Abstract
BackgroundGround waters are an important resource of water supply for human health and activities. Groundwater uses and applications are often related to its composition, which is increasingly influenced by human activities.In fact the water quality of groundwater is affected by many factors including precipitation, surface runoff, groundwater flow, and the characteristics of the catchment area. During the years 2004-2007 the Agricultural and Food Authority of Apulia Region has implemented the project “Expansion of regional agro-meteorological network” in order to assess, monitor and manage of regional groundwater quality. The total wells monitored during this activity amounted to 473, and the water samples analyzed were 1021. This resulted in a huge and complex data matrix comprised of a large number of physical-chemical parameters, which are often difficult to interpret and draw meaningful conclusions. The application of different multivariate statistical techniques such as Cluster Analysis (CA), Principal Component Analysis (PCA), Absolute Principal Component Scores (APCS) for interpretation of the complex databases offers a better understanding of water quality in the study region.ResultsForm results obtained by Principal Component and Cluster Analysis applied to data set of Foggia province it’s evident that some sampling sites investigated show dissimilarities, mostly due to the location of the site, the land use and management techniques and groundwater overuse. By APCS method it’s been possible to identify three pollutant sources: Agricultural pollution 1 due to fertilizer applications, Agricultural pollution 2 due to microelements for agriculture and groundwater overuse and a third source that can be identified as soil run off and rock tracer mining.ConclusionsMultivariate statistical methods represent a valid tool to understand complex nature of groundwater quality issues, determine priorities in the use of ground waters as irrigation water and suggest interactions between land use and irrigation water quality.
Highlights
Ground waters are an important resource of water supply for human health and activities
The groundwater quality is affected by many factors including precipitation, surface runoff, groundwater flow, and the characteristics of the catchment area
The application of different multivariate statistical techniques such as cluster analysis, principal component analysis, source apportionment by multiple linear regression on absolute principal component scores for interpretation of the complex databases offers a better understanding of water quality in the study region
Summary
Ground waters are an important resource of water supply for human health and activities. Groundwater uses and applications are often related to its composition, which is increasingly influenced by human activities. The water quality of groundwater is affected by many factors including precipitation, surface runoff, groundwater flow, and the characteristics of the catchment area. The total wells monitored during this activity amounted to 473, and the water samples analyzed were 1021 This resulted in a huge and complex data matrix comprised of a large number of physical-chemical parameters, which are often difficult to interpret and draw meaningful conclusions. Ground water serves a number of important functions for humanity and nature. These functions are often related to groundwater composition, which is increasingly influenced by human activities. The groundwater quality is affected by many factors including precipitation, surface runoff, groundwater flow, and the characteristics of the catchment area. Groundwater flow depends on natural factors (e.g. elevation differences and lithology) and on human interventions (e.g. groundwater extraction and drainage)
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