Ordinary kriging approach to predicting long-term particulate matter concentrations in seven major Korean cities.
- Author:
Sun Young KIM
1
;
Seon Ju YI
;
Young Seob EUM
;
Hae Jin CHOI
;
Hyesop SHIN
;
Hyoung Gon RYOU
;
Ho KIM
Author Information
- Publication Type:introduction
- Keywords: Exposure prediction; Health effect; Kriging; Long-term exposure; Particulate matter
- MeSH: Air Pollution; Busan; Cohort Studies; Korea; Particulate Matter*; Seoul; Spatial Analysis*
- From:Environmental Health and Toxicology 2014;29(1):e2014012-
- CountryRepublic of Korea
- Language:English
- Abstract: OBJECTIVES: Cohort studies of associations between air pollution and health have used exposure prediction approaches to estimate individual-level concentrations. A common prediction method used in Korean cohort studies is ordinary kriging. In this study, performance of ordinary kriging models for long-term particulate matter less than or equal to 10 mum in diameter (PM10) concentrations in seven major Korean cities was investigated with a focus on spatial prediction ability. METHODS: We obtained hourly PM10 data for 2010 at 226 urban-ambient monitoring sites in South Korea and computed annual average PM10 concentrations at each site. Given the annual averages, we developed ordinary kriging prediction models for each of the seven major cities and for the entire country by using an exponential covariance reference model and a maximum likelihood estimation method. For model evaluation, cross-validation was performed and mean square error and R-squared (R2) statistics were computed. RESULTS: Mean annual average PM10 concentrations in the seven major cities ranged between 45.5 and 66.0 mug/m3 (standard deviation=2.40 and 9.51 mug/m3, respectively). Cross-validated R2 values in Seoul and Busan were 0.31 and 0.23, respectively, whereas the other five cities had R2 values of zero. The national model produced a higher crossvalidated R2 (0.36) than those for the city-specific models. CONCLUSIONS: In general, the ordinary kriging models performed poorly for the seven major cities and the entire country of South Korea, but the model performance was better in the national model. To improve model performance, future studies should examine different prediction approaches that incorporate PM10 source characteristics.
