4 research outputs found
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Comparing Building and Neighborhood-Scale Variability of CO₂ and O₃ to Inform Deployment Considerations for Low-Cost Sensor System Use.
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
Comparing Building and Neighborhood-Scale Variability of CO2 and O3 to Inform Deployment Considerations for Low-Cost Sensor System Use
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
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Understanding Our Local Environment: Developing Novel Approaches To Quantify and Apportion Ambient VOCs With Low-Cost Sensors
In this dissertation, we demonstrate the application of low-cost air quality sensors to better understanding our local environment. Specifically, my work has focused on the application of arrays of low-cost sensors and methods of analysis that improve our ability to attribute local sources of volatile organic compounds (VOCs).
Low-cost sensors have been widely applied to the study of air quality at smaller spatial and temporal scales than was previously feasible. The research that is detailed in Chapter 2 built upon existing low-cost sensor research in order to develop an approach to both quantifying the concentrations of several compounds and also classifying the mixture based on the source that is likely to have emitted the detected gases. This research involved a chamber study where a large sensor array was exposed to complex gas mixtures that simulated realistic pollution sources. These data were used to validate the proposed methodology that involved a two-step process to accomplish the quantification and classification goals. The results of this approach show the feasibility of using low-cost sensors to directly estimate the effect of local sources of VOCs based on their chemical fingerprints.</p