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 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.

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 The Simplified Expression of Machine Learning and Multivariate Statistical Analysis Based on the Centering Matrix

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Zhen Xue ◽  
Liangliang Zhang
Keyword(s):  
Machine Learning ◽  
Statistical Analysis ◽  
Quadratic Form ◽  
Multivariate Statistical Analysis ◽  
Null Space ◽  
Predictive Ability ◽  
Principal Component ◽  
Original Data ◽  
Multivariate Statistical ◽  
Analysis Theory

In machine learning (ML) algorithms and multivariate statistical analysis (MVA) problems, it is usually necessary to center (zero centered or mean subtraction) the original data. The centering matrix plays an important role in this process. The full consideration and use of its properties may contribute to the speed or stability improvement of some related ML algorithms. Therefore, in this paper, we discussed in detail the properties of the centering matrix, proved some previously known properties, and deduced some new properties. The involved properties mainly consisted of the centering, quadratic form, spectral decomposition, null space, projection, exchangeability, Kronecker square, and so on. Based on this, we explored the simplified expression role of the centering matrix in the principal component analysis (PCA) and regression analysis theory. The results show that the sum of deviation squares which is widely used in regression theory and variance analysis can be expressed by the quadratic form with the centering matrix as the kernel matrix. The ML algorithms introducing the centering matrix can greatly simplify the learning process and improve predictive ability.

Relationship Between Major And Trace Elements In Ulaanbaatar Soils: A Study Based On Multivariate Statistical Analysis

2019 ◽  
Vol 12 (3) ◽  
pp. 199-212 ◽  
Author(s):  
Elena V. Shabanova ◽  
Ts. Byambasuren ◽  
G. Ochirbat ◽  
Irina E. Vasil'eva ◽  
B. Khuukhenkhuu ◽  
...  
Keyword(s):  
Trace Elements ◽  
Statistical Analysis ◽  
Principal Components ◽  
Multivariate Statistical Analysis ◽  
Urban Soils ◽  
Thermal Power ◽  
Principal Component ◽  
Major Elements ◽  
Multivariate Statistical ◽  
Data Set

This article focuses on the relationships between major (Si, Al, Mg, Fe, Ca, Na, K, S, P and Ti) and potentially toxic trace (Ag, As, B, Ba, Bi, Co, Cd, Cr, Cu, F, Ge, Mo, Mn, Li, Ni, Pb, Sb, Sn, Sr, Tl, V and Zn) elements in Ulaanbaatar surface soils and also sources of the trace elements in the soils distinguished by the methods of multivariate statistical analysis. Results of exploratory data analysis of 325 Ulaanbaatar soil samples show the accumulation of Ca, S, B, Bi, Cu, Mo, Pb, Sb, Sn, Sr and Zn in urban soils. The major elements were grouped by cluster analysis in tree associations characterizing main soil fractions: sandy P-(K-Na-Si), clayey (Mg-Ti-Fe-Al) and silty (S-Ca). The factor analysis shows that silty fraction is enriched in major elements of both natural and anthropogenic origin. The principal component analysis from 32 variables extracted nine principal components with 82.49% of the cumulative explained variance. The results of cluster and factor analyses well agree and reaffirm the enrichment causes of potentially toxic elements are a coal combustion at thermal power stations (B, Bi, Ca, Mo, S and Sr) and traffic emissions (Cu, Pb, Sn and Zn). Spatial distributions of trace elements in the districts of Ulaanbaatar city were obtained by ordinary kriging. It is illustrated that the different principal components define the various origins and patterns of accumulation of trace elements in soils. The supplementation of data set by the concentration of organic carbon and the species of elements could help to identify the sources of such elements as P, Ni, Al, Fe, Ca, Ba, Bi, Cr, Zn, Sr and Sb in urban soils more completely.

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.

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.

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