Multivariate statistical techniques such as principal component (PCA) and cluster analyses, were applied for the interpretation of chemical composition of groundwater in a zone of Colline Metallifere (southern Tuscany, Italy). The aim was to classify groundwater into hydrochemical facies and groups, and to identify the main sources and hydrogeochemical processes governing the spatial and temporal variation of groundwater chemistry. Multivariate statistical analysis involved the main physico-chemical parameters (temperature, pH, electrical conductivity, redox potential) and the major ions (Ca2+, Mg2+, Na+, K+, HCO3-, Cl-, SO42-) of about 200 groundwater samples. Water samples were collected at 5 sites (springs and monitoring wells) from 2004 to 2014, over an area of about 3 km2 in the zone of Montioni (Grosseto). This area was affected by a widespread circulation of hydrothermal fluids that altered rocks (silicization and kaolinite alteration) and formed alunite deposits as well as manganese and cinnabar mineralizations. The results of PCA analysis allowed to identify the hydrochemical facies of groundwater (SO4-Ca, HCO3-Ca and Cl-Na), the contribution of each hydrochemical facies to groundwater chemistry in each collection site, as well as the influence of water table fluctuations. PCA analysis revealed a component characterized by potassium, iron and manganese ions likely related to the alunite deposits and hydrothermal mineralizations present throughout the study area. This feature is important to define the origin of the HCO3-Ca and Cl-Na waters. Cluster analysis carried out with the k-means method, categorized the sampling locations into five dissimilar groups, and showed that there were temporal variations of groundwater chemistry within the same group. This study demonstrates that multivariate statistical analysis is a useful and powerful tool for the characterization and interpretation of groundwater chemistry in a zone affected by hydrothermal circulation.

Russo, C., Bianchi, S., PROTANO, G., & SALLEOLINI, M. (2015). Multivariate statistical characterization of groundwater chemistry: a case study in a zone affected by hydrothermal circulation. In Il Pianeta Dinamico: sviluppi e prospettive a 100 anni da Wegener - Congresso congiunto SIMP-AIV-SoGeI-SGI (pp.136-136). Roma : Società Geologica Italiana.

Multivariate statistical characterization of groundwater chemistry: a case study in a zone affected by hydrothermal circulation

PROTANO, GIUSEPPE;SALLEOLINI, MASSIMO
2015

Abstract

Multivariate statistical techniques such as principal component (PCA) and cluster analyses, were applied for the interpretation of chemical composition of groundwater in a zone of Colline Metallifere (southern Tuscany, Italy). The aim was to classify groundwater into hydrochemical facies and groups, and to identify the main sources and hydrogeochemical processes governing the spatial and temporal variation of groundwater chemistry. Multivariate statistical analysis involved the main physico-chemical parameters (temperature, pH, electrical conductivity, redox potential) and the major ions (Ca2+, Mg2+, Na+, K+, HCO3-, Cl-, SO42-) of about 200 groundwater samples. Water samples were collected at 5 sites (springs and monitoring wells) from 2004 to 2014, over an area of about 3 km2 in the zone of Montioni (Grosseto). This area was affected by a widespread circulation of hydrothermal fluids that altered rocks (silicization and kaolinite alteration) and formed alunite deposits as well as manganese and cinnabar mineralizations. The results of PCA analysis allowed to identify the hydrochemical facies of groundwater (SO4-Ca, HCO3-Ca and Cl-Na), the contribution of each hydrochemical facies to groundwater chemistry in each collection site, as well as the influence of water table fluctuations. PCA analysis revealed a component characterized by potassium, iron and manganese ions likely related to the alunite deposits and hydrothermal mineralizations present throughout the study area. This feature is important to define the origin of the HCO3-Ca and Cl-Na waters. Cluster analysis carried out with the k-means method, categorized the sampling locations into five dissimilar groups, and showed that there were temporal variations of groundwater chemistry within the same group. This study demonstrates that multivariate statistical analysis is a useful and powerful tool for the characterization and interpretation of groundwater chemistry in a zone affected by hydrothermal circulation.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11365/1007363