Journal of Korean Society for Atmospheric Environment (한국대기환경학회지)

Korean Society for Atmospheric Environment (한국대기환경학회)
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Land Use Regression Model for Assessing Exposure and Impacts of Air Pollutants in School Children

Land Use Regression 모델을 이용한 수도권 초등학교 대기오염 노출 분석

  • Lee, Ji-Young (Department of Social and Preventive Medicine, Inha University Medical College) ;
  • Leem, Jong-Han (Department of Social and Preventive Medicine, Inha University Medical College) ;
  • Kim, Hwan-Cheol (Department of Social and Preventive Medicine, Inha University Medical College) ;
  • Hwang, Seung-Sik (Department of Social and Preventive Medicine, Inha University Medical College) ;
  • Jung, Dal-Young (Department of Social and Preventive Medicine, Inha University Medical College) ;
  • Park, Myung-Sook (Department of Social and Preventive Medicine, Inha University Medical College) ;
  • Kim, Jung-Ae (Department of Social and Preventive Medicine, Inha University Medical College) ;
  • Lee, Je-Joon (Department of Statistics, Inha University) ;
  • Park, No-Wook (Department of Geoinformatic Engineering, Inha University) ;
  • Kang, Sung-Chan (Department of Social and Preventive Medicine, Inha University Medical College)
  • 이지영 (인하대학교 의과대학 사회의학교실) ;
  • 임종한 (인하대학교 의과대학 사회의학교실) ;
  • 김환철 (인하대학교 의과대학 사회의학교실) ;
  • 황승식 (인하대학교 의과대학 사회의학교실) ;
  • 정달영 (인하대학교 의과대학 사회의학교실) ;
  • 박명숙 (인하대학교 의과대학 사회의학교실) ;
  • 김정애 (인하대학교 의과대학 사회의학교실) ;
  • 이재준 (인하대학교 통계학과) ;
  • 박노욱 (인하대학교 지리정보공학과) ;
  • 강성찬 (인하대학교 의과대학 사회의학교실)
  • Received : 2012.06.19
  • Accepted : 2012.09.24
  • Published : 2012.10.31
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Abstract

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.

Keywords

References

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