Articles | Volume 16, issue 1
https://doi.org/10.5194/amt-16-169-2023
© Author(s) 2023. 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-16-169-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
13 Jan 2023
Research article |
| 13 Jan 2023
| 13 Jan 2023
Identifying optimal co-location calibration periods for low-cost sensors
University of Connecticut Health Center, Department of Public Health Sciences, UConn School of Medicine, 263 Farmington Avenue, Farmington, CT 06032-1941, USA
Bloomberg School of Public Health, Environmental Health and Engineering, Johns Hopkins University, 615 N Wolfe St, Baltimore, MD 21205-2103, USA
SEARCH (Solutions for Energy, Air, Climate and Health) Center, Yale University, New Haven, CT 06520, USA
Colby Buehler
SEARCH (Solutions for Energy, Air, Climate and Health) Center, Yale University, New Haven, CT 06520, USA
Chemical and Environmental Engineering, Yale University, P.O. Box 208286, New Haven, CT 06520, USA
Abhirup Datta
Bloomberg School of Public Health, Department of Biostatistics, Johns Hopkins University, 615 N Wolfe Street, Baltimore, MD 21205-2103, USA
Drew R. Gentner
SEARCH (Solutions for Energy, Air, Climate and Health) Center, Yale University, New Haven, CT 06520, USA
Chemical and Environmental Engineering, Yale University, P.O. Box 208286, New Haven, CT 06520, USA
Kirsten Koehler
Bloomberg School of Public Health, Environmental Health and Engineering, Johns Hopkins University, 615 N Wolfe St, Baltimore, MD 21205-2103, USA
SEARCH (Solutions for Energy, Air, Climate and Health) Center, Yale University, New Haven, CT 06520, USA
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Cited
12 citations as recorded by crossref.
- Assessing the spatial transferability of calibration models across a low-cost sensors network V. Malyan et al. 10.1016/j.jaerosci.2024.106437
- A scalable framework for harmonizing, standardization, and correcting crowd-sourced low-cost sensor PM2.5 data across Europe A. Hassani et al. 10.1016/j.jenvman.2025.125100
- Air Quality Sensor Experts Convene: Current Quality Assurance Considerations for Credible Data K. Barkjohn et al. 10.1021/acsestair.4c00125
- Spatiotemporal Analysis of Complex Emission Dynamics in Port Areas Using High-Density Air Sensor Network J. Pan et al. 10.3390/toxics12100760
- 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
- Seasonally optimized calibrations improve low-cost sensor performance: long-term field evaluation of PurpleAir sensors in urban and rural India M. Campmier et al. 10.5194/amt-16-4357-2023
- Fundamentals of low-cost aerosol sensor design and operation J. Ouimette et al. 10.1080/02786826.2023.2285935
- Improving the quantification of peak concentrations for air quality sensors via data weighting C. Frischmon et al. 10.5194/amt-18-3147-2025
- Innovations in Air Quality Monitoring: Sensors, IoT and Future Research S. Shahid et al. 10.3390/s25072070
- Calibration and validation-based assessment of low-cost air quality sensors J. Dong et al. 10.1016/j.scitotenv.2025.179364
- Performance validation and calibration conditions for novel dynamic baseline tracking air sensors in long-term field monitoring H. Mei et al. 10.5194/amt-18-1771-2025
- Understanding the effect of temperature and relative humidity on sensor sensitivities in field environments and improving the calibration models of multiple electrochemical carbon monoxide (CO) sensors in a tropical environment R. Ariyaratne et al. 10.1016/j.snb.2023.133935
11 citations as recorded by crossref.
- Assessing the spatial transferability of calibration models across a low-cost sensors network V. Malyan et al. 10.1016/j.jaerosci.2024.106437
- A scalable framework for harmonizing, standardization, and correcting crowd-sourced low-cost sensor PM2.5 data across Europe A. Hassani et al. 10.1016/j.jenvman.2025.125100
- Air Quality Sensor Experts Convene: Current Quality Assurance Considerations for Credible Data K. Barkjohn et al. 10.1021/acsestair.4c00125
- Spatiotemporal Analysis of Complex Emission Dynamics in Port Areas Using High-Density Air Sensor Network J. Pan et al. 10.3390/toxics12100760
- 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
- Seasonally optimized calibrations improve low-cost sensor performance: long-term field evaluation of PurpleAir sensors in urban and rural India M. Campmier et al. 10.5194/amt-16-4357-2023
- Fundamentals of low-cost aerosol sensor design and operation J. Ouimette et al. 10.1080/02786826.2023.2285935
- Improving the quantification of peak concentrations for air quality sensors via data weighting C. Frischmon et al. 10.5194/amt-18-3147-2025
- Innovations in Air Quality Monitoring: Sensors, IoT and Future Research S. Shahid et al. 10.3390/s25072070
- Calibration and validation-based assessment of low-cost air quality sensors J. Dong et al. 10.1016/j.scitotenv.2025.179364
- Performance validation and calibration conditions for novel dynamic baseline tracking air sensors in long-term field monitoring H. Mei et al. 10.5194/amt-18-1771-2025
1 citations as recorded by crossref.
Latest update: 28 Aug 2025
Short summary
We assessed five pairs of co-located reference and low-cost sensor data sets (PM2.5, O3, NO2, NO, and CO) to make recommendations for best practices regarding the field calibration of low-cost air quality sensors. We found diminishing improvements for calibration periods longer than about 6 weeks for all sensors and that co-location can be minimized if the period is strategically selected and monitored so that the calibration period is representative of the desired measurement setting.
We assessed five pairs of co-located reference and low-cost sensor data sets (PM2.5, O3, NO2,...
