Characterization of groundwater contamination using factor analysis

 The effluent contamination of groundwater at two industrial sites at Visakhapatnam, India, was studied using factor analysis. Thirty groundwater samples near a zinc smelter plant and 19 from the polymers plant were analyzed for specific conductance, chloride, bicarbonate, sulfate, calcium, magnesium, sodium, and potassium. The data were subjected to R-mode factor analysis and the factor scores transferred to areal maps. While magnesium and sulfate are the dominant contaminants at the zinc site, sodium, chloride, and bicarbonate from the effluent are affecting groundwater in the polymers area. Contour maps for each factor suggest the areal extension of the contaminants. Received: 1 March 1995 · Accepted: 18 September 1995     

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.     

Assessing hydrochemical evolution of groundwater in limestone terrain via principal component analysis

This paper describes the use of multivariate statistical analysis to trace hydrochemical evolution in a limestone terrain at Zagros region, Iran. The study area includes a deep confined aquifer, overlaid by an unconfined aquifer. The method involves the use of principal component analysis (PCA) to assess and evaluate the hydrochemical evolution based on chemical and isotope variables of 12 piezometers drilled in both the unconfined and confined aquifers. First PCA on all variables shows that water–rock interaction under different conditions with respect to the atmospheric CO₂ is the main process responsible for chemical constituents. As a result, combinations of several ratios such as Ca/TDS, SO₄/TDS and Mg/TDS with physico-chemical and isotope variables reveal different hydrochemical evolution trend in the aquifers. Second PCA on the selective samples and variables reveals that displacement of the unconfined samples from dry to wet season follows a refreshing trend towards river samples that is characterized by reducing electrical conductivity and increasing sulphate and tritium contents. However, the refreshing trend cannot be traced in the confined aquifer samples suggesting no recharge from river to the confined aquifer. Third PCA reveals that, chemical composition of water samples in the unconfined aquifer tends to have considerable difference from each other in the end of recharge period. In contrast, the confined aquifer samples have a tendency to show similar chemical composition during recharge period in comparison to end of dry period. This difference is caused by different mechanism of recharge in the unconfined aquifer (through the whole aquifer surface) and the confined aquifer (through the limited recharge area).     

Spatial variations of groundwater vulnerability using cluster analysis

An investigation on quality of groundwater has been carried out in the river basin of Varaha located in Visakhapatnam District, Andhra Pradesh to find out the factors that are responsible for spatial variations of water vulnerability. The study area is underlain by the Precambrian rocks of Eastern Ghats over which the Recent Formations occur. Groundwater is a prime source for drinking and irrigation. The quality of groundwater is fresh and brackish with dominance of the latter. Groundwater samples are categorized into two major clusters A and B, using the dendrogram of cluster analyses. Out of these two major clusters, five sub-clusters I to V in the pre-monsoon season and six sub-clusters I to VI in the post-monsoon season are identified. The sub-clusters I to IV of pre-monsoon and I to V of post-monsoon seasons of the cluster A are characterized by less mineralized groundwater compared to those of V of pre-monsoon and VI of post-monsoon seasons of the cluster B, which represent highly mineralized groundwater. The low to high mineral content follows gradually from upstream to the downstream area, being higher in post-monsoon season in both the clusters A and B, depending upon the source, mineral dissolution, and precipitation, solubility and leaching of ions, ion exchange and adsorption processes. Spatial distributions of the sub-clusters give clues to understand the factors that cause variations of groundwater vulnerability at a specific site, vis-a-vis local and regional lithological and non-lithological influences. As a result, the quality of groundwater on a regional scale changes from Na⁺ > Mg²⁺ >Ca²⁺ > K⁺: HCO ₃ ^? > Cl^? > SO ₄ ^2? > NO ₃ ^? > F^? in the cluster A to Na⁺ > Mg²⁺ >Ca²⁺ > K⁺: Cl^? > HCO ₃ ^? > SO ₄ ^2? > NO ₃ ^? > F^? in the cluster B, following the topography. The classification of the area into the zones of relative groundwater vulnerability with respect to drinking water quality of the chemical composition of the sub-clusters helps the planners to identify the specific locations, where the inferior quality of groundwater can occur, for taking the remedial measures.     

地下水污染场地水质空间相关性分析

地下水污染场地的监控需要依据水文地质条件、地下水流场和溶质运移特征,利用水化学的示踪作用开展空间分析,以识别地下水和溶质运移特征。该方法具有简易和经济的优点。以台湾苗栗县某化工污染场地为例,通过对地下水中多种离子浓度的空间等值线分析及对比,判断污染物的来源和分布范围。采用离子浓度的统计特征值P95、P75、P50、P25构成的等值线,作为判断离子空间分布特征的依据,并形成离子之间对比的统一标准。所有离子都采用浓度P95等值线包围的区域作为其污染源,其它三种等值线表示可能出现的污染物扩散范围。在此基础上通过对比发现10种离子的污染源集中出现在场地一个范围内,并形成一个污染带,表明它们的来源具有密切的联系。10种离子P75等值线划出的污染物分布范围同样比较集中,但几乎都分布在污染带的南部,显示离子的迁移方向和迁移距离是一致的。根据离子空间分布的相似性将其分为三组,空间分布相似性高的离子组同时出现在一个区域的机会更多。通过多种水化学成分识别地下水流场和溶质运移特征,提高了结论的可信度,为污染物监控提供参考。     

Multiple group principal component analysis

Common Principal Component Analysis is a generalization of standard principal components to several groups under the rigid mathematical assumption of equality of all latent vectors across groups (i.e., principal component directions), whereas the latent roots are allowed to vary between groups (differing inflations of dispersion ellipsoids). In practice, data that fulfill these strict requirements are relatively rare. Examples from palaeontology are used to illustrate the principles. Compositional data can be made to fit the Common Principal Component (CPC) model by the appropriate logratio covariance matrix.     

Isolation of wells contaminated by tannery industries using principal component analysis

The groundwater in the upper Kodaganar basin is contaminated due to the discharge of effluents from tannery industries. The water in the wells, whose physico-chemical characteristics are altered due to the influence of the effluents, is statistically analyzed. The physico-chemical variables such as EC, Na⁺, K⁺, Ca²⁺, Mg²⁺, F^?, Cl^?, HCO₃ ^?,CO₃ ^2?, NO₃ ^?, SO₄ ^2?, pH, and Cr_total were used for this study. An attempt was made to identify the contaminated wells based on suitability for drinking, suitability for industrial requirements, and through principal component analysis (PCA). Classification based on suitability helped in identifying the contaminated wells. However, this resulted in failure when identifying the wells that are contaminated by tanneries. PCA has proved to be effective in the segregation of contaminated wells influenced by tannery industries. The physico-chemical variables that are 13 in number are transformed into two orthogonal components and Eigen values based on the variance. The Eigen values are used to select the first two principal components PC1 (7.26) and PC2 (2.24) that accounted for 73.04% variance in the data. The components of the variables and the wells are plotted in a biplot to isolate the contaminated samples. The contaminated samples are analyzed in the spatial domain in geographic information system and found to be clustered around the tannery belt. The study reveals that 35% of the samples are contaminated due to discharge from tannery industries.     

Water quality of the Chhoti Gandak River using principal component analysis,Ganga Plain,India

Chhoti Gandak is a meandering river which originates in the terai area of the Ganga Plain and serves as a lifeline for the people of Deoria district, Uttar Pradesh. It travels a distance of about 250 km and drains into Ghaghara near Gothani, Siwan district of Bihar. It has been observed that people of this region suffer from water-borne health problems; therefore water samples were collected to analyse its quality along the entire length of Chhoti Gandak River.     

Spatial and statistical assessment of nitrate contamination in groundwater:Case of Sais Basin,Morocco

The objective of this study is to evaluate the nitrate contamination in the plioquaternary aquifer of Sais Basin based on a statistical approach. A total of 98 samples were collected in the cultivated area during the spring and autumn period of 2018. The results show that 55% and 57% of the samples in spring and autumn respectively exceed the threshold fixed by WHO(50 mg/L). However, nitrate concentrations do not show seasonal and spatial variation(p0.05). The results of the correlation matrix, principal component analysis(PCA), and hierarchical cluster analysis(HCA) suggest that nitrate pollution is related to anthropogenic source. Moreover, multiple linear regression results show that NO_3 is more positively explained in the spring period by Ca and SO_4 and negatively explained by pH and HCO_3. Regarding the autumn period, nitrate pollution is positively explained by Ca and negatively by pH. This study proposes a useful statistical platform for assessing nitrate pollution in groundwater.     

Detecting groundwater contamination of a river in Georgia, USA using baseflow sampling

Algal blooms and fish kills were reported on a river in coastal Georgia (USA) downstream of a poultry-processing plant, prompting officials to conclude the problems resulted from overland flow associated with over-application of wastewater at the plant’s land application system (LAS). An investigation was undertaken to test the hypothesis that contaminated groundwater was also playing a significant role. Weekly samples were collected over a 12-month period along an 18 km reach of the river and key tributaries. Results showed elevated nitrogen concentrations in tributaries draining the plant and a tenfold increase in nitrate in the river between the tributary inputs. Because ammonia concentrations were low in this reach, it was concluded that nitrate was entering via groundwater discharge. Data from detailed river sampling and direct groundwater samples from springs and boreholes were used to isolate the entry point of the contaminant plume. Analysis showed two separate plumes, one associated with the plant’s unlined wastewater lagoon and another with its LAS spray fields. The continuous discharge of contaminated groundwater during summer low-flow conditions was found to have a more profound impact on river-water quality than periodic inputs by overland flow and tributary runoff.     

Spatial analyses of groundwater levels using universal kriging

For water levels, generally a non-stationary variable, the technique of universal kriging is applied in preference to ordinary kriging as the interpolation method. Each set of data in every sector can fit different empirical semivariogram models since they have different spatial structures. These models can be classified as circular, spherical, tetraspherical, pentaspherical, exponential, gaussian, rational quadratic, hole effect, K-bessel, J-bessel and stable. This study aims to determine which of these empirical semivariogram models will be best matched with the experimental models obtained from groundwater-table values collected from Mustafakemalpasa left bank irrigation scheme in 2002. The model having the least error was selected by comparing the observed water-table values with the values predicted by empirical semivariogram models. It was determined that the rational quadratic empirical semivariogram model is the best fitted model for the studied irrigation area.     

Geochemistry of Bizerte lagoon affluent water, Northern Tunisia: principal component analysis

The monthly geochemical study of Bizerte lagoon principal affluent water consists in characterizing the water geochemical facies and their inorganic pollution degree by nutrients. The major elements analysis shows calcium sulfate to chloride calcium balanced facies. The geochemical analysis of water nutritive salts shows generally a good to excellent quality. Wadi Guenniche is considered more polluted as we recorded the highest nutrients contents. The principal component analysis of the connections between the physicochemical and geochemical parameters of Bizerte lagoon affluent water show that the low salinities, the turbidity, and the low contents of major sodium, chloride ions, and nutritive elements are the major factors of the environment controlling the good quality of this fresh water.     

Application of principal component analysis to understand variability of rainfall

The usefulness of principal component analysis for understanding the temporal variability of monsoon rainfall is studied. Monthly rainfall data of Karnataka, spread on 50 stations for a period of 82 years have been analysed for interseasonal and interannual variabilities. A subset of the above data comprising 10 stations from the coherent west zone of Karnataka has also been investigated to bring out statistically significant interannual signals in the southwest monsoon rainfall. Conditional probabilities are proposed for a few above normal/below normal transitions. A sample prediction exercise for June–July using such a transition probability has been found to be successful.     

Assessment of regional drought vulnerability and risk using principal component analysis and a Gaussian mixture model

Due to the complex characteristics of drought, drought risk needs to be quantified by combining drought vulnerability and drought hazard. Recently, the major focus in drought vulnerability has been on how to calculate the weights of indicators to comprehensively quantify drought risk. In this study, principal component analysis (PCA), a Gaussian mixture model (GMM), and the equal-weighting method (EWM) were applied to objectively determine the weights for drought vulnerability assessment in Chungcheong Province, located in the west-central part of South Korea. The PCA provided larger weights for agricultural and industrial factors, whereas the GMM computed larger weights for agricultural factors than did the EWM. The drought risk was assessed by combining the drought vulnerability index (DVI) and the drought hazard index (DHI). Based on the DVI, the most vulnerable region was CCN9 in the northwestern part of the province, whereas the most drought-prone region based on the DHI was CCN12 in the southwest. Considering both DVI and DHI, the regions with the highest risk were CCN12 and CCN10 in the southern part of the province. Using the proposed PCA and GMM, we validated drought vulnerability using objective weighting methods and assessed comprehensive drought risk considering both meteorological hazard and socioeconomic vulnerability.

     

Environmental factors of road slope stability in mountain area using principal component analysis and hierarchy cluster

Nine environmental factors of 147 roadside soil samples were administered in Sichuan Basin of China and principal component analysis was conducted using the Pearson correlation matrix. The results show that the first four principal components whose eigenvalue is over 1.00 can be extracted. The first principal component which is consisted of rock type, soil type, weathering degree, and soil depth is the most important factor of all. The geographical position which is consisted of altitude, longitude, and latitude is included in the second and the third principal components. The fourth principal component shows that the terrain factor influences the rock slope stability. The hierarchy cluster shows that rock type and soil type play the maximum positive correlation, while the slope and the aspect present the maximum negative correlation.     

Thoughts on use of principal component analysis in petrogenetic problems

The use of principal component analysis in studying chemical trends in volcanic rock suites is described. It is suggested that eigenvectors generated from a correlation matrix, rather than a covariance matrix, could be used in this context. In the latter situation many elements are swamped by silicon's numerical size and range. In the former situation the alkalies and titanium begin to show their true importance.     

Statistical inverse analysis based on genetic algorithm and principal component analysis: Method and developments using synthetic data

This study concerns the identification of parameters of soil constitutive models from geotechnical measurements by inverse analysis. To deal with the non‐uniqueness of the solution, the inverse analysis is based on a genetic algorithm (GA) optimization process. For a given uncertainty on the measurements, the GA identifies a set of solutions. A statistical method based on a principal component analysis (PCA) is, then, proposed to evaluate the representativeness of this set. It is shown that this representativeness is controlled by the GA population size for which an optimal value can be defined. The PCA also gives a first‐order approximation of the solution set of the inverse problem as an ellipsoid. These developments are first made on a synthetic excavation problem and on a pressuremeter test. Some experimental applications are, then, studied in a companion paper, to show the reliability of the method. Copyright © 2009 John Wiley & Sons, Ltd.     

Mapping groundwater contamination risk using GIS and groundwater modelling. A case study from the Gaza Strip, Palestine

Increasing pressure on water resources worldwide has resulted in groundwater contamination, and thus the deterioration of the groundwater resources and a threat to the public health. Risk mapping of groundwater contamination is an important tool for groundwater protection, land use management, and public health. This study presents a new approach for groundwater contamination risk mapping, based on hydrogeological setting, land use, contamination load, and groundwater modelling. The risk map is a product of probability of contamination and impact. This approach was applied on the Gaza Strip area in Palestine as a case study. A spatial analyst tool within Geographical Information System (GIS) was used to interpolate and manipulate data to develop GIS maps of vulnerability, land use, and contamination impact. A groundwater flow model for the area of study was also used to track the flow and to delineate the capture zones of public wells. The results show that areas of highest contamination risk occur in the southern cities of Khan Yunis and Rafah. The majority of public wells are located in an intermediate risk zone and four wells are in a high risk zone.