Literature DB >> 28399503

National-scale exposure prediction for long-term concentrations of particulate matter and nitrogen dioxide in South Korea.

Sun-Young Kim1, Insang Song2.   

Abstract

The limited spatial coverage of the air pollution data available from regulatory air quality monitoring networks hampers national-scale epidemiological studies of air pollution. The present study aimed to develop a national-scale exposure prediction model for estimating annual average concentrations of PM10 and NO2 at residences in South Korea using regulatory monitoring data for 2010. Using hourly measurements of PM10 and NO2 at 277 regulatory monitoring sites, we calculated the annual average concentrations at each site. We also computed 322 geographic variables in order to represent plausible local and regional pollution sources. Using these data, we developed universal kriging models, including three summary predictors estimated by partial least squares (PLS). The model performance was evaluated with fivefold cross-validation. In sensitivity analyses, we compared our approach with two alternative approaches, which added regional interactions and replaced the PLS predictors with up to ten selected variables. Finally, we predicted the annual average concentrations of PM10 and NO2 at 83,463 centroids of residential census output areas in South Korea to investigate the population exposure to these pollutants and to compare the exposure levels between monitored and unmonitored areas. The means of the annual average concentrations of PM10 and NO2 for 2010, across regulatory monitoring sites in South Korea, were 51.63 μg/m3 (SD = 8.58) and 25.64 ppb (11.05), respectively. The universal kriging exposure prediction models yielded cross-validated R2s of 0.45 and 0.82 for PM10 and NO2, respectively. Compared to our model, the two alternative approaches gave consistent or worse performances. Population exposure levels in unmonitored areas were lower than in monitored areas. This is the first study that focused on developing a national-scale point wise exposure prediction approach in South Korea, which will allow national exposure assessments and epidemiological research to answer policy-related questions and to draw comparisons among different countries.
Copyright © 2017 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Exposure prediction; Korea; National model; Nitrogen dioxide; Particulate matter

Mesh:

Substances:

Year:  2017        PMID: 28399503     DOI: 10.1016/j.envpol.2017.03.056

Source DB:  PubMed          Journal:  Environ Pollut        ISSN: 0269-7491            Impact factor:   8.071


  14 in total

1.  Assessing NO2 Concentration and Model Uncertainty with High Spatiotemporal Resolution across the Contiguous United States Using Ensemble Model Averaging.

Authors:  Qian Di; Heresh Amini; Liuhua Shi; Itai Kloog; Rachel Silvern; James Kelly; M Benjamin Sabath; Christine Choirat; Petros Koutrakis; Alexei Lyapustin; Yujie Wang; Loretta J Mickley; Joel Schwartz
Journal:  Environ Sci Technol       Date:  2020-01-14       Impact factor: 9.028

Review 2.  Fine-Scale Air Pollution Models for Epidemiologic Research: Insights From Approaches Developed in the Multi-ethnic Study of Atherosclerosis and Air Pollution (MESA Air).

Authors:  Kipruto Kirwa; Adam A Szpiro; Lianne Sheppard; Paul D Sampson; Meng Wang; Joshua P Keller; Michael T Young; Sun-Young Kim; Timothy V Larson; Joel D Kaufman
Journal:  Curr Environ Health Rep       Date:  2021-06

3.  Association between Long-Term Exposure to Particulate Matter Air Pollution and Mortality in a South Korean National Cohort: Comparison across Different Exposure Assessment Approaches.

Authors:  Ok-Jin Kim; Sun-Young Kim; Ho Kim
Journal:  Int J Environ Res Public Health       Date:  2017-09-23       Impact factor: 3.390

4.  Genome-wide DNA methylation and long-term ambient air pollution exposure in Korean adults.

Authors:  Mi Kyeong Lee; Cheng-Jian Xu; Megan U Carnes; Cody E Nichols; James M Ward; Sung Ok Kwon; Sun-Young Kim; Woo Jin Kim; Stephanie J London
Journal:  Clin Epigenetics       Date:  2019-02-28       Impact factor: 6.551

5.  Air Pollution and Incidence of Lung Cancer by Histological Type in Korean Adults: A Korean National Health Insurance Service Health Examinee Cohort Study.

Authors:  Da Hye Moon; Sung Ok Kwon; Sun-Young Kim; Woo Jin Kim
Journal:  Int J Environ Res Public Health       Date:  2020-02-02       Impact factor: 3.390

6.  Gender Difference in the Effects of Outdoor Air Pollution on Cognitive Function Among Elderly in Korea.

Authors:  Hyunmin Kim; Juhwan Noh; Young Noh; Sung Soo Oh; Sang-Baek Koh; Changsoo Kim
Journal:  Front Public Health       Date:  2019-12-10

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

Authors:  Kyung-Duk Min; Ho-Jang Kwon; KyooSang Kim; Sun-Young Kim
Journal:  Int J Environ Res Public Health       Date:  2017-06-25       Impact factor: 3.390

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

Authors:  Seon-Ju Yi; Changwoo Shon; Kyung-Duk Min; Hwan-Cheol Kim; Jong-Han Leem; Ho-Jang Kwon; Soyoung Hong; KyooSang Kim; Sun-Young Kim
Journal:  Biomed Res Int       Date:  2017-09-13       Impact factor: 3.411

9.  Web-Based Visualization of Scientific Research Findings: National-Scale Distribution of Air Pollution in South Korea.

Authors:  Yeonkyeong Park; Insang Song; Jeeeun Yi; Seon-Ju Yi; Sun-Young Kim
Journal:  Int J Environ Res Public Health       Date:  2020-03-26       Impact factor: 3.390

10.  Long-Term Ambient Air Pollution Exposures and Brain Imaging Markers in Korean Adults: The Environmental Pollution-Induced Neurological EFfects (EPINEF) Study.

Authors:  Jaelim Cho; Young Noh; Sun Young Kim; Jungwoo Sohn; Juhwan Noh; Woojin Kim; Seong-Kyung Cho; Hwasun Seo; Gayoung Seo; Seung-Koo Lee; Seongho Seo; Sang-Baek Koh; Sung Soo Oh; Hee Jin Kim; Sang Won Seo; Dae-Seock Shin; Nakyoung Kim; Ho Hyun Kim; Jung Il Lee; Changsoo Kim
Journal:  Environ Health Perspect       Date:  2020-11-20       Impact factor: 9.031

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