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

Abstract

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.

Association between exposure to traffic-related air pollution and pediatric allergic diseases based on modeled air pollution concentrations and traffic measures in Seoul, Korea: a comparative analysis

“…These road variables represented road density and proximity computed as the sum of lengths of major roads multiplied by road widths and the number of lanes, and the distance to the closest major road within 300 m from children's homes, respectively. Major roads were defined as national highways or roads with six lanes or more [32]. This comparison was done based on the same input data and health analysis models including the identical covariates.…”

“…2). The geographic variables included potential air pollution sources for eight categories of traffic, demographic characteristics, land use, transportation facilities, physical geography, emissions, vegetation, and altitude [32].…”

Air Pollution Monitoring Design for Epidemiological Application in a Densely Populated City

“…All source data were collected or generated in 2010, except land use data, which were generated in 2007, and updated for some areas in 2009. The details on their relationships with air pollution and computation procedure were published elsewhere [ 13 ]. The variables were computed as two types of metrics: proximity and density.…”

Association between Exposure to Traffic-Related Air Pollution and Prevalence of Allergic Diseases in Children, Seoul, Korea

“…We chose 300 m as the distance affected by traffic, as previous studies showed exponential decrease of air pollution concentrations at 300 m distant from the major roads [25,37]. Computation procedure for distances and sums of road lengths were described in previously published work in detail [38].…”