• Journal of Wetlands Research
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• v.11 no.3
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• pp.145-151
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• 2009

Recently the water quality management policy has been changed from managing the point source to controlling the nonpoint sources (NPSs) because of TMDL program. Most NPSs are accumulated on the surface during dry periods. These accumulated pollutants are washed-off during a storm event and highly impairing the water quality of the receiving water bodies. Usually NPS has high uncertainty and is hard to control because of the variability of the rainfall and watershed characteristics. Also, NPS is derived from various land uses. The Ministry of Environment (MOE) is studying and monitoring the pollutant loads from each land use since 2007 to determine the unit pollutant loads. This research was a part of long-term monitoring program conducted to characterize the washoff and provide the mean EMC of artificial grassland. The average EMCs result of BOD, COD, DOC, SS, TN, NH4-N, NO3-N, TP, and PO4-P of the artificial grassland were deterined to 8.2, 17.5, 11.3, 110.1, 3.07, 0.20, 0.75, 0.86 and 0.08 mg/L, respectively. The results of statistical analysis conducted showed a low correlation to the contaminants.

• The Journal of Engineering Geology
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• v.20 no.4
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• pp.401-407
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• 2010

This study was carried out to produce the characteristics of pollutant loads caused by a cherry tree plot as a nonpoint sources(NPS) unit in agricultural areas. The relationship between rainfall and runoff didn't show a good coefficient with 0.5. Despite precipitation amount was less than 20 mm, runoff occurred with $0.5\;m^3$ because of high rainfall intensity of 8.8 mm/hr. In contrast, runoff was not occurred when precipitation amount was 47.4 mm in one case. In that case the primal effect on runoff was not precipitation amount. Correlation between load of pollutants such as BOD, COD, TN and TP and runoff' volumes showed significantly positive values which were more than r = 0.92 for all pollutants except SS(r = 0.71). SS could be a proper factor for estimating pollutant loads of BOD, COD, TN and TP because of a high correlation more than r = 0.73 between SS load and pollutant loads of BOD, COD, TN and TP. Both Organics and nutrient pollutants could be reduced if we control SS in runoff. The highest concentration of TN was detected in the event which was affected by fertilization activities directly. Therefore fertilization must be considered as a function of impact parameters on TN load in agricultural areas.

• The Journal of Engineering Geology
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• v.19 no.3
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• pp.365-371
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• 2009

This paper address the characteristics of loading pollutants caused by the unit agricultural area to establish an efficient management method in NPS (non-point source). The relationship between rainfall and runoff shows good coefficient with 0.92, when the event which shows relatively long antecedent dry days is excepted. The impact of runoff volume on the runoff coefficient can be described by the rainfall intensity strongly. The pollutant EMCs (event mean concentrations) in runoff increased by the increase of antecedent dry days due to dry soil conditions. As the similar pattern of pollutant's loads such as TSS, BOD, COD, TN and TP, it is cleared that other pollutants can be removed when TSS is removed. Therefore the system using only runoff coefficients is not sufficient for the prediction of pollutant loads. It is necessary to consider soil conditions such as rainfall, antecedent dry day, antecedent rainfall etc. for the prediction system.

• Journal of Environmental Science International
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• v.23 no.3
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• pp.511-520
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• 2014

The MFFn(Mass first flush), EMCs(Event mean concentrations) and runoff loads were analyzed for various rainy events(monitoring data from 2011 to 2012) in transportation area(rail road in station). The pollutant EMCs by volume of stormwater runoff showed the BOD5 9.6 mg/L, COD 29.9 mg/L, SS 16.7 mg/L, T-N 3.271 mg/L, T-P 0.269 mg/L in the transportation areas(Railroad in station). The average pollutant loading by unit area of stormwater runoff showed the BOD5 $27.26kg/km^2$, COD $92.55kg/km^2$, SS $50.35kg/km^2$, T-N $10.13kg/km^2$ and T-P $10.13kg/km^2$ in the transportation areas. Estimated NCL-curve(Normalized cumulated-curve) was evaluated by comparison with observed MFFn. MFFn was estimated by varying n-value from 10% to 90% on the rainy events. The n-value increases, MFFn is closed to '1'. As time passed, the rainfall runoff was getting similar to ratio of pollutants accumulation. The result of a measure of the strength of the linear relationship between observed data and expected data under model was good.

• Journal of Korean Society of Rural Planning
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• v.24 no.4
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• pp.99-119
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• 2018

Unit load factor, which is used for the quantification of non-point pollution in watersheds, has the limitation that it does not reflect spatial characteristics of soil, topography and temporal change due to the interannual or seasonal variability of precipitation. Therefore, we developed the method to estimate a watershed-scale non-point pollutant load using seasonal forecast data that forecast changes of precipitation up to 6 months from present time for watershed-scale water quality management. To establish a preemptive countermeasure against non-point pollution sources, it is possible to consider the unstructured management plan which is possible over several months timescale. Notably, it is possible to apply various management methods such as control of sowing and irrigation timing, control of irrigation through water management, and control of fertilizer through fertilization management. In this study, APEX-Paddy model, which can consider the farming method in field scale, was applied to evaluate the applicability of seasonal forecast data. It was confirmed that the rainfall amount during the growing season is an essential factor in the non-point pollution pollutant load. The APEX-Paddy model for quantifying non-point pollution according to various farming methods in paddy fields simulated similarly the annual variation tendency of TN and TP pollutant loads in rice paddies but showed a tendency to underestimate load quantitatively.

• Journal of Korean Society on Water Environment
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• v.27 no.6
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• pp.784-793
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• 2011

Activities on golf courses are believed to contribute to the degradation of water quality in receiving waters due to the excessive use of farm chemicals including fertilizers and pesticides. The objective of this study was to collect basic data that could explain the characteristics of non-point source (NPS) pollution discharged from a golf course. Twenty seven water quality monitoring was conducted at a golf course during the rainy season of 2008 and 2009. The results indicated that the ranges of the Event Mean Concentration (EMC) at the golf course were $BOD_5$ 1.8~11.3 (ave. 5.6) mg/L, $COD_{Mn}$ 19.2~51.4 (ave. 39.6) mg/L, TOC 11.0~31.0 (ave. 16.8) mg/L, TN 1.545~16.098 (ave. 5.623) mg/L, TP 0.230~4.528 (ave. 1.525) mg/L, and SS 2.2~57.3 (ave. 10.1) mg/L. The unit loads of the golf course estimated were $BOD_5$ $3.35kg/km^2/day$, SS $6.43kg/km^2/day$, $COD_{Mn}$ $30.00kg/km^2/day$, TN $4.04kg/km^2/day$, TP $1.14kg/km^2/day$, and TOC $12.16kg/km^2/day$. Golf courses are currently classified as a grass field in which the unit loads are different from golf courses. Therefore, it was recommended that golf courses need to be separated from the grass field when the surveys and modelings for Total Maximum Daily Load (TMDL) development and the evaluation of TMDL implementation were performed.

• Journal of Korea Water Resources Association
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• v.40 no.8
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• pp.585-600
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• 2007

The hydrologic cycle and BOD pollutant loads of all sub-watersheds were analyzed using HSPF (Hydrological Simulation Program-Fortran). At first, sensitivity analyses to water quantity (peak discharge and total volume) and quality (BOD peak concentrations and total loads) were conducted and some critical Parameters were selected. For more precise simulation, the study watershed was divided into four parts according to the landuse characteristics and used climate data and so calibrated and verified respectively. It was found that as the urban area ratio increases in the downstream direction, baseflow decreases (11.1 % $\rightarrow$ 5.0%) and the ratio of direct runoff volume(42.5 % $\rightarrow$ 56.9 %), BOD concentration (3.3 mg/L $\rightarrow$ 15.0 mg/L) and unit loads (55.4 kg/ha/year $\rightarrow$ 354.5 kg/ha/year) increase.

• Journal of the Korean Society of Hazard Mitigation
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• v.8 no.6
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• pp.155-160
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• 2008

The urban areas have various landuses such as residential, commercial, industrial and official purposes that are highly concerned with human activities. The other landuses are relating to vehicle activities, which are roads, parking lots, bridges, parks etc. The mainly using landuses by human activities are possessing three different areas that are buildings, parking lots/roads and landscapes. Of these areas, the buildings and landscapes can be classified as non-pollution areas. However, the parking lots or roads are classifying as the main pollution areas because of vehicle activities. Therefore, the landuses arising the nonpoint pollution during a storm in urban areas are roads and parking lots. The vehicles are emitting lots of nonpoint pollutants such as metals and particulate matters and it is impacting on water qualities and aqua-ecosystems nearby the city areas. Therefore, this research was conducted for characterizing the pollutant types and determining the EMCs (Event Mean Concentrations) and unit pollutant loads during a storm. The monitoring was performed on 9 locations such as highways, service area, tollgates, parking lot and bridges. All of the landuses selected for monitoring are concerned with transportation. The results can be effectively used to predict the pollutant loading before urban planning and to select the BMPs (Best Management Practices) for reducing the pollution.

• Journal of Wetlands Research
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• v.11 no.1
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• pp.75-82
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• 2009

The regulation of emission concentration for stream water qualities doesn't control quantitative increase on pollution loads, it has limits for improvement of water qualities. Total water pollution load management system(TMDL) can control the total amount of pollutant in waste water which is allowed to assign and control the total discharged pollutant loads in a permissible level. When it comes to generated wastewater value of TMDL system, there is difference between calculated value based on individual pollutant unit load and observed value. Calculated sewer inflow, calculated sewer outflow, measured sewer inflow, and measured sewer outflow at dry season are $26,460.9m^3$/d, $17,778.6m^3$/d, $17,106.1m^3$/d and $19,033.9m^3$/d respectively, Calculated sewer inflow, calculated sewer outflow, measured sewer inflow, and measured sewer outflow at rainy season are $49,512.2m^3$/d, $18,628.7m^3$/d, $30,918.2m^3$/d, $19,700.7m^3$/d respectively. This result presents the necessity to acquire the precise observed data to fulfill the efficient TMDL system.

• Journal of Korean Society on Water Environment
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• v.38 no.6
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• pp.306-315
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• 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.