• 환경위생공학
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• 제18권3호통권49호
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• pp.1-10
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• 2003

This study was aimed to investigate the influence on the vicinity by air pollutant generated from facility of the district heating located in local small town. We selected the seven areas arround the surroundings of facility of the district heating, compared the air quality evaluated before and after operations of the facility, and estimated the diffusion of air pollutant exhausted from the facility using a ISC model. The result was that the concentration of TSP before and after operations of the facility was 89${\sim}$94${\mu}$g/m$^3$,and 72${\sim}$81${\mu}$g/m$^3$, respectively and the latter showed a decline in concentration. Also, there was no relationship between straight distance from the facility of the district heating and the concentration of TSP. This result was applicable to cases of PM-10 and SO$_2$. We also investigated the influence on the air around the neighbored area by air pollutant produced from facility of the district heating using ISCLT3 model. The adding-concentrations of TSP, SO$_2$,NO$_2$, and CO were 0.0019${\sim}$0.00183${\mu}$g/m$^3$, 0.0029${\sim}$0.5648ppb, 0.2924${\sim}$l.9837ppb,and 0.0087${\sim}$0.0590ppb, respectively. It is predicted that each concentration is added to pollutant exhausted from facility of the district heating and is about 1/100${\sim}$1/180,000 of present air quality. This has a tiny influence on general air quality. According to this analysis, the concentration of air pollutant is less effected to pollutants expected by the facility of the district heating than other pollutants emitted from mobil source or industrial complex, and etc.

• 대한토목학회논문집
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• 제26권2D호
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• pp.335-340
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• 2006

임하호 유역은 지질 및 지형이 토사유실에 취약한 구조를 가지고 있어 강우발생시 많은 토사가 호소로 유입되어 고탁수의 원인이 되고 있다. 특히 임하호유역의 농경지가 주로 하천주변에 분포하고 있어 강우시 토사유실로 인한 탁수발생이 큰 지역이다. 따라서, 탁수저감을 위한 수변구역의 체계적인 관리와 대책 마련을 위해서는 수변구역에서 발생하는 토사유실량의 영향을 평가하는 것이 중요하다. 본 연구에서는 GIS 기반 RUSLE 모형을 선정하여 수변구역에서의 토사유실 비율을 평가한 결과 약 12.23%로서 임하호 전체유역과의 면적비율(9.95%) 보다 높게 나타남을 알 수 있었다. 이러한 결과는 수변구역 주변의 농경지비율(27.24%)이 전체유역에 대한 농경지비율(14.96%) 보다 높은 특성이 반영된 것으로 해석된다. 또한 소유역별 분석결과를 볼 때 수변구역중 대곡천 유역이 가장 높은 토사유실량 분포를 나타냈으며, 반변천_10 그리고 서시천 순서로 나타났다.

• KIEAE Journal
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• 제5권3호
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• pp.41-48
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• 2005

Before occupation of an apartment housing, the builders are required to inform the test result of IAQ to the public. However, there is no simplified method to predict IAQ before measurement of pollutant concentration. In this study, a simplified way of predicting IAQ based on the distribution of indoor pollutant concentration is proposed. 7 different cases of air change rate have been simulated through CFD analysis to get the distribution ratio of each pollutant material and then simplified functions were used with CRIAQ1 values derived from CFD simulation to evaluate by comparing the influence of each material in the indoor pollutant concentration. Again, a lot of efforts which can improve the indoor air quality have been performed. Materials used in indoor space are labeled with their pollutant emission level. Installation of ventilation system in residential buildings will be regulated by a building codes sooner or later. But it is important to understand the fact that layout of walls, location or size of openings will influence the indoor air flow and pollutant concentration. And location of emitting material influences to indoor air pollutants distribution. But until now there is few recognition and consideration of these factors. Therefore, in this paper the effects of these factors is proved and some kind of guideline is made for designers after a comparison of typical apartment floor plan and a new type plan with their average pollutant concentration and its distribution of each room. CFD(Computational Fluid Dynamics) program was used to show the indoor air flow and pollutant concentration distribution. For this purpose, a typical $100m^2$ apartment floor plan was chosen as a case study model and several alternatives were reviewed to improve the IAQ performance. The simulation took place in the condition of natural ventilation through windows.

• 상하수도학회지
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• 제26권1호
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• pp.47-54
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• 2012

This study analyzed influence factors and the correlation among pollutants which affect occurrence of leaked pollution based on the long-term runoff flow and water quality investigation results to understand the characteristics of highway rainfall runoff pollution load. According to the result of correlation analysis on TSS (Total Suspended Solid) concentration, anteceded dry days, rainfall intensity, traffic volume and etc. as major influence factors of highway rainfall runoff pollution loads, the correlations were weak or scarce in most items. These results might be attributed that runoff pollutant concentration changes vary severely on changes of rainfall intensity and rainfall duration within rainfall and it is affected by disturbances of vehicles and street cleaning and etc. as characteristics of the highway. While Cu, Fe and Zn which are discharged with high concentrations out of heavy metals showed high correlation with particulate matter, organic matter(COD), nutrient(TN, TP), Ni and Pb showed relatively low correlation in a correlation evaluation by pollutant. Significant correlation with traffic volumes was not shown and TSS concentration even decreased in accordance with increase of the traffic volume. In the comparison with precedent studies, it was considered necessary additional analysis of the effects of rainfall section analysis, road type, disturbances of surface contaminants by vehicles, rainfall and climate conditions, surrounding terrains etc.

• 한국물환경학회지
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• 제23권1호
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• pp.161-168
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• 2007

The main aim of this work is focus on the Geum river water quality evaluation of pollution data obtained by monitoring measurement during the period 2001-2005. The complex data matrix 19 (entire monitoring stations)*13 (parameters), 60 (month)*13 (parameters) and 20 (season)*13 (parameters) were treated with different multivariate techniques such as factor analysis/principal component analysis (FA/PCA). FA/PCA identified two factor (19*13) classified pollutant Loading factor (BOD, COD, pH, Cond, T-N, T-P, $NH_3$-N, $NO_3$-N, $PO_4$-P, Chl-a), seasonal factor (water temp, SS) and three Factor (60*13, 20*13) classified pollutant Loading factor (BOD, COD, Cond, T-N, T-P, $NH_3$-N, $NO_3$-N, $PO_4$-P), seasonal factor (water temp, SS) and metabolic factor (Chl-a, pH). Loadings of pollutant factor is potent influence main factor in the Geum river which is explained by loadings of pollutant factor at whole sampling stations (71.16%), month (52.75%) and season (56.57%) of main water quality stations. Result of this study is that pollutant loading factor is affected at Gongju 1, 2, Buyeo 1, 2, Gangkyeong, Yeongi stations by entire stations and entire month (Gongju 1, Cheongwon stations), April, May, July and August (buyeo 1) by month. Also the pollutant Loading factor is season gives an influence in winter (Gongju 1, buyeo 1) from main sampling stations, but Cheongwon characteristic is non-seasonal influenced. This study presents necessity and usefulness of multivariate statistic techniques for evaluation and interpretation of large complex data set with a view to get better information data effective management of water sources.

• 한국관개배수논문집
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• 제18권2호
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• pp.54-65
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• 2011

This study was performed to analyze the influence of pollutant loads characteristics on the point and non-point sources in Saemangeum watershed area using Hydrological Simulation Program, Fortran (HSPF). The simulation items were flow, BOD, T-N, and T-P(2007~2010). The pollutant loads trend reflects the precipitation. Specifically, the point source loads were almost constant, but the non-point source loads were influenced in the precipitation. It was found that the effect of non-point source is larger than point source. The water quality had a clear trend by the season. However, pollutant loads did not show distinct seasonal changes. The reason is that the pollutant concentration is diluted by the increased flow at summer season. Therefore, it is important to control the non-point source in order to manage water quality in the region. For the management of Saemangeum lake, it is need to control of supplied pollutant loads from Saemangeum watershed.

• 한국해안해양공학회지
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• 제12권3호
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• pp.149-155
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• 2000

마산만의 오염물질 수지변화를 파악하여 하수처리장 건설 효과를 정량적으로 분석하였다. 하수처리장 건설에 의한 유역의 오염부하량 삭감효과는 명확한 것으로 추정되었으나, 해역의 수질개선에 미치는 영향은 그에 상응하는 오염부하량 증가로 인하여 뚜렷하게 효과를 보이고 있지 않은 것으로 추정되었다. 하수처리장 건설에 의한 마산만(I 영역)의 오염부하량 삭감효과는 COD, SS, TN, TP 항목의 경우 각각 17.6%, 68.9%, 66.7%, 38%에 해당된다. 반면, 진해만 북부 방류해역(II 영역)은 하수처리장 방류수의 직접적인 오염부하와 오염된 퇴적물에 의한 용출부하(간접적인 효과)가 가중되고 있다. 따라서, 하수처리장 건설로 인하여 방류해역의 환경은 오히려 악화되어 가고 있는 것으로 파악되었다.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2006년도 학술발표회 논문집
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• pp.908-912
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• 2006

임하호 유역은 지질 및 지형이 토사유실에 취약한 구조를 가지고 있어 강우발생시 많은 토사가 호소로 유입되어 고탁수의 원인이 되고 있다. 특히 임하호유역의 농경지가 주로 하천주변에 분포하고 있어 강우시 토사유실로 인한 탁수발생이 큰 지역이다. 따라서, 탁수저감을 위한 수변구역의 체계적인 관리와 대책 마련을 위해서는 수변구역에서 발생하는 토사유실량의 영향을 평가하는 것이 중요하다. 본 연구에서는 GIS 기반 RUSLE 모형을 선정하여 수변구역에서의 토사유실 비율을 평가한 결과 약 12.23%로서 임하호 전체유역과의 면적비율(9.95%) 보다 높게 나타남을 알 수 있었다. 이러한 결과는 수변구역 주변의 농경지비율(27.24%)이 전체유역에 대한 농경지비율(14.96%) 보다 높은 특성이 반영된 것으로 해석된다. 또한 소유역별 분석결과를 볼 때 수변구역중 대곡천 유역이 가장 높은 토사유실량 분포를 나타냈으며, 반변천_10 그리고 서시천 순서로 나타났다.

• 한국수자원학회논문집
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• 제55권8호
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• pp.577-588
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• 2022

본 연구는 금강의 조류 성장에 대한 제한영양소와 수질에 영향을 미치는 주요 지류를 파악하고 수질개선을 위한 관리대안을 제시하는 것을 목적으로 수행되었다. 금강 대청댐 하류에 위치한 5개 수질측정소에서 약 8년간(2013~202) 환경부의 물환경정보시스템(water.nier.go.kr)과 수자원관리정보시스템(wamis.go.kr)에서 14개의 수질항목의 자료를 분석하였다. 금강의 4대강 수중보 수문 개방 시 TP(총인)와 수온은 하천 하류의 조류 성장에 큰 영향을 미친다. 본 연구에는 수질변수간의 상관관계를 규명하고 금강의 조류 성장에 영향을 미치는 중요인자를 파악하고자 하였다. 최하류에 위치한 백제보수질측정소(WQ5)에서 TP와 수온은 Chl-a와 특별히 높은 상관관계를 보여 조류 번식에 상당한 영향을 미친다는 것을 나타냈다. 또한 본 연구에서는 금강의 양대 지류인 갑천과 미호천의 주요 오염원을 식별하고 우선순위를 지정하기 위해 주성분분석(Principal Component Analysis, PCA) 방법을 이 적용하였다. PCA방법을 이용하여 갑천과 미호천의 수질에 영얗ㅇ을 미치는 3대 오염원을 각각 파악하였다. 갑천의 경우 폐수처리장과 도시·농업 오염이 주요 오염원으로, 미호천의 경우 농지, 도시, 산림이 주요 오염원으로 각각 확인되었다. PCA는 금강 및 그 지류의 수질오염원을 구체적으로 파악하는 데 효과적인 것으로 판단되어 수질관리 전략의 효율을 제고하는 데에, 활용될 수 있을 것으로 보인다.

• 환경위생공학
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• 제24권3호
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• pp.37-54
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• 2009

The study has analyzed impacts and intensity of weather that affect $PM_{10}$ concentration based on PM10 forecast conducted by the city of Seoul in order to identify ways to improve the accuracy of PM10 forecast. Variables that influence $PM_{10}$ concentration include not only velocity and direction of the wind and rainfalls, but also those including secondary particulate matter, which were identified to greatly influence the concentration in complicated manner as well. In addition, same variables were found to have different impacts depending on seasons and conditions of other variables. The study found out that improving accuracy of $PM_{10}$ concentration forecast face some limits as it is greatly influenced by the weather. As an estimation, this study assumed that basic research units and artificially estimated pollutant emissions, study on mechanisms of secondary particulate matter productions, observatory compliment, and enhanced forecaster's expertise are needed for better forecast.