Using multivariate techniques as a strategy to guide optimization projects for the surface water quality network monitoring in the Velhas river basin,Brazil

Surface water quality monitoring networks are usually deployed and rarely re-evaluated with regard to their effectiveness. In this sense, this work sought to evaluate and to guide optimization projects for the water quality monitoring network of the Velhas river basin, using multivariate statistical methods. The cluster, principal components, and factorial analyses, associated with non-parametric tests and the analysis of violation to the standards set recommended by legislation, identified the most relevant water quality parameters and monitoring sites, and evaluated the sampling frequency. Thermotolerant coliforms, total arsenic, and total phosphorus were considered the most relevant parameters for characterization of water quality in the river basin. The monitoring sites BV156, BV141, BV142, BV150, BV137, and BV153 were considered priorities for maintenance of the network. The multivariate statistical analysis showed the importance of a monthly sampling frequency, specifically the parameters considered most important.     

Water quality assessment and source identification of Daliao river basin using multivariate statistical methods

Multivariate statistical methods, such as cluster analysis (CA), discriminant analysis (DA) and principal component analysis (PCA), were used to analyze the water quality dataset including 13 parameters at 18 sites of the Daliao River Basin from 2003-2005 (8424 observations) to obtain temporal and spatial variations and to identify potential pollution sources. Using Hierarchical CA it is classified 12 months into three periods (first, second and third period) and the 18 sampling sites into three groups (groups A, B and C). Six significant parameters (temperature, pH, DO, BOD(5), volatile phenol and E. coli) were identified by DA for distinguishing temporal or spatial groups, with close to 84.5% correct assignment for temporal variation analysis, while five parameters (DO, NH(4)(+)-N, Hg, volatile phenol and E. coli) were discovered to correctly assign about 73.61% for the spatial variation analysis. PCA is useful in identifying five latent pollution sources for group B and C (oxygen consuming organic pollution, toxic organic pollution, heavy metal pollution, fecal pollution and oil pollution). During the first period, sites received more oxygen consuming organic pollution, toxic organic pollution and heavy metal pollution than those in the other two periods. For group B, sites were mainly affected by oxygen consuming organic pollution and toxic organic pollution during the first period. The level of pollution in the second period was between the other two periods. For group C, sites were mainly affected by oil pollution during the first period and oxygen consuming organic pollution during the third period. Furthermore, source identification of each period for group B and group C provided useful information about seasonal pollution. Sites were mainly affected by fecal pollution in the third period for group B, indicating the character of non-point source pollution. In addition, all the sites were also affected by physical-chemistry pollution. In the second and third period for group B and second period for group C sites were also affected by natural pollution.     

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.     

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.     

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.     

Multi-objective, decision-based assessment of a water quality monitoring network in a river system

Water quality monitoring network design has historically tended to use experience, intuition and subjective judgement in locating monitoring stations. Better design procedures to optimize monitoring systems need to simultaneously identify significant planning objectives and consider a number of social, economic and environmental constraints. The consideration of multiple objectives may require further decision analysis to determine the preference weights associated with the objectives to aid in the decision-making process. This may require the application of an optimization study to extract such information from decision makers or experts and to evaluate the overall effectiveness of locating strategies. This paper assesses the optimal expansion and relocation strategies of a water quality monitoring network using a two-stage analysis. The first stage focuses on the information retrieval of preference weights with respect to the designated planning objectives. With the aid of a pre-emptive goal programming model, data analysis is applied to obtain the essential information from the questionnaire outputs. The second stage then utilizes a weighted multi-objective optimization approach to search for the optimal locating strategies of the monitoring stations in the river basin. Practical implementation is illustrated by a case study in the Kao-Ping River Basin, south Taiwan.     

Application of water quality indices and analysis of the surface water quality monitoring network in semiarid North-Central Chile

Surface water quality has increasing importance worldwide and is particularly relevant in the semiarid North-Central Chile, where agriculture and mining activities are imposing heavy pressure on limited water resources. The current study presents the application of a water quality index in four watersheds of the 29°-33°S realm for the period 1999-2008, based on the Canadian Council of Ministers for the Environment approach and the Chilean regulation for irrigation water quality. In addition, two modifications to the index are tested and a comprehensive characterization of the existing monitoring network is performed through cluster analysis. The basins studied show fairly good water quality in the overall, specially the Limarí basin. On the other hand, the lower index values were obtained for the headwaters of Elqui, associated with the El Indio mining district. The first modification of the indicator (i.e., to consider parameters differentially according to their effect on human health or the environment) did not produce major differences with respect to the original index, given the generally good water quality. The second modification (i.e., to consider as threshold values the more restrictive figures derived from a set of regulations) yielded important differences in the indicator values. Finally, an adequate characterization of the monitoring network was obtained. The results presented spatial coherence and the information can be used as a basis for the optimization of the monitoring network if required.     

Assessment of surface water quality using multivariate statistical techniques: a case study of Behrimaz Stream, Turkey

Multivariate statistical techniques, such as cluster analysis (CA), principal component analysis, and factor analysis, were applied for the evaluation of temporal/spatial variations and for the interpretation of a water quality data set of the Behrimaz Stream, obtained during 1 year of monitoring of 20 parameters at four different sites. Hierarchical CA grouped 12 months into two periods (the first and second periods) and classified four monitoring sites into two groups (group A and group B), i.e., relatively less polluted (LP) and medium polluted (MP) sites, based on similarities of water quality characteristics. Factor analysis/principal component analysis, applied to the data sets of the two different groups obtained from cluster analysis, resulted in five latent factors amounting to 88.32% and 88.93% of the total variance in water quality data sets of LP and MP areas, respectively. Varifactors obtained from factor analysis indicate that the parameters responsible for water quality variations are mainly related to discharge, temperature, and soluble minerals (natural) and nutrients (nonpoint sources: agricultural activities) in relatively less polluted areas; and organic pollution (point source: domestic wastewater) and nutrients (nonpoint sources: agricultural activities and surface runoff from villages) in medium polluted areas in the basin. Thus, this study illustrates the utility of multivariate statistical techniques for analysis and interpretation of data sets and, in water quality assessment, identification of pollution sources/factors and understanding temporal/spatial variations in water quality for effective stream water quality management.     

Assessment of surface water quality of the Ceyhan River basin, Turkey

In this study, surface water quality of the Ceyhan River basin were assessed and examined with 13 physico-chemical parameters in 31 stations in 3 months during the period of 2005. Multivariate statistical techniques were applied to identify characteristics of the water quality in the studied stations. Nutrients, Cl^??? and Na^?+? affected mostly to the stations of Erkenez 2, S?r 2, and S?r 3 in the ordination diagram of correspondence analysis. Three factors were extracted by principal component analysis, which explains 79.14% of the total variation. The first factor (PC1) captures variables of EC, DO, NO $_{2}^{\; -}$ , PO $_{4}^{\; \equiv }$ , Cl^???, SO $_{4}^{\; =}$ , Na^?+?, and Ca^?+?+?. The second factor (PC2) is significantly related to pH, NH $_{3}^{\; -}$ , and Mg^?+?+?, while water temperature (T) and NO $_{3}^{\; -}$ accounted for the greatest loading for factor 3 (PC3). The stations were divided into three groups for PC1, two groups for PC2, and three groups for PC3 by hierarchical cluster analysis. The stations in the vicinity of cities presented low dissolved oxygen and high concentration of physico-chemical parameter levels. The stations of Erkenez 2, S?r 2, S?r 3, and Aksu 4 located near the city of Kahramanmara? were characterized by an extremely high pollution due to discharge of wastewater from industry and domestic. P?narba?? and Elbistan stations were also influenced by household wastewater of the city of Elbistan. According to criteria of Turkish Water Pollution Control Regulation, Erkenez 2, S?r 2, and S?r 3 stations have high polluted water. This study suggests that it is urgent to control point pollutions, and all wastewater should be purified before discharge to the Ceyhan River basin.     

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.     

Chironomidae and Oligochaeta for water quality evaluation in an urban river in southeastern Brazil

Considering the importance of benthic macroinvertebrates for diagnosis of variations in the ecological conditions of aquatic habitats, the aim of this study was to investigate the structure of the Chironomidae and Oligochaeta assemblages along an organic pollution gradient. The fauna specimens were obtained with the use of artificial substrates, and the environmental variables were recorded at five sites of the São Lourenço River, during 12 months. Metrics of the assemblage and detrended correspondence analysis were used to verify the response of the fauna to the pollution gradient. Procrustes analysis was used to verify whether the data on the Chironomidae and Oligochaeta assemblages, as well as the taxonomic and numerical resolution of these groups, provide similar results in relation to the pollution gradient. The richness, evenness, and taxonomic composition of the Chironomidae and Oligochaeta assemblages varied significantly among the collection sites, with distinct conservation conditions. Genera of the subfamilies Orthocladiinae and Tanypodinae were associated with the sites upstream of the urban area, where the dissolved oxygen levels are higher. Species of Oligochaeta and the genus Chironomus were associated with more organically polluted sites. No concordance was observed in the response of the Chironomidae and Oligochaeta assemblages in relation to the environmental variables, indicating the need to use both groups in biomonitoring studies. On the other hand, both the data on composition (presence or absence) and those on the lowest taxonomic resolution (abundance of subfamilies) were effective to diagnose the pollution gradient in the river studied. Therefore, when the environmental conditions along a river’s gradient are contrasting, we suggest the use of the lowest taxonomic resolution of Chironomidae and Oligochaeta in biomonitoring. That procedure considerably reduces the assessment time, besides being a method that can be used by people not specializing in the taxonomy of groups.     

Groundwater quality and its suitability for domestic and agricultural use in Tondiar river basin,Tamil Nadu,India

Assessment of suitability of groundwater for domestic and agricultural purposes was carried out in Tondiar river basin, Tamil Nadu, India. The study area covers an area of 315 km² and lies in a semiarid region. Groundwater is the major source for domestic and agricultural activity in this area. Groundwater samples were collected from 45 wells during pre-monsoon and post-monsoon period in the year 2006. The water samples were analysed for physical and chemical characteristics. Suitability of groundwater for irrigation was evaluated based on salinity hazard, sodium percent, sodium adsorption ratio, residual sodium carbonate, US salinity diagram, Wilcox’s diagram, Kelly’s ratio and permeability index. Ca-HCO₃, mixed Ca–Mg–Cl and Na–Cl were the dominant groundwater types. High hardness and electrical conductivity in this area makes the groundwater unsuitable for drinking and agricultural purposes. Concentration of trace elements (Mn, Cu, Zn, Pb and Ni) did not exceed the permissible limit for drinking and agricultural purposes. Majority of the groundwater samples were unsuitable for domestic and agricultural purposes except for 31% and 36%, which were suitable for drinking and irrigation purposes, respectively.     

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.     

Assessment of surface water quality using multivariate statistical techniques in red soil hilly region: a case study of Xiangjiang watershed, China

In the study, multivariate statistical methods including factor, principal component and cluster analysis were applied to analyze surface water quality data sets obtained from Xiangjiang watershed, and generated during 7 years (1994-2000) monitoring of 12 parameters at 34 different profiles. Hierarchical cluster analysis grouped 34 sampling sites into three clusters, including relatively less polluted (LP), medium polluted (MP) and highly polluted (HP) sites, and based on the similarity of water quality characteristics, the watershed was divided into three zones. Factor analysis/principal component analysis, applied to analyze the data sets of the three different groups obtained from cluster analysis, resulted in four latent factors accounting for 71.62%, 71.77% and 72.01% of the total variance in water quality data sets of LP, MP and HP areas, respectively. The PCs obtained from factor analysis indicate that the parameters for water quality variations are mainly related to dissolve heavy metals. Thus, these methods are believed to be valuable to help water resources managers understand complex nature of water quality issues and determine the priorities to improve water quality.     

Efficiency of a standardized artificial substrate for biological monitoring of river water quality

Before using macroinvertebrates in water quality assessment in the Chusovaya River (Russia, the Urals, 50°55N, 60° E), preliminary results of three sampling methods were compared: handnet, circular shovel and a standardized artificial substrate sampler. The artificial substrate consisted of glass marbles ( 20 mm). To compare the efficiency of these sampling methods the total numbers of taxa found at each location per sampling data were considered to be 100%. The highest efficiency was reached with the artificial substrate sampler. 75–100% of the taxa at the different locations were collected with this sampler. Only 5–19% and 10–20% of the taxa at each location per sampling date were collected with the circular shovel in the sand and gravel substrate respectively, being the lowest efficiency. Intermediate results were obtained with the hand net. 23–38% of the taxa were collected with this net. Based on these results and requirements placed upon sampling methods in general, the standardized artificial substrate sampler has been considered to be an optimal sampling device for macroinvertebrates in biological monitoring.     

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.     

Impact of human activities on the quality of river water: The case of Evrotas River catchment basin,Greece

The impact of point (domestic and industrial effluents) and non-point (agricultural land runoff) pollution sources on the quality of the receiving waters of the Evrotas River (Laconia, Greece) was investigated during a monitoring study from August 1991 to August 1992. The part of the river which was located near the city of Sparta was seasonally influenced by the discharge of effluents from orange juice plants (operating during winter) and by the discharge of septage for the emptying of cesspools which are serving part of the city. The low dilution of incoming pollutants (septage) during the low water flow in summer lead to the decreasing self-purification capacity of the river and the development of septicity conditions in some of its parts. In the vicinity of intensively cultivated areas, the high concentrations of nitrogen and phosphorus which were detected in the river water during winter and spring may be partly attributed to the leaching of the applied fertilizers because of nirogen mobilization and soil erosion, following the season's precipitations. The protection of the Evrotas River water Quality must therefore include adequate treatment of the septage produced in the area, as well as the construction of wastewater treatment plants for the major industries of the area. The non-point pollution could be controlled by the restoration of the Evrotas riparian vegetation, together with a more rational use of fertilizers in the area.