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.     

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.     

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。     

Statistical interpretation on seasonal variations of groundwater quality in Ramanathapuram coastal tract,Tamil Nadu,India

Groundwater availability depends on its accessibility, as well as on its quality. Factor analysis (FA) has been used to analyze quality problems and provide strategies for water resources exploitation. The present study demonstrated the use of factor analysis to evaluate temporal variations in groundwater quality and find latent sources of water pollution in coastal areas of Ramanathapuram District, Tamil Nadu, India. The data set included data of eleven water quality parameters viz., pH, electrical conductivity, salinity, total dissolved solids, total alkalinity, calcium hardness, magnesium hardness, total hardness, chloride and fluoride for two different seasons (pre- and post-monsoon) in 2012. FA of the two seasons resulted in two latent factors accounting for 80.38 % of total variance for pre-monsoon (summer) and 73.03 % for post-monsoon (winter) in the water quality data sets. The results obtained from FA prove that the groundwater quality in winter is better than that of summer. Langelier Saturation Index was used to find out scaling and corrosive tendency of the groundwater samples for the study area. Karl Pearson correlation matrix was used to study the correlation between the studied water quality parameters. Hence, the analysis suggests that FA techniques are useful tools for identification of influence of various quality parameters on overall nature of the groundwater.     

Spatial and temporal study of nitrate concentration in groundwater by means of coregionalization

 Spatial and temporal behavior of hydrochemical parameters in groundwater can be studied using tools provided by geostatistics. The cross-variogram can be used to measure the spatial increments between observations at two given times as a function of distance (spatial structure). Taking into account the existence of such a spatial structure, two different data sets (sampled at two different times), representing concentrations of the same hydrochemical parameter, can be analyzed by cokriging in order to reduce the uncertainty of the estimation. In particular, if one of the two data sets is a subset of the other (that is, an undersampled set), cokriging allows us to study the spatial distribution of the hydrochemical parameter at that time, while also considering the statistical characteristics of the full data set established at a different time. This paper presents an application of cokriging by using temporal subsets to study the spatial distribution of nitrate concentration in the aquifer of the Lucca Plain, central Italy. Three data sets of nitrate concentration in groundwater were collected during three different periods in 1991. The first set was from 47 wells, but the second and the third are undersampled and represent 28 and 27 wells, respectively. Comparing the result of cokriging with ordinary kriging showed an improvement of the uncertainty in terms of reducing the estimation variance. The application of cokriging to the undersampled data sets reduced the uncertainty in estimating nitrate concentration and at the same time decreased the cost of the field sampling and laboratory analysis. Received: 23 July 1997 · Accepted: 31 March 1998     

Ground water quality classification by decision tree method in Ardebil region,Iran

A decision tree-based approach is proposed to predict ground water quality based on the United States Salinity Laboratory (USSL) diagram using the data from aquifers in agricultural lands of Ardebil province, northwest of Iran. Several combinations of hydro chemical parameters of groundwater and monthly precipitation with different lag time were considered to find an accurate and economical alternative for groundwater quality classification. The performance evaluation was based on the number of correctly classified instances (CCI) and kappa statistics. The results suggested the suitability of decision tree-based classification approach for the used data sets. The overall average of CCI and kappa statistic for the prediction of groundwater quality classes based on the USSL diagram was 0.88 and 0.83 %, respectively. Principal component analysis (PCA) was also used to determine the important parameters for groundwater quality classification. The results showed that groundwater quality classification by decision tree is more precise and efficient in comparison with PCA. The best alternative could evaluate groundwater quality class with only two parameters: electrical conductivity and cumulative precipitation of 11 months earlier. The developed model is able to predict water quality class by only two variables and this lead to a reduction in the number of variables analyzed on a routine basis, resulting in a significant reduction in laboratory costs and latency times between the sampling moment and the outcome of the laboratory analyses.     

北京平原地下水水位监测网优化

文章在北京市地下水水位监测现状基础上,分潜水和承压水对北京平原地下水监测网的监测密度和监测频率进行了优化设计。主要采用编制地下水动态类型图的方法进行了地下水水位监测网的优化,克里金插值法能定量评价依据监测网观测值绘制的地下水水位等高线的精度,因而可以用来评价监测优化结果。并根据时间序列分析和统计检验提供的定量标准优化了地下水水位监测频率。优化后,北京平原共有监测孔400眼,其中利用原有监测孔300眼,新设计监测孔100眼,手工监测频率由原来的每月6次优化为每月1次,专项高频率监测可以由地下水自动监测仪实现。文中还对地下水自动监测仪(DIVER)的监测结果和手工监测结果进行了对比评价,提出了地下水水位监测网的维护、管理措施和信息发布方式。     

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.     

Groundwater characterization and quality assessment,and sources of pollution in Madinah,Saudi Arabia

Groundwater quality in the Madinah city is increasingly endangered by expanding urbanization, industrial activities, and intensified agricultural land use. In order to investigate the pollution of Madinah groundwater resources, 32 samples have been gathered and examined for major, trace, and nutrient components. Results of groundwater characterization and groundwater quality assessment show that Na⁺ and Cl^? are the main anion and cation in the groundwater, respectively. Depletion of HCO₃ that interacts with water increases salinity. Cluster analysis and principal component analysis were applied in the current study to obtain relationship between parameters and sampling site in order to identify the factors and sources influencing groundwater quality. The CA allowed the formation of three clusters between the sampling wells reflecting differences on water quality at different locations. Four major PCs were extracted, which accounted 86.05 % variance of the original data structure. Forty-four percent of the groundwater samples have high values of NO₃, due to human and agricultural activities. Four samples in the southwestern part of the study area show high content of Pb, Cd, Cr, Ni, As, and Al. This may be due to the influence of anthropogenic activities that resulted from the southwestern industrial area of Madinah. The present study illustrates explicitly the stress on groundwater quality and its vulnerability in the aquifer system.     

国家地下水监测工程(自然资源部分)西辽河平原监测区建设成果概述

为落实国家地下水监测工程与地下水质监测工作任务,实现对西辽河平原地下水动态的有效监测,国家地下水监测工程(自然资源部分)在西辽河平原监测区共布设了国家级地下水自动监测井117眼,其中新建监测井93眼,改建机民井24眼,安装自动监测仪器117套。监测区控制面积57 000 km²,主要监测层位为第四系松散沉积物孔隙水含水层,监测层位最大深度为206 m。建成了国家地下水监测工程信息服务系统,提升了地下水监测信息获取、分析、共享和服务能力。该项目的实施大幅度提高了监测区地下水的监测频次以及信息的时效性、可靠性和准确性。     

Assessing the impact of natural and anthropogenic activities on groundwater quality in coastal alluvial aquifers of the lower Liaohe River Plain,NE China

The lower Liaohe River Plain (LRP) is an economically and ecologically important area situated on an alluvial plain, where anthropogenic activities are very intensive. Field investigations were conducted in the LRP and 15 water quality parameters surveyed at 216 wells during September and October of 2009 and 2010. These showed significant variation in the hydrochemistry of groundwater throughout the plain. A Piper plot was used to identify the major geochemical processes occurring in the entire plain. Principal components analysis (PCA) was used to identify various underlying natural and anthropogenic processes that created these distinct water types. The Stuyfzand classification was used to subdivide and interpret the complex groundwater hydrochemistry of the Liaohe River delta. Five principal components (PCs) were extracted in terms of PCA, which can be invoked to explain 82% of the total variance in water quality parameters. The PCA results can be categorized by five major factors: (1) Holocene transgression and mixing; (2) surface water infiltration; (3) multi-factor processes; (4) rainfall and agricultural fertilizer contamination; and (5) Geogenic F enrichment. This study demonstrates that the great variation of groundwater hydrochemistry in the LRP should be attributed to both natural and anthropogenic processes.     

Hydrogeochemical characteristics of groundwater from the two main aquifers in the Red River Delta,Vietnam

In the Red River Delta, situated in the northern part of Vietnam, nearly its entire population depends solely on groundwater for daily water consumptions. For this reason, groundwater quality assessments must be carefully carried out using hydrogeochemical properties, to ensure effective groundwater resource planning for the Delta’s present and future groundwater use. In this study, the spatial and seasonal changes in the hydrogeochemical characteristics of groundwater in the two main aquifers of the RRD were investigated by analyzing the physicochemical data obtained in 2011 from 31 conjunctive wells in the Delta’s Holocene unconfined aquifer (HUA) and Pleistocene confined aquifer (PCA) using the Piper diagram and the Gibbs diagram. Results of the data analysis show that the groundwater in both aquifers in the upstream area of the delta is dominated by the [Ca²⁺–HCO₃⁻] water-type, while the [Na⁺–Cl⁻] dominates along the middle-stream and downstream areas. Seasonal changes in the hydrogeochemical facies in both aquifers, comparing the results for the dry and the rainy seasons, were detected in about one third of the sampling wells, which were mainly located at the upstream portion of the Delta. The hydrogeochemical facies of HUA were different from that of PCA by about 45% of the sampling wells in both the dry and the rainy seasons, which were found mostly in the upstream and middle-stream areas.     

The hydrochemical facies and anomalous fluoride content in the Serra Geral aquifer system,southern Brazil: a GIS approach with tectonic and principal component analysis

Groundwater with high fluoride content and water mixture patterns were studied in Serra Geral aquifer system (SGAS) using three aspects, principal component analysis (PCA), tectonic scenery and hydrochemical interpretation from 309 groundwater chemical data information from deep wells. A four-component model is suggested and explains 81% of total variance in the PCA. Six hydrochemical facies were identified. These facies suggest two different fluoride sources. Tectonic approach shows the relationship between defined hydrochemical facies and regional fracture control. The applied methodology reveals a minimum level to understand hydrochemical mixtures. The fluoride enrichment mechanisms into the groundwater are comprised in advance to guide the future uses of SGAS to the public supply.     

Applying the scores of multivariate statistical analyses to characterize the relationships between the hydrochemical properties and groundwater conditions in respect of the monsoon variation in Kapas Island,Terengganu, Malaysia

The different factors (seasonal changes) and variables (physicochemical) controlling the groundwater hydrochemistry of Kapas Island were identified using multivariate techniques principal component analysis (PCA), discriminant analysis (DA) and hierarchy cluster analysis (HCA). In the present study, the hydrochemistry of 216 groundwater samples, consisting of information concerning the in situ parameters and major ions in six monitoring boreholes, was studied and compared in two different monsoon seasons. The dominant variables derived from four components by PCA in the pre-monsoon indicated the influence of the salinity process, while the dominant variables derived from three components in the post-monsoon mostly indicated on the mineralization process. The DA gave the final variables after discriminating the insignificant variables based on the pre- and post-monsoon classifications. This provided important data reduction in terms of the mineralization process, as it only discriminated physical variables (TDS, EC, salinity, DO and temperature). Based on the HCA result, samples belonging to stations KW 3 and KW 4 were under Ca-rich water, while the remaining boreholes were grouped in Na-rich water.     

Artificial neural network prediction of sulfate and SAR in an unconfined aquifer in southeastern Turkey

In many rural communities, groundwater is used to meet the water demand of the community and for the irrigation of cultivating areas. The quality of groundwater can be adversely affected by agricultural activities and finally groundwater quality may become unsuitable for human consumption and irrigation, as in the Harran Plain. Hence, monitoring groundwater quality by cost-effective techniques is necessary, as especially unconfined aquifers are vulnerable to contamination. This study presents an artificial neural network model predicting sodium adsorption ratio (SAR) and sulfate concentration in the unconfined aquifer of the Harran Plain. Samples from 24 observation wells were analyzed monthly for 1?year. Electrical conductivity, pH, groundwater level, temperature, total hardness and chloride were used as input parameters in the predictions. The best back-propagation (BP) algorithm and neuron numbers were determined for the optimization of the model architecture. The Levenberg?CMarquardt algorithm was selected as the best of 12 BP algorithms and optimal neuron number was determined as 20 for both parameters. The model tracked the experimental data very closely both for SAR (R?=?0.96) and sulfate (R?=?0.98). Hence, it is possible to manage groundwater resources in a more cost-effective and easier way with the proposed model application.     

Geological impacts on groundwater pollution: a case study in Khuzestan Province

The main aims of this study were to examine the sources of pollution with an emphasis on geogenic sources and to predict the groundwater quality with reasonable accuracy. For this purpose, factor analysis/principal component analysis and partial least squares regression were used to analyze a data set of groundwater quality containing 17 parameters measured at 45 different sampling wells in Andimeshk Aquifer during 2006–2013 time period. Factor analysis identified three factors, which were responsible for the data structure explaining 78.3 % of the total variance of the data set. There were various sources of groundwater contamination, based on factor analysis, in which geological formations next to agricultural activities had the most influential effects. Partial least squares regression could predict the quality of groundwater according to the value of water quality index. The amounts of R-squared (0.79) and MSE (0.21) using seven PLS components showed that this method has been successful in the prediction of water quality in the study area.     

Statistical evaluation of fluoride contamination in groundwater resources of Santiago del Estero Province,Argentina

This study investigates the suitability of statistical techniques for evaluating the fluoride content and the groundwater quality from Robles Department (RD) and Banda Department (BD) in Santiago del Estero (Argentina). For the original statistical study, evaluation of nine parameters (fluoride, pH, conductivity, atmospheric and water temperature, total dissolved solids, chloride, hardness, and alkalinity) of 110 collected underground water samples from 23 dispersed rural areas was proposed. Groundwater samples were obtained by sampling taken from wells at different depths. Fluoride levels were determined by a standard colorimetric method in two seasonal periods, the dry (from April to September) and rainy (from October to March) period. The analytical results obtained for physicochemical parameters such as pH, total dissolved solids (TDS), and temperature does not reveal any notable difference between the rainy and dry seasons studied. In both seasons, the atmospheric temperature average was 22 ​°C. With respect to fluoride content, approximately 50% of the analysed groundwater samples exceeded the limit established by current legislation (1.0 ​mg/L), obtaining concentration levels in the range of 0.01–2.80 ​mg/L. This study demonstrates the usefulness of the univariate statistical method (quartiles calculation, interquartile range IQR), multivariate principal component analysis (PCA), and cluster analysis to establish a better understanding of the state of the contamination of the waters in the region studied.     

An integrated approach for assessment of groundwater quality in and around uranium mineralized zone,Gogi region,Karnataka, India

Assessment of groundwater quality is an important aspect of water security, which is the key to ensure sustainable development. The objective of the study is to bring out an integrated approach for assessment of groundwater quality for drinking and irrigation purposes. Gogi region, Karnataka, India was chosen as the study area due to the effect of the presence of medium-grade uranium deposits. An integrated approach including the concentration of major ions, trace elements and uranium was employed to investigate the quality of groundwater. Totally, 367 groundwater samples were collected periodically from 52 wells distributes over the Gogi region and the parameters such as pH, electrical conductivity, total dissolved solids (TDS), Ca²⁺, Mg²⁺, Na⁺, K⁺, Cl^?, SO₄ ^2?, NO₃ ^?, Zn, Pb, Cu, and uranium of groundwater were analysed. Spatial distribution maps of various chemical constituents were prepared using geographic information system and its temporal variation was plotted in box and whisker plot. The analytical data were compared with Bureau of Indian Standards and World Health Organisation standards to determine drinking water quality and parameters such as salinity hazard, alkalinity hazard and percent sodium were estimated to assess the irrigation quality. Multivariate statistical analysis by cluster analysis was also performed which results in two groups consisting of wells with unsuitable water for drinking purposes. Groundwater in about 15% of the sampling wells were found to be unsuitable for domestic purpose based on TDS and about 17% were unsuitable based on uranium concentration. Finally, integration of spatial variation in TDS and uranium reveals that about 25% of the wells were unsuitable for domestic purposes. It is suggested that such an integrated approach needs to be formulated considering major ions, trace elements and radioactive elements for proper assessment of water quality. Implementation of managed aquifer recharge structures in the study area is suggested since it would potentially reduce the concentration of ions.     

Assessment of natural and anthropogenic impacts in groundwater,utilizing multivariate statistical analysis and inverse distance weighted interpolation modeling: the case of a Scopia basin (Central Greece)

In Scopia basin, central Greece, a hydrochemical investigation was completed. Groundwater samples from 41 sites were used to assess the natural and anthropogenic impacts in groundwater, utilizing the principal component analysis (PCA) involved with the inverse distance weighted (IDW) interpolation modeling and hierarchical cluster analysis (HCA). Best fit model to explain the spatial distribution of both hydrochemical parameters and PCA was chosen by optimizing the IDW interpolator’s parameters. Precision of the model was picked based on less root-mean-squared prediction error (RMSPE) amongst predicted and actual values measured at the same locations. Groundwater exhibit Ca–Mg–HCO₃ as the dominant hydrochemical type and their greater part are mixed waters with non-dominant ion. Interpolation models demonstrate high estimations of nitrates in zones with agricultural activities and high estimations of nickel and chromium in regions with the strong presence of ultrabasic rocks. Dominant part of the groundwater samples surpasses in many cases the European Community (EC) drinking water permissible limits. Thus, they are unsuitable for human consumption. PCA illustrated four factors, which clarified 80.62% of the aggregate variance of data and HCA classified two statistically significant clusters of sampling sites. Results show natural procedures ascribed to the weathering of the minerals contained in the ultrabasic rocks and anthropogenic influences related to the use of fertilizers and wastewater leak. In light of FAO standards and Richards’s classification, the groundwaters are reasonable for irrigation purposes, featuring waters with low sodium hazard and moderate salinity hazard.     

Assessment of groundwater quantity and quality and saltwater intrusion in the Damghan basin,Iran

This study describes the groundwater quantity and quality conditions in the Damghan aquifer in Iran. The quantitative analysis of data obtained from observation wells indicates overexploitation of groundwater during recent years, which has resulted in deterioration of water quality. The mean water level has declined about 7.4 m between years of 1966 and 2010. The hydrochemical facies of water collected from sampling wells were investigated though Piper and Chadha diagrams, and the general dominant type of water in the study area was determined as Na-Cl. The quality assessment examined the suitability of groundwater for drinking and irrigation purposes. Compared to the World Health Organization (WHO) guidelines for drinking water, all regions were found to have unpotable groundwater. Furthermore, unsuitability of groundwater for agricultural applications due to high salinity was observed through analysis of major quality indicators. The saltwater intrusion was investigated by ionic ratio analyses and was determined to be the main factor contributing to high salinity and deterioration of the groundwater quality in the Damghan basin.