This study was conducted to clarify the faunistic information of the genus Choristoneura in Korea. The genus Choristoneura belongs to the tribe Archipini of the family Tortricidae, including 46 species in the world. Among them, Choristoneura albaniana (Walker) is the only one species with a Holarctic distribution (Dang, 1992; Fagua et al., 2014). In Korea, 7 species of the genus Choristoneura have been reported up to date. But taxonomic location of some species in the genus are still confused: e.g. transfer of Choristoneura evanidana to Archips; Choristoneura simonyi to Xenotemna and so on. Also, it is necessary to identify correctly the genus Choristoneura, especially known as the forest pest. In this study, we rearrange and re-place the species, including nomenclatural changes according the current study.

The determining the appropriate dosage of coagulant is very important, because dosage of coagulant in the coagulation process for wastewater affects removing the amount of pollutants, cost, and producing sludge amount. Accordingly, in this study, in order to determine the optimal PAC dosage in the coagulation process, CCD (Central composite design) was used to proceed experimental design, and the quadratic regression models were constructed between independent variables (pH, influent turbidity, PAC dosage) and each response variable (Total coliform, E.coli, PSD (Particle size distribution) (‹10 μm), TP, PO4-P, and CODcr) by the RSM (Response surface methodology). Also, Considering the various response variables, the optimum PAC dosage and range were derived. As a result, in order to maximize the removal rate of total coliform and E.coli, the values of independent variables are the pH 6-7, the influent turbidity 100-200 NTU, and the PAC dosage 0.07-0.09 ml/L. For maximizing the removal rate of TP, PO4-P, CODcr, and PSD(‹10 μm), it is required for the pH 9, the influent turbidity 200-250 NTU, and the PAC dosage 0.05-0.065 ml/L. In the case of multiple independent variables, when the desirable removal rate for total coliform, E.coli, TP, and PO4-P is 90-100 % and that for CODcr and PSD(‹10 μm) is 50-100 %, the required PAC dosage is 0.05-0.07 ml/L in the pH 9 and influent turbidity 200-250 NTU. Thus, if the influent turbidity is high, adjusting pH is more effective way in terms of cost since a small amount of PAC dosage is required.

A novel disaggregation model that combines a machine learning model and kriging of residuals is presented to map precipitation at a fine scale from coarse scale precipitation data. Random forest (RF) and fine scale auxiliary variables are used to estimate trend components at a fine scale. Residual components are then estimated by area-to-point residual kriging. A case study of spatial disaggregation of TRMM monthly precipitation data acquired over the Korean peninsula is carried out to illustrate the potential of the presented disaggregation method. From the evaluation results, the presented method outperformed the RF-based disaggregation method that only considers trend components and ignores residual components, in terms of accuracy statistics and the ability of coherent predictions. This case study indicates that accounting for residual components by applying a proper spatial prediction method such as area-to-point kriging is very important in spatial disaggregation of coarse scale spatial data, even though advanced regression models such as RF could have high goodness of fit for the quantification of relationships between a target attribute and auxiliary variables.