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.     

R-mod factor analysis, a popular multivariate statistical technique to evaluate water quality in Khaf-Sangan basin, Mashhad, Northeast of Iran

Water quality data are required in order to compare chemical water analyses and identify water masses. R-mode factor analysis, a popular multivariate statistical tool, has been effectively used for groundwater quality studies. In this paper, the R-mode factor analysis was applied to 50 groundwater samples collected from pumping wells in the Sangan-Khaf basin which is located in the southeast of Mashhad, northeast Iran. The groundwater samples were analysed for chemical parameters. The factor analysis was then performed on the chemical data set. It can be suggested that four factors in R-mode analysis explain more than 94.31% of the total variance. The contribution of each factor at sample points, factor score, was calculated. The spatial distribution of the factor scores for each factor was mapped separately. Since the Sangan iron mine south of the study area probably affects groundwater aquifer, therefore, such studies can be used to manage the groundwater quality in the study area.     

Groundwater classification using multivariate statistical methods: Southern Ghana

This study demonstrates the strength of R-mode factor analysis and Q-mode hierarchical cluster analysis in determining spatial groundwater salinity groups in southeastern Ghana. Three hundred and eighty three (383) groundwater samples were taken from six hydrogeological terrains and surface water bodies and analyzed for the concentrations of the major ions, electrical conductivity and pH. Q-mode hierarchical cluster analysis and R-mode factor analysis were respectively used to spatially classify groundwater samples and determine the probable sources of variation in groundwater salinity. The quality of groundwater for irrigation was then determined using three major indices. The analyses revealed two major sources of variation in groundwater salinity: silicate mineral weathering on one hand, and seawater intrusion and anthropogenic contamination on the other. A plot of the factor scores for the two major sources of variation in the salinity revealed trends which can be used in hydrogeological mapping and assist in drilling potable water boreholes in southeastern Ghana. This study also revealed four major spatial groundwater groups: low salinity, acidic groundwaters which are mainly derived from the Birimian and Togo Series aquifers; low salinity, moderate to neutral pH groundwaters which draw membership mainly from samples of the Voltaian, Buem and Cape Coast granitoids; very high salinity waters which are not suitable for most domestic and irrigation purposes and are mainly from the Keta Basin aquifers; and intermediate salinity groundwaters consisting of groundwater from the Keta basin aquifers with minor contributions from the other major terrains. The major water type identified in this study is the Ca–Mg–HCO₃ type, which degrades into predominantly Na–Cl–SO₄ more saline groundwaters towards the coast.     

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.     

Delineation of groundwater provenance in a coastal aquifer using statistical and isotopic methods,Southeast Tanzania

Rapid population growth and urbanization has placed a high demand on freshwater resources in southeast costal Tanzania. In this paper, we identify the various sources of groundwater and the major factors affecting the groundwater quality by means of multivariate statistical analyses, using chemical tracers and stable isotope signatures. The results from hierarchical cluster analyses show that the groundwater in the study area may be classified into four groups. A factor analysis indicates that groundwater composition is mainly affected by three processes, accounting for 80.6% of the total data variance: seawater intrusion, dilution of groundwater by recharge, and sewage infiltration. The hydrochemical facies of shallow groundwater was mostly of the Na–Ca–Cl type, although other water types were also observed. The deep groundwater samples were slightly to moderately mineralized and they were of the NaHCO₃ type. This water type is induced mainly by dissolution of carbonate minerals and modified by ion exchange reactions. The signal from the stable isotope composition of the groundwater samples corresponded well with the major chemical characteristics. In the shallow groundwater, both high-nitrate and high-chloride concentrations were associated with localized stable isotope enrichments which offset the meteoric isotopic signature. This is inferred to be due to the contamination by influx of sewage, as well as intrusion by seawater. The depleted stable isotope values, which coincides with a chemical signature for the deep aquifer indicates that this deep groundwater is derived from infiltration in the recharge zone followed by slow lateral percolation. This study shows that a conceptual hydrogeochemical interpretation of the results from multivariate statistical analysis (using HCA and FA) on water chemistry, including isotopic data, provides a powerful tool for classifying the sources of groundwater and identifying the significant factors governing the groundwater quality. The derived knowledge generated by this study constitutes a conceptual framework for investigating groundwater characteristics. This is a prerequisite for developing a sound management plan, which is a requirement for ensuring a sustainable exploitation of the deep aquifer water resource in the coastal area of Tanzania.     

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.     

Factor and cluster analysis of water quality data of the groundwater wells of Kushtia, Bangladesh: Implication for arsenic enrichment and mobilization

The study area is located in the southwestern part of Bangladesh. Twenty-six groundwater samples were collected from both shallow and deep tube wells ranging in depth from 20 to 60 m. Multivariate statistical analyses including factor analysis, cluster analysis and multidimensional scaling were applied to the hydrogeochemical data. The results show that a few factors adequately represent the traits that define water chemistry. The first factor of Fe and HCO₃ is strongly influenced by bacterial Fe (III) reduction which would raise both Fe and HCO₃ concentrations in water. Na, Cl, Ca, Mg and PO₄ are grouped under the second factor representing the salinity sources of waters. The third factor, represented by As, Mn, SO₄ and K is related to As mobilization processes. Cluster analysis has been applied for the interpretation of the groundwater quality data. Initially Piper methods have been employed to obtain a first idea on the water types in the study area. Hierarchical cluster analysis was carried out for further classification of water types in the study area. Twelve components, namely, pH, Fe, Mn, As, Ca, Mg, Na, K, HCO₃, Cl, SO₄ and NO₃ have been used for this purpose. With hierarchical clustering analysis the water samples have been classified into 3 clusters. They are very high, high and moderately As-enriched groundwater as well as groundwater with elevated SO₄.     

基于人为活动影响识别的区域地下水水质演化预测:以石家庄地区为例

地下水的水质演化预测一直以来是一个难点,单纯依赖于历史数据所反映的水质演化趋势预测未来水质,存在较大的局限性。以石家庄地区为研究区,提出将现状人为活动对地下水的影响程度作为校正因子,修订历史数据所表现出来的水质演化趋势,预测未来水质变化的方法。并以石家庄地区浅层地下水代表性指标总硬度、TDS、$NO_3^-$、Fe4项指标作为预测指标,尝试预测了2025年代表性指标的演化情况。结果表明:TDS和总硬度恶化的区域主要为灵寿县东南方向、藁城区和无极县靠近滹沱河河道附近等区域;Fe恶化的区域主要为石家庄市区中部以及东南部等区域; $NO_3^-$恶化的主要区域为鹿泉区与灵寿县交界附近、石家庄市区西北以及藁城区靠近滹沱河河道附近等区域。预测结果与研究区水文地质条件、人为污染源的排放以及地下水的开采等因素影响下的情况较为吻合。     

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.     

Assessment of arsenic health risk and source apportionment of groundwater pollutants using multivariate statistical techniques in Chapai-Nawabganj district,Bangladesh

This study assessed arsenic health risk to the local residents through oral and dermal exposure pathways of drinking water and to investigate source apportionment of groundwater pollutants using multivariate statistical techniques in the Chapai-Nawabganj district, Bangladesh. Groundwater samples collected from shallow tube well and dug well at the depth ranges (15-60 m) were analyzed for physio-chemical parameters and trace elements. Most of the studied physio-chemical parameters were found within their respective permissible limits. However, total As, Fe and Mn concentrations exceeded Bangladesh and WHO guideline values. The assessment of arsenic health risk reveals that children as compared to adults are found at a higher risk as the values of hazard quotients (HQ) >1 in the most of the groundwater samples. This level of arsenic contamination should have medium to high chronic risk and medium carcinogenic risk when compared with US EPA guidelines which can cause serious health hazard. The results of principal component analysis (PCA) and factor analysis (CA) indicate that geogenic (interaction of water and basement rock) and anthropogenic (agrochemicals, agricultural fertilizer and domestic sewage) sources are responsible for variation in arsenic and other physio-chemical parameters in the groundwater aquifer of the study area. Furthermore,the inter-correlation of arsenic with metals and ions were also calculated by correlation matrix and linear regression analysis. The outcomes of this study will help to meet the challenge of sustainable groundwater quality management in Bangladesh and enhancing better vision of potential health risk of local inhabitants in the study area.     

Hydrochemical characteristics and geochemistry evolution of groundwater in the plain area of the Lake Baiyangdian watershed,North China Plain

Water cycle and water quality in the Lake Baiyangdian watershed of the North China Plain have undergone great changes due to over-pumping of groundwater and wastewater discharge.In this paper,hydrogeochemical data was collected to analyze the hydrochemical characteristics and geochemistry evolution of groundwater.The study area was divided into two typical parts.One was in the upstream plain area,where over-pumping had resulted in significant decline of groundwater level;the other one was located in the downstream area near the Fu River and Lake Baiyangdian(Lake BYD region).In addition to the natural weathering of minerals,excessive fertilizer was also a main factor of higher ion concentration in groundwater.According to studies,due to good permeability,these regions were easy to be polluted even with deep groundwater depth.However,upstream shallow groundwater and surface water,including lake water,domestic along with industrial wastewater were all sources of present shallow groundwater in the Lake BYD region.Results indicated that anthropogenic activities rather than minerals much matter to the groundwater in these regions.Particularly,wastewater largely decided the groundwater quality,which suggested that the management and restoration of surface water quality was crucial to groundwater protection.     

Identification of influencing factors for groundwater quality variation using multivariate analysis

This paper reports the results of multivariate analysis on the major-ions data present in groundwater collected in a hydrogeochemical survey of a hard rock terrain in Andhra Pradesh, India. The use of factor analysis is proposed, as a first step, for identifying the processes influencing the hydrogeochemical variations in groundwater. As a result of the application factor analysis, three factors were obtained, which explained characterization of groundwater quality and identified the sources for the presence of ions and its variations in concentrations and their geochemical processes. The natural dissolution of existing rocks of granites and granitic gneisses is the common influencing source for its hydrogeochemical character in the study area, but at some localized zones, the human-influenced sources of agricultural, industrial and urbanization are the dominating factors. The spatial and seasonal variation of the hydrogeochemical processes associated with the factor variables in the groundwater quality of the study area were analysed and evaluated.     

多元校正分析在水环境地球化学领域中的应用

多元校正分析法应用于水环境地球化学研究,可对复杂的分析数据进行处理和解析,反映水质状况与污染之间的各种关系,建立水质评价和预测模型,从而科学地服务于治理水污染。评述了多元分析方法如主成分分析、偏最小二乘法、人工神经网络等在水环境地球化学研究中的应用、进展和前景。     

Integrated geostatistics and GIS techniques for assessing groundwater contamination in Al Arish area, Sinai, Egypt

Sustainable development in El Arish area of North Sinai, Egypt, is retarded by serious environmental problems, where the land-use and land cover of the region is changing over present time. The impact of human activities in the study area is accompanied by the destruction and over-exploitation of the environment. This study applies multivariate statistics (factor and cluster analyses) and GIS techniques to identify both anthropogenic and natural processes affecting the groundwater quality in the Quaternary sands aquifer. The aim of this study was to investigate the impacts on groundwater resources, the potential pollution sources, and to identify the main anthropogenic inputs of both nutrients and trace metal. Since the depth to the water table is shallow especially in the northern part (<4?m), and the aquifer was exposed on the ground surface, it has poor buffering capacity and the pollution risk is very high. Groundwater chemistry in this coastal region has complex contaminant sources, where intensive farming activities and untreated wastes put stress on groundwater quality. Several areal distribution maps were constructed for correlating water quality with possible contributing factors such as location, land-use, and aquifer depth. These maps identified both anthropogenic and natural processes affecting groundwater quality of the studied aquifer. Cluster analysis was used to classify water chemistry and determine the hydrochemical groups, Q-mode dendrogram is interpreted and there are three main clusters. Factor analyses identify the potential contamination sources affecting groundwater hydrochemistry such as: nitrate, sulfate, phosphate and potassium fertilizers, pesticides, sewage pond wastes, and salinization due to circulation of dissolved salts in the irrigation water itself.     

Risk assessment of groundwater pollution using sensitivity analysis and a worst-case scenario analysis

This paper illustrates how sensitivity analysis and a worst-case scenario analysis can be useful tools in risk assessment of groundwater pollution. The approach is applied to a study area in Hungary with several known groundwater pollution sources and nearby drinking water production wells. The main concern is whether the contamination sources threaten the drinking water wells of the area. A groundwater flow and transport model is set up to answer this question. Due to limited data availability, the results of this model are associated with large uncertainty. Sensitivity analysis and a worst-case scenario analysis are applied to estimate this uncertainty and build confidence in the model results.     

Assessing the inconsistency between groundwater vulnerability and groundwater quality: the case of Chapala Marsh, Mexico

Aquifer systems present intrinsic properties such as vulnerability, which is identified as the potential risk of groundwater pollution by contaminants generated by human activity. When there are surface sources of pollution, usually there is a direct relationship between high vulnerability and decreased water quality. Nevertheless, this relationship is not observed in all aquifers and so the causative circumstances of inconsistencies between aquifer vulnerability and water quality have been investigated. This work addresses the vulnerability assessment of the Chapala Marsh area, Mexico, using SINTACS analysis. The Chapala Marsh aquifer is characterized by a granular structure and a fractured recharge zone; there are natural and anthropogenic sources of pollution. The results show discrepancies between the vulnerability indices and groundwater quality, as indicated by the existence of vulnerable areas with good water quality and vice versa. This is because the SINTACS method works well when contaminants have only vertical movement. For scenarios with lateral movement of contaminants, the method of geographic weighted regression (GWR) is used to model the influence of potential sources of contaminants on the water quality.     

Tracking sources of groundwater nitrate contamination using nitrogen and oxygen stable isotopes at Beijing area,China

The identification of sources and behavior of contaminants is important to control and manage groundwater quality of aquifer systems in urban areas. In this study, hydrogeochemistry of major constituents and stable isotope ratios of nitrate in groundwater were determined to identify contamination sources and transformation processes occurring in soils and deeper groundwater of Beijing with intense human activities. The nitrogen and oxygen isotopic compositions of nitrate in pore water extracts from groundwater samples indicate at least three potential sources of nitrate in groundwaters at Beijing. Stable isotope analyses from this study site, which has atmospheric, chemical fertilizer and human waste nitrate sources, provide a tool to distinguish nitrate sources in a confined aquifer where concentrations alone do not. These data indicate that the most common sources of high nitrate concentrations in groundwater at Beijing are wastewater and denitrification process occurred specially in the Central area. NO₃–N and cation and anion concentrations (Ca²⁺, Mg²⁺ Cl^? and SO ₄ ² ) showed strong correlations indicating that they originated from the same sources. This study demonstrates that a thorough evaluation of hydrodynamic and hydrochemical parameters with dual isotopes of NO₃ ^? constitutes an effective approach for identifying sources and transformation processes of NO₃ ^? in deeper groundwater systems.     

济南泉域岩溶水水化学特征及其成因

济南泉域水质逐年变差,查明其污染来源和影响因素,对岩溶水资源开发利用及生态环境保护具有十分重要的意义,而对包含补给径流区的全区岩溶水系统分析尚未见报道.综合运用水化学(Piper三线图、离子比例系数、相关分析)和多元统计(因子分析、聚类分析)方法分析地下水水化学特征,探讨了不同区域水质影响因素及影响强度.因子分析反映了灰岩水岩作用、工业和生活污染、白云岩水岩作用、农牧业和生活污染对水化学组分的影响,贡献率依次为33.1%、28.4%、12.0%和11.8%.分析结果表明:研究区岩溶水水质受水岩作用和人类活动的双重影响;南部补给区、西郊及其以西排泄区水质优良,主要受碳酸盐岩溶解的影响;直接补给径流区部分岩溶水受农牧业和农村生活污染的影响,NO₃-含量较高;东郊排泄区、城区及近郊开采区受工业及城镇生活污染的影响,水质较差,少数地段SO₄2-、NO₃-、TDS和总硬度超标.      

黄土梁峁地貌矿井水水质时空变异评估与关键控制因子水源识别

针对黄土梁峁地区砂岩-泥岩互层覆岩突水水源难以准确判别以及不同水源所占比例无法定量化问题,在系统采集了陕北朱家峁煤矿地表水、地下水水样基础上,采用数理统计、Piper三线图、Gibbs图、常规水化学特征比值法,揭示了不同含水系统水化学演化规律和水源补给关系。通过分析水体稳定同位素组成演变特征,明确了不同环境背景下各类水源水质时空变异性。在此基础上,以常规、同位素特征比因子作为判别指标,构建了基于T-球型模糊聚集算子TOPSIS法(T-TOPSIS)、粗糙集理论(RST)、D-S证据理论(DSET)与单指标未知测度函数(SIUMF)的突水水源混合比例计算模型。结果表明,研究区地表水、地下水水化学类型虽均以Na-SO4·Cl为主,但两者受控因素有着明显差别,即地表水水化学成分受硅酸盐岩风化和蒸发结晶共同控制;地下水成分则主要受控于蒸发结晶作用。此外,经历了强烈蒸发作用的地表水对地下水存在一定程度的补给作用。T-TOPSISRST-DSET-SIUMF突水水源混合比例判别模型表明,陕北朱家峁煤矿50%以上的突水水源为顶板砂岩水和地表沙空沟水。三维高密度电法探测结果验证了该模型的准确性。     

PLS法在重庆巫山县邓家屋场滑坡稳定性影响因素分析中的应用

滑坡各影响因素之间存在较强的相关性。偏最小二乘回归(PLS)方法在一个算法下同时实现了回归建模(多元线性回归分析)、数据结构简化(主成分分析)以及两组变量间的相关分析(典型相关分析),给因变量之间存在较高相关性的回归带来极大的便利。为了探讨PLS方法在滑坡稳定性影响因素分析中的适用性,选取位于三峡水库区巫山县的邓家屋场滑坡为试验区。在考虑三峡水库蓄水后引起的地下水力坡度变化、地震因素以及建筑物因素对邓家屋场滑坡稳定性的影响条件下,建立了滑坡稳定性系数与以上指标的PLS回归方程,以期达到对邓家屋场滑坡稳定性影响因素敏感性的分析目标。计算建立的回归模型为:K*=0.181468×C*+0.274876×φ*-0.611369×J*-0.238604×α*-0.105219×ΔW*。表达式各因素影响力的排序为:地下水力坡度>滑动面内摩擦角>地震加速度>滑动面内聚力>滑体均匀加载。结果表明,采用偏最小二乘回归方法对滑坡稳定性影响因素进行分析具有物理意义明确、计算简单、建模效果好、解释性强的特点,是一种可行的解决方案。