Articles | Volume 15, issue 21
https://doi.org/10.5194/amt-15-6309-2022
© 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-15-6309-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
02 Nov 2022
Research article |
| 02 Nov 2022
| 02 Nov 2022
Calibrating networks of low-cost air quality sensors
Priyanka deSouza
CORRESPONDING AUTHOR
Department of Urban and Regional Planning, University of Colorado Denver, CO 80202, USA
CU Population Center, University of Colorado, Boulder, CO 80302, USA
Ralph Kahn
NASA Goddard Space Flight Center, Greenbelt, MD, 20771, USA
Tehya Stockman
Denver Department of Public Health and Environment, Denver CO 80202, USA
Department of Civil, Environmental, and Architectural Engineering,
University of Colorado, Boulder, CO 80309, USA
William Obermann
Denver Department of Public Health and Environment, Denver CO 80202, USA
Ben Crawford
Department of Geography and Environmental Sciences, University of
Colorado, Denver, CO 80202, USA
An Wang
Senseable City Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
James Crooks
Division of Biostatistics and Bioinformatics, National Jewish Health, Denver, CO 2930, USA
Department of Epidemiology, University of Colorado at Denver – Anschutz Medical Campus, Denver, CO 129263, USA
Jing Li
Department of Geography and the Environment, University of Denver, Denver, CO 80210, USA
Patrick Kinney
Boston University School of Public Health, Boston, MA 02118, USA
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https://doi.org/10.1016/j.envint.2019.105329, 2020.
Short summary
How sensitive are the spatial and temporal trends of PM2.5 derived from a network of low-cost sensors to the calibration adjustment used? How transferable are calibration equations developed at a few co-location sites to an entire network of low-cost sensors? This paper attempts to answer this question and offers a series of suggestions on how to develop the most robust calibration function for different end uses. It uses measurements from the Love My Air network in Denver as a test case.
How sensitive are the spatial and temporal trends of PM2.5 derived from a network of low-cost...
