Articles | Volume 11, issue 2
https://doi.org/10.5194/amt-11-1087-2018
© Author(s) 2018. 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-11-1087-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
23 Feb 2018
Research article |
| 23 Feb 2018
| 23 Feb 2018
A low-cost particulate matter (PM2.5) monitor for wildland fire smoke
Scott Kelleher
Department of Mechanical Engineering, Colorado State University, Fort
Collins, USA
Casey Quinn
Department of Environmental and Radiological Health Sciences, Colorado
State University, Fort Collins, USA
Daniel Miller-Lionberg
Department of Mechanical Engineering, Colorado State University, Fort
Collins, USA
John Volckens
CORRESPONDING AUTHOR
Department of Mechanical Engineering, Colorado State University, Fort
Collins, USA
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Cited
39 citations as recorded by crossref.
- A multi-modal wildfire prediction and early-warning system based on a novel machine learning framework R. Bhowmik et al. https://doi.org/10.1016/j.jenvman.2023.117908
- Evaluating Wildfire Smoke Transport Within a Coupled Fire‐Atmosphere Model Using a High‐Density Observation Network for an Episodic Smoke Event Along Utah's Wasatch Front D. Mallia et al. https://doi.org/10.1029/2020JD032712
- Wildfire Smoke Adjustment Factors for Low-Cost and Professional PM2.5 Monitors with Optical Sensors W. Delp & B. Singer https://doi.org/10.3390/s20133683
- Summertime Trends in U.S. Population Exposure to Fine Particulate Air Pollution (PM2.5) from Wildfire Smoke J. Volckens https://doi.org/10.1021/acs.estlett.4c00770
- Atomic Spectrometry Update – a review of advances in environmental analysis J. Bacon et al. https://doi.org/10.1039/C8JA90044B
- Wearable sensors for respiratory protection in occupational settings J. Devkota & H. Sharma https://doi.org/10.1016/j.jsr.2026.02.006
- Particulate Matter Sensors Mounted on a Robot for Environmental Aerosol Measurements A. Cashikar et al. https://doi.org/10.1061/(ASCE)EE.1943-7870.0001569
- Gaussian process regression model for dynamically calibrating and surveilling a wireless low-cost particulate matter sensor network in Delhi T. Zheng et al. https://doi.org/10.5194/amt-12-5161-2019
- Field Evaluation of Low-Cost PM Sensors (Purple Air PA-II) Under Variable Urban Air Quality Conditions, in Greece I. Stavroulas et al. https://doi.org/10.3390/atmos11090926
- Wildfire Early Warning System Based on a Smart CO2 Sensors Network A. De Rango et al. https://doi.org/10.3390/s25072012
- Assessment of the impact of sensor error on the representativeness of population exposure to urban air pollutants T. Leo Hohenberger et al. https://doi.org/10.1016/j.envint.2022.107329
- Summary of PM2.5 measurement artifacts associated with the Teledyne T640 PM Mass Monitor under controlled chamber experimental conditions using polydisperse ammonium sulfate aerosols and biomass smoke R. Long et al. https://doi.org/10.1080/10962247.2023.2171156
- Field Evaluation of Low-Cost Particulate Matter Sensors for Measuring Wildfire Smoke A. Holder et al. https://doi.org/10.3390/s20174796
- A low-cost monitor for simultaneous measurement of fine particulate matter and aerosol optical depth – Part 3: Automation and design improvements E. Wendt et al. https://doi.org/10.5194/amt-14-6023-2021
- Aerosol breezes drive cloud and precipitation increases G. Leung & S. van den Heever https://doi.org/10.1038/s41467-023-37722-3
- The mental health and well-being effects of wildfire smoke: a scoping review D. Eisenman & L. Galway https://doi.org/10.1186/s12889-022-14662-z
- Assessing the distributional characteristics of PM10, PM2.5, and PM1 exposure profile produced and propagated from a construction activity D. Cheriyan et al. https://doi.org/10.1016/j.jclepro.2020.124335
- Multi-Output Machine-Learning Prediction of Volatile Organic Compounds (VOCs): Learning from Co-Emitted VOCs A. Eid et al. https://doi.org/10.3390/environments12070216
- Regional aerosol transport and vertical mixing: factors contributing to an air pollution event in a highly urbanized Andean valley G. Ayala-Parra et al. https://doi.org/10.1016/j.atmosres.2025.108512
- Application of low-cost particulate matter sensors for air quality monitoring and exposure assessment in underground mines: A review N. Amoah et al. https://doi.org/10.1007/s12613-021-2378-z
- 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
- Statistical field calibration of a low-cost PM2.5 monitoring network in Baltimore A. Datta et al. https://doi.org/10.1016/j.atmosenv.2020.117761
- Smoke Patterns around Prescribed Fires in Australian Eucalypt Forests, as Measured by Low-Cost Particulate Monitors O. Price & H. Forehead https://doi.org/10.3390/atmos12111389
- Smart sensors, smart calibration: Applications in machine learning for coal dust monitoring N. Amoah et al. https://doi.org/10.1016/j.gsme.2025.09.010
- A national crowdsourced network of low-cost fine particulate matter and aerosol optical depth monitors: results from the 2021 wildfire season in the United States E. Wendt et al. https://doi.org/10.1039/D3EA00086A
- A low-cost monitor for measurement of fine particulate matter and aerosol optical depth – Part 2: Citizen-science pilot campaign in northern Colorado B. Ford et al. https://doi.org/10.5194/amt-12-6385-2019
- Uncertainty in Health Impact Assessments of Smoke From a Wildfire Event M. Johnson & F. Garcia‐Menendez https://doi.org/10.1029/2021GH000526
- Network of low-cost air quality sensors for monitoring indoor, outdoor, and personal PM2.5 exposure in Seattle during the 2020 wildfire season J. He et al. https://doi.org/10.1016/j.atmosenv.2022.119244
- A review of research on particulate matter pollution in the construction industry D. Cheriyan & J. Choi https://doi.org/10.1016/j.jclepro.2020.120077
- A Multipollutant Smoke Emissions Sensing and Sampling Instrument Package for Unmanned Aircraft Systems: Development and Testing K. Nelson et al. https://doi.org/10.3390/fire2020032
- The Impacts of Prescribed Fire on PM2.5 Air Quality and Human Health: Application to Asthma-Related Emergency Room Visits in Georgia, USA R. Huang et al. https://doi.org/10.3390/ijerph16132312
- A Survey on IoT Ground Sensing Systems for Early Wildfire Detection: Technologies, Challenges, and Opportunities C. Chan et al. https://doi.org/10.1109/ACCESS.2024.3501336
- Spatiotemporal modeling of long-term PM2.5 concentrations and population exposure in Greece, using machine learning and statistical methods A. Kakouri et al. https://doi.org/10.1016/j.scitotenv.2024.178113
- Calibration of low-cost particulate matter sensors for coal dust monitoring N. Amoah et al. https://doi.org/10.1016/j.scitotenv.2022.160336
- Community-Based Measurements Reveal Unseen Differences during Air Pollution Episodes K. Kelly et al. https://doi.org/10.1021/acs.est.0c02341
- Application and evaluation of a low-cost PM sensor and data fusion with CMAQ simulations to quantify the impacts of prescribed burning on air quality in Southwestern Georgia, USA R. Huang et al. https://doi.org/10.1080/10962247.2021.1924311
- A low-cost monitor for simultaneous measurement of fine particulate matter and aerosol optical depth – Part 1: Specifications and testing E. Wendt et al. https://doi.org/10.5194/amt-12-5431-2019
- Environmental Particulate Matter (PM) Exposure Assessment of Construction Activities Using Low-Cost PM Sensor and Latin Hypercubic Technique M. Khan et al. https://doi.org/10.3390/su13147797
- Quantitative Assessment of Soldering-Induced PM2.5 Exposure Using a Distributed Sensor Network in Instructional Laboratory Settings I. Kinsella et al. https://doi.org/10.3390/air3020016
39 citations as recorded by crossref.
- A multi-modal wildfire prediction and early-warning system based on a novel machine learning framework R. Bhowmik et al. https://doi.org/10.1016/j.jenvman.2023.117908
- Evaluating Wildfire Smoke Transport Within a Coupled Fire‐Atmosphere Model Using a High‐Density Observation Network for an Episodic Smoke Event Along Utah's Wasatch Front D. Mallia et al. https://doi.org/10.1029/2020JD032712
- Wildfire Smoke Adjustment Factors for Low-Cost and Professional PM2.5 Monitors with Optical Sensors W. Delp & B. Singer https://doi.org/10.3390/s20133683
- Summertime Trends in U.S. Population Exposure to Fine Particulate Air Pollution (PM2.5) from Wildfire Smoke J. Volckens https://doi.org/10.1021/acs.estlett.4c00770
- Atomic Spectrometry Update – a review of advances in environmental analysis J. Bacon et al. https://doi.org/10.1039/C8JA90044B
- Wearable sensors for respiratory protection in occupational settings J. Devkota & H. Sharma https://doi.org/10.1016/j.jsr.2026.02.006
- Particulate Matter Sensors Mounted on a Robot for Environmental Aerosol Measurements A. Cashikar et al. https://doi.org/10.1061/(ASCE)EE.1943-7870.0001569
- Gaussian process regression model for dynamically calibrating and surveilling a wireless low-cost particulate matter sensor network in Delhi T. Zheng et al. https://doi.org/10.5194/amt-12-5161-2019
- Field Evaluation of Low-Cost PM Sensors (Purple Air PA-II) Under Variable Urban Air Quality Conditions, in Greece I. Stavroulas et al. https://doi.org/10.3390/atmos11090926
- Wildfire Early Warning System Based on a Smart CO2 Sensors Network A. De Rango et al. https://doi.org/10.3390/s25072012
- Assessment of the impact of sensor error on the representativeness of population exposure to urban air pollutants T. Leo Hohenberger et al. https://doi.org/10.1016/j.envint.2022.107329
- Summary of PM2.5 measurement artifacts associated with the Teledyne T640 PM Mass Monitor under controlled chamber experimental conditions using polydisperse ammonium sulfate aerosols and biomass smoke R. Long et al. https://doi.org/10.1080/10962247.2023.2171156
- Field Evaluation of Low-Cost Particulate Matter Sensors for Measuring Wildfire Smoke A. Holder et al. https://doi.org/10.3390/s20174796
- A low-cost monitor for simultaneous measurement of fine particulate matter and aerosol optical depth – Part 3: Automation and design improvements E. Wendt et al. https://doi.org/10.5194/amt-14-6023-2021
- Aerosol breezes drive cloud and precipitation increases G. Leung & S. van den Heever https://doi.org/10.1038/s41467-023-37722-3
- The mental health and well-being effects of wildfire smoke: a scoping review D. Eisenman & L. Galway https://doi.org/10.1186/s12889-022-14662-z
- Assessing the distributional characteristics of PM10, PM2.5, and PM1 exposure profile produced and propagated from a construction activity D. Cheriyan et al. https://doi.org/10.1016/j.jclepro.2020.124335
- Multi-Output Machine-Learning Prediction of Volatile Organic Compounds (VOCs): Learning from Co-Emitted VOCs A. Eid et al. https://doi.org/10.3390/environments12070216
- Regional aerosol transport and vertical mixing: factors contributing to an air pollution event in a highly urbanized Andean valley G. Ayala-Parra et al. https://doi.org/10.1016/j.atmosres.2025.108512
- Application of low-cost particulate matter sensors for air quality monitoring and exposure assessment in underground mines: A review N. Amoah et al. https://doi.org/10.1007/s12613-021-2378-z
- 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
- Statistical field calibration of a low-cost PM2.5 monitoring network in Baltimore A. Datta et al. https://doi.org/10.1016/j.atmosenv.2020.117761
- Smoke Patterns around Prescribed Fires in Australian Eucalypt Forests, as Measured by Low-Cost Particulate Monitors O. Price & H. Forehead https://doi.org/10.3390/atmos12111389
- Smart sensors, smart calibration: Applications in machine learning for coal dust monitoring N. Amoah et al. https://doi.org/10.1016/j.gsme.2025.09.010
- A national crowdsourced network of low-cost fine particulate matter and aerosol optical depth monitors: results from the 2021 wildfire season in the United States E. Wendt et al. https://doi.org/10.1039/D3EA00086A
- A low-cost monitor for measurement of fine particulate matter and aerosol optical depth – Part 2: Citizen-science pilot campaign in northern Colorado B. Ford et al. https://doi.org/10.5194/amt-12-6385-2019
- Uncertainty in Health Impact Assessments of Smoke From a Wildfire Event M. Johnson & F. Garcia‐Menendez https://doi.org/10.1029/2021GH000526
- Network of low-cost air quality sensors for monitoring indoor, outdoor, and personal PM2.5 exposure in Seattle during the 2020 wildfire season J. He et al. https://doi.org/10.1016/j.atmosenv.2022.119244
- A review of research on particulate matter pollution in the construction industry D. Cheriyan & J. Choi https://doi.org/10.1016/j.jclepro.2020.120077
- A Multipollutant Smoke Emissions Sensing and Sampling Instrument Package for Unmanned Aircraft Systems: Development and Testing K. Nelson et al. https://doi.org/10.3390/fire2020032
- The Impacts of Prescribed Fire on PM2.5 Air Quality and Human Health: Application to Asthma-Related Emergency Room Visits in Georgia, USA R. Huang et al. https://doi.org/10.3390/ijerph16132312
- A Survey on IoT Ground Sensing Systems for Early Wildfire Detection: Technologies, Challenges, and Opportunities C. Chan et al. https://doi.org/10.1109/ACCESS.2024.3501336
- Spatiotemporal modeling of long-term PM2.5 concentrations and population exposure in Greece, using machine learning and statistical methods A. Kakouri et al. https://doi.org/10.1016/j.scitotenv.2024.178113
- Calibration of low-cost particulate matter sensors for coal dust monitoring N. Amoah et al. https://doi.org/10.1016/j.scitotenv.2022.160336
- Community-Based Measurements Reveal Unseen Differences during Air Pollution Episodes K. Kelly et al. https://doi.org/10.1021/acs.est.0c02341
- Application and evaluation of a low-cost PM sensor and data fusion with CMAQ simulations to quantify the impacts of prescribed burning on air quality in Southwestern Georgia, USA R. Huang et al. https://doi.org/10.1080/10962247.2021.1924311
- A low-cost monitor for simultaneous measurement of fine particulate matter and aerosol optical depth – Part 1: Specifications and testing E. Wendt et al. https://doi.org/10.5194/amt-12-5431-2019
- Environmental Particulate Matter (PM) Exposure Assessment of Construction Activities Using Low-Cost PM Sensor and Latin Hypercubic Technique M. Khan et al. https://doi.org/10.3390/su13147797
- Quantitative Assessment of Soldering-Induced PM2.5 Exposure Using a Distributed Sensor Network in Instructional Laboratory Settings I. Kinsella et al. https://doi.org/10.3390/air3020016
Saved (final revised paper)
Latest update: 15 Aug 2026
Short summary
Wildland fires, whether natural or prescribed, are increasing in frequency and magnitude worldwide. Smoke from these fires poses a threat to public and environmental health. This paper describes the development and evaluation of a low-cost monitor for wildland fire smoke. The monitor was designed to be compact, capable of real-time and time-integrated PM2.5 measures, and powered by a combination of solar and battery power. Initial field testing of this device gave encouraging results.
Wildland fires, whether natural or prescribed, are increasing in frequency and magnitude...
