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.     

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.     

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.     

Assessment of temporal and spatial water quality in international Gomishan Lagoon,Iran, using multivariate analysis

Coastal lagoon ecosystems are vulnerable to eutrophication, which leads to the accumulation of nutrients from the surrounding watershed over the long term. However, there is a lack of information about methods that could accurate quantify this problem in rapidly developed countries. Therefore, various statistical methods such as cluster analysis (CA), principal component analysis (PCA), partial least square (PLS), principal component regression (PCR), and ordinary least squares regression (OLS) were used in this study to estimate total organic matter content in sediments (TOM) using other parameters such as temperature, dissolved oxygen (DO), pH, electrical conductivity (EC), nitrite (NO₂), nitrate (NO₃), biological oxygen demand (BOD), phosphate (PO₄), total phosphorus (TP), salinity, and water depth along a 3-km transect in the Gomishan Lagoon (Iran). Results indicated that nutrient concentration and the dissolved oxygen gradient were the most significant parameters in the lagoon water quality heterogeneity. Additionally, anoxia at the bottom of the lagoon in sediments and re-suspension of the sediments were the main factors affecting internal nutrient loading. To validate the models, R², RMSECV, and RPDCV were used. The PLS model was stronger than the other models. Also, classification analysis of the Gomishan Lagoon identified two hydrological zones: (i) a North Zone characterized by higher water exchange, higher dissolved oxygen and lower salinity and nutrients, and (ii) a Central and South Zone with high residence time, higher nutrient concentrations, lower dissolved oxygen, and higher salinity. A recommendation for the management of coastal lagoons, specifically the Gomishan Lagoon, to decrease or eliminate nutrient loadings is discussed and should be transferred to policy makers, the scientific community, and local inhabitants.     

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.     

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.     

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.     

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.     

Multivariate statistical techniques for the assessment of seasonal variations in surface water quality of pasture ecosystems

This study investigates the applicability of multivariate statistical techniques including cluster analysis (CA), discriminant analysis (DA), and factor analysis (FA) for the assessment of seasonal variations in the surface water quality of tropical pastures. The study was carried out in the TPU catchment, Kuala Lumpur, Malaysia. The dataset consisted of 1-year monitoring of 14 parameters at six sampling sites. The CA yielded two groups of similarity between the sampling sites, i.e., less polluted (LP) and moderately polluted (MP) at temporal scale. Fecal coliform (FC), NO₃, DO, and pH were significantly related to the stream grouping in the dry season, whereas NH₃, BOD, Escherichia coli, and FC were significantly related to the stream grouping in the rainy season. The best predictors for distinguishing clusters in temporal scale were FC, NH₃, and E. coli, respectively. FC, E. coli, and BOD with strong positive loadings were introduced as the first varifactors in the dry season which indicates the biological source of variability. EC with a strong positive loading and DO with a strong negative loading were introduced as the first varifactors in the rainy season, which represents the physiochemical source of variability. Multivariate statistical techniques were effective analytical techniques for classification and processing of large datasets of water quality and the identification of major sources of water pollution in tropical pastures.     

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.     

Application of multivariate statistical techniques in the assessment of groundwater quality in seawater intrusion area in Bafra Plain, Turkey

Multivariate statistical techniques such as cluster analysis and principal component analysis were performed on 28 groundwater wells in Bafra Plain. Cluster analysis results show that the groundwater in the study area is classified into three groups (A, B, and C), and factor analysis indicates that groundwater is composed of 89.64 % of total variance of 12 variables and is mainly affected by three factors. Factor 1 (seawater salinization) includes concentrations of electrical conductivity, TDS, Cl^?, Na⁺, and sodium adsorption ratio, factor 2 (mixing water) includes δ¹⁸O, δD, and T, and factor 3 (fresh) includes Ca²⁺. For determination of the source of water, Ca/Cl, Cl/HCO₃, Mg/Cl, and Ca/Na as initials and Mg/Ca and SO₄/Cl as molar rates which were identified, the rates had been found to be very useful. Cluster analysis was made by using these rates and the waters were classified in two groups (group 1 and group 2). First group waters were affected by seawater, and the second group were very less affected by freshwater or seawater. According to the comparison of two different parameters, group 1 comprised group A and group B-2, -3, and -4 from the same wells, and group 2 comprised group B-1 and group C from the same well. As a result of this study, it could be said that multivariate statistical methods gave very useful results for the determination of the source.     

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.     

Assessment of trace elements in the shell layers and soft tissues of the pearl oyster Pinctada radiata using multivariate analyses: a potential proxy for temporal and spatial variations of trace elements

Concentrations of trace elements (Cd, Cu, Ni, Pb, V, and Zn) were determined in the soft tissues (adductor muscle and gills) of the pearl oyster Pinctada radiata and surficial sediments from two sampling sites located in the northern part of the Persian Gulf by Graphite Furnace Atomic Absorption Spectrophotometer (GFAAS). Moreover, the levels of Li, Mg, Al, Mn, Fe, Cu, Sr, Ba, Pb, and Zn were measured in two shell layers (prismatic and nacreous) using Laser Ablation Inductively Coupled Plasma Mass Spectrometer (LA-ICP-MS). There were significant differences between the sampling sites with regard to mean concentrations of Cu, Mn, and Al in the prismatic layers of the shells. But in terms of the soft tissues, only in the case of Ni accumulation in the muscle significant differences between the sites could be observed. No significant differences could be found between the sites from the elements concentrations in the sediments point of view. The levels of Cd, Cu, Ni, and Zn in the gills were markedly higher than those in the muscle. Concentrations of Mn, Mg, Li, and Cu in the prismatic layer were significantly higher than in the nacreous but the reverse case could be found for Sr. The patterns of metal occurrence in the selected tissues, shell layers, and sediments exhibited the following descending order: Zn, Ni?>?Cd, Cu?>?V, and Pb and Zn, Ni, Cd?>?Cu, V, and Pb for muscle and gills, respectively; Zn?>?Cu, Ni, Pb, Cd, and V for sediments; Mg?>?Sr, Mn, Li, Al, Fe, Ba, Cu, Pb, and Zn for the prismatic layer; and Sr, Mg?>?Mn, Al, Fe, Li, Ba, Cu, Pb, and Zn for the nacreous layer. In most cases, the temporal variations of the elements levels in the prismatic layer were clearer than those in the nacreous layer (especially for Li, Mg, Mn, Pb, and Fe). Comparison of the gained data from this study with the other relevant researches shows that in most cases the levels of the elements in this investigation either fell within the range for other world areas or were lower. Generally, it can be concluded that the shell (especially prismatic layer) of P. radiata can be considered as a suitable proxy for temporal and spatial variations of the trace elements (and probably some environmental parameters) in the study area.     

Assessment of temporal variations of water quality in inland water bodies using atmospheric corrected satellite remotely sensed image data

The spatial distribution of silicate, ammonium, nitrate, nitrite, phosphate, chlorophyll a and dissolved oxygen in Obidos lagoon was obtained by surveying five sites in eight campaigns, between October 2004 and October 2006. A confined inner branch of the lagoon showed higher availability of ammonium (1.2-81 micromol l(-1)), phosphate (1.9-17 micromol l(-1)), silicate (0.85-86 micromol l(-1)) and chlorophyll a (0.30-18 microg l(-1)) than other sites (0.47-25 micromol l(-1), 0.10-3.9 micromol l(-1), 0.47-25 micromol l(-1), 0.25-11 microg l(-1), respectively). According to several trophic classification tools, that branch is considered eutrophic to polytrophic, emphasising its deteriorated conditions, while the rest of the lagoon is of better quality. In autumn/winter nutrients were inversely correlated to salinity (r > 0.93) reflecting the freshwater inputs enriched in nitrogen and phosphorous compounds to the inner branch. In warmer periods, dissolved oxygen concentrations dropped during the night, and sediments of the branch become an important source of ammonium and phosphate. The low DIN:P ratio (median = 10) obtained in the branch, which suggests an excess of phosphate, that increased in warmer periods and changed the limiting nutrient in the entire lagoon. These results emphasize the spatial heterogeneity of water quality in Obidos lagoon, its seasonal variability, and the importance of recognising these distributions before defining homogenous water body on the scope of Water Framework Directive.     

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.