• Journal of Korean Society on Water Environment
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• v.37 no.6
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• pp.520-530
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• 2021

Multivariate statistical analysis and an environmental hydrological model were applied for investigating the causes of water pollution and providing best management practices for water quality improvement in urban and agricultural watersheds. Principal component analysis (PCA) and cluster analysis (CA) for water quality time series data show that chemical oxygen demand (COD), total organic carbon (TOC), suspended solids (SS) and total phosphorus (T-P) are classified as non-point source pollutants that are highly correlated with river discharge. Total nitrogen (T-N), which has no correlation with river discharge and inverse relationship with water temperature, behaves like a point source with slow and consistent release. Biochemical oxygen demand (BOD) shows intermediate characteristics between point and non-point source pollutants. The results of the PCA and CA for the spatial water quality data indicate that the cluster 1 of the watersheds was characterized as upstream watersheds with good water quality and high proportion of forest. The cluster 3 shows however indicates the most polluted watersheds with substantial discharge of BOD and nutrients from urban sewage, agricultural and industrial activities. The cluster 2 shows intermediate characteristics between the clusters 1 and 3. The results of hydrological simulation program-Fortran (HSPF) model simulation indicated that the seasonal patterns of BOD, T-N and T-P are affected substantially by agricultural and livestock farming activities, untreated wastewater, and environmental flow. The spatial analysis on the model results indicates that the highly-populated watersheds are the prior contributors to the water quality degradation of the river.

• Journal of Environmental Science International
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• v.11 no.12
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• pp.1281-1289
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• 2002

This study was conducted to evaluate water quality in the Keum River using multivariate analysis. The analysis data in Keum river made use of surveyed data by the ministry of environment from January 1994 to December 2001. Thirteen water quality parameter were determined on each sample. The results was summarized as follow; Water quality in the Keum River could be explained up to 71.39% by four factors which were included in loading of organic matter and nutrients by the tributaries (32.88%), seasonal variation (16.09%), loading of pathogenic bacteria by domestic sewage of Gapcheon (13.39%) and internal metabolism in estuary as lakes(9.03%). For spatial variation of factor score, four group was classified by each factor characterization. Station 1 and 2 was influenced by Daechung dam, station 3 was affected by domestic sewage of Gapcheon, station 10~12 was affected by estuary dyke and the rest station. The result of cluster analysis by station was classified into four group that has different water quality characteristics. In monthly cluster analysis, three group was classified according to seasonal characteristic. Also, in yearly cluster analysis, three group was classified. It is necessary to control the pollutant loadings by Gapcheon inflow domestic sewage in Daejeon city for the sake of water quality management of Keum river.

• Journal of Environmental Science International
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• v.5 no.6
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• pp.763-770
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• 1996

This study was performed to analyze the variation characteristics of writer qulity, correlation analysis of water quality data at each site and among the items of water Quality data. Water quality for analysis was monthly values of water temperature, pH, dissolved oxygen, biochemical oxygen demand, chemical oxygen demand, suspended solid, 7-N and T-P checked in Daecheong Lake from January to December, 1995. It was analyzed variation of monthly water qulity was well from February to April, water temperature and COD seemed to have high correlationships at all sites. Regression equation is COD = 0.07 Water temperature +1.23 ($R^2$: 0.7616) . Results of the correlation analysis of water quality data showed that DO had high correlationships between site 1 and site 2, BOD did site 1 and 3, COD did site 1 and 2, 55 did site 5 and 6, 7-N did 2 and 3, 7-P did site 4 and 6. Regression equations for estimate of water quality data are as follows. $DO_1$=4.46+0.59 DO, ($R^2$=0.8868), $BOD_1$ = 0, 52+0.63 BOD3 ($R^2$ = 0.6390) $COD_2$ = 0.44+0.71 $COD_1$ ($R^2$ = 0.9183), SS6 = 0.89+0.7055.($R^2$ = 0.9155) $TN_3$ = 0.151 +0.886 $TN_2$ ($R^2$ = 0.9415), $TP_4$ = 0.004+5.758 $TP_6$ ($R^2$ = 0.9669)

• Journal of Environmental Impact Assessment
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• v.20 no.1
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• pp.25-36
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• 2011

In this study, the accurate water quality analysis in rivers, including the non-point source is performed. First of all, watershed model, SWAT(Soil and Water Assessment Tool) was applied to analyze the impact of the non-point source in study area. And then, water quality analysis integrating the point source and the non-point source is implemented using QUALKO model. For more exact simulation, it should be the calibration and verification of variables and parameters which are needed for simulation. In addition, the importance of considering the non-point source was confirmed in river water quality simulation. BOD, TN, TP were analysed, and the results shows that BOD, TN and TP concentration was increased to 16.8%, 8.2% and 25.8% respectively. The more accurate estimate will be carried if use of reliable measurements and watershed simulation be done in models linking process. The suggested technique will improve the accuracy of the water quality analysis. The methodologies presented in this study will contribute to basin-wide water quantity and quality management.

• Proceedings of the Membrane Society of Korea Conference
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• 1997.06a
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• pp.1-20
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• 1997

The present standard of drinking water quality is not reached to the guidelines of WHO and US EPA recommended. The appraisal of safety is not appropriate by the results of intermittent and limitted analysis. 45 items of drinking water quality are regulated in the Korean standard and 9 items for inspection designated by Seoul City. This report is the results of analysis of the water quality in the water stations of Seoul which are concerned with the items of Korean water quality standard and the priolity pollutnats recommended by WHO. In the results, 45 items of water quality, and the priolity pollutants were not exceeded to the standard and criteria, while DDT, heptachlor-epoxide, THMs, benzo(a)pyrene, Ba, Al, Gross beta, $^{226}$Ra, $^{90}$Sr were detected, the levels were not exceeded to the WHO guidelines. In ordes to evalute the safety of drinking water quality, besides of the existed items of standard, new hazardouse pollutants should be considered monitored continenously. For the regulation of hazardous pollutants, it may be introduced from the risk assessment. According to the relevant assessment, the acceptable risk of pollutants estimated could be applied to set the water quality standard or recommendations or quidelines as well as the number of monitoring.

• Journal of Korean Society of Water and Wastewater
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• v.29 no.6
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• pp.625-632
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• 2015

The study of water environment system using a multivariate analysis in Changnyeong-Haman weir section has been conducted. The purpose of this study is to establish better understanding related water qualities in the Changnyeong-Haman weir section which can provide useful information. The data were consisted of water quality data and algae data including WT(water temperature), pH, DO, EC, COD, SS, T-N, $NH_3-N$, T-P, $PO_4-P$, Chl-a, TOC, d-silica, t-silica, Cyanobacteria, Diatoms, and Green algae. Statistical analyses used in this study were correlation analysis, principal components, and factor analysis. According to correlation analysis on COD and TOC, it revealed that the each value of correlation coefficient was 0.843. On the other result, a negative correlation was observed between diatoms and d-silica. Furthermore, the results of principal component analysis to the overall water quality were classified into four main factors with contribution rate 81.071%.

• Journal of Korean Society on Water Environment
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• v.38 no.1
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• pp.1-9
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• 2022

The Yeongsan River, one of the four major rivers in Korea, shows the highest degree of water pollution compared to the other major rivers. The construction and opening of two weirs, Seungchon and Juksan, induced fluctuations in the hydrologic conditions and water quality of the river. To investigate the water quality changes caused by the opening of the weir in 2017, this study analyzed the water quality data using the non-parametric Wilcoxon signed-rank test and the three-dimensional spatiotemporal plots. The non-parametric statistical test results showed that the concentration of all parameters has increased after 2017 at a significance level of 0.05. For the parameters that showed the highest degree of change, chlorophyll-a and suspended solids, the median values have increased by more than 30% after weir opening. Visual analysis additionally showed the spatial changes in the Yeongsan River. Generally, the sites above the Seungchon weir showed higher pollution levels than those above the Juksan weir. In time series, visual analysis results also showed the trend of rising concentration for all water quality parameters, indicating that the opening of two weirs had a significant effect on the change in water quality of the Yeongsan River.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2004.05a
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• pp.117-122
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• 2004

This study investigates the spatial and temporal distribution characteristics and relationships among water quality parameters, which based on 6 years' data(from 1989 to 1994) measured at 16 stations on Jinhae Bay. The results of these analysis, monthly variations range between surface and bottom layer of water quality had a tendency to increase and decrease, and appeared to be at the maximum value in August. The relationships between concentration of COD ana nutrients(DIN and DIP), which obtained by correlation analysis of water quality, were shown $85\%$ and $74\%$, respectively. Using the cluster analysis to develop the division of the sea basin by the dendrogram, before and after dredging of Masan bay and operating a sewage disposal plant, the variation characteristics of water quality of Jinhae Bay were discussed. Through it, we can see the serious pollution of northen sea basin of Jinhae Bay(B2) although dredging Masan bay and operating the sewage disposal plant. As the results, it doesn't appear the improvement effect of water quality in spite of carrying out the effort of water quality improvement.

• Journal of Environmental Impact Assessment
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• v.20 no.5
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• pp.765-777
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• 2011

According to the total pollution load management system, exact prediction and analysis of water quality and discharge has been required in order to allocate the amount of pollution load to each local government. In this study, QUAL2E model was used for comparison with other water quality models and improve the inadequate to forecast future water quality. And Various calibration and verification methods were applied to deal with existing uncertainties of parameter during modeling water quality. For user convenience, A GUI(Graphical User Interface) system named "QL2-XP" model is developed by object-oriented language for the user convenience and practical usage. Suggested GUI system consist of hydraulic analysis, water quality analysis, optimized model calibration processes, and postprocessing the simulation results. Therefore this model will be effectively utilized to manage practical and efficient water quality.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2005.10a
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• pp.614-619
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• 2005

The object of this study was to presented regression equations for obtaining simply and quickly values of water quality items, BOD, COD, T-N, and T-P. Regression equations obtained to analyze relationships for water quality items to land use types in agricultural reservoir watersheds. In order to derive regression equations, a multiple linear regression analysis was used in this studying reservoirs. In this regression analysis, a independent values used land used types and dependent values used BOD, COD, T-N, T-P values in water quality items. The results showed that numbers of regression equation ranging above 0.90 in a multiple correlation coefficient (MCC) was not found, ranging from 0.70 to 0.90 in the MCC was 6, ranging from 0.40 to 0.70 in the MCC was 20, and ranging from 0.20 to 0.40 in the MCC was 4. The results of this study can be used as a basic information for evaluating simply and quickly water quality for proposing and designing steps in water quality policy.