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

Multivariate statistical techniques, such as cluster analysis and principal component analysis (PCA), were applied for evaluation of spatial variations and interpretation of large complex water quality data set of the Ganga river basin, generated during one year (2013-2014) monitoring of eight water parameters at seven different sites. Hierarchical cluster analysis grouped seven sampling sites into three clusters, i.e., relatively low polluted (LP), medium polluted (MP) and highly polluted (HP) sites based on the similarity of water quality characteristics. Principal component analysis produced three significant main components and explaining more than 82.9% of the variance (anthropogenic and industrial effect) that present 57.1%, 13.8% and 12% respectively of the total variance of water quality in Ganga river. The result reveals that Turbidity, Dissolved oxygen and Biochemical oxygen demand are the parameters that are most important in assessing variations of water quality. Water quality index based on eight parameters (Turbidity, DO, BOD, COD, pH, TS, TSS and TDS) calculated for all the sites are found to be medium to bad. Thus, this study illustrates the usefulness of multivariate statistical techniques for analysis, interpretation of complex data sets and understanding spatial variations in water quality for effective river water quality management. The study reveals that untreated industrial and municipal discharges are the major source of the pollution to the Ganga river. Implementation of suitable management plan along with proper sewerage treatment network, maintaining sufficient dilution flow, artificial aeration and watershed management will control the pollution in the Ganga river.

Three representative areas (lowland, semi-mountainous, and coastal) have been selected for the collection of drinking water samples, and a total number of 28 physical, chemical, and biological parameters per water sample have been determined and analyzed. The mean values of the physical and chemical parameters were found to be within the limits mentioned in the 98/83/EEC directive. The analysis of biological parameters shows that many of the water samples are inadequate for human consumption because of the presence of bacteria. Cluster analysis (CA) first was used to classify sample sites with similar properties and results in three groups of sites; discriminant analysis (DA) was used to construct the best discriminant functions to confirm the clusters determined by CA and evaluate the spatial variations in water quality. The standard mode discriminant functions, using 17 parameters, yielded classification matrix correctly assigning 96.97% of the cases. In the stepwise mode, the DA produced a classification matrix with 96.36% correct assignments using only ten parameters (EC, Cl-, NO3-, HCO3-, CO3(-2), Ca+2, Na+, Zn, Mn, and Pb). CA and factor analysis (FA) are used to characterize water quality and assist in water quality monitoring planning. CA proved that two major groups of similarity (six subclusters) between 17 physicochemical parameters are formed, and FA extracts six factors that account for 66.478% of the total water quality variation, when all samples' physicochemical data set is considered. It is noteworthy that the classification scheme obtained by CA is completely confirmed by principal component analysis.

The present study is an evaluation of temporal/spatial variations of surface water quality using multivariate statistical techniques, comprising cluster analysis (CA), principal component analysis (PCA), factor analysis (FA) and discriminant analysis (DA). Eleven water quality parameters were monitored at 38 different sites in Can Tho City, a Mekong Delta area of Vietnam from 2008 to 2012. Hierarchical cluster analysis grouped the 38 sampling sites into three clusters, representing mixed urban-rural areas, agricultural areas and industrial zone. FA/PCA resulted in three latent factors for the entire research location, three for cluster 1, four for cluster 2, and four for cluster 3 explaining 60, 60.2, 80.9, and 70% of the total variance in the respective water quality. The varifactors from FA indicated that the parameters responsible for water quality variations are related to erosion from disturbed land or inflow of effluent from sewage plants and industry, discharges from wastewater treatment plants and domestic wastewater, agricultural activities and industrial effluents, and contamination by sewage waste with faecal coliform bacteria through sewer and septic systems. Discriminant analysis (DA) revealed that nephelometric turbidity units (NTU), chemical oxygen demand (COD) and NH₃ are the discriminating parameters in space, affording 67% correct assignation in spatial analysis; pH and NO₂ are the discriminating parameters according to season, assigning approximately 60% of cases correctly. The findings suggest a possible revised sampling strategy that can reduce the number of sampling sites and the indicator parameters responsible for large variations in water quality. This study demonstrates the usefulness of multivariate statistical techniques for evaluation of temporal/spatial variations in water quality assessment and management.

Rural inhabitants depend on groundwater for their drinking water in the Lakes region of central Cote d’Ivoire. The aim of this study is to identify the factors responsible of the water mineralisation and the sources of major ions in groundwater within the area. 123 groundwater samples were collected and analysed for various physico-chemical parameters. Multivariate statistical techniques including Principal Component Analysis (PCA) and Cluster Analysis (CA) were applied on the dataset to evaluate the geochemical processes that control the hydrogeochemistry of groundwater. Ca-HCO3 and Mg-Ca-HCO3 hydrochemical facies predominate the groundwater samples. The first 4 factors explain 79.139% of the total variance, their loading allowing the interpretation of hydrogeochemical processes in the area. The sources of ions in the groundwater are due to two main factors: natural mineralisation and anthropogenic activities. The silicates mineral weathering such as acid hydrolysis of silicates provide the major part of the ions in groundwater such as Ca2+, Mg2+, and HCO3-. The concentrations of NO-3 and Cl- in groundwater were due to anthropogenic activities such as farming. Reduction-oxidation process produced Mn and Fe. The results of this study show the usefulness of multivariate statistical techniques in hydrochemical studies.

This paper presents a comprehensive study on the quality of surface and groundwater in an environmentally sensitive karstic watershed strongly pressed by urban growth. The objective of the study was to assess the integrated effects of human activities and natural characteristics of karstic environments on the quality of surface and groundwater using multivariate statistical techniques. Data from 18 physical, chemical, and microbiological water quality variables obtained throughout a hydrological year were analyzed using principal components analysis (PCA) and cluster analysis. The PCA was carried out individually for surface water and groundwater. Our goal was to study the behavior of the water quality variables at each of these systems, as well as to infer the importance of these variables on the dynamics of the water resources in the region. Our results for surface water showed that the 18 original variables could be reduced to five principal components which together accounted for 69% of the total variation in the data, whereas for groundwater, 70% of the total variation in the data was explained by five principal components. In order to evaluate the impacts of anthropic activities on the water quality at the monitoring locations, the cluster analysis was applied to the ten sampling points. The analysis identified five clusters, two of which are formed by points with low contamination levels and three where the anthropic interference is noticeable. The results suggest the existence of specific contamination sources in many points, including in the groundwater, and highlight the natural vulnerability of the karstic environments.

Multivariate statistical techniques, such as principal component analysis (PCA) , cluster analysis (CA) were applied for the evaluation of spatial variations and the interpretation of a large complex water quality data set of the lakes in Wuhan, generated in 2009 monitoring of 21 parameters at 70 different lakes (1470 observations) located at the 7 core districts of Wuhan, Hubei Province, China. Results reveal that Potassium Permanganate Index, Biochemical oxygen demand (BOD), Ammonical nitrogen NH4-N, Total Phosphate TP, Total nitrogen, Chl-a were the parameters that are the most important ones in assessing variations of water quality in the lake. Hierarchical cluster analysis grouped 70 lakes into three clusters, i.e., relatively less polluted (LP), medium polluted (MP) and highly polluted (HP) lakes, based on the similarity of water quality characteristics. Thus, this study illustrates the usefulness of multivariate statistical techniques for analysis and interpretation of complex data sets, and in water quality assessment, identification of pollution sources/factors and understanding temporal/spatial variations in water quality for effective lakes water quality management.This study suggests that PCA and CA techniques are useful tools for identification of important surface water quality monitoring stations and parameters.

Temporal and spatial water quality data are essential to evaluate human health risks. Understanding the interlinking variations between water quality and socio-economic development is the key for integrated pollution management. In this study, we applied several multivariate approaches, including trend analysis, cluster analysis, and principal component analysis, to a 15-year dataset of water quality monitoring (1999 to 2013) in the Thi Vai estuary, Southern Vietnam. We discovered a rapid improvement for most of the considered water quality parameters (e.g., DO, NH4, and BOD) by step trend analysis, after the pollution abatement in 2008. Nevertheless, the nitrate concentration increased significantly at the upper and middle parts and decreased at the lower part of the estuary. Principal component (PC) analysis indicates that nowadays the water quality of the Thi Vai is influenced by point and diffuse pollution. The first PC represents soil erosion and stormwater loads in the catchment (TSS, PO4, and Fetotal); the second PC (DO, NO2, and NO3) determines the influence of DO on nitrification and denitrification; and the third PC (pH and NH4) determines point source pollution and dilution by seawater. Therefore, this study demonstrated the need for stricter pollution abatement strategies to restore and to manage the water quality of the Thi Vai Estuary.

Multivariate statistical techniques, including cluster analysis (CA), principal component analysis (PCA), factor analysis (FA) and discriminant analysis (DA), were used to evaluate temporal and spatial variations and to interpret a large and complex water quality data sets collected from the Songhua River Basin. The data sets, which contained 14 parameters, were generated during the 7-year (1998-2004) monitoring program at 14 different sites along the rivers. Three significant sampling locations (less polluted sites, moderately polluted sites and highly polluted sites) were detected by CA method, and five latent factors (organic, inor-ganic, petrochemical, physiochemical, and heavy metals) were identified by PCA and FA methods. The re-sults of DA showed only five parameters (temperature, pH, dissolved oxygen, ammonia nitrogen, and nitrate nitrogen) and eight parameters (temperature, pH, dissolved oxygen, biochemical oxygen demand, ammonia nitrogen, nitrate nitrogen, volatile phenols and total arsenic) were necessarily in temporal and spatial varia-tions analysis, respectively. Furthermore, this study revealed the major causes of water quality deterioration were related to inflow of effluent from domestic and industrial wastewater disposal.

In order to study the spatiotemporal variations characteristic of water quality and potential pollution sources of Qujiang River, the water quality data of twelve water quality parameters at three monitoring sections (Tuanbaoling, Baita, and Sailong) from 2015 to 2019 were analyzed by using comprehensive pollution index (CPI) and multivariate statistical techniques (MST). The water quality parameters of Qujiang River basically meet the class 3 value of environmental quality standards for surface water (GB3838-2002, China). CPI varies from 0.62 to 1.06 and the water quality is characterized by slight pollution at the three monitoring sections. Cluster analysis (CA) results show that the months can be divided into three groups on the basis of similarities of the water quality characteristics: Group 1 (dry season), which includes January-April and November–December; Group 2 (flood season), that is, July; Group 3 (flat season), which consists of May–June and August–October. Principal component analysis (PCA) results identify four principal components (PCs) for the dry season and flood season, and five PCs for the flat season, thus explaining 58.23%, 82.94%, and 73.23% of the total variance, respectively. The results of the independent sample t-test show significant differences among the pH, Permanganate index (CODMn), Ammonia nitrogen (NH3–N), Total nitrogen (TN), Fecal coliform (F.coli), and (Flow) Q in the three monitoring sections. Moreover, the pollution is more serious in Baita than Tuanbaoling and Sailong Section and the main problem in the Qujiang River is the high water organic and nitrogen nutrient pollutant content. Hence, monitoring and protection need to be strengthened in the Baita section of Qujiang River.

ABSTRACTThis study is focused on the character and classification of Yamuna River water quality using secondary data. The application of water quality index (WQI), cluster analysis (CA), principal component analysis (PCA), multiple regression analysis (MRA) and discriminant analysis (DA) was investigated to assess the water quality status and evaluate the critical parameters with identification of most prominent polluted sites. During investigation of WQI, 67% of the total monitoring area falls under bad and very bad categories. CA classified 19 monitoring sites into 3 groups based on the properties of water quality. PCA obtained two principle components which suggest that domestic, industrial, municipal effluent and agricultural runoff are important pollution sources of Yamuna River. DA result indicates pH, COD, DO as most significant parameters, and are responsible for discriminating the groups. MRA obtained that TC, FC, NH4, COD, BOD and pH are dependable factors for controlling the river water quality. The trend line of Yamuna River water quality through simple regression indicated that the trend values of COD, BOD, NH4, TKN, TC and FC are continuously increasing in the river system while the negative trend line of DO suggested that the water quality of Yamuna could be under hypoxic condition in near future.

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.

In this study, multivariate statistical techniques, such as discriminant, factor /principal component and cluster analyses were applied to water quality data set monitored in pre- and post- monsoon for twenty five locations during three years to investigate seasonal and spatial variations in river water quality. The variables were mainly divided into two categories viz., non-conservative – DO, BOD, COD, nitrates and phosphates and conservative parameters – TDS, conductivity, alkalinity, hardness, calcium, magnesium, sodium, potassium and sulfates. Trivial elevated values of all non conservative Characteristics in pre-monsoon and some conservative parameters (SO4, Cl) in post-monsoon period reflected contribution on temporal effect on surface water. Results of principal component analysis evinced that all the parameters equally and significantly contribute to water quality variations in the river basin for both the seasons. Factor 1 and factor 2 analysis revealed the inverse relation of DO, indicating the control of dissolved oxygen on organic load and nutrients in different seasons. Hierarchical cluster analysis grouped twenty five stations into three clusters in pre-monsoon and six clusters in post- monsoon with similar water quality features. Third clustered group of former and sixth of latter consisted one station (St.25), exhibiting significant spatial variation in physico-chemical composition. Key words: Multivariate analysis, cauvery, cluster, conservative parameters, temporal and spatial variations.

Monitoring water quality of River Ganga using multivariate techniques and WQI (Water Quality Index) in Western Himalayan region of Uttarakhand, India

This study integrated conventional statistical tools with neighbourhood linkage to propose the statistical diagnosis approach. Fourteen monitoring wells in Kaohsiung Science Park were selected as study case, and lab data of routine groundwater analysis including pH, EC, hardness, TDS, TOC, ammonia, nitrate, nitrite, chloride, sulphate, fluoride, phenols, Fe, Mn, As, and temperature were subjected to principal component and cluster analysis. Principal component analysis (PCA) was utilized to reflect those chemical data with the greatest correlation, and PCA results identified five major principal components (PCs) representing 74.6% of cumulative variance. Based on the monitoring data between 2005 and 2008, the extracted information from the PCA mirrored the potential sources of groundwater contamination as acid leakage, arsenic dissolution, salinization, mineralization, and fluoride release. Cluster analysis (CA) was used to evaluate the similarities of water quality in groundwater samples, and five clusters were assigned in two-step clustering for corresponding with the number of PCs, i.e. the potential sources of groundwater contamination. The interpreted facts from CA illustrated that the classified monitoring wells in each cluster properly match up with the identified processes. With the aid of neighbourhood linkage, the domain of groundwater contamination can be spatially outlined by mapping the neighbouring wells within the identical cluster. Therefore, the nature of underlying processes affecting groundwater quality was explored by statistical diagnosis.

This study investigates and proposes a reduction in the number of water quality monitoring stations, parameters and develops the best input combination for water quality modelling using artificial neural network and multivariate statistical technique. Fourteen water quality physicochemical parameters acquired from eight monitoring sites for 8 years (2006–2013) were investigated. Hierarchical agglomerative cluster analyses (HACA) classify the eight monitoring sites into two significant clusters. Principal component analysis (PCA) accounted for more than 82% of the total variance and attributes the sources of pollution to critical anthropogenic activities, surface run-off and weathering of parent rocks. Furthermore, sensitivity analyses percentage contribution of pollutants revealed dissolved oxygen as the most significant parameter responsible for the pollution (66.3%), followed by ammonia nitrogen (14.4%), chemical oxygen demand (9.4%) and biochemical oxygen demand (5.3%). The result for source category apportionment assigned 39% to rock weathering, 25% anthropogenic activities, 20% surface run-off, 11% faecal waste, 3.4% human and natural factors and 1.4% erosion of river bank. In addition, three input combination models (model 1, 2 and 3) were developed in order to identify the best that can predict water quality index (WQI) at a very high precision. Model 2 using the principal component scores before varimax rotation appears to have the best prediction capability at node eight with coefficient of determination (R2) = 0.999 and root mean square error (RMSE) = 0.159. These findings justify the use of environmetrics modelling technique to reveal the pattern of water quality for decision making by government and stakeholders.