Environ Health Toxicol.  2015 ;30(1):e2015010. 10.5620/eht.e2015010.

Computation of geographic variables for air pollution prediction models in South Korea

Affiliations
  • 1Department of Geography, Seoul National University, Seoul, Korea.
  • 2Department of Geography, State University of New York at Buffalo, NY, USA.
  • 3Department of Occupational and Environmental Medicine, Inha University School of Medicine, Incheon, Korea.
  • 4Institute of Health and Environment, Seoul National University, Seoul, Korea. puha0@snu.ac.kr

Abstract

Recent cohort studies have relied on exposure prediction models to estimate individual-level 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.

Keyword

Air pollution; Cohort study; Exposure prediction; Geographical information system

MeSH Terms

Air Pollution*
Altitude
Buffers
Cohort Studies
Family Characteristics
Geographic Information Systems
Geography
Housing
Information Services
Korea*
Seoul
Transportation
Buffers
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