Low Cost, Multi-Pollutant Sensing System Using Raspberry Pi for Indoor Air Quality Monitoring

He Zhang, Ravi Srinivasan and Vikram Ganesan
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He Zhang: UrbSys (Urban Building Energy, Sensing, Controls, Big Data Analysis, and Visualization) Laboratory, M.E. Rinker, Sr. School of Construction Management, University of Florida, Gainesville, FL 32603, USA
Ravi Srinivasan: UrbSys (Urban Building Energy, Sensing, Controls, Big Data Analysis, and Visualization) Laboratory, M.E. Rinker, Sr. School of Construction Management, University of Florida, Gainesville, FL 32603, USA
Vikram Ganesan: Department of Mechanical and Aerospace Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, FL 32603, USA

Sustainability, 2021, vol. 13, issue 1, 1-15

Abstract: Deteriorating levels of indoor air quality is a prominent environmental issue that results in long-lasting harmful effects on human health and wellbeing. A concurrent multi-parameter monitoring approach accounting for most crucial indoor pollutants is critical and essential. The challenges faced by existing conventional equipment in measuring multiple real-time pollutant concentrations include high cost, limited deployability, and detectability of only select pollutants. The aim of this paper is to present a comprehensive indoor air quality monitoring system using a low-cost Raspberry Pi-based air quality sensor module. The custom-built system measures 10 indoor environmental conditions including pollutants: temperature, relative humidity, Particulate Matter (PM) 2.5 , PM 10 , Nitrogen dioxide (NO 2 ), Sulfur dioxide (SO 2 ), Carbon monoxide (CO), Ozone (O 3 ), Carbon dioxide (CO 2 ), and Total Volatile Organic Compounds (TVOCs). A residential unit and an educational office building was selected and monitored over a span of seven days. The recorded mean PM 2.5 , and PM 10 concentrations were significantly higher in the residential unit compared to the office building. The mean NO 2 , SO 2 , and TVOC concentrations were comparatively similar for both locations. Spearman rank-order analysis displayed a strong correlation between particulate matter and SO 2 for both residential unit and the office building while the latter depicted strong temperature and humidity correlation with O 3 , SO 2 , PM 2.5 , and PM 10 when compared to the former.

Keywords: indoor air quality; smart environment monitoring (SEM); sensors; raspberry Pi (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (1)

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