Spatio-temporal distribution and chemical characterization of groundwater quality of a wastewater irrigated system: A case study

Jampani, Mahesh; Huelsmann, Stephan; Liedl, Rudolf; Sonkamble, Sahebrao; Ahmed, Shakeel; Amerasinghe, Priyanie H.

doi:10.1016/j.scitotenv.2018.04.347

Cited by 47 publications

(17 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The application of multivariate statistical methods to environmental monitoring datasets is beneficial to highlight information that is not available at first glance, especially when the results of the univariate analysis reveal statistically significant associations for several dataset variables. The factor analysis (FA), principal component analysis (PCA), and cluster analysis (CA) are the most commonly used multivariate analysis methods for complex environmental datasets assessment . Complex structures within the datasets could be extracted without losing any information by reducing them to a few dominating factors .…”

Section: Methodsmentioning

confidence: 99%

“…Complex structures within the datasets could be extracted without losing any information by reducing them to a few dominating factors . FA/PCA is a convenient data reduction method that uses the extraction of eigenvalues and eigenvectors from the correlation matrix . Each factor is extracted by means of the PCA method and the interpretation is based on the rotated factors and loadings (a measure of how much the variable contributes to the factor) .…”

Section: Methodsmentioning

confidence: 99%

“…FA/PCA is a convenient data reduction method that uses the extraction of eigenvalues and eigenvectors from the correlation matrix . Each factor is extracted by means of the PCA method and the interpretation is based on the rotated factors and loadings (a measure of how much the variable contributes to the factor) . The suitability of the data that is to be applied to FA/PCA is assessed using Kaiser‐Meyer‐Olkin (KMO) and Bartlett's sphericity tests …”

Section: Methodsmentioning

confidence: 99%

“…The factor analysis (FA), principal component analysis (PCA), and cluster analysis (CA) are the most commonly used multivariate analysis methods for complex environmental datasets assessment. [1,[20][21][22] Complex structures within the datasets could be extracted without losing any information by reducing them to a few dominating factors. [23] FA/PCA is a convenient data reduction method that uses the extraction of eigenvalues and eigenvectors from the correlation matrix.…”

Section: Descriptive Statistics Univariate and Multivariate Methodsmentioning

confidence: 99%

“…[20] Each factor is extracted by means of the PCA method and the interpretation is based on the rotated factors and loadings (a measure of how much the variable contributes to the factor). [20] The suitability of the data that is to be applied to FA/PCA is assessed using Kaiser-Meyer-Olkin (KMO) and Bartlett's sphericity tests. [3,24,25] A well-recognized method for identifying groups called clusters from multivariate data objects is cluster analysis (CA).…”

Section: Descriptive Statistics Univariate and Multivariate Methodsmentioning

confidence: 99%

See 4 more Smart Citations

Assessment of Khibiny Alkaline Massif groundwater quality using statistical methods and water quality index

2019

View full text Add to dashboard Cite

Groundwater is a major source for the water supply of households in the mining‐intensive area of Khibiny Alkaline Massif, Kola Peninsula, in the Arctic. There are an increasing number of signs of groundwater quality deterioration in the area caused by the presence of elevated aluminum concentrations. Groundwater quality studies using univariate and multivariate statistical methods and the Water Quality Index were conducted to analyze a field dataset including 12 groundwater quality parameters monitored between 1999 and 2012. Descriptive statistics showed that the monitored water did not meet the established drinking water standards for aluminum concentration and pH level. The calculated Spearman correlation coefficient matrix revealed statistically significant associations (α‐level = .05) between Al concentrations and pH values, concentrations of SO42−, NO3−, Cl−, and TDS. Factor analysis using the principal component analysis extraction method (FA/PCA) identified four major influencing factors. Altogether the factors captured 67.53% of the dataset total variance. The outcomes of the hierarchical cluster analysis (HCA) revealed that the 12 monitored groundwater quality parameters can be grouped into three clusters where the concentration of Al and pH level formed a separate cluster. The calculated score values of the Canadian Council of Ministers of the Environment Water Quality Index indicated a deterioration of groundwater quality over the monitoring period.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Descriptive Statistics Univariate and Multivariate Methodsmentioning

confidence: 99%

Section: Descriptive Statistics Univariate and Multivariate Methodsmentioning

confidence: 99%

See 3 more Smart Citations

Assessment of Khibiny Alkaline Massif groundwater quality using statistical methods and water quality index

2019

View full text Add to dashboard Cite

show abstract

Using multivariate statistical techniques and geochemical modelling to identify factors controlling the evolution of groundwater chemistry in a typical transitional area between Taihang Mountains and North China Plain

Liu

Shou

Yeh

et al. 2020

Hydrological Processes

View full text Add to dashboard Cite

Identifying the key factors controlling groundwater chemical evolution in mountain-plain transitional areas is crucial for the security of groundwater resources in both headwater basins and downstream plains. In this study, multivariate statistical techniques and geochemical modelling were used to analyse the groundwater chemical data from a typical headwater basin of the North China Plain. Groundwater samples were divided into three groups, which evolved from Group A with low mineralized Ca-HCO 3 water, through Group B with moderate mineralized Ca-SO 4 -HCO 3 water, to Group C with highly saline Ca-SO 4 and Ca-Cl water. Water-rock interaction and nitrate contamination were mainly responsible for the variation in groundwater chemistry. Groundwater chemical compositions in Group A were mainly influenced by dissolution of carbonates and cation exchange, and suffered less nitrate contamination, closely relating to their locations in woodland and grassland with less pronounced human interference. Chemical evolution of groundwater in Groups B and C was gradually predominated by the dissolution of evaporites, reverse ion exchange, and anthropogenic factors. Additionally, the results of the inverse geochemical model showed that dedolomitization caused by gypsum dissolution, played a key role in the geochemical evolution from Group A to Group B. Heavy nitrate enrichment in most groundwater samples of Groups B and C was closely associated with the land-use patterns of farmland and residential areas. Apart from the high loads of chemical fertilizers in irrigation return flow as the main source for nitrate contamination, the stagnant zones, flood irrigation pattern, mine drainage, and groundwater-exploitation reduction program were also important contributors for such high mineralization and heavy NO 3 − contents in Group C. The important findings of this work not only provide the conceptual framework for the headwater basin but also have important implications for sustainable management of groundwater resources in other headwater basins of the North China Plain. K E Y W O R D S geochemical modelling, groundwater evolution, headwater basin, hydrochemistry, multivariate statistical analysis, North China Plain

show abstract

The Nexus Approach as a Tool for Resources Management in Resilient Cities and Multifunctional Land-Use Systems

Hülsmann

Jampani

2020

A Nexus Approach for Sustainable Development

View full text Add to dashboard Cite

Spatio-temporal distribution and chemical characterization of groundwater quality of a wastewater irrigated system: A case study

Cited by 47 publications

References 31 publications

Assessment of Khibiny Alkaline Massif groundwater quality using statistical methods and water quality index

Assessment of Khibiny Alkaline Massif groundwater quality using statistical methods and water quality index

Using multivariate statistical techniques and geochemical modelling to identify factors controlling the evolution of groundwater chemistry in a typical transitional area between Taihang Mountains and North China Plain

The Nexus Approach as a Tool for Resources Management in Resilient Cities and Multifunctional Land-Use Systems

Contact Info

Product

Resources

About