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http://dx.doi.org/10.7780/kjrs.2018.34.3.2

Empirical Estimation and Diurnal Patterns of Surface PM2.5 Concentration in Seoul Using GOCI AOD  

Kim, Sang-Min (Environmental Satellite Center, Climate and Air Quality Research Department, National Institute of Environmental Research)
Yoon, Jongmin (Environmental Satellite Center, Climate and Air Quality Research Department, National Institute of Environmental Research)
Moon, Kyung-Jung (Environmental Satellite Center, Climate and Air Quality Research Department, National Institute of Environmental Research)
Kim, Deok-Rae (Environmental Satellite Center, Climate and Air Quality Research Department, National Institute of Environmental Research)
Koo, Ja-Ho (Department of Atmospheric Sciences, Yonsei University)
Choi, Myungje (Department of Atmospheric Sciences, Yonsei University)
Kim, Kwang Nyun (Department of Atmospheric Sciences, Chungnam National University)
Lee, Yun Gon (Department of Atmospheric Sciences, Chungnam National University)
Publication Information
Korean Journal of Remote Sensing / v.34, no.3, 2018 , pp. 451-463 More about this Journal
Abstract
The empirical/statistical models to estimate the ground Particulate Matter ($PM_{2.5}$) concentration from Geostationary Ocean Color Imager (GOCI) Aerosol Optical Depth (AOD) product were developed and analyzed for the period of 2015 in Seoul, South Korea. In the model construction of AOD-$PM_{2.5}$, two vertical correction methods using the planetary boundary layer height and the vertical ratio of aerosol, and humidity correction method using the hygroscopic growth factor were applied to respective models. The vertical correction for AOD and humidity correction for $PM_{2.5}$ concentration played an important role in improving accuracy of overall estimation. The multiple linear regression (MLR) models with additional meteorological factors (wind speed, visibility, and air temperature) affecting AOD and $PM_{2.5}$ relationships were constructed for the whole year and each season. As a result, determination coefficients of MLR models were significantly increased, compared to those of empirical models. In this study, we analyzed the seasonal, monthly and diurnal characteristics of AOD-$PM_{2.5}$model. when the MLR model is seasonally constructed, underestimation tendency in high $PM_{2.5}$ cases for the whole year were improved. The monthly and diurnal patterns of observed $PM_{2.5}$ and estimated $PM_{2.5}$ were similar. The results of this study, which estimates surface $PM_{2.5}$ concentration using geostationary satellite AOD, are expected to be applicable to the future GK-2A and GK-2B.
Keywords
GOCI AOD; $PM_{2.5}$; vertical correction; humidity correction; temporal variations;
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