• Journal of Korean Society on Water Environment
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• v.29 no.5
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• pp.625-629
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• 2013

The objective of this study is to develop and evaluate simple and multiple regression models for Total Organic Carbon (TOC) concentration estimation in stream flow. For development (using water quality data in 2012) and evaluation (using water quality data in 2011) of regression models, we used water quality data from downstream of Yeongsan river basin during 2011 and 2012, and correlation analysis between TOC and water quality parameters was conducted. The concentrations of TOC were positively correlated with Chemical Oxygen Demand (COD), Biochemical Oxygen Demand (BOD), TN (Total Nitrogen), Water Temperature (WT) and Electric Conductivity (EC). From these results, simple and multiple regression models for TOC estimation were developed as follows : $TOC=0.5809{\times}BOD+3.1557$, $TOC=0.4365{\times}COD+1.3731$. As a result of the application evaluation of the developed regression models, the multiple regression model was found to estimate TOC better than simple regression models.

• Korean Journal of Soil Science and Fertilizer
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• v.46 no.5
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• pp.327-332
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• 2013

This survey was conducted to obtain basic data about organic matter such as BOD (Biochemical Oxygen Demand), COD (Chemical Oxygen Demand), DOC (Dissolved Organic Carbon) and TOC (Total Organic Carbon) in agricultural water in Gyeongnam province. BOD and COD are currently used for water quality indices, but adoption of TOC is being suggested. The surface water samples were collected at 39 locations in Gyeongnam province in April, July and October. Average concentrations of BOD, COD, DOC and TOC were 1.6, 3.3, 5.3 and $5.7mg\;L^{-1}$, respectively. In July, average concentrations of BOD and COD were 2.4 and $3.9mg\;L^{-1}$, respectively, showing these values were higher than other months. Average concentrations of TOC in April, July and October were 4.8, 7.4 and $4.8mg\;L^{-1}$, respectively. This suggested that the highest value of TOC was in July. Average decomposition efficiencies for BOD-C/TOC and COD-C/TOC were 11.4 and 23.9%, respectively. Correlation coefficient of TOC and DOC was higher by 0.995, whereas that of TOC and COD was lower by 0.763. Especially, TOC and DOC were highly related than others.

• Journal of Wetlands Research
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• v.14 no.4
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• pp.503-518
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• 2012

Models estimating carbon budget at land surface are mainly interested in vertical flux of carbon. On the other hand, studies on horizontal flux are obviously lacked to confirm that relationship between the hydrological flux of organic carbon discharged from catchment and terrestrial carbon production, a relation between Total Organic Carbon(TOC) and Gross Primary Production(GPP) tried analysis through cross correlation. The best correlation structure is correlation between GPP and TOC of flow-weighted mean concentration from watershed without delay. Furthermore, cross correlation analysis was performed by consider periodicity. The correlation between TOC and GPP in summer was similar to correlation without periodicity. Therefore, correlation between GPP and TOC was most regulated by the correlation between GPP and TOC at summer. As a result, the vegetation carbon and organic carbon from watershed is recognized a close relationship on the seasonal. Therefore, future research is correlation analyzing between vegetation variables according season, GPP and TOC, we are expected to use quantitative understanding that horizontal flux flow of carbon from the surface.

• Journal of Korean Society of Water and Wastewater
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• v.33 no.2
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• pp.141-150
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• 2019

Total organic carbon(TOC) was introduced as the water quality index of the rivers and lakes in 2013. This paper evaluated factors affecting effluent TOC concentrations and treated and discharged loads of existing publicly owned treatment works(POTWs). For selected POTWs with greater treatment capacity than $500m^3/day$, factorial analysis was used to consider effects of kinds of biological treatment processes, inflow of other types of wastewater(industrial, livestock, landfill leachate wastewater, etc.) with domestic wastewater, sewer separation rate, and effluent discharging zones in which different effluent criteria applied. As a result, those factors did not show significant effect on effluent TOC concentration of POTWs in effluent discharging zone I and II. However, In effluent discharging zone III and IV, kinds of biological treatment processes, the inclusion of other waste in influent of domestic wastewater, and the sewer separation rate were significant factors. The treated TOC load in POTWs was also not affected significantly by the variables set in this study. On the other hand, those three factors influenced significantly on the TOC load discharged to water bodies. The sum of factorial effects and the contribution rate of three factors to the discharged TOC load was 60.23 and 41%, 59.57 and 41%, and 42.04 and 18%, respectively.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.195-195
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• 2023

유기물오염은 호수와 하천에서 가장 흔히 나타나는 오염의 형태이다. 개발도상국에서는 하수처리시설이나 생활용수처리시설에서 유기물이 처리되지 않아 수중의 유기물함량이 증가하여 피해가 나타난다. 물환경보전법에 따라 유기물질을 측정하는 지표에는 BOD, COD, TOC가 있다. 그 중 BOD, COD 측정은 절차가 복잡하고 오래 걸리며, 생물분해가 불가능하거나 유독물질이 존재하는 유기물질인 경우에는 낮은 결과 또는 측정이 불가능하다. 이러한 이유로 환경부에서는 TOC중심관리로 전환되고 있다. 대부분의 수질항목 측정의 경우 현장시료를 채취하여 실험실에서 분석하는 수분석을 이용한다. 하지만 수분석의 경우 인력이 많이 투입되고 분석수가 한정적이며, 정교한 과학 장치를 갖춘 전문 실험실을 필요로 한다. 반면에 TOC는 짧은 시간 내에 측정이 가능하고, 안정적인 산화로 유기물질 총량을 정확하게 측정이 가능하다. 또한 위해성 물질을 발생시키지 않는 친환경적 방법이라 환경오염 감시에 많이 활용된다. 본 연구에서 분광측정기 중 하나인 S-CAN을 통해 하천의 TOC분석을 진행하였다. 대상지는 안동에 위치한 하천실험센터와 금호강 진천천으로 S-CAN측정과 채취한 시료의 수분석 결과를 비교하였다. 이를 통해 TOC측정 및 기기에 대한 정확도분석을 실시하였다. 본 연구를 통해 S-CAN을 활용한 TOC중심의 수질모니터링하여 수질의 특성을 탐구하고, TOC기초연구를 통한 유기물오염도 지표로서 해석할 수 있다. 수분석 자료를 활용한 정확도 비교를 통해 하천 TOC센서 측정 적용에 대한 기초자료 제공과 측정기기의 현장적용성을 판단 할 수 있다. TOC측정 및 분석을 통해 유기물질량을 실시간으로 정확하게 측정하게 되면 수질관리 효율성이 높아지고, 하천에 유입되는 유기물질량과 농도를 정확하게 파악하여 실효성 있는 정책 수립 및 효율적인 수질관리를 할 수 있다.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.26 no.4
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• pp.207-216
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• 2014

Total organic carbon (TOC) is an important indicator used as an direct biological index in the research field of the marine carbon cycle. It is possible to produce the sufficient TOC estimation data by using the Chemical Oxygen Demand(COD) data because the available TOC data is relatively poor than the COD data. The outlier detection and treatment (removal) should be carried out reasonably and objectively because the equation for a COD-TOC conversion is directly affected the TOC estimation. In this study, it aims to suggest the optimal regression model using the available salinity, COD, and TOC data observed in the Korean coastal zone. The optimal regression model is selected by the comparison and analysis on the changes of data numbers before and after removal, variation coefficients and root mean square (RMS) error of the diverse detection methods of the outlier and influential observations. According to research result, it is shown that a diagnostic case combining SIQR (Semi - Inter-Quartile Range) boxplot and Cook's distance method is most suitable for the outlier detection. The optimal regression function is estimated as the TOC(mg/L) = $0.44{\cdot}COD(mg/L)+1.53$, then determination coefficient is showed a value of 0.47 and RMS error is 0.85 mg/L. The RMS error and the variation coefficients of the leverage values are greatly reduced to the 31% and 80% of the value before the outlier removal condition. The method suggested in this study can provide more appropriate regression curve because the excessive impacts of the outlier frequently included in the COD and TOC monitoring data is removed.

• Journal of Environmental Science International
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• v.27 no.4
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• pp.261-266
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• 2018

In this study, the organic matter of effluents from sewage treatment plants, located in the Nakdong watershed was investigated. Regression equations were computed using treated sewage data to convert the chemical oxygen demand(COD) concentrations, which are mostly available from an open database, into total organic carbon(TOC) concentrations. The average concentration of organic matter in the sewage treatment plant effluents were 2.2~16.8 mg/L for COD and 3.4~14.3 mg/L for TOC. The concentrations of COD were positively correlated with the TOC concentrations. The correlation between COD and TOC was relatively high, at 0.865(p<0.01). Based on these results, regression analysis was conducted. The regression equation for TOC was $1.651{\times}COD_{Mn}-0.084$ ($R^2=0.84$). Furthermore, organic matter-related databases for more sewage treatment plants need to be built in order to establish TOC standards and manage the water quality.

• Environmental Engineering Research
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• v.10 no.3
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• pp.105-111
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• 2005

The applicability of the USEPA's (United States Environmental Protection Agency) three criteria of enhanced coagulation (criterion 1-TOC level less than 2 mg/l. before chlorination; criterion II-% requirement of TOC removal; criterion III-point of diminishing return) for Korean waters was evaluated in this study. This study also investigated an effect of enhanced coagulation on turbidity removal, and attempted to identify the best coagulant for enhanced coagulation. Three different waters were used in this study: one river water and two lake waters. five different coagulants were used: alum, liquid alum, PACl, ferric chloride with and without water. Results of this study showed that all three criteria were achievable for the tested waters. For these waters, controlling criterion was found to be different depending upon raw water characteristics. When initial Toe level was low(< 4 mg/L), criterion I (< 2 mg/L) could be the controlling criterion. As TOC level increased, criterion II (% TOC removal) became the controlling criteria. It was possible to achieve different goals of turbidity and TOC removals. Although the optimum region of TOC removal was more acidic than that of turbidity removal, there was no conflict between these two removals. The best coagulant was found to be different depending upon the evaluation tool: maximum and optimum removal. ferric chloride was more effective than alum in terms of the maximum TOC removal, while Al-based coagulant such as alum or PACl was the best coagulant in terms of the optimum TOC removal.

• Journal of Korea Water Resources Association
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• v.56 no.11
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• pp.815-822
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• 2023

Organic pollution is one of the most common forms of water contamination. Under the Water Quality Conservation Act, indicators for measuring organic substances include BOD, COD, and TOC. Analysis of BOD and COD is labor-intensive, and in the case of organic substances where biological decomposition is not feasible or toxic substances are present, the accuracy is often low. Therefore, the Ministry of Environment is shifting towards TOC-centric management. With advancements in sensor technology today, various parameters can be monitored using sensors. In this study, digital monitoring of river TOC using a spectrophotometer called Spectro::lyser V3 was conducted. Initially, experiments were carried out at the Andong River Experiment Center to assess the applicability of the measurement equipment. Subsequently, data collected at the confluence of the Nakdong River was analyzed for the spatial distribution of TOC using the Kriging technique. This research proposes the utilization of sensors for river TOC monitoring and spatial distribution analysis. Real-time monitoring of changes in river TOC concentration can serve as fundamental data for pollution monitoring and response. Sensor-based river monitoring offers advantages in terms of temporal resolution and real-time data acquisition. When various spatial information interpretation methods are applied, it is expected to contribute to diverse studies such as aquatic ecological health, river water source selection, and stratification analysis in the future.

• Journal of Environmental Science International
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• v.24 no.6
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• pp.743-753
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• 2015

In this study, it is an object to develop a regression model for the estimation of TOC (total organic carbon) concentration using investigated data for three years from 2010 to 2012 in the Gam Stream unit watershed, and applied in 2009 to verify the applicability of the regression model. TOC and $COD_{Mn}$ (chemical oxygen demand) were appeared to be derived the highest correlation. TOC was significantly correlated with 5 variables including BOD (biological oxygen demand), discharge, SS (suspended solids), Chl-a (chlorophyll a) and TP (total phosphorus) of p<0.01. As a result of PCA (principal component analysis) and FA (factor analysis), COD, TOC, SS, discharge, BOD and TP have been classified as a first factor. TOCe concentration was estimated using the model developed as an independent variable $BOD_5$ and $COD_{Mn}$. R squared value between TOC and measurement TOC is 0.745 and 0.822, respectively. The independent variable were added step by step while removing lower importance variable. Based on the developed optimal model, R squared value between measurement value and estimation value for TOC was 0.852. It was found that multiple independent variables might be a better the estimation of TOC concentration using the regression model equation(in a given sites).