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Land Use Regression 모델을 이용한 수도권 초등학교 대기오염 노출 분석
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저자명
이지영,임종한,김환철,황승식,정달영,박명숙,김정애,이재준,박노욱,강성찬,Lee. Ji-Young,Leem. Jong-Han,Kim. Hwan-Cheol,Hwang. Seung-Sik,Jung. Dal-Young,Park. Myun
간행물명
한국대기환경학회지
권/호정보
2012년|28권 5호|pp.571-580 (10 pages)
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한국대기환경학회
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이 논문은 한국과학기술정보연구원과 논문 연계를 통해 무료로 제공되는 원문입니다.
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기타언어초록

Epidemiologic studies of air pollution need accurate exposure assessments at unmonitored locations. A land use regression (LUR) model has been used successfully for predicting traffic-related pollutants, although its application has been limited to Europe, North America, and a few Asian region. Therefore, we modeled traffic-related pollutants by LUR then examined whether LUR models could be constructed using a regulatory monitoring network in Metropolitan area in Korea. We used the annual-mean nitrogen dioxide ($NO_2$) in 2010 in the study area. Geographic variables that are considered to predict traffic-related pollutants were classified into four groups: road type, traffic intensity, land use, and elevation. Using geographical variables, we then constructed a model to predict the monitored levels of $NO_2$. The mean concentration of $NO_2$ was 30.71 ppb (standard deviation of 5.95) respectively. The final regression model for the $NO_2$ concentration included five independent variables. The LUR models resulted in $R^2$ of 0.59. The mean concentration of $NO_2$ of elementary schools was 34.04 ppb (standard deviation of 5.22) respectively. The present study showed that even if we used regulatory monitoring air quality data, we could estimate $NO_2$ moderately well. These analyses confirm the validity of land use regression modeling to assign exposures in epidemiological studies, and these models may be useful tools for assessing health effects of long-term exposure to traffic related pollution.