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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- Minimized Training of Machine Learning-Based Calibration Methods for Low-Cost O3 Sensors S. Tondini et al. https://doi.org/10.1109/JSEN.2023.3339202
- Design and Evaluation of a Calibration Chamber for Low-Cost PM Sensors–Part 1: Number Concentration-Based Assessment V. Malyan et al. https://doi.org/10.1021/acsestair.5c00262
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- Particle number size distribution evaluation of Plantower PMS5003 low-cost PM sensors – a field experiment A. Caseiro et al. https://doi.org/10.1039/D4EA00086B
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- Low-Cost Investigation into Sources of PM2.5 in Kinshasa, Democratic Republic of the Congo D. Westervelt et al. https://doi.org/10.1021/acsestair.3c00024
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- Dynamic and stationary monitoring of air pollutant exposures and dose during marathons C. Ribalta et al. https://doi.org/10.1016/j.scitotenv.2024.171997
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- Efficient field correction of low-cost particulate matter sensors using machine learning, mixed multiplicative/additive scaling and extended calibration inputs S. Koziel et al. https://doi.org/10.1038/s41598-025-02069-w
- A State of the Science Review of Wildfire-Specific Fine Particulate Matter Data Sources, Methods, and Models A. Orr et al. https://doi.org/10.1289/EHP15672
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- Data Insights for Sustainable Cities: Associations between Google Street View-Derived Urban Greenspace and Google Air View-Derived Pollution Levels M. Sabedotti et al. https://doi.org/10.1021/acs.est.3c05000
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- 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. https://doi.org/10.3390/s24175767
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66 citations as recorded by crossref.
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- Effect of environmental conditions on the performance of a low-cost atmospheric particulate matter sensor B. Macías-Hernández et al. https://doi.org/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 https://doi.org/10.3390/atmos15070797
- Mapping trends and analyzing key themes in low-cost sensors for air quality monitoring K. Alhasa et al. https://doi.org/10.1007/s12145-025-01927-5
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- Calibration and performance evaluation of PM2.5 and NO2 air quality sensors for environmental epidemiology M. Chacón-Mateos et al. https://doi.org/10.5194/amt-18-4061-2025
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- Promoting quality in low-cost gas sensor devices for real-world applications W. Reimringer & C. Bur https://doi.org/10.3389/fsens.2023.1317533
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- Minimized Training of Machine Learning-Based Calibration Methods for Low-Cost O3 Sensors S. Tondini et al. https://doi.org/10.1109/JSEN.2023.3339202
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- Forecasting the Exceedances of PM2.5 in an Urban Area S. Logothetis et al. https://doi.org/10.3390/atmos15050594
- Particle number size distribution evaluation of Plantower PMS5003 low-cost PM sensors – a field experiment A. Caseiro et al. https://doi.org/10.1039/D4EA00086B
- Machine Learning–Based Calibration and Performance Evaluation of Low-Cost Internet of Things Air Quality Sensors M. Taştan https://doi.org/10.3390/s25103183
- Low-Cost Investigation into Sources of PM2.5 in Kinshasa, Democratic Republic of the Congo D. Westervelt et al. https://doi.org/10.1021/acsestair.3c00024
- A Network Calibration Approach Improves the Accuracy and Long-Term Stability of a Low-Cost Air Quality Mesonet in New York City E. Hojeily et al. https://doi.org/10.1021/acsestair.5c00205
- Assessing the spatial transferability of calibration models across a low-cost sensors network V. Malyan et al. https://doi.org/10.1016/j.jaerosci.2024.106437
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- Low-Cost CO2 Sensors: On-Site Performance Evaluation and Co-Location Correction Procedure for Reliable Ventilation Assessments in Schools D. Honan et al. https://doi.org/10.3390/s26041265
- 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. https://doi.org/10.1016/j.indenv.2024.100008
- Indoor air quality assessment using low-cost sensors, and impact of outdoors Y. Dahima & A. Vaishya https://doi.org/10.1007/s11869-025-01843-z
- Field calibration of low-cost particulate matter sensors using artificial neural networks and affine response correction S. Koziel et al. https://doi.org/10.1016/j.measurement.2024.114529
- First long-term air quality assessment in Luanda, Angola: Performance evaluation of a low-cost monitoring station against reference equipment A. da Silva et al. https://doi.org/10.1016/j.apr.2025.102746
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- Unmanned aerial vehicles and low-cost sensors for air quality monitoring: A comprehensive review of applications across diverse emission sources . Vishal et al. https://doi.org/10.1016/j.scs.2025.106409
- AirMLP: A Multilayer Perceptron Neural Network for Temporal Correction of PM2.5 Values in Turin M. Casari et al. https://doi.org/10.3390/s23239446
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- Evolving trends in application of low-cost air quality sensor networks: challenges and future directions E. Bagkis et al. https://doi.org/10.1038/s41612-025-01216-4
- 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. https://doi.org/10.1016/j.scitotenv.2023.162336
- A BIM-Oriented Framework for Integrating IoT-Based Air Quality Monitoring Systems Using the AllBIMclass Classification E. Renard-Julián et al. https://doi.org/10.3390/app151910409
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- Dynamic and stationary monitoring of air pollutant exposures and dose during marathons C. Ribalta et al. https://doi.org/10.1016/j.scitotenv.2024.171997
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- Data-Driven Machine Learning Calibration Propagation in A Hybrid Sensor Network for Air Quality Monitoring I. Vajs et al. https://doi.org/10.3390/s23052815
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- Advancing low-cost air quality monitor calibration with machine learning methods S. Sousan et al. https://doi.org/10.1016/j.envpol.2025.126191
- Practical Guidance for Using PurpleAir Particle Monitors for Indoor and Outdoor Measurements in Community Field Studies M. Wang et al. https://doi.org/10.1007/s44408-025-00048-4
- Design and Evaluation of a Calibration Chamber for Low-Cost PM Sensors–Part 2: Mass Concentration-Based Assessment V. Malyan et al. https://doi.org/10.1021/acsestair.6c00161
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- Correlating Air Pollution Concentrations and Vehicular Emissions in an Italian Roadway Tunnel by Means of Low Cost Sensors S. De Vito et al. https://doi.org/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. https://doi.org/10.1021/acs.est.3c05000
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- 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. https://doi.org/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. https://doi.org/10.1021/acsearthspacechem.2c00257
- Causality-Driven Feature Selection for Calibrating Low-Cost Airborne Particulate Sensors Using Machine Learning V. Sooriyaarachchi et al. https://doi.org/10.3390/s24227304
- Efficient calibration of cost-efficient particulate matter sensors using machine learning and time-series alignment S. Koziel et al. https://doi.org/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. https://doi.org/10.3390/s24175767
- Decarbonizing the Transportation Sector: A Review on the Role of Electric Vehicles Towards the European Green Deal for the New Emission Standards D. Rimpas et al. https://doi.org/10.3390/air3020010
- Field-based calibration and operation of low-cost sensors for particulate matter by linear and nonlinear methods C. Mai et al. https://doi.org/10.1016/j.apr.2025.102676
- Spatio-temporal analysis of bicyclists’ PM2.5 exposure levels in a medium sized urban agglomeration M. Tames et al. https://doi.org/10.1007/s10661-024-13356-w
- Comparing spatial and temporal variabilities between the Vaisala AQT530 monitor and reference measurements R. Papaconstantinou et al. https://doi.org/10.5194/amt-19-63-2026
- Using low-cost sensors for source attribution and health assessment: An air quality study in Brownsville, Texas S. Pinakana et al. https://doi.org/10.1016/j.aeaoa.2025.100405
- Low-Cost Air Quality Sensors: Biases, Corrections and Challenges in Their Comparability I. Hayward et al. https://doi.org/10.3390/atmos15121523
Saved (final revised paper)
Latest update: 09 Jun 2026
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...
