• Korean Journal of Microbiology
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• v.51 no.4
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• pp.374-381
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• 2015

Occurrences of coastal dredged materials are ever increasing due to port construction, navigational course maintenance and dredging of polluted coastal sediments. Ocean dumping of the coastal dredged materials has become virtually prohibited as London Treaty will be enacted as of the year 2012. It will be necessary to treat and recycle the dredged materials that may carry organic pollutants and heavy metals in a reasonable and effective process: collection of the dredged materials, liquid and solid separation, and treatment of organic compounds and heavy metals. In this study we have developed a continuous bioreactor system that can treat a mixture of silt and particulate organic matter using a microbial consortium (BM-S-1). The steady-state operation conditions were: pH (7.4-7.5), temperature ($16^{\circ}C$), DO (7.5-7.9), and salt concentration (3.4-3.7%). The treatment efficiencies of SCOD, T-N and T-P of the mixture were 95-96%, 92-99%, and 79-97%. The system was also effective in removal of heavy metals such as Zn, Ni, and Cr. Levels of MLSS during three months operation period were 11,000-19,000 mg/L. Interestingly, there was little sludge generated during this period of operation. The augmented microbial consortium seemed to be quite active in the removal of the organic component (30%) present in the dredged material in association with indigenous bacteria. The dominant phyla in the treatment processes were Proteobacteria and Bacteroidetes while dominant genii were Marinobacterium, Flaviramulus, Formosa, Alteromonadaceae_uc, Flavobacteriaceae_uc. These results will contribute to a development of a successful bioremediation technology for various coastal and river sediments with a high content of organic matter, inorganic nutrients and heavy metals, leading to a successful reuse of the polluted dredged sediments.

• Korean Journal of Ecology and Environment
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• v.43 no.2
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• pp.319-337
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• 2010

Major objective of this study was to determine interannual and seasonal water quality along with characteristics of longitudinal gradients along the reservoir axis of the riverine zone (Rz)-to-lacustrine zone (Lz). Water quality dataset of five years during 2003~2007 used here were obtained from Ministry of Environment, Korea and ten physical, chemical and biological parameters were analyzed in the study. Similarity analysis, based on moropho-hydrological variables of reservoir surface area, watershed area, total inflow, and outflow, showed that the reservoirs were categorized as three groups of large-dam reservoirs (Chungju Reservoir, Daecheong Reservoir and Soyang Reservoir), mid-size reservoirs (Andong Reservoir, Yongdam Reservoir, Juam Reservoir and Hapcheon Reservoir), and small-size reservoirs (Hoengseong Reservoir and Buan Reservoir). According to the data comparison of high-flow year (2003) vs. lowflow year (2005), dissolved oxygen (DO), pH, biological oxygen demand (BOD), suspended solids (SS), total nitrogen (TN), total phosphorus (TP), chlorophyll-a (CHL) and electrical conductivity (EC) declined along the longitudinal axis of Rz to Lz and water transparency, based on Secchi depth (SD), increased along the axis. These results indicate that transparency was a function of Values of pH, DO, SS, SD, and EC at each site were greater in the low-flow year (2005) than the high-flow year (2003), whereas values of BOD, COD, TN, TP and CHL were greater in the high-flow year (2003). When values of TN, TP, CHL and SD in nine reservoirs were compared in the three zones of Rz, Tz, and Lz, values of TN, TP and CHL declined along longitudinal gradients and SD showed the opposite due to the sedimentation processes from the water column. Values of TN were not statistically correlated with TP values. The empirical linear models of TP-CHL and CHL-SD showed significant (p<0.05, $R^2$>0.04). In the mid-size reservoirs, the variation of CHL was explained ($R^2$=0.2401, p<0.0001, n=239) by the variation of TP. The affinities in the correlation analysis of mid-size reservoirs were greater in the CHL-SD model than any other empirical models, and the CHL-SD model had an inverse relations. In the meantime, water quality variations was evidently greater in Daecheong Reservoir than two reservoirs of Andong Reservoir and Hoengseong Reservoir as a result of large differences of water quality by long distance among Rz, Tz and Lz.

• Journal of Korea Water Resources Association
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• v.55 no.11
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• pp.931-939
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• 2022

In Korea, about two-thirds of the precipitation is concentrated in the summer season, so the problem of turbidity in the summer flood season varies from year to year. Concentrated rainfall due to abnormal rainfall and extreme weather is on the rise. The inflow of turbidity caused a sudden increase in turbidity in the water, causing a problem of turbidity in the dam reservoir. In particular, in Korea, where rivers and dam reservoirs are used for most of the annual average water consumption, if turbidity problems are prolonged, social and environmental problems such as agriculture, industry, and aquatic ecosystems in downstream areas will occur. In order to cope with such turbidity prediction, research on turbidity modeling is being actively conducted. Flow rate, water temperature, and SS data are required to model turbid water. To this end, the national measurement network measures turbidity by measuring SS in rivers and dam reservoirs, but there is a limitation in that the data resolution is low due to insufficient facilities. However, there is an unmeasured period depending on each dam and weather conditions. As a sensor for measuring turbidity, there are Optical Backscatter Sensor (OBS) and YSI, and a sensor for measuring SS uses equipment such as Laser In-Situ Scattering and Transmissometry (LISST). However, in the case of such a high-tech sensor, there is a limit due to the stability of the equipment. Therefore, there is an unmeasured period through analysis based on the acquired flow rate, water temperature, SS, and turbidity data, so it is necessary to develop a relational expression to calculate the SS used for the input data. In this study, the AEM3D model used in the Water Resources Corporation SURIAN system was used to improve the accuracy of prediction of turbidity through the turbidity-SS relationship developed based on the measurement data near the dam outlet.