Articles | Volume 15, issue 21
https://doi.org/10.5194/amt-15-6309-2022
© 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-15-6309-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
02 Nov 2022
Research article |
| 02 Nov 2022
| 02 Nov 2022
Calibrating networks of low-cost air quality sensors
Priyanka deSouza
CORRESPONDING AUTHOR
Department of Urban and Regional Planning, University of Colorado Denver, CO 80202, USA
CU Population Center, University of Colorado, Boulder, CO 80302, USA
Ralph Kahn
NASA Goddard Space Flight Center, Greenbelt, MD, 20771, USA
Tehya Stockman
Denver Department of Public Health and Environment, Denver CO 80202, USA
Department of Civil, Environmental, and Architectural Engineering,
University of Colorado, Boulder, CO 80309, USA
William Obermann
Denver Department of Public Health and Environment, Denver CO 80202, USA
Ben Crawford
Department of Geography and Environmental Sciences, University of
Colorado, Denver, CO 80202, USA
An Wang
Senseable City Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
James Crooks
Division of Biostatistics and Bioinformatics, National Jewish Health, Denver, CO 2930, USA
Department of Epidemiology, University of Colorado at Denver – Anschutz Medical Campus, Denver, CO 129263, USA
Jing Li
Department of Geography and the Environment, University of Denver, Denver, CO 80210, USA
Patrick Kinney
Boston University School of Public Health, Boston, MA 02118, USA
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Cited
36 citations as recorded by crossref.
- Evaluating Indoor Air Quality in Residential Environments: A Study of PM2.5 and CO2 Dynamics Using Low-Cost Sensors K. Shah et al. 10.3390/environments11110237
- 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
- Investigating the Sensitivity of Low-Cost Sensors in Measuring Particle Number Concentrations across Diverse Atmospheric Conditions in Greece and Spain G. Kosmopoulos et al. 10.3390/s23146541
- Dynamic and stationary monitoring of air pollutant exposures and dose during marathons C. Ribalta et al. 10.1016/j.scitotenv.2024.171997
- Effect of environmental conditions on the performance of a low-cost atmospheric particulate matter sensor B. Macías-Hernández et al. 10.1016/j.uclim.2023.101753
- Source Tracing of PM2.5 in a Metropolitan Area Using a Low-Cost Air Quality Monitoring Network: Case Study of Denver, Colorado, USA N. Afshar-Mohajer & M. Shaban 10.3390/atmos15070797
- Performance evaluation of MeteoTracker mobile sensor for outdoor applications F. Barbano et al. 10.5194/amt-17-3255-2024
- A Case Study of Air Quality and a Health Index over a Port, an Urban and a High-Traffic Location in Rhodes City I. Logothetis et al. 10.3390/air1020011
- From planetary scenarios to planetary sensing: Models, observations, and political legibility D. Pendergrass 10.1177/20530196241270716
- Calibrating low-cost sensors using MERRA-2 reconstructed PM2.5 mass concentration as a proxy V. Malyan et al. 10.1016/j.apr.2023.102027
- A novel approach combining indoor mobile measurements and interpretable machine learning to unveil highly-resolved indoor air pollution Z. Tang et al. 10.1016/j.buildenv.2025.112552
- A Systematic Study of Popular Software Packages and AI/ML Models for Calibrating In Situ Air Quality Data: An Example with Purple Air Sensors S. Smith et al. 10.3390/s25041028
- Data-Driven Machine Learning Calibration Propagation in A Hybrid Sensor Network for Air Quality Monitoring I. Vajs et al. 10.3390/s23052815
- Promoting quality in low-cost gas sensor devices for real-world applications W. Reimringer & C. Bur 10.3389/fsens.2023.1317533
- Systematic framework for quantitative assessment of Indoor Air Quality under future climate scenarios; 2100s Projection of a Belgian case study M. Pourkiaei et al. 10.1016/j.jobe.2024.109611
- Evaluating the Performance of Low-Cost PM2.5Sensors in Mobile Settings P. deSouza et al. 10.1021/acs.est.3c04843
- Minimized Training of Machine Learning-Based Calibration Methods for Low-Cost O3 Sensors S. Tondini et al. 10.1109/JSEN.2023.3339202
- Forecasting the Exceedances of PM2.5 in an Urban Area S. Logothetis et al. 10.3390/atmos15050594
- Particle number size distribution evaluation of Plantower PMS5003 low-cost PM sensors – a field experiment A. Caseiro et al. 10.1039/D4EA00086B
- Low-Cost Investigation into Sources of PM2.5 in Kinshasa, Democratic Republic of the Congo D. Westervelt et al. 10.1021/acsestair.3c00024
- Correlating Air Pollution Concentrations and Vehicular Emissions in an Italian Roadway Tunnel by Means of Low Cost Sensors S. De Vito et al. 10.3390/atmos14040679
- Data Insights for Sustainable Cities: Associations between Google Street View-Derived Urban Greenspace and Google Air View-Derived Pollution Levels M. Sabedotti et al. 10.1021/acs.est.3c05000
- Assessing the spatial transferability of calibration models across a low-cost sensors network V. Malyan et al. 10.1016/j.jaerosci.2024.106437
- Air Quality Sensor Experts Convene: Current Quality Assurance Considerations for Credible Data K. Barkjohn et al. 10.1021/acsestair.4c00125
- Spatiotemporal distribution prediction for PM2.5 based on STXGBoost model and high-density monitoring sensors in Zhengzhou High Tech Zone, China S. Zhao et al. 10.1016/j.jenvman.2024.123682
- Inter- versus Intracity Variations in the Performance and Calibration of Low-Cost PM2.5 Sensors: A Multicity Assessment in India S. V et al. 10.1021/acsearthspacechem.2c00257
- Spatial and temporal variation of cooking-emitted particles in distinct zones using scanning mobility particle sizer and a network of low-cost sensors R. Dhiman et al. 10.1016/j.indenv.2024.100008
- Causality-Driven Feature Selection for Calibrating Low-Cost Airborne Particulate Sensors Using Machine Learning V. Sooriyaarachchi et al. 10.3390/s24227304
- Field calibration of low-cost particulate matter sensors using artificial neural networks and affine response correction S. Koziel et al. 10.1016/j.measurement.2024.114529
- Efficient calibration of cost-efficient particulate matter sensors using machine learning and time-series alignment S. Koziel et al. 10.1016/j.knosys.2024.111879
- Feasibility Study on the Use of NO2 and PM2.5 Sensors for Exposure Assessment and Indoor Source Apportionment at Fixed Locations M. Chacón-Mateos et al. 10.3390/s24175767
- AirMLP: A Multilayer Perceptron Neural Network for Temporal Correction of PM2.5 Values in Turin M. Casari et al. 10.3390/s23239446
- Spatio-temporal analysis of bicyclists’ PM2.5 exposure levels in a medium sized urban agglomeration M. Tames et al. 10.1007/s10661-024-13356-w
- A nested machine learning approach to short-term PM2.5 prediction in metropolitan areas using PM2.5 data from different sensor networks J. Li et al. 10.1016/j.scitotenv.2023.162336
- Low-Cost Air Quality Sensors: Biases, Corrections and Challenges in Their Comparability I. Hayward et al. 10.3390/atmos15121523
- An analysis of degradation in low-cost particulate matter sensors P. deSouza et al. 10.1039/D2EA00142J
35 citations as recorded by crossref.
- Evaluating Indoor Air Quality in Residential Environments: A Study of PM2.5 and CO2 Dynamics Using Low-Cost Sensors K. Shah et al. 10.3390/environments11110237
- 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
- Investigating the Sensitivity of Low-Cost Sensors in Measuring Particle Number Concentrations across Diverse Atmospheric Conditions in Greece and Spain G. Kosmopoulos et al. 10.3390/s23146541
- Dynamic and stationary monitoring of air pollutant exposures and dose during marathons C. Ribalta et al. 10.1016/j.scitotenv.2024.171997
- Effect of environmental conditions on the performance of a low-cost atmospheric particulate matter sensor B. Macías-Hernández et al. 10.1016/j.uclim.2023.101753
- Source Tracing of PM2.5 in a Metropolitan Area Using a Low-Cost Air Quality Monitoring Network: Case Study of Denver, Colorado, USA N. Afshar-Mohajer & M. Shaban 10.3390/atmos15070797
- Performance evaluation of MeteoTracker mobile sensor for outdoor applications F. Barbano et al. 10.5194/amt-17-3255-2024
- A Case Study of Air Quality and a Health Index over a Port, an Urban and a High-Traffic Location in Rhodes City I. Logothetis et al. 10.3390/air1020011
- From planetary scenarios to planetary sensing: Models, observations, and political legibility D. Pendergrass 10.1177/20530196241270716
- Calibrating low-cost sensors using MERRA-2 reconstructed PM2.5 mass concentration as a proxy V. Malyan et al. 10.1016/j.apr.2023.102027
- A novel approach combining indoor mobile measurements and interpretable machine learning to unveil highly-resolved indoor air pollution Z. Tang et al. 10.1016/j.buildenv.2025.112552
- A Systematic Study of Popular Software Packages and AI/ML Models for Calibrating In Situ Air Quality Data: An Example with Purple Air Sensors S. Smith et al. 10.3390/s25041028
- Data-Driven Machine Learning Calibration Propagation in A Hybrid Sensor Network for Air Quality Monitoring I. Vajs et al. 10.3390/s23052815
- Promoting quality in low-cost gas sensor devices for real-world applications W. Reimringer & C. Bur 10.3389/fsens.2023.1317533
- Systematic framework for quantitative assessment of Indoor Air Quality under future climate scenarios; 2100s Projection of a Belgian case study M. Pourkiaei et al. 10.1016/j.jobe.2024.109611
- Evaluating the Performance of Low-Cost PM2.5Sensors in Mobile Settings P. deSouza et al. 10.1021/acs.est.3c04843
- Minimized Training of Machine Learning-Based Calibration Methods for Low-Cost O3 Sensors S. Tondini et al. 10.1109/JSEN.2023.3339202
- Forecasting the Exceedances of PM2.5 in an Urban Area S. Logothetis et al. 10.3390/atmos15050594
- Particle number size distribution evaluation of Plantower PMS5003 low-cost PM sensors – a field experiment A. Caseiro et al. 10.1039/D4EA00086B
- Low-Cost Investigation into Sources of PM2.5 in Kinshasa, Democratic Republic of the Congo D. Westervelt et al. 10.1021/acsestair.3c00024
- Correlating Air Pollution Concentrations and Vehicular Emissions in an Italian Roadway Tunnel by Means of Low Cost Sensors S. De Vito et al. 10.3390/atmos14040679
- Data Insights for Sustainable Cities: Associations between Google Street View-Derived Urban Greenspace and Google Air View-Derived Pollution Levels M. Sabedotti et al. 10.1021/acs.est.3c05000
- Assessing the spatial transferability of calibration models across a low-cost sensors network V. Malyan et al. 10.1016/j.jaerosci.2024.106437
- Air Quality Sensor Experts Convene: Current Quality Assurance Considerations for Credible Data K. Barkjohn et al. 10.1021/acsestair.4c00125
- Spatiotemporal distribution prediction for PM2.5 based on STXGBoost model and high-density monitoring sensors in Zhengzhou High Tech Zone, China S. Zhao et al. 10.1016/j.jenvman.2024.123682
- Inter- versus Intracity Variations in the Performance and Calibration of Low-Cost PM2.5 Sensors: A Multicity Assessment in India S. V et al. 10.1021/acsearthspacechem.2c00257
- Spatial and temporal variation of cooking-emitted particles in distinct zones using scanning mobility particle sizer and a network of low-cost sensors R. Dhiman et al. 10.1016/j.indenv.2024.100008
- Causality-Driven Feature Selection for Calibrating Low-Cost Airborne Particulate Sensors Using Machine Learning V. Sooriyaarachchi et al. 10.3390/s24227304
- Field calibration of low-cost particulate matter sensors using artificial neural networks and affine response correction S. Koziel et al. 10.1016/j.measurement.2024.114529
- Efficient calibration of cost-efficient particulate matter sensors using machine learning and time-series alignment S. Koziel et al. 10.1016/j.knosys.2024.111879
- Feasibility Study on the Use of NO2 and PM2.5 Sensors for Exposure Assessment and Indoor Source Apportionment at Fixed Locations M. Chacón-Mateos et al. 10.3390/s24175767
- AirMLP: A Multilayer Perceptron Neural Network for Temporal Correction of PM2.5 Values in Turin M. Casari et al. 10.3390/s23239446
- Spatio-temporal analysis of bicyclists’ PM2.5 exposure levels in a medium sized urban agglomeration M. Tames et al. 10.1007/s10661-024-13356-w
- A nested machine learning approach to short-term PM2.5 prediction in metropolitan areas using PM2.5 data from different sensor networks J. Li et al. 10.1016/j.scitotenv.2023.162336
- Low-Cost Air Quality Sensors: Biases, Corrections and Challenges in Their Comparability I. Hayward et al. 10.3390/atmos15121523
1 citations as recorded by crossref.
Latest update: 21 Feb 2025
Short summary
How sensitive are the spatial and temporal trends of PM2.5 derived from a network of low-cost sensors to the calibration adjustment used? How transferable are calibration equations developed at a few co-location sites to an entire network of low-cost sensors? This paper attempts to answer this question and offers a series of suggestions on how to develop the most robust calibration function for different end uses. It uses measurements from the Love My Air network in Denver as a test case.
How sensitive are the spatial and temporal trends of PM2.5 derived from a network of low-cost...
