Nitrate contamination of groundwater: A conceptual management framework

Almasri, Mohammad N.

doi:10.1016/j.eiar.2006.11.002

Cited by 180 publications

(110 citation statements)

References 46 publications

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“…The origins of groundwater salinization are diverse such as (1) seawater intrusion (Park et al 2005;Bouchaou et al 2008;Custodio 2010;Cruz et al 2011); (2) evapo-concentration (concentration by evapotranspiration) of airborne salts (Alcalá and Custodio 2008;Guan et al 2010); (3) hydrogeological characteristics of the aquifer (Ben Moussa et al 2011;Farber et al 2007); (4) water-rock interaction, such as dissolution, leaching and hydrolysis of minerals (Van der Weijden and Pacheco 2003; Abid et al 2011;Jalali 2007) and (5) human influence, such as return flows from irrigated agricultural activities (Oren et al 2004;Almasri 2007;García-Garizabal and Causape 2011).…”

Section: Introductionmentioning

confidence: 99%

Combining the advantages of neural networks using the concept of committee machine in the groundwater salinity prediction

Barzegar

Moghaddam

2016

Model. Earth Syst. Environ.

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Salinity is one of the main factors in groundwater quality monitoring. The main objectives of this study are to investigate and compare the accuracy of three different neural computing techniques, multi-layer perceptron neural network (MLP), radial basis function neural network (RBFNN), and generalized regression neural network (GRNN), in prediction of groundwater salinity of the Tabriz plain confined aquifer, expressed by electrical conductivity [EC (lS/cm)], and to employ an integrated method to combine the advantages of neural network models utilizing the concept of committee machine. To develop the models, 93 data records of groundwater samples were collected from East Azarbaijan regional water company. The data set including Ca 2? , Mg 2? , Na ? , SO 4 2-and Cl -concentrations as the inputs and salinity [EC (lS/ cm)] as an output were divided into two subsets; training and testing based on cross validation approach. After training and testing of the models, the performance of the models were evaluated using root mean square errors (RMSE), determination coefficient (R 2 ) and mean absolute error (MAE). The performance criteria of the constructed neural network models showed that RBFNN model has the best performance in predicting salinity. The committee neural network (CNN) combined the results of salinity predicted from MLP, RBFNN and GRNN, each of them has a weight factor showing its contribution in overall prediction. The optimal weights were derived by a genetic algorithm (GA). The results of salinity prediction derived from CNN showed that the CNN performs better than any one of the individual ANNs acting alone for predicting groundwater salinity.

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Section: Introductionmentioning

confidence: 99%

Combining the advantages of neural networks using the concept of committee machine in the groundwater salinity prediction

Barzegar

Moghaddam

2016

Model. Earth Syst. Environ.

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“…Many areas of the United States and other countries have reported significant contamination of groundwater from septic tanks. Ground water contamination is usually related to the density of septic systems 7 .Nitrogen in organic form and ammonium can be converted by bacteria in aerobic conditions into nitrite and nitrate, a process termed 'nitrification'. Nitrate in anaerobic systems can be reduced by other strains of bacteria to nitrous oxide or nitrogen gas, by 'denitrification'.…”

Section: Introductionmentioning

confidence: 99%

Extent of Nitrate and Nitrite Pollution in Ground Water of Rural Areas of Lucknow, U.P., India

Verma¹,

Rawat²,

More³

2014

Curr World Environ

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The present world is facing problems with a wide variety of pollutants. Water pollution is a major global problem. It has been suggested that it is the leading worldwide cause of deaths and diseases and that it accounts for the deaths of more than 14,000 people daily in Lucknow Capital of Uttar Pradesh in India.Nitrate and Nitrite pollution is one of groundwater's most commonly identified contaminants, an indicator of serious pollution as they are associated with septic waste and agricultural endeavours, leads to numerous health problems to human beings and animals.4 rural areas of Lucknow were selected and 15 samples from each station to check the level of nitrite and nitrate parameters in groundwater. Further our studies reveal that the extent of nitrate and nitrite varied with reference to sampled site and maximum nitrate was found to be 250.224 and maximum nitrite was 1.8998 both are high.

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“…Many studies have shown high correlation between agriculture and high nitrate concentrations in groundwater (Oakes and Young, 1981;Bundy et al, 1994;Voudouris et al, 2004;Torr, 2009). Extensive use of fertilizers is considered to be the main source of nitrates that reach groundwater worldwide (Almasri, 2007;Darnault et al, 2005;Carey et al, 2009). However, nitrate pollution is not restricted to rural areas; untreated sewage of urban areas is characterized by high contents of NH 4 + .…”

Section: Introductionmentioning

confidence: 99%

Nitrate mass balance in agricultural areas of intensive fertilizer application: the North Maresme aquifer system case study (Spain)

Hernández-Espriú

Domínguez-Mariani

Reyna-Gutiérrez

et al. 2013

BSGM

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Nitrate pollution is a worldwide problem in groundwater, as it may limit water supply and increase health risk when high concentrations are present. The North Maresme coastal alluvial groundwater-dependent agrosystem, one of the most heavily nitrate polluted aquifers in Spain, is located 70 km northeast from Barcelona. Field monitoring campaigns performed in this aquifer yielded nitrate concentrations up to ten times the permitted limit for drinking water suggested by the World Health Organization (50 mg/l), with a maximum concentration of 567 mg/l and a mean of 137.5 mg/l. Nitrate contamination was quantified by means of a mass balance, taking into account the main hydrogeological and anthropogenic processes that control the behavior of this ion in the aquifer, such as recharge, groundwater extraction, irrigation and fertilizer application to crops. Nitrate balance is an integrative approach for assessing nitrate loading based on linking different sources of nitrates to a groundwater balance. This methodology considers nitrate as a conservative ion because the geological-geochemical medium behaves under oxidant conditions. Results show that the studied aquifer system receives, yields and retains approximately 935, 844 and 91 tons of NO 3 -/year, respectively. This methodology can be applied to other nitrate-contaminated aquifers with similar conditions to improve management of fertilizers practices in areas of intensive agriculture.Keywords: nitrate mass balance, nitrate contamination, groundwater balance, fertilizers, Maresme. Resumen

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Nitrate contamination of groundwater: A conceptual management framework

Cited by 180 publications

References 46 publications

Combining the advantages of neural networks using the concept of committee machine in the groundwater salinity prediction

Combining the advantages of neural networks using the concept of committee machine in the groundwater salinity prediction

Extent of Nitrate and Nitrite Pollution in Ground Water of Rural Areas of Lucknow, U.P., India

Nitrate mass balance in agricultural areas of intensive fertilizer application: the North Maresme aquifer system case study (Spain)

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