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

Author

Listed:
  • 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)

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.

Suggested Citation

  • He Zhang & Ravi Srinivasan & Vikram Ganesan, 2021. "Low Cost, Multi-Pollutant Sensing System Using Raspberry Pi for Indoor Air Quality Monitoring," Sustainability, MDPI, vol. 13(1), pages 1-15, January.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:1:p:370-:d:474173
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    References listed on IDEAS

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    1. He Zhang & Ravi Srinivasan, 2020. "A Systematic Review of Air Quality Sensors, Guidelines, and Measurement Studies for Indoor Air Quality Management," Sustainability, MDPI, vol. 12(21), pages 1-38, October.
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    Cited by:

    1. Muhammad Khan & Numan Khan & Miroslaw J. Skibniewski & Chansik Park, 2021. "Environmental Particulate Matter (PM) Exposure Assessment of Construction Activities Using Low-Cost PM Sensor and Latin Hypercubic Technique," Sustainability, MDPI, vol. 13(14), pages 1-20, July.

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