| Prediction models using outdoor environmental data for real-time PM10 concentrations in daycare centers, kindergartens, and elementary schools | |
|---|---|
| 년도 | 2021 |
| 날짜 | 2021 Jan |
| 페이지 / 학회지명 |
187:107371 / Building and Environment |
| 논문저자 | Sooyoung Guak 1, Kangjin Kim 2, Wonho Yang 3, Sungho Won 2 4, Hunjoo Lee 5, Kiyoung Lee 1 4 |
| Link |
https://www.sciencedirect.com/science/article/pii/S036013232030740X
190회 연결
|
1 Department of Environmental Health Sciences, Graduate School of Public Health, Seoul National University, Seoul, South Korea 2 Department of Health Sciences, Graduate School of Public Health, Seoul National University, Seoul, South Korea 3 Department of Occupational Health, Daegu Catholic University, Gyeongsan, South Korea 4 Institute of Health and Environment, Seoul National University, Seoul, South Korea 5 Chem.I.Net Inc., Seoul, South Korea Abstract Children spend a considerable amount of time in daycare centers, kindergartens, and elementary schools. Poor indoor air quality (IAQ) in the educational facilities can affect the health of the children and impair their academic performance. The prediction of real-time PM10 concentration could be useful to intervene the problem of poor IAQ. This study developed models to predict real-time indoor PM10 concentration in the daycare centers, kindergartens, and elementary schools using outdoor environmental data. Indoor PM10 concentrations were measured in 54 daycare centers, 12 kindergartens, and 21 elementary schools in Seoul, South Korea, using a real-time monitor (AirGuard K) over a period of one year. Multiple linear regression models were used to predict real-time indoor PM10 concentration in these educational facilities using outdoor PM10 and meteorological data as input variable. Four formations (original, ratio of indoor-to-outdoor, root-transformation, and log-transformation) for dependent variable were compared to determine the best performance of the model. A 10-fold cross-validation method was used to evaluate the accuracy of the prediction models. Daycare centers showed the highest indoor PM10 concentration. Root-transformed models with high accuracy were developed to predict the real-time indoor PM10 concentration in educational facilities every 10 min. The R2 of the prediction models were 0.64 in the daycare centers, 0.45 in the kindergartens, and 0.43 in the elementary schools. The 24 h profile of the predicted indoor PM10 was similar to the measured PM10 concentration. The prediction models could provide real-time PM10 levels in educational facilities without direct indoor measurement and observation. Keywords: Children; Indoor air quality; PM10; Prediction model; Real-time. |
|
연구성과