• Journal of Korean Society on Water Environment
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• v.32 no.6
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• pp.511-519
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• 2016

National Institute of Environmental Research (NIER) has developed the Korean Reach File (KRF) for scientific and systematic analysis of variables related to water quality, pollutant sources and aquatic ecosystems in consideration of steam reach networks. The KRF provides a new framework for data production, storage, management and analysis for water related variables in relation to spatial characteristics, connections, and topologies of stream reaches. However, the current version of KRF (ver.2) has limited applicability because its nodes include not only the stream points based on topological characteristics but also those based on water quality monitoring stations, which may undermine its generality. In this study, a new version of KRF (ver.3) was designed and established to overcome the weak point of version 2. The version 3 is a generalization of the old KRF graphic data and it integrates the attribute data while separating it from the graphic data to minimize additional work that is needed for data association and search. We tested the KRF (ver.3) on actual cases and convenience and adaptability for each application was verified. Further research should focus on developing a database link model and real-world applications that are targeted to process event data.

• Journal of Korean Society on Water Environment
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• v.27 no.2
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• pp.228-234
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• 2011

The goal of water quality management on stream and watershed is to focus not on discharged loads management but on a water quality management. Discharged loads management is not goal of water quality management but way for perform with total maximum daily loads management. It is necessary to estimate the relation between non-point source with stromwater runoff (NPSSR) and water quality to select a watershed where it is required to manage NPSSR for water quality improvement. To evaluate the effects of NPSSR on stream's water quality, we compare the aspects of water quality in dry and wet seasons using flow duration curve analysis based on flow rate variation data by actual surveying. In this study we attempt to quantify the variation characteristic of water quality and estimate the Inflow characteristic of pollution source with water quality and flow rate monitoring on 10 watersheds. We try to estimate water quality and flow rate by regression analysis and try again regression analysis with each high and low water quality data more than estimations. An analysis of relation between water quality and flow rate of 10 watersheds shows that the water quality of the Nonsan and the Ganggyeong streams had been polluted by NPSSR pollutants. Other eight streams were important point source more than NPSSR. It is wide variation range of $BOD_5$ also high average concentration of $BOD_5$. We have to quantify water quality variation by cv1 in wet season and cv365 in dry season with comparing the estimate of high water quality and low water quality. This method can be used to indicator for water quality variation according to flow rate.

• Journal of Korean Society on Water Environment
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• v.28 no.3
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• pp.426-435
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• 2012

The variation of stream flow is the one of the most important factors which influence on that of water quality in the unit watershed. The target water quality goal is established and permissible load is allotted in the base of the standard flow condition along with its water quality for the management of Total Maximum Daily Loads (TMDLs). A standard flow selected could cause problems in the load allotment if it was not properly arranged. This study reviewed the acquisition of water quality data, the self-variation and the retainability in water quality on the specific flow conditions. This study also proposed the median and the adjusted average flow condition out of general flow conditions as alternative standard flow conditions. It is considered that the alternatives can make the water quality data easily acquired and the water quality representativeness more enhanced on the standard flow conditions.

• Journal of Korean Society on Water Environment
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• v.24 no.5
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• pp.592-597
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• 2008

Current water quality standard (WQS) in Korea is based on the protection of human health, not considering the protection of aquatic organisms. Most of chemicals can be toxic to ecological biota as well as human. Health of aquatic biota is closely related to the human health via food chain, therefore ecological risk based-WQS needs to be developed to protect the aquatic ecosystem. In this study, we selected the 31 chemicals in the Project entitled 'Development of integrated methodology for evaluation of water environment'. The methodology for calculating water quality criteria was derived from the Australian and New Zealand processes for deriving guideline trigger value for aquatic ecosystem. The available ecotoxicity data were collected from US EPA's ECOTOXicology Database (ECOTOX), TOX-2000 Database, European Chemicals Bureau (ECB)'s International Uniform Chemical Information Database (IUCLID) and Environmental Protection Agency (US EPA)'s report 'Ambient Water Quality Criteria (AWQC)'. The aquatic toxicity data for the Korean species were selected for risk assessment to reflect the Korean water environment. The monitoring values were calculated from the water quality monitoring data four main Korean rivers. We suggested the order of priorities of chemicals based on ecological risk assessment. We expect that these results can be useful information for establishing the WQS for the protection of aquatic ecosystem.

• Magazine of the Korean Society of Agricultural Engineers
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• v.42 no.5
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• pp.103-113
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• 2000

A decision support system DSS-WQMRA (Decision Support System-Water Quality Management in Rural Area) was developed to help regional planners for the water quality management in a rural basin. The integrated model DSS-WQMRA, written in JAVA, includes four subsystems such as a GIS, a database, water quality simulation models and a decision model. In the system, the GIS deals with landuse and the location of pollutant sources. The database manages each data and supplies input data for various water quality simulation models. the water quality simulation model is composed of the GWLF( Generalized Watershed Loading Function), PCLM(Pollutant Loading Calculation Module) and the WASP5 model. The decision model based on mixed integer programming is designed to determine optimal costs and thus allow the selection of managemental practices to meet the water quality criteria. The methodology was tested with an example application in the Bokha River Basin, Kyunggi Province in Korea. It was proved that the integrated model DSS-WQMRA could be very useful for water quality management including the non-point source pollution in rural areas.

• Journal of Environmental Science International
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• v.25 no.3
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• pp.361-371
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• 2016

Ministry of Environment has been operating water quality monitoring network in order to obtain the basic data for the water environment policies and comprehensively understand the water quality status of public water bodies such as rivers and lakes. The observed water quality data is very important to analyze by applying statistical methods because there are seasonal fluctuations. Typically, monthly water quality data has to analyze that the transition comprise a periodicity since the change has the periodicity according to the change of seasons. In this study, trends, SOM and RDA analysis were performed at the Mulgeum station using water quality data for temperature, BOD, COD, pH, SS, T-N, T-P, Chl-a and Colon-bacterium observed from 1989 to 2013 in the Nakdong River. As a result of trends, SOM and RDA, the Mulgeum station was found that the water quality is improved, but caution is required in order to ensure safe water supply because concentrations in water quality were higher in the early spring(1~3 month) the most.

• Journal of Korean Society of Water and Wastewater
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• v.21 no.3
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• pp.305-314
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• 2007

Forecasting of water quality variation is not an easy process due to the complicated nature of various water quality factors and their interrelationships. The objective of this study is to test the applicability of neural network models to the forecasting of the water quality at Dalchun station in Han River. Input data is consist of monthly data of concentration of DO, BOD, COD, SS and river flow. And this study selected optimal neural network model through changing the number of hidden layer based on input layer(n) from n to 6n. After neural network theory is applied, the models go through training, calibration and verification. The result shows that the proposed model forecast water quality of high efficiency and developed web-based water quality forecasting system after extend model

• Journal of Korean Society on Water Environment
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• v.20 no.4
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• pp.321-326
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• 2004

The objective of this study is to test the applicability of neural network models to forecast water quality at Naesa and Pyongchang river. Water quality data devided into rainy day and non-rainy day to find characteristics of them. The mean and maximum data of rainy day show higher than those of non-rainy day. And discharge correlate with TOC at Pyongchang river. Neural network model is trained to the correlation of discharge with water quality. As a result, it is convinced that the proposed neural network model can apply to the analysis of real time water quality monitoring.

• Journal of Wetlands Research
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• v.1 no.1
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• pp.17-28
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• 1999

The purpose of this study was to analysis water quality monitoring data and to estimate future trends using ARIMA model of time series analysis. Water quality data in Chin yang reservoir were used with monthly monitoring interval during past 7 years. The variations of water quality parameters with periodicity and trend could be estimated by multiplicative ARIMA models and the statistical tests showed a good agreement with the observed data. Therefore, the monthly values of water quality parameters could be forecasted using these models.

• Journal of Environmental Impact Assessment
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• v.18 no.6
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• pp.401-409
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• 2009

Multivariate statistical approaches to classify sampling sites with measuring their similarity by water quality data and understand the characteristics of classified clusters have been discussed for the optimal water quality monitering network. For empirical study, data of two years (2005, 2006) at the 9 sampling sites with the combination of 2 depth levels and 7 important variables related to water quality is collected in Yongdam reservoir. The similarity among sampling sites is measured with Euclidean distances of water quality related variables and they are classified by hierarchical clustering method. The clustered sites are discussed with principal component variables in the view of the geographical characteristics of them and reducing the number of measuring sites. Nine sampling sites are clustered as follows; One cluster of 5, 6, and 7 sampling sites shows the characteristic of low water depth and main stream of water. The sites of 2 and 4 are clustered into the same group by characteristics of hydraulics which come from that of main stream. But their changing pattern of water quality looks like different since the site of 2 is near to dam. The sampling sites of 3, 8, and 9 are individually positioned due to the different tributary.