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http://dx.doi.org/10.7780/kjrs.2019.35.6.1.4

Estimating Fine Particulate Matter Concentration using GLDAS Hydrometeorological Data  

Lee, Seulchan (Department of Water Resources, Sungkyunkwan University)
Jeong, Jaehwan (Department of Water Resources, Sungkyunkwan University)
Park, Jongmin (Department of Civil Engineering, Sungkyunkwan University)
Jeon, Hyunho (Department of Civil Engineering, Sungkyunkwan University)
Choi, Minha (School of Civil, Architectural Engineering & Landscape Architecture, Sungkyunkwan University)
Publication Information
Korean Journal of Remote Sensing / v.35, no.6_1, 2019 , pp. 919-932 More about this Journal
Abstract
Fine particulate matter (PM2.5) is not only affected by anthropogenic emissions, but also intensifies, migrates, decreases by hydrometeorological factors. Therefore, it is essential to understand relationships between the hydrometeorological factors and PM2.5 concentration. In Korea, PM2.5 concentration is measured at the ground observatories and estimated data are given to locations where observatories are not present. In this way, the data is not suitable to represent an area, hence it is impossible to know accurate concentration at such locations. In addition, it is hard to trace migration, intensification, reduction of PM2.5. In this study, we analyzed the relationships between hydrometeorological factors, acquired from Global Land Data Assimilation System (GLDAS), and PM2.5 by means of Bayesian Model Averaging (BMA). By BMA, we also selected factors that have meaningful relationship with the variation of PM2.5 concentration. 4 PM2.5 concentration models for different seasons were developed using those selected factors, with Aerosol Optical Depth (AOD) from MODerate resolution Imaging Spectroradiometer (MODIS). Finally, we mapped the result of the model, to show spatial distribution of PM2.5. The model correlated well with the observed PM2.5 concentration (R ~0.7; IOA ~0.78; RMSE ~7.66 ㎍/㎥). When the models were compared with the observed PM2.5 concentrations at different locations, the correlation coefficients differed (R: 0.32-0.82), although there were similarities in data distribution. The developed concentration map using the models showed its capability in representing temporal, spatial variation of PM2.5 concentration. The result of this study is expected to be able to facilitate researches that aim to analyze sources and movements of PM2.5, if the study area is extended to East Asia.
Keywords
Particulate matter; PM2.5 Modeling; BMA; MODIS AOD; GLDAS;
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