Assessment of groundwater water quality in central and southern Gulf Coast aquifer,TX using principal component analysis

Principal component analysis has been applied for source identification and to assess factors affecting concentration variations. In particular, this study utilizes principal component analysis (PCA) to understand groundwater geochemical characteristics in the central and southern portions of the Gulf Coast aquifer in Texas. PCA, along with exploratory data analysis and correlation analysis is applied to a spatially extensive multivariate dataset in an exploratory mode to conceptualize the geochemical evolution of groundwater. A general trend was observed in all formations of the target aquifers with over 75 % of the observed variance explained by the first four factors identified by the PCA. The first factor consisted of older water subjected to weathering reactions and was named the ionic strength index. The second factor, named the alkalinity index explained greater variance in the younger formations rather than in the older formations. The third group represented younger waters entering the aquifers from the land surface and was labeled the recharge index. The fourth group which varied between aquifers was either the hardness index or the acidity index depending on whether it represented the influences of carbonate minerals or parameters affecting the dissolution of fluoride minerals, respectively. The PCA approach was also extended to the well scale to determine and identify the geographic influences on geochemical evolution. It was found that wells located in outcrop areas and near rivers and streams had a larger influence on the factors suggesting the importance of surface water–groundwater interactions.     

Assessment of water quality and identification of pollution sources of three lakes in Kashmir, India, using multivariate analysis

Multivariate statistical techniques, such as cluster analysis, principal component analysis (PCA) and factor analysis (FA) were applied to evaluate and interpret the water quality data set for 13 parameters at 10 different sites of the three lakes in Kashmir, India. Physicochemical parameters varied significantly (p?<?0.05) among the sampling sites. Hierarchical cluster analysis grouped 10 sampling sites into three clusters of less polluted, moderately polluted and highly polluted sites, based on similarity of water quality characteristics. FA/PCA applied to data sets resulted in three principal components accounting for a cumulative variance of 69.84, 65.05 and 71.76% for Anchar Lake, Khushalsar Lake and Dal Lake, respectively. Factor analysis obtained from principal components (PCs) indicated that factors responsible for accelerated eutrophication of the three lakes are domestic waste waters, agricultural runoff and to some extent catchment geology. This study assesses water quality of three lakes through multivariate statistical analysis of data sets for effective management of these lakes.     

Defining element associations and inferring geological processes from total element concentrations in Australian catchment outlet sediments: Multivariate analysis of continental-scale geochemical data

In this paper, the geochemical composition of surficial regolith is statistically analysed and compared to independent geoscientific datasets to infer processes governing regolith composition. Surface (0–10 cm depth) and sub-surface (∼60–80 cm depth) transported sediment samples from the National Geochemical Survey of Australia were analysed for total element content in both coarse (<2 mm) and fine (<75 μm) grain-size fractions. Multi-element total content data was obtained from mainly XRF and total digestion ICP-MS analysis, of which the 50 elements satisfying data quality criteria, plus Loss on Ignition, are used herein.Censored data (<lower limit of detection) was imputed using a nearest neighbour-based analysis. The compositional data was transformed using centered log ratios (clr) to circumvent closure issues. A Principal Component Analysis (PCA) was then performed on the dataset. The first four PCs account for 59% of the variance in the dataset. Both negative and positive loadings of each of these PCs relate to geological processes consistent with the element associations they represent as well as the spatial distribution patterns they produce. The positive loadings of PC1 represent the accumulation of resistant minerals rich in Rare Earth Elements (REEs) that results from intense weathering, except in southeastern Australia where they reflect REE-enriched igneous and sedimentary rocks. Negative PC1 loadings represent secondary minerals formed during weathering (carbonates, sulfates, Fe-oxyhydroxides). Negative PC2 loadings are a mixture of elements (e.g., Co, Mn, Zn, V) characterising mafic and ultramafic minerals; conversely negative PC3 loadings (e.g., K, Rb, Na, Sr, Ca) represent more felsic minerals. Spatial distributions of the PCs are compared with independent spatial information from geological maps, airborne radiometric and spaceborne spectroscopic datasets. The differences between surface and sub-surface and between coarse and fine grain-size fractions are analysed. The implied processes (e.g., lithological control, weathering, transport, secondary mineral precipitation) overall match well with this new geochemical evidence. Future work directions with this dataset include lithological prediction and mineral prospectivity analysis.     

Spatial–temporal characteristics of surface water quality in the Taihu Basin,China

Taihu Basin is one of the most developed and industrialized regions in China. In the last two decades, rapid development of economy as well as an increase in population has resulted in an increase of pollutants produced and discharged into rivers and lakes. Much more attention has been paid on the serious water pollution problems due to high frequency of algal blooming. The dataset, obtained during the period 2001–2002 from the Water Resources Protection Bureau of the Taihu Basin, consisted of eight physicochemical variables surveyed monthly at 22 sampling sites in the Taihu Basin, China. Principal component analysis (PCA) and cluster analysis (CA) were used to identify the characteristics of the surface water quality in the studied area. The temporal and spatial variations of water quality were also evaluated by using the fuzzy synthetic evaluation (FSE) method. PCA extracted the first two principal components (PCs), explaining 86.18% of the total variance of the raw data. Especially, PC1 (73.72%) had strong positive correlation with DO, and was negatively associated with COD_Mn, COD, BOD, NH₄ ⁺–N, TP and TN. PC2 (12.46%) was characterized by pH. CA showed that most sites were highly polluted by industrial and domestic wastewater which contributed significantly to PC1. The sites located in the west of Lake Taihu were influenced by farmland runoff which may contribute to nitrogen pollution of Lake Taihu, whereas the monitoring sites in the eastern of Lake Taihu demonstrated that urban residential subsistence and domestic wastewater are the major contaminants. FSE indicates that there is no obvious variance between 2001 and 2002 among most sites. Only several sites free from point-source pollution appear to exhibit good water quality through the studied period.     

Assessment of surface water quality using multivariate statistical analysis techniques: a case study from Ghrib dam,Algeria

This study explores the water quality status and pollution sources in Ghrib Dam, Algeria. It allows us to obtain more accurate information on water quality by applying a series of multivariate statistical techniques, including principal component analysis (PCA)/factor analysis (FA), hierarchical cluster analysis (CA), and multiple regression analysis (MRA). On 19 physicochemical parameters dataset over 5 years and from 6 different sites located in and around the lake. One-way analysis of variance (ANOVA) was used to investigate the statistically considerable spatial and seasonal differences. The results of ANOVA suggest that there exist a statistically significant temporal variation in the water quality of the dam for all parameters. On the other hand, only organic matter has a statistically significant spatial variation. In the multiple linear models, an association between organic and inorganic parameters was found; their origin comes from the mechanical erosion process of agricultural lands in the watershed. The PCA/FA identifies five dominant factors as responsible of the data structure, explaining more than 94.96% of the total variance in the water quality dataset. This suggests that the variations in water compounds’ concentration are mainly related to the multiple anthropogenic activities, as well as natural processes. The results of cluster analysis demonstrate that the sampling stations were divided in two similar groups, which indicates spatial homogeneity. While seasonal grouping has showed that the source of pollution was related to the level of runoff in the seasons.     

Identifying key hydrochemical processes in a confined aquifer of an arid basin using multivariate statistical analysis and inverse modeling

Tertiary fractured permeable confined aquifer, which covered about 70 % of the studying area, played an important role in alleviating drinking water shortages. However, about 58 and 79 % of the groundwater samples exceeded the desirable limits for fluoride (1.5 mg/L) and TDS (1,000 mg/L). Two multivariate statistical methods, hierarchical cluster analysis (HCA) and principal components analysis (PCA), were applied to a subgroup of the dataset in terms of their usefulness for groundwater classification, as well as to identify the key processes controlling groundwater geochemistry. In the PCA, two principal factors have been extracted, which could explain 73 % of the total data variability. Among them, factor 1 revealed the source of groundwater salinity and factor 2 explained the elevated fluoride. Two major groups were classified by HCA and Group 1 was near the groundwater recharge zone and Group 2 was mainly distributed over the groundwater discharge zone. Inverse modeling (NETPATH) results indicated that the hydrochemical evolution was primarily controlled by (1) the dissolution of mirabilite, gypsum and halite for the source of groundwater salinity; (2) the release of the adsorbed fluoride through desorption or through competition with HCO₃ ^? under alkalinity condition for the elevated fluoride in the groundwater.     

基于主成分和半参数的大坝变形监测回归模型

主成分分析在一定程度上可以解决大坝变形监测回归模型因子间的复共线性,然而当提取的主成分信息不充分时,主成分回归用于大坝安全预测可能失效。提出以主成分分析提取的主成分作为半参数回归的参数分量,剩余成分和模型误差作为未知的非参数分量对主成分回归进行补偿,建立一种基于主成分和半参数的大坝变形监测混合回归模型。利用某大坝实测资料进行建模分析,结果表明该混合模型能克服回归因子间的复共线性,避免半参数回归补偿最小二乘估计中法矩阵的病态性,比传统的主成分回归和逐步回归模型具有更好的拟合和预报精度。     

Spatial variation assessment of groundwater quality using multivariate statistical analysis(Case Study: Fasa Plain,Iran)

Groundwater is considered as one of the most important sources for water supply in Iran. The Fasa Plain in Fars Province, Southern Iran is one of the major areas of wheat production using groundwater for irrigation. A large population also uses local groundwater for drinking purposes. Therefore, in this study, this plain was selected to assess the spatial variability of groundwater quality and also to identify main parameters affecting the water quality using multivariate statistical techniques such as Cluster Analysis (CA), Discriminant Analysis (DA), and Principal Component Analysis (PCA). Water quality data was monitored at 22 different wells, for five years (2009-2014) with 10 water quality parameters. By using cluster analysis, the sampling wells were grouped into two clusters with distinct water qualities at different locations. The Lasso Discriminant Analysis (LDA) technique was used to assess the spatial variability of water quality. Based on the results, all of the variables except sodium absorption ratio (SAR) are effective in the LDA model with all variables affording 92.80% correct assignation to discriminate between the clusters from the primary 10 variables. Principal component (PC) analysis and factor analysis reduced the complex data matrix into two main components, accounting for more than 95.93% of the total variance. The first PC contained the parameters of TH, Ca²⁺, and Mg²⁺. Therefore, the first dominant factor was hardness. In the second PC, Cl^-, SAR, and Na⁺ were the dominant parameters, which may indicate salinity. The originally acquired factors illustrate natural (existence of geological formations) and anthropogenic (improper disposal of domestic and agricultural wastes) factors which affect the groundwater quality.     

Multivariate statistical analysis of geochemical data of groundwater in Veeranam catchment area,Tamil Nadu

The study of hydrogeochemistry of the Mio-Pliocene sedimentary rock aquifer system in Veeranam catchment area produced a large geochemical dataset. Groundwater samples were collected at 52 sites over 963.86 km² area and analyzed for major ions. The large number of data can lead to difficulties in the integration, interpretation and representation of the results. Two multivariate statistical methods, Hierarchical cluster analysis (HCA) and Factor analysis (FA), were applied to a subgroup of the dataset to evaluate their usefulness to classify the groundwater samples, and to identify geochemical processes controlling groundwater geochemistry. Hydrochemical data for 52 groundwater samples were subjected to Q- and R- mode factor and cluster analysis. R-mode analysis reveals the inter-relations among the variables studied and the Q-mode analysis reveals the inter-relations among the samples studied. The R-mode factor analysis shows that Ca, Mg and Cl with HCO₃ account for most of the electrical conductivity, total dissolved solids and total hardness of groundwater. The ‘single dominance’ nature of the majority of the factors in the R-mode analysis indicates non-mixing or partial mixing of different types of groundwater. Both Q-mode factor and Q-mode cluster analyses indicate an exchange between the river water and the groundwater in the vicinity. The rock water interaction like flood basin back swamp deposits of silty clayey formation is the major cause for the cluster II classification. Cluster classification map reveals that 58% of the study area comes under cluster II classification.     

对应聚类分析

对应聚类分析是一种多元统计分析方法。它吸取了对应分析和聚类分析的主要优点,在充分利用多维空间信息的基础上,该方法将变量类别、样品类别和它们间的对应关系清晰显示于单—图件,即对应聚类谱系图。近年来的许多实例说明该方法在地质和勘查地球化学中的应用是有成效的。本文详细叙述了对应聚类分析的计算方法,通过一些验证性实例介绍了方法效果,并简要讨论了为什么能获得较好效果的原因。     

Distribution of uranium in German bottled and tap water

The results presented in this paper on uranium in bottled and tap water were determined within the scope of the project “European Groundwater Geochemistry: Bottled Water” of the Geochemistry Expert Group of EuroGeoSurveys. The analyses of bottled water provide an inexpensive approach to obtain information about European groundwater geochemistry. For this study, the uranium concentrations in 1785 European mineral water samples were analyzed by ICP–QMS in the BGR laboratories. The dataset is used to obtain a first impression about natural concentration levels and variation of uranium in groundwater (and bottled water) at the German and European scale.     

基于主成分分析的反射光谱在岩石学中的应用——以哈密地区为例

本文首先总结并分析了岩矿常见光谱特征的波长位置及其产生原因。然后基于主成分分析技术对哈密42条岩石实验室光谱进行了定量分析。前两个主成分共包含了96．7％的信息量，代表了原始信息。第一主成分反映了岩石总体反射率大小，称为反照率因子；第二主成分反映了光谱曲线斜率变化，称为形状因子。使用这两个主成分可以区分该区主要岩类，达到了数据压缩和分类的目的。第三和第四主成分尽管所占信息很少，但反映了岩石蚀变信息。为了突出蚀变岩石光谱吸收特征，进而又对经过连续统去除后的光谱进行主成分分析，结果发现，在新生成的第三和第四主成分图上，4种矿石被清晰区分开来。     

Spatial variability analysis of precipitation in northwest Iran

The spatial variability of precipitation was investigated in the northwestern corner of Iran using data collected at 24 synoptic stations from 1986 to 2015. Principal component analysis (PCA) and cluster analysis (CA) were used to regionalize precipitation in the study area. Eleven precipitation variables were averaged and arranged as an input matrix for the R-mode PCA to identify the precipitation patterns. Results suggest that the study area can be divided into four spatially homogeneous sub-zones. In addition, the spatial patterns of annual precipitation were identified by applying the T-mode PCA and CA to the annual precipitation data. The delineated spatial patterns revealed three distinct sub-regions. The resultant maps were compared with the spatial distribution of the rotated principal components (PCs). Results pointed out that the delineated clusters are characterized by different precipitation variability; and using different precipitation parameters can lead to different spatial patterns of precipitation over northwest Iran.     

Preliminary groundwater quality assessment in the central region of Ghana

Insufficient knowledge of the hydrogeochemistry of aquifers in the Central Region of Ghana has necessitated a preliminary water quality assessment in some parts of the region. Major and minor ions, and trace metal compositions of groundwater have been studied with the aim of evaluating hydrogeochemical processes that are likely to impair the quality of water in the study area. The results show that groundwater in the area is weakly acidic with mean acidity being 5.83 pH units. The dominant cation in the area is Na, followed by K, Ca, and Mg, and the dominant anion is Cl^?, followed by HCO₃ ^? and SO₄ ^2?. Two major hydrochemical facies have been identified as Na–Cl and Na–HCO_3, water types. Multivariate statistical techniques such as cluster analysis (CA) and factor analysis/principal component analysis (PCA), in R mode, were employed to examine the chemical compositions of groundwater and to identify factors that influenced each. Q-mode CA analysis resulted in two distinct water types as established by the hydrochemical facies. Cluster 1 waters contain predominantly Na–Cl. Cluster 2 waters contain Na–HCO₃ and Na–Cl. Cluster 2 waters are fresher and of good quality than cluster 1. Factor analysis yielded five significant factors, explaining 86.56% of the total variance. PC1 explains 41.95% of the variance and is contributed by temperature, electrical conductivity, TDS, turbidity, SO₄ ^2?, Cl^?, Na, K, Ca, Mg, and Mn and influenced by geochemical processes such as weathering, mineral dissolution, cation exchange, and oxidation–reduction reactions. PC2 explains 16.43% of the total variance and is characterized by high positive loadings of pH and HCO₃ ^?. This results from biogenic activities taking place to generate gaseous carbon dioxide that reacts with infiltrating water to generate HCO₃ ^?, which intend affect the pH. PC3 explains 11.17% of the total variance and is negatively loaded on PO₄ ^3? and NO₃ ^? indicating anthropogenic influence. The R-mode PCA, supported by R-mode CA, have revealed hydrogeochemical processes as the major sources of ions in the groundwater. Factor score plot revealed a possible flow direction from the northern sections of the study area, marked by higher topography, to the south. Compositional relations confirmed the predominant geochemical process responsible for the various ions in the groundwater as mineral dissolution and thus agree with the multivariate analysis.     

Application of the chemometric approach to evaluate the spatial variation of water chemistry and the identification of the sources of pollution in Langat River, Malaysia

The current study presents the application of selected chemometric techniques—hierarchical cluster analysis (HCA) and principal component analysis (PCA)—to evaluate the spatial variation of the water chemistry and to classify the pollution sources in the Langat River. The HCA rendered the sampling stations into two clusters (group 1 and group 2) and identified the vulnerable stations that are under threat. Group1 (LY 1 to LY 14) is associated with seawater intrusion, while group 2 (LY 15 to LY 30) is associated with agricultural and industrial pollution. PCA analysis was applied to the water datasets for group 1 resulting in four components, which explained 85 % of the total variance while group 2 extracted six components, explaining 88 % of the variance. The components obtained from PCA indicated that seawater intrusion, agricultural and industrial pollution, and geological weathering were potential sources of pollution to the study area. This study demonstrated the usefulness of the chemometric techniques on the interpretation of large complex datasets for the effective management of water resources.     

Geometric morphometric assessment of the fossil bears of Namur,Belgium: Allometry and ecomorphology

The Namur area in Belgium is useful to study brown (Ursus arctos) and cave bears (Ursus spelaeus) as the assemblage contains little temporal and no geographical variation. Here, we aim to assess ontogenetic allometry within cave bears, as well as ecomorphological differences between adult brown bears (n = 9), adult cave bears (n = 5) and juvenile cave bears (n = 3). Landmarks for 3D digitization of the mandible were chosen based on the taphonomical damage of the specimens. Extant brown bears and extinct Pleistocene brown and cave bears were digitized with a Microscribe G2. Generalized Procrustes superimposition was performed on the coordinates. Allometry was studied using regression analysis. Principal component analysis (PCA) was conducted to assess ecomorphological differences between the groups. 61% of the shape variance within juvenile and adult cave bears was predicted by size (n = 8, p < 0.01). The juvenile cave bears have relatively deep horizontal rami. In adult cave bears, the horizontal ramus is much narrower dorsoventrally. Juvenile cave bears have a small masseteric fossa and a short coronoid process, whereas both are larger, relative to mandible size, in adult cave bears. This made juvenile cave bears likely less effective masticators than fully grown cave bears. In the PCA, principal component (PC) 1 accounts for 45.0% of the total variance and PC2 accounts for 27.6%. Fossil U. arctos from Namur fall within the 95% confidence interval of modern North American U. arctos on both PCs, but are more similar to cave bears than the average extant brown bear. From the similarity of fossil and modern brown bears, it can be deduced that the diet of fossil brown bears was probably also within the range of their modern North American conspecifics, although they might have been more efficient at masticating plant matter.     

Multivariate analysis of sediment data from the upper and middle Odra River (Poland)

River pollution data are characterized by high variability. Multivariate statistical methods help to determine a complex set of these multidimensional data and to extract latent information (e.g. differently polluted areas, discharges). The chemometric methods can handle interactions between different pollutants and relationships among various sampling locations. This study presents an application of multivariate data analysis in the field of environmental pollution. The dataset consists of As, Cd, Cr, Cu, Fe, Ni, Mn, Pb and Zn contents of sediment samples collected in the upper and middle Odra River (Poland) in three sampling campaigns (November 1998, June 1999, and May 2000). As chemometric tools cluster analysis (CA), multivariate analysis of variance and discriminant analysis (MVDA) and factor analysis (FA) were used to investigate the matrix of 60 sampling points.     

Assessment of physico-chemical and microbiological surface water quality using multivariate statistical techniques: a case study of the Wadi El-Bey River,Tunisia

Located in the northeastern part of Tunisia, Wadi El Bey drains the watershed through farmland, industrial, and urban areas of the region. It serves to discharge treated wastewater of different types. In this work, the variations of the water quality of Wadi El Bey were studied and evaluated, during 2 years (2012–2013), using multivariate statistical techniques such as principal component analysis (PCA) and cluster analysis (CA). In addition, the similarities or dissimilarities among the sampling points were as well analyzed to identify spatial and temporal variations. The results obtained based on the cluster analysis, led to identify three similar water quality zones: relatively polluted (LP), moderately polluted (MP), and highly polluted (HP). The inorganic and organic parameters, temperature, conductivity, dissolved oxygen, chemical oxygen demand, salmonella, and enterococcus, seemed to be the most significant parameters of water quality. Three factors were identified as responsible for the data structure, explaining 60.95% of the total variance. The first factor is the physical and non-organic chemical parameters explaining 23.48% of the total variance. The second and third factors are, respectively, the microbiological (21.26%) and organic-nutrient (16.2%).This study shows that multivariate statistical methods can help the water managers to understand the factors affecting the water quality.     

Cluster analysis applied to regional geochemical data: Problems and possibilities

Cluster analysis can be used to group samples and to develop ideas about the multivariate geochemistry of the data set at hand. Due to the complex nature of regional geochemical data (neither normal nor log-normal, strongly skewed, often multi-modal data distributions, data closure), cluster analysis results often strongly depend on the preparation of the data (e.g. choice of the transformation) and on the clustering algorithm selected. Different variants of cluster analysis can lead to surprisingly different cluster centroids, cluster sizes and classifications even when using exactly the same input data. Cluster analysis should not be misused as a statistical “proof” of certain relationships in the data. The use of cluster analysis as an exploratory data analysis tool requires a powerful program system to test different data preparation, processing and clustering methods, including the ability to present the results in a number of easy to grasp graphics. Such a tool has been developed as a package for the R statistical software. Two example data sets from geochemistry are used to demonstrate how the results change with different data preparation and clustering methods. A data set from S-Norway with a known number of clusters and cluster membership is used to test the performance of different clustering and data preparation techniques. For a complex data set from the Kola Peninsula, cluster analysis is applied to explore regional data structures.