Earticle

본 연구에서는 전라북도에 존재하는 인공호인 옥정호의 기상 및 수문 현황을 파 악함과 더불어 측정망 결과를 바탕으로 수질과 환경인자들 간의 상호관련성 영향 을 파악하고자 하였다. 또한 수질 및 조류 분석과 더불어 통계 분석을 수행하여 종 합적으로 식물성 플랑크톤의 성장에 영향을 미치는 인자를 파악하고자 하였다. 수질측정망 결과로는 하천이 강우로 인한 영향을 직접적으로 받기 때문에 수질 농도의 변동성이 호소에서보다 크게 나타났다. 또한 총인과 유기물질의 농도가 유 사하게 나온 것으로 보아 강우로 인해 비점오염원으로부터 오염물질과 함께 인 화 합물이 입자성 인의 형태로 유입되어 나타난 결과로 판단된다. 현장조사는 2017년과 2018년에 각각 하천 3지점, 호수내 5지점, 총 8지점을 선 정하여 조사를 수행하였으며, N/P ratio는 측정망 결과와 수질분석 결과 모두 대부분이 인 제한환경으로 나타났다. 부영양화지수 분석 결과 대부분 중영양 환경으로 나타났다. 조류 분석 결과로는 조사 기간 전반적으로 규조류가 우점하는 경향을 보 였다. 다변량 통계 분석인 주성분 분석을 통하여 나타난 결과에서는 2017년, 2018년 모두 4개의 요인이 추출되었으며, 각각 74.6%, 78.3%의 설명력을 가지는 것으로 나타났다. 2017년과 2018년의 분석결과는 각각의 순서는 다르지만 수체의 기상 및 수환경인자의 변화, 유기물과 입자성 인의 유입으로 인한 조류의 성장, 기상인자의 변화, 남조류 고유의 특성으로 유사하게 나타났다. 본 연구 결과를 종합한 결과 옥정호에서는 수질인자 중 제한영양염으로 작용하 는 T-P에 대한 관리가 필요하다고 판단되었다. 또한 상류 유입하천은 유량 변화에 따라 유입되는 영양염류 및 유기물 영향이 크게 작용하였기에 옥녀동천의 수질을 관리함으로서 옥정호내 조류의 성장을 제어할 필요가 있다고 판단된다.

The purpose of this study was to find out the meteorological and hydrological status and the trend through the results of the water quality monitoring network of Okjung reservoir at Jeollabuk-do. By analyzing the water quality, algae and statistical analysis, we tried to find out factors influencing the phytoplankton growth. As a result of the water quality monitoring network, The concentration change in the river monitoring network was greater than that in the lake because it was affected by rainfall directly. T-P and Organic matter concentration showed similar tendencies because particulate phosphorus flow into water body with pollutants due to rainfall. The water samples were collected from three points in the river and five points in the lake in 2017 and 2018. N/P ratio was observed to be P-limited condition in both the monitoring network and the water quality analysis. The phytoplankton community was found to be mainly dominated of diatoms during the study period. Principal component analysis of 2017 and 2018 identified 4 component that explained 74.6% and 78.3% respectively. During the study period, these components were attributed to change of meteorological and environment factor, growth of phytoplankton by inflow of organic matter and particulate phosphorus, concentrated precipitation in the summer, environmental changes causing growth of cyanobacteria. As a result of this study, T-P management is necessary because it acts as a limiting nutrient among water quality factors. And the upstream inflow river has a large change in the concentration of nutrients and organic matters entered as the flow rate changes. Therefore, it is necessary to control the growth of algae by managing the water quality of upstream inflow river.

Background: This study was performed to analyze the agronomic characteristics of 300 perilla germplasms to develop a perilla cultivar for leaves using principal component (PC) and cluster analyses. Methods and Results: In total, 300 perilla germplasms were analyzed in this study. The morpho- logical diversity and relationships among 300 perilla germplasms were assessed using principal component and cluster analyses. The perilla cultivar for leaf use requires resources with a flower- ing date later than 105 days and a short node length; we selected 74 resources with a flowering date later than 105 days and a length of less than l00 ㎝. The coefficients of variation of the perilla ger- mplasms were the highest for the days to flowering and days to maturation, whereas they were the lowest for the stem and flower colors. PC analysis revealed the eigenvalues and contributions respective to each PC as follows: PC1, 4.592 and 41.75%; PC2, 1.774 and 16.13%; and PC3, 1.280 and 11.64%. According to cluster analysis, the genetic resources were divided into four groups, comprising 118, 110, 69, and 3 resources, respectively. Conclusions: Clusters I and Ⅱ were found the most suitable for breeding materials, considering flowering days, initial shape, and leaf size, which are relevant traits for developing leaf-only varieties.

본 연구는 인쇄회로기판(PCB)의 RAM시료를 대상으로 선별을 통한 주요 금속의 회수율을 제고하기 위해레이저 유도붕괴 분광기(LIBS)로 스펙트럼 데이터를 얻어 주성분분석(PCA)과 심층신경망 기법을 적용했다. 레이저를 RAM 시료 표면294 지점에 조사해 스펙트럼을 얻고 기판을 검정(K), 노랑(Y), 초록(G)로 구분해 데이터가 구분이되는지 살펴보았다. PCA 분석과 SEM-EDS 분석결과의 비교를 통해 각 부분의 선별 가능성과 기준이 될 수 있는 파장, 포함된 주요 금속 조성비를 확인하였다. 추후 공정 자동화를 위해 심층신경망을 통한 각 부분 자동 분류 가능성을 타진하였고 98% 이상의 분류 정확도를 보았다. 따라서 본 연구를 통해 폐전기전자제품(WEEE)을 폐금속 회수 공정에 투입하기 전에 이를 선별할 수 있는 새로운 방법론을 만드는 데 활용할 수 있을 것으로 기대된다.

In this study, we collected spectral data using laser induced breakdown spectrometry. Our results were processed by a principle component analysis (PCA) and a deep neural network to improve the separation efficiency of metals from RAM samples of a printed circuit board (PCB). The spectra were collected from 294 spots on the sample surface and subsequently divided into three groups, i.e., Black (K), Yellow (Y), and Green (G) by visual inspection of the surface color. We identified the specific wavelengths that were usable as separation criteria as well as the main elemental composition of each part by comparing the PCA and scanning electron microscopy-electron dispersive spectroscopy ()results. We also confirmed the possibility of automatic separation with a deep neural network whose separation accuracy was more than 98%. Our results can be used to create a new automatic separation process for waste electronic electrical equipment.

본 연구에서는 결정질 기반암에 위치하는 12개 시추공의 지하수 수질을 분석하여, 다변량 통계 분석법을 활용하여 지하수 수질 진화 특성 및 성분 기원을 평가하였다. 지하수 수질 유형은 Na(Ca)-HCO3형과 Ca-HCO3형이 가장 우세하여, 물-암석 반 응에 의한 직접적인 양이온 교환 반응(Ca2+ → Na+)을 지시하며, 현장 지하수 특성과 실내 지하수 분석 결과에 기초한 연구 지역의 지하수 수질 진화는 초기 내지 중간 정도의 단계를 지시하는 것으로 사료된다. 다변량 분석 결과, 인위적인 기원인 NO3 -와 다른 성분들 간의 상관성을 살펴보면, Na+, Cl-와 양의 상관성을 나타난다. 염무의 기원인 Cl-와는 Na+, SO4 2-, Mg2+, K+ 와 양의 상관성을 나타낸다. 그러나 다른 성분들(Ca2+, Fe2+, HCO3 -, F-, SiO2)과는 상관성이 나타나지 않는다. Cl- 농도가 일반 적인 지하수 수질 범위에 포함되고 NO3 - 농도는 먹는물 수질기준치 이하로서 농도가 매우 낮으며, 대부분의 광물에 대해서 지하수 화학성분들은 불포화상태를 지시한다. 따라서, 연구지역의 수질 성분들은 대부분 물-암석 반응을 통한 자연적인 기 원을 지시하고 부분적으로는 자연적인 염무와 농업과 관련된 인위적인 오염으로부터 기인된다.

This study evaluated the evolution stage and origin of chemical components of 12 boreholes at crystalline bedrock using multivariate statistical and groundwater quality analyses. Groundwater types are mostly belonged to Na(Ca)-HCO3 and Ca-HCO3 types, indicating that directly reaction of cation exchange (Ca2+ → Na+) prevailed. The degree of groundwater evolution is included the range from low to intermediate stage based on field and laboratory analytical conditions. As a result of multivariate statistical analysis, a typical indicator of groundwater contamination, NO3-, has the positive correlation with Na+ and Cl-. The origin of sea spary (Cl-) has the positive correlation with Na+, SO4 2-, Mg2+, and K+, while not correlation with Ca2+, Fe2+, HCO3 -, F-, and SiO2. The concentration of Cl- and NO3 - belongs to general quality of groundwater and not exceeds over the Korean standard for drinking water. And the negative values of saturation index of minerals are calculated with chemical components in groundwater. Therefore, most of chemical components of groundwater in the study area are originated from natural process between rock and groundwater, whereas some of components are derived from sea spary and anthropogenic sources related to agricultural activities.

The purpose of this paper is to develop a multivariate statistical model for boxing matches. The model can estimate which factors affect match outcomes more effectively. This research employs a logistic regression model in which boxing match outcome, a dichotomous variable is used as a dependent variable. This study suggests that among a variety of independent variables, win/loss ratio, use of different punching techniques and state of mind prior to the match appear to play an important role in determining the match outcome.

A systematic methodology is developed for the electrofacies determination from wireline log data using multivariate statistical analysis. To consider corresponding contribution of each log and reduce the computational dimension, multivariate logs are transformed into a single variable through principal components analysis. Resultant principal components logs are segmented using the statistical zonation method to enhance the efficiency and quality of the interpreted results. Hierarchical cluster analysis is then used to group the segments into electrofacies. Optimal number of groups is determined on the basis of the ratio of within-group variance to total variance and core data. This technique is applied to the wells in the Korea Continental Shelf. The results of field application demonstrate that the prediction of lithology based on the electrofacies classification matches well to the core and the cutting data with high reliability This methodology for electrofacies classification can be used to define the reservoir characteristics which are helpful to the reservoir management.

A diagnosis methodology for thickness quality in hot finishing mill is proposed based on multivariate statistical analysis. The thickness of hot strip is a key quality factor that is measured by x-ray thickness gauge. Currently, the thickness quality is guaranteed by upper and lower limit of thickness deviation from target thickness. But if any over-limit is occurred, there is no in-line method to identify the causes. In this paper, many parameters are extracted from the thickness deviation signal such as mean deviation(top, middle, tail), rms deviation(top, middle, tail) and peak deviation(top, middle, tail) as time domain parameters ...

In this research, we have attempted to estimate the water quality of fish farms in terms of parameters such as water temperature, dissolved oxygen, pH, and salinity by employing observational data obtained from a coastal ocean observatory of a national institution located close to the fish farm. We requested and received marine data comprising nine factors including water temperature from Korea Hydrographic and Oceanographic Administration. For verifying our results, we also established an experimental fish farm in which we directly placed the sensor module of an optical mode, YSI-6920V2, used for self-cleaning inside fish tanks and used the data measured and recorded by a environment monitoring system that was communicating serially with the sensor module. We investigated the differences in water temperature and salinity among three areas - Goheung Balpo, Yeosu Odongdo, and the experimental fish farm, Keumho. Water temperature did not exhibit significant differences but there was a difference in salinity (significance <5%). Further, multiple regression analysis was performed to estimate the water quality of the fish farm at Keumho based on the data of Goheung Balpo. The water temperature and dissolved-oxygen estimations had multiple regression linear relationships with coefficients of determination of 98% and 89%, respectively. However, in the case of the pH and salinity estimated using the oceanic environment with nine factors, the adjusted coefficient of determination was very low at less than 10%, and it was therefore difficult to predict the values. We plotted the predicted and measured values by employing the estimated regression equation and found them to fit very well; the values were close to the regression line. We have demonstrated that if statistical model equations that fit well are used, the expense of fish-farm sensor and system installations, maintenances, and repairs, which is a major issue with existing environmental information monitoring systems of marine farming areas, can be reduced, thereby making it easier for fish farmers to monitor aquaculture and mariculture environments.

Metabolic profiling of cactus samples collected on Jeju Island (Opuntia ficus-indica) and in Sinan, Jeonnam province (O. humifusa) was carried out using high performance liquid chromatography-electrospray ionization mass spectroscopy (HPLC-ESI-MS) and multivariate statistical analysis. The samples were classified into two characteristic groups and the flavonoid glycosides accounting for the observed variations were identified by MS/MS analysis. This approach provided an overview of the differences among plant products originating from differences in the genetic nature of plants.

To classify Glycyrrhiza species, samples of different species were analyzed by $^1H$ NMR-based metabolomics technique. Partial least squares discriminant analysis (PLS-DA) was used as the multivariate statistical analysis of the 1H NMR data sets. There was a clear separation between various Glycyrrhiza species in the PLS-DA derived score plots. The PLS-DA model was validated, and the key metabolites contributing to the separation in the score plots of various Glycyrrhiza species were lactic acid, alanine, arginine, proline, malic acid, asparagine, choline, glycine, glucose, sucrose, 4-hydroxy-phenylacetic acid, and formic acid. The compounds present at relatively high levels were glucose, and 4-hydroxyphenylacetic acid in G. glabra; lactic acid, alanine, and proline in G. inflata; and arginine, malic acid, and sucrose in G. uralensis. This is the first study to perform the global metabolomic profiling and differentiation of Glycyrrhiza species using $^1H$ NMR and multivariate statistical analysis.

The metabolic discrimination of safflowers from various geographical origins was performed using 1H nuclear magnetic resonance (NMR) spectroscopy followed by principal components analysis. With a combination of these techniques, safflower samples from different origins could be discriminated using the first two principal components (PC) of the 1H NMR spectra of the 50% methanol fractions. PC1 and PC2 accounted cumulatively for 91.3% of the variation in all variables. The major peaks in the 1H NMR spectra that contributed to the discrimination were assigned to fatty acid (terminal CH3), lactic acid, acetic acid, choline derivatives, glycine, and safflower yellow derivatives. In this study, we suggest that various types of safflower can be discriminated using PCA and 1H NMR spectra.

The root of Astragalus membranaceus is a traditional folk medicine that has been used for many therapeutic purposes in Asia. It reportedly acts as an immunostimulant, tonic, hepatoprotective, diuretic, antidiabetic, analgesic, expectorant, sedative, and anticancer drug. In this study, metabolomic profiling was applied to the roots of A. membranaceus of different ages using NMR coupled with two multivariate statistical analysis methods: such as principal components analysis (PCA) and canonical discriminant analysis (CDA). This allowed various metabolites to be assigned in NMR spectra, including $\gamma$-aminobutyric acid (GABA), aspartic acid, succinic acid, glutamic acid, glutamine, N-acetyl aspartic acid, acetic acid, arginine, alanine, threonine, lactic acid, and valine. The score plot from PCA and also CDA allowed a clear separation between samples according to age.

Background: Ginsenosides are not only the principal bioactive components but also the important indexes to the quality assessment of Panax ginseng Meyer. Their contents in cultivated ginseng vary with the growth environment and age. The present study aimed at evaluating the significant difference between 36 cultivated ginseng of different cultivation areas and ages based on the simultaneously determined contents of 14 ginsenosides. Methods: A high-performance liquid chromatography (HPLC) coupled with triple quadrupole mass spectrometer (MS) method was developed and used in the multiple reaction-monitoring (MRM) mode (HPLC-MRM/MS) for the quantitative analysis of ginsenosides. Multivariate statistical analysis, such as principal component analysis and partial least squares-discriminant analysis, was applied to discriminate ginseng samples of various cultivation areas and ages and to discover the differentially accumulated ginsenoside markers. Results: The developed HPLC-MRM/MS method was validated to be precise, accurate, stable, sensitive, and repeatable for the simultaneous determination of 14 ginsenosides. It was found that the 3- and 5-yr-old ginseng samples were differentiated distinctly by all means of multivariate statistical analysis, whereas the 4-yr-old samples exhibited similarity to either 3- or 5-yr-old samples in the contents of ginsenosides. Among the 14 detected ginsenosides, Rg1, Rb1, Rb2, Rc, 20(S)-Rf, 20(S)-Rh1, and Rb3 were identified as potential markers for the differentiation of cultivation ages. In addition, the 5-yr-old samples were able to be classified in cultivation area based on the contents of ginsenosides, whereas the 3- and 4-yr-old samples showed little differences in cultivation area. Conclusion: This study demonstrated that the HPLC-MRM/MS method combined with multivariate statistical analysis provides deep insight into the accumulation characteristics of ginsenosides and could be used to differentiate ginseng that are cultivated in different areas and ages.

In this study, multivariate statistical analysis was applied to analyze the trend of water quality and water quality in the Fourth reservoir over the last 10 years. Correlation analysis between water quality variables showed that, the correlation coefficient between COD and TOC, which is an indirect indicator of organic matter, showed a high correlation of 0.608, while water temperature showed a positive correlation with pH of 0.515 and a negative correlation with DO of -0.716. Principal component and factor analyses showed that the four major components were extracted from the total water quality and contributed 72.9% of the total variance. When analyzed for cyanobacteria occurrence, three major components were extracted and contributed 67.2% of the total variance. Factor loadings analysis of water quality variables on the factors identified the first factors as COD and TOC. Water quality of the Fourth reservoir was found to be influenced by seasonal effects such as rainfall and organic matter, in addition to the effects of phytoplankton proliferation and nutrient influx.

면적(面積)이 광대(廣大)하며 지형(地形)은 험준(險峻)한 산림(山林)에서 그의 축적(蓄積)을 조사(調査)하기에는 여러 난점(難點)이 많다. 중량(重量)과 용적(容積)에 비(比)하여 그 가격(價格)이 저렴(低廉)한 임목(林木)의 축적(蓄積)을 조사(調査)하는데 많은 시간(時間)과 노력(勞力)을 소비(消費)함은 산림경영상(山林經營上) 아주 비경제적(非經濟的)이다. 그러므로 항공사진(航空寫眞)만을 가지고 용이(容易)하게 ha 당(當) 임분재적(林分材積)을 추정(推定)할 수 있도록 임분재적표(林分材積表)를 작성(作成)하고 임분재적(林分材積)과 관련(關聯)있는 인자(因子)의 재적추정(材積推定)에의 관여성(關與性)을 구명(究明)하였다. 재료(材料)는 임업시험장(林業試驗場) 광릉시험림내(光陵試驗林內)에 식재(植栽)한 잣나무림(林) 114개소(個所)이며 근래(近來)에 발달(發達)한 통계학(統計學)의 일종(一種)인 다변량해석(多變量解析)과 수량화(數量化)의 이론(理論)을 응용(應用)하였으며 계산(計算)은 한국과학기술연구소(韓國科學技術硏究所)(K.I.S.T.)의 전자계산기연구실(電子計算機硏究室)에서 행(行)하였다. 조제(調製)된 임분재적표(林分材積表)의 중상관계수(重相關係數)는 0.85~0.88 로 상당(相當)히 높은 정확도(正確度)를 갖고 있다.

Surveys of stock volume on steep and vast expanse of mountains, involves various difficulties. And it is extremely uneconomical in forest management point of view, to spend lots of time and man power for surveying such tree volume as the value is much cheaper in comparison with volume and weight. Therefore, a stand volume table estimate easely stock volume per hectare basis from aerial photographs was prepared and correlations to stand volume among factors affecting tree volumation, were studied. Data were 114 places selected from planted Korean white pine, Pinus koreiensis Sieb. et Zucc. stands in Kwangnung Experiment Forest and were computed and analysed by the means of the quantification in the multi-variate statistical analysis. Electronic Data Processing System was applied for data processing at Korean Instiute of Science and Technology. Coefficients of multiple correlations of stand volume table was ranged 0.85~0.88.

A condition monitoring methodology for rotating machinery is proposed based on multivariate statistical analysis. The CMS usually are using the vibration signal amplitude such as acceleration RMS, peak and velocity RMS to detect machine faults but the information is not so enough that CMS cannot perform reliable monitoring. So new parameters are added such as shape factor, crest factor, kurtosis and skewness as time domain parameters and spectrum amplitude of rotating frequency, $2^{nd}$ harmonics and gear mesh frequency etc. as frequency domain parameters. Many parameters are combined to represent the machine state using the Hotelling's $T^2$ statistics. The proposed methodology is tested in laboratory and the on-line experiment has shown that the proposed methodology offers a reliable monitoring for rotating machinery.

지구온난화로 인해 세계 곳곳에서 기온상승이 관측되고 있으며, 이는 전지구적 기후시스템의 변화를 보여주는 대표적인 예이다. 온도를 비롯한 강수량, 풍속, 증발량 등의 기상학적, 수문학적 인자들이 각각 서로에게 영향을 주고 받으며 복잡하게 변화할 것이고, 그 변화폭도 점점 커질 것이다. 증발에 영향을 미치는 인자들은 크게 세 가지로 나뉘는데, 태양복사에너지, 온도, 바람, 기압, 습도와 같은 기상학적인자, 증발표면의 특성인자 그리고 수질인자로 분류할 수 있다. 증발에 영향을 주는 인자들은 예전부터 알려져 있지만 이들 간의 복잡한 상호작용에 대해 정확히 이해하기는 쉽지 않다. 본 연구에서는 댐유역의 증발량에 영향을 미치는 기상인자 파악을 위해 2008부터 2016년까지 관측된 낙동강수계 내 안동댐과 남강댐의 기상자료(기온, 강수량, 풍속, 상대습도, 기압, 일사량, 일조시간, 전운량)를 이용한 변화를 분석하였으며, 다변량 통계기법인요인분석을 통해 증발량과 상관성이 높은 인자들을 분류하였다. 안동댐과 남강댐 공통적으로 증발량과 기온, 기압이 같은 요인으로 분류되고 높은 상관성을 보였으며, 강수량, 일조시간, 일사량, 전운량이 같은 요인으로 분류되었다. 국내의 증발량 측정지점에 대한 추가적인 분석과 영향인자를 이용한 다변량회귀식과 인공신경망 통해 증발량 미측정 지점의 증발량 산정이 가능할 것으로 판단된다.

본 연구는 다변량 통계 분석법을 이용하여 지리산 서부 천연림을 대상으로 산림 피복형을 분류하기 위해 실시하였다. 점표본법에 의한 식생자료를 바탕으로, 수종-표본점 곡선, 계층적 군집분석, 지표종분석, 다중판별분석 등의 다변량 통계 분석법을 이용하여 식생자료를 분석하였다. 수종-표본점 곡선에서는 산림 피복형 분류에서 전혀 영향력이 없는 수종들을 예외값으로 제거하였다. 예외값을 제외한 산림식생정보를 바탕으로 계층적 군집분석을 이용하여 연구대상지를 2~10개의 클러스터로 분류하였으며, 지표종분석을 통해 연구대상지의 적정 클러스터 수는 7개인 것으로 파악되었다. 이를 통계적으로 검증하기 위해 다중판별분석을 실시하였고, 91.3%가 정확하게 분류되어, 연구대상지 산림 피복형의 개수는 7개가 적당한 것으로 나타났다. 각 클러스터 상층의 우점수종 비율에 따라 신갈나무순림, 중생혼합림, 신갈나무-졸참나무림, 구상나무-신갈나무림, 들메나무림, 졸참나무림, 서어나무림으로 산림 피복형을 명명하였다.

This study was conducted to classify forest cover types using the multivariate statistical analysis in the natural forest of western Mt. Jiri. On the basis of the vegetation data by point quarter sampling, the adopted analytical methods were species-area curve (SAC), hierarchical cluster analysis (HCA), indicator species analysis (ISA), and multiple discriminant analysis (MDA). SAC selected the outlier tree species which was likely to have no influence on the classification of forest cover types, excluded from all analytical process. Based on forest vegetative information, HCA classified the study area into 2 to 10 clusters and ISA indicated that the optimal number of clusters were seven. MDA was taken to test the clusters that classified with HCA and ISA. The seven clusters were classified appropriately as overall classification success were 91.3%. The classified forest cover types were named by the ratio of the dominant species in the upper layer of each cluster. They were (1) Quercus mongolica Pure forest, (2) Mixed mesophytic forest, (3) Q. mongolica - Q. serrata forest, (4) Abies koreana - Q. mongolica forest, (5) Fraxinus mandshurica forest, (6) Q. serrata forest, and (7) Carpinus laxiflora forest.

강우는 우리의 일상생활뿐만 아니라 각종 산업 활동 및 레저스포츠 등 많은 분야에 영향을 주는 중요한 기상 현상이다. 최근 들어 우리나라는 기후변화로 인해 과거 장마기간에 발생하였던 강우집중 현상이 장마기간 전후되어 발생하는 게릴라성 집중호우나 급작스런 이상강우의 형태를 보이고 있으며 그로 인해 홍수 예 경보체계 구축에 대한 중요성이 높아지고 있다. 하지만 우리나라의 경우 홍수 예 경보 체계 및 수공구조물 건설 등이 국가하천 위주로 운영되어 소하천에 대한 연구와 대책이 부족한 실정이다. 최근 이상호우에 의한 홍수피해 사례를 보면 지방하천 혹은 소하천으로부터 발생한 재산 및 인명피해가 크다는 것을 알 수 있으며 이를 관리하기 위한 시스템 구축이 절실한 상황이다. 특히 도시하천의 홍수방재를 위해서는 안정적이고 체계적인 도시하천관리가 필요한데 이를 위해서는 도시하천의 특성을 정확히 파악하는 것이 매우 중요하다. 국내에서 하천의 특성에 관한 연구동향을 살펴보면 대부분의 연구가 하천수질 관점에서 하천특성을 규명하는 목적으로 수행 되었고 홍수 방재를 위한 수문학적인 관점에서의 하천 특성을 도출하는 연구는 미비한 실정이다. 수문학적 관점에서의 하천특성은 유역면적, 도달시간, 형상계수 등 유역특성인자를 말하며 국내 하천에 적용 할 수 있는 대표 유역특성인자를 도출을 위해서는 통계학적인 방법이 필수적이다. 그 중에서도 요인분석, 주성분분석, 군집분석 등 다변량 통계기법을 이용한 분석 필요하다. 본 연구에서는 GIS분석과 지자체에서 제공하는 소하천정비기본계획을 통해 도시하천의 유역특성인자를 선정하고, 수문분야에서의 적용성이 높은 다변량 통계분석기법을 이용하여 우리나라를 대표할 수 있는 도시하천의 특성 및 권역별 도시하천의 특성을 도출하였다. 또한 대조군에 적용하여 도출된 특성의 적정성을 평가하였고 이를 통해 도시하천 관리에 이용될 수 있는 도시하천의 특성을 제시하고자 한다.

지황(Rehmannia glutinosa)은 국내 약재시장에서 많이 유통되지만, 수입 의존이 높은 대표적인 약용작물이다. 원산지 등 재배 환경의 차이는 대사체 종류와 조성의 차이를 유발할 수 있으며 대사체 분석법을 활용하여 원산지를 과학적으로 규명할 수 있다. 본 연구에서는 국내산과 중국산 지황을 핵자기공명 분광법(NMR)과 고분해능 질량분석법(UPLC-QTOF/MS) 기반의 멀티플랫폼 대사체분석과 통계분석을 활용하여 원산지를 판별하였다. 지황의 1차 대사체 33개, 2차 대사체 17개의 정성 분석 결과 및 비표적 분석 데이터의 다변량통계분석 비교를 통해 국내산과 중국산의 차이를 확인할 수 있었다. 원산지 판별 마커에 사용되는 바이오마커 분석결과, 원산지 판별에 기인한 대사체는 malate 등 7종의 유기산이 기여율이 높음을 확인하였으며, 지황의 원산지 판별에 유용하게 사용될 수 있음을 시사하였다.

Rehmannia glutinosa is a widely used medicinal herb in the domestic market. The type and composition of metabolites and the efficacy of medicinal herbs can vary depending on variances in cultivation environment, such as origin. Metabolomic approaches can be used to objectively determine the geographical origin. In this study, Korean and Chinese R. glutinosa were analyzed using a multi-platform based on nuclear magnetic resonance spectroscopy and ultra-performance liquid chromatography quadrupole time of flight mass spectrometry. A multivariate statistical analysis of non-targeted analysis data and a comparison of quantitative analysis results of primary and secondary metabolites confirmed the differences between Korean and Chinese R. glutinosa. In the biomarker analysis, the metabolites resulting from the discrimination of geographical origin included various of organic acids such as malate, etc. It was found that organic acids can change based on environmental circumstances and that they play a significant role in growth, such as solubilizing nutrients.