The use of multivariate statistical methods for optimization of the surface water quality network monitoring in the Paraopeba river basin,Brazil

This study sought to evaluate and propose adjustments to the water quality monitoring network of surface freshwaters in the Paraopeba river basin (Minas Gerais, Brazil), using multivariate statistical methods. A total of 13,560 valid data were analyzed for 19 water quality parameters at 30 monitoring sites, over a period of 5 years (2008–2013). The cluster analysis grouped the monitoring sites in eight groups based on similarities of water quality characteristics. This analysis made it possible to detect the most relevant monitoring stations in the river basin. The principal components analysis associated with non-parametric tests and the analysis of violation of the standards prescribed by law, allowed for identifying the most relevant parameters which must be maintained in the network (thermotolerant coliforms, total manganese, and total phosphorus). The discharge of domestic sewage and industrial wastewater, that from mining activities and diffuse pollution from agriculture and pasture areas are the main sources of pollution responsible for the surface water quality deterioration in this basin. The BP073 monitoring site presents the most degraded water quality in the Paropeba river basin. The monitoring sites BP094 and BP092 are located geographically close and they measure similar water quality, so a possible assessment of the need to maintain only one of the two in the monitoring network is suggested. Therefore, multivariate analyses were efficient to assess the adequacy of the water quality monitoring network of the Paraopeba river basin, and it can be used in other watersheds.     

Evaluation of surface water quality characteristics by using multivariate statistical techniques: A case study of the Euphrates river basin, Turkey

The surface water quality of the Euphrates river basin in Turkey are evaluated by using the multivariate statistical techniques known as factor analysis (FA) and multidimensional scaling (MDS) analysis. When FA was applied to the water quality data obtained from the 15 different surface water quality monitoring stations, two factors were identified, which were responsible from the 86.02% of the total variance of the water quality in the Euphrates river basin. The first factor called the urban land use factor explained 44.20% of the total variance and the second factor called the agricultural use factor explained 41.81% of the total variance. MDS technique showed that electrical conductivity (EC), percent sodium (Na%) and total salt are the most important variables causing difference in the water quality analysis.     

Use of water quality index and multivariate statistical techniques for the assessment of spatial variations in water quality of a small river

Rapid urban development has led to a critical negative impact on water bodies flowing in and around urban areas. In the present study, 25 physiochemical and biological parameters have been studied on water samples collected from the entire section of a small river originating and ending within an urban area. This study envisaged to assess the water quality status of river body and explore probable sources of pollution in the river. Weighted arithmetic water quality index (WQI) was employed to evaluate the water quality status of the river. Multivariate statistical techniques namely cluster analysis (CA) and principal component analysis (PCA) were applied to differentiate the sources of variation in water quality and to determine the cause of pollution in the river. WQI values indicated high pollution levels in the studied water body, rendering it unsuitable for any practical purpose. Cluster analysis results showed that the river samples can be divided into four groups. Use of PCA identified four important factors describing the types of pollution in the river, namely (1) mineral and nutrient pollution, (2) heavy metal pollution, (3) organic pollution, and (4) fecal contamination. The deteriorating water quality of the river was demonstrated to originate from wide sources of anthropogenic activities, especially municipal sewage discharge from unplanned housing areas, wastewater discharge from small industrial units, livestock activities, and indiscriminate dumping of solid wastes in the river. Thus, the present study effectively demonstrates the use of WQI and multivariate statistical techniques for gaining simpler and meaningful information about the water quality of a lotic water body as well as to identify of the pollution sources.     

Application of multivariate statistical methods for groundwater physicochemical and biological quality assessment in the context of public health

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.     

Holistic approach for quantification and identification of pollutant sources of a river basin by analyzing the open drains using an advanced multivariate clustering

Global scarcity of freshwater has been gearing towards an unsustainable river basin management and corresponding services to the humans. It needs a holistic approach, which exclusively focuses on effective river water quality monitoring and quantification and identification of pollutant sources, in order to address the issue of sustainability. These days, rivers are heavily contaminated due to the presence of organic and metallic pollutants released from several anthropogenic sources, such as industrial effluents, domestic sewage, and agricultural runoff. It is astonishing to note that even in many developing countries, most of these contaminants are carried through open drains, which enter river premises without proper treatment. Such practice not only devastates riverine ecosystem but also gives rise to deadly diseases, such as minimata and cancer in humans. Considering these issues, the present study develops a novel approach towards simultaneous identification of major sources of pollution in the rivers, along with critical pollutants and locations using an advanced hierarchical cluster and multivariate statistical analysis. A systematic approach has been developed by agglomerating both R-mode and Q-mode analysis, which develops monoplots, two-dimensional biplots, rotated component matrices, and dendrograms (using “SPSS” and “Analyse It” software) to reveal relationships among various quality parameters to identify the pollutant sources along with clustering of critical sampling sites and pollutants. A case study of the Ganges River Basin of India has been considered to demonstrate the efficacy and usefulness of the model by analyzing 85 open drains. Both organic and metallic pollutants are analyzed simultaneously as well as separately to get a holistic understanding of all the relationships and to broaden the perspective of water characterization. Results provide a comprehensive guidance to the policy makers and water managers to optimize corrective efforts, minimize further damage, and improve the water quality condition to ensure sustainable development of the river basin.     

Water quality assessment of Ogun river, South West Nigeria

The quality of Ogun river in South-West, Nigeria was studied by a field survey for a period of 1 year (covering dry season and rainy season). Water samples were collected from thirteen sites and analysed for physico-chemical and bacteriological parameters as well as heavy metals using standard methods. Generally, the values obtained for turbidity, phosphate, oil and grease, iron and faecal coliform from all the sites in both seasons were above the maximum acceptable limit set by the World Health Organization (WHO) for drinking water. Also, the manganese content from all the sites in the dry season, lead concentrations from three sites in the dry season and cadmium concentrations from some sites in both seasons were above the WHO limit. The values obtained for total dissolved solids, dissolved oxygen and chloride at site M in the dry season and nitrate at site J in the rainy season were also above the WHO limit. Pollution of Ogun river water along its course is evidenced by the high concentrations of pollution indicators, nutrients and trace metals above the acceptable limit. This poses a health risk to several rural communities who rely on the river primarily as their source of domestic water. The study showed a need for continuous pollution monitoring programme of surface waters in Nigeria.     

Evaluation of spatial and temporal variations in marine sediments quality using multivariate statistical techniques

Spatial and temporal variations of sediment quality in Matanzas Bay (Cuba) were studied by determining a total of 12 variables (Zn, Cu, Pb, As, Ni, Co, Al, Fe, Mn, V, CO₃ ^2?, and total hydrocarbons (THC). Surface sediments were collected, annually, at eight stations during 2005–2008. Multivariate statistical techniques, such as principal component (PCA), cluster (CA), and lineal discriminant (LDA) analyses were applied for identification of the most significant variables influencing the environmental quality of sediments. Heavy metals (Zn, Cu, Pb, V, and As) and THC were the most significant species contributing to sediment quality variations during the sampling period. Concentrations of V and As were determined in sediments of this ecosystem for the first time. The variation of sediment environmental quality with the sampling period and the differentiation of samples in three groups along the bay were obtained. The usefulness of the multivariate statistical techniques employed for the environmental interpretation of a limited dataset was confirmed.     

Water quality index applied to rivers in the Vistula river basin in Poland

Summary A new method of a water quality index has been proposed. The unit indices were determined from the values of individual parameters using continuous functions. The base for such functions were the four water quality classes used in Poland. The summarized WQI is the square root of the harmonic mean of squares of unit indices. Using this mean we have eliminated the use of weights of parameters. Parameters are divided into basic parameters (7) and other additional parameters (19). The additional parameter is considered only if its unit index is lower than WQI from basic parameters. For many measurements at one point the guaranteed WQI has been calculated. The points of WQI were connected and the curves of WQI along the river were obtained.A method of WQI calculating and preparation of WQI curves has been shown using as an example the Pilica river in Poland. The WQI was then calculated for 31 rivers in the Vistula river basin by measuring points.     

Identification of mangrove water quality by multivariate statistical analysis methods in Pondicherry coast,India

Different multivariate statistical analysis such as, cluster analysis, principal component analysis, and multidimensional scale plot were employed to evaluate the trophic status of water quality for four monitoring stations. The present study was carried out to determine the physicochemical parameters of water and sediment characteristics of Pondicherry mangroves—southeast coast of India, during September 2008–December 2010. Seasonal variations of different parameters investigated were as follows: salinity (10.26–35.20 psu), dissolved oxygen (3.71–5.33 mg/L), pH (7.05–8.36), electrical conductivity (26.41–41.33 ms⁻¹), sulfide (1.98–40.43 mg/L), sediment texture sand (39.54–87.31%), silt (9.89–32.97%), clay (3.06–31.20%), and organic matter (0.94–4.64%). pH, temperature, salinity, sand, silt, clay, and organic matter indicated a correlation at P < 0.01. CA grouped the four seasons in to four groups (pre-monsoon, monsoon, post-monsoon, summer) and the sampling sites in to three groups. PCA identified the spatial and temporal characteristics of trophic stations and showed that the water quality was worse in stations 3 and 4 in the Pondicherry mangroves.     

Analysis of spatial and temporal water pollution patterns in Lake Dianchi using multivariate statistical methods

Various multivariate statistical methods including cluster analysis (CA), discriminant analysis (DA), factor analysis (FA), and principal component analysis (PCA) were used to explain the spatial and temporal patterns of surface water pollution in Lake Dianchi. The dataset, obtained during the period 2003–2007 from the Kunming Environmental Monitoring Center, consisted of 12 variables surveyed monthly at eight sites. The CA grouped the 12 months into two groups, August–September and the remainder, and divided the lake into two regions based on their different physicochemical properties and pollution levels. The DA showed the best results for data reduction and pattern recognition in both temporal and spatial analysis. It calculated four parameters (TEMP, pH, COD_Mn, and Chl-a) to 85.4% correct assignment in the temporal analysis and three parameters (BOD, NH 2009/11/20Heavy metals (Cd, Cu, Fe, Mn and Zn) concentrations were determined in different tissues (muscle, kidney, liver, brain, gonads, heart and feathers) of Glaucous Gulls (Larus hyperboreus) from Bjørnøya and Jan Mayen. The age and spatial dependent variations in heavy metals were quantified and interpreted in view of the three chemometric techniques, i.e. non-parametric Mann–Whitney U test, redundancy gradient analysis and detrended correspondence analysis. The Glaucous Gulls from Bjørnøya contained significantly higher (p?<?0.05) levels of Cd, Cu and Zn than those inhabited Jan Mayen. Adult birds were characterized by greater (p?<?0.01) concentration of muscle, hepatic and renal heavy metals in comparison to chicks. Insignificantly higher slope constant Zn/Cd for the liver than for the kidney may reflect insignificant Cd exposure. Estimate of transfer factor (TF) allows us to assess variations in heavy metal concentrations during the individual development of Glaucous Gulls. It may be stated that there is a distinct increase of bioaccumulation of all the studied metals during subsequent stages of the bird life.     

Assessing soil quality indicator under different land use and soil erosion using multivariate statistical techniques

Soil degradation associated with soil erosion and land use is a critical problem in Iran and there is little or insufficient scientific information in assessing soil quality indicator. In this study, factor analysis (FA) and discriminant analysis (DA) were used to identify the most sensitive indicators of soil quality for evaluating land use and soil erosion within the Hiv catchment in Iran and subsequently compare soil quality assessment using expert opinion based on soil surface factors (SSF) form of Bureau of Land Management (BLM) method. Therefore, 19 soil physical, chemical, and biochemical properties were measured from 56 different sampling sites covering three land use/soil erosion categories (rangeland/surface erosion, orchard/surface erosion, and rangeland/stream bank erosion). FA identified four factors that explained for 82 % of the variation in soil properties. Three factors showed significant differences among the three land use/soil erosion categories. The results indicated that based upon backward-mode DA, dehydrogenase, silt, and manganese allowed more than 80 % of the samples to be correctly assigned to their land use and erosional status. Canonical scores of discriminant functions were significantly correlated to the six soil surface indices derived of BLM method. Stepwise linear regression revealed that soil surface indices: soil movement, surface litter, pedestalling, and sum of SSF were also positively related to the dehydrogenase and silt. This suggests that dehydrogenase and silt are most sensitive to land use and soil erosion.     

Using multivariate statistical methods to assess the groundwater quality in an arsenic-contaminated area of Southwestern Taiwan

Groundwater is a major water resource in Southwestern Taiwan; hence, long-term monitoring of water quality is essential. The study aims to assess the hydrochemical characteristics of water in the arsenic-contaminated aquifers of Choushui River alluvial fan and Chianan Plain, Taiwan using multivariate statistical methods, namely, factor analysis (FA), cluster analysis (CA), and discriminant analysis (DA). Factor analysis is applied to reveal the processes controlling the hydrochemistry of groundwater. Cluster analysis is applied to spatially categorize the collected water samples based on the water quality. Discriminant analysis is then applied to elucidate key parameters associated with the occurrence of elevated As concentration (>10 μg L(-1)) in groundwater. Major water types are characterized as Na-Ca-Cl and Na-Mg-Cl in the Choushui River alluvial fan and Chianan Plain, respectively. Inorganic species of arsenic (As), particularly As(III), prevail in these two groundwater catchments, and their levels are higher in the Chianan Plain than in the Choushui River alluvial fan. Through FA, three factors, namely, the degree of salination, As reduction, and iron (Fe) reduction, are determined and denoted irrespective of some differences between the factorial compositions. Spatial distribution patterns of factors As reduction and Fe reduction imply that the redox zonation is delineated by As- and Fe-dominance zones separately. The results of CA demonstrate that three main groups can be properly explained by the factors extracted via FA. Three- (Fe(2+), Fe(3+), and NH (4) (+) ) and four-parameters (Fe(2+), Fe(3+), NH (4) (+) , and Ca(2+)) derived from discriminant analysis for Choushui River alluvial fan and Chianan Plain are elucidated as key parameters affecting the distribution of As-contained groundwater. The analytical results indicate that the reductive dissolution of Fe minerals is prerequisite for the mobilization of As, whereas the shift of redox condition from Fe- to As-reducing leads to the accumulation of dissolved As in this area.     

Application of multivariate statistical techniques in the assessment of surface water quality in Uluabat Lake, Turkey

The application of different multivariate statistical approaches for the interpretation of a complex data matrix obtained during the period 2004-2005 from Uluabat Lake surface water is presented in this study. The dataset consists of the analytical results of a 1 year-survey conducted in 12 sampling stations in the Lake. Twelve parameters (T, pH, DO, [Formula: see text], NH(4)-N, NO(2)-N, NO(3)-N, [Formula: see text], BOD, COD, TC, FC) were monitored in the sampling sites on a monthly basis (except December 2004, January and February 2005, a total of 1,296 observations). The dataset was treated using cluster analysis, principle component analysis and factor analysis on principle components. Cluster analysis revealed two different groups of similarities between the sampling sites, reflecting different physicochemical properties and pollution levels in the studied water system. Three latent factors were identified as responsible for the data structure, explaining 77.35% of total variance in the dataset. The first factor called the microbiological factor explained 32.34% of the total variance. The second factor named the organic-nutrient factors explained 25.46% and the third factor called physicochemical factors explained 19.54% of the variances, respectively.     

Assessment of water quality using multivariate statistical techniques in the coastal region of Visakhapatnam,India

The present study was intended to develop a Water Quality Index (WQI) for the coastal water of Visakhapatnam, India from multiple measured water quality parameters using different multivariate statistical techniques. Cluster analysis was used to classify the data set into three major groups based on similar water quality characteristics. Discriminant analysis was used to generate a discriminant function for developing a WQI. Discriminant analysis gave the best result for analyzing the seasonal variation of water quality. It helped in data reduction and found the most discriminant parameters responsible for seasonal variation of water quality. Coastal water was classified into good, average, and poor quality considering WQI and the nutrient load. The predictive capacity of WQI was proved with random samples taken from coastal areas. High concentration of ammonia in surface water during winter was attributed to nitrogen fixation by the phytoplankton bloom which resulted due to East India Coastal Current. This study brings out the fact that water quality in the coastal region not only depends on the discharge from different pollution sources but also on the presence of different current patterns. It also illustrates the usefulness of WQI for analyzing the complex nutrient data for assessing the coastal water and identifying different pollution sources, considering reasons for seasonal variation of water quality.     

Arsenic concentration variability, health risk assessment, and source identification using multivariate analysis in selected villages of public water system, Lahore, Pakistan

This paper reports high levels and variability in arsenic (As) levels at locations identified as one of the highest As-contaminated locations in Pakistan. Groundwater pollution related to arsenic has been reported since many years in the areas lying in outskirts of District Lahore, Pakistan. A comparative study is done to determine temporal variations of As from three villages, i.e., Kalalanwala (KLW), Manga Mandi (MM), and Shamki Bhattian (SKB). Seventy-three percent of the 30 investigated samples ranging in depth from 20 to 200 m, show an increasing trend in variations of As concentration over a time span of 4 years and 87 % of samples exceeded the WHO standard of 10 μg/L for As while 77 % of samples have As concentration >50 μg/L (national standard). Further results indicate that high levels of As is accompanied with increase pH (r?=?0.8) favoring desorption of As from minerals at higher pH under oxidizing conditions. For health risk assessment of arsenic, the average daily dose, hazard quotient (HQ), and cancer risk were calculated. The residents of the studied areas had toxic risk index in the order of SKB>KLW>MM, with 87 % of samples exceeding the typical toxic risk index 1.00 (ranging from 2.3–48.6) which was 83 % (ranging from 0.3–41) 4 years before. The results of the present study therefore indicate that arsenic concentrations are increasing in the area, which needs an immediate attention to provide alternate sources of water to save people at risk.     

Groundwater arsenic contamination in Brahmaputra river basin: a water quality assessment in Golaghat (Assam), India

Distribution of arsenic (As) and its compound and related toxicology are serious concerns nowadays. Millions of individuals worldwide are suffering from arsenic toxic effect due to drinking of As-contaminated groundwater. The Bengal delta plain, which is formed by the Ganga?CPadma?CMeghna?CBrahmaputra river basin, covering several districts of West Bengal, India, and Bangladesh is considered as the worst As-affected alluvial basin. The present study was carried out to examine As contamination in the state of Assam, an adjoining region of the West Bengal and Bangladesh borders. Two hundred twenty-two groundwater samples were collected from shallow and deep tubewells of six blocks of Golaghat district (Assam). Along with total As, examination of concentration levels of other key parameters, viz., Fe, Mn, Ca, Na, K, and Mg with pH, total hardness, and SO $_{4}^{2-}$ , was also carried out. In respect to the permissible limit formulated by the World Health Organization (WHO; As 0.01 ppm, Fe 1.0 ppm, and Mn 0.3 ppm for potable water), the present study showed that out of the 222 groundwater samples, 67%, 76.4%, and 28.5% were found contaminated with higher metal contents (for total As, Fe, and Mn, respectively). The most badly affected area was the Gamariguri block, where 100% of the samples had As and Fe concentrations above the WHO drinking water guideline values. In this block, the highest As and Fe concentrations were recorded 0.128 and 5.9 ppm, respectively. Tubewell water of depth 180 ± 10 ft found to be more contaminated by As and Fe with 78% and 83% of the samples were tainted with higher concentration of such toxic metals, respectively. A strong significant correlation was observed between As and Fe (0.697 at p < 0.01), suggesting a possible reductive dissolution of As?CFe-bearing minerals for the mobilization of As in the groundwater of the region.     

Application of multivariate statistical techniques in the assessment of water quality in the Southwest New Territories and Kowloon, Hong Kong

The application of different multivariate statistical techniques for the interpretation of a complex data matrix obtained during 2000?C2007 from the watercourses in the Southwest New Territories and Kowloon, Hong Kong was presented in this study. The data set consisted of the analytical results of 23 parameters measured monthly at 16 different sampling sites. Hierarchical cluster analysis grouped the 12 months into two periods and the 16 sampling sites into three groups based on similarity in water quality characteristics. Discriminant analysis (DA) provided better results both temporally and spatially. DA also offered an important data reduction as it only used four parameters for temporal analysis, affording 84.2% correct assignations, and eight parameters for spatial analysis, affording 96.1% correct assignations. Principal component analysis/factor analysis identified four latent factors standing for organic pollution, industrial pollution, nonpoint pollution, and fecal pollution, respectively. KN1, KN4, KN5, and KN7 were greatly affected by organic pollution, industrial pollution, and nonpoint pollution. The main pollution sources of TN1 and TN2 were organic pollution and nonpoint pollution, respectively. Industrial pollution had high effect on TN3, TN4, TN5, and TN6.