Compositional multivariate statistical analysis of thermal groundwater provenance: A hydrogeochemical case study from Ireland

Blake, Sarah; Henry, Tiernan; Murray, John; Flood, Rory; Müller, Mark; Jones, Alan G.; Rath, Volker

doi:10.1016/j.apgeochem.2016.05.008

Cited by 65 publications

(37 citation statements)

References 56 publications

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“…In as much as multivariate statistical assessment (MSA) allows samples with identical chemical and physical attributes to be categorized together, trend patterns that provide essential information about the controlling factors of geochemical compositions may not be identified immediately (Güler et al, 2012). Therefore, it is important to merge the results obtained from the MSA with other analysis to provide an elaborate interpretation of the data structure (Blake et al, 2016).…”

Section: Compositional Data Analysis (Cda)mentioning

confidence: 99%

An integrated assessment of land-use change impact, seasonal variation of pollution indices and human health risk of selected toxic elements in sediments of River Atuwara, Nigeria

Emenike

Tenebe

Neris

et al. 2020

Environmental Pollution

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River sediments contain environmental fingerprints that provide useful ecological information. However, the geochemistry of River Atuwara sediments has received less attention over the years. One hundred and twenty-six sediments from 21 locations were collected over a two-season period from River Atuwara, and a detailed investigation of the land use and land cover (LULC) change between 1990 and 2019, analysis of selected toxic and potentially toxic metal(oid)s (TPTM) (Cu, As, Cd, Pb, Ni, Cr, Zn, Fe, Co and Al) using ICP-OES, pollution index assessment, potential source identification (using center logtransformation approach), potential ecological, and human health risk assessment were conducted. The results of the LULC change revealed that the built-up area increased by 95.58 km 2 , at an average rate of 3.186 km 2 /year over the past 30 years. The mean concentration of metal(oid)s increased in the order of Cd < As < Cr < Pb < Co < Ni < Cu < Zn < Fe < Al, and Cd < As < Cr < Co < Pb < Ni < Cu < Zn < Fe < Al during the dry and wet seasons, respectively. Meanwhile, the statistical analysis of the data spectrum inferred possible contamination from lithological and anthropogenic sources. According to the pollution load index, 90.48% of the sediment samples are polluted by the metal(oid)s. Potential ecological risk assessment identified Ni, As, and Cd as problematic to the ecological community of River Atuwara. Regarding the metal-specific hazard quotient via ingestion route, the risks are in order of Co [ As [ Pb > Cr > Cd > Al > Ni > Cu > Zn > Fe for both seasons and the carcinogenic risk for children via ingestion route presented a value higher than the safe limits for As, Cd, Cr, and Ni during both seasons. This outcome highlights the need for prompt action towards the restoration of environmental quality for communities surrounding River Atuwara.

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Section: Compositional Data Analysis (Cda)mentioning

confidence: 99%

An integrated assessment of land-use change impact, seasonal variation of pollution indices and human health risk of selected toxic elements in sediments of River Atuwara, Nigeria

Emenike

Tenebe

Neris

et al. 2020

Environmental Pollution

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“…Multivariate statistical analysis has been extensively applied to analyze the complex hydrogeochemical processes taking place in the aquifer [15,17,20,26,27,35]. In this study, the PCA was chosen to analyze the hydrochemical dataset.…”

Section: Principal Component Analysis (Pca)mentioning

confidence: 99%

Hydrogeochemistry of Shallow Groundwater in a Karst Aquifer System of Bijie City, Guizhou Province

Jiang

Deng

et al. 2017

Water

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Abstract:In this study, the major chemical compositions of 159 shallow groundwater samples incorporated saturation index (SI) and Principal Component Analysis (PCA) were employed to evaluate the mainly geochemical processes that control the hydrogeochemical evolution of groundwater in a typical karst area, Bijie city, Guizhou Province. The groundwater samples in this study area were dominated of HCO 3 -Ca, HCO 3 -Ca-Mg, and HCO 3 -SO 4 -Ca types. The PCA suggested that four principal components could explain 88.85% of the total variance of 10 parameters, indicating that the hydrogeochemical evolution of groundwater was mainly controlled by the dissolution/precipitation of carbonates, gypsum, and halite minerals, cation exchange, and anthropogenic activities. To be specific, the enrichment of Ca 2+ , Mg 2+ , HCO 3 − and SO 4 2− in groundwater were primarily affected by the dissolution of dolomite and gypsum minerals, and the role of calcite dissolution was relatively weaker because most groundwater samples were saturated with respect to calcite. Besides, cation exchange was another factor that may affect the concentration of Ca 2+ and Mg 2+ in groundwater, and the concentration of SO 4 2− can also be influenced by coal mining activity. In addition, the concentrations of Na + and Cl − in groundwater were likely influenced by the dissolution of halite, cation exchange, and human activities.

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“…Selecting a suitable method to discriminate background and anomaly values is the first and most important step in mineral exploration [6]. Because of large data sets in geochemical exploration projects, it is crucial to identify geochemical background and potential analytical and sampling errors [20][21][22]. Several procedures can be used to identify data outliers.…”

Section: Anomaly Recognition Methodsmentioning

confidence: 99%

The Use of Univariate and Multivariate Analyses in the Geochemical Exploration, Ravanj Lead Mine, Delijan, Iran

2017

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A geochemical exploration program was applied to recognize the anomalous geochemical haloes at the Ravanj lead mine, Delijan, Iran. Sampling of unweathered rocks were undertaken across rock exposures on a 10 × 10 meter grid (n = 302) as well as the accessible parts of underground mine A (n = 42). First, the threshold values of all elements were determined using the cutoff values used in the exploratory data analysis (EDA) method. Then, for further studies, elements with lognormal distributions (Pb, Zn, Ag, As, Cd, Co, Cu, Sb, S, Sr, Th, Ba, Bi, Fe, Ni and Mn) were selected. Robustness against outliers is achieved by application of central log ratio transformation to address the closure problems with compositional data prior to principle components analysis (PCA). Results of these analyses show that, in the Ravanj deposit, Pb mineralization is characterized by a Pb-Ba-Ag-Sb ± Zn ± Cd association. The supra-mineralization haloes are characterized by barite and tetrahedrite in a BaTh -Ag-Cu-Sb-As-Sr association and sub-mineralization haloes are comprised of pyrite and tetrahedrite, probably reflecting a Fe-Cu-As-Bi-Ni-Co-Mo-Mn association. Using univariate and multivariate geostatistical analyses (e.g., EDA and robust PCA), four anomalies were detected and mapped in Block A of the Ravanj deposit. Anomalies 1 and 2 are around the ancient orebodies. Anomaly 3 is located in a thin bedded limestone-shale intercalation unit that does not show significant mineralization. Drilling of the fourth anomaly suggested a low grade, non-economic Pb mineralization.

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Compositional multivariate statistical analysis of thermal groundwater provenance: A hydrogeochemical case study from Ireland

Cited by 65 publications

References 56 publications

An integrated assessment of land-use change impact, seasonal variation of pollution indices and human health risk of selected toxic elements in sediments of River Atuwara, Nigeria

An integrated assessment of land-use change impact, seasonal variation of pollution indices and human health risk of selected toxic elements in sediments of River Atuwara, Nigeria

Hydrogeochemistry of Shallow Groundwater in a Karst Aquifer System of Bijie City, Guizhou Province

The Use of Univariate and Multivariate Analyses in the Geochemical Exploration, Ravanj Lead Mine, Delijan, Iran

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