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Evaluation of IAQ Management Using an IoT-Based Indoor Garden

Author

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  • Ho-Hyun Kim

    (Department of Information, Communication and Technology Convergence, ICT Environment Convergence, Pyeongtaek University, 3825 Seodong-daero, Pyeongtaek-si 17869, Gyeonggi-do, Korea)

  • Min-Jung Kwak

    (Department of Data Information and Statistics in Pyeongtaek University, 3825, Seodong-daero, Pyeongtaek-si 17869, Gyeonggi-do, Korea)

  • Kwang-Jin Kim

    (Urban Agriculture Research Division, National Institute of Horticulture and Herbal Science, 100, Nongsaengmyeong-ro, Iseo-myeon, Wanju-gun 55365, Jeollabuk-do, Korea)

  • Yoon-Kyung Gwak

    (Life & Industry Environmental R&D Center in Pyeongtaek University, 3825, Seodong-daero, Pyeongtaek-si 17869, Gyeonggi-do, Korea)

  • Jeong-Hun Lee

    (Life & Industry Environmental R&D Center in Pyeongtaek University, 3825, Seodong-daero, Pyeongtaek-si 17869, Gyeonggi-do, Korea)

  • Ho-Hyeong Yang

    (Life & Industry Environmental R&D Center in Pyeongtaek University, 3825, Seodong-daero, Pyeongtaek-si 17869, Gyeonggi-do, Korea)

Abstract

This study was designed to verify the effectiveness of smart gardens by improving indoor air quality (IAQ) through the installation of an indoor garden with sensor-based Internet-of-Things (IoT) technology that identifies pollutants such as particulate matter. In addition, the study aims to introduce indoor gardens for customized indoor air cleaning using the data and IoT technology. New apartments completed in 2016 were selected and divided into four households with indoor gardens installed and four households without indoor gardens. Real-time data and data on PM 2.5 , CO 2 , temperature, and humidity were collected through an IoT-based IAQ monitoring system. In addition, in order to examine the effects on the health of occupants, the results were analyzed based on epidemiological data, prevalence data, current maintenance, and recommendation criteria, and were presented and evaluated as indices. The indices were classified into a comfort index, which reflects the temperature and humidity, an IAQ index, which reflects PM 2.5 and CO 2 , and an IAQ composite index. The IAQ index was divided into five grades from “good” to “hazardous”. Using a scale of 1 to 100 points, it was determined as follows: “good (0–20)”, “moderate (21–40)”, “unhealthy for sensitive group (41–60)”, “bad (61–80)”, “hazardous (81–100)”. It showed an increase in the “good” section after installing the indoor garden, and the “bad” section decreased. Additionally, the comfort index was classified into five grades from “very comfortable” to “very uncomfortable”. In the comfort index, the “uncomfortable” section decreased, and the “comfortable” section increased after the indoor garden was installed.

Suggested Citation

  • Ho-Hyun Kim & Min-Jung Kwak & Kwang-Jin Kim & Yoon-Kyung Gwak & Jeong-Hun Lee & Ho-Hyeong Yang, 2020. "Evaluation of IAQ Management Using an IoT-Based Indoor Garden," IJERPH, MDPI, vol. 17(6), pages 1-14, March.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:6:p:1867-:d:332097
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    References listed on IDEAS

    as
    1. Yanxiao Cao & Fei Li & Yanan Wang & Yu Yu & Zhibiao Wang & Xiaolei Liu & Ke Ding, 2019. "Assisted Deposition of PM 2.5 from Indoor Air by Ornamental Potted Plants," Sustainability, MDPI, vol. 11(9), pages 1-10, May.
    2. Lihui Huang & Zhongnan Pu & Mu Li & Jan Sundell, 2015. "Characterizing the Indoor-Outdoor Relationship of Fine Particulate Matter in Non-Heating Season for Urban Residences in Beijing," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-17, September.
    3. Gonçalo Marques & Rui Pitarma, 2016. "An Indoor Monitoring System for Ambient Assisted Living Based on Internet of Things Architecture," IJERPH, MDPI, vol. 13(11), pages 1-14, November.
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    Cited by:

    1. Belfiore, Alessandra & Cuccurullo, Corrado & Aria, Massimo, 2022. "IoT in healthcare: A scientometric analysis," Technological Forecasting and Social Change, Elsevier, vol. 184(C).

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