Articles | Volume 15, issue 11
https://doi.org/10.5194/amt-15-3353-2022
© Author(s) 2022. 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-15-3353-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
09 Jun 2022
Research article |
| 09 Jun 2022
| 09 Jun 2022
Performance characterization of low-cost air quality sensors for off-grid deployment in rural Malawi
Ashley S. Bittner
Department of Civil, Construction and Environmental Engineering, North
Carolina State University, Raleigh, NC 27606, USA
Eben S. Cross
QuantAQ, Inc., Somerville, MA 02143, USA
David H. Hagan
QuantAQ, Inc., Somerville, MA 02143, USA
Carl Malings
NASA Postdoctoral Program Fellow, Goddard Space Flight Center,
Greenbelt, MD 20771, USA
Eric Lipsky
Department of Energy Engineering, Penn State Greater Allegheny
University, McKeesport, PA 15132, USA
Department of Civil, Construction and Environmental Engineering, North
Carolina State University, Raleigh, NC 27606, USA
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Atmos. Meas. Tech., 17, 6735–6749, https://doi.org/10.5194/amt-17-6735-2024, https://doi.org/10.5194/amt-17-6735-2024, 2024
Short summary
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.
Eric A. Wendt, Casey Quinn, Christian L'Orange, Daniel D. Miller-Lionberg, Bonne Ford, Jeffrey R. Pierce, John Mehaffy, Michael Cheeseman, Shantanu H. Jathar, David H. Hagan, Zoey Rosen, Marilee Long, and John Volckens
Atmos. Meas. Tech., 14, 6023–6038, https://doi.org/10.5194/amt-14-6023-2021, https://doi.org/10.5194/amt-14-6023-2021, 2021
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Fine particulate matter air pollution is one of the leading contributors to adverse health outcomes on the planet. Here, we describe the design and validation of a low-cost, compact, and autonomous instrument capable of measuring particulate matter levels directly, via mass sampling, and optically, via mass and sunlight extinction measurements. We demonstrate the instrument's accuracy relative to reference measurements and its potential for community-level sampling.
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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.
We present findings from a 1-year pilot deployment of low-cost integrated air quality sensor...
