[논문]IoT 기반 지하역사 내 바이오필터시스템 설치에 따른 실내공기질 변화 및 영향 요인 분석

양호형; 김형주; 방성원; 조흔우; 김호현

doi:10.5668/jehs.2021.47.5.410

IoT 기반 지하역사 내 바이오필터시스템 설치에 따른 실내공기질 변화 및 영향 요인 분석
Analysis of Changes and Factors Influencing IAQ in Subway Stations Using IoT Technology after Bio-Filter System Installation 원문보기

韓國環境保健學會誌 = Journal of environmental health sciences, v.47 no.5, 2021년, pp.410 - 424

양호형 (평택대학교 생활 및 산업 환경R&D 센터) , 김형주 (건국대학교 생물공학과) , 방성원 ((주)가든포유) , 조흔우 ((주)에어콕) , 김호현 (평택대학교 생활 및 산업 환경R&D 센터)

Abstract ▼ AI-Helper

Background: Subway stations have the characteristics of being located underground and are a representative public-use facility used by an unspecified number of people. As concerns about indoor air quality (IAQ) increase, various management measures are being implemented. However, there are few systematic studies and cases of long-term continuous measurement of underground station air quality. Objectives: The purpose of this study is to analyze changes and factors influencing IAQ in subway stations through real-time continuous long-term measurement using IoT-based IAQ sensing equipment, and to evaluate the IAQ improvement effect of a bio-filter system. Methods: The IAQ of a subway station in Seoul was measured using IoT-based sensing equipment. A bio-filter system was installed after collecting the background concentrations for about five months. Based on the data collected over about 21 months, changes in indoor air quality and influencing factors were analyzed and the reduction effect of the bio-filter system was evaluated. Results: As a result of the analysis, PM10, PM2.5, and CO2 increased during rush hour according to the change in the number of passengers, and PM10 and PM2.5 concentrations were high when a PM warning/watch was issued. There was an effect of improving IAQ with the installation of the bio-filter system. The reduction rate of a new-bio-filter system with improved efficiency was higher than that of the existing bio-filter system. Factors affecting PM2.5 in the subway station were the outdoor PM2.5, platform PM2.5, and the number of passengers. Conclusions: The IAQ in a subway station is affected by passengers, ventilation through the air supply and exhaust, and the spread of particulate matter generated by train operation. Based on these results, it is expected that IAQ can be efficiently improved if a bio-filter system with improved efficiency is developed in consideration of the factors affecting IAQ and proper placement.

주제어

표/그림 (16)

그림 Fig. 1. Location of IAQ measuring equipment and bio-filter systems
그림 Fig. 2. Structure of bio-filter system (A) Structure of bio-filter system, (B) Air purification principle of bio-filter system
표 Table 1. Detailed specification of IoT sensing device
그림 Fig. 3. Installation of bio-filter system (A) New bio-filter system, (B) Bio-filter system
표 Table 2. Detailed specifications of installed plants
표 Table 3. Comparison of PM₁₀, PM_2.5 and CO₂ concentration by factors
그림 Fig. 4. Comparison of PM₁₀ and PM_2.5 concentration by factors (A) Time in weekdays and weekend, (B) Peak/off-peak, (C) Weekdays/weekend, (D) Season, (E) Particulate matters warning/watch issued
그림 Fig. 5. Comparison of CO₂ concentration by factors (A) Time in weekdays and weekend, (B) Peak/off-peak, (C) Weekdays/weekend, (D) Season, (E) Particulate matters warning/watch issued
그림 Fig. 6. Comparison of IAQ according to the bio-filter system installation by sampling location (A) PM₁₀, (B) PM_2.5, (C) CO₂ *Bio-filter system, **Outside of bio-filter system, ***New bio-filter system, ****Shelter, *****Right side of transfer passage, ******Left side of transfer passage
표 Table 4. Comparison of indoor air quality in subway stations according to bio-filter system installation by equipment location
표 Table 5. Reduction rate according to bio-filter system installation
그림 Fig. 7. Changes in the number of subway passengers pre month by line (A) Line A, (B) Line B, (C) Line C
표 Table 6. Stepwise regression model of the indoor PM_2.5 concentration in the subway station
표 Table 6. Continued
표 Table 7. Stepwise regression model of the indoor CO₂ concentration in the subway station
표 Table 7. Continued

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저자의 다른 논문 :

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