engineering Science, University of Oxford, Parks Road,
Oxford, OX1 3PJ, UK
Francis D. Pope
School of Geography, Earth and Environmental Sciences, University of
Birmingham, Edgbaston, Birmingham, B15 2TT, UK
Ajit Singh
School of Geography, Earth and Environmental Sciences, University of
Birmingham, Edgbaston, Birmingham, B15 2TT, UK
G. Neil Thomas
Institute of Applied Health Research, University of Birmingham,
Edgbaston, Birmingham, B15 2TT, UK
Brian Stacey
Ricardo Energy & Environment, The Gemini Building, Fermi Avenue,
Harwell, Didcot, OX11 0QR, UK
Suzanne Bartington
Institute of Applied Health Research, University of Birmingham,
Edgbaston, Birmingham, B15 2TT, UK
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Total article views: 4,835 (including HTML, PDF, and XML)
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Total article views: 1,646 (including HTML, PDF, and XML)
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Cited
26 citations as recorded by crossref.
- Growth Stage-Specific Modeling of Chlorophyll Content in Korla Pear Leaves by Integrating Spectra and Vegetation Indices M. Yu et al.
- Smart Multi-Sensor Calibration of Low-Cost Particulate Matter Monitors E. Villanueva et al.
- A negative emission internal combustion engine vehicle? F. Leach
- Low-cost electrochemical gas sensing of vertical differences in wintertime air composition (CO, NO, NO2, O3) in Fairbanks, Alaska T. Roberts et al.
- Enhancing accuracy of air quality sensors with machine learning to augment large-scale monitoring networks K. Ravindra et al.
- Low-Cost Sensor Systems and IoT Technologies for Indoor Air Quality Monitoring: Instrumentation, Models, Implementation, and Perspectives for Validation S. Lopes et al.
- A comprehensive review on advancements in sensors for air pollution applications T. Seesaard et al.
- Improving Low-Cost Optical PM Sensor Accuracy in Humid Conditions via Aerosol Liquid Water Estimation Using U.S. EPA CSN Data Y. Guo et al.
- Air Pollution Monitoring Using Cost-Effective Devices Enhanced by Machine Learning Y. Colléaux et al.
- The impact of COVID-19 public health restrictions on particulate matter pollution measured by a validated low-cost sensor network in Oxford, UK T. Bush et al.
- Mapping trends and analyzing key themes in low-cost sensors for air quality monitoring K. Alhasa et al.
- Insights into the utility of small form air quality monitoring in health care environments: lessons learned from the University Hospitals Birmingham NHS Foundation Trust N. Cowell et al.
- Low-Cost Air Quality Sensors: Biases, Corrections and Challenges in Their Comparability I. Hayward et al.
- Advancements in Air Quality Monitoring Systems: A Comprehensive Review of Emerging Technologies for Enhancing Environmental Health S. Ahmed et al.
- Optimisation of the adaptive neuro-fuzzy inference system for adjusting low-cost sensors PM concentrations M. Casari et al.
- Nowcasting street-level NO2 concentrations using Gaussian processes M. Schoucair & M. van Reeuwijk
- Empowering communities: Advancements in air quality monitoring and citizen engagement H. Relvas et al.
- Design and Analysis of a Low-Cost Device for Detecting Prime Factors of Fire Ignition in Environment M. Chatterjee et al.
- Advancements in air quality monitoring: a systematic review of IoT-based air quality monitoring and AI technologies A. Garcia et al.
- Multi-City Bi-Pollutant Calibration of Low-Cost PM Sensors Using Machine Learning I. Yaqoob et al.
- A study on a low-cost real time MEMS based modular Indoor Environmental Quality monitor sensor N. Nanos et al.
- Assessment of Sensor Data from an Air Quality Monitoring Network—The Need for Machine Learning-Based Recalibration and Its Relevance in Health Impact Analysis of Local Pollution Events V. Petrić et al.
- Multi-Scenario Validation and Assessment of a Particulate Matter Sensor Monitor Optimized by Machine Learning Methods H. Tang et al.
- What can we learn from nested IoT low‐cost sensor networks for air quality? A case study of PM2.5 in Birmingham, UK N. Cowell et al.
- Comprehensive comparison of correction techniques for low-cost air quality sensors: the impact of device type and deployment environment I. Hayward et al.
- A low-cost IBBCEAS platform integrated with hierarchical Bayesian-optimized neural networks for high-precision urban CO2 monitoring J. Chen et al.
26 citations as recorded by crossref.
- Growth Stage-Specific Modeling of Chlorophyll Content in Korla Pear Leaves by Integrating Spectra and Vegetation Indices M. Yu et al.
- Smart Multi-Sensor Calibration of Low-Cost Particulate Matter Monitors E. Villanueva et al.
- A negative emission internal combustion engine vehicle? F. Leach
- Low-cost electrochemical gas sensing of vertical differences in wintertime air composition (CO, NO, NO2, O3) in Fairbanks, Alaska T. Roberts et al.
- Enhancing accuracy of air quality sensors with machine learning to augment large-scale monitoring networks K. Ravindra et al.
- Low-Cost Sensor Systems and IoT Technologies for Indoor Air Quality Monitoring: Instrumentation, Models, Implementation, and Perspectives for Validation S. Lopes et al.
- A comprehensive review on advancements in sensors for air pollution applications T. Seesaard et al.
- Improving Low-Cost Optical PM Sensor Accuracy in Humid Conditions via Aerosol Liquid Water Estimation Using U.S. EPA CSN Data Y. Guo et al.
- Air Pollution Monitoring Using Cost-Effective Devices Enhanced by Machine Learning Y. Colléaux et al.
- The impact of COVID-19 public health restrictions on particulate matter pollution measured by a validated low-cost sensor network in Oxford, UK T. Bush et al.
- Mapping trends and analyzing key themes in low-cost sensors for air quality monitoring K. Alhasa et al.
- Insights into the utility of small form air quality monitoring in health care environments: lessons learned from the University Hospitals Birmingham NHS Foundation Trust N. Cowell et al.
- Low-Cost Air Quality Sensors: Biases, Corrections and Challenges in Their Comparability I. Hayward et al.
- Advancements in Air Quality Monitoring Systems: A Comprehensive Review of Emerging Technologies for Enhancing Environmental Health S. Ahmed et al.
- Optimisation of the adaptive neuro-fuzzy inference system for adjusting low-cost sensors PM concentrations M. Casari et al.
- Nowcasting street-level NO2 concentrations using Gaussian processes M. Schoucair & M. van Reeuwijk
- Empowering communities: Advancements in air quality monitoring and citizen engagement H. Relvas et al.
- Design and Analysis of a Low-Cost Device for Detecting Prime Factors of Fire Ignition in Environment M. Chatterjee et al.
- Advancements in air quality monitoring: a systematic review of IoT-based air quality monitoring and AI technologies A. Garcia et al.
- Multi-City Bi-Pollutant Calibration of Low-Cost PM Sensors Using Machine Learning I. Yaqoob et al.
- A study on a low-cost real time MEMS based modular Indoor Environmental Quality monitor sensor N. Nanos et al.
- Assessment of Sensor Data from an Air Quality Monitoring Network—The Need for Machine Learning-Based Recalibration and Its Relevance in Health Impact Analysis of Local Pollution Events V. Petrić et al.
- Multi-Scenario Validation and Assessment of a Particulate Matter Sensor Monitor Optimized by Machine Learning Methods H. Tang et al.
- What can we learn from nested IoT low‐cost sensor networks for air quality? A case study of PM2.5 in Birmingham, UK N. Cowell et al.
- Comprehensive comparison of correction techniques for low-cost air quality sensors: the impact of device type and deployment environment I. Hayward et al.
- A low-cost IBBCEAS platform integrated with hierarchical Bayesian-optimized neural networks for high-precision urban CO2 monitoring J. Chen et al.
Saved (final revised paper)
Latest update: 20 May 2026
Short summary
Poor air quality is a human health risk which demands high-spatiotemporal-resolution monitoring data to manage. Low-cost air quality sensors present a convenient pathway to delivering these needs, compared to traditional methods, but bring methodological challenges which can limit operational ability. In this study within Oxford, UK, we develop machine learning methods to improve the quality of low-cost sensors for NO2, PM10 (particulate matter) and PM2.5 and demonstrate their effectiveness.
Poor air quality is a human health risk which demands high-spatiotemporal-resolution monitoring...
