금번 연구에서는 낙동강 유역내 주요하천을 대상으로 자연도 평가와 수질을 분석하였다. 하천 자연도의 종합평가 결과는 의 1등급과 4등급으로 평가되었으며, 부문별 평가 결과는 하천형태의 평가지수가 로 2등급에서 4등급으로, 하천환경의 평가지수는 의 1등급에서 4등급으로 평가되었다. 수질 평가 결과는 수소이온농도는 이었으며, 생물화학적 산소요구량은 , 용존산소량 , 부유물질은 이었다. 하천 자연도 평가 지표의 독립성과 유사성을 판단하기 위하여 하천 자연도

본 연구에서는 우리나라 강우량과 광범위한 해수면 온도(SST)와의 공간적인 상관성 분석을 위해서 Wavelet 분석과 독립성분분석을 결합하여 계절강우량의 주요성분을 추출하였다. 계절강우량의 Wavelet분석결과 2년과 8년 사이의 주기에서 강한 스펙트럼을 나타내었으며 모든 계절강우량에 대하여 저빈도 특성을 확인할 수 있었다. Wavelet에서 추출된 Scale Average Wavelet Power(SAWP)를 대상으로 독립성분분석을 실시하였으며 계절에

본 연구는 Moussa(2003)에 의하여 개발된 등가타원을 기반으로 한 신집수형상디스크립터에 대한 소개 및 수문학적 적용성 평가를 목적으로 한다. 두 디스크립터 a+b, a+b+과 지체시간 사이의 상관성 분석이 수행되었고 이를 Nash 모형 매개변수들의 대표치 산정에 적용하였다. 본 연구의 실용성을 조사하기 위하여 한강 수계의 평창강 유역, 금강 수계의 보청천 유역 그리고 낙동강 수계의 위천 유역들에 각각 이 디스크립터특이 적용되었다. 그 결과, 두

This study tried to construct a direction in regional planning concerning the structural relationship between the ratio of aged population and the industrial characteristics. We investigated this structural relationship incorporating the aged population index and the number of classified companies. We applied diverse statistical analyses to understand the relationship. We classified the number of companies to reflect regional industrial characteristics using the principal component analysis. We applied a multiple regression model to understand the relationship between these two indices. The aged population index represents the degree of being old divided by the ratio of juvenile population and aged population. We found that such industries as manufacturing, service, and conveyance increase the ratio of juvenile population. However, industries such as tourism, waterworks, forestry, agriculture and etc. have a positive effect on the aged population index. In addition to these findings, we believe that the efficacy of this study is the possibility that can be used as the basic data when central or local autonomous entities need to adopt rural development planning.

The relationship between the distribution of sea surface temperature(SST) and dinoflagellate(Cochlodinium polykrikoides) bloom areas were studied. The SST data were derived from the infrared channels of AVHRR(Advanced Very High Resolution Radiometer) sensor on NOAA(National Oceanic and Atmospheric Administration) 12 and 14 satellites during 1995-1998. The initial water temperature at C. polykrikoides bloom was about 21℃ at the coastal areas of the South Sea and along the shore of the East Sea of Korea during the summer season of 1995. The northern limit of red tides was coincident with that of 21℃ isothermal line in the East Sea. The red tides that initially bloomed at the coast of Pohang on September 21, 1995 moved to the coast of Uljin on September 26, 1995. The skipped appearance of the red tides in the areas between Pohang and Uljin was due to the East Korean Warm Current, which was moving offshore from Pohang to approach to Uljin. The cold water which was formed by tidal front in the western coast of the South Sea and by upwelling water from deep layer in the southeastern coast of the Korean peninsula played a role in blocking the spreading of red tides during summer season in 1997 and 1998. In conclusion, the distribution of red tides appeared to be dependent on the initial water temperature at red tides bloom. The SST at the red tides varied from 21℃ to 25℃ ; 21℃, 23℃, 24℃ and 24-25℃ in 1995, 1996, 1997 and 1998, respectively.

The purpose of this study is providing basic data to control the air pollutants from solid waste incinerator. Incinerating the waste wood, the electrostatic precipitator had the best collection efficiency. The leather incineration had the same collection efficiency as synthetic resin incineration. And the coarse particle collection efficiency was high. As you know in correlation of leather incineration, pollutants produced a from incinerator are mostly fine particles. If the scrubber used only in the process produced a lot of fine particles. It is adequate to use the above control devices, together with high efficiency collector like bag-filter. To select the adequate control devices, it is required to investigate the size distribution before establishing control devices.

In the Atmosphere under the various physical and chemical condition different chemical reactions occur and there are a number of air pollutants which are generated by photochemical reaction by absorbing solar energy. Therefor various testing simulation was done as foundation work to develop the numerical model for the prediction of concentration of air pollutants. It was shown change of major air pollutants concentration in according to variation of photodissociation speed constant, K_1 and initial condition of air pollutants concentration which plays major role in photochemical reaction. The photochemical reaction model which was used in this study is found to be useful for understanding relationship among the concentration of reacting air pollutants and the prediction of concentration of air pollutants in urban atmosphere

This study was performed to analyze the variation characteristics of water qulity, correlation analysis of water quality data at each site and among the items of water quality data. Water quality for analysis was monthly values of water temperature, pH, dissolved oxygen, biochemical oxygen demand, chemical oxygen demand, suspended solid, T-N and T-P checked in Daecheong Lake from January to December, 1995. It was analyzed variation of monthly water qulity was well from February to April, water temperature and COD seemed to have high correlationships at all sites. Regression equation is COD = 0.07 Water temperature + 1.23 (R^2 = 0.7616) . Results of the correlation analysis of water quality data showed that DO had higt correlationships between site 1 and site 2, BOD did site 1 and 3, COD did site 1 and 2, SS did site 5 and 6, T-N did 2 and 3, T-P did site 4 and 6. Regression equations for estimate of water quality data are as follows. DO_1 = 4.46 + 0.59 DO_2 (R^2 = 0.8868), BOD_1 = 0.52 + 0.63 BOD_3 (R^2 = 0.6390) COD_2 = 0.44 + 0.71 COD_1 (R^2 = 0.9183), SS_6 = 0.89 + 0.70 SS_5 (R^2 = 0.9155) TN_3 = 0.151 + 0.886 TN_2 (R^2 = 0.9415), TP_4 = 0.004 + 0.758 TP_6 (R^2 = 0.9669)