• 환경위생공학
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• 제18권4호
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• pp.6-14
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• 2003

A spring water quality was depend on the aquifer soil status. However, water quality was rapidly contaminated by artificial affects. In the contaminate components, the heavy metals were significantly important because the heavy metals influence the plants and the animals. But, it is difficult to find out how the heavy metal can affect in the water quality. According to the group analysis and the factor analysis, water quality management was advanced. The experimental area was divided into three region and six factor. The six factor could not define the overall water quality, however this method were one of the useful methods.

• Water Engineering Research
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• 제3권2호
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• pp.143-153
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• 2002

As the natural flows in rivers dramatically decrease during drought season in Korea, a deterioration of river water quality is accelerated. Thus, consideration of downstream water quality responding to changes in reservoir release is essential for an integrated watershed management with regards to water quantity and quality. In this study, water quality models based on artificial neural networks (ANNs) method were developed using historical downstream water quality (rm $\NH_3$-N) data obtained from a water treatment plant in Geum river and reservoir release data from Daechung dam. A nonlinear multiple regression model was developed and compared with the ANN models. In the models, the rm NH$_3$-N concentration for next time step is dependent on dam outflow, river water quality data such as pH, alkalinity, temperature, and rm $\NH_3$-N of previous time step. The model parameters were estimated using monthly data from Jan. 1993 to Dec. 1998, then another set of monthly data between Jan. 1999 and Dec. 2000 were used for verification. The predictive performance of the models was evaluated by comparing the statistical characteristics of predicted data with those of observed data. According to the results, the ANN models showed a better performance than the regression model in the applied cases.

• 한국물환경학회지
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• 제28권2호
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• pp.320-331
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• 2012

A combination system of catch canal and constructed wetland was designed and suggested to improve water quality in gagricultural region of lower Dong-jin river basin. In order to evaluate an water quality improvement efficiency of the designed combination system, the NPS-WET model was applied in this study. Simulation result of the NPS-WET shown that the nutrient load removal rate of constructed wetland was BOD, T-N, T-P and SS was 30.7~39.0%, 46~60%, 40.7~57.0% and 68.2~74.7%, respectively. Nutrients reduction of constructed wetland was higher in growing season than winter season because vital activity of microorganism, macrophyte and algae was augmented with high air and water temperature. Effluents from constructed wetland can affect water-quality of catch canal drains, especially, water-quality on junction point to Dong-jin river. Water-quality improvement in low-flowed catch canal (Un-san) was more significant than in high-flowed catch canal (Won-pyeong). In conclusion, a feasible design of constructed wetland is necessary to treat large quantity of receiving water. The NPS-WET is useful tool for assessing water-quality improvement efficiency using constructed wetland.

• 한국물환경학회지
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• 제22권6호
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• pp.1045-1051
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• 2006

Geumgang canal is planned to connect Geumgang lake with Saemangeum lake to accelerate desalinization and dilute polluted water to improve water quality in Saemangeum lake. The purpose of this study is to evaluate the impact of water quality on Geumgang lake by diversion of its lake flow to Saemangeum lake. WASP5 model was used to estimate water quality of Geumgang lake. Model calibration and verification were done for water quality data for 2001 and 2002. Water quality concentrations in Geumgang lake were simulated for 4 scenarios, which were considered whether the Geumgang canal is built or not. As a result of simulations, there was little impact on water quality in Geumgang lake, though a little of the Geumgang lake flow diverted to Saemangeum lake. As the Geumgang canal is planned to divert the Geumgang water flow which were discharged into the sea through sluice gates when canal is not built, it is thought that there will be little change by diversion of water flow.

• 한국물환경학회지
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• 제29권6호
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• pp.842-846
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• 2013

In 2012, the Korean Ministry of Environment (MOE) added 3 new water quality standards for the protection of human health; specifically, regarding 1,4-dioxane, formaldehyde, and hexachlorobenzene. In this study, we assimilated the water quality standards of these 3 substances from other countries, with respect to surface water quality standards for human health protection and drinking water standards. We subsequently investigated how these standard values were derived. 1,4-Dioxane is managed as an environmental standard for human health in Japan, and as a drinking water quality standard in WHO, New Zealand, and Japan with respect to both carcinogenic and non-carcinogenic effects. In New York, the oncogenic effects of formaldehyde in drinking water intake is considered, whereas WHO, Australia, New Zealand, and Japan also assess the non-carcinogenic effects of formaldehyde when setting their standards. USEPA and New York have a water quality standard for human health protection with respect to hexachlorobenzene based on carcinogenic effects. This study focuses on deriving water quality standards for the 3 new substances, or obtaining baseline information to revise the values of existing substances in the future.

• 상하수도학회지
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• 제18권4호
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• pp.453-460
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• 2004

The objective of this research is to develop the water quality management model to achieve the water quality goal and the minimization of the waste load abatement cost. Most of existing water quality management model can calculate BOD and DO. In addition to those variables, N and P are included in the management model of this study. With a genetic algorithm, calculation results from the mathematical water quality model can be used directly in this management model. Developed management model using genetic algorithm was applicated for the Youngsan River basin. To verify the management model, water quality and pollution source of the Youngsan River had been investigated. Treatment types and optimum treatment costs of the existing and planned WWTPs in the baisn were calculated from the model. The results of genetic algorithm indicate that Kwangju and Naju WWTP have to do the advanced treatment to achieve the water quality goal about BOD, DO and TP. Total annual treatment cost including the upgrade cost of existing WWTPs in the Youngsan River basin was about 50.3 billion Won.

• 한국습지학회지
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• 제7권1호
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• pp.81-91
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• 2005

본 연구에서는 하천에서의 흐름과 수질변화를 실시간으로 감시할 수 있는 수문 수질관측시스템을 구축하고자 한다. 이를 통해 하천에서의 오염사고에 대비한 수질감시와 수질변화를 모의할 수 있는 하천관리시스템을 구축하고자 한다. 횡성댐 상류 계천 유역에 시험유역을 선정하였으며, 보다 정확한 수문 및 수질자료를 얻기 위해 우량관측소 3개소, 수위관측소 3개소 및 수질관측소 1개소를 설치하여 실시간 수문 수질관측시스템을 운영하고 있다. 이와 같이 구축된 관측시스템을 통해 강우량, 수위, 유속, 유량 및 수질 등과 같은 자료들을 축적하고, 다양한 분야의 활용을 위해 자유롭게 공개함으로써 관측자료에 대한 활용도를 높이고자 한다.

• 한국농공학회논문집
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• 제48권1호
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• pp.91-102
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• 2006

Most projects of tideland reclamation with dike construction produce estuarine reservoirs, which may result in water quality problems due to blocking of natural flow of stream water to the sea. External loadings to the reservoirs through tributaries are major concerns in a concerned water quality management. The water quality of a reservoir is greatly influenced by watershed drainage, and accurate estimation of pollutant is indispensable for in the reservoir management. Concentrations of the microorganisms in stream water and conventional parameters were monitored in the 13 water quality monitoring sites located in a rural watershed of Hwaong estuarine reservoir. The indicator of microorganisms showed strong correlation between them, and regression equations with $R^2\geq0.70$ may be used fur estimating one from other microorganisms. The relationships between water quality parameters obtained in this study may be used to infer one unknown pollutant concentrations from the measured pollutant loadings. This methodology could be applied to other areas where the watershed characteristics are not significantly different from the study area. High concentrations of nitrogen was observed in water quality monitoring sites affected by urban land uses and numbers of livestock in wet day as well as dry day, due to the influent of diffuse sources.

• 한국농공학회논문집
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• 제46권1호
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• pp.99-105
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• 2004

The present agricultural water quality standards are set by a policy goal. This is intended for water quality management of public water resources, but not for the use of water resources. These standards were not determined by considering the influence of water quality on the safety of agricultural produce and the growth, yield and quality of agricultural crops. Thus, this study was carried out to investigate the influence of irrigation water quality on the growth, yield, and grain quality of rice and acquire fundamental knowledges to set up irrigation water quality standards. The pot experiment was conducted with 4 treatments using irrigation waters with various total nitrogen concentrations (control, 1, 5, 10, 20mg/L) and replicated four times with randomized block design. The results of this study showed that plant height, number of tiller, plant dry weight, the uptake of N, P, and K, and rice protein contents tended to increase as the T-N concentration in irrigation water was increased. In addition, grain yield at T-N 20 mg/L was significantly higher than in the control, but the percentage of head rice was slightly lower due to the increase of green kernel and white belly/core kernel.

• 농업과학연구
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• 제47권4호
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• pp.1029-1037
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• 2020

The machine learning algorithm has been widely used in water-related fields such as water resources, water management, hydrology, atmospheric science, water quality, water level prediction, weather forecasting, water discharge prediction, water quality forecasting, etc. However, water quality prediction studies based on the machine learning algorithm are limited compared to other water-related applications because of the limited water quality data. Most of the previous water quality prediction studies have predicted monthly water quality, which is useful information but not enough from a practical aspect. In this study, we predicted the dissolved oxygen (DO) using recurrent neural network with long short-term memory model recurrent neural network long-short term memory (RNN-LSTM) algorithms with hourly- and daily-datasets. Bugok Bridge in Oncheoncheon, located in Busan, where the data was collected in real time, was selected as the target for the DO prediction. The 10-month (temperature, wind speed, and relative humidity) data were used as time prediction inputs, and the 5-year (temperature, wind speed, relative humidity, and rainfall) data were used as the daily forecast inputs. Missing data were filled by linear interpolation. The prediction model was coded based on TensorFlow, an open-source library developed by Google. The performance of the RNN-LSTM algorithm for the hourly- or daily-based water quality prediction was tested and analyzed. Research results showed that the hourly data for the water quality is useful for machine learning, and the RNN-LSTM algorithm has potential to be used for hourly- or daily-based water quality forecasting.