Literature DB >> 12837260

Evaluating factors influencing groundwater vulnerability to nitrate pollution: developing the potential of GIS.

Iain R Lake1, Andrew A Lovett, Kevin M Hiscock, Mark Betson, Aidan Foley, Gisela Sünnenberg, Sarah Evers, Steve Fletcher.   

Abstract

The 1991 EU Nitrate Directive was designed to reduce water pollution from agriculturally derived nitrates. England and Wales implemented this Directive by controlling agricultural activities within their most vulnerable areas termed Nitrate Vulnerable Zones. These were designated by identifying drinking water catchments (surface and groundwater), at risk from nitrate pollution. However, this method contravened the Nitrate Directive because it only protected drinking water and not all waters. In this paper, a GIS was used to identify all areas of groundwater vulnerable to nitrate pollution. This was achieved by constructing a model containing data on four characteristics: the quality of the water leaving the root zone of a piece of land; soil information; presence of low permeability superficial (drift) material; and aquifer properties. These were combined in a GIS and the various combinations converted into a measure of vulnerability using expert knowledge. Several model variants were produced using different estimates of the quality of the water leaving the root zone and contrasting methods of weighting the input data. When the final models were assessed all produced similar spatial patterns and, when verified by comparison with trend data derived from monitored nitrate concentrations, all the models were statistically significant predictors of groundwater nitrate concentrations. The best predictive model contained a model of nitrate leaching but no land use information, implying that changes in land use will not affect designations based upon this model. The relationship between nitrate levels and borehole intake depths was investigated since there was concern that the observed contrasts in nitrate levels between vulnerability categories might be reflecting differences in borehole intake depths and not actual vulnerability. However, this was not found to be statistically important. Our preferred model provides the basis for developing a new set of groundwater Nitrate Vulnerable Zones that should help England and Wales to comply with the EU Nitrate Directive.

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Year:  2003        PMID: 12837260     DOI: 10.1016/s0301-4797(03)00095-1

Source DB:  PubMed          Journal:  J Environ Manage        ISSN: 0301-4797            Impact factor:   6.789


  7 in total

1.  Mapping of coastal aquifer vulnerable zone in the south west coast of Kanyakumari, South India, using GIS-based DRASTIC model.

Authors:  S Kaliraj; N Chandrasekar; T Simon Peter; S Selvakumar; N S Magesh
Journal:  Environ Monit Assess       Date:  2014-11-19       Impact factor: 2.513

2.  The nitrate time bomb: a numerical way to investigate nitrate storage and lag time in the unsaturated zone.

Authors:  L Wang; A S Butcher; M E Stuart; D C Gooddy; J P Bloomfield
Journal:  Environ Geochem Health       Date:  2013-06-26       Impact factor: 4.609

3.  Spatial and seasonal distribution of nitrate-N in groundwater beneath the rice-wheat cropping system of India: a geospatial analysis.

Authors:  Parvesh Chandna; M L Khurana; Jagdish K Ladha; Milap Punia; R S Mehla; Raj Gupta
Journal:  Environ Monit Assess       Date:  2010-09-24       Impact factor: 2.513

4.  Nitrates and phosphates in cave waters of Kraków-Częstochowa Upland, southern Poland.

Authors:  Jacek Różkowski; Kazimierz Różkowski; Oimahmad Rahmonov; Hanna Rubin
Journal:  Environ Sci Pollut Res Int       Date:  2017-09-22       Impact factor: 4.223

5.  Impact of intensive agricultural practices on drinking water quality in the Evros region (NE Greece) by GIS analysis.

Authors:  C Nikolaidis; P Mandalos; A Vantarakis
Journal:  Environ Monit Assess       Date:  2007-09-16       Impact factor: 2.513

6.  Assessment of groundwater nitrate contamination hazard in a semi-arid region by using integrated parametric IPNOA and data-driven logistic regression models.

Authors:  Hossein Mojaddadi Rizeei; Omer Saud Azeez; Biswajeet Pradhan; Hayder Hassan Khamees
Journal:  Environ Monit Assess       Date:  2018-10-04       Impact factor: 2.513

7.  Groundwater vulnerability to pollution mapping of Ranchi district using GIS.

Authors:  R Krishna; J Iqbal; A K Gorai; G Pathak; F Tuluri; P B Tchounwou
Journal:  Appl Water Sci       Date:  2014-05-17
  7 in total

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