scholarly journals Application of multivariate statistical analysis in the assessment of groundwater quality of Tan Thanh district, Ba Ria – Vung Tau province

2017 ◽  
Vol 1 (M2) ◽  
pp. 66-72
Author(s):  
Au Hai Nguyen ◽  
Ngan Thi Khanh Phan ◽  
Thuy Thi Thanh Hoang ◽  
Ngoc Nguyen Hong Phan
Keyword(s):  
Cluster Analysis ◽  
Statistical Analysis ◽  
Groundwater Quality ◽  
Multivariate Statistical Analysis ◽  
Anthropogenic Pollution ◽  
Dry Season ◽  
Wet Season ◽  
Multivariate Statistical ◽  
Rock Interaction ◽  
And Cluster Analysis

In the present study, Multivariate Statistical Analysis (MSA) such as Principle Component Analysis (PCA) and Cluster Analysis (CA) were applied to determine the temporal and spatial variations of groundwater quality in Tan Thanh district, Ba Ria – Vung Tau province. Groundwater samples were collected from 18 monitoring wells in April (dry season) and October (wet season) during the year 2012. Fifteen parameters (pH, TH, TDS, Cl-, F-, NO3-, SO42-, Cr6+, Cu2+, Ca2+, Mg2+, Na+, K+, HCO3- and Fe2+) were selected for MSA. PCA identified a reduced number of mean three latent factors of groundwater quality. Three factors called salinization, water-rock interaction and anthropogenic pollution explanined 70,5% (dry season) and 71.28% (wet season) of the variances. Cluster analysis revealed two main different groups of similarities between the sampling sites. This study presents the necessity of MSA in order to extract more precise information from a huge minitoring data, which will be usefull to groundwater quality management.

Source identification of groundwater pollution with the aid of multivariate statistical analysis

2005 ◽  
Vol 5 (6) ◽  
pp. 281-288 ◽  
Author(s):  
T.N. Wu ◽  
Y.C. Huang ◽  
M.S. Lee ◽  
C.M. Kao
Keyword(s):  
Cluster Analysis ◽  
Statistical Analysis ◽  
Groundwater Quality ◽  
Multivariate Statistical Analysis ◽  
Principal Component ◽  
Ammonia Concentration ◽  
Mineral Dissolution ◽  
Agricultural Activity ◽  
Multivariate Statistical ◽  
The Impact

With the aid of multivariate statistical analysis, this study attempted to predict possible underlying processes, attribute their influence, and isolate the distribution of sources that might threaten groundwater quality. Tainan County, Taiwan was employed as a case study, and 34 monitoring wells were sampled for routine lab analysis. Lab data of groundwater quality including pH, EC, hardness, chloride, sulfate, ammonia, nitrate, Fe, Mn, As, Zn, TOC and TDS were subjected to factor and cluster analysis. Principal component analysis (PCA) was utilized to reflect those chemical data with the greatest correlation, whereas cluster analysis (CA) was used to evaluate the similarities of water quality in groundwater samples. By utilizing PCA, the identified four major principal components (PCs) representing 78.8% of cumulative variance were able to interpret the most information contained in the data. PC 1 reflects the dominance of salinization, which was characterized by the elevated concentrations of EC, hardness, chloride and sulfate in groundwater. PC 2 with the positive loadings of TOC and pH but negative loading of nitrate is thought to be representative of organic pollution within the aquifer. PC 3 is regarded as mineralization factor on the basis of the loadings of manganese and zinc. PC 4 shows a strong monotonic relationship with ammonia concentration in the groundwater revealing the linkage with agricultural activity. CA results illustrated that coastal area was partially salinized as a result of seawater intrusion and part of salinization zone was also subjected to the impact of mineral dissolution.

scholarly journals Relationship Between Aquifer Processes and Groundwater Quality: A Case of Olbanita Aquifer System, Lower Baringo Basin, Kenya Rift

2019 ◽  
Vol 12 ◽  
pp. 117862211988991
Author(s):  
Benjamin Sosi ◽  
Albert Getabu ◽  
Samson Maobe ◽  
Justus Barongo
Keyword(s):  
Electrical Conductivity ◽  
Cluster Analysis ◽  
Groundwater Quality ◽  
Ph Control ◽  
Dry Season ◽  
Integrative Approach ◽  
Wet Season ◽  
Rock Interaction ◽  
External Ph ◽  
Carbonate Equilibrium

A hydrogeochemical relation has been hypothesized through the analyses of physiochemical data of a fractured volcanic rock aquifer located in the Lower Baringo Basin, Kenyan Rift. Data sets included 15 individual metrics determined in 42 dry and wet season water samples obtained from 6 boreholes in the area. Aquifer evolutionary theory was postulated using sequential principal component analysis (PCA) and hierarchical cluster analysis. To eliminate the effects of scale dimensionality, PCA decomposed the variable data into 4 factors, namely, electrical conductivity, salinity, alkalinity, and carbonate equilibrium with external pH control for the dry season and salinity, carbonate equilibrium with external pH control, alkalinity, and electrical conductivity for the wet season. The main result depicted a major shift in the variability factor from electrolytic conductivity (34.8%) in the dry season to salinity (23.5%) in the wet season. Ward’s linkage cluster analysis partitioned the aquifer into 2 spatially discrete associations; the western and the eastern entities, respectively, in spite of their shared recharge area. These agglomerative scheduling validated in an integrative approach (with groundwater flow predictions using a calibrated petrophysical groundwater model for the area) linked the 4 factors to aquifer processes and 3 pathways: fault permeability, weathering processes, and water-rock interaction. Statistical approaches are, therefore, useful in the conceptualization of pollutant sources and their attenuation for effective groundwater quality management.

LOGICAL DECISION RULES FOR AUTOMATIC DISCOVERY OF KNOWLEDGE IN EXPERT SYSTEMS DATABASE

1989 ◽  
Vol 03 (01) ◽  
pp. 135-145
Author(s):  
G.S. LBOV
Keyword(s):  
Cluster Analysis ◽  
Regression Analysis ◽  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
Decision Rules ◽  
Multivariate Statistical ◽  
Automatic Discovery ◽  
And Cluster Analysis ◽  
Statistical Results ◽  
Statistical Analysis Methods

We consider the class of logical decision rules and its applications for the solution of various problems of multivariate statistical analysis: discriminant and regression analysis, and cluster analysis. Some useful properties of the statistical analysis methods using the class under consideration are shown. Particular attention is paid to the possibility of presenting statistical results (unlike all other methods) in a language close to a natural language of logical statements.

Quality Evaluation of Canned Whole Tomatoes

1995 ◽  
Vol 1 (2-3) ◽  
pp. 97-104 ◽  
Author(s):  
S. Porretta
Keyword(s):  
Cluster Analysis ◽  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
Quality Evaluation ◽  
Chemical Properties ◽  
Sensory Attributes ◽  
Tomato Juice ◽  
Multivariate Statistical ◽  
Tomato Variety ◽  
Physico Chemical

The physico-chemical properties of commercial canned whole tomatoes (i.e., peeled tomatoes with about 30% tomato juice as packing medium) and the contribution of various analytical parameters to some sensory attributes were evaluated using multivariate statistical analysis. In addition, cluster analysis was used to determine the existence of significant qualitative differences between the old and famous San Marzano variety (as described on the commercial labels by the manufacturers) and traditional (without any specification on the tomato variety) canned whole tomatoes.

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