• Korean Journal of Ecology and Environment
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• v.50 no.1
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• pp.169-185
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• 2017

Physicochemical properties of water quality were analyzed to understand the long-term variations in Lake Youngrang from 1998 to 2015. Nonparametric statistical methods were applied to deduct correlation among water quality parameters and water quality trend. In total observations(N=64), the Secchi depth (SD) transparency showed significant positive correlation with salinity (r=0.458) and highly significant negative correlation with chlorophyll-a (r= -0.649) for p<0.0001 in two-tailed test of Spearman's rank correlation. Significant negative correlations of SD were observed with chemical oxygen demand (COD), total phosphorus (TP) and total nitrogen (TN). These correlation patterns were very similar in rainy (N=25) and non-rainy (N=39) periods too. Chlorophyll-a (Chl-a) had significant correlation with COD. Sen's slope test was performed along with Mann-Kendall trend test (significance ${\alpha}=0.05$, two-tailed) to find water quality trend. Positive trends were observed for SD and salinity with Sen's slopes 0.012 and 0.385, respectively (p<0.0001). Negative significant trends were observed for total nitrogen (TN) and Chl-a with Sen's slopes -0.02 (p<0.0001) and -0.346 (p=0.0010), respectively. Temperature, COD and phosphorus components had no trends. Carlson's trophic state index (TSI) for SD, TP and Chl-a were obtained in the ranges of 46~80, 37~82 and 39~82, respectively. Trophic index values suggest that Lake Youngrang was mesoeutrophic to eutrophic and there could be possibility of anoxia during the summer and dominance of blue-green algae. Excess nutrient inputs from external and internal sources were the causes of eutrophication in this lake. The findings of this study would be helpful to recognize water quality variables to manage the water body.

• ALGAE
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• v.23 no.4
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• pp.257-268
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• 2008

Species composition, standing crop and dominant species of phytoplankton were investigated at 6 sites of Galgok stream and Incheon river which run into Gomso Bay, Jeollanam-do in April, August and November 2007. Total of 210 taxa were found which were classified as 6 classes, 12 orders, 25 families, 66 genera, 177 species, 27 varieties, 5 forms and 1 unidentified species. These river and stream had lower concentration of T-N and T-P compared to that of others; however, the site 2 of Galgok stream in summer was hypertrophic in T-P and also near hypertrophic in TN, and the site 1 in Incheon river during fall appeared to be hypertrophic in T-N, and the site 3 in summer showed near hypertrophic level in T-P. Determining the trophic status of the water quality based on chlorophyll a (chl-a), the site 3 of Galgok stream in spring and the site 3 of Incheon river in fall were oligotrophic; moreover, 6 sites including the site 2 and 3 of Galgok stream in summer were mesotrophic, and 9 sites including all sites of Galgok stream in fall turned out to be eutrophic. Particularly, the site 1 of Galgok stream in summer was hypertrophic, having 58.19 mg chl-a m$^{-3}$. There was a conspicuous difference between two values of standing crops based on chl-a and cell counting. This discrepancy may have occurred because of abundant cyanophytes and exclusion of picoplankton cells in cell counting. In the study, 5 cyanophytes, Synechocystis aquatilis, Microcystis aeruginosa, M. flos-aquae, Oscillatoria angustissima, O. limnetica and 2 diatoms, Thalassiosira bramaputrae and Navicula viridula var. rostellata were abundant. Based on the T-N, T-P values, standing crops and cell counting in Galgok stream and Incheon river were between mesotrophic and eutrophic conditions.

• Journal of Conservation Science
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• v.26 no.3
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• pp.259-268
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• 2010

To evaluate the biological pollution class, Chl-a concentration and color were examined in 8 stone cultural heritages of Korea from April to June 2007. In the case of Chl-a, it had the range of 16.2 to $136.6{\mu}g{\cdot}g^{-1}$, and for the range of the colors, it came out from green to black. As the result of the evaluation, the Three storied stone pagoda in Mulgeol-ri and the Three storied stone pagoda in Suta Temple both showed class 3 and the other 6 stone cultural heritages showed the classes above 4, proving severe biological pollution. The total of 32 aerial algae taxa were found in 8 stone cultural heritages. Out of the 32 taxa, 16 taxa were found in Cyanophyceae, 13 taxa in Chlorophyceae, and 3 taxa in Bacillariophyceae. The correlation coefficient showed 0.43, as a result of the correlation analysis between Chl-a and the biological pollution class. Two among all the aerial algae taxa, Trebouxia sp, and Chroococcus bituminosus, showed high correlation coefficients of 0.89 and 0.65 with Chl-a, in the correlation analysis. High correlation coefficients from 0.54 to 0.65 were shown in a correlation analysis between the biological pollution class and the aerial algae taxa, which are Chroococcus bituminosus, Aphanocapsa grevillei, Xenococcus acervatus, Chlorella vulgaris, and Synechocystis pevalekii.

• Journal of the Korean Society of Marine Environment & Safety
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• v.24 no.7
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• pp.898-906
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• 2018

To investigate the effect of yellow dust on phytoplankton, a field survey and physiological experiments were carried out in the waters near Taean Peninsula from April 22 to 26, 2006, when yellow dust occurred. Phytoplankton populations during the yellow dust period were in the range of $26{\sim}290{\times}10^3cells{\cdot}L^{-1}$, a somewhat low standing crop. An increase in diatoms (a main taxonomic group), especially benthic diatoms such as Paralia sulcate, a typical species for active mixed sea water areas, was also remarkable. In addition, the Chl-a concentration after yellow dust exceeded the Chl-a concentration change range according to the tide before yellow dust. As the concentration of yellow sand increased in a yellow sand treatment experiment, primary productivity decreased, and the maximum assimilation number showed the same tendency. In the 48h culture experiment, primary productivity of the test group was lower than that of the control group at the early stage (T0) of yellow sand treatment, but after 48 hours (T48), the test group showed higher primary productivity than the control group. In particular, the primary productivity of the test group significantly increased to 321 % after 48 hours. Therefore, strong physical environment accompanied by yellow dust may temporarily inhibit the growth of phytoplankton in the waters adjacent to China in the early stage of yellow dust, but the formation of stable water mass has also been identified as a potential factor promoting the growth of phytoplankton.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1541-1555
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• 2022

Red tide has potential to harm marine ecology and aquaculture. Research on detecting red tide using various optical remote sensors has been conducted, but most of existing algorithms for detecting red tide has limitations, especially in shallow coastal waters with high levels of suspended sediment. For enhanced understanding of the optical behavior of red tide waters, analysis on remote sensing reflectance and water constituent is becoming increasingly important. This study analyzed the optical remote sensing data and water quality variables(Chl-a_(Spec), SPM, a_ph, a_d, Turbidity, Chl-a_(HPLC), Dominant species) of red tide waters. The data were collected from ship-based campaigns. In addition to the research on detecting red tide, the remote sensing reflectance and extinction coefficients for mesodinium and cochlodinium species were also analyzed. Through the analysis, it was possible to estimate the red tide chlorophyll concentration based on a specific wavelength of the remote sensing reflectance. The study found that chlorophyll concentration and phytoplankton absorption coefficient were highly correlated(R²=0.9), and that the RE_diff formula provided a more accurate estimate of red tide concentration than the B-G ratio.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.5
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• pp.427-434
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• 2023

Oysters are an important organism, accounting for an average of 76% of shellfish aquaculture production. In this study, optimal habitat was searched for by calculating the Habitat Suitability Index (HSI) using water temperature, salinity, hydrodynamics, DO, SS and Chl.a in Jaran Bay. As a result, the inside of Jaran Bay was found to be a more suitable habitat than the outside with a wide entrance and rapid hydrodynamics. Oyster production and HSI showed a significant correlation (0.710, p<0.05). Hydrodynamics helps the growth of aquaculture organisms, such as food supply through seawater exchange in the bay, which showed a high correlation (0.709, p<0.05) with oyster production. It was found to have a greater effect on habitat suitability than Chl.a. The results of this study are expected to be helpful for the efficient conservation, use and management of coastal fisheries.

• Journal of Wetlands Research
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• v.18 no.3
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• pp.301-312
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• 2016

This study has performed comparative analysis on characteristics of contaminated 35 tributaries on seasonal variation/point discharge load/pollutant distribution of water quality factors(8) in order to understand the effect of the watershed in Nakdong River Basin. As a results, the water quality of $BOD_5$(Biochemical Oxygen Demand), Chl-a(Chlorophyll a) and Fecal E. Coli shows II grade at tributaries of more than 50% without COD(Chemical Oxygen Demand), TP(Total Phosphate), TOC(Total Oxygen Carbon) and TN(Total Nitrogen) factors. The specific discharge(Q) were occupied about 54.4% (19 sites) as $0.05m^3/sec/km^2$ value. Among these results, the contaminant level of Dalseocheon, Hyeonjicheon, Seokkyocheon 1, Uriyeongcheon and Dasancheon was also high, which has to consider a discharged pollutant load(kg/day). The 35 major tributaries of Nakdong River were included in 7 mid-watershed, such as Nakdong Waegwan, Geumho River, Nakdong Goryung, Nakdong Changnyung, Nam River, Nakdong Milyang, Nakdong River Hagueon. Especially, the discharged pollutant load of Nam River and Geumho River also was high according to the amount of discharge such as Kachang dam, Gongsan dam and Nam river dam. Seasonal difference of the water quality factors such as $BOD_5$, TN, SS and Q was observed largely, on the other hand the TP and Chl-a was not. This is guessed due to the precipitation effect of site, biological and physicochemical degradation properties of pollutant and etc. The co-relationship between the seasonal difference and water quality factors was observed using a Pearson correlation coefficients. Besides, the Multiple Regression analysis using a Stepwise Regression method was conducted to understand the effect between seasonal difference and water quality factors/regression equations. As a result, the Multiple Regression analysis was adapted in the spring, summer and autumn without the winter, which was observed high at spring, summer and autumn in the order COD/TP, Chl-a/TOC, TOC/COD/$BOD_5$ water quality factors, respectively.

• Korean Journal of Ecology and Environment
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• v.45 no.2
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• pp.174-189
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• 2012

The objective of this study was to determine the weight-length relations and condition factor (K) of Zacco platypus, along the trophic gradients from oligotrophic to eutrophic state in six reservoir ecosystems ($B_aR$, $Y_yR$, $J_yR$, $G_pR$, $Y_dR$, and $M_sR$), during 2008~2010. The species was selected as a sentinel species for the study, due to its wide distribution and wide trophic gradient. The analysis of trophic state index (TSI), based on total phosphorus (TP) and chlorophyll-a (Chl-a), indicated that reservoirs of $Y_yR$ and $B_aR$ were classified as to be in an oligotrophic state (30~40), the $J_yR$ and $G_pR$ as mesotrophic (40~50), and the $Y_dR$ and $M_sR$ as eutrophic state (50~70). Total 47 species and 26,226 individuals were sampled from 6 reservoirs and sensitive species dominated in the oligotrophic reservoirs ($Y_yR$ and $B_aR$). In the mean time, the tolerant speciesdominated the community in the mesotrophic ($J_yR$ and $G_pR$) and eutrophic ($Y_dR$ and $M_sR$) reservoirs. Regression analysis of body weight, against the total length, indicated that the regression coefficient (b value) was lower in the oligotrophic reservoir (2.77~2.79) than the mesotrophic (3.07~3.17) and eutrophic reservoirs (3.15~ 3.21). This result suggests that the population growth rate Zacco platypus reflected the trophic gradients of the reservoirs. The analysis of condition factor (K) against the total length showed positive slopes (b>3.0) in mesotrophic and eutrophic reservoirs, and a negative slope (b<3.0) in oligotrophic reservoir. The variation of the regression slope of "b" in Z. platypus was accounted for 79.7% [$b=0.012{\times}TSI(TP)+2.395$, p=0.017] by the variation of TSI (TP) and 82.2% [$b=0.013{\times}TSI(Chl-a)+2.36$, p=0.013] by the variation of TSI (Chl-a). The proportion of DELT abnormality increased as the trophic state increases in the reservoirs. The overall data suggest that the growth of the fish populations, based on the length-weight relations and condition factor, reflected the trophic state of nutrient and phytoplankton biomass of the reservoir waters. Thus, in spite of the tolerant characteristics of Z. platypus, hypertrophic states might negatively affect the health of the population.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.655-667
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• 2023

Algal bloom outbreaks are frequently reported around the world, and serious water pollution problems arise every year in Korea. It is necessary to protect the aquatic ecosystem through continuous management and rapid response. Many studies using satellite images are being conducted to estimate the concentration of chlorophyll-a (Chl-a), an indicator of algal bloom occurrence. However, machine learning models have recently been used because it is difficult to accurately calculate Chl-a due to the spectral characteristics and atmospheric correction errors that change depending on the water system. It is necessary to consider the factors affecting algal bloom as well as the satellite spectral index. Therefore, this study constructed a dataset by considering water quality, hydrological and meteorological factors, and sentinel-2 images in combination. Representative ensemble models random forest and extreme gradient boosting (XGBoost) were used to predict the concentration of Chl-a in eight weirs located on the Nakdong river over the past five years. R-squared score (R²), root mean square errors (RMSE), and mean absolute errors (MAE) were used as model evaluation indicators, and it was confirmed that R² of XGBoost was 0.80, RMSE was 6.612, and MAE was 4.457. Shapley additive expansion analysis showed that water quality factors, suspended solids, biochemical oxygen demand, dissolved oxygen, and the band ratio using red edge bands were of high importance in both models. Various input data were confirmed to help improve model performance, and it seems that it can be applied to domestic and international algal bloom detection.

• Journal of Wetlands Research
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• v.26 no.3
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• pp.197-203
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• 2024

The algal blooms in rivers can negatively affect water source management and water treatment processes, necessitating continuous management. In this study, a multi-classification model was developed to predict the concentration of chlorophyll-a (chl-a), one of the key indicators of algal blooms, using Tabular Prior Fitted Networks (TabPFN), a novel deep learning algorithm known for its relatively superior performance on small tabular datasets. The model was developed using daily observation data collected at Buyeo water quality monitoring station from January 1, 2014, to December 31, 2022. The collected data were averaged to construct input data sets with measurement frequencies of 1 day, 3 days, 6 days, 12 days. The performance comparison of the four models, constructed with input data on observation frequencies of 1 day, 3 days, 6 days, and 12 days, showed that the model exhibits stable performance even when the measurement frequency is longer and the number of observations is smaller. The macro average for each model were analyzed as follows: Precision was 0.77, 0.76, 0.83, 0.84; Recall was 0.63, 0.65, 0.66, 0.74; F1-score was 0.67, 0.69, 0.71, 0.78. For the weighted average, Precision was 0.76, 0.77, 0.81, 0.84; Recall was 0.76, 0.78, 0.81, 0.85; F1-score was 0.74, 0.77, 0.80, 0.84. This study demonstrates that the chl-a prediction model constructed using TabPFN exhibits stable performance even with small-scale input data, verifying the feasibility of its application in fields where the input data required for model construction is limited.