• 자원환경지질
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• 제57권3호
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• pp.329-342
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• 2024

The exponential increase in nitrate pollution of river water poses an immediate threat to public health and the environment. This contamination is primarily due to various human activities, which include the overuse of nitrogenous fertilizers in agriculture and the discharge of nitrate-rich industrial effluents into rivers. As a result, the accurate prediction and identification of contaminated areas has become a crucial and challenging task for researchers. To solve these problems, this work leads to the prediction of nitrate contamination using machine learning approaches. This paper presents a novel approach known as Grey Wolf Optimizer (GWO) based on the Stacked Ensemble approach for predicting nitrate pollution in the Cauvery Delta region of Tamilnadu, India. The proposed method is evaluated using a Cauvery River dataset from the Tamilnadu Pollution Control Board. The proposed method shows excellent performance, achieving an accuracy of 93.31%, a precision of 93%, a sensitivity of 97.53%, a specificity of 94.28%, an F1-score of 95.23%, and an ROC score of 95%. These impressive results underline the demonstration of the proposed method in accurately predicting nitrate pollution in river water and ultimately help to make informed decisions to tackle these critical environmental problems.

• 한국지하수토양환경학회지:지하수토양환경
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• 제13권4호
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• pp.40-53
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• 2008

지하수의 오염 예측 기법의 개선을 위하여 미국 환경청(U.S. EPA)에서 개발된 지하수 오염 취약성 평가방법인 DRASTIC 모델(Aller et al., 1987), Panagopoulos et al.(2006)가 제안한 M-DRASTIC, Rupert(1999)가 제안한 LSDG 방법을 충남 금산 지역에 적용하였다. 충남 금산 지역은 농업을 비롯한 다양한 토지이용 특성과 아울러 다양한 지질, 지형, 토양 분포를 나타내어 지하수 오염예측 기법의 개선을 위한 연구에 최적의 조건을 갖추고 있다. DRASTIC 평가를 위하여 149개의 충적층 관정에 대한 수질 및 수리지질 조사가 수행되었으며, 지하수의 질산염 이온의 농도와 각 예측 방법으로부터 도출된 지수와의 상관관계 분석을 통하여 예측방법의 효용성을 평가하였다. EPA DRASTIC은 지하수 심도, 순 충진량, 대수층 매질, 토양 매질, 지형 경사, 비포화대 매질, 수리전도도 등 수리지질학적 인자들을 이용하여 지하수 오염 취약성을 상대적으로 평가하는 방법으로, 지하수의 잠재오염원에 대한 정보가 포함되지 않으므로 지하수 오염을 예측하는데 비효율적이다. 본 연구 결과, 관정 주변 150 m 영역의 DRASTIC 지수와 해당 관정의 질산염 이온 농도의 상관관계는 0.058로 낮게 나타났다. 한편, M-DRASTIC의 경우 DRASTIC과 사용하는 인자는 같으나 등급과 가중치를 실제 질산염 이온 농도의 비율로부터 산출한다. 등급만을 수정하였을 경우 0.245, 등급과 가중치를 모두 수정하였을 경우 질산염 이온 농도와의 상관관계는 0.400로 지하수 오염 예측율이 개선되었다. LSDG 방법은 토지이용(Land use), 토양 배수(Soil drainage), 지하수면 심도(Depth to water), 지질(Geology)를 특성에 따라서 구분하고 해당 지역의 질산염 이온 농도 평균의 차이를 통계적으로 분석하여 등급을 산정하는 기법으로, 금산 지역에 적용한 결과 질산염 이온 농도와의 상관관계가 0.415로 개선되었다. 결과적으로 LSDG를 적용하였을 경우 EPA DRASTIC 보다 질산염 이온 농도와의 상관관계가 0.357만큼 개선되었다. M-DRASTIC과 LSDG의 예측율이 증가하는 것은, 이 방법들의 등급과 가중치에는 현재의 오염현황이 반영되기 때문으로 질산염 이온 오염 가능성을 귀납적으로 예측하기 때문이다. LSDG의 예측율이 가장 높은 이유는 LSDG에는 잠재오염원으로 분류되는 토지이용이 포함되었기 때문인 것으로 판단된다.

• 한국수산과학회지
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• 제30권5호
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• pp.782-793
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• 1997

식물 플랑크톤의 인위적인 용승조건 하에서 생리적 적응 (shift-up)을 알아보기 위하여, 안정동위원소인 $^{15}N-KNO_3$를 이용하여 실험실에서 Dunaliella tertiolecta의 질산염 흡수 능력을 측정하였다. 그 결과 예상과는 달리 최대 질소비 질산염 흡수 속도 $(V_{NO3})$와 초기 질산염 농도 사이에는 유의성 있는 상관관계가 나타나지 않았다. 그러나 최대 질산염 운반 속도 $(\rho_{NO3})$와 $25\;{\mu}M$ 이하의 초기 질산염 농도 사이에는 강한 상관성이 나타났으며, 이는 배양 세포의 생리적인 상태에 의한 영향에도 기인한다. $\rho_{NO3}$의 증가는 주로 입자성 유기 질소 농도의 증가와 함께 부분적으로는 $V_{NO3}$의 증가에 기인한다. 식물 플랑크톤 개체군이 심하게 shift-down되었을 경우 질산염 흡수의 생리적 적응은 높은 초기 질산염 농도에서 주목할 만큼 저해되었다. 최대 $V_{NO3}$ 또는 $\rho_{NO3}$이 나타나는 시기는 초기 질산염 농도와 관련이 있다. 높은 초기 질산염 농도에서는 $V_{NO3}$와 $\rho_{NO3}$의 최대치가 낮은 초기 질산염 농도에서보다 $1\~2$일 정도 늦게 나타났다. 이는 Zimmerman et al. (1987)의 shift-up 모델에서의 예측과 상반되는 결과이다. Shift-up 과정은 명백히 내부적인 시간순서와 초기 질산염 농도에 의하여 조절되지만, $V_{NO3}$의 크기는 질산염 농도의 변화에 거의 영향을 받지 알았다.

• 한국대기환경학회지
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• 제9권2호
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• pp.154-159
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• 1993

Theoretical prediction of the equilibrium of temperature and relative humidity dependance involving $HNO_{3(g)}-NH_{3(g)}$ and $NH_4NH_{3(s, aq)}$ was compared with atmospheric measurement of particulate nitrate$(NO_3^-)$, Ammonia-Nitric Acid partial pressure product $([$NH_{3(g)}][HNO_{3(g)}]ppb^2$) by a triple filter pack sampler from Oct 1991 to July 1992. The measured $HNO_3NH_3$ concentration product K was greater than equilibrium constant $K_p$ calculated from thermodynamic data of $NH_4NO_{3(s, aq)}-HNO_{3(g)}-NH_{3(g)}$ during fall, winter and spring. But K was lower than $K_p$ in summer. K was greater than $K_p$ as the result of supersaturation by air pollution, particularly anthropogenic $NH_3$.The reason of $K < K_p$ was due to removal of particulate nitrate$(NO_3^-)$ by rainout and washout. $NH_4NO_3$ which consists mainly of particulate nitrate is formed by reaction between $HNO_3$ and $NH_3$. As a result of the removal of particulate nitrate$(NO_3^-)$ by rainout and washout, concentrations of $HNO_3$ and $NH_3$ are decreased by equilibrium transfer(Le Chatelier's Law) in atmosphere.

• 한국연초학회지
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• 제35권1호
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• pp.1-6
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• 2013

Near InfraRed Spectroscopy(NIRS) is a quick and accurate analytical method to measure multiple components in tobacco manufacturing process. This study was carried out to develop calibration equation of near infrared spectroscopy for the prediction of the amount of chemical components and hot water solubles(HWS) of reconstituted tobacco leaf. Calibration samples of reconstituted tobacco leaf were collected from every lot produced during one year. The calibration equation was formulated as modified partial least square regression method (MPLS) by analyzing laboratory actual values and mathematically pre-treated spectra. The accuracy of the acquired equation was confirmed with the standard error of prediction(SEP) of chemical components in reconstituted tobacco leaf samples, indicated as coefficient of determination($R^2$) and prediction error of sample unacquainted, followed by the verification of model equation of laboratory actual values and these predicted results. As a result of monitoring, the standard error of prediction(SEP) were 0.25 % for total sugar, 0.03 % for nicotine, 0.03 % for chlorine, 0.16 % for nitrate, and 0.38 % for hot water solubles. The coefficient of determination($R^2$) were 0.98 for total sugar, 0.97 for nicotine, 0.96 for chlorine, 0.98 for nitrate and 0.92 for hot water solubles. Therefore, the NIRS calibration equation can be applicable and reliable for determination of chemical components of reconstituted tobacco leaf, and NIRS analytical method could be used as a rapid and accurate quality control method.

• 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.

• ALGAE
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• 제34권3호
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• pp.237-251
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• 2019

The dinoflagellate genus Alexandrium is known to often form harmful algal blooms causing human illness and large-scale mortality of marine organisms. Therefore, the population dynamics of Alexandrium species are of primary concern to scientists and aquaculture farmers. The growth rate of the Alexandrium species is the most important parameter in prediction models and nutrient conditions are critical parameters affecting the growth of phototrophic species. In Korean coastal waters, Alexandrium affine and Alexandrium fraterculus, of similar sizes, often form red-tide patches together. Thus, to understand bloom dynamics of A. affine and A. fraterculus, growth rates and nitrate uptake of each species as a function of nitrate ($NO_3$) concentration at $100{\mu}mol\;photons\;m^{-2}s^{-1}$ under 14-h light : 10-h dark and continuous light conditions were determined using a nutrient repletion method. With increasing $NO_3$ concentration, growth rates and $NO_3$ uptake of A. affine or A. fraterculus increased, but became saturated. Under light : dark conditions, the maximum growth rates of A. affine and A. fraterculus were 0.45 and $0.42d^{-1}$, respectively. However, under continuous light conditions, the maximum growth rate of A. affine slightly increased to $0.46d^{-1}$, but that of A. fraterculus largely decreased. Furthermore, the maximum nitrate uptake of A. affine and A. fraterculus under light : dark conditions were 12.9 and $30.1pM\;cell^{-1}d^{-1}$, respectively. The maximum nitrate uptake of A. affine under continuous light conditions was $16.4pM\;cell^{-1}d^{-1}$. Thus, A. affine and A. fraterculus have similar maximum growth rates at the given $NO_3$ concentration ranges, but they have different maximum nitrate uptake rates. A. affine may have a higher conversion rate of $NO_3$ to body nitrogen than A. fraterculus. Moreover, a longer exposure time to the light may confer an advantage to A. affine over A. fraterculus.

• 한국지하수토양환경학회지:지하수토양환경
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• 제8권2호
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• pp.55-61
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• 2003

본 연구의 목적은 반응벽체 매질로서 제올라이트의 표면특성을 계면활성제로 변화시켜 제조한 SMZ로 충진된 컬럼 시험을 수행함으로써 질산성 질소의 제거특성을 조사하는 것이다. SMZ컬럼 실험에서 얻어진 파과곡선의 해석결과를 이용하여 일차원 이류확산모델을 통해 예측되는 반응벽체의 질산성 질소(N $O_3$$^{-}$-N)제거효과와 실제 소규모 반응벽체 설계의 기본 인자 도출을 통한 설계방법론을 제시하였다 SMZ충진 컬럼 내 질산성 질소의 파과에 대한 예측이 선형 평형 흡착 이동모델을 이용하여 수행될 수 있음을 알 수 있었다. 유량을 변화시키면서 수행한 파과실험을 통해 얻은 파과시간과 반감기( $t_{\frac{1}{2}}$)는 유량의 크기에 반비례함을 알 수 있었다. 질산성 질소농도가 50mg/L인 지하수를 10 $m^3$/day의 처리용량으로 음용수 수질기준인 10mg/L로 감소시키고자 할 경우, 300 ton의 SMZ를 사용하여 약 6년 간 (5.8년)매질의 교체 없이 SMZ 반응벽체를 사용할 수 있을 것으로 예측되었으며, 초기 건설비용과 조사비용 등을 제외하고는 6년에 한 번씩 교체를 위한 매질비용으로 약 1억 6천 5백만 원과 주기적으로 간단한 유지관리와 모니터링 비용이 소요될 것으로 예측되었다.

• Journal of Biosystems Engineering
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• 제36권2호
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• pp.96-108
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• 2011

Several methods of in-field measurements of Nitrogen and other soil properties using cores extracted by a hydraulic soil sampler were evaluated. A prototype core scanner was built to accommodate Veris Technologies commercial Vis-NIRS equipment. The testing result for pH, P and Mg were close to RPD (Ratio of Prediction to Deviation = Standard deviation/RMSE) of 2, however the scanner could not achieve the goal of RPD of 2 on some other properties, especially on nitrate nitrogen ($NO_3$) and potassium (K). In situ NIRS/EC probe showed similar results to the core scanner; pH, P and Mg were close to RPD of 2, while $NO_3$ and K were RPD of 1.5 and 1.2, respectively. Correlations between estimations using the probe and the core scanner were strong, with $r^2$ > 0.7 for P, Mg, Total N, Total C and CEC. Preliminary results for mid-IR spectroscopy showed an $r^2$ of 0.068 and an RMSE for nitrate (N) of 18 ppm, even after the removal of calcareous samples and possible N outlier. After removal of calcareous samples on a larger sample set, results improved considerably with an $r^2$ of 0.64 and RMSE of 6 ppm. However, this was only possible after carbonate samples were detected and eliminated, which would not be feasible under in-field measurements. Testing of $NO_3$ and K ion-selective electrodes (ISEs) revealed promising results, with acceptable errors measuring soil solutions containing nitrate and potassium levels that are typical of production agriculture fields.

• Corrosion Science and Technology
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• 제20권2호
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• pp.52-61
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• 2021

This study using experimental design and linear regression technique was implemented in order to predict the pitting potential of stainless steel in marine environments, with the target materials being AL-6XN and STS 316L. The various variables (inputs) which affect stainless steel's pitting potential included the pitting resistance equivalent number (PRNE), temperature, pH, Cl^- concentration, sulfate levels, and nitrate levels. Among them, significant factors affecting pitting potential were chosen through an experimental design method (screening design, full factor design, analysis of variance). The potentiodynamic polarization test was performed based on the experimental design, including significant factor levels. From these testing methods, a total 32 polarization curves were obtained, which were used as training data for the linear regression model. As a result of the model's validation, it showed an acceptable prediction performance, which was statistically significant within the 95% confidence level. The linear regression model based on the full factorial design and ANOVA also showed a high confidence level in the prediction of pitting potential. This study confirmed the possibility to predict the pitting potential of stainless steel according to various variables used with experimental linear regression design.