scholarly journals Hydro-Geochemical Assessment of Groundwater Quality in Aseer Region, Saudi Arabia

Water ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 1847 ◽  
Author(s):  
Javed Mallick ◽  
Chander Singh ◽  
Mohammed AlMesfer ◽  
Anand Kumar ◽  
Roohul Khan ◽  
...  
Keyword(s):  
Saudi Arabia ◽  
Groundwater Quality ◽  
Structural Equation ◽  
Anthropogenic Activities ◽  
Principal Component ◽  
Statistical Technique ◽  
Climatic Conditions ◽  
Geochemical Modelling ◽  
Multivariate Statistical ◽  
Study Region

Saudi Arabia is an arid country with very limited water resources. The absence of surface water bodies along with erratic rainfall renders groundwater as the most reliable source of potable water in arid and semi-arid regions globally. Groundwater quality is determined by aquifer characteristics regional geology and it is extensively influenced by both natural and anthropogenic activities. In the recent past, several methodologies have been adopted to analyze the quality of groundwater and associated hydro-geochemical process i.e., multivariate statistical analysis, geochemical modelling, stable isotopes, a redox indicator, structural equation modelling. In the current study, statistical methods combined with geochemical modelling and conventional plots have been used to investigate groundwater and related geochemical processes in the Aseer region of Saudi Arabia. A total of 62 groundwater samples has been collected and analyzed in laboratory for major cations and anions. Groundwater in the study region is mostly alkaline with electrical conductivity ranging from 285–3796 μS/cm. The hydro-geochemical characteristics of groundwater are highly influenced by extreme evaporation. Climatic conditions combined with low rainfall and high temperature have resulted in a highly alkaline aquifer environment. Principal component analysis (PCA) yielded principal components explaining 79.9% of the variance in the dataset. PCA indicates ion exchange, soil mineralization, dissolution of carbonates and halite are the major processes governing the groundwater geochemistry. Groundwater in this region is oversaturated with calcite and dolomite while undersaturated with gypsum and halite which suggests dissolution of gypsum and halite as major process resulting into high chloride in groundwater. The study concludes that the combined approach of a multivariate statistical technique, conventional plots and geochemical modelling is effective in determining the factors controlling the groundwater quality.

Exploring competitiveness and wellbeing in Italy by spatial principal component analysis

2021 ◽  
pp. 141-146
Author(s):  
Carlo Cusatelli ◽  
Massimiliano Giacalone ◽  
Eugenia Nissi
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Well Being ◽  
Component Analysis ◽  
Measurement Model ◽  
Statistical Technique ◽  
Multivariate Statistical ◽  
Spatial Principal Component Analysis ◽  
Italian Provinces ◽  
Reflective Measurement

Well being is a multidimensional phenomenon, that cannot be measured by a single descriptive indicator and that, it should be represented by multiple dimensions. It requires, to be measured by combination of different dimensions that can be considered together as components of the phenomenon. This combination can be obtained by applying methodologies knows as Composite Indicators (CIs). CIs are largely used to have a comprehensive view on a phenomenon that cannot be captured by a single indicator. Principal Component Analysis (PCA) is one of the most popular multivariate statistical technique used for reducing data with many dimension, and often well being indicators are obtained using PCA. PCA is implicitly based on a reflective measurement model that it non suitable for all types of indicators. Mazziotta and Pareto (2013) in their paper discuss the use and misuse of PCA for measuring well-being. The classical PCA is not suitable for data collected on the territory because it does not take into account the spatial autocorrelation present in the data. The aim of this paper is to propose the use of Spatial Principal Component Analysis for measuring well being in the Italian Provinces.

scholarly journals Detection of Circulating Tumor Cells Using Membrane-Based SERS Platform: A New Diagnostic Approach for ‘Liquid Biopsy’

Nanomaterials ◽  
2019 ◽  
Vol 9 (3) ◽  
pp. 366 ◽  
Author(s):  
Agnieszka Kamińska ◽  
Tomasz Szymborski ◽  
Evelin Witkowska ◽  
Ewa Kijeńska-Gawrońska ◽  
Wojciech Świeszkowski ◽  
...  
Keyword(s):  
Tumor Cells ◽  
Circulating Tumor Cells ◽  
Principal Component ◽  
Cell Types ◽  
Surface Enhanced Raman Spectroscopy ◽  
Statistical Technique ◽  
Diagnostic Efficiency ◽  
Multivariate Statistical ◽  
Multi Stage ◽  
Filtration Enrichment

The detection and monitoring of circulating tumor cells (CTCs) in blood is an important strategy for early cancer evidence, analysis, monitoring of therapeutic response, and optimization of cancer therapy treatments. In this work, tailor-made membranes (MBSP) for surface-enhanced Raman spectroscopy (SERS)-based analysis, which permitted the separation and enrichment of CTCs from blood samples, were developed. A thin layer of SERS-active metals deposited on polymer mat enhanced the Raman signals of CTCs and provided further insight into CTCs molecular and biochemical composition. The SERS spectra of all studied cells—prostate cancer (PC3), cervical carcinoma (HeLa), and leucocytes as an example of healthy (normal) cell—revealed significant differences in both the band positions and/or their relative intensities. The multivariate statistical technique based on principal component analysis (PCA) was applied to identify the most significant differences (marker bands) in SERS data among the analyzed cells and to perform quantitative analysis of SERS data. Based on a developed PCA algorithm, the studied cell types were classified with an accuracy of 95% in 2D PCA to 98% in 3D PCA. These results clearly indicate the diagnostic efficiency for the discrimination between cancer and normal cells. In our approach, we exploited the one-step technology that exceeds most of the multi-stage CTCs analysis methods used and enables simultaneous filtration, enrichment, and identification of the tumor cells from blood specimens.

scholarly journals Wine Traceability Using Chemical Analysis, Isotopic Parameters, and Sensory Profiles

Beverages ◽  
2018 ◽  
Vol 4 (3) ◽  
pp. 54 ◽  
Author(s):  
Federica Bonello ◽  
Maria Cravero ◽  
Valentina Dell’Oro ◽  
Christos Tsolakis ◽  
Aldo Ciambotti
Keyword(s):  
Statistical Analysis ◽  
Geographical Origin ◽  
Central Italy ◽  
Principal Component ◽  
Climatic Conditions ◽  
Sensory Profile ◽  
Red Wines ◽  
Multivariate Statistical ◽  
Sensory Parameters ◽  
Sensory Profiles

NMR/IRMS techniques are now widely used to assess the geographical origin of wines. The sensory profile of a wine is also an interesting method of characterizing its origin. This study aimed at elaborating chemical, isotopic, and sensory parameters by means of statistical analysis. The data were determined in some Italian white wines—Verdicchio and Fiano—and red wines—Refosco dal Peduncolo Rosso and Nero d’Avola—produced from grapes grown in two different regions with different soil and climatic conditions during the years 2009–2010. The grapes were cultivated in Veneto (northwest Italy) and Marches (central Italy). The results show that the multivariate statistical analysis PCA (Principal Component Analysis) of all the data can be a useful tool to characterize the vintage and identify the origin of wines produced from different varieties. Moreover, it could discriminate wines of the same variety produced in regions with different soil and climatic conditions.

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 A SYSTEMATIC REVIEW OF STRUCTURAL EQUATION MODEL (SEM)

2020 ◽  
Vol 1 (2) ◽  
pp. 27-39
Author(s):  
H. O. Owolabi ◽  
J. K. Ayandele ◽  
D. D. Olaoye
Keyword(s):  
Structural Equation Model ◽  
Measurement Errors ◽  
Structural Equation ◽  
Statistical Technique ◽  
Equation Model ◽  
Equation Modeling ◽  
Weak Assumption ◽  
Multivariate Statistical ◽  
Software Packages ◽  
Complex Relationships

Structural Equation Model (SEM) is a multivariate statistical technique that has been explored to test relationships between variables. The use of SEM to analyze relationship between variables is premised on the weak assumption of path analysis, regression analysis and so on; that variables are measured without error. This review thus sheds light on the meaning of SEM, its assumptions, steps and some of the terms used in SEM. The importance of item parcelling to SEM and its methods were briefly examined. It also dealt on the stages involved in SEM, similarities and differences between SEM and conventional statistical methods, software packages that can be used for SEM. This article employed systematic literature review method because it critically synthesized research studies and findings on structural equation modeling (SEM). It could be concluded that SEM is useful in analyzing a set of relationships between variables using diagrams. SEM can also be useful in minimizing measurement errors and in enhancing reliability of constructs. Based on this, it is recommended that SEM should be employed to test relationship between variables since it can explore complex relationships among variables such as direct, indirect, spurious, hierarchical and non-hierarchical.

scholarly journals Lithofacies Identification and Multivariate Analysis of Groundwater Chemistry in Coastal Aquifers in Koko Area of the Western Niger Delta

Hydrology ◽  
2019 ◽  
Vol 6 (2) ◽  
pp. 31 ◽  
Author(s):  
Oghenero Ohwoghere-Asuma ◽  
Kizito Aweto ◽  
Chukwuma Ugbe
Keyword(s):  
Groundwater Quality ◽  
Principal Component ◽  
Organic Pollution ◽  
Clayey Sand ◽  
Multivariate Statistical ◽  
Subsurface Geology ◽  
Dissolved Ions ◽  
Considerable Impact ◽  
And Cluster Analysis

Understanding aquifer lithofacies and depth of occurrence, and what factors influence its quality and chemistry are of paramount importance to the management of groundwater resource. Subsurface lithofacies distribution was characterized by resistivity and validated with available subsurface geology. Resistivity values varied from less than 100 Ωm to above 1000 Ωm. Lithofacies identified includes clay, clayey sand, sand and peat. Shallow unconfined and confined aquifers occurred at depths ranging from 0 to 12 m and 18 to 63 m, respectively. Geochemistry and multivariate statistical analysis consisting of principal component analysis (PCA) and cluster analysis (CA) were used for the determination of quality and groundwater evolution. Groundwater types depicted by Piper plots were Ca3+, Cl− and Na+, Cl−, which was characterized by low dissolved ions, slightly acidic and Fe2+. The dominant variables influencing groundwater quality as returned by PCA were organic pollution resulting from swampy depositional environment, anthropogenic effects resulting from septic and leachates from haphazard dumpsites mixing with groundwater from diffuse sources. In addition, the weathering and dissolution of aquifer sediments rich in feldspar and clay minerals have considerable impact on groundwater quality. CA depicted two distinct types of groundwater that are significantly comparable to those obtained from Piper plots.

Sign in / Sign up

Export Citation Format

Share Document