Salman, N, Khan, MW, Lim, M et al. (3 more authors) (2021) Use of Multiple Low Cost Carbon Dioxide Sensors to Measure Exhaled Breath Distribution with Face Mask Type and Wearing Behaviour. Sensors, 21 (18). 6204. ISSN 1424-8220
Abstract
The use of cloth face coverings and face masks has become widespread in light of the COVID-19 pandemic. This paper presents a method of using low cost wirelessly connected carbon dioxide (CO₂) sensors to measure the effects of properly and improperly worn face masks on the concentration distribution of exhaled breath around the face. Four types of face masks are used in two indoor environment scenarios. CO₂ as a proxy for exhaled breath is being measured with the Sensirion SCD30 CO₂ sensor, and data are being transferred wirelessly to a base station. The exhaled CO2 is measured in four directions at various distances from the head of the subject, and interpolated to create spatial heat maps of CO2 concentration. Statistical analysis using the Friedman’s analysis of variance (ANOVA) test is carried out to determine the validity of the null hypotheses (i.e., distribution of the CO₂ is same) between different experiment conditions. Results suggest CO₂ concentrations vary little with the type of mask used; however, improper use of the face mask results in statistically different CO₂ spatial distribution of concentration. The use of low cost sensors with a visual interpolation tool could provide an effective method of demonstrating the importance of proper mask wearing to the public.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
Keywords: | face mask; CO2 sensors; COVID-19; data interpolation |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Robotics, Autonomous Systems & Sensing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 29 Sep 2021 12:34 |
Last Modified: | 29 Sep 2021 12:34 |
Status: | Published |
Publisher: | MDPI |
Identification Number: | 10.3390/s21186204 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:178572 |