Multivariate statistical analysis supporting the hydrochemical characterization of groundwater and surface water: a case study in northern Italy

Rotiroti, Marco; Zanotti, Chiara; Fumagalli, Letizia; Taviani, Sara; Stefania, Gennaro A.; Patelli, M; Nava, Veronica; Soler, Valentina; Sacchi, Elisa; Leoni, Barbara

doi:10.3301/rol.2019.17

Cited by 9 publications

(7 citation statements)

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“…Various techniques that are commonly used to assess the groundwater quality are Water Quality Index (WQI), Piper diagram, Gibbs plot for drinking purpose and Sodium Absorption Ratio (SAR), Wilcox diagram, US Salinity Laboratory (USSL) diagram, kelly ratio, permeability index, percentage sodium, magnesium hazards for irrigation purpose (Chitsazan et al, 2019, He et al, 2019, Adimalla et al, 2019b. In addition to the above, multivariate statistical techniques such as principal component analysis and hierarchical cluster analysis methods are also used to evaluate the water quality by categorizing the similarity of chemical compounds and degree of contamination in water (Rotiroti et al, 2019, Merz et al, 2019, Masoud et al, 2018, Ferchichi et al, 2018, Ibrahim et al, 2019, Shankar et al,2011d The significant objective of the current study is to measure the chemical characteristics of groundwater in Yercadu Taluk, Salem, India to predict its suitability for drinking and agricultural purposes. The specific objectives of the study include the evaluation of the hydro-chemical characteristics of groundwater for drinking purpose, calculation of WQI and irrigation indices to ensure the water quality for drinking and agricultural purpose respectively.…”

Section: Introductionmentioning

confidence: 99%

Non-Carcinogenic Risk Assessment of Groundwater in Southern Part of Salem District in Tamilnadu, India

Balamurugan¹,

Kumar²,

Shankar

et al. 2020

J. Chil. Chem. Soc.

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The main objective of this study was to evaluate the groundwater quality for domestic, agriculture use and to describe fluoride contamination in groundwater and their impacts on human health. 67 groundwater samples were collected and analyzed for major ions. Water Quality Index (WQI), Piper diagram and Gibbs diagrams were calculated to measure the suitability of groundwater for drinking purpose. The hazards index value was calculated to estimate the noncarcinogenic risk to adult (male, female) and children suggested by the United States Environmental Protection Agency (USEPA). The irrigation indices were calculated to evaluate the quality of water for irrigation purpose. Statistical methods such as principal component and hierarchical cluster analysis were used to analyses the interrelationship of data. Hydrochemistry of the samples shows, the major ions in the order of Ca 2+ >Mg 2+ >Na + and Cl->SO4in the study area. WQI value of groundwater, 74.62% of sample locations are good and 25.38 % of sample locations need primary treatment for drinking purpose. The results of the hazards index show that 65.67% of the sample locations exceeds the tolerable limit for non-carcinogenic risk (greater than one) for children higher than the risk level for Male and female. Statistical report of PCA and HCA reveals that Ca-Na-HCO3-F has positive loading and TDS-EC has negative loading. The study results show that rock-water interaction and anthropogenic activities are the major factors that influence the quality of groundwater. The continuous intake of excess concentration fluoride causes bone diseases and teeth problems.

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Section: Introductionmentioning

confidence: 99%

Non-Carcinogenic Risk Assessment of Groundwater in Southern Part of Salem District in Tamilnadu, India

Balamurugan¹,

Kumar²,

Shankar

et al. 2020

J. Chil. Chem. Soc.

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“…In this regard, developing and implementing a comprehensive strategy for the water consumption management based on the hydro-ecological characteristics of the Kuban River basin at a regional scale is highly important for many large cities of Kuban. The effective principle for consolidating the hydro-chemical data sets is known to be a multivariate cluster analysis (Rotiroti et al, 2019). Multivariate statistical methods are often used to characterise and assess surface and freshwater quality, as well as to establish patterns of temporal and spatial changes in drainage basins characteristics caused induced by natural and anthropogenic factors (Hyvlud et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Assessing the Pollution Level in the Kuban River Basin by Multivariate Cluster Analysis

Snezhko

Benin

Lukyanets

et al. 2020

AJW

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Considering features of hydrological conditions for hydro-chemical system, this paper analyses the performance of the hydro-ecological status of the Kuban river basin.. The results of the study on water chemical composition depending on the distance from the source are presented. By comparing the results with the reference values of water quality, increased aluminium, zinc, and copper content was established. Respective dendrograms of hydro-ecological studies obtained according to performed analysis for the Kuban River and its tributaries are presented. The relevance of the findings received is p<0.0005 and the correlation coefficient corresponds to 0.935...1. The results of multivariate cluster analysis showed that the Kuban basin has an increased content of particular heavy metals such as aluminium, copper, and zinc.

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“…2 Improved hydrochemical characterization of aquifers in such highly heterogeneous areas, decoupling the relation between groundwater quality and aquifer lithology, could enhance their effective management. [6][7][8] Pazand 9 observed that sustainable development and management of the quality of groundwater resources in arid and semiarid lands is achievable through improved understanding of geochemical evolution and groundwater processes.…”

Section: Introductionmentioning

confidence: 99%

“…6 Güler et al 10 noted that the interpretation of associations exclusively based on graphical techniques is fraught with difficulties owing to the simultaneous nature of aquifer processes and their effects. Recent studies by Nwankwoala, 11 Pazand, 9 Moghimi, 12 and Rotiroti et al 6 established the value of multivariate techniques such as principal component analysis (PCA) and hierarchical clustering analysis (HCA) in the provision of quantitative measures of correlation between water quality parameters and fundamental aquifer processes. PCA is widely utilized to characterize groundwater pollution sources, 9 while HCA is used to deduce spatial variability among sampling sites.…”

Section: Introductionmentioning

confidence: 99%

“…PCA is widely utilized to characterize groundwater pollution sources, 9 while HCA is used to deduce spatial variability among sampling sites. 13 An integrative application of PCA and HCA was employed by Rotiroti et al 6 and Moghimi 12 to interpret processes affecting groundwater hydrochemistry and by Yidana et al, 7 to evaluate evolutionary trends of groundwater dynamics and by Majeed et al 14 to assess spatial patterns of pollutants in water. Several investigators 15,16 utilized z-score or log conversions to normalize physical and chemical data acquired from groundwater sources.…”

Section: Introductionmentioning

confidence: 99%

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Relationship Between Aquifer Processes and Groundwater Quality: A Case of Olbanita Aquifer System, Lower Baringo Basin, Kenya Rift

Sosi

Getabu

Maobe

et al. 2019

Air, Soil and Water Research

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A hydrogeochemical relation has been hypothesized through the analyses of physiochemical data of a fractured volcanic rock aquifer located in the Lower Baringo Basin, Kenyan Rift. Data sets included 15 individual metrics determined in 42 dry and wet season water samples obtained from 6 boreholes in the area. Aquifer evolutionary theory was postulated using sequential principal component analysis (PCA) and hierarchical cluster analysis. To eliminate the effects of scale dimensionality, PCA decomposed the variable data into 4 factors, namely, electrical conductivity, salinity, alkalinity, and carbonate equilibrium with external pH control for the dry season and salinity, carbonate equilibrium with external pH control, alkalinity, and electrical conductivity for the wet season. The main result depicted a major shift in the variability factor from electrolytic conductivity (34.8%) in the dry season to salinity (23.5%) in the wet season. Ward’s linkage cluster analysis partitioned the aquifer into 2 spatially discrete associations; the western and the eastern entities, respectively, in spite of their shared recharge area. These agglomerative scheduling validated in an integrative approach (with groundwater flow predictions using a calibrated petrophysical groundwater model for the area) linked the 4 factors to aquifer processes and 3 pathways: fault permeability, weathering processes, and water-rock interaction. Statistical approaches are, therefore, useful in the conceptualization of pollutant sources and their attenuation for effective groundwater quality management.

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Multivariate statistical analysis supporting the hydrochemical characterization of groundwater and surface water: a case study in northern Italy

Cited by 9 publications

References 0 publications

Non-Carcinogenic Risk Assessment of Groundwater in Southern Part of Salem District in Tamilnadu, India

Non-Carcinogenic Risk Assessment of Groundwater in Southern Part of Salem District in Tamilnadu, India

Assessing the Pollution Level in the Kuban River Basin by Multivariate Cluster Analysis

Relationship Between Aquifer Processes and Groundwater Quality: A Case of Olbanita Aquifer System, Lower Baringo Basin, Kenya Rift

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