A methodology for space-time classification of groundwater quality

Environ Monit Assess. 2006 Apr;115(1-3):95-117. doi: 10.1007/s10661-006-6547-3. Epub 2006 Feb 15.

Abstract

Safeguarding groundwater from civil, agricultural and industrial contamination is matter of great interest in water resource management. During recent years, much legislation has been produced stating the importance of groundwater as a source for drinking water supplies, underlining its vulnerability and defining the required quality standards. Thus, schematic tools, able to characterise the quality and quantity of groundwater systems, are of very great interest in any territorial planning and/or water resource management activity. This paper proposes a groundwater quality classification method which has been applied to a real aquifer, starting from several studies published by the Italian National Hydrogeologic Catastrophe Defence Group (GNDCI). The methodology is based on the concentration values of several parameters used as indexes of the natural hydro-chemical water condition and of potential man-induced modifications of groundwater quality. The resulting maps, although representative of the quality, do not include any information on its evolution in time. In this paper, this "stationary" classification method has been improved by crossing the quality classes with three indexes of temporal behaviour during recent years. It was then applied to data from monitoring campaigns, performed in spring and autumn, from 1990 to 1996, in the plain of Modena aquifer (central Italy). The results are reported in the form of space-time classification table and maps.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Environmental Monitoring* / methods
  • Environmental Monitoring* / statistics & numerical data
  • Fresh Water / analysis*
  • Quality Control
  • Space-Time Clustering
  • Water Pollution / statistics & numerical data*
  • Water Supply / legislation & jurisprudence
  • Water Supply / standards*
  • Water Supply / statistics & numerical data