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Sensors (Basel). 2018 Apr 26;18(5). pii: E1349. doi: 10.3390/s18051349.

Comparing Building and Neighborhood-Scale Variability of CO₂ and O₃ to Inform Deployment Considerations for Low-Cost Sensor System Use.

Author information

1
Environmental Engineering, University of Colorado Boulder, Boulder, CO 80309, USA. ashley.collier@colorado.edu.
2
Mechanical Engineering, University of Colorado Boulder, Boulder, CO 80309, USA. evan.coffey@colorado.edu.
3
Mechanical Engineering, University of Colorado Boulder, Boulder, CO 80309, USA. jacob.thorson@colorado.edu.
4
Preventive Medicine, University of Southern California, Los Angeles, CA 90089, USA. jillj@usc.edu.
5
Mechanical Engineering, University of Colorado Boulder, Boulder, CO 80309, USA. hannigan@colorado.edu.

Abstract

The increased use of low-cost air quality sensor systems, particularly by communities, calls for the further development of best-practices to ensure these systems collect usable data. One area identified as requiring more attention is that of deployment logistics, that is, how to select deployment sites and how to strategically place sensors at these sites. Given that sensors are often placed at homes and businesses, ideal placement is not always possible. Considerations such as convenience, access, aesthetics, and safety are also important. To explore this issue, we placed multiple sensor systems at an existing field site allowing us to examine both neighborhood-level and building-level variability during a concurrent period for CO₂ (a primary pollutant) and O₃ (a secondary pollutant). In line with previous studies, we found that local and transported emissions as well as thermal differences in sensor systems drive variability, particularly for high-time resolution data. While this level of variability is unlikely to affect data on larger averaging scales, this variability could impact analysis if the user is interested in high-time resolution or examining local sources. However, with thoughtful placement and thorough documentation, high-time resolution data at the neighborhood level has the potential to provide us with entirely new information on local air quality trends and emissions.

KEYWORDS:

air quality; best-practices; citizen science; gas-phase pollutants; low-cost sensors; spatial variability

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