IoT 모니터링 기반 빅데이터 활용 민감군 다중이용시설 실내공기질 특성 분석 (Analysis of indoor air quality characteristics of sensitive groups using big data based on IoT monitoring)

서지정보

· 발행기관 : 한국냄새환경학회
· 수록지 정보 : 실내환경 및 냄새 학회지 / 23권 / 2호 / 115 ~ 123페이지
· 저자명 : 김호현

초록

The objective of this study is to analyze the indoor air quality of multi-use facilities using an IoT-based monitoring and control system. Thise study aims to identify effective management strategies and propose policy improvements. This research focused on 50 multi-use facilities, including daycare centers, medical centers, and libraries. Data on PM10, PM2.5, CO2, temperature, and humidity were collected 24 hours a day from June 2019 to April 2020. The analysis included variations in indoor air quality by season, hour, and day of the week (including both weekdays and weekends). Additionally, ways to utilize IoT monitoring systems using big data were propsed. The reliability analysis of the IoT monitoring network showed an accuracy of 81.0% for PM10 and 76.1% for PM2.5. Indoor air quality varied significantly by season, with higher particulate matter levels in winter and spring, and slightly higher levels on weekends compared to weekdays. There was a positive correlation found between outdoor and indoor pollutant levels. Indoor air quality management in facilities requires season-specific strategies, particularly during the winter and spring. Furhtermore, enhanced management is necessary during weekends due to higher pollutant levels.

영어초록

The objective of this study is to analyze the indoor air quality of multi-use facilities using an IoT-based monitoring and control system. Thise study aims to identify effective management strategies and propose policy improvements. This research focused on 50 multi-use facilities, including daycare centers, medical centers, and libraries. Data on PM10, PM2.5, CO2, temperature, and humidity were collected 24 hours a day from June 2019 to April 2020. The analysis included variations in indoor air quality by season, hour, and day of the week (including both weekdays and weekends). Additionally, ways to utilize IoT monitoring systems using big data were propsed. The reliability analysis of the IoT monitoring network showed an accuracy of 81.0% for PM10 and 76.1% for PM2.5. Indoor air quality varied significantly by season, with higher particulate matter levels in winter and spring, and slightly higher levels on weekends compared to weekdays. There was a positive correlation found between outdoor and indoor pollutant levels. Indoor air quality management in facilities requires season-specific strategies, particularly during the winter and spring. Furhtermore, enhanced management is necessary during weekends due to higher pollutant levels.

참고자료

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