Groundwater nitrate contamination is a source of rising concern that has been faced throughthe introduction of several regulations in different countries. However the methodologies used in thedefinition of Nitrate Vulnerable Zones are not included in the regulations. The aim of this work was tocompare different methodologies, used to asses groundwater nitrate contamination risks, based onparametric systems or simulation modelling. The work was carried out in Piedmont, Italy, in an areacharacterised by intensive animal husbandry, high N load, a shallow water table and a coarse type ofsub-soil sediments. Only N loads from agricultural non-point sources were considered. Differentmethodologies with different level of information have been compared to determine the groundwaternitrate contamination risk assessment: N load, IPNOA index, the intrinsic contamination risk fromnitrates, leached N and N concentration of the soil solution estimated by the simulation model. Thegood correlation between the IPNOA index and the intrinsic nitrate contamination risk revealed thatthe parameters that describe the soil in this area did not lead to a different classification of the parcels.The intrinsic nitrate contamination risk was greatly influenced by N fertilisation, however the effect ofthe soils increased the variability in comparison to the IPNOA index. The leached N and Nconcentration in the leaching were closely correlated. The dilution effect of percolated water wasalmost negligible. Both methodologies were slightly correlated to the N fertilisation and the twoindexes. The correlations related to the intrinsic nitrate contamination risk was higher than thoserelated to IPNOA, and this means that the effect of taking into account soil parameters increases thecorrelation to the prediction of the simulation model.

Groundwater nitrate contamination risk assessment: a comparison of parametric systems and simulation modelling / Sacco, D; Offi, M; DE MAIO, Marina; Grignani, C.. - In: AMERICAN JOURNAL OF ENVIRONMENTAL SCIENCES. - ISSN 1553-345X. - ELETTRONICO. - 3:(2007), pp. 117-125. [10.3844/ajessp.2007.117.125]

Groundwater nitrate contamination risk assessment: a comparison of parametric systems and simulation modelling

DE MAIO, MARINA;
2007

Abstract

Groundwater nitrate contamination is a source of rising concern that has been faced throughthe introduction of several regulations in different countries. However the methodologies used in thedefinition of Nitrate Vulnerable Zones are not included in the regulations. The aim of this work was tocompare different methodologies, used to asses groundwater nitrate contamination risks, based onparametric systems or simulation modelling. The work was carried out in Piedmont, Italy, in an areacharacterised by intensive animal husbandry, high N load, a shallow water table and a coarse type ofsub-soil sediments. Only N loads from agricultural non-point sources were considered. Differentmethodologies with different level of information have been compared to determine the groundwaternitrate contamination risk assessment: N load, IPNOA index, the intrinsic contamination risk fromnitrates, leached N and N concentration of the soil solution estimated by the simulation model. Thegood correlation between the IPNOA index and the intrinsic nitrate contamination risk revealed thatthe parameters that describe the soil in this area did not lead to a different classification of the parcels.The intrinsic nitrate contamination risk was greatly influenced by N fertilisation, however the effect ofthe soils increased the variability in comparison to the IPNOA index. The leached N and Nconcentration in the leaching were closely correlated. The dilution effect of percolated water wasalmost negligible. Both methodologies were slightly correlated to the N fertilisation and the twoindexes. The correlations related to the intrinsic nitrate contamination risk was higher than thoserelated to IPNOA, and this means that the effect of taking into account soil parameters increases thecorrelation to the prediction of the simulation model.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/1641932
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