Controlling nitrate pollution: An integrated approach

O’Shea, Lucy; Wade, Andrew J.

doi:10.1016/j.landusepol.2008.10.017

Cited by 30 publications

(12 citation statements)

References 45 publications

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“…(1) The right amount of denitrification granular sludge was placed in a 1.5 mL centrifuge tube and soaked with 4% paraformaldehyde for 4 h at 4 • C. (2) The granular sludge samples were washed three times by 1 × PBS (phosphate-buffered saline). (3) The samples were spotted on a gelatin-coated slide, treated with 50, 80 and 100% ethanol dehydration for 3 min, respectively, and allowed to air dry as previously described.…”

Section: Fluorescent In Situ Hybridization Assaysmentioning

confidence: 99%

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Effective and characteristics of anthraquinone-2,6-disulfonate (AQDS) on denitrification byParacoccus versutussp.GW1

Guo

Lian

et al. 2013

Environmental Technology

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The effects of anthraquinone-2,6-disulfonate (AQDS) on microbial populations and the biocatalysing characteristics of AQDS on denitrifying strain GW1 are discussed. The results showed that microbial population abundances were 30, 6, 12 and 18% during the different periods (1st, 5th, 10th and 20th days). Strain GW1 utilized AQDS as an electron acceptor in the respiration chain and reduced AQDS to hydroquinone (QH2). The nitrate removal efficiency of strain GW1 was increased 1.14-1.63 fold with 0.04-0.32 mmol/L AQDS. A linear correlation was found between the nitrate removal reaction constant k[gNO3(-)- N/(gVSS x d)] (where VSS is volatile suspended solids) and AQDS concentration (CAQDS), which was k = 19.332 C(AQDS) + 11.115 (R2 = 0.9749). The stabilized oxidation-reduction potential (ORP) values with AQDS were lower 22-92 mv than the control during the denitrification process. The concentration of nitrite sequentially accumulated up to around 250 mg/L when nitrate have removed completely. The results suggested that AQDS as redox mediator was capable of biocatalysing the nitrate and nitrite removal rate.

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Section: Fluorescent In Situ Hybridization Assaysmentioning

confidence: 99%

“…[1,2] Nitrate pollutants can cause eutrophication of water environment, deterioration of water quality and potential hazard to human. [3][4][5] Wastewater treatment of nitrate can be divided into two main categories: physicochemical and biological technologies.…”

Section: Introductionmentioning

confidence: 99%

Effective and characteristics of anthraquinone-2,6-disulfonate (AQDS) on denitrification byParacoccus versutussp.GW1

Guo

Lian

et al. 2013

Environmental Technology

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“…Nitrogen leaching from agricultural land is a main pollutant in many countries in the world [7,8]. In agricultural areas of the European Union (EU), fertilizer contribution as nonpoint source pollution to the surface water is estimated to be 55% [9].…”

Section: Introductionmentioning

confidence: 99%

Modeling Agricultural Land Management to Improve Understanding of Nitrogen Leaching in an Irrigated Mediterranean Area in Southern Turkey

Karnez¹,

Sagir²,

Gavan³

et al. 2017

Water Quality

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Nitrogen (N) cycle dynamics and its transport in the ecosystem were always an attracting subject for the researchers. Calculation of N budget in agricultural systems with use of different empirical statistical methods is common practice in OECD and EU countries. However, these methodologies do not include climate and water cycle as part of the process. On the other hand, big scale studies are labor and work intensive. As a solution, various computer modeling approaches have been used to predict N budget and related N parameters. One of them is internationally established Soil and Water Assessment (SWAT) model, which was developed especially for modeling agricultural catchments. The aim of this study was to improve understanding of N leaching with simulation of agricultural land management (fertilization, irrigation, and plant species) in hydrological heavily modified watershed with irrigation-depended agriculture under Mediterranean climate. The study was conducted in Lower Seyhan River Plain Irrigation District (Akarsu) of 9495 ha in Cukurova region of southern Turkey. Intensive and extensive water and nitrogen monitoring data (2008)(2009)(2010)(2011)(2012)(2013)(2014), soil properties, cropping pattern, and crop rotation were used for the SWAT model build, calibration, and validation of the model.

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“…In addition, the learning abilities of the ANNs have enabled them to be adaptive to the changes in the system [13]. There are a number of studies into the prediction and modelling of nitrate in various water reservoirs, such as Şengörür and Öz [14] in Mahmudiye Stream; Garcet et al [15], Yesilnacar et al [16] in Harran Plain, O'Shea and Wade [17] in Kennet Catchment, Kunkel et al [18] in Weser Basin, Peña-Haro et al [19] in Castilla-La Mancha Region, İleri et al [20] in Uluabat Lake, Aguilera et al [21] in coastal waters in a Spanish tourist area, Palani et al [22] in Singapore coastal waters, Singh et al [23] in Gomti River (India). This study aims to estimate the changes in the amount of nitrate in Yeşilırmak Watershed using surface water and underground water of the nitrate content determined by General Directorate of State Hydraulic Works using Artificial Neural Networks (ANNs).…”

Section: Introductionmentioning

confidence: 99%

Modelling Nitrate Prediction of Groundwater and Surface Water Using Artificial Neural Networks

Benzer¹,

Benzer²

2018

Journal of Polytechnic

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Bu makaleye şu şekilde atıfta bulunabilirsiniz(To cite to this article): Benzer S. ve Benzer R., "Modelling nitrate prediction of groundwater and surface water using artificial neural networks", Politeknik Dergisi, 21(2): 321-325, (2018).Erişim linki (To link to this article): http://dergipark.gov.tr/politeknik/archive DOI: 10.2339/politeknik.385434 Politeknik Dergisi, 2018;21(2):321-325 Journal of Polytechnic, 2018;21(2) ABSTRACTThis study aims to estimate the changes in the amount of nitrate in Yeşilırmak Watershed using surface water and underground water of the nitrate content determined by General Directorate of State Hydraulic Works using Artificial Neural Networks (ANNs). This study was conducted in 2010 at 30 stations (9 groundwater, 18 surface water and 3 closed water source) in Yeşilırmak Watershed. Nitrate ranged from 0.341 to 77.700 mg/L, with an average value of 17.870 mg/L. In this study, changes in the amount of nitrate in Amasya using groundwater and surface water in the basin of the nitrate content determined by the Provincial Directorate of Agriculture modeling was presented with an approach based on Artificial Neural Networks (ANNs) and predict the nitrate value for the year of 2020 and 2030. Thus, the nitrate levels of water samples obtained from 30 stations water supplies found to be under the limits of Turkish and international codex of drinking water intended for human consumption. Yapay Sinir Ağları Kullanılarak Yer Altı ve YüzeySularındaki Nitrat Tahmininin Modellenmesi

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Controlling nitrate pollution: An integrated approach

Cited by 30 publications

References 45 publications

Effective and characteristics of anthraquinone-2,6-disulfonate (AQDS) on denitrification byParacoccus versutussp.GW1

Effective and characteristics of anthraquinone-2,6-disulfonate (AQDS) on denitrification byParacoccus versutussp.GW1

Modeling Agricultural Land Management to Improve Understanding of Nitrogen Leaching in an Irrigated Mediterranean Area in Southern Turkey

Modelling Nitrate Prediction of Groundwater and Surface Water Using Artificial Neural Networks

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