• Journal of Korea Water Resources Association
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• v.30 no.1
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• pp.3-13
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• 1997

Modifications of Prandtl's mixing length theory were used to obtain a power velocity distribution in which the coefficient and exponent are variable over a range from 1/4 to 1/7. A simple suspended-sediment concentration distribution was developed which can be associated with this modified velocity distribution. Using nominal values of ${\beta}$=1.0, $\kappa$=0.4 and visual accumulation tube values of fall velocity, the comparison between theory and field measurements by the USGS on the Rio Grande is fair. Doubling the value of the exponent results in a good comparison. Further research is needed to be able to better choose ${\beta}$, $\kappa$, and fall velocity values, but such research will not be able to account for the effects of large-scale turbulence and secondary flows. In a pragmatic sense, a special set of fairly detailed measurements can establish coefficients and exponents for any gaging site.

• Journal of the Korean earth science society
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• v.38 no.4
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• pp.283-292
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• 2017

In this study, the empirical models were established to estimate the concentrations of surface-level $PM_{2.5}$ over Seoul, Korea from 1 January 2012 to 31 December 2013. We used six different multiple linear regression models with aerosol optical thickness (AOT), ${\AA}ngstr{\ddot{o}}m$ 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 $PM_{2.5}$ and the estimated $PM_{2.5}$ concentrations using $M_6$ model were correlations (R=0.62) and root square mean error ($RMSE=10.70{\mu}gm^{-3}$). 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 $PM_{2.5}$. Also, the result calculated for $PM_{2.5}$ using empirical multi linear regression model will be useful as a method to enable monitoring of atmospheric environment from satellite and ground meteorological data.