장유호 Yuho Jang , 손봉호 Bongho Son , 저소웅 Shaoxiong Chu , 임봉수 Bongsu Lim

DOI:10.15681/KSWE.2019.35.5.399 Vol.35(No.5) 399-408, 2019

The average annual water quality at Yongchon Bridge, Bonggok 2 Bridge, and Gasuwon Bridge in upstream of Gapcheon in 2018 was Ib grade (good) in organic matters, including BOD and TOC compared to the local environmental standard in Daejeon. However, their monthly changes for TOC partially exceeded the standard during the busy farming season at Bonggok 2 Bridge and Gasuwon Bridge. Although the annual average TP was within the standard, the monthly change at Bonggok 2 Bridge partially exceeded the standard. For Dugyecheon, the annual average water quality in 2018 at Umyeon-dong Bridge and Wonjeong Bridge, which are downstream of the Gyeryong public sewage treatment plant, exceeded the local environmental standard in BOD. COD was exceeded, and TP was within the standard. It seemed that the causes of deteriorated water quality downstream of Dugyecheon were discharges of agricultural water from agricultural land and effluent from the Gyeryong public sewage treatment plant. Assuming the pollution load of 100% based at the Mulangil point of the mainstream of Gapcheon, the ratio of BOD load and TOC load were as high as 58% and 47%, respectively. At the basin of Bonggok 2 Bridge and Mulangil, the loads downstream of Dugyecheon including the Gyeryong public sewage treatment plant were as high as 43% for TN and 56% for TP, respectively, indicating that Dugyecheon had a major impact on the water quality at the mainstream of Gapcheon.

Dugyecheon, Local environmental standard in Daejeon, Management plan of water quality, Pollution load, Upstream of Gapcheon

이준호 Lee Jun Ho , 신용균 Shin Young Gyun

DOI:10.15681/KSWE.2019.35.5.409 Vol.35(No.5) 409-417, 2019

In this study, a 480 m3/day pilot device was constructed through laboratory experiments based on the Ministry of Environment manual. The purpose of this study was to analyze the characteristics of CSO treatment and backwashing characteristics by applying the pilot device to the field. The purpose of this study was to acquire the basic data necessary for the design and operation management of the real scale filtration type non-point pollution control system. The filtration was conducted while maintaining the linear velocity of 20m/hour. The CSO treatment efficiencies of the pilot devices were 0.4-76.1%(mean 49.0 %), SS 51.4-91.6%(mean 77.8%), COD 22.2-59.4% (mean 38.3%) and TP 14.5-52.6%(mean 38.1%),respectively. The correlation coefficient between SS and the turbidity of influent water was 0.90, higher than that of CSO. To operate the treatment system effectively, the turbidity can be easily measured in real time as the monitoring item is the most appropriate because SS is the main target substance of the non-point source. As a result of analyzing the adsorbent treatment characteristics of PP filter material applied to this pilot device, the average particle diameter range of influent was 4.6-40.1 ㎛(mean 21.2 ㎛) and the treated water was 0.9-24.5 ㎛(mean 6.4 ㎛), respectively. Particles of approximately 10m or less are leached out, and so it is necessary to compensate for the raw water containing micro particulate matter.

Backwashing, CSO, Filtration, PSD

이용호 Yongho Lee , 쑨밍하오 Minghao Sun , 박대원 Daewon Pak

DOI:10.15681/KSWE.2019.35.5.418 Vol.35(No.5) 418-424, 2019

In this study, TiO₂ nanotubes with different morphologies were prepared in the electrolyte consisting of ethylene glycol, ammonium fluoride(NH₄F), and deionized water(H₂O) by controlling the voltage and time in the anodization method. Thicknesses and pore sizes of these TiO₂ nanotubes were measured to interpret the relationship between anodization conditions and TiO₂ nanotube morphologies. Element contents in the TiO₂ nanotubes were detected for further analysis of TiO₂ nanotube characteristics. Photoelectrolyticdecolorization efficiencies of the TiO₂ nanotube plates with various morphologies were tested to clarify the morphology that a highly active TiO₂ nanotube plate should have. Influences of applied voltage in photoelectrolysis processes and sodium sulfate(Na₂SO₄) concentration in wastewater on the decolorization efficiency were also studied. To save the equipment investment cost in photoelectrolysis methods, a two-photoelectrode system that uses the TiO₂ nanotube plates as photoanode and photocathode instead of adding other counter electrodes was studied. Compared with single-photoelectrode system that uses the TiO₂ nanotube plate as photoanode and titanium plate as cathode on the view of the treatment of dye wastewater containing different amounts of salt. As a result, a considerably suitable voltage was strictly needed for enhancing the photoelectrolyticdecolorization effect of the two-photoelectrode system but if salts exist in wastewater, an excellent increase in the decolorization efficiency can be obtained.

Decolorization efficiency, Dye wastewater, Photoelectrolysis, TiO₂ nanotube plate

고우석 Woo-seok Go , 윤춘경 Yoon Chun Gyeong , 이한필 Rhee Han-pil , 황순진 Hwang Soon-jin , 이상우 Lee Sang-woo

DOI:10.15681/KSWE.2019.35.5.425 Vol.35(No.5) 425-431, 2019

Recently, people have been attracting attention to the good quality of water resources as well as water welfare. to improve the quality of life. This study is a papers on the prediction of benthic macroinvertebrate index (BMI), which is a aquatic ecological health, using the machine learning based CFS (Correlation-based Feature Selection) method and the random forest model to compare the measured and predicted values of the BMI. The data collected from the Han River’s branch for 10 years are extracted and utilized in 1312 data. Through the utilized data, Pearson correlation analysis showed a lack of correlation between single factor and BMI. The CFS method for multiple regression analysis was introduced. This study calculated 10 factors(water temperature, DO, electrical conductivity, turbidity, BOD, NH3-N, T-N, PO4-P, T-P, Average flow rate) that are considered to be related to the BMI. The random forest model was used based on the ten factors. In order to prove the validity of the model, R2, %Difference, NSE (Nash-Sutcliffe Efficiency) and RMSE (Root Mean Square Error) were used. Each factor was 0.9438, -0.997, and 0,992, and accuracy rate was 71.6% level. As a result, These results can suggest the future direction of water resource management and Pre-review function for water ecological prediction.

Aquatic ecology, BMI, CFS(Correlation-based Feature Selection), Machine Running, Random Forest

김민규 Kim Minkyu , 윤춘경 Yoon Chun Gyeong , 이한필 Rhee Han-pil , 황순진 Hwang Soon-jin , 이상우 Lee Sang-woo

DOI:10.15681/KSWE.2019.35.5.432 Vol.35(No.5) 432-438, 2019

The purpose of this study is to predict Trophic Diatom Index (TDI) in tributaries of the Han River watershed using the random forest algorithm. The one year (2017) and supplied aquatic ecology health data were used. The data includes water quality(BOD, T-N, NH₃-N, T-P, PO₄-P, water temperature, DO, pH, conductivity, turbidity), hydraulic factors(water width, average water depth, average velocity of water), and TDI score. Seven factors including water temperature, BOD, T-N, NH₃-N, T-P, PO₄-P, and average water depth are selected by the Correlation Feature Selection. A TDI prediction model was generated by random forest using the seven factors. To evaluate this model, 2017 data set was used first. As a result of the evaluation, R², % Difference, NSE(Nash-Sutcliffe Efficiency), RMSE(Root Mean Square Error) and accuracy rate show that this model is compatible with predicting TDI. To be more concrete, R² is 0.93, % Difference is -0.37, NSE is 0.89, RMSE is 8.22 and accuracy rate is 70.4%. Also, additional evaluation using data set more than 17 times the measured point was performed. The results were similar when the 2017 data set were used. The Wilcoxon Signed Ranks Test shows there was no statistically significant difference between actual and predicted data for the 2017 data set. These results can specify the elements which probably affect aquatic ecology health. Also, these will provide direction relative to water quality management for a watershed that must be continuously preserved.

Aquatic ecology health, Correlation-based Feature Selection, Random forest, TDI Prediction