• Communications for Statistical Applications and Methods
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• 제27권5호
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• pp.547-568
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• 2020

Modeling the statistical autocorrelations in spatial data is often achieved through the estimation of the variograms, where the selection of the appropriate valid variogram model, especially for small samples, is crucial for achieving precise spatial prediction results from kriging interpolations. To estimate such a variogram, we traditionally start by computing the empirical variogram (traditional Matheron or robust Cressie-Hawkins or kernel-based nonparametric approaches). In this article, we conduct numerical studies comparing the performance of these empirical variograms. In most situations, the nonparametric empirical variable nearest-neighbor (VNN) showed better performance than its competitors (Matheron, Cressie-Hawkins, and Nadaraya-Watson). The analysis of the spatial groundwater dataset used in this article suggests that the wave variogram model, with hole effect structure, fitted to the empirical VNN variogram is the most appropriate choice. This selected variogram is used with the ordinary kriging model to produce the predicted pollution map of the nitrate concentrations in groundwater dataset.

• 한국환경농학회지
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• 제16권2호
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• pp.199-205
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• 1997

한강 상류와 하류의 농업 형태가 유역의 토양 및 수질오염에 미치는 영향을 평가하기 위하여 농업 형태가 다른 대표 소유역을 선정하여 소유역 하천수와 지하수질에 대한 현황을 조사하였다. 조사 대상지는 강원도 횡성군 둔내면 주천강 지류의 일반 농업지대, 평창군 대관령일대의 대규모 축산 단지와 고랭지 농업지대, 춘천시 소양강 지류 농업 지대일원, 그리고 경기도 구리시 왕숙천 일대의 도시 근교 농업지대이었다. 조사결과 중금속은 특정한 시기의 왕숙천을 제외하고 불검출 또는 미량으로 큰 문제가 없는 것으로 나타났으나, 질소와 인, 특히 질산태 질소의 오염은 하천수와 지하수 일부에서 높은 수준으로 나타났다. 조사된 소유역의 하천수 수질중 pH, EC, COD등은 농업 용수로 적합한 수준이었으나, 시설 원예 지대에서 관개용으로 사용하고 있는 일부 지하수 중 질산태 질소의 농도가 높은 상태로 작물을 재배할 때 물관리와 시비 관리에 세심한 주의를 하여야 하는 수준이었다. 작물 재배를 위해 농업용수를 사용할 때에는 EC와 질산태 질소의 농도 수준을 동시에 고려하여 적절히 사용하는 것이 바람직하다.

• KSBB Journal
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• 제27권2호
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• pp.75-85
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• 2012

Although, microbial arsenic mobilization by dissimilatory arsenate-reducing bacteria (DARB) and the practical use to the removal technology of arsenic from contaminated soil are expected, most previous research mainly has been focused on the geochemical circulation of arsenic. Therefore, in this review we summarized the previously reported DARB to grasp the characteristic for bioremediation of arsenic. Evidence of microbial growth on arsenate is presented based on isolate analyses, after which a summary of the physiology of the following arsenate-respiring bacteria is provided: Chrysiogenes arsenatis strain BAL-$1^T$, Sulfurospirillum barnesii, Desulfotomaculum strain Ben-RB, Desulfotomaculum auripigmentum strains OREX-4, GFAJ-1, Bacillus sp., Desulfitobacterium hafniense DCB-$2^T$, strain SES-3, Citrobacter sp. (TSA-1 and NC-1), Sulfurospirillum arsenophilum sp. nov., Shewanella sp., Chrysiogenes arsenatis BAL-$1^T$, Deferribacter desulfuricans. Among the DARB, Citrobacter sp. NC-1 is superior to other dissimilatory arsenate-reducing bacteria with respect to arsenate reduction, particularly at high concentrations as high as 60 mM. A gram-negative anaerobic bacterium, Citrobacter sp. NC-1, which was isolated from arsenic contaminated soil, can grow on glucose as an electron donor and arsenate as an electron acceptor. Strain NC-1 rapidly reduced arsenate at 5 mM to arsenite with concomitant cell growth, indicating that arsenate can act as the terminal electron acceptor for anaerobic respiration (dissimilatory arsenate reduction). To characterize the reductase systems in strain NC-1, arsenate and nitrate reduction activities were investigated with washed-cell suspensions and crude cell extracts from cells grown on arsenate or nitrate. These reductase activities were induced individually by the two electron acceptors. Tungstate, which is a typical inhibitory antagonist of molybdenum containing dissimilatory reductases, strongly inhibited the reduction of arsenate and nitrate in anaerobic growth cultures. These results suggest that strain NC-1 catalyzes the reduction of arsenate and nitrate by distinct terminal reductases containing a molybdenum cofactor. This may be advantageous during bioremediation processes where both contaminants are present. Moreover, a brief explanation of arsenic extraction from a model soil artificially contaminated with As (V) using a novel DARB (Citrobacter sp. NC-1) is given in this article. We conclude with a discussion of the importance of microbial arsenate reduction in the environment. The successful application and use of DARB should facilitate the effective bioremediation of arsenic contaminated sites.

• 식물조직배양학회지
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• 제27권1호
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• pp.31-37
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• 2000

인삼 (Panax ginseng C.A. Meyer)의 뿌리로부터 유도한 모상근의 proton 방출은 철이온의 결핍 또는 nitrate나 ammonium이온에 의해 증가되었다. 철이온 결핍에 의한 수소이온 방출의 증가는 정단 조직에서 나타나는 반면에 nitrate와 ammonium 이온에 의한 수소이온 방출은 기저부위에서 높게 나타났다. 배지의 pH변화에 따른 무기이온의 흡수는 무기이온의 종류에 따라 다양한 양상을 나타냈다. $Ca^{2＋}$, $Mg^{2＋}$ 그리고 Fe$^{2＋}$ 는 pH 4.8 에서 가장 높은 흡수를 나타냈으며, Mn$^{2＋}$, $Zn^{2＋}$ 그리고 Cu$^{2＋}$ 는 pH 3.8에서 가장 높았다. HPLC를 이용한 유리당 및 ginsenoside분석에서, 인삼모상근 유리당 구성의 주성분은 sucrose, glucose 그리고 fructose 였으며, glucose와 fructose는 pH 6.8에서 sucrose는 pH 4.8 에서 함량이 가장 높았다. 반면에 ginsenosides와 phenolic compound의 함량은 MS 배지의 표준인 pH 5.8 에서 가장 높았다. pH 변화에 대한 인삼모상근의 이러한 생리적 반응은 인삼근의 적변유발과 관련된 무기이온의 mobilization 및 흡수기작의 구명을 위한 모델 시스템으로 이용할 수 있는 가능성을 제시하였다.

• Ocean Science Journal
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• 제43권4호
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• pp.195-208
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• 2008

This paper evaluates whether a thermodynamic ocean-carbon model can be used to predict the monthly mean global fields of the surface-water partial pressure of $CO_2$ ($pCO_{2SEA}$) from sea surface salinity (SSS), temperature (SST), and/or nitrate ($NO_3$) concentration using previously published regional total inorganic carbon ($C_T$) and total alkalinity ($A_T$) algorithms. The obtained $pCO_{2SEA}$ values and their amplitudes of seasonal variability are in good agreement with multi-year observations undertaken at the sites of the Bermuda Atlantic Timeseries Study (BATS) ($31^{\circ}50'N$, $60^{\circ}10'W$) and the Hawaiian Ocean Time-series (HOT) ($22^{\circ}45'N$, $158^{\circ}00'W$). By contrast, the empirical models predicted $C_T$ less accurately at the Kyodo western North Pacific Ocean Time-series (KNOT) site ($44^{\circ}N$, $155^{\circ}E$) than at the BATS and HOT sites, resulting in greater uncertainties in $pCO_{2SEA}$ predictions. Our analysis indicates that the previously published empirical $C_T$ and $A_T$ models provide reasonable predictions of seasonal variations in surface-water $pCO_{2SEA}$ within the (sub) tropical oceans based on changes in SSS and SST; however, in high-latitude oceans where ocean biology affects $C_T$ to a significant degree, improved $C_T$ algorithms are required to capture the full biological effect on $C_T$ with greater accuracy and in turn improve the accuracy of predictions of $pCO_{2SEA}$.

• Journal of Applied Biological Chemistry
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• 제44권3호
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• pp.135-139
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• 2001

To observe the changes in isotopic composition (${\delta}^{15}N$) of $NO_3{^-}$ during denitrification, an incubation experiment using soil treated with nitrification inhibitor (2-chloro-6-trichloromethyl-pyridine) under water-saturated condition was conducted for 153 h. The $NO_3-N$ concentration decreased from 73.3 to $20.6mg\;kg^{-1}$ during the incubation period, with denitrification rate constant of $0.00905h^{-1}$, and ${\delta}^{15}N$ values of $NO_3-N$ increased from +0.9 to +25.5‰ with decreasing the $NO_3-N$ concentration. The increase in the ${\delta}^{15}N$ values of $NO_3-N$ is due to kinetic isotope fractionation, which always results in $^{15}N$ enrichment of the substrate. The isotopic fractionation factor calculated in this study was 1.0196, an indication that 1.96% more $^{14}NO_3{^-}$ reacted at a given time interval than a comparable number of $^{15}NO_3{^-}$. The ${\delta}^{15}N$ values measured through the incubation study showed a good agreement with the results calculated from the Fochts isotope fractionation model. Our results suggest that when the ${\delta}^{15}N$ of $NO_3{^-}$ is used for tracing the fate of N, the kinetic isotope fractionation associated with denitrification must be taken into consideration.

• 한국작물학회:학술대회논문집
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• 한국작물학회 2017년도 9th Asian Crop Science Association conference
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• pp.4-4
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• 2017

Atmospheric carbon dioxide enrichment ($eCO_2$) often increases soil nitrous oxide ($N_2O$) emissions, but the underlying mechanisms are not fully understood. Emerging evidence suggests that $eCO_2$ alters plant N preference in favor of ammonium ($NH_4{^+}-N$) over nitrate ($NO_3{^-}-N$). Yet, whether and how this attributes to the enhancement of $N_2O$ emissions has not been investigated. We examined the effects of $eCO_2$ on soil $N_2O$ emissions in the presence of two N forms ($NH_4{^+}-N$ or $NO_3{^-}-N$), using wheat (Triticum aestivum L.) as a model plant. Our results showed that N forms dominated $eCO_2$ effects on plant and microbial N utilization, and thus soil $N_2O$ emissions. Elevated $CO_2$ significantly increased the rate and the sum of $N_2O$ emissions by three to four folds when $NO_3{^-}-N$, but not $NH_4{^+}-N$, was supplied. Enhanced $N_2O$ emission was related to the reduced plant $NO_3{^-}-N$ uptake in wheat. We propose a new conceptual model in which $eCO_2$-inhibition of plant $NO_3{^-}-N$ uptake and/or $CO_2$-enhancement of soil labile C enhances the N and/or C availability for denitrifiers and increases the intensity and/or the duration of $N_2O$ emissions. Together, these findings suggest that to enhance plant N use efficiency and reduce $N_2O$ emission, crop breeding and management need to consider altered plant preference of N sources under future $CO_2$ scenarios.

• 한국환경과학회지
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• 제12권2호
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• pp.217-225
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• 2003

Samples of size-fractionated PM10 (airborne particulate matter with aerodynamic diameter less than $10\mu\textrm{m}$) were collected at an urban site in Jeju city from May to September 2002. The mass concentration and chemical composition of the samples were measured. The data sets were then applied to the CMB receptor model to estimate the source contribution of PM10 in Jeju area. The average PM10 mass concentration was 28.80$\mu\textrm{g}/m^3$ ($24.6~33.49\mu\textrm{g}/m^3$), and the FP (fine particle with aerodynamic diameter less than $2.l\mu\textrm{m}$ fraction in PM10 was approximately 8% higher than the CP (coarse particle with aerodynamic diameter greater than $2.l\mu\textrm{m}$ and less than $10\mu\textrm{m}$ fraction in PM10. The CP composition was obviously different from the FP composition, that is, the most abundant water soluble species was nitrate ion in the FP, but sulfate ion in the CP. Also sulfur was the most dominant element in the FP, however, sodium was that in the CP. From CMB receptor model results, it was found that road dust was the largest contributor to the CP mass concentration (45% of the CP) and ammonium nitrate, domestic boiler, and marine aerosol were major sources to the CP mass. However, the secondary aerosol was the most significant contributor to the FP mass concentration (45% of the FP). In this study, it was suggested that the contributions of soil dust and gasoline vehicle became very low due to collinearity with road dust and diesel vehicle, respectively.

• Advances in environmental research
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• 제5권3호
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• pp.153-167
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• 2016

We applied multilayer perceptron (MLP) and radial basis function (RBF) neural network in upstream and downstream water quality stations of the Karaj Reservoir in Iran. For both neural networks, inputs were pH, turbidity, temperature, chlorophyll-a, biochemical oxygen demand (BOD) and nitrate, and the output was dissolved oxygen (DO). We used an MLP neural network with two hidden layers, for upstream station 15 and 33 neurons in the first and second layers respectively, and for the downstream station, 16 and 21 neurons in the first and second hidden layer were used which had minimum amount of errors. For learning process 6-fold cross validation were applied to avoid over fitting. The best results acquired from RBF model, in which the mean bias error (MBE) and root mean squared error (RMSE) were 0.063 and 0.10 for the upstream station. The MBE and RSME were 0.0126 and 0.099 for the downstream station. The coefficient of determination ($R^2$) between the observed data and the predicted data for upstream and downstream stations in the MLP was 0.801 and 0.904, respectively, and in the RBF network were 0.962 and 0.97, respectively. The MLP neural network had acceptable results; however, the results of RBF network were more accurate. A sensitivity analysis for the MLP neural network indicated that temperature was the first parameter, pH the second and nitrate was the last factor affecting the prediction of DO concentrations. The results proved the workability and accuracy of the RBF model in the prediction of the DO.

• 한국분무공학회지
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• 제20권1호
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• pp.53-58
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• 2015

In this study, we prepare the Ag nanofluids synthesized by the chemical reduction method and measure the extinction coefficient of those nanofluids at a wavelength of 632.8 nm. The Ag nanofluids are synthesized by the chemical reduction method using silver nitrate ($AgNO_3$) and sodium borohydride ($NaBH_4$) in water and ethylene glycol (EG). For stable dispersion of Ag particles in the base liquids, polyvinyl pyrrolidone (PVP) is added as a surfactant. The extinction coefficient of manufactured Ag nanofluids is measured by an in-house developed measurement system at the wavelength of 632.8 nm. The results show that the extinction coefficient of water-based and EG-based Ag nanofluids is linearly increased with respect to the particle loadings. Moreover, it is shown that the extinction coefficient of EG-based Ag nanofludis is higher than that of water-based Ag nanofluids. Finally we compare the experimental results with both the Maxwell-Garnett model and Rayleigh scattering approximation model, and they demonstrate that the Rayleigh scattering approximation model is reasonably predict the extinction coefficient of Ag nanofluids using hydraulic diameter of silver nanoparticle.