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

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 i...

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Autores principales: Eum, Youngseob, Song, Insang, Kim, Hwan-Cheol, Leem, Jong-Han, Kim, Sun-Young
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Korean Society of Environmental Health and Toxicology 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4662093/
https://www.ncbi.nlm.nih.gov/pubmed/26602561
http://dx.doi.org/10.5620/eht.e2015010
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author Eum, Youngseob
Song, Insang
Kim, Hwan-Cheol
Leem, Jong-Han
Kim, Sun-Young
author_facet Eum, Youngseob
Song, Insang
Kim, Hwan-Cheol
Leem, Jong-Han
Kim, Sun-Young
author_sort Eum, Youngseob
collection PubMed
description 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.
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spelling pubmed-46620932015-12-01 Computation of geographic variables for air pollution prediction models in South Korea Eum, Youngseob Song, Insang Kim, Hwan-Cheol Leem, Jong-Han Kim, Sun-Young Environ Health Toxicol Special Topic 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. The Korean Society of Environmental Health and Toxicology 2015-10-23 /pmc/articles/PMC4662093/ /pubmed/26602561 http://dx.doi.org/10.5620/eht.e2015010 Text en Copyright © 2015 The Korean Society of Environmental Health and Toxicology This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Special Topic
Eum, Youngseob
Song, Insang
Kim, Hwan-Cheol
Leem, Jong-Han
Kim, Sun-Young
Computation of geographic variables for air pollution prediction models in South Korea
title Computation of geographic variables for air pollution prediction models in South Korea
title_full Computation of geographic variables for air pollution prediction models in South Korea
title_fullStr Computation of geographic variables for air pollution prediction models in South Korea
title_full_unstemmed Computation of geographic variables for air pollution prediction models in South Korea
title_short Computation of geographic variables for air pollution prediction models in South Korea
title_sort computation of geographic variables for air pollution prediction models in south korea
topic Special Topic
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4662093/
https://www.ncbi.nlm.nih.gov/pubmed/26602561
http://dx.doi.org/10.5620/eht.e2015010
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