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 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.

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 Multivariate Statistical Analysis of Hydrochemical Data and Stable Isotopes of Groundwater Contaminated with Nitrate at Huay Sai Royal Development Study Center and Adjacent Areas in Phetchaburi Province, Thailand

Water ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 1127 ◽  
Author(s):  
Srilert Chotpantarat ◽  
Tewanopparit Parkchai ◽  
Wanlapa Wisitthammasri
Keyword(s):  
Stable Isotopes ◽  
Statistical Analysis ◽  
Groundwater Quality ◽  
Multivariate Statistical Analysis ◽  
Principal Component ◽  
Potassium Nitrate ◽  
Multivariate Statistical ◽  
Hydrochemical Data ◽  
Hydrogeological Characteristics ◽  
Groundwater Wells

Due to the continuous expansion in agriculture production and industry for many years, groundwater usage has been increasing, with a decrease in groundwater levels in many cases. In addition, in some areas, groundwater quality has degraded due to agrochemical contamination from agricultural areas. The aims of this research pertains to aquifers as follows: (1) to evaluate hydrochemical characteristics of groundwater using multivariate statistical analysis, including principal component analysis (PCA) and hierarchical cluster analysis (HCA), and (2) to integrate the stable isotopes 18O and 2H with hydrochemical data to evaluate the origin of the groundwater and indirectly identify the pollution sources of groundwater contaminated with nitrate (NO3). Water samples were collected from 60 groundwater wells with different hydrogeological characteristics and land use types in both the rainy season (in October) and the summer seasons (in February) in the Cha Am district of Phetchaburi Province. The groundwater was separated into 3 types: Ca-Na-Cl, Ca-Na-HCO3-Cl, and Na-Cl. Two groundwater wells (no. 19 and 41), which were located southeast and southwest of the study area, had relatively high NO3− concentrations (47 mg/L NO3 and 50 mg/L NO3, respectively) that were higher than the groundwater quality standards. These two wells corresponded to the second group that was exposed by HCA. The PCA results revealed the influence of seawater intrusion. Furthermore, multivariate statistical analysis (PC 2) revealed that the NO3− that is mainly released from potassium nitrate (KNO3), for example, during pineapple cultivation, directly contaminated the groundwater system.

Evaluation of Groundwater Quality at an Industrial Park Site Zone Using Statistical Analyses: A Case Study in Taiwan

2013 ◽  
Vol 457-458 ◽  
pp. 1581-1584
Author(s):  
Bo Ming Yang ◽  
Zong Han Yang ◽  
Jong Kang Liu ◽  
Hui Yu Lee ◽  
Chih Ming Kao
Keyword(s):  
Statistical Analysis ◽  
Groundwater Quality ◽  
Quality Indicators ◽  
Principal Components ◽  
Multivariate Statistical Analysis ◽  
Organic Pollutant ◽  
Principal Component ◽  
Original Data ◽  
Industrial Park ◽  
Multivariate Statistical

Multivariate statistical analysis explains the huge and complicated current situation of the original data efficiently, concisely, and explicitly. It simplifies the original data into representative factors, or bases on the similarity between data to cluster and identify clustering outcome. In this study, the statistical software SPSS 12.0 was used to perform the multivariate statistical analysis to evaluate characteristics of groundwater quality at an industrial park site located in Kaohsiung, Taiwan. Results from the principal component analysis (PCA) and factor analyses (FA) show that seven principal components could be compiled from 20 groundwater quality indicators obtained from groundwater analyses, which included background factor, salt residua factor, hardness factor, ethylene chloride factor, alkalinity factor, organic pollutant factor, and chloroform factor. Among the seven principal components, the major influencing components were salinization factor and acid-base factor. Results show that the seven principal component factors were able to represent 89.6% of the total variability for 20 different groundwater quality indicators. Groundwater monitoring wells were classified into seven groups according to the partition of homogeneity and similarity using the two-phase cluster analysis (CA). The clustering results indicate that chlorides such as 1,1-dichloroethylene, 1,1-dichloroethane, and cis-1,2-dichloroethylene had the highest concentrations among the clusters. This indicates that groundwater at nearby areas may be polluted by chlorinated organic compounds. Results from the correlation analysis by Fisher coefficient formula show that the cluster results of seven groups of groundwater wells had 100 and 80% accuracies using discriminant and cross-validation analyses, respectively. This implies that high accuracy can be obtained when discriminant and cluster analyses are applied for data evaluation.

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 Investigating the Impact of Anthropogenic and Natural Sources of Pollution on Quality of Water in Upper Indus Basin (UIB) by Using Multivariate Statistical Analysis

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Mansoor A. Baluch ◽  
Hashim Nisar Hashmi
Keyword(s):  
Water Quality ◽  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
Quality Parameters ◽  
Anthropogenic Source ◽  
Indus River ◽  
Multivariate Statistical ◽  
Quality Of Water ◽  
The Impact

Water quality of the Indus River around the upper basin and the main river was evaluated with the help of statistical analysis. In order to analyze the similarities and dissimilarities for identifying the spatial variations in water quality of the Indus River and sources of contamination, multivariate statistical analysis, i.e., principle component analysis (PCA), cluster analysis, and descriptive analysis, was done. Data of 8 physicochemical quality parameters from 64 sampling stations belonging to 6 regions (labeled as M1, M2, M3, M4, M5, and M6) were used for analysis. The parameters used for assessing the water quality were pH, dissolved oxygen (DO), oxygen reducing potential (ORP), electrical conductivity (EC), total dissolved solids (TDS), salinity (%), and concentration of arsenic (As) and lead (Pb), respectively. PCA assisted in extracting and recognizing the responsible variation factors of water quality over the region, and the results showed three underlying factors including anthropogenic source pollution along with runoff due to rain and soil erosion were responsible for explaining the 93.87% of total variance. The parameters which were significantly influenced by anthropogenic impact are DO, EC, TDS (negative), and concentration of Pb (positive), while the concentration of As, % salinity, and ORP are affected by erosion and runoff due to rain. The worst pollution situation for regions M1 and M6 was due to the concentration of As which was approximately 400 μg/l (i.e., 40 times higher than minimum WHO recommendation). Furthermore, the results also indicated that, in the Indus River, three monitoring stations and five quality parameters are sufficient to have a reasonable confidence about the quality of water in this most important reserve of Pakistan.

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