• 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 the Korean earth science society
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• v.44 no.4
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• pp.291-306
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• 2023

Shallow groundwater in rural areas is primarily polluted by agricultural activities. Nitrate-nitrogen is an indicator of artificial pollution. In this study, the hydrochemical characteristics and nitrate-nitrogen pollution of shallow groundwater were examined in two agricultural villages (Hyogyo-ri and Sinan-ri) in Chungcheongnam-do Province, Korea. Physicochemical quality analysis of shallow groundwater and stream water in the field, and chemical analysis in the laboratory were conducted from July 2020 to October 2021. In Hygyo-ri and Sinan-ri villages, shallow groundwater mainly belonged to the Ca-Cl, Ca-H CO₃, Na-HCO₃, and Na-Cl types, whereas stream water predominantly belonged to the Ca-HCO₃ type. The nitrate-nitrogen concentration in shallow groundwater varied depending on the season, displaying an increased concentration of nitrate-nitrogen in the dry season compared to the rainy season. Stream water may be influenced by runoff into villages from the surrounding area, although both shallow groundwater and stream water are affected by artificial pollution. In addition, the nitrate-nitrogen concentration in stream water was lower than that in shallow groundwater.

• Membrane and Water Treatment
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• v.10 no.4
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• pp.313-320
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• 2019

Increasing the concentration of nitrate ions in the soil solution and then leaching it to underground aquifers increases the concentration of nitrate in the water, and can cause many health and ecological problems. This study was conducted to evaluate the vulnerability of Meymeh aquifer to nitrate pollution. In this research, sampling of 10 wells was performed according to standard sampling principles and analyzed in the laboratory by spectrophotometric method, then; the nitrate concentration zonation map was drawn by using intermediate models. In the drastic model, the effective parameters for assessing the vulnerability of groundwater aquifers, including the depth of ground water, pure feeding, aquifer environment, soil type, topography slope, non-saturated area and hydraulic conductivity. Which were prepared in the form of seven layers in the ARC GIS software, and by weighting and ranking and integrating these seven layers, the final map of groundwater vulnerability to contamination was prepared. Drastic index estimated for the region between 75-128. For verification of the model, nitrate concentration data in groundwater of the region were used, which showed a relative correlation between the concentration of nitrate and the prepared version of the model. A combination of two vulnerability map and nitrate concentration zonation was provided a qualitative aquifer classification map. According to this map, most of the study areas are within safe and low risk, and only a small portion of the Meymeh Aquifer, which has a nitrate concentration of more than 50 mg / L in groundwater, is classified in a hazardous area.

• Proceedings of the Korea Air Pollution Research Association Conference
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• 1999.04a
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• pp.111-112
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• 1999

• The Journal of Engineering Geology
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• v.12 no.4
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• pp.471-484
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• 2002

Alluviums in the Keum River watershed cover an areal extent of $3,029{\;}\textrm{km}^2$ and contain about 8.1 billion tons of groundwater. However, the waters are severely polluted by nitrate, possibly due to the application of nitrogen fertilizer (>250 N kg/ha) on agricultural land. This paper aims to elucidate the pollution status and behaviors of nitrate in alluvial groundwaters in the Keum River watershed area, based on regional hydrogeochemical study. Most of the collected samples (n = 186) are polluted by nitrate (average = 42.2 mg/L, maximum = 295 mg/L). About 29% of the samples have the nitrate concentrations exceeding Korean Drinking Water Standard (44 mg/L $NO_3$). The distribution of nitrate concentrations in the study area is largely dependant on geochemical environments of alluvial aquifers. In particular, the decrease of redox potential of alluvial groundwaters showed a good correlation with the decreases of nitrate, iron, and manganese concentrations. Thus, the change of redox state in alluvial aquifers, likely reflecting their sedimentary environments, controls both the behavior and fate of nitrogen compounds and their natural attenuation (denitrification) in aquifers. A carbon-rich, silty layer within alluvium strata forms a reducing condition and possesses a buffering capacity on nitrate pollution.

• Journal of Korean Society of Water and Wastewater
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• v.25 no.6
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• pp.989-994
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• 2011

Nitrate contamination of groundwater is a common problem throughout intensive agriculture areas (non-point source pollution). Current processes (e.g. ion exchange and membrane separation) for nitrate removal have various disadvantages. The objective of this study was to evaluate electrochemical method such as electroreduction using bipolar ZVI packed bed electrolytic cell to remove nitrate from groundwater at field pilot. In addition ammonia stripping tower continuously removed up to 77.0% of ammonia. Bipolar ZVI packed bed electrolytic cell also removed E.coli. In the field pilot experiment for groundwater in 'I' city (average nitrate 30~35 mg N/L, pH 6.4), maximum 99.9% removal of nitrate was achieved in the applied 600 V.

• Journal of Korean Society on Water Environment
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• v.27 no.1
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• pp.120-128
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• 2011

Korea Groundwater Quality Monitoring Network is a database of annual groundwater quality survey results to prevent groundwater pollution. We estimated contamination index (CI) values for each type of land use, and analyzed temporal trends of pollutant concentration data in the Groundwater Quality Monitoring Network from 2001 to 2009. Among the pollutants considered in the database, the concentrations of nitrate and chloride were higher than their standards. In the case of nitrate, recreation parks, golf courses and general waste dumping regions showed increasing trends according to linear regression analysis, whereas industrial complexes and residential regions of urgan and recreation parks showed increasing trends in the chloride concentration data. According to multiple variable linear regression analysis, EC, pH and topography were major factors influencing CI values. These results suggest that groundwater with a high CI value and increasing trend is vulnerable for potential contamination, which requires more careful groundwater pollution control.

• 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.

• Journal of the Society of Disaster Information
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• v.16 no.3
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• pp.542-549
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• 2020

Purpose: As nitrate nitrogen produced during fermentation of liquid fertilizer is a pollution indicator of water, in this study, four research areas where liquid fertilizer was sprayed were selected, and a model was designed to estimate the concentration of nitrate nitrogen pollution. Method: Prior to shooting on site, a spectrum library was constructed by dividing the ratio of liquid fertilizer into 5 groups: 0%, 25%, 50%, 75%, and 100%. PLSR (Partial least squares regression) method was applied to hyperspectral images acquired in the study area based on the aspect of spectrum. Result: The behavior of nitrate nitrogen was confirmed by 1st and 2nd differentiation of the spectrum of the constructed liquid fertilizer. PLSR concentration estimation modeling was implemented using images from field experiments and compared with actual concentration of nitrate nitrogen. Conclusion: When comparing the PLSR concentration estimation model with the actual concentration of nitrate nitrogen, it was measured that the detection is possible in high concentration areas where the concentration of nitrate nitrogen is 70mg/kg or more.

• Economic and Environmental Geology
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• v.57 no.2
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• pp.271-280
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• 2024

This study examines the European Union (EU)'s policies on managing nitrate contamination in groundwater and provides implications for the future groundwater management in South Korea. Initiated by the 1991 Nitrate Directive, the EU has pursued a multifaceted approach to reduce agricultural nitrate pollution through sustainable ('good') farming practices, regular nitrate level monitoring, and designating Nitrate Vulnerable Zones. Further policy integrations, like the Water Framework Directive and Groundwater Directive, have established comprehensive protection strategies, including the use of pollutant threshold values. Recently, the 2019 Green Deal escalated efforts against nitrates, aligning with broader environmental and climate objectives. This review aims to explore these developments, highlighting key mitigation strategies against nitrate pollution, and providing valuable insights for the future sustainable groundwater nitrate management in South Korea, emphasizing the importance of preventive measures and collaborative efforts to restore and improve groundwater quality.