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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 환경독성보건학회 Environmental Analysis Health and Toxicology Environmental Health and Toxicology Vol.30
- 발행연도
- 2015.12
- 수록면
- 70 - 83 (14page)
이용수
초록· 키워드
오류제보하기
Recent cohort studies have relied on exposure prediction models to estimate individuallevel air pollution concentrations because individual air pollution measurements are not available for cohort locations. For such prediction models, geographic variables related to pollution sources are important inputs. We demonstrated the computation process of geographic variables mostly recorded in 2010 at regulatory air pollution monitoring sites in South Korea. On the basis of previous studies, we finalized a list of 313 geographic variables related to air pollution sources in eight categories including traffic, demographic characteristics, land use, transportation facilities, physical geography, emissions, vegetation, and altitude. We then obtained data from different sources such as the Statistics Geographic Information Service and Korean Transport Database. After integrating all available data to a single database by matching coordinate systems and converting non-spatial data to spatial data, we computed geographic variables at 294 regulatory monitoring sites in South Korea. The data integration and variable computation were performed by using ArcGIS version 10.2 (ESRI Inc., Redlands, CA, USA). For traffic, we computed the distances to the nearest roads and the sums of road lengths within different sizes of circular buffers. In addition, we calculated the numbers of residents, households, housing buildings, companies, and employees within the buffers. The percentages of areas for different types of land use compared to total areas were calculated within the buffers. For transportation facilities and physical geography, we computed the distances to the closest public transportation depots and the boundary lines. The vegetation index and altitude were estimated at a given location by using satellite data. The summary statistics of geographic variables in Seoul across monitoring sites showed different patterns between urban background and urban roadside sites. This study provided practical knowledge on the computation process of geographic variables in South Korea, which will improve air pollution prediction models and contribute to subsequent health analyses.
목차
Introduction
Conceptual Background of Geographic Variables
Data Acquisition
Data Integration
Variable Computation
Discussion
Conclusion
References
참고문헌 (45)
참고문헌 신청
Pope CA 3rd. / 2002 / Lung cancer, cardiopulmonary mortality, and long-term exposure to fine particulate air pollution / JAMA 287(9):1132~1141
Laden F. / 2006 / Reduction in fine particulate air pollution and mortality : extended follow-up of the Harvard Six Cities study / Am J Respir Crit Care Med 173(6):667~672
Hoek G. / 2008 / A review of land-use regression models to assess spatial variation of outdoor air pollution / Atmos Environ 42(33):7561~7578
Jerrett M. / 2005 / A review and evaluation of intraurban air pollution exposure models / J Expo Anal Environ Epidemiol 15(2):185~204
Eeftens M. / 2012 / Development of Land Use Regression models for PM(2.5), PM(2.5) absorbance, PM(10) and PM(coarse) in 20 European study areas; results of the ESCAPE project / Environ Sci Technol 46(20):11195~11205
Laden F. / 2006 / Reduction in fine particulate air pollution and mortality : extended follow-up of the Harvard Six Cities study / Am J Respir Crit Care Med 173(6):667~672
Hoek G. / 2008 / A review of land-use regression models to assess spatial variation of outdoor air pollution / Atmos Environ 42(33):7561~7578
Jerrett M. / 2005 / A review and evaluation of intraurban air pollution exposure models / J Expo Anal Environ Epidemiol 15(2):185~204
Eeftens M. / 2012 / Development of Land Use Regression models for PM(2.5), PM(2.5) absorbance, PM(10) and PM(coarse) in 20 European study areas; results of the ESCAPE project / Environ Sci Technol 46(20):11195~11205
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UCI(KEPA) : I410-ECN-0101-2017-539-000798882