• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.465-465
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• 2022

하천 미세플라스틱 측량을 위해 많은 연구자들이 노력하고 있으나 모든 하천에서의 미세플라스틱 실측은 현실적으로 어렵다. 미세플라스틱의 물 환경에 대한 영향을 이해하고 예측하기 위해서는 오염원과 오염원 운송 매개체로서 물 환경에 존재하는 플라스틱의 물리적 거동을 아는 것이 매우 중요하다. 따라서 미세플라스틱의 하천 내 물리적 거동을 과학적으로 규명하고 하천 미세플라스틱오염을 예측가능한 물리모형의 도출이 요구된다. 본 연구에서는 물리기반 하천수질모형인 Water Quality Analysis Simulation Program(WASP8)의 Advanced Eutrophication Module을 이용하여 상용 수질 물리모형의 하천에서의 미세플라스틱 거동 모의 가능성을 검토하였다. 이를 위해 미세플라스틱과 유사한 거동을 보이는 수질지표를 대리인자로 하여 기존에 알려져 있는 물리모형(WASP8의 Advanced Eutrophication Module)을 이용해 미세플라스틱의 하천 내 거동을 설명하고 예측 가능한 모델을 개발하고자 한다. 본 연구결과로부터 하천 미세플라스틱 오염 분석 및 예측의 기초자료를 마련한다.

• Clean Technology
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• v.19 no.3
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• pp.295-299
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• 2013

This study was purposed and performed to evaluate removal efficiency of non-point source pollution in the process and system based on bio-retention design criteria regulated by EPA. Basic Column Reactors (BCR) were prepared for optimal determinations of inflow rate of first rainfall runoff and composition and ratio of soil layers. Removal efficiencies of non-point source pollution from synthetic runoff and real first rainfall runoff, directly sampled from motor way and parking lot, were analyzed, respectively. Removal efficiency of SS, BOD, COD, T-N, and T-P were all shown to be more than 80%.

• Spatial Information Research
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• v.14 no.4 s.39
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• pp.363-377
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• 2006

Mean annual soil loss was calculated and critical soil erosion areas were identified for the Congaree River Basin in South Carolina, USA using the Revised Universal Soil Loss Equation (RUSLE) model. In the RUSLE model, the mean annual soil loss (A) can be calculated by multiplying rainfall-runoff erosivity (R), soil erodibility (K), slope length and steepness (LS), crop-management (C), and support practice (P) factors. The critical soil erosion areas can be identified as the areas with soil loss amounts (A) greater than the soil loss tolerance (T) factor More than 10% of the total area was identified as a critical soil erosion area. Among seven subwatersheds within the Congaree River Basin, the urban areas of the Congaree Creek and the Gills Creek subwatersheds as well as the agricultural area of the Cedar Creek subwatershed appeared to be exposed to the risk of severe soil loss. As a prototype model for examining future effect of human and/or nature-induced changes on soil erosion, the RUSLE model customized for the area was embedded into ESRI ArcGIS ArcMap 9.0 using Visual Basic for Applications. Using the embedded model, users can modify C, LS, and P-factor values for each subwatershed by changing conditions such as land cover, canopy type, ground cover type, slope, type of agriculture, and agricultural practice types. The result mean annual soil loss and critical soil erosion areas can be compared to the ones with existing conditions and used for further soil loss management for the area.

• Journal of Wetlands Research
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• v.17 no.3
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• pp.303-310
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• 2015

Recently the water quality management policy gives priority to management the point source. Point pollution sources have definite emission points and are discharged to one point through a pipe. But Nonpoint pollution source (NPS) has uncertain pathway, pollutant load and runoff characteristics unlike point pollution sources, making them difficult to manage. Thus, the Korea government plans to develop and equip facilities that help reduce NPS so as to manage them more easily. But removal efficiency of Best Management Practice (BMPs) is in influenced by rainfall, hydrologic condition like natural phenomenon, so factors of removal efficiency are difficult. Thus there is a need for multilateral research about many factors that affect removal efficiency for removal facility design of proper non-point pollution. In this research, mapping, vegetation coverage and retention time were investigated in the case of factors that affect removal efficiency in grassed swale, a nature-type non-point removal facility. Grassed swale obtained changed of coverage using Braun-Blanquet within swale and retention time was obtained from point that rainfall effluent enters into swale to the time that first outflow starts. Besides, correlation analysis was obtained using pearson correlation analysis method. As a result, it was shown that removal efficiency increases as retention time is longer in grassed swale and that retention time increases as vegetation coverage is higher.

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2000.11a
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• pp.157-159
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• 2000

일반적으로 광화학반응에 의해 생성되는 오존은 전구물질(precursor)의 발생지역 뿐만아니라 바람에 따라 수송(transport)되어 오염원이 없는 외곽지역에 영향을 주기도 하는데, 지역적 규모(regional scale)이상의 수평적인 오존 수송을 나타낼 수 있다. 따라서 오존오염은 국지적 현상과 수송에 의한 복합적인 오염현상으로 나타나므로 지리적으로 인접한 수도권지역은 오존 및 전구물질의 유ㆍ출입이 발생하여 서풍일 경우 인천, 서울, 구리, 춘천까지 시간대별로 고농도의 오존이 관측되기도 한다. (중략)

• Proceedings of the Korea Society of Environmental Toocicology Conference
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• 2002.10a
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• pp.160-160
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• 2002

호소 수질에 영향을 미치는 대표적 인자로 외부 유입물질과 퇴적물을 들 수 있다. 따라서, 호소 수질 관리를 위해서는 호소로 유입되는 점ㆍ비점 오염원 관리와 함께, 퇴적층 관리도 수반되어야만 한다. 특히, 호소수가 주요 식수원으로 사용되는 경우에는 엄격한 퇴적토질 평가가 이루어져야 할 것이다. 본 연구에서는 소양호 퇴적층에 대한 환경지질학적 조사 연구의 일환으로 다양한 bioassays 기법(the battery of bioassays)을 적용하여 퇴적토의 오염상태를 평가하였다.

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2001.11a
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• pp.209-210
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• 2001

공장 굴뚝이나 차량과 같은 오염원에서 배출된 오염물질은 대기 중에서 확산과정을 거쳐 수용체(Receptor)에 도달하여 영향을 미치게 되는데, 확산을 결정하는 주요인자로는 오염물 배출조건 및 농도, 대기 기상조건, 주변지형조건, 오염물의 반응성 및 침착성 등이 있다. 대기환경 영향평가 및 대기질 관리성책수립 등을 위해서 복잡한 대기확산 과정을 거쳐서 수용체에 도달하는 오염물질의 농도에 대한 정량적인 값의 추정이 필요하다. (중략)

• Proceedings of the Korea Multimedia Society Conference
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• 2003.05b
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• pp.592-595
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• 2003

미세 먼지(PM-10)는 인체에 깊은 영향을 미치는 대기 오염원 중의 하나로써, 발생 원인에 대한 규명이 어렵고 기초 자료의 부족으로 통계적 분석 역시 어려워 예보가 지난한 실정이다. 본 연구에서는 기상 및 환경 인자들과 PM-10과의 상관 관계를 분석하고, 다층 인식자 신경망 모델을 이용하여 미세 먼지 예경보 시스템을 구축하였다. 실험 결과 RMSE가 0.04～0.09로 매우 정확한 성능을 보였다.

• Journal of the Korean Association of Geographic Information Studies
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• v.3 no.1
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• pp.12-22
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• 2000

Recently urbanization and industrialization around the Nak-dong river watershed have lead to the regional environmental problems. In this viewpoint, we took up variables which were related to watershed environment, and found out spatial and environmental properties of the Nak-dong river using factor analysis, ANOVA test and geographic information system. The results may be summarized as follows; three common factors which were named as urban, agricultural and industrial pollutant factor extracted from statistical methods. Spatial distribution of watershed environment could be found by connection attributes of factor scores derived from factor analysis to digital map using GIS. According to the results, distribution of pollutant sources were concentrated in the main stream of the Nak-dong river and its tributaries, Kum-ho river. So it is necessary to manage the watershed environment in due consideration of environmental carrying capacity.

• Journal of Korea Water Resources Association
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• v.55 no.8
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• pp.577-588
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• 2022

This study aims to identify the limiting nutrient for algal growth in the Geum River and the significant pollutant sources from the tributaries affecting the water quality and to provide a management alternative for an improvement of water quality. An eight-year of daily data (2013~2020) were collected from the Water Environment Information System (water.nier.go.kr) and Water Resources Management Information System (wamis.go.kr). 14 water quality variables were analyzed at five water quality monitoring stations in the Geum River (WQ1-WQ5). In the Geum River, the water quality variables, especially Chl-a vary greatly in downstream of the river. In the open weir gate operation, TP (total phosphorus) and water temperature greatly influence the growth of algae in downstream of the river. A correlation analysis was used to identify the relationship between variables and investigate the factor affecting algal growth in the Geum River. At the downstream station (WQ5), TP and Temp have shown a strong correlation with Chl-a, indicating they significantly influence the algal bloom. The principal component analysis (PCA) was applied to identify and prioritize the major pollutant sources of the two major tributaries of the river, Gab-cheon and Miho-cheon. PCA identifies three major pollutant sources for Gab-cheon and Miho-cheon, respectively. For Gab-cheon, wastewater treatment plant, urban, and agricultural pollutions pollution are identified as significant pollutant sources. For Miho-cheon, agricultural, urban, and forest land are identified as major pollutant sources. PCA seems to be effective in identifying water pollutant sources for the Geum River and its tributaries in detail and thus can be used to develop water quality management strategies.