• Journal of Wetlands Research
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• v.25 no.4
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• pp.306-314
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• 2023

Stream sediments are an important component of water quality management because they are receptors of various pollutants such as heavy metals and organic matters emitted from upland sources and can be secondary pollution sources, adversely affecting water environment. To effectively manage the stream sediments, identification of primary sources of sediment contamination and source-associated control strategies will be required. We evaluated the performance of machine learning models in identifying primary sources of sediment contamination based on the physico-chemical properties of stream sediments. A total of 356 stream sediment data sets of 18 quality parameters including 10 heavy metal species(Cd, Cu, Pb, Ni, As, Zn, Cr, Hg, Li, and Al), 3 soil parameters(clay, silt, and sand fractions), and 5 water quality parameters(water content, loss on ignition, total organic carbon, total nitrogen, and total phosphorous) were collected near abandoned metal mines and industrial complexes across the four major river basins in Korea. Two machine learning algorithms, linear discriminant analysis (LDA) and support vector machine (SVM) classifiers were used to classify the sediments into four cases of different combinations of the sampling period and locations (i.e., mine in dry season, mine in wet season, industrial complex in dry season, and　industrial complex in wet season). Both models showed good performance in the classification, with SVM outperformed LDA; the accuracy values of LDA and SVM were 79.5% and 88.1%, respectively. An SVM ensemble model was used for multi-label classification of the multiple contamination sources inlcuding landuses in the upland areas within 1 km radius from the sampling sites. The results showed that the multi-label classifier was comparable performance with sinlgle-label SVM in classifying mines and industrial complexes, but was less accurate in classifying dominant land uses (50~60%). The poor performance of the multi-label SVM is likely due to the overfitting caused by small data sets compared to the complexity of the model. A larger data set might increase the performance of the machine learning models in identifying contamination sources.

• Journal of Korean Society of Forest Science
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• v.84 no.4
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• pp.465-478
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• 1995

The objective of this study was to investigate the impacts of large-scale timber harvesting on the environment of a mature hardwood forest. To achieve the objective, the effects of harvesting on forest environmental factors were analyzed quantitatively using the field data measured in the study sites of Seoul National University Research Forests [(Mt.) Paekunsan] for two years(1993-1994) following timber harvesting. The field data include information on vegetation, soil mesofauna, physicochemical characteristics of soil, surface water runoff, water quality in the stream, and hillslope erosion. For comparison, field data for each environmental factor were collected in forest areas disturbed by logging and undisturbed, separately. The results of this study were as follows : The diversity of vegetational species increased in the harvested sites. However, the similarity index value of species between harvested and non-harvested sites was close to each other. Soil bulk density and soil hardness were increased after timber harvesting, respectively. The level of organic matter, total-N, avail $P_2O_5$, CEC($K^+$, $Na^+$, $Ca^{{+}{+}}$, $Mg^{{+}{+}}$) in the harvested area were found decreased. While the population of Colembola spp., and Acari spp. among soil mesofauna in harvested sites increased by two to seven times compared to those of non-harvested sites during the first year, the rates of increment decreased in the second year. However, those members of soil mesofauna in harvested sites were still higher than those of non-harvested sites in the second year. The results of statistical analysis using the stepwise regression method indicated that the diversity of soil mesofauna were significantly affected by soil moisture, soil bulk density, $Mg^{{+}{+}}$, CEC, and soil temperature at soil depth of 5(0~10)cm in the order of importance. The amount of surface water runoff on harvested sites was larger than that of non-harvested sites by 28% in the first year and 24.5% in the second year after timber harvesting. The level of BOD, COD, and pH in the stream water on the harvested sites reached at the level of the domestic use for drinking in the first and second year after timber harvesting. Such heavy metals as Cd, Pb, Cu, and organic P were not found. Moreover, the level of eight factors of domestic use for drinking water designated by the Ministry of Health and Welfare of Korea were within the level of the first class in the quality of drinking water standard. The study also showed that the amount of hillslope erosion in harvested sites was 4.77 ton/ha/yr in the first year after timber harvesting. In the second year, the amount decreased rapidly to 1.0 ton/ha/yr. The impact of logging on hillslope erosion in the harvested sites was larger than that in non-harvested sites by seven times in the first year and two times in the second year. The above results indicate that the large-scale timber harvesting cause significant changes in the environmental factors. However, the results are based on only two-year field observation. We should take more field observation and analyses to increase understandings on the impacts of timber harvesting on environmental changes. With the understandings, we might be able to improve the technology of timber harvesting operations to reduce the environmental impacts of large-scale timber harvesting.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.127-146
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• 2017

Services using artificial intelligence have begun to emerge in daily life. Artificial intelligence is applied to products in consumer electronics and communications such as artificial intelligence refrigerators and speakers. In the financial sector, using Kensho's artificial intelligence technology, the process of the stock trading system in Goldman Sachs was improved. For example, two stock traders could handle the work of 600 stock traders and the analytical work for 15 people for 4weeks could be processed in 5 minutes. Especially, big data analysis through machine learning among artificial intelligence fields is actively applied throughout the financial industry. The stock market analysis and investment modeling through machine learning theory are also actively studied. The limits of linearity problem existing in financial time series studies are overcome by using machine learning theory such as artificial intelligence prediction model. The study of quantitative financial data based on the past stock market-related numerical data is widely performed using artificial intelligence to forecast future movements of stock price or indices. Various other studies have been conducted to predict the future direction of the market or the stock price of companies by learning based on a large amount of text data such as various news and comments related to the stock market. Investing on commodity asset, one of alternative assets, is usually used for enhancing the stability and safety of traditional stock and bond asset portfolio. There are relatively few researches on the investment model about commodity asset than mainstream assets like equity and bond. Recently machine learning techniques are widely applied on financial world, especially on stock and bond investment model and it makes better trading model on this field and makes the change on the whole financial area. In this study we made investment model using Support Vector Machine among the machine learning models. There are some researches on commodity asset focusing on the price prediction of the specific commodity but it is hard to find the researches about investment model of commodity as asset allocation using machine learning model. We propose a method of forecasting four major commodity indices, portfolio made of commodity futures, and individual commodity futures, using SVM model. The four major commodity indices are Goldman Sachs Commodity Index(GSCI), Dow Jones UBS Commodity Index(DJUI), Thomson Reuters/Core Commodity CRB Index(TRCI), and Rogers International Commodity Index(RI). We selected each two individual futures among three sectors as energy, agriculture, and metals that are actively traded on CME market and have enough liquidity. They are Crude Oil, Natural Gas, Corn, Wheat, Gold and Silver Futures. We made the equally weighted portfolio with six commodity futures for comparing with other commodity indices. We set the 19 macroeconomic indicators including stock market indices, exports & imports trade data, labor market data, and composite leading indicators as the input data of the model because commodity asset is very closely related with the macroeconomic activities. They are 14 US economic indicators, two Chinese economic indicators and two Korean economic indicators. Data period is from January 1990 to May 2017. We set the former 195 monthly data as training data and the latter 125 monthly data as test data. In this study, we verified that the performance of the equally weighted commodity futures portfolio rebalanced by the SVM model is better than that of other commodity indices. The prediction accuracy of the model for the commodity indices does not exceed 50% regardless of the SVM kernel function. On the other hand, the prediction accuracy of equally weighted commodity futures portfolio is 53%. The prediction accuracy of the individual commodity futures model is better than that of commodity indices model especially in agriculture and metal sectors. The individual commodity futures portfolio excluding the energy sector has outperformed the three sectors covered by individual commodity futures portfolio. In order to verify the validity of the model, it is judged that the analysis results should be similar despite variations in data period. So we also examined the odd numbered year data as training data and the even numbered year data as test data and we confirmed that the analysis results are similar. As a result, when we allocate commodity assets to traditional portfolio composed of stock, bond, and cash, we can get more effective investment performance not by investing commodity indices but by investing commodity futures. Especially we can get better performance by rebalanced commodity futures portfolio designed by SVM model.

• Economic and Environmental Geology
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• v.42 no.3
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• pp.177-193
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• 2009

The Samgwang deposit consists of eight massive mesothermal quartz veins that filled NE and NW-striking fractures along fault zones in Precambrian granitic gneiss of the Gyeonggi massif. The mineralogy and paragenesis of the veins allow two separate discrete mineralization episodes(stage I=quartz and calcite stage, stage II-calcite stage) to be recognized, temporally separated by a major faulting event. The ore minerals are contained within quartz and calcite associated with fracturing and healing of veins that occurred during both mineralization episodes. The hydrothermal alteration of stage I is sericitization, chloritization, carbonitization, pyritization, silicification and argillization. Sericitic zone occurs near and at quartz vein and include mainly sericite, quartz, and minor illite, carbonates and chlorite. Chloritic zone occurs far from quartz vein and is composed of mainly chlorite, quartz and minor sericite, carbonates and epidote. Fe/(Fe+Mg) ratios of sericite and chlorite range 0.45 to 0.50(0.48$\pm$0.02) and 0.74 to 0.81(0.77$\pm$0.03), and belong to muscovite-petzite series and brunsvigite, respectiveIy. Calculated $Al_{IV}$-FE/(FE+Mg) diagrams of sericite and chlorite suggest that this can be a reliable indicator of alteration temperature in Au-Ag deposits. Calculated activities of chlorite end member are $a3(Fe_5Al_2Si_3O_{10}(OH)_6$=0.0275${\sim}$0.0413, $a2(Mg_5Al_2Si_3O_{10}(OH)_6$=1.18E-10${\sim}$7.79E-7, $a1(Mg_6Si_4O_{10}(OH)_6$=4.92E-10${\sim}$9.29E-7. It suggest that chlorite from the Samgwang deposit is iron-rich chlorite formed due to decreasing temperature from high temperature(T>450$^{\circ}C$). Calculated ${\alpha}Na^+$, ${\alpha}K^+$, ${\alpha}Ca^{2+}$, ${\alpha}Mg^{2+}$ and pH values during wallrock alteration are 0.0476($400^{\circ}C$), 0.0863($350^{\circ}C$), 0.0154($400^{\circ}C$), 0.0231($350^{\circ}C$), 2.42E-11($400^{\circ}C$), 7.07E-10($350^{\circ}C$), 1.59E-12($400^{\circ}C$), 1.77E-11($350^{\circ}C$), 5.4${\sim}$6.4($400^{\circ}C$), 5.3${\sim}$5.7($350^{\circ}C$)respectively. Gain elements(enrichment elements) during wallrock alteration are $TiO_2$, $Fe_2O_3(T)$,CaO, MnO, MgO, As, Ag, Cu, Zn, Ni, Co, W, V, Br, Cs, Rb, Sc, Bi, Nb, Sb, Se, Sn and Lu. Elements(Ag, As, Zn, Sc, Sb, Rb, S, $CO_2$) represents a potential tools for exploration in mesothermal and epithermal gold-silver deposits.

• Journal of Preventive Medicine and Public Health
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• v.29 no.1 s.52
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• pp.15-26
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• 1996

It has been studied that a variety of fauna and flora are sensitive biological indicators which reflect the severity of regional pollution of heavy metals, but in the center of part of Taegu City the controversial issue of lead poisoning attributable to the atmosphere which contains an increased concentrations of lead has been raised recently, it is usually hard to find suitable plants or animal in the areas with heavy traffic. Pigeons are ubiquitous in and around Taegu City area, inhabiting even the most densely populated areas with heavy traffic. With its small body size, high metabolic turnover, and rather limited mobility, a pigeon, as a biological indicator is expected. This study was conducted to monitor lead pollution in the Taegu and Kyongju City in Korea. We measured the lead content of the various tissue of three groups of feral pigeon(Columba livia) and soil and atmospheric lead concentration. First group was obtained in heavy traffic area in Taegu City, the second group was obtained a park in Taegu City and the third group was obtained light traffic area in Kyongju City. The air and soil lead concentration of heavy traffic area in Taegu City was $0.11{\mu}g/m^3,\;4.96{\mu}g/g$, that of park in Taegu City was $0.05{\mu}g/m^3,\;2.65{\mu}g/g$ and that of light traffic area in Kyongju City was $0.03{\mu}g/m^3,\;0.01{\mu}g/g$. The lead content of lung, blood, kidney, femur and liver of feral pigeons in heavy traffic area in Taegu City was significantly higher than pigeons obtained in a park in Taegu City and low traffic density area in Kyongju City(p<0.01). But stomach lead content of three group did not reflect a significant difference. In this study positive correlation was found between atmospheric lead concentrations and the concentration of lead in the pigeon's lung(r=0.5040, p<0.001), blood(r=0.3322, p<0.01), kidney(r=0.4824, p<0.001), femur(r=0.7214, p<0.001) and liver(r=0.4836, p<0.01). We can also found positive correlation between soil lead concentrations and the concentration of lead in the pigeon's femur(r=0.4850, p<0.001), kidney(r=0.4850, p<0.001) and liver(r=0.4386, p<0.01). In the pigeon's tissue there were significant correlations between concentration of lead in the blood and kidney(r=4818, p<0.001), femur(r=0.6157, p<0.001) and liver(r=0.3889, p<0.001). In conclusion, at the heavy traffic area in Taegu City, lead concentrations found in the atmosphere and soil are reflected in the lead concentrations of different tissue of urban pigeons. It is suggested that the tissue of pigeons can be good biological indicators of environmental lead pollution.

• Economic and Environmental Geology
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• v.30 no.4
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• pp.289-301
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• 1997

The Boguk cobalt mine is located within the Cretaceous Gyeongsang Sedimentary Basin. Major ore minerals including cobalt-bearing minerals (loellingite, cobaltite, and glaucodot) and Co-bearing arsenopyrite occur together with base-metal sulfides (pyrrhotite, chalcopyrite, pyrite, sphalerite, etc.) and minor amounts of oxides (magnetite and hematite) within fracture-filling $quartz{\pm}actinolite{\pm}carbonate$ veins. These veins are developed within an epicrustal micrographic granite stock which intrudes the Konchonri Formation (mainly of shale). Radiometric date of the granite (85.98 Ma) indicates a Late Cretaceous age for granite emplacement and associated cobalt mineralization. The vein mineralogy is relatively complex and changes with time: cobalt-bearing minerals with actinolite, carbonates, and quartz gangues (stages I and II) ${\rightarrow}$ base-metal sulfides, gold, and Fe oxides with quartz gangues (stage III) ${\rightarrow}$ barren carbonates (stages IV and V). The common occurrence of high-temperature minerals (cobalt-bearing minerals, molybdenite and actinolite) with low-temperature minerals (base-metal sulfides, gold and carbonates) in veins indicates a xenothermal condition of the hydrothermal mineralization. High enrichment of Co in the granite (avg. 50.90 ppm) indicates the magmatic hydrothermal derivation of cobalt from this cooling granite stock, whereas higher amounts of Cu and Zn in the Konchonri Formation shale suggest their derivations largely from shale. The decrease in temperature of hydrothermal fluids with a concomitant increase in fugacity of oxygen with time (for cobalt deposition in stages I and II, $T=560^{\circ}C-390^{\circ}C$ and log $fO_2=$ >-32.7 to -30.7 atm at $350^{\circ}C$; for base-metal sulfide deposition in stage III, $T=380^{\circ}-345^{\circ}C$ and log $fO_2={\geq}-30.7$ atm at $350^{\circ}C$) indicates a transition of the hydrothermal system from a magmatic-water domination toward a less-evolved meteoric-water domination. Sulfur isotope data of stage II sulfide minerals evidence that early, Co-bearing hydrothermal fluids derived originally from an igneous source with a ${\delta}^{34}S_{{\Sigma}S}$ value near 3 to 5‰. The remarkable increase in ${\delta}^{34}S_{H2S}$ values of hydrothermal fluids with time from cobalt deposition in stage II (3-5‰) to base-metal sulfide deposition in stage III (up to about 20‰) also indicates the change of the hydrothermal system toward the meteoric water domination, which resulted in the leaching-out and concentration of isotopically heavier sulfur (sedimentary sulfates), base metals (Cu, Zn, etc.) and gold from surrounding sedimentary rocks during the huge, meteoric water circulation. We suggest that without the formation of the later, meteoric water circulation extensively through surrounding sedimentary rocks the Boguk cobalt deposits would be simple veins only with actinolite + quartz + cobalt-bearing minerals. Furthermore, the formation of the meteoric water circulation after the culmination of a magmatic hydrothermal system resulted in the common occurrence of high-temperature minerals with later, lower-temperature minerals, resulting in a xenothermal feature of the mineralization.

• Economic and Environmental Geology
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• v.46 no.2
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• pp.179-198
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• 2013

The Boziguoer A-type granitoids in Baicheng County, Xinjiang, belong to the northern margin of the Tarim platform as well as the neighboring EW-oriented alkaline intrusive rocks. The rocks comprise an aegirine or arfvedsonite quartz alkali feldspar syenite, an aegirine or arfvedsonite alkali feldspar granite, and a biotite alkali feldspar syenite. The major rock-forming minerals are albite, K-feldspar, quartz, arfvedsonite, aegirine, and siderophyllite. The accessory minerals are mainly zircon, pyrochlore, thorite, fluorite, monazite, bastnaesite, xenotime, and astrophyllite. The chemical composition of the alkaline granitoids show that $SiO_2$ varies from 64.55% to 72.29% with a mean value of 67.32%, $Na_2O+K_2O$ is high (9.85~11.87%) with a mean of 11.14%, $K_2O$ is 2.39%~5.47% (mean = 4.73%), the $K_2O/Na_2O$ ratios are 0.31~0.96, $Al_2O_3$ ranges from 12.58% to 15.44%, and total $FeO^T$ is between 2.35% and 5.65%. CaO, MgO, MnO, and $TiO_2$ are low. The REE content is high and the total ${\sum}REE$ is $(263{\sim}1219){\times}10^{-6}$ (mean = $776{\times}10^{-6}$), showing LREE enrichment HREE depletion with strong negative Eu anomalies. In addition, the chondrite-normalized REE patterns of the alkaline granitoids belong to the "seagull" pattern of the right-type. The Zr content is $(113{\sim}1246){\times}10^{-6}$ (mean = $594{\times}10^{-6}$), Zr+Nb+Ce+Y is between $(478{\sim}2203){\times}10^{-6}$ with a mean of $1362{\times}10^{-6}$. Furthermore, the alkaline granitoids have high HFSE (Ga, Nb, Ta, Zr, and Hf) content and low LILE (Ba, K, and Sr) content. The Nb/Ta ratio varies from 7.23 to 32.59 (mean = 16.59) and the Zr/Hf ratio is 16.69~58.04 (mean = 36.80). The zircons are depleted in LREE and enriched in HREE. The chondrite-normalized REE patterns of the zircons are of the "seagull" pattern of the left-inclined type with strong negative Eu anomaly and without a Ce anomaly. The Boziguoer A-type granitoids share similar features with A1-type granites. The average temperature of the granitic magma was estimated at $832{\sim}839^{\circ}C$. The Boziguoer A-type granitoids show crust-mantle mixing and may have formed in an anorogenic intraplate tectonic setting under high-temperature, anhydrous, and low oxygen fugacity conditions.

• Korean Journal of Environmental Agriculture
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• v.18 no.3
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• pp.250-258
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• 1999

The purpose of this study was to compare heavy metal concentrations in uncontaminated soil with those in soil influenced by industrial activities, and to investigate the relationship between change of heavy metal content and the kind of industry at the Iksan 2nd Industrial Complex that has started since 1995. Soils sampled in 0-3 cm and 3-6 cm soil depth, respectively were analized for content of Cd, Cu, Ni, Pb and Zn. The content of Cd in soil layer of 0 to 3 cm was 0.07-4.37ppm range, average concentration was 0.516ppm and 3-6 cm was 0.07-8.52ppm range, average concentration was 0.380ppm. Area of the chemicals, dyes and metal products manufacturing were higher than the other manufacturing area in Industrial Complex. The content of Cu in soil layer of 0 to 3 cm was 0.61-42.62ppm range, average concentration was 11.087ppm and 3-6 cm was 0.16-35.45ppm range, average concentration was 7.578ppm. Area of the metal products manufacturing were higher than the other manufacturing area in Industrial Complex. The content of Ni in soil layer of 0 to 3 cm was 0.19-15.93ppm range, average concentration was 5.525ppm and 3-6 cm was 0.39-15.59ppm range, average concentration was 5.310ppm. Area of the metal and chemical products manufacturing were higher than the other manufacturing area in Industrial Complex. The content of Pb in soil layer of 0 to 3 cm was 3.10-55.75ppm range, average concentration was 23.543ppm and 3-6 cm was 3.35-46.55ppm range, average concentration was 19.198ppm. Area of the chemicals and metal products manufacturing were higher than the other manufacturing area in Industrial Complex. The content of Zn in soil layer of 0 to 3 cm was 26.50-943.00ppm range, average concentration was 158.329ppm and 3-6 cm was 35.45-882.45ppm range, average concentration was 127.914ppm. Area of the chemicals and metal products manufacturing were higher than the other manufacturing area in Industrial Complex. As the result, this study was to compare Cd, Cu, Ni, Pb, Zn average concentration in uncontaminated soil of world with those in soil, that Cu, Ni were uncontaminated concentration level, Cd was somewhat higher compare with the concentration level of world, Pb and Zn were very higher. Soil contaminated degree of Iksan 2nd Industrial Complex was known a difference by type of industrial activities(chemical, dyes and metal of products)

• Journal of Food Hygiene and Safety
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• v.34 no.1
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• pp.40-51
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• 2019

Ginseng has a unique production system that is different from those used for other crops. It is subject to the Ginseng Industry Act., requires a long-term cultivation period of 4-6 years, involves complicated cultivation characteristics whereby ginseng is not produced in a single location, and many ginseng farmers engage in mixed-farming. Therefore, to bring the production of Ginseng in line with GAP standards, it is necessary to better understand the on-site practices of Ginseng farmers according to established control points, and to provide a proper action plan for improving efficiency. Among ginseng farmers in Korea who applied for GAP certification, 77.6% obtained it, which is lower than the 94.1% of farmers who obtained certification for other products. 13.7% of the applicants were judged to be unsuitable during document review due to their use of unregistered pesticides and soil heavy metals. Another 8.7% of applicants failed to obtain certification due to inadequate management results. This is a considerably higher rate of failure than the 5.3% incompatibility of document inspection and 0.6% incompatibility of on-site inspection, which suggests that it is relatively more difficult to obtain GAP certification for ginseng farming than for other crops. Ginseng farmers were given an average of 2.65 points out of 10 essential control points and a total 72 control points, which was slightly lower than the 2.81 points obtained for other crops. In particular, ginseng farmers were given an average of 1.96 points in the evaluation of compliance with the safe use standards for pesticides, which was much lower than the average of 2.95 points for other crops. Therefore, it is necessary to train ginseng farmers to comply with the safe use of pesticides. In the other essential control points, the ginseng farmers were rated at an average of 2.33 points, lower than the 2.58 points given for other crops. Several other areas of compliance in which the ginseng farmers also rated low in comparison to other crops were found. These inclued record keeping over 1 year, record of pesticide use, pesticide storages, posts harvest storage management, hand washing before and after work, hygiene related to work clothing, training of workers safety and hygiene, and written plan of hazard management. Also, among the total 72 control points, there are 12 control points (10 required, 2 recommended) that do not apply to ginseng. Therefore, it is considered inappropriate to conduct an effective evaluation of the ginseng production process based on the existing certification standards. In conclusion, differentiated certification standards are needed to expand GAP certification for ginseng farmers, and it is also necessary to develop programs that can be implemented in a more systematic and field-oriented manner to provide the farmers with proper GAP management education.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.15 no.1
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• pp.1-25
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• 1982

Limnological study on the physico-chemical properties and biological characteristics of the Lake Ok-Jeong was made from May 1980 to August 1981. For the planktonic organisms in the lake, species composition, seasonal change and diurnal vertical distribution based on the monthly plankton samples were investigated in conjunction with the physico-chemical properties of the body of water in the lake. Analysis of temperature revealed that there were three distinctive periods in terms of vertical mixing of the water column. During the winter season (November-March) the vertical column was completely mixed, and no temperature gradient was observed. In February temperature of the whole column from the surface to the bottom was $3.5^{\circ}C$, which was the minimum value. With seasonal warming in spring, surface water forms thermoclines at the depth of 0-10 m from April to June. In summer (July-October) the surface mixing layer was deepened to form a strong thermocline at the depth of 15-25 m. At this time surface water reached up to $28.2^{\circ}C$ in August, accompanied by a significant increase in the temperature of bottom layer. Maximum bottom temperature was $r5^{\circ}C$ which occurred in September, thus showing that this lake keeps a significant turbulence Aehgh the hypolimnial layer. As autumn cooling proceeded summer stratification was destroyed from the end of October resulting in vertical mixing. In surface layer seasonal changes of pH were within the range from 6.8 in January to 9.0 in guutuost. Thighest value observed in August was mainly due to the photosynthetic activity of the phytoplankton. In the surface layer DO was always saturated throughout the year. Particularly in winter (January-April) the surface water was oversaturated (Max. 15.2 ppm in March). Vertical variation of DO was not remarkable, and bottom water was fairly well oxygenated. Transparency was closely related to the phytoplankton bloom. The highest value (4.6 m) was recorded in February when the primary production was low. During summer transparency decreased hand the lowest value (0.9 m) was recorded in August. It is mainly due to the dense blooming of gnabaena spiroides var. crassa in the surface layer. A. The amount of inorganic matters (Ca, Mg, Fe) reveals that Lake Ok-Jeong is classified as a soft-water lake. The amount of Cl, $NO_3-N$ and COD in 1981 was slightly higher than those in 1980. Heavy metals (Zn, Cu, Pb, Cd and Hg) were not detectable throughout the study period. During the study period 107 species of planktonic organisms representing 72 genera were identified. They include 12 species of Cyanophyta, 19 species of Bacillariophyta, 23 species of Chlorophyta, 14 species of Protozoa, 29 species of Rotifera, 4 species of Cladocera and 6 species of Copepoda. Bimodal blooming of phytoplankton was observed. A large blooming ($1,504\times10^3\;cells/l$ in October) was observed from July to October; a small blooming was present ($236\times10^3\;cells/l$ in February) from January to April. The dominant phytoplankton species include Melosira granulata, Anabaena spiroides, Asterionella gracillima and Microcystis aeruginota, which were classified into three seasonal groups : summer group, winter group and the whole year group. The sumner group includes Melosira granulate and Anabaena spiroides ; the winter group includes Asterionella gracillima and Synedra acus, S. ulna: the whole year group includes Microtystis aeruginosa and Ankistrodesmus falcatus. It is noted that M. granulate tends to aggregate in the bottom layer from January to August. The dominant zooplankters were Thermocpclops taihokuensis, Difflugia corona, Bosmina longirostris, Bosminopsis deitersi, Keratelle quadrata and Asplanchna priodonta. A single peak of zooplankton growth was observed and maximum zooplankton occurrence was present in July. Diurnal vertical migration was revealed by Microcystis aeruginosa, M. incerta, Anabaena spiroides, Melosira granulata, and Bosmina longirostris. Of these, M. granulata descends to the bottom and forms aggregation after sunset. B. longirostris shows fairly typical nocturnal migration. They ascends to the surface after sunset and disperse in the whole water column during night. Foully one species of fish representing 31 genera were collected. Of these 13 species including Pseudoperilnmpus uyekii and Coreoleuciscus splendidus were indigenous species of Korean inland waters. The indicator species of water quality determination include Microcystis aeruginosa, Melosira granulata, Asterionelta gracillima, Brachionus calyciflorus, Filinia longiseta, Conochiloides natans, Asplanchna priodonta, Difflugia corona, Eudorina elegans, Ceratium hirundinella, Bosmina longirostris, Bosminopsis deitersi, Heliodiaptomus kikuchii and Thermocyclops taihokuensis. These species have been known the indicator groups which are commonly found in the eutrophic lakes. Based on these planktonic indicators Lake Ok-Jeong can be classified into an eutrophic lake.