Articles | Volume 17, issue 3
https://doi.org/10.5194/amt-17-1051-2024
© 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-17-1051-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
13 Feb 2024
Research article |
| 13 Feb 2024
| 13 Feb 2024
Towards a hygroscopic growth calibration for low-cost PM2.5 sensors
Milan Y. Patel
Department of Chemistry, University of California Berkeley, Berkeley, CA 94720, USA
Pietro F. Vannucci
Department of Chemistry, University of California Berkeley, Berkeley, CA 94720, USA
Jinsol Kim
Department of Earth Sciences, University of Southern California, Los Angeles, CA 90089, USA
William M. Berelson
Department of Earth Sciences, University of Southern California, Los Angeles, CA 90089, USA
Department of Chemistry, University of California Berkeley, Berkeley, CA 94720, USA
Department of Earth and Planetary Sciences, University of California Berkeley, Berkeley, CA 94720, USA
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Cited
28 citations as recorded by crossref.
- Harmonizing low-cost and regulatory air quality monitoring networks with interpretable semi-supervised learning: Reducing exposure misclassification in underrepresented communities D. Tang et al. https://doi.org/10.1016/j.jhazmat.2025.137893
- Evaluating PurpleAir Sensors: Do They Accurately Reflect Ambient Air Temperature? J. Tse & L. Liang https://doi.org/10.3390/s25103044
- 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
- Validation and Correction of PM2.5 Mass Concentrations from Consumer-Grade Personal Exposure and Outdoor Fixed Monitors: A Two-Year Colocation Study in Colorado A. Khalili Hollo et al. https://doi.org/10.1021/acsenvironau.6c00020
- A Scalable Calibration Method for Enhanced Accuracy in Dense Air Quality Monitoring Networks A. Winter et al. https://doi.org/10.1021/acs.est.4c08855
- Multimodal Approach for Assessing Emissions and Transport of Greenhouse Gases and Air Pollutants from the January 2025 Los Angeles Wildfires P. Vannucci et al. https://doi.org/10.1021/acsestair.5c00430
- Observational Inferences of NOx and CO Emission Factors for Vehicles and Homes in the San Francisco Bay Area Y. Zhu et al. https://doi.org/10.1021/acsestair.5c00004
- 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
- Transferability of Machine Learning‐Based Calibration Models for Low‐Cost PM2.5 Sensors Across Heterogeneous African Cities P. Adong & E. Bainomugisha https://doi.org/10.1002/met.70228
- Enhancing the spatio-temporal resolution of urban PM2.5 mapping via large-scale mobile monitoring data and machine learning R. Xu et al. https://doi.org/10.1016/j.apr.2026.102977
- Calibration of PurpleAir low-cost particulate matter sensors: model development for air quality under high relative humidity conditions M. Mathieu-Campbell et al. https://doi.org/10.5194/amt-17-6735-2024
- Sustained Performance of Low-Cost Air Quality Sensors in Long-Term Deployments A. Winter et al. https://doi.org/10.1021/acssensors.5c00566
- Analytical performance and calibration strategies of low-cost particulate matter sensors for indoor and workplace monitoring—a review Z. Feng et al. https://doi.org/10.1039/D5AY01554E
- 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
- Machine Learning-Based Calibration of Low-Cost PM2.5 Sensors Using Location-Specific Environmental Covariates and Feature Engineering Strategies H. Gökozan https://doi.org/10.3390/chemosensors14070154
- Breathe Providence: an integrated approach to siting a low-cost air monitoring network G. Berg et al. https://doi.org/10.1088/1748-9326/ae2ca4
- Urban Pollution Burden in Sub-Saharan African (SSA) Cities: Characterizing PM2.5, PM10, and Black Carbon in Two Contrasting Environments in Accra, Ghana J. Nimo et al. https://doi.org/10.1021/acsestair.5c00469
- 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. https://doi.org/10.1021/acsestair.5c00225
- Correction of humidity and sensor aging effects in low-cost PM 2.5 light scattering sensors for improved measurement accuracy T. Kumpika et al. https://doi.org/10.1080/26395940.2026.2616098
- 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
- City-scale calibration of a low-cost PM2.5 network for regulatory-compliant air-quality assessment R. Blaga et al. https://doi.org/10.1371/journal.pclm.0000780
- 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
- The effect of particle size and composition on plantower reported PM2.5 concentrations W. Malm et al. https://doi.org/10.1016/j.atmosenv.2026.122108
- Reliability Assessment of Low-Cost PM Sensors Under High Humidity and High PM Level Outdoor Conditions G. Kumar et al. https://doi.org/10.1109/JSEN.2025.3592796
- Enhancing accuracy of air quality sensors with machine learning to augment large-scale monitoring networks K. Ravindra et al. https://doi.org/10.1038/s41612-024-00833-9
- Plume Detection and Emissions Quantification Potential Using a Dense Sensor Network M. Patel et al. https://doi.org/10.1021/acsestair.5c00069
- Low-Cost Source Apportionment (LoCoSA) of air pollution - literature review of the state of the art D. Bousiotis et al. https://doi.org/10.1016/j.scitotenv.2025.180257
- Exploration of a practical approach to providing RH corrections to low cost sensor networks S. Lekamge & H. Oswin https://doi.org/10.1038/s41612-025-01115-8
28 citations as recorded by crossref.
- Harmonizing low-cost and regulatory air quality monitoring networks with interpretable semi-supervised learning: Reducing exposure misclassification in underrepresented communities D. Tang et al. https://doi.org/10.1016/j.jhazmat.2025.137893
- Evaluating PurpleAir Sensors: Do They Accurately Reflect Ambient Air Temperature? J. Tse & L. Liang https://doi.org/10.3390/s25103044
- 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
- Validation and Correction of PM2.5 Mass Concentrations from Consumer-Grade Personal Exposure and Outdoor Fixed Monitors: A Two-Year Colocation Study in Colorado A. Khalili Hollo et al. https://doi.org/10.1021/acsenvironau.6c00020
- A Scalable Calibration Method for Enhanced Accuracy in Dense Air Quality Monitoring Networks A. Winter et al. https://doi.org/10.1021/acs.est.4c08855
- Multimodal Approach for Assessing Emissions and Transport of Greenhouse Gases and Air Pollutants from the January 2025 Los Angeles Wildfires P. Vannucci et al. https://doi.org/10.1021/acsestair.5c00430
- Observational Inferences of NOx and CO Emission Factors for Vehicles and Homes in the San Francisco Bay Area Y. Zhu et al. https://doi.org/10.1021/acsestair.5c00004
- 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
- Transferability of Machine Learning‐Based Calibration Models for Low‐Cost PM2.5 Sensors Across Heterogeneous African Cities P. Adong & E. Bainomugisha https://doi.org/10.1002/met.70228
- Enhancing the spatio-temporal resolution of urban PM2.5 mapping via large-scale mobile monitoring data and machine learning R. Xu et al. https://doi.org/10.1016/j.apr.2026.102977
- Calibration of PurpleAir low-cost particulate matter sensors: model development for air quality under high relative humidity conditions M. Mathieu-Campbell et al. https://doi.org/10.5194/amt-17-6735-2024
- Sustained Performance of Low-Cost Air Quality Sensors in Long-Term Deployments A. Winter et al. https://doi.org/10.1021/acssensors.5c00566
- Analytical performance and calibration strategies of low-cost particulate matter sensors for indoor and workplace monitoring—a review Z. Feng et al. https://doi.org/10.1039/D5AY01554E
- 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
- Machine Learning-Based Calibration of Low-Cost PM2.5 Sensors Using Location-Specific Environmental Covariates and Feature Engineering Strategies H. Gökozan https://doi.org/10.3390/chemosensors14070154
- Breathe Providence: an integrated approach to siting a low-cost air monitoring network G. Berg et al. https://doi.org/10.1088/1748-9326/ae2ca4
- Urban Pollution Burden in Sub-Saharan African (SSA) Cities: Characterizing PM2.5, PM10, and Black Carbon in Two Contrasting Environments in Accra, Ghana J. Nimo et al. https://doi.org/10.1021/acsestair.5c00469
- 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. https://doi.org/10.1021/acsestair.5c00225
- Correction of humidity and sensor aging effects in low-cost PM 2.5 light scattering sensors for improved measurement accuracy T. Kumpika et al. https://doi.org/10.1080/26395940.2026.2616098
- 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
- City-scale calibration of a low-cost PM2.5 network for regulatory-compliant air-quality assessment R. Blaga et al. https://doi.org/10.1371/journal.pclm.0000780
- 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
- The effect of particle size and composition on plantower reported PM2.5 concentrations W. Malm et al. https://doi.org/10.1016/j.atmosenv.2026.122108
- Reliability Assessment of Low-Cost PM Sensors Under High Humidity and High PM Level Outdoor Conditions G. Kumar et al. https://doi.org/10.1109/JSEN.2025.3592796
- Enhancing accuracy of air quality sensors with machine learning to augment large-scale monitoring networks K. Ravindra et al. https://doi.org/10.1038/s41612-024-00833-9
- Plume Detection and Emissions Quantification Potential Using a Dense Sensor Network M. Patel et al. https://doi.org/10.1021/acsestair.5c00069
- Low-Cost Source Apportionment (LoCoSA) of air pollution - literature review of the state of the art D. Bousiotis et al. https://doi.org/10.1016/j.scitotenv.2025.180257
- Exploration of a practical approach to providing RH corrections to low cost sensor networks S. Lekamge & H. Oswin https://doi.org/10.1038/s41612-025-01115-8
Saved (final revised paper)
Latest update: 23 Sep 2026
Short summary
Low-cost particulate matter (PM) sensors are becoming increasingly common in community monitoring and atmospheric research, but these sensors require proper calibration to provide accurate reporting. Here, we propose a hygroscopic growth calibration scheme that evolves in time to account for seasonal changes in hygroscopic growth. In San Francisco and Los Angeles, CA, applying a seasonal hygroscopic growth calibration can account for sensor biases driven by the seasonal cycles in PM composition.
Low-cost particulate matter (PM) sensors are becoming increasingly common in community...
