Groundwater Nitrate Contamination Risk Assessment: A Comparison of Parametric Systems and Simulation Modelling
Dario Sacco, Marco Offi, Marina De Maio and Carlo Grignani
DOI : 10.3844/ajessp.2007.117.125
American Journal of Environmental Sciences
Volume 3, Issue 3
Groundwater nitrate contamination is a source of rising concern that has been faced through the introduction of several regulations in different countries. However the methodologies used in the definition of Nitrate Vulnerable Zones are not included in the regulations. The aim of this work was to compare different methodologies, used to asses groundwater nitrate contamination risks, based on parametric systems or simulation modelling. The work was carried out in Piedmont, Italy, in an area characterised by intensive animal husbandry, high N load, a shallow water table and a coarse type of sub-soil sediments. Only N loads from agricultural non-point sources were considered. Different methodologies with different level of information have been compared to determine the groundwater nitrate contamination risk assessment: N load, IPNOA index, the intrinsic contamination risk from nitrates, leached N and N concentration of the soil solution estimated by the simulation model. The good correlation between the IPNOA index and the intrinsic nitrate contamination risk revealed that the 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 of the soils increased the variability in comparison to the IPNOA index. The leached N and N concentration in the leaching were closely correlated. The dilution effect of percolated water was almost negligible. Both methodologies were slightly correlated to the N fertilisation and the two indexes. The correlations related to the intrinsic nitrate contamination risk was higher than those related to IPNOA, and this means that the effect of taking into account soil parameters increases the correlation to the prediction of the simulation model.
© 2007 Dario Sacco, Marco Offi, Marina De Maio and Carlo Grignani. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.