• Economic and Environmental Geology
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• v.57 no.3
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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.

• Journal of Soil and Groundwater Environment
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• v.13 no.4
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• pp.40-53
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• 2008

Groundwater pollution prediction methods have been developed to plan the sustainable groundwater usage and protection from potential pollution in many countries. DRASTIC established by US EPA is the most widely used groundwater vulnerability mapping method. However, the DRASTIC showed limitation in predicting the groundwater contamination because the DRASTIC method is designed to embrace only hydrogeologic factors. Therefore, in this study, three different methods were applied to improve a groundwater pollution prediction method: US EPA DRASTIC, Modified-DRASTIC suggested by Panagopoulos et al. (2006), and LSDG (Land use, Soil drainage, Depth to water, Geology) proposed by Rupert (1999). The Modified-DRASTIC is the modified version of the DRASTIC in terms of the rating scales and the weighting coefficients. The rating scales of each factor were calculated by the statistical comparison of nitrate concentrations in each class using the Wilcoxon rank-sum test; while the weighting coefficients were modified by the statistical correlation of each parameter to nitrate concentrations using the Spearman's rho test. The LSDG is a simple rating method using four factors such as Land use, Soil drainage, Depth to water, and Geology. Classes in each factor are compared by the Wilcoxon rank-sum test which gives a different rating to each class if the nitrate concentration in the class is significantly different. A database of nitrate concentrations in groundwaters from 149 wells was built in Keumsan area. Application of three different methods for assessing the groundwater pollution potential resulted that the prediction which was represented by a correlation (r) between each index and nitrate was improved from the EPA DRASTIC (r = 0.058) to the modified rating (r = 0.245), to the modified rating and weights (r = 0.400), and to the LSDG (r = 0.415), respectively. The LSDG seemed appropriate to predict the groundwater pollution in that it contained land use as a factor of the groundwater pollution sources and the rating of each class was defined by a real pollution nitrate concentration.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.30 no.5
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• pp.782-793
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• 1997

To study the physiological adaptation (shift-up) of phytoplankton under the simulated upwelling conditions, nitrate uptake capacity of Dunaliella tertiolecta batch culture was measured in the laboratory using the stable isotope $^{15}N-KNO_3$. Contrary to the expected, there was no significant relationship between the maximum $V_{NO3}$ (nitrogen specific nitrate uptake rate) and the initial nitrate concentration. However, there was a strong relationship between the maximum $\rho_{NO3}$ (nitrate transport rate) and the initial nitrate concentration of $<25\;{\mu}M$, which was also influenced by the physiological status of the culture. The increase in $V_{NO3}$ was mainly due to the increase in PON (particulate organic nitrogen) concentration and partly due to the increase in $V_{NO3}$. When the phytoplankton population was severely shifted-down, the physiological adaptation of nitrate uptake was significantly inhibited at high initial nitrate concentrations. The timing of the maximum $V_{NO3}$ or $\rho_{NO3}$ was related to the initial nitrate concentration. At higher initial nitrate concentrations, maxima in $V_{NO3}$ and $\rho_{NO3}$ occurred 1 or 2 days later than at lower nitrate concentrations. This relationship was the opposite to the prediction from the shift-up model of Zimmerman et al. (1987), The shift-up process is apparently controlled by an internal time sequence and the initial nitrate concentration, but the magnitude of $V_{NO3}$ was affected little by changes in nitrate concentration.

• Journal of Korean Society for Atmospheric Environment
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• v.9 no.2
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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.

• Journal of the Korean Society of Tobacco Science
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• v.35 no.1
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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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• v.5 no.3
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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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• v.34 no.3
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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.

• Journal of Soil and Groundwater Environment
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• v.8 no.2
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• pp.55-61
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• 2003

The objective of this study was to investigate the characteristics of nitrate removal by conducting the column test in order to see the performance of surfactant modified zeolite (SMZ) as a permeable reactive barrier material. The prediction of nitrate removal was tested using the one-dimensional advective-dispersive model fitted to the experimental breakthrough curve. A methodology for scaling up to in-situ permeable reactive barrier was also proposed. The breakthrough of nitrate in the column packed with SMZ was well predicted using linear equilibrium adsorption model. The breakthrough time and half-life obtained by breakthrough experiment with variation of flowrates were decreased with the increase of flowrates. When 10㎥/day of groundwater containing the 50 mg/l of nitrate is to be treated to satisfy the potable water quality criteria (10 mg/l) by SMZ reactive barrier, 300 tons of SMZ and about 6 years of breakthrough time will be required, suggesting that 165 million wons are needed as barrier material expenses in each 6 years besides the initial design and construction expenses and the minimal monitoring and maintenance expenses.

• Journal of Biosystems Engineering
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• v.36 no.2
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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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• v.20 no.2
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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.