Articles | Volume 17, issue 22
https://doi.org/10.5194/amt-17-6735-2024
© Author(s) 2024. 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-17-6735-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
26 Nov 2024
Research article |
| 26 Nov 2024
| 26 Nov 2024
Calibration of PurpleAir low-cost particulate matter sensors: model development for air quality under high relative humidity conditions
Martine E. Mathieu-Campbell
Center for Geospatial Analytics, North Carolina State University, Raleigh, NC 27695, USA
Chuqi Guo
Department of Forestry and Environmental Resources, North Carolina State University, Raleigh, NC 27695, USA
Andrew P. Grieshop
Department of Civil, Construction and Environmental Engineering, North Carolina State University, Raleigh, NC 27695, USA
Jennifer Richmond-Bryant
CORRESPONDING AUTHOR
Center for Geospatial Analytics, North Carolina State University, Raleigh, NC 27695, USA
Department of Forestry and Environmental Resources, North Carolina State University, Raleigh, NC 27695, USA
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Cited
19 citations as recorded by crossref.
- Calibration Model for Purple Air monitored PM2.5 across the United States D. Pruthi et al. https://doi.org/10.1016/j.envpol.2026.128935
- Air Quality Mapping Using PM2.5 Sensors on Urban Buses I. González-de-Castro et al. https://doi.org/10.1109/JIOT.2026.3672932
- PurpleAir Sensor Deployment Trends and Uncertainties C. Chung & A. Rohr https://doi.org/10.3390/s26061789
- Evaluating and Calibrating Low-Cost Air Quality Sensors in Contrasting Aerosol Regimes P. Gupta et al. https://doi.org/10.1007/s44408-026-00146-x
- Remote Calibrating a National-Wide Dense Low-Cost Sensor Network for Hyperlocal Air Surveillance W. Wang et al. https://doi.org/10.1021/acs.est.6c03270
- Spatial-Aware Uncertainty Quantification for Low-Cost PM2.5 Sensors: GeoConformal Prediction vs. Monte Carlo Dropout Under Wildfire Distribution Shift A. Malarvizhi et al. https://doi.org/10.3390/atmos17070692
- Accuracy and Equivalence of Particle Number Concentration Measurements (0.3–10 µm) from a Low-Cost Sensirion SPS30 Compared with the OPS 3330 Under Field Conditions T. Gorzelnik et al. https://doi.org/10.3390/su18168097
- Calibration of Low-Cost Sensors for PM10 and PM2.5 Based on Artificial Intelligence for Smart Cities R. Gómez et al. https://doi.org/10.3390/s26030796
- 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
- Retrieval of bulk hygroscopicity from PurpleAir PM2.5 sensor measurements J. Psotka et al. https://doi.org/10.5194/amt-18-3135-2025
- Intelligence frameworks for environmental pollution assessment: a review on air and water quality monitoring systems A. Pati et al. https://doi.org/10.1016/j.measurement.2025.119122
- AIoT-Powered Real-Time Sensing and Calibration of Low-Cost Particulate Matter Sensors Using the TCM Network G. Msigwa et al. https://doi.org/10.1109/JIOT.2025.3631631
- Fundamental understanding of low-cost sensor response S. Kookkal & S. Dhaniyala https://doi.org/10.1080/02786826.2026.2649803
- Spatial distribution of persistent components of particulate matter near a hazardous waste thermal treatment facility C. Guo et al. https://doi.org/10.1016/j.envpol.2025.127524
- Global and regional calibration of low-cost PM 2.5 sensors (PurpleAir): assessing the transferability of U.S.-based corrections worldwide E. Ginsburg et al. https://doi.org/10.1080/02786826.2026.2676293
- Persistent chemicals in particulate matter (PM) near a hazardous waste thermal treatment facility C. Guo et al. https://doi.org/10.1016/j.apr.2025.102769
- From sensor noise to spatial insight: A pipeline for detecting and visualizing air pollution events in urban environments S. Zafeirelli et al. https://doi.org/10.1016/j.aeaoa.2026.100474
- CyanoHABs and CAPs: assessing community-based monitoring of PM2.5 with regional sources of pollution in rural, northeastern North Carolina H. Plaas et al. https://doi.org/10.1039/D5EA00020C
- Correction of PM2.5 underestimation in low-cost sensors under elevated dust loading using only sensor measurements K. Kaur et al. https://doi.org/10.5194/amt-19-1077-2026
19 citations as recorded by crossref.
- Calibration Model for Purple Air monitored PM2.5 across the United States D. Pruthi et al. https://doi.org/10.1016/j.envpol.2026.128935
- Air Quality Mapping Using PM2.5 Sensors on Urban Buses I. González-de-Castro et al. https://doi.org/10.1109/JIOT.2026.3672932
- PurpleAir Sensor Deployment Trends and Uncertainties C. Chung & A. Rohr https://doi.org/10.3390/s26061789
- Evaluating and Calibrating Low-Cost Air Quality Sensors in Contrasting Aerosol Regimes P. Gupta et al. https://doi.org/10.1007/s44408-026-00146-x
- Remote Calibrating a National-Wide Dense Low-Cost Sensor Network for Hyperlocal Air Surveillance W. Wang et al. https://doi.org/10.1021/acs.est.6c03270
- Spatial-Aware Uncertainty Quantification for Low-Cost PM2.5 Sensors: GeoConformal Prediction vs. Monte Carlo Dropout Under Wildfire Distribution Shift A. Malarvizhi et al. https://doi.org/10.3390/atmos17070692
- Accuracy and Equivalence of Particle Number Concentration Measurements (0.3–10 µm) from a Low-Cost Sensirion SPS30 Compared with the OPS 3330 Under Field Conditions T. Gorzelnik et al. https://doi.org/10.3390/su18168097
- Calibration of Low-Cost Sensors for PM10 and PM2.5 Based on Artificial Intelligence for Smart Cities R. Gómez et al. https://doi.org/10.3390/s26030796
- 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
- Retrieval of bulk hygroscopicity from PurpleAir PM2.5 sensor measurements J. Psotka et al. https://doi.org/10.5194/amt-18-3135-2025
- Intelligence frameworks for environmental pollution assessment: a review on air and water quality monitoring systems A. Pati et al. https://doi.org/10.1016/j.measurement.2025.119122
- AIoT-Powered Real-Time Sensing and Calibration of Low-Cost Particulate Matter Sensors Using the TCM Network G. Msigwa et al. https://doi.org/10.1109/JIOT.2025.3631631
- Fundamental understanding of low-cost sensor response S. Kookkal & S. Dhaniyala https://doi.org/10.1080/02786826.2026.2649803
- Spatial distribution of persistent components of particulate matter near a hazardous waste thermal treatment facility C. Guo et al. https://doi.org/10.1016/j.envpol.2025.127524
- Global and regional calibration of low-cost PM 2.5 sensors (PurpleAir): assessing the transferability of U.S.-based corrections worldwide E. Ginsburg et al. https://doi.org/10.1080/02786826.2026.2676293
- Persistent chemicals in particulate matter (PM) near a hazardous waste thermal treatment facility C. Guo et al. https://doi.org/10.1016/j.apr.2025.102769
- From sensor noise to spatial insight: A pipeline for detecting and visualizing air pollution events in urban environments S. Zafeirelli et al. https://doi.org/10.1016/j.aeaoa.2026.100474
- CyanoHABs and CAPs: assessing community-based monitoring of PM2.5 with regional sources of pollution in rural, northeastern North Carolina H. Plaas et al. https://doi.org/10.1039/D5EA00020C
- Correction of PM2.5 underestimation in low-cost sensors under elevated dust loading using only sensor measurements K. Kaur et al. https://doi.org/10.5194/amt-19-1077-2026
Saved (final revised paper)
Latest update: 19 Aug 2026
Short summary
The main source of measurement error from particulate matter PurpleAir sensors is relative humidity. Recent bias correction methods have not focused on the humid southeastern United States (US). To provide high-quality spatial and temporal data to inform community exposure in this area, our study developed and evaluated PurpleAir correction models for use in the warm–humid climate zones of the US. We found improved performance metrics, with error metrics decreasing by 16–23 % for our models.
The main source of measurement error from particulate matter PurpleAir sensors is relative...
