Publications

Choo, GH; Lee, KT; Jeong, MJ (2017). Analysis of Empirical Multiple Linear Regression Models for the Production of PM2.5 Concentrations. JOURNAL OF THE KOREAN EARTH SCIENCE SOCIETY, 38(4), 283-292.

Abstract
In this study, the empirical models were established to estimate the concentrations of surface-level PM2.5 over Seoul, Korea from 1 January 2012 to 31 December 2013. We used six different multiple linear regression models with aerosol optical thickness (AOT), Angstrom exponents (AE) data from Moderate Resolution Imaging Spectroradiometer (MODIS) aboard Terra and Aqua satellites, meteorological data, and planetary boundary layer depth (PBLD) data. The results showed that M-6 was the best empirical model and AOT, AE, relative humidity (RH), wind speed, wind direction, PBLD, and air temperature data were used as input data. Statistical analysis showed that the result between the observed PM2.5 and the estimated PM2.5 concentrations using M-6 model were correlations (R=0.62) and root square mean error (RMSE=10.70 mu g m(-3)). In addition, our study show that the relation strongly depends on the seasons due to seasonal observation characteristics of AOT, with a relatively better correlation in spring (R=0.66) and autumntime (R=0.75) than summer and wintertime (R was about 0.38 and 0.56). These results were due to cloud contamination of summertime and the influence of snow/ice surface of wintertime, compared with those of other seasons. Therefore, the empirical multiple linear regression model used in this study showed that the AOT data retrieved from the satellite was important a dominant variable and we will need to use additional weather variables to improve the results of PM2.5. Also, the result calculated for PM2.5 using empirical multi linear regression model will be useful as a method to enable monitoring of atmospheric environment from satellite and ground meteorological data.

DOI:
10.5467/JKESS.2017.38.4.283

ISSN:
1225-6692