scholarly journals Integrated Hydrochemistry and Statistical Methods to Investigate Groundwater Origin and Salinization Process in Arid Regions (Central-eastern Tunisia)

2021 ◽  
Author(s):  
Ali Souei ◽  
Fadoua Hamzaoui-Azaza ◽  
Taher Zouaghi ◽  
Chafik Oueslati
Keyword(s):  
Statistical Methods ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Exchange Reactions ◽  
Multivariate Statistical ◽  
Groundwater Origin ◽  
Geochemical Study ◽  
Hydrogeological Study ◽  
Scatter Plots ◽  
Recharge Rates

Abstract Hydrochemistry is a discipline widely used given the groundwater quantitative and qualitative reliability in the hydrogeological study. The geochemical study of groundwater in the Nadhour-Sisseb-El Alem basin aimed to characterize the water chemistry, determination of the physicochemical parameters and chemical facies well as and the mineralization processes. The Piper and Durov diagrams and scatter plots, conventional classification techniques, are applied to evaluate the geochemical processes. Samples are classified using two multivariate statistical methods, Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA). Waters compositions are affected by cation exchange reactions in the intercalated clay, resulting in a Na+ increase, and peaks of K+, Ca2+ and Mg2+. PCA analyses show that the water samples have been classified into 8 groups. The waters quality deterioration is caused essentially by; overexploitation, decreased in freshwater recharge rates, climate condition; height evaporation low precipitation, artificial recharge by dam water, and irrigation return water.

scholarly journals Geochemical classification of groundwater using multivariate statistical analysis in Latvia

2016 ◽  
Vol 47 (4) ◽  
pp. 799-813 ◽  
Author(s):  
Inga Retike ◽  
Andis Kalvans ◽  
Konrads Popovs ◽  
Janis Bikse ◽  
Alise Babre ◽  
...  
Keyword(s):  
Trace Elements ◽  
Statistical Methods ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Ion Chemistry ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
Gypsum Dissolution ◽  
Major Ion

Multivariate statistical methods – principal component analysis (PCA) and hierarchical cluster analysis (HCA) – are applied to identify geochemically distinct groundwater groups in the territory of Latvia. The main processes observed to be responsible for groundwater chemical composition are carbonate and gypsum dissolution, fresh and saltwater mixing and ion exchange. On the basis of major ion concentrations, eight clusters (C1–C8) are identified. C6 is interpreted as recharge water not in equilibrium with most sediment forming minerals. Water table aquifers affected by diffuse agricultural influences are found in C3. Groundwater in C4 reflects brine or seawater admixture and gypsum dissolution in C5. C7 and C2 belong to typical bicarbonate groundwater resulting from calcite and dolomite weathering. Extremely low Cl− and SO42− are observed in C8 and described as pre-industrial groundwater or a solely carbonate weathering result. Finally, C1 seems to be a poorly defined subgroup resulting from mixing between other groups. This research demonstrates the validity of applying multivariate statistical methods (PCA and HCA) on major ion chemistry to distribute characteristic trace elements in each cluster even when incomplete records of trace elements are present.

It is better an approximate answer to the right question than the exact answer to the wrong question : the case of the psychometric analysis of the ASQ:SE

2020 ◽  
Author(s):  
Luis Anunciacao ◽  
janet squires ◽  
J. Landeira-Fernandez
Keyword(s):  
Internal Structure ◽  
Statistical Methods ◽  
Principal Component ◽  
Psychological Theory ◽  
Published Data ◽  
Multivariate Statistical ◽  
Exact Answer ◽  
Wide Range ◽  
Ages And Stages Questionnaire ◽  
The Right

One of the main activities in psychometrics is to analyze the internal structure of a test. Multivariate statistical methods, including Exploratory Factor analysis (EFA) and Principal Component Analysis (PCA) are frequently used to do this, but the growth of Network Analysis (NA) places this method as a promising candidate. The results obtained by these methods are of valuable interest, as they not only produce evidence to explore if the test is measuring its intended construct, but also to deal with the substantive theory that motivated the test development. However, these different statistical methods come up with different answers, providing the basis for different analytical and theoretical strategies when one needs to choose a solution. In this study, we took advantage of a large volume of published data (n = 22,331) obtained by the Ages and Stages Questionnaire Social-Emotional (ASQ:SE), and formed a subset of 500 children to present and discuss alternative psychometric solutions to its internal structure, and also to its subjacent theory. The analyses were based on a polychoric matrix, the number of factors to retain followed several well-known rules of thumb, and a wide range of exploratory methods was fitted to the data, including EFA, PCA, and NA. The statistical outcomes were divergent, varying from 1 to 6 domains, allowing a flexible interpretation of the results. We argue that the use of statistical methods in the absence of a well-grounded psychological theory has limited applications, despite its appeal. All data and codes are available at https://osf.io/z6gwv/.

Multivariate Techniques of Use in Biological Research

1969 ◽  
Vol 5 (1) ◽  
pp. 67-77 ◽  
Author(s):  
S. C. Pearce
Keyword(s):  
Principal Component Analysis ◽  
Statistical Methods ◽  
Matrix Algebra ◽  
Principal Component ◽  
Component Analysis ◽  
Canonical Analysis ◽  
Biological Research ◽  
Multivariate Techniques ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical

SUMMARYMultivariate statistical methods are used increasingly in biological research to investigate the responses of organisms considered as a whole, whereas established statistical methods are usually concerned with measured characteristics considered one at a time. Multivariate techniques are mostly explained in terms of matrix algebra, which is a way of dealing with groups of numbers rather than individual ones. A brief description is given of some elementary results of matrix algebra and a method is presented whereby hypotheses can be generated about interrelations within an organism. Two techniques, principal component analysis and canonical analysis, are described in greater detail. It is emphasized that hypotheses need to be tested even though they have been generated by objective statistical means.

scholarly journals Groundwater quality assessment using principal component analysis and hierarchical cluster analysis in Alaçam, Turkey

2021 ◽  
Author(s):  
Mehmet Taşan ◽  
Yusuf Demir ◽  
Sevda Taşan
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Groundwater Quality ◽  
Seawater Intrusion ◽  
Hierarchical Cluster Analysis ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Component Analysis ◽  
Quality Parameters ◽  
Multivariate Statistical

Abstract This study assessed groundwater quality in Alaçam, where irrigations are performed solely with groundwaters and samples were taken from 35 groundwater wells at pre and post irrigation seasons in 2014. Samples were analyzed for 18 water quality parameters. SAR, RSC and %Na values were calculated to examine the suitability of groundwater for irrigation. Hierarchical cluster analysis and principal component analysis were used to assess the groundwater quality parameters. The average EC value of groundwater in the pre-irrigation period was 1.21 dS/m and 1.30 dS/m after irrigation in the study area. It was determined that there were problems in two wells pre-irrigation and one well post-irrigation in terms of RSC, while there was no problem in the wells in terms of SAR. Piper diagram and cluster analysis showed that most groundwaters had CaHCO3 type water characteristics and only 3% was NaCl- as the predominant type. Seawater intrusion was identified as the primary factor influencing groundwater quality. Multivariate statistical analyses to evaluate polluting sources revealed that groundwater quality is affected by seawater intrusion, ion exchange, mineral dissolution and anthropogenic factors. The use of multivariate statistical methods and geographic information systems to manage water resources will be beneficial for both planners and decision-makers.

scholarly journals Water quality assessment and geochemical processes in the unconfined coastal Boa Viagem Aquifer, Recife, NE Brazil

2021 ◽  
pp. 56-77
Author(s):  
Thyego Silva ◽  
Mariucha Lima ◽  
Teresa Leitão ◽  
Tiago Martins ◽  
Mateus Albuquerque
Keyword(s):  
Water Quality ◽  
Quality Assessment ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Water Quality Assessment ◽  
Multivariate Statistical Analyses ◽  
Multivariate Statistical ◽  
Rock Interaction ◽  
Groundwater Samples ◽  
Water Rock Interaction

A hydrochemical study was conducted on the Quaternary Aquifer, in Recife, Brazil. Groundwater samples were collected in March–April 2015, at the beginning of the rainy season. Conventional graphics, ionic ratios, saturation indices, GIS mapping, and geostatistical and multivariate statistical analyses were used to water quality assessment and to characterize the main hydrochemical processes controlling groundwater’s chemistry. Q-mode hierarchical cluster analysis separated the samples into three clusters and five sub-clusters according to their hydrochemical similarities and facies. Principal Component Analysis (PCA) was employed to the studied groundwater samples where a three-factor model explains 80% of the total variation within the dataset. The PCA results revealed the influence of seawater intrusion, water-rock interaction, and nitrate contamination. The physico-chemical parameters of ~30% groundwaters exceed the World Health Organization (WHO) guidelines for drinking water quality. Nitrate was found at a concentration >10 mg NO3−/L in ~21% of the wells and exceeded WHO reference values in one. The integrated approach indicates the occurrence of the main major hydrogeochemical processes occurring in the shallow marine to alluvial aquifer as follow: 1) progressive freshening of remaining paleo-seawater accompanying cation exchange on fine sediments, 2) water-rock interaction (i.e., dissolution of silicates), and 3) point and diffuse wastewater contamination, and sulfate dissolution. This study successfully highlights the use of classical geochemical methods, GIS techniques, and multivariate statistical analyses (hierarchical cluster and principal component analyses) as complementary tools to understand hydrogeochemical processes and their influence on groundwater quality status to management actions, which could be used in similar alluvial coastal aquifers.

scholarly journals Long-Term Ripening Evaluation of Ewes’ Cheeses by Fourier-Transformed Infrared Spectroscopy under Real Industrial Conditions

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Jonathan Andrade ◽  
Cristina Guimarães Pereira ◽  
Thamiris Ranquine ◽  
Cosme Antonio Azarias ◽  
Maria José Valenzuela Bell ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
First Trimester ◽  
Multivariate Statistical ◽  
Peptide Bonds ◽  
Physicochemical Changes ◽  
Fast Pace ◽  
The Times ◽  
Atr Spectroscopy

The ripening changes over time of special cheeses (Pecorino, ewes’ ripe, and Gouda) made with ewes’ milk were evaluated using FTIR/ATR spectroscopy during approximately one year. The midinfrared FTIR/ATR analyses were carried out in different ripening times between the cheese varieties and processed by means of multivariate statistical approaches. Overall, during the maturation, we observed a downward trend of the absorbance intensity of the amide group peaks (1700 to 1500 cm−1), which is linked to the breakdown of peptide bonds. Similar behavior was obtained for the lipidic region (3000 to 2800 cm−1 and 1765 to 1730 cm−1). Hierarchical cluster analysis and principal component analysis allowed the evaluation of the physicochemical changes of the cheeses. The proteolysis occurs in a fast pace during the first trimester of the ripening process, and the lipids are converted to smaller species as the times goes by. Our results indicate that infrared spectroscopy can be a useful tool in determining optimal temporal parameters in stages involving the development, production, and even a possible estimation of shelf life of cheeses.

Evaluation of colon cancer elements contents in serum using statistical methods

2007 ◽  
Vol 61 (5) ◽  
Author(s):  
D. Milde ◽  
J. Macháček ◽  
V. Stužka
Keyword(s):  
Statistical Methods ◽  
Tnm Classification ◽  
Principal Component ◽  
Absorption Spectrometry ◽  
Acid Mixture ◽  
Multivariate Statistical Methods ◽  
Serum Samples ◽  
Multivariate Statistical ◽  
Colorectal Cancer Patients

AbstractClassification of normal and different cancer groups (TNM classification) with univariate and multivariate statistical methods according to the contents of Cu, Fe, Mn, Se, and Zn in blood serum is discussed. All serum samples were digested by acid mixture in a microwave mineralization unit prior to the analysis by atomic absorption spectrometry. Results show that univariate methods can distinguish normal and cancer groups. Level of selenium evaluated as arithmetic mean with its standard deviation in colorectal cancer patients was (42.61 ± 23.76) µg L−1. Retransformed mean was used to evaluate levels of managanese (11.99 ± 1.71) µg L−1, copper (1.05 ± 0.06) mg L−1, zinc (2.14 ± 0.21) mg L−1, and iron (1.82 ± 0.22) mg L−1. Conclusions of multivariate statistical procedures (principal component analysis, hierarchical, and k-means clustering) do not correlate very well with the division of serum samples according to the TNM classification.

Spatial distribution and sources of dissolved trace metals in surface water of the Wei River, China

2013 ◽  
Vol 67 (4) ◽  
pp. 817-823 ◽  
Author(s):  
Li Jing ◽  
Li Fadong ◽  
Liu Qiang ◽  
Song Shuai ◽  
Zhao Guangshuai
Keyword(s):  
Drinking Water ◽  
Trace Metals ◽  
Trace Metal ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Environmental Data ◽  
Multivariate Statistical ◽  
Drinking Water Standard ◽  
The Impact ◽  
Wei River

For this study, 34 water samples were collected along the Wei River and its tributaries. Multivariate statistical analyses were employed to interpret the environmental data and to identify the natural and anthropogenic trace metal inputs to the surface waters of the river. Our results revealed that Zn, Se, B, Ba, Fe, Mn, Mo, Ni and V were all detected in the Wei River. Compared to drinking water guidelines, the primary trace metal pollution components (B, Ni, Zn and Mn) exceeded drinking water standard levels by 47.1, 50.0, 44.1 and 26.5%, respectively. Inter-element relationships and landscape features of trace metals conducted by hierarchical cluster analysis (HCA) identified a uniform source of trace metals for all sampling sites, excluding one site that exhibited anomalous concentrations. Based on the patterns of relative loadings of individual metals calculated by principal component analysis (PCA), the primary trace metal sources were associated with natural/geogenic contributions, agro-chemical processes and discharge from local industrial sources. These results demonstrated the impact of human activities on metal concentrations in the Wei River.

scholarly journals Pyrolysis - Field lonization Mass Spectrometry - A New Method for Direct, Rapid Characterization of Tobacco

1986 ◽  
Vol 13 (5) ◽  
pp. 219-227 ◽  
Author(s):  
HR Schulten
Keyword(s):  
Mass Spectrometry ◽  
High Vacuum ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Mass Range ◽  
Linear Mapping ◽  
Mapping Technique ◽  
Ionization Mass ◽  
Multivariate Statistical ◽  
Rapid Characterization

AbstractFor the first time, pyrolysis - field ionization (Py-FI) mass spectra of the tobacco blends of three different cigarette brands have been recorded in the mass range up to 1000 mass units and evaluated by operational fingerprinting techniques. Due to the high reproducibility of the applied methods, all three tobacco blends could be differentiated clearly with several univariate or multivariate statistical methods. Feature scaling with Fisher ratios revealed that the signal at m/z 93, mainly due to aniline, is the most suited to distinguishing the tobacco blends analysed. Principal component analysis showed the variety of pyrolytic reactions during the thermal decomposition of tobacco in high vacuum. It revealed that, in addition to aniline, lignin-related signals can also be used for a clear differentiation. From the whole pattern of Py-FI mass spectrum, nearest-neighbour relationships are visualized by the non-linear mapping technique and further classification of tobacco blends is obtained by hierarchical cluster analysis. A thorough chemical interpretation of the data obtained should give new insights into the structure of tobacco and its pyrolytic decomposition. Pyrolysis - soft ionization mass spectrometry in combination with pattern recognition techniques appears to provide a useful tool for future investigations connected with the quality control of commercial tobacco products.

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