Geochemical modeling and multivariate statistical evaluation of trace elements in arsenic contaminated groundwater systems of Viterbo Area, (Central Italy)

As contamination in groundwater has been reported from various regions of the Indian subcontinent but no data related to heavy metal contamination of groundwater has been reported for the Bahraich area in the Indo-Gangetic plains. We report the first dataset on arsenic contamination and groundwater hydrogeochemistry, in Bahraich. This includes concentrations of heavy metal such as As, Mn, and Fe, along with major cations (Na+, K+, Ca2+and Mg2+) and anions (F-, Cl-, NO3-, SO42- and PO43-), and dissolved organic carbon (DOC), along with various physico-chemical parameters such as EC, pH, and Eh from samples collected during two extensive field campaigns conducted during pre-monsoon, and post-monsoon seasons respectively. The combined use of geochemical modeling and multivariate statistical approaches such as principal component analysis (PCA) and correlation analysis (CA) suggest several processes affecting the geochemistry of groundwater including the lithological characteristics of aquifers and anthropogenic activities.The groundwater of the study area predominantly belongs to the Ca-Mg-HCO3 type hydrochemical facies. HCO3&#8722;/Na+ and Ca2+/Na+ signatures of groundwater indicate the influence of silicate weathering and carbonate dissolution processes with the insignificant role of evaporate dissolution mechanism. As concentration was found to range from 0.6 &#956;g/L to ~100 &#956;g/L with almost 40% of the collected samples exceeding the WHO defined limit of 10 &#956;g/L for drinking water. 70 % of the groundwater samples were found to have very high Fe concentrations exceeding the WHO guideline of 0.3 mg/l in drinking water. Mn concentrations in the groundwater samples were relatively low with only ~10 % of the samples exceeding the WHO defined limit for Mn (400 &#956;g/L). The majority of the groundwater samples were found to be anoxic in nature showing low NO3&#8722; & SO42- concentrations, high Fe & Mn and DOC concentrations, and negative Eh values.Results from this study show that the reductive dissolution mechanism of iron oxyhydroxide is the dominant mechanism responsible for arsenic release in groundwater of the region, ruling out any role of sulfide oxidation and alkali desorption.&#160;&#160;

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Multivariate statistical analysis of nutrients and trace elements in plants and soil from northwestern Russia

Plant and Soil ◽

10.1007/s11104-009-9910-7 ◽

2009 ◽

Vol 322 (1-2) ◽

pp. 219-228 ◽

Cited By ~ 18

Author(s):

I. Shtangeeva ◽

D. Alber ◽

G. Bukalis ◽

B. Stanik ◽

F. Zepezauer

Keyword(s):

Trace Elements ◽

Statistical Analysis ◽

Multivariate Statistical Analysis ◽

Multivariate Statistical ◽

Northwestern Russia

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Multivariate Statistical Analysis and Geochemical Modeling to Characterize the Surface Water of Oued Chemora Basin, Algeria

Natural Resources Research ◽

10.1007/s11053-014-9239-7 ◽

2014 ◽

Vol 23 (4) ◽

pp. 379-391 ◽

Cited By ~ 7

Author(s):

Ammar Tiri ◽

Noureddine Lahbari ◽

Abderrahmane Boudoukha

Keyword(s):

Statistical Analysis ◽

Surface Water ◽

Multivariate Statistical Analysis ◽

Geochemical Modeling ◽

Multivariate Statistical

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Khipu Transcription Typologies: A Corpus-Based Study of theTextos Andinos

Ethnohistory ◽

10.1215/00141801-8801912 ◽

2021 ◽

Vol 68 (2) ◽

pp. 311-341

Author(s):

Manuel Medrano

Keyword(s):

Statistical Analysis ◽

Sixteenth Century ◽

Multivariate Statistical Analysis ◽

Statistical Evaluation ◽

Multivariate Statistical ◽

Historical Interpretation ◽

Spanish Conquest ◽

Textual Interpretation ◽

Early Colonial ◽

And Diffusion

AbstractDespite ongoing efforts to compile both Andean khipus and their written colonial references, initiatives in this domain have emphasized the benefits of aggregation vis-à-vis preservation and diffusion, largely forgoing opportunities to analyze khipu data in aggregate. This article introduces multivariate statistical analysis to colonial khipu texts, enlisting the aid of a heretofore little-studied source: the Textos Andinos, a compilation of sixteenth-century Spanish transcriptions of Indigenous khipu “readings.” The largest syntactically annotated corpus of khipu transcriptions to date is compiled. Textual interpretation informs an exegetical typology of “paper khipus”—a division of the texts into distinguishable categories. The initial typology is expanded using the outcome of its statistical evaluation. Pre- versus postconquest content and the incorporation of currency emerge as the primary distinguishing attributes of khipu transcriptions. The expanded typology in turn enables the assessment of previous hypotheses in the study of paper khipus, responding to criticisms of their generalizability; suggestions of a diminishment in khipu complexity following the Spanish conquest are revisited and corroborated to this effect. A corpus-based study of khipu transcriptions offers a promising inroad to negotiating the highly mediated conditions of their original creation while expanding the study of khipus in the early colonial Andes. The aggregative methodology is proposed to ethnohistorians as an additional strategy for complementing and enriching historical interpretation.

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Multivariate Analysis and Geochemical Signatures of Shallow Groundwater in the Main Urban Area of Chongqing, Southwestern China

Water ◽

10.3390/w12102833 ◽

2020 ◽

Vol 12 (10) ◽

pp. 2833

Author(s):

Si Chen ◽

Zhonghua Tang ◽

Jian Wang ◽

Jialing Wu ◽

Chang Yang ◽

...

Keyword(s):

Geochemical Modeling ◽

Shallow Groundwater ◽

Poor Quality ◽

Easy Access ◽

Multivariate Statistical ◽

Drinking Purposes ◽

Groundwater Samples ◽

New Findings ◽

Group 2 ◽

Group 1

Hydrogeochemical processes and quality assessment for shallow groundwater are pivotal issues to be solved in many regions over the world due to the easy access of shallow groundwater. In this study, eighty-six water samples were collected from shallow aquifers of Chongqing, China, during July–September 2019. Multivariate statistical techniques, major ion ratios, and geochemical modeling were integrated to investigate hydrogeochemical characteristics and controlling factors. Afterwards, groundwater quality in spatial was classified by entropy-weighted water quality index and geographic information system (GIS) spatial analysis. Groundwater samples were alkaline and possessed high total dissolved solids (TDS) values. Two-group samples were distinguished as mix cations-HCO3-SO4 (group 1) and Ca-Mg-SO4 (group 2) facies. Hydrogeochemical compositions of group 1 samples were dominated by silicate dissolution and ion exchange, while sulfate and calcite dissolution were the main factors influencing the hydrogeochemical characteristics of group 2. The overall quality of groundwater samples varied greatly from excellent quality to extremely poor quality. Group 1 samples were found with excellent and good quality and suitable for drinking purposes. Group 2 samples were all unsuitable for direct drinking purposes as the quality varied from medium to extremely poor, but were spatially limited. Groundwaters with relatively poor quality were due to dissolution of sulfate that were locally enriched in the aquifers. The new findings of this study are expected to provide the reference for future management and sustainable exploitation of groundwater in Chongqing.

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