Assessment of groundwater quality using multivariate statistical techniques in Hashtgerd Plain, Iran

Multivariate statistical techniques, such as cluster analysis (CA), factor analysis (FA), principal component analysis (PCA), and discriminant analysis (DA), were applied for the evaluation of variations and the interpretation of a large complex groundwater quality data set of the Hashtgerd Plain. In view of this, 13 parameters were measured in groundwater of 26 different wells for two periods. Hierarchical CA grouped the 26 sampling sites into two clusters based on the similarity of groundwater quality characteristics. FA based on PCA, was applied to the data sets of the two different groups obtained from CA, and resulted in three and five effective factors explaining 79.56 and 81.57% of the total variance in groundwater quality data sets of the two clusters, respectively. The main factors obtained from FA indicate that the parameters influencing groundwater quality are mainly related to natural (dissolution of soil and rock), point source (domestic wastewater) and non-point source pollution (agriculture and orchard practices) in the sampling sites of Hashtgerd Plain. DA provided an important data reduction as it uses only three parameters, i.e., electrical conductivity (EC), magnesium (Mg²⁺) and pH, affording more than 98% correct assignations, to discriminate between the two clusters of groundwater wells in the plain. Overall, the results of this study present the effectiveness of the combined use of multivariate statistical techniques for interpretation and reduction of a large data set and for identification of sources for effective groundwater quality management.     

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.     

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.     

Water pollution sources assessment by multivariate statistical methods in the Tahtali Basin,Turkey

In this study, multivariate statistical methods including factor, principal component and cluster analysis were applied to surface water quality data sets obtained from the Tahtali River Basin, Turkey. Factor and principal components analysis results revealed that surface water quality was mainly controlled by agricultural uses and domestic discharges. Cluster analysis generated two clusters. Based on the locations of the sites consisted by each cluster and variable concentrations at these stations, it was concluded that agricultural discharges strongly affected north and northeast part of the region. These methods are believed to assist water managers to understand complex nature of water quality issues and determine priorities to improve water quality.     

Spatial characteristics of surface water quality in the Haicheng River (Liao River basin) in Northeast China

Spatial variations of the water quality in the Haicheng River during April and October 2009 were evaluated for the national monitoring program on water pollution control and treatment in China. The spatial autocorrelation analysis with lower Moran’s I values displayed the spatial heterogeneity of the 12 physicochemical parameters among all the sampling sites of the river. The one-way ANOVA showed that all variables at different sampling sites had significant spatial differences (p < 0.01). Based on the similarity of water quality characteristics, cluster analysis grouped the 20 sampling sites into three clusters, related with less polluted, moderately polluted and highly polluted sites. The factor analysis extracted three major factors explaining 76.4 % of the total variance in the water quality data set, i.e., integrated pollution factor, nitrogen pollution factor and physical factor. The results revealed that the river has been severely polluted by organic matter and nitrogen. The major sources leading to water quality deterioration are complex and ascribed to anthropogenic activities, e.g., domestic and industrial wastewater discharges, agricultural runoff, and animal rearing practices.     

Chemometric approach integrated with capillary electrophoresis for the investigation of public water system sources and quality

A chemometric approach coupled with capillary electrophoresis based on the hierarchical cluster analysis and principal component analysis has been applied for the investigation of the water quality in the Golcuk-Isparta region (Lake District of Turkey). In the research area, Egirdir Lake, Golcuk Lake and surrounding ground and domestic waters have been utilized as drinking water resources. Golcuk Lake is distinctive in terms of high fluoride content (3.50 ± 0.21 mg/mL) which is endemic in volcanic areas where the water flow through volcanic rocks and sediments. Based on the analysis of major anions chloride, sulfate, nitrate and fluoride with capillary electrophoresis, twenty-four drinking water sampling sites in the research area were classified into four classes using the hierarchical cluster and principal component analysis. Combining the research area investigation results of hierarchical cluster and principal component analysis, it was found that fluoride concentration is the major diagnostic variable to determine the quality of drinking waters, and all the other anions are the important classification factors to predict the resources of the drinking water samples, individually. To sum up, this study reveals the potential of the use of capillary electrophoresis in combination with chemometric techniques for the determination of the quality and origin of drinking waters.     

Correlations between algae and water quality: factors driving eutrophication in Lake Taihu, China

Rapid population increase and economic growth in eastern China has lead to the degradation of many water bodies in the region, such as Lake Taihu, the third largest freshwater lake in China. Using data from recent investigations, the correlations between algae (measured as chlorophyll-a) and water quality indices in Lake Taihu were described by multivariate statistical analyses, and the key driving factors for the lake eutrophication were identified by principal component analysis. Results revealed strong spatiotemporal variation in the correlations between algae and water quality indices, suggesting that the limiting factor for the dominant algae growth depends on seasonality and location and it is necessary to reduce both nitrogen and phosphorus inputs for a long-term eutrophication control in this hyper-eutrophic system. Water temperature was another important controlling factor for algal growth in the lake. Using principal component analysis, nutrient contaminations from anthropogenic and natural inputs were identified as the key driving factor for the water quality problems of the lake. Moreover, five principal components were extracted and characterized with high spatial and seasonal variations in Lake Taihu. The key driving factors were believed to influence spatial variations including heavily polluted areas located in the northern and northwestern parts of the lake, where many manufacturing factories were built and wastewater from domestic and industrial plants was discharged. Based on this analysis, attention should be paid to effective land management, industrial wastewater treatment, and macrophytic vegetation restoration to reduce the pollutant loads and improve water quality. Principal component analysis was found to be a useful and effective method to reduce the number of analytical parameters without notably impairing the quality of information in this study.     

Characterization of underground tunnel water hydrochemical system and uses through multivariate statistical methods: a case study from Maddhapara Granite Mine,Dinajpur, Bangladesh

A quality study of the drained water from Maddhapara Granite Mine underground tunnel was undertaken to study their hydrochemical variations and suitability for various uses employing chemical analysis, basic statistics, correlation matrix (r), cluster analysis, principal component/factor analyses, and ANOVA as the multivariate statistical methods. The results of chemical analysis of water show the modest variation in their ionic assemblage among different sampling points of the tunnel where Ca–HCO₃ type of hydrochemical facies is principally dominated. The correlation matrix shows a very strong to very weak positive, even negative, correlation relationship, suggesting the influence of different processes such as geochemical, biochemical processes, and multiple anthropogenic sources on controlling the hydrochemical evolution and variations of water in the mine area. Cluster analysis confirms that cluster 1 contains 68.75% of total samples, whereas cluster 2 contains 31.25%. On the whole, the dominated chemical ions of first cluster groups are Ca and HCO₃, suggesting a natural process similar to dissolution of carbonate minerals. The second cluster group consisted of Cl^? and SO₄ ^2? ions representing natural and anthropogenic hydrochemical process. The results of PCA/FA analysis illustrate that different processes are involved in controlling the chemical composition of groundwater in the mine area. The factor 1 loadings showed that pH, EC, TDS, Na, Mg, chloride, and sulfate which have high loading in this factor are expected to come from carbonate dissolution to oxidation conditions. One-way ANOVA describes the significance of dependent variables with respect to independent variables. ANOVA gives us the idea that EC, K⁺, Fe^total, SO ₄ ² , As, and Pb are the most important factors in controlling spatial differences in water quality in this tunnel. But different results have been encountered for different independent variables which might be due to dissimilar sources of water. From the qualitative analysis, it is clear that water quality is not very favorable for aquatic creatures as well as for drinking purposes. The water can be used for irrigation purposes without any doubt as SAR and RSC analysis provides good results. Moreover, the results of this research confirmed that the application of multivariate statistical analysis methods is apposite to inferring complex water quality data sets with its possible pollution sources. At the end, this research recommends (1) as water becomes more and more important, water treatment plants should be built before the water being used; (2) a detailed water step utilization plan should be set beforehand to guarantee tunnel water being used effectively; and (3) after the water being used for agriculture, elements in crops should be monitored continuously to ensure that ions and compounds that come from the tunnel water are lower than guideline values for human beings health.     

基于多元统计方法的河流水质空间分析

基于聚类分析和判别分析探讨了河流水质空间分析方法,旨在识别采样点的空间相似性与差异性,从而为水质监测网络优化提供支持。该方法首先利用kurtosis和Skewness检验数据分布特征和进行数据对数转化与标准化处理;然后利用聚类分析进行空间相似性分析,确定空间尺度分类情况;最后利用判别分析识别显著性污染指标,以此反映上述空间尺度分类的差异性。以香港后海湾水质管制区为例,结果表明:①通过对数转化显著改善数据分布特征,使绝大部分污染指标呈正态或接近正态分布;②该区域采样点在个案链锁距离与最大链锁距离之比(D_link/D_max)×100<35处明显分为3类,它们分别代表轻度、中度、重度污染3种类型,且后两者属于采样点主要属于营养盐和重金属污染类型,需要控制其生活污水、畜牧污染、工业污染和地表径流污染;③后退式判别分析具有良好的指标降维能力,仅需7个显著性污染指标(pH,NH₃-N,NO₃-N,F.coil,Fe,Ni和Zn)可以反映整体水质的空间差异性,且具有90.65%的正确判别能力;④归纳起来,从3类采样点中选择一个或多个、监测7个显著性污染指标即可全面反映后海湾水质管制区的水质空间特征,实现水质监测网络优化。     

Classification of rivers based on water quality assessment using factor analysis in Taizi River basin, northeast China

A method for river classification based on water quality assessment (WQA) was introduced using factor analysis (FA) in this paper. Sixty-nine sampling sites and 20 water quality parameters in Taizi River basin were selected for monitoring and analysis. Five factors were determined in FA, denoted as general, hardness, trophic, nitrogen pollution, and physical factors. The total factor scores (TFSs) of the WQA results from all sampling sites were calculated by the eigenvalue and factor score of each factor. The TFSs of 69 sites were interpolated with the measure of inverse distance weighted in the river buffer zone generated by ArcGIS 9.2 software to form a continuous spatial distribution along river channels. All streams were divided into five classes marked “excellent”, “good”, “fair”, “poor”, and “seriously polluted”. The classification result showed that the water quality of Taizi River basin deteriorated gradually from the mountain area to the plain area. Sewage and intensive human activities contributed to the deterioration of water quality since towns and farmland were dotted densely along the river basin.     

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.     

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.     

Evaluation of spatial and seasonal variations in surface water quality using multivariate statistical techniques

In this study, spatial and seasonal variations of water quality in Haraz River Basin were evaluated using multivariate statistical techniques, such as cluster analysis, principal component analysis and factor analysis. Water quality data collected from 8 sampling stations in river during 4 seasons (Summer and Autumn of 2007, Winter and Spring of 2008) were analyzed for 10 parameters (dissolved oxygen, Fecal Coliform, pH, water temperature, biochemical oxygen demand, nitrate, total phosphate, turbidity, total solid and discharge). Cluster analysis grouped eight sampling stations into three clusters of similar water quality features and thereupon the whole river basin may be categorized into three zones, i.e. low, moderate and high pollution. The principle component analysis/factor analysis assisted to extract and recognize the factors or origins responsible for water quality variations in four seasons of the year. The natural parameters (temperature and discharge), the inorganic parameter (total solid) and the organic nutrients (nitrate) were the most significant parameters contributing to water quality variations for all seasons. Result of principal component analysis and factor analysis evinced that, a parameter that can be significant in contribution to water quality variations in river for one season, may less or not be significant for another one.     

Risk characterization and surface water quality assessment of Manas River,Assam (India) with an emphasis on the TOPSIS method of multi-objective decision making

The present study centers on the investigation of surface water quality with the aid of quality indices and explores the application of a multi-objective decision-making method (TOPSIS) in arranging decisions for policy makers on the basis of overall ranking of the sampling locations. A case study has been performed on the Manas River, Assam (India). Water Quality Index (WQI) involving physico-chemical parameters, and heavy metal pollution index (HPI) and contamination index (CI) involving heavy metal influences were employed for water quality assessment. WQI graded two sampling locations “very poor” and all other locations “poor”. HPIs of all the locations were below the critical value of 100, but the CI depicted that two locations were “moderately contaminated”. Risk assessment to human health was done using hazard quotient and hazard index. Cluster analysis (CA) demonstrated site similarity by grouping the relatively more polluted and less polluted (LP) sites into two major clusters. However, there surfaced difficulty in discerning the overall water quality, as all the three quality indices included different parameters and contradicted each other. A multi-objective decision-making tool, TOPSIS was therefore employed for ranking the locations on the basis of their relative pollution levels. The novelty of the study reflects in the identification of the relatively more or relatively less polluted sites within the same cluster in CA by the application of TOPSIS. The study justifies the effectiveness of TOPSIS method in prioritizing decisions in complex scenarios for policy makers.     

Monitoring the seasonal dynamics of physicochemical parameters from Atoyac River basin (Puebla), Central Mexico: multivariate approach

Spatiotemporal variations of ten physicochemical parameters in the water quality of Atoyac River basin, Central Mexico, were obtained from 22 sampling sites (66 samples in total) located all along the basin for three different seasons (dry, rainy and winter). Multivariate statistical techniques such as correlation matrix, factor analysis (FA) and cluster analysis (CA) were used as a tool to understand the process. Physicochemical parameters such as temperature (T), pH, conductivity (λ), dissolved oxygen (DO), spectral absorption coefficient (SAC), oxidation–reduction potential (ORP), turbidity, 5-day biochemical oxygen demand (BOD₅), chemical oxygen demand (COD) and total suspended solids (TSS) were analyzed. Extremely high values of pH (10.24), conductivity (1870 µS/cm) and reduced redox potential (?370.1 mV) were observed in the dry season, whereas elevated TSS of 2996 mg/L was detected during the rainy season. The results elucidated high influence from the adjoining industrial, agricultural and urban zones, making the river unsuitable for life. FA generated varifactors, which accounted for cumulative % of 75.04 (dry), 76.22 (rainy) and 79.96 (winter) clearly grouping the external factors responsible for these significant values indicating the source of contamination. Cluster analysis facilitated the ease of classifying the sampling sites based on the similarities of physicochemical parameters. This study carried out in different seasons using multivariate statistical techniques would definitely prove to be an efficient tool for the restoration and establishing the real-time monitoring stations along this important river basin of Mexico.     

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.     

Dissolved trace elements and heavy metals from the shallow lakes in the middle and lower reaches of the Yangtze River region,China

Dissolved trace elements and heavy metals of waters and sediments in the ten shallow lakes in the middle and lower reaches of the Yangtze River region were determined to identify their composition and spatial distribution, and to assess the extent of their environmentally detrimental effects by comparison with water and sediment quality guidelines. Results indicated that As and Pb were the main pollutants in lake waters and Mn and Hg the potential ones, while As, Cu and Pb were the main pollutants in lake sediments. Their spatial distribution indicated that Daye Lake was seriously polluted by metals, which was corroborated by cluster analysis. Higher concentrations of trace elements have been found in lakes downstream of the Yangtze River delta, and higher concentrations of metals have been recorded in sediments of upstream lakes, suggesting that metals in water were more sensitive to anthropogenic activities and that metals in sediment were mainly controlled by minerals. Correlation analyses demonstrated that there were stronger associations among metals in lake sediments than those in lake waters, and their good relationships suggested the common sources. Further research on the subject will help develop water quality management with the aim of restoring shallow lakes in the Yangtze River.     

Evaluation of graphical and multivariate statistical methods for classification of water chemistry data

A robust classification scheme for partitioning water chemistry samples into homogeneous groups is an important tool for the characterization of hydrologic systems. In this paper we test the performance of the many available graphical and statistical methodologies used to classify water samples including: Collins bar diagram, pie diagram, Stiff pattern diagram, Schoeller plot, Piper diagram, Q-mode hierarchical cluster analysis, K-means clustering, principal components analysis, and fuzzy k-means clustering. All the methods are discussed and compared as to their ability to cluster, ease of use, and ease of interpretation. In addition, several issues related to data preparation, database editing, data-gap filling, data screening, and data quality assurance are discussed and a database construction methodology is presented. The use of graphical techniques proved to have limitations compared with the multivariate methods for large data sets. Principal components analysis is useful for data reduction and to assess the continuity/overlap of clusters or clustering/similarities in the data. The most efficient grouping was achieved by statistical clustering techniques. However, these techniques do not provide information on the chemistry of the statistical groups. The combination of graphical and statistical techniques provides a consistent and objective means to classify large numbers of samples while retaining the ease of classic graphical presentations. Electronic Publication