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
Related authors
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Ashley S. Bittner, Eben S. Cross, David H. Hagan, Carl Malings, Eric Lipsky, and Andrew P. Grieshop
Atmos. Meas. Tech., 15, 3353–3376, https://doi.org/10.5194/amt-15-3353-2022, https://doi.org/10.5194/amt-15-3353-2022, 2022
Short summary
Short summary
We present findings from a 1-year pilot deployment of low-cost integrated air quality sensor packages in rural Malawi using calibration models developed during collocation with US regulatory monitors. We compare the results with data from remote sensing products and previous field studies. We conclude that while the remote calibration approach can help extract useful data, great care is needed when assessing low-cost sensor data collected in regions without reference instrumentation.
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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...
