Robust cluster analysis for detecting physico-chemical typologies of freshwater from wells of the plain of Friuli (northeastern Italy)

An approach for the characterisation of the groundwater system of the southern plain of FriuliVenezia Giulia Region (Italy) is proposed on the basis of its physico-chemical composition, in order to detect multivariate patterns for unpolluted waters typical of specific areas in the plain, as well as for eventual polluted zones. The analytical data are relative to 38 wells (depth ranging from 20 to 200m) sampled in three different periods along a year. Ten physicochemical parameters were determined: conductivity, temperature, dissolved oxygen, calcium, magnesium, chlorides, nitrates, sulphates, atrazine and desethylatrazine. Cluster analysis (CA) provides the methodological bases for detecting the classes of freshwater being typical for the considered plain: partitioning around medoids (PAM) and fuzzy clustering are considered. The number of classes to be characterised and the clustering algorithm are selected by comparing the average silhouette index for models counting from 2 to 10 clusters; six classes obtained by PAM partition the data set at best. Plotting the frequencies of cluster membership for each well on a map permits the association of the six classes of waters to five easily recognisable geographical areas and to one group of two wells that are highly polluted by nitrates and triazines. Averages and ranges of values for physico-chemical parameters of each class can be provided according to this methodology, defining a set of values being characteristic for the composition of waters belonging to the classes of wells identified in the considered plain.