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, CO₂, 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 CO₂, 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.

Background and objective: Plants are a natural and environmentally friendly way to improve indoor air quality. To evaluate indoor air quality, it is important to continuously measure and identify the influencing factors. This study aimed to identify the factors affecting PM2.5 concentration in indoor spaces with indoor garden installations.Methods: Factors influencing the concentration of indoor, airborne PM2.5 were monitored based on Internet of Things (IoT) technology. Ten households in South Korea were surveyed and categorized into Groups A (households without an indoor garden) and B (households with an indoor garden). An IoT-based device was used to monitor the indoor PM2.5 concentration and several environmental factors, including the outdoor PM2.5 (µg⋅m-3) and carbon dioxide (mL⋅m-3) concentrations, temperature (°C), and relative humidity (%). Further, the seasonal (spring, summer, fall, and winter) and temporal (dawn, morning, afternoon, and evening) variations in indoor PM2.5 concentration were monitored.Results: The indoor PM2.5 concentration decreased from 17.7 µg⋅m-3 to 16.7 µg⋅m-3, and from 15.5 µg⋅m-3 to 12.5 µg⋅m-3 in Groups A and B, respectively. A regression analysis showed that the indoor PM2.5 concentration was not significantly affected by the installation of the indoor garden (living rooms: p = .1577; kitchen: p = .4974); however, was influenced by the outdoor air conditions, as well as seasonal and temporal factors. Additionally, a subgrouping model demonstrated a statistical relationship between indoor garden installation and the environmental factors.Conclusion: These findings can assist in establishing guidelines for indoor air quality management.

Ho-Hyeong Yang, Hyung-Joo Kim, Sung-Won Bang, Heun-Woo Cho, and Ho-Hyun Kim. J Environ Health Sci. 2021;47:410-24. https://doi.org/10.5668/JEHS.2021.47.5.410

Ho-Hyeong Yang, Hyung-Joo Kim, Sung-Won Bang, Heun-Woo Cho, Hyeong-Seok Lee, Seung-Won Han, Kwang-Jin Kim, and Ho-Hyun Kim. J Environ Health Sci. 2023;49:295-304. https://doi.org/10.5668/JEHS.2023.49.6.295

Chi-Ku Choi, Ho-Hyeong Yang, Ho-Hyun Kim, and Hyuk-Ku Kwon. J Environ Health Sci. 2023;49:183-9. https://doi.org/10.5668/JEHS.2023.49.4.183