하준수 Junsoo Ha , 허다니엘상두 Daniel Sangdu Hur , 임채언 Chaieon Im , 정동희 Donghee Jung , 임영성 Youngseong Lim , 주진경 Jinkyong Ju

DOI:10.15681/KSWE.2022.38.6.267 Vol.38(No.6) 267-274, 2022

This paper is a result of research conducted on the 800,000 m3/d capacity of A Water Treatment Plant (WTP) and 400,000 m3/d capacity of B WTP plant in operation in the Nakdong River region. We evaluated the effect of algae broom on the WTP operation based on the running data of both WTP and the data on the pre-oxidation process field test for algae control using sodium permanganate (SPM) at the B WTP. The study results showed that during the algal bloom period, the coagulant dose increased by 102% in A WTP and 58% in B WTP, respectively, and the chlorine dose also increased by 38% and 29%, respectively, which may affect Total trihalomethane (THM) production. Data such as algal populations and Chl-a, residual chlorine and THM, algal populations, and ozone dose appeared also highly correlated, confirming that algal broom affects WTP operations, including water quality and chemical dosage. As a result of the field test of B WTP, THMs appeared lower than that of the control, suggesting the possibility of the SPM pre-oxidation process as an alternative to algae-related water quality management. Furthermore, in terms of GHG emissions due to energy consumption, it was observed that the pre-oxidation process using SPM was approximately 10.8%, which is a very low ratio compared to the pre-ozonation process. Therefore, these results suggest that the SPM pre-oxidation process can be recommended as an alternative to low-carbon water purification technology.

Algae bloom, Chemical dosages, Greenhouse gas, GHG, Sodium permanganate, SPM, Water treatment plant, WTP

장준영 JunYoung Jang , 김주원 JooWon Kim , 황유훈 YuHoon Hwang , 김기팔 KiPal Kim , 신현상 HyunSang Shin , 임병란 ByungRan Lim

DOI:10.15681/KSWE.2022.38.6.275 Vol.38(No.6) 275-281, 2022

To maintain water quality after water treatment, monitoring whether the quality of treated tap water quality changes is essential. However, current investigations are insufficient to prevent secondary contamination in drinking water supply systems. This study investigated Gyeonggi’s e apartment where a red water problem occurred and monitored the water quality and corrosiveness of the overall water supply system to the apartment from June 2021 to April 2022. In a comparison of drinking water quality after water treatment and the influent of the reservoir, turbidity and heavy metal concentrations were increased and residual chlorine was decreased due to increases in temperature. Correlation analysis and principal component analysis (PCA) indicated that a low level of residual chlorine may cause the abscission of Mn²⁺ and Fe²⁺ through microorganism activation, which also causes a high level of turbidity. The corrosion index (LI) in the influent of the reservoir tank was increased due to Ca²⁺ and temperature. These results indicate that the corrosiveness of drinking water and the deterioration of drinking water quality were mainly increased between the drinking water treatment plant and the reservoir tank’s influent. The findings provide clear evidence that it is essential to manage water supply systems and reservoir tanks to prevent the secondary contamination of drinking water.

Corrosion index, Drinking water, Turbidity, Underground reservoir, Water quality

이수현 Su-Hyeon Lee

DOI:10.15681/KSWE.2022.38.6.282 Vol.38(No.6) 282-291, 2022

Since the efficiency of wastewater treatment using microalgae differs depending on the metabolic characteristics of the species, it is important to understand the characteristics of target algae prior to the application in wastewater treatment. In this study, for the application of Arthrospira platensis to wastewater treatment, which is a filamentous alkaliphilic cyanobacteria, basic species specificity was identified and the possibility of application to wastewater treatment was investigated. As a result of the species specificity investigation, the specific growth rate between pH 7.0 and 11.0 showed the highest value near pH 9 at 0.25/day. The reason for the relatively low growth(0.08/day) at pH 11 was thought to be the CA(carbonic anhydrase) enzyme that is involved in carbon fixation during photosynthesis has the highest activity at pH 8.0 to 9.0, and at pH 11, CA activity was relatively low. In addition, A. platensis showed optimal growth at 400 PPFD(photosynthetic photon flux density) and 30℃, and this means that cyanobacteria such as A. platensis have a larger number of PS-I(photosystem I) than that of PS-II(photosystem II). It was speculated that it was because higher light intensity and temperature were required to sufficiently generate electrons to transfer to PS-I. Regarding the applicability of A. platensis, it was suggested that if a system using the synergistic effect of co-culture of A. platensis and bacteria was developed, a more efficient system would be possible. And different from single cocci, filamentous A. platensis expected to have a positive impact on harvesting, which is very important in the latter part of the wastewater treatment process.

Arthrospira platensis, Species specificity, Microalgal wastewater treatment, Alkaliphilic microalgae, CO₂ concentrating mechanism (CCM)

심선희 Sun-Hee Shim , 김유흔 Yu-Heun Kim , 이혜원 Hye Won Lee , 김민 Min Kim , 최정현 Jung Hyun Choi

DOI:10.15681/KSWE.2022.38.6.292 Vol.38(No.6) 292-305, 2022

The Yeongsan River, one of the four largest rivers in South Korea, has been facing difficulties with water quality management with respect to algal bloom. The algal bloom menace has become bigger, especially after the construction of two weirs in the mainstream of the Yeongsan River. Therefore, the prediction and factor analysis of Chlorophyll-a (Chl-a) concentration is needed for effective water quality management. In this study, Chl-a prediction model was developed, and the performance evaluated using machine and deep learning methods, such as Deep Neural Network (DNN), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost). Moreover, the correlation analysis and the feature importance results were compared to identify the major factors affecting the concentration of Chl-a. All models showed high prediction performance with an R² value of 0.9 or higher. In particular, XGBoost showed the highest prediction accuracy of 0.95 in the test data. The results of feature importance suggested that Ammonia (NH₃-N) and Phosphate (PO₄-P) were common major factors for the three models to manage Chl-a concentration. From the results, it was confirmed that three machine learning methods, DNN, RF, and XGBoost are powerful methods for predicting water quality parameters. Also, the comparison between feature importance and correlation analysis would present a more accurate assessment of the important major factors.

Chlorophyll-a, Deep learning, Feature importance, Machine learning, Yeongsan river

이기순 Gi-Soon Lee , 백종훈 Jonghun Baek , 최지연 Ji Yeon Choi , 이영재 Youngjea Lee , 신동석 Dong Seok Shin , 하돈우 Don-Woo Ha

DOI:10.15681/KSWE.2022.38.6.306 Vol.38(No.6) 306-315, 2022

In this study, long-term measurement data were applied to the LOADEST model and used as an analysis tool to identify and interpret trends in pollution load. The LOADEST model is a regression equation-based pollution load estimation program developed by the United States Geological Survey (USGS) to estimate the change in the pollution load of rivers according to flow rate and time and provides 11 regression equations for pollution load evaluation. As a result of simulating the Gwangjuchen2, Pungyeongjeongchen, and Pyeongdongchen in the Yeongbon B unit basin in the middle and upper reaches of the Yeongsan River with the LOADEST model using water quality and flow measurement data, lower values were observed for the Gwangjuchen2 and Pyeongdongchen, whereas the Pungyeongjeongchen had higher values. This was judged to be due to the characteristics of the LOADEST model related to data continuity. According to the parameters estimated by the LOADEST model, pollutant trends were affected by increases in the flow. In addition, variability increased with time, and BOD and T-P were affected by the season. Thus, the LOADEST model can contribute to water quality management as an analytical tool for long-term data monitoring.

LOADEST, Pollutant loads, Regression model, Youngsan river

최정화 Jounghwa Choi , 권재옥 Jaeog Kwon , 이미선 Miseon Lee , 장욱 Wook Jang , 최근화 Geunhwa Choi , 고은혜 Eunhye Ko , 심서현 Seohyeon Sim , 조창우 Changwoo Cho

DOI:10.15681/KSWE.2022.38.6.316 Vol.38(No.6) 316-326, 2022

This study aimed to investigate the causes of the increasing Total Phosphate(TP) in the mainstream of Mangyeong river over the past 10 years, and suggested a reduction plan of about 3 points. First, the high TP concentration was continuously released in the discharge outlet of the Haepo bridge stormwater pipeline. The average TP concentration was 5.066 mg/L and values as high as 29.470 mg/L were measured. The highest pollution contribution rate to the Mangyeong river was more than 70 %. The cause of the pollution was expected to take place somewhere in Wanju Industrial Complex. Second, the average TP concentration of wastewater-treated effluent in the H factory was 0.405 mg/L. If a TP reduction facility is additionally installed in the H factory, it will help reduce TP uptake by Lake Saemangeum. Third, the TP concentration of untreated non-point source point flowing into the Samrae stream was very high with an average of 2.828 mg/L. Also, the pollution contribution rate of Samraecheon 2 to Mangyeong river was 21.8 % on average and up to 58 %. The pollution contribution rate was also high during the agricultural season and the winter, during which the flow rate is decreased. Investigation of these three points may be continuously needed, and analysis results and policy proposals presented to Jeollabukdo and Wanjugun to manage pollution sources.