• Korean Journal of Ecology and Environment
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• v.34 no.4 s.96
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• pp.267-276
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• 2001

The downstream reach of the Han River adjoining Seoul in Korea was the upper boundary of an estuary where tidal effect on the flow rate could be exerted. According to the comprehensive river regulation project, the river was channelize dand impounded by two overflow dams, which provided favorable condition for algal growth in this sewage polluted eutrophic reach. In this study primary productivity of phytoplankton was measured in the down reach and the autochthonous and allochthonous organic carbon loadings were estimated. Primary production of phytoplankton measured by C-14 uptake and P-I model method ranged from 140 to $4,890\;mgC\;m^{-2}\;d^{-1}$ (median value $1,865\;mgC\;m^{-2}\;d^{-1}$) showing the level of eutrophic lakes. Phytoplankton density that varied according to water flow rate was highest in spring. Allochthonous organic carbon loading was dominated by sewage input through tributaries in most of days except flood flow period. The average proportion of autochthonous carbon generation by phytoplankton was 40.9%, which is very high proportion for a lotic habitat.

• Economic and Environmental Geology
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• v.38 no.1
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• pp.67-78
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• 2005

Riverbank filtration has been used in advanced countries for 150 years. In Korea, investigations for producing riverbank filtrate started in the Han River, Nakdong River, Geum River, Yeongsan River and Seomjin River basins in the 1990s. The lower part of the Nakdong River has a poorer water quality than the upper part of the river. A water balance analysis and groundwater flow modeling were conducted for the riverbanks of the Nakdong River in Daesan-Myeon, Changwon City. The results of the water balance analysis revealed the groundwater infiltration rate into the aquifer to be 245.26 mm/year (19.68% of the average annual precipitation, 1,251.32 mm). Direct runoff accounts for 153.49 mm/year, evapotranspiration is 723.95 mm/year and baseflow is 127.63 mm/year. According to the groundwater flow modeling, 65% of the total inflow to the pumping wells originates from the Nakdong River, 13% originates from the aquifer in the rectilinear direction, and 22% originates from the aquifer in the parallel direction. The particle tracking model shows that a particle moving from the river toward the pumping wells travels 100 m in 50 days and a particle from the aquifer toward the pumping wells travels 100 m in 100 days.

• Journal of Korean Society of Disaster and Security
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• v.12 no.2
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• pp.25-34
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• 2019

The efficiency of sewage treatment was analyzed by statistical method based on the water quality and operation data of the sewage treatment plant applying the oxidation method. The obtained water quality data were pH, temperature, BOD, SS, T-N, ${NH_4}^+-N$, and T-P of influent and discharge water. Data analysis was performed by correlation analysis, ANOVA analysis, and cluster analysis. As a result of the statistical analysis, the influent flow rate in the sewage treatment plant was the highest in summer. The average inflow flow rate was $3.000m^3/s$. According to Box plot results, COD, and T-P concentrations of effluents were not significantly different from season to season. The Pearson correlation analysis showed strong positive correlation between BOD, COD, T-N, and T-P in influent flow. Seasonal BOD and T-N concentrations were highest in winter and COD and T-P in seasonal influences. BOD showed a strong negative correlation with the water temperature, but showed a positive correlation with other operating factors such as HRT, SRT and C/N. The higher the influent temperature, the lower the BOD concentration. Therefore, retention time was shortened and BOD treatment efficiency was lowered. It was found that T-N had a higher retention time and a higher concentration than DO concentration. On the other hand, T-P did not show a significant correlation with operating factors.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.12
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• pp.192-199
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• 2020

The blower erosion phenomenon was investigated to develop a long-life blower with a volume flow rate of 10,000 ㎥/min with the required total pressure efficiency of 83% or more. The blower performance and blower erosion were predicted through numerical analysis by computational fluid dynamics(CFD). The conditions used for numerical analysis were an air volume of 16,200 ㎥/min, a rotation speed of 893 rpm, and a temperature of 330℃. The specific gravity, particle size, and amount of the dust was 3.15, 90 ㎛~212 ㎛, and is 265 kg/min, respectively. To examine the effects of a dust deflector on erosion, erosion analysis was performed by comparing the models with and without a dust deflector. Numerical analysis showed that when the dust deflector is installed, the average tended to decrease by 167% in the impeller and 133% in the boss. CFD using the Finne's model for erosion revealed a parallel restitution coefficient of 1 and a perpendicular restitution coefficient of 0.1. The blower performance of case 5 was 691.7 mmAq, and the efficiency was 83.3% when the rotation speed and the air volume flow rate were 880 rpm and 16,200 ㎥/min, respectively.

• Journal of The Korean Society of Agricultural Engineers
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• v.59 no.6
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• pp.61-71
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• 2017

The tracer gas method has an advantage that can estimate total and local ventilation rate by tracing air flow. However, the field measurement using tracer gas has disadvantages such as danger, inefficiency, and high cost. Therefore, the aim of this study was to evaluate ventilation rate in pig house by using the thermal distribution data rather than tracer gas. Especially, LMA (Local Mean Age), which is an index based on the age of air theory, was used to evaluate the ventilation rate in pig house. Firstly, the field experiment was conducted to measure micro-climate inside pig house, such as the air temperature, $CO_2$ concentration and wind velocity. And then, LMA was calculated based on the decay of $CO_2$ concentration and air temperature, respectively. This study compared between LMA determined by $CO_2$ concentration and air temperature; the average error and root mean square error were 3.76 s and 5.34 s. From these results, it was determined that thermal distribution data could be used for estimation of LMA. Finally, CFD (Computational fluid dynamic) model was validated using LMA and wind velocity. The mesh size was designed to be 0.1 m based on the grid independence test, and the Standard $k-{\omega}$ model was eventually chosen as the proper turbulence model. The developed CFD model was highly appropriate for evaluating the ventilation rate in pig house.

• Journal of the Korean Association of Geographic Information Studies
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• v.9 no.4
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• pp.81-94
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• 2006

The water quality impacts of forest fragmentation at the Doam-dam watershed were evaluated in this study. For this ends, the watershed scale model, Soil and Water Assessment Tool (SWAT) model was utilized. To exclude the effects of different magnitude and patterns in weather, the same weather data of 1985 was used because of significant differences in precipitation in year 1985 and 2000. The water quality impacts of forest fragmentation were analyzed temporarily and spatially because of its nature. The flow rates for Winter and Spring has increased with forest fragmentations by $8,366m^3/month$ and $72,763m^3/month$ in the S1 subwatershed, experiencing the most forest fragmentation within the Doam-dam watershed. For Summer and Fall, the flow rate has increased by $149,901m^3/month$ and $107,109m^3/month$, respectively. It is believed that increased flow rates contributed significant amounts of soil erosion and diffused nonpoint source pollutants into the receiving water bodies. With the forest fragmentation in the S1 watershed, the average sediment concentration values for Winter and Spring increased by 5.448mg/L and 13.354mg/L, respectively. It is believed that the agricultural area, which were forest before the forest fragmentation, are responsible for increased soil erosion and sediment yield during the spring thaw and snow melts. For Spring and Fall, the sediment concentration values increased by 20.680mg/L and 24.680mg/L, respectively. Compared with Winter and Spring, the increased precipitation during Summer and Fall contributed more soil erosion and increased sediment concentration value in the stream. Based on the results obtained from the analysis performed in this study, the stream flow and sediment concentration values has increased with forest fragmentation within the S1 subwatershed. These increased flow and soil erosion could contribute the eutrophication in the receiving water bodies. This results show that natural functionalities of the forest, such as flood control, soil erosion protection, and water quality improvement, can be easily lost with on-going forest fragmentation within the watershed. Thus, the minimize the negative impacts of forest fragmentation, comprehensive land use planning at watershed scale needs to be developed and implemented based on the results obtained in this research.

• Journal of Korea Water Resources Association
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• v.46 no.9
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• pp.921-932
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• 2013

Runoff data availability is a substantial factor for precise flood control such as flood frequency or flood forecasting. However, runoff depths and/or peak discharges for small watersheds are rarely measured which are necessary components for hydrological analysis. To compensate for this discrepancy, a lumped concept such as a Storage Function Method (SFM) was applied for the partitioned Choongju Dam Watershed in Korea. This area was divided into 22 small watersheds for measuring the capability of spatial extension of runoff data. The chosen total number of flood events for searching parameters of SFM was 21 from 1991 to 2009. The parameters for 22 small watersheds consist of physical property based (storage coefficient: k, storage exponent: p, lag time: $T_l$) and flood event based parameters (primary runoff ratio: $f_1$, saturated rainfall: $R_{sa}$). Saturated rainfall and base flow from event based parameters were explored with respect to inflow at Choongju Dam while other parameters for each small watershed were fixed. When inflow of Choongju Dam was optimized, Youngchoon and Panwoon stations obtained average of Nash-Sutcliffe Efficiency (NSE) were 0.67 and 0.52, respectively, which are in the satisfaction condition (NSE > 0.5) for model evaluation. This result is showing the possibility of spatial data extension using a lumped concept model.

• Korean Chemical Engineering Research
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• v.45 no.3
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• pp.249-257
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• 2007

The periodic square wave (PSW) model was successfully applied to the optimal design of a batch-storage network. The network structure can cover any type of batch production, distribution and inventory system, including recycle streams. Here we extend the coverage of the PSW model to multitasking semi-continuous processes as well as pure continuous and batch processes. In previous solutions obtained using the PSW model, the feedstock composition and product yield were treated as known constants. This constraint is relaxed in the present work, which treats the feedstock composition and product yield as free variables to be optimized. This modification makes it possible to deal with the pooling problem commonly encountered in oil refinery processes. Despite the greater complexity that arises when the feedstock composition and product yield are free variables, the PSW model still gives analytic lot sizing equations. The ability of the proposed method to determine the optimal plant design is demonstrated through the example of a high density polyethylene (HDPE) plant. Based on the analytical optimality results, we propose a practical process optimality measure that can be used for any kind of process. This measure facilitates direct comparison of the performance of multiple processes, and hence is a useful tool for diagnosing the status of process systems. The result that the cost of a process is proportional to the square root of average flow rate is similar to the well-known six-tenths factor rule in plant design.

• Journal of Ecology and Environment
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• v.45 no.4
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• pp.277-286
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• 2021

Background: As trade increases, the influx of various alien species and their spread to new regions are prevalent and no longer a special problem. Anthropogenic activities and climate changes have made the distribution of alien species out of their native range common. As a result, alien species can be easily found anywhere, and they have nothing but only a few differences in intensity. The prevalent distribution of alien species adversely affects the ecosystem, and a strategic management plan must be established to control them effectively. To this end, hot spots and cold spots were analyzed according to the degree of distribution of invasive alien plants, and major environmental factors related to hot spots were found. We analyzed the 10,287 distribution points of 126 species of alien plants collected through the national survey of alien species by the hierarchical model of species communities (HMSC) framework. Results: The explanatory and fourfold cross-validation predictive power of the model were 0.91 and 0.75 as AUC values, respectively. The hot spots of invasive plants were found in the Seoul metropolitan area, Daegu metropolitan city, Chungcheongbuk-do Province, southwest shore, and Jeju island. Generally, the hot spots were found where the higher maximum temperature of summer, precipitation of winter, and road density are observed, but temperature seasonality, annual temperature range, precipitation of the summer, and distance to river and sea were negatively related to the hot spots. According to the model, the functional traits accounted for 55% of the variance explained by the environmental factors. The species with higher specific leaf areas were more found where temperature seasonality was low. Taller species preferred the bigger annual temperature range. The heavier seed mass was only preferred when the max temperature of summer exceeded 29 ℃. Conclusions: In this study, hot spots were places where 2.1 times more alien plants were distributed on average than non-hot spots (33.5 vs 15.7 species). The hot spots of invasive plants were expected to appear in less stressful climate conditions, such as low fluctuation of temperature and precipitation. Also, the disturbance by anthropogenic factors or water flow had positive influences on the hot spots. These results were consistent with the previous reports about the ruderal or competitive strategies of invasive plants instead of the stress-tolerant strategy. The functional traits are closely related to the ecological strategies of plants by shaping the response of species to various environmental filters, and our result confirmed this. Therefore, in order to effectively control alien plants, it is judged that the occurrence of disturbed sites in which alien plants can grow in large quantities is minimized, and the river management of waterfronts is required.

• Journal of Korea Water Resources Association
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• v.56 no.7
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• pp.439-449
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• 2023

Recent advancements in data measuring technology have facilitated the installation of various sensors, such as pressure meters and flow meters, to effectively assess the real-time conditions of water distribution systems (WDSs). However, as cities expand extensively, the factors that impact the reliability of measurements have become increasingly diverse. In particular, demand data, one of the most significant hydraulic variable in WDS, is challenging to be measured directly and is prone to missing values, making the development of accurate data generation models more important. Therefore, this paper proposes an adversarially trained autoencoder (ATAE) model based on generative deep learning techniques to accurately estimate demand data in WDSs. The proposed model utilizes two neural networks: a generative network and a discriminative network. The generative network generates demand data using the information provided from the measured pressure data, while the discriminative network evaluates the generated demand outputs and provides feedback to the generator to learn the distinctive features of the data. To validate its performance, the ATAE model is applied to a real distribution system in Austin, Texas, USA. The study analyzes the impact of data uncertainty by calculating the accuracy of ATAE's prediction results for varying levels of uncertainty in the demand and the pressure time series data. Additionally, the model's performance is evaluated by comparing the results for different data collection periods (low, average, and high demand hours) to assess its ability to generate demand data based on water consumption levels.