scholarly journals Application of Multivariate Statistical Analysis to Identify Water Sources in A Coastal Gold Mine, Shandong, China

2019 ◽  
Vol 11 (12) ◽  
pp. 3345 ◽  
Author(s):  
Guowei Liu ◽  
Fengshan Ma ◽  
Gang Liu ◽  
Haijun Zhao ◽  
Jie Guo ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Statistical Analysis ◽  
Water Samples ◽  
Multivariate Statistical Analysis ◽  
Water Inrush ◽  
Principal Component ◽  
Component Analysis ◽  
Gold Mine ◽  
Mining Operations ◽  
Multivariate Statistical

Submarine mine water inrush has become a problem that must be urgently solved in coastal gold mining operations in Shandong, China. Research on water in subway systems introduced classifications for the types of mine groundwater and then established the functions used to identify each type of water sample. We analyzed 31 water samples from −375 m underground using multivariate statistical analysis methods. Cluster analysis combined with principle component analysis and factor analysis divided water samples into two types, with one type being near the F3 fault. Principal component analysis identified four principle components accounting for 91.79% of the total variation. These four principle components represented almost all the information about the water samples, which were then used as clustering variables. A Bayes model created by discriminant analysis demonstrated that water samples could also be divided into two types, which was consistent with the cluster analysis result. The type of water samples could be determined by placing Na+ and CHO3− concentrations of water samples into Bayes functions. The results demonstrated that F3, which is a regional fault and runs across the whole Xishan gold mine, may be the potential channel for water inrush, providing valuable information for predicting the possibility of water inrush and thus reducing the costs of the mining operation.

scholarly journals Economic Causes of Extremism Intellectual (Thoughts) Views of Graduate Students Using Multivariate Statistical Techniques

2016 ◽  
Vol 9 (7) ◽  
pp. 160
Author(s):  
Hasan Abdullah Al-Dajah
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Factor Analysis ◽  
Statistical Analysis ◽  
Graduate Students ◽  
Multivariate Statistical Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Multivariate Statistical ◽  
The Impact

The present study investigated the impact of the economic reasons on the intellectual (thoughts) extremism, and the statement of the most important indicators in the economic factor that lead to extremism from the views of graduate students. The study problem based on the following question: What are economic factors leading to the extremism of the intellectual(Thoughts)? Correlation coefficient, Principal component analysis (PCA), varimax (F) rotated factor analysis, and dendrogram cluster analysis (DCA) were assessed for the economic impacts that leads to extremism(Thoughts). Multivariate statistical analysis of the dataset and correlation analysis suggested that the strong positive correlations are commonly associated in the poverty and lack of interest in remote areas for major cities Center. Multivariate statistical analysis such as principal component analysis, varimax rotated factor analysis, and dendrogram cluster analysis allowed the identification of three main factors controlling that lead to extremism from the views of graduate students. The extracted factors are as follows: low living expenses, poverty and substantial deprivation, and unequal opportunities and unemployment associations related to prevalence of corruption phase.

Risk estimation and multivariate statistical analysis of the heavy metal content of drinking water samples

2018 ◽  
Vol 34 (10) ◽  
pp. 714-725
Author(s):  
Rajan Jakhu ◽  
Rohit Mehra
Keyword(s):  
Heavy Metals ◽  
Heavy Metal ◽  
Drinking Water ◽  
Statistical Analysis ◽  
Water Samples ◽  
Multivariate Statistical Analysis ◽  
Inductively Coupled Plasma ◽  
Principal Component ◽  
Multivariate Statistical ◽  
Drinking Water Samples

Drinking water samples of Jaipur and Ajmer districts of Rajasthan, India, were collected and analyzed for the measurement of concentration of heavy metals. The purpose of this study was to determine the sources of the heavy metals in the drinking water. Inductively coupled plasma mass spectrometry was used for the determination of the heavy metal concentrations, and for the statistical analysis of the data, principal component analysis and cluster analysis were performed. It was observed from the results that with respect to WHO guidelines, the water samples of some locations exceeded the contamination levels for lead (Pb), selenium (Se), and mercury (Hg), and with reference to the EPA guidelines, the samples were determined unsuitable for drinking because of high concentrations of Pb and Hg. Using multivariate statistical analysis, we determined that copper, manganese, arsenic, Se, and Hg were of anthropogenic origin, while Pb, copper, and cadmium were of geogenic origin. The present study reports the dominance of the anthropogenic contributions over geogenics in the studied area. The sources of the anthropogenic contaminants need to be investigated in a future study.

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 Seasonal distribution and population dynamics of limnic microalgae and their association with physico-chemical parameters of river Noyyal through multivariate statistical analysis

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Pandian Suresh Kumar ◽  
Jibu Thomas
Keyword(s):  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
Sampling Site ◽  
Principal Component ◽  
Environmental Parameters ◽  
Diversity Indices ◽  
Population Diversity ◽  
Chemical Parameters ◽  
Multivariate Statistical ◽  
Physico Chemical

Abstract The present investigation embarks on understanding the relationship between microalgal species assemblages and their associated physico-chemical parameter dynamics of the catchment region of river Noyyal. Totally, 142 microalgae cultures belonging to 10 different families were isolated from five different sites during four seasons and relative percentage distribution showed that Scenedesmaceae (36.6%) and site S1 (26.4%) with predominant microalgae population. Diversity indices revealed that microalgae communities were characterized by high Hʹ index, lower Simpson dominance, and Margalef index value with indefinite patterns of annual variations. Results showed that variation in the physico-chemical parameters in each sampling site has its impact on the microalgae population during each season. Multivariate statistical analysis viz., Karl Pearson’s correlation coefficient, principal component analysis, and canonical correspondence analysis were applied on microalgae species data, to evaluate the seasonal relationship between microalgae and physico-chemical parameters. The findings of our study concluded that the physico- chemical parameters influenced the dominant taxa of microalgae Chlorellaceae, Scenedesmaceae and Chlorococcaceae in river Noyyal and gives a base data for the seasonal and dynamic relationship between environmental parameters and microalgae population.

EVALUATION AND MODELLING OF GROUND WATER QUALITY DATA OF ALLAHABAD CITY BY ENVIRONMETRIC METHODS

2016 ◽  
Vol 2 (4) ◽  
pp. 211
Author(s):  
Girdhari Lal Chaurasia ◽  
Mahesh Kumar Gupta ◽  
Praveen Kumar Tandon
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Factor Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Sampling Location ◽  
Data Sets ◽  
Multivariate Statistical ◽  
Positive Loading

Water is an essential resource for all the organisms, plants and animals including the human beings. It is the backbone for agricultural and industrial sectors and all the small business units. Increase in human population and economic activities have tremendously increased the demand for large-scale suppliers of fresh water for various competing end users.The quality evaluation of water is represented in terms of physical, chemical and Biological parameters. A particular problem in the case of water quality monitoring is the complexity associated with analyzing the large number of measured variables. The data sets contain rich information about the behavior of the water resources. Multivariate statistical approaches allow deriving hidden information from the data sets about the possible influences of the environment on water quality. Classification, modeling and interpretation of monitored data are the most important steps in the assessment of water quality. The application of different multivariate statistical techniques, such as cluster analysis (CA), principal component analysis (PCA) and factor analysis (FA) help to identify important components or factors accounting for most of the variances of a system. In the present study water samples were analyzed for various physicochemical analyses by different methods following the standards of APHA, BIS and WHO and were subjected to further statistical analysis viz. the cluster analysis to understand the similarity and differences among the various sampling stations.  Three clusters were found. Cluster 1 was marked with 3 sampling locations 1, 3 & 5; Cluster-2 was marked with sampling location-2 and cluster-3 was marked with sampling location-4. Principal component analysis/factor analysis is a pattern reorganization technique which is used to assess the correlation between the observations in terms of different factors which are not observable. Observations correlated either positively or negatively, are likely to be affected by the same factors while the observations which are not correlated are influenced by different factors. In our study three factors explained 99.827% of variances. F1 marked  51.619% of total variances, high positive strong loading with TSS, TS, Temp, TDS, phosphate and moderate with electrical conductivity with loading values of 0.986, 0.970, 0.792, 0.744, 0.695,  0.701, respectively. Factor 2 marked 27.236% of the total variance with moderate positive loading with total alkalinity & temp. with loading values 0.723 & 0.606 respectively. It also explained the moderate negative loading with conductivity, TDS, and chloride with loading values -0.698, -0.690, -0.582. Factor F 3 marked 20.972 % of the variances with positive loading with PH, chloride, and phosphate with strong loading of pH 0.872 and moderate positive loading with chloride and phosphate with loading values 0.721, and 0.569 respectively. 

Rapid Differentiation and Identification of Shigella sonnei and Escherichia coli O157: H7 by Fourier Transform Infrared Spectroscopy and Multivariate Statistical Analysis

2014 ◽  
Vol 926-930 ◽  
pp. 1116-1119 ◽  
Author(s):  
Li Jun Yang ◽  
Jing Wang ◽  
Zhao Jie Li ◽  
Xiao Hua Song ◽  
Yu Min Liu ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Infrared Spectroscopy ◽  
Statistical Analysis ◽  
Fourier Transform Infrared Spectroscopy ◽  
Multivariate Statistical Analysis ◽  
Principal Component ◽  
Fourier Transform Infrared ◽  
Shigella Sonnei ◽  
Multivariate Statistical ◽  
Simple Method

Fourier transform infrared spectroscopy (FTIR) combined with multivariate statistical analysis was applied to differentiate and identify Shigella sonnei and Escherichiacoli O157: H7. FTIR absorption spectra from 4000-600 cm-1 were collected from sampling 10 μL of bacterial suspention. The spectra between 1800 and 900 cm-1 highlighted the most distinctive variations and were the most useful for characterizing the selected microorganisms. Spectra of the two bacteria were noticeably segregated with distinct clustering by principal component analysis (PCA). Further more, another cluster model of hierarchical cluster analysis (HCA) was established and could also gave a good separation between the two bacteria. These results demonstrate that FTIR technology has considerable potential as a rapid, accurate and simple method for differentiating and identifying bacteria.

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.

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.

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 Multivariate statistical analysis to support the minimum streamflow regionalization

2015 ◽  
Vol 35 (5) ◽  
pp. 838-851 ◽  
Author(s):  
Abrahão A. A. Elesbon ◽  
Demetrius D. da Silva ◽  
Gilberto C. Sediyama ◽  
Hugo A. S Guedes ◽  
Carlos A. A. S. Ribeiro ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Statistical Analysis ◽  
River Basin ◽  
Multivariate Statistical Analysis ◽  
Annual Rainfall ◽  
Morphometric Characteristics ◽  
Multivariate Statistical ◽  
Independent Variables ◽  
Homogeneous Regions ◽  
Doce River

ABSTRACT This study aimed to develop a methodology based on multivariate statistical analysis of principal components and cluster analysis, in order to identify the most representative variables in studies of minimum streamflow regionalization, and to optimize the identification of the hydrologically homogeneous regions for the Doce river basin. Ten variables were used, referring to the river basin climatic and morphometric characteristics. These variables were individualized for each of the 61 gauging stations. Three dependent variables that are indicative of minimum streamflow (Q7,10, Q90 and Q95). And seven independent variables that concern to climatic and morphometric characteristics of the basin (total annual rainfall – Pa; total semiannual rainfall of the dry and of the rainy season – Pss and Psc; watershed drainage area – Ad; length of the main river – Lp; total length of the rivers – Lt; and average watershed slope – SL). The results of the principal component analysis pointed out that the variable SL was the least representative for the study, and so it was discarded. The most representative independent variables were Ad and Psc. The best divisions of hydrologically homogeneous regions for the three studied flow characteristics were obtained using the Mahalanobis similarity matrix and the complete linkage clustering method. The cluster analysis enabled the identification of four hydrologically homogeneous regions in the Doce river basin.

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