Preprints
https://doi.org/10.5194/amt-2022-65
https://doi.org/10.5194/amt-2022-65
 
08 Mar 2022
08 Mar 2022
Status: this preprint is currently under review for the journal AMT.

Calibrating Networks of Low-Cost Air Quality Sensors

Priyanka deSouza1, Ralph Kahn2, Tehya Stockman3,4, William Obermann3, Ben Crawford5, An Wang6, James Crooks7,8, Jing Li9, and Patrick Kinney10 Priyanka deSouza et al.
  • 1Department of Urban and Regional Planning, University of Colorado Denver, 80202
  • 2NASA Goddard Space Flight Center, Greenbelt MD
  • 3Denver Department of Public Health and Environment, USA
  • 4Department of Civil, Environmental, and Architectural Engineering, University of Colorado Boulder, Boulder, Colorado 80309, United States
  • 5Department of Geography and Environmental Sciences, University of Colorado Denver, 80202
  • 6Senseable City Lab, Massachusetts Institute of Technology, Cambridge 02139
  • 7Division of Biostatistics and Bioinformatics, National Jewish Health, 2930
  • 8Department of Geography and the Environment, University of Denver, Denver, CO, USA
  • 9Department of Epidemiology, University of Colorado at Denver - Anschutz Medical Campus, 129263
  • 10Boston University School of Public Health, Boston, MA, USA

Abstract. Ambient fine particulate matter (PM2.5) pollution is a major health risk. Networks of low-cost sensors (LCS) are increasingly being used to understand local air pollution variation. However, measurements from LCS have uncertainties which can act as a potential barrier for effective decision-making. LCS data thus need to be calibrated to obtain better quality PM2.5 estimates. In order to develop correction factors, LCS are typically co-located with gold-standard reference monitors. A calibration equation is then developed that relates the raw output of the LCS as closely as possible to measurements from the reference monitor. This calibration algorithm is then typically transferred to measurements from monitors in the network. Calibration algorithms tend to be evaluated based on their performance at co-location sites. It is often implicitly assumed that the conditions at the relatively sparse co-location sites are representative of the LCS network, overall. Little work has been done to explicitly evaluate the sensitivity of the LCS network hotspot detection, and spatial and temporal PM2.5 trends to the correction method applied. This paper provides a first look at how transferable different calibration methods are using a dense network of Love My Air LCS monitors in Denver. It offers a series of transferability metrics that can be applied to other networks and offers suggestions for which calibration method would be most useful for different end goals. Finally, it develops a set of best practice suggestions on calibrating LCS networks.

Priyanka deSouza et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on amt-2022-65', Anonymous Referee #1, 14 Apr 2022
  • RC2: 'Comment on amt-2022-65', Anonymous Referee #2, 28 Apr 2022

Priyanka deSouza et al.

Priyanka deSouza et al.

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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.