• Journal of The Korean Society of Agricultural Engineers
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• v.47 no.1
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• pp.93-102
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• 2005

A prototype surface flow constructed wetland was built in the upstream area of Sihwa reclaimed tidal lands to improve the water quality of Lake Sihwa by treating severely polluted stream water. In this study, we monitored hydrology, macrophyte (Phragmites communis Trin,) growth, and water quality in the Banwol and Donghwa wetlands to evaluate their performance during the initial period after the completion of wetland construction, The average removal efficiency($\%$) in each wetland was relatively low compared with the performance data from the North America Wetland Treatment System Database (NADB), which mainly includes urban sewage-treatment wetlands. However, the average removal rates per unit area ($g/m^{2}/day$) were 0.72, 0.72 and 0.51 (BOD), 2,04, 2.46 and 0.70 (SS), 0.89, 0.43 and 1.09 (TN) and 0.02, 0.02 and 0.02 (TP) in the Banwol and Donghwa wetlands and NADB, respectively. The overall performance of the Banwol and Donghwa wetlands was within the expected range of the wetland system processes contributing the reduction of the pollutant load to Lake Sihwa during the initial period of wetland operation. Considering the low influent concentration, high hydraulic loading rate, and insufficient macrophyte growth since the wetland was constructed, better performance is expected if an improved operational scheme is adopted.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.6
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• pp.1-6
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• 2020

Road movement pollutant air environment big data is a link between real-time traffic data such as vehicle type, speed, and load using AVC, VDS, WIM, and DTG, which are always traffic volume survey equipment, and road shape (uphill, downhill, turning section) data using GIS. It consists of traffic flow data. Also, unlike general data, a lot of data per unit time is generated and has various formats. In particular, since about 7.4 million cases/hour or more of large-scale real-time data collected as detailed traffic flow information are collected, stored and processed, a system that can efficiently process data is required. Therefore, in this study, an open source-based data parallel processing performance optimization study is conducted for the visualization of big data in the air environment of road transport pollution.

• Journal of Environmental Impact Assessment
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• v.26 no.1
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• pp.68-77
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• 2017

In order to estimate the non-point pollution loads from each watersheds among 209 watersheds, the calibration and validation of HSPF model were carried out based on 2012 in 2013 years. In the case of flow rate, R2 of calibration and validation were 0.71~0.93 and 0.71~0.79, which were relatively good values. With the respect to calibration of water quality, % differences between measured and simulated values were 0.4 ~ 9.7 of DO, BOD 0.5 ~ 30.2% and TN 1.9~28.6% except for Hwhangkang B site. In case of validation, DO was 0.2 ~ 13.7%, BOD 1.3~23% and TN 0.5~24.3% excluding Hwhangkang B. However, since the concentration of TP was very small compared with other items, the range of difference was large as 0.8~55.3%. level. As the result of calculating annual accumulative BOD loads for each watershed, it was found that RCH 123 (Uryeong, Gyeongsangnamdo), RCH 121 (Jinju, Gyeongsangnamdo) and RCH 92 (Daegu) were the high ranked. The unit watersheds including various landuse type susch as forest and agricultural sites in mainstream areas have a higher BOD nonpoint pollution load than those in dam regions. However, the results of the annual cumulative loading of the basins for nutrients did not appear to be consistent with the BOD annual cumulative loading ranks. Other factors that represent watershed characteristics such as landslope and soiltypes, including landuse pattern, have been found to be closely related to nonpoint pollutant loads.

• Journal of Environmental Impact Assessment
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• v.27 no.4
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• pp.353-366
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• 2018

The purpose of this study was assessment the quality of water by using the statistical analysis technique of the Water flow and water quality from January 2012 to December 2016 at the unit basin for total pollutant load management system (TPLMS) in the Imjin River. Water flow and water quality were monitored at an average of 8 day intervals, 11 parameters were used for correlation analysis, principal component analysis (PCA), factor analysis (FA), and cluster analysis (CA). The Hierarchical CA was classified into three according to the change of space, such as natural rivers, urban rivers, point with large influence of point pollution source, it was found that the type of contamination source the similarity of water quality affected the classification of cluster. Using one-way analysis of variance (ANOVA) and post-hoc Analysis, there were statistically significant differences between mean values among the clusters. Correlation analysis showed the correlation coefficient between $COD_{Mn}$ and TOC was 0.951 (p<0.01) and the correlation was statistically significantly higher. According to the result PCA and FA, 3 principal components can explaining 72% of the total variations in water quality characteristics and main factor was EC, $BOD_5$, $COD_{Mn}$, TN, TP and TOC indirect indicators of organic matter and nutrients were influenced. This study presented the regression equation obtained by applying the factor scores to the multiple linear regression analysis and concluded that the management Indirect indicators of organic matter and nutrients is important for water quality management in the Imjin River basin.

• Korean Journal of Soil Science and Fertilizer
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• v.40 no.1
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• pp.18-24
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• 2007

Paddy fields are apparently nonpoint source pollution and influence water environment. In order to improve water quality in rivers or lakes, to low nutrient load from paddy fields are required. To establish comprehensive plan to control agricultural non-point source pollution, it is imperative to get a quantitative evaluation on pollutants and pollution load from paddy fields. A field monitoring study was carried out to investigate the water balance and losses of nutrients from fields in Sumjin river basin. The size of paddy fields was 115 ha and the fields were irrigated from a pumping station. The observed total nitrogen loads from paddy fields were larger than those of the unit loads determined by Ministry of Environment data (MOE). It is because the nitrogen fertilization level at the studied field was higher than the recommended rate and the high irrigation and subsequent drainage amount. On the contrary, total phosphorus loads were less than those addressed by MOE since phosphorus fertilization level was lower than that of standard level. Therefore, it was found that fertilization, irrigation, and drainage management are key factors to determine nutrient losses from paddy fields. When the runoff losses of nutrients were compared to applied chemical fertilizer, it was found that 42 to 60% of nitrogen lost via runoff while runoff losses of phosphorus account for 1.3 to 7.6% of the total applied amount during the entire year.

• Journal of Korea Water Resources Association
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• v.36 no.4
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• pp.533-545
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• 2003

This study analyzed several storm events observed in the Seolma-chun basin to derive the characteristic velocity of GIUH (Geomophological Instantaneous Unit Hydrograph) as well as its variability. Especially, this study focused on the variation of characteristic velocity due to the change of rainfall characteristics. The IUH of the Seolma-chun basin was derived using the HEC-1, whose peak discharge and time were then compared with those of the GIUH to derive the characteristic velocities. The characteristics velocities were analyzed by comparing with the GcIUH (Geomorphoclimatic IUH) as well as the characteristics of rainfall. Results are summarized as follows. (1) The characteristic velocity of GIUH was estimated higher with higher variability than the GcIUH, but their trends were found similar (2) Total amount of effective rainfall (or, mean effective rainfall) well explains the characteristic velocity of GIUH. This could be assured by the regression analysis, whose coefficient of determination was estimated about 0.6. (3) The duration and the maximum intensity of rainfall were found not to affect significantly on the characteristic velocity of GIUH. The coefficients of determination were estimated less than 0.3 for all cases considered. (4) For the rainfall events used in this study, the characteristic velocities of GIUH were found to follow the Gaussian distribution with its mean and the standard deviation 0.402 m/s and 0.173 m/s, respectively. Most of the values are within the range of 0.4∼0.5 m/s, and its coefficient of variation was estimated to be 0.43, much less than that of the runoff itself (about 1.0).