Spatio-temporal variations in water quality of Nullah Aik-tributary of the river Chenab, Pakistan

This study reports the spatio-temporal changes in water quality of Nullah Aik, tributary of the Chenab River, Pakistan. Stream water samples were collected at seven sampling sites on seasonal basis from September 2004 to April 2006 and were analyzed for 24 water quality parameters. Most significant parameters which contributed in spatio-temporal variations were assessed by statistical techniques such as Hierarchical Agglomerative Cluster Analysis (HACA), Factor Analysis/Principal Components Analysis (FA/PCA), and Discriminant Function Analysis (DFA). HACA identified three different classes of sites: Relatively Unimpaired, Impaired and Less Impaired Regions on the basis of similarity among different physicochemical characteristics and pollutant level between the sampling sites. DFA produced the best results for identification of main variables for temporal and spatial analysis and separated eight parameters (DO, hardness, sulphides, K, Fe, Pb, Cr and Zn) that accounted 89.7% of total variations of spatial analysis. Temporal analysis using DFA separated six parameters (E.C., TDS, salinity, hardness, chlorides and Pb) that showed more than 84.6% of total temporal variation. FA/PCA identified six significant factors (sources) which were responsible for major variations in water quality dataset of Nullah Aik. The results signify that parameters identified by statistical analyses were responsible for water quality change and suggest the possibility of industrial, municipal and agricultural runoff, parent rock material contamination. The results suggest dire need for proper management measures to restore the water quality of this tributary for a healthy and promising aquatic ecosystem and also highlights its importance for objective ecological policy and decision making process.     

Assessment of water quality monitoring for the optimal sensor placement in lake Yahuarcocha using pattern recognition techniques and geographical information systems

Pollution and the eutrophication process are increasing in lake Yahuarcocha and constant water quality monitoring is essential for a better understanding of the patterns occurring in this ecosystem. In this study, key sensor locations were determined using spatial and temporal analyses combined with geographical information systems (GIS) to assess the influence of weather features, anthropogenic activities, and other non-point pollution sources. A water quality monitoring network was established to obtain data on 14 physicochemical and microbiological parameters at each of seven sample sites over a period of 13 months. A spatial and temporal statistical approach using pattern recognition techniques, such as cluster analysis (CA) and discriminant analysis (DA), was employed to classify and identify the most important water quality parameters in the lake. The original monitoring network was reduced to four optimal sensor locations based on a fuzzy overlay of the interpolations of concentration variations of the most important parameters.     

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.     

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.     

Spatiotemporal variation of groundwater quality using integrated multivariate statistical and geostatistical approaches in Amol–Babol Plain,Iran

In recent years, groundwater quality has become a global concern due to its effect on human life and natural ecosystems. To assess the groundwater quality in the Amol–Babol Plain, a total of 308 water samples were collected during wet and dry seasons in 2009. The samples were analysed for their physico-chemical and biological constituents. Multivariate statistical analysis and geostatistical techniques were applied to assess the spatial and temporal variabilities of groundwater quality and to identify the main factors and sources of contamination. Principal component analysis (PCA) revealed that seven factors explained around 75 % of the total variance, which highlighted salinity, hardness and biological pollution as the dominant factors affecting the groundwater quality in the Plain. Two-way analysis of variance (ANOVA) was conducted on the dataset to evaluate the spatio-temporal variation. The results showed that there were no significant temporal variations between the two seasons, which explained the similarity between six component factors in dry and wet seasons based on the PCA results. There are also significant spatial differences (p?>?0.05) of the parameters under study, including salinity, potassium, sulphate and dissolved oxygen in the plain. The least significant difference (LSD) test revealed that groundwater salinity in the eastern region is significantly different to the central and western side of the study area. Finally, multivariate analysis and geostatistical techniques were combined as an effective method for demonstrating the spatial structure of multivariate spatial data. It was concluded that multiple natural processes and anthropogenic activities were the main sources of groundwater salinization, hardness and microbiological contamination of the study area.     

Application of multivariate statistical techniques to evaluation of water quality in the Mała Wełna River (Western Poland)

The paper presents the results of determinations of physico-chemical parameters of the Ma?a We?na waters, a river situated in Wielkopolska voivodeship (Western Poland). Samples for the physico-chemical analysis were taken in eight gauging cross-sections once a month between May and November 2006. To assess the physico-chemical composition of surface water, use was made of multivariate statistical methods of data analysis, viz. cluster analysis (CA), factor analysis (FA), principal components analysis (PCA), and discriminant analysis (DA). They made it possible to observe similarities and differences in the physico-chemical composition of water in the gauging cross-sections, to identify water quality indicators suitable for characterising its temporal and spatial variability, to uncover hidden factors accounting for the structure of the data, and to assess the impact of man-made sources of water pollution.     

Heavy metal enrichment in the seagrasses of Lakshadweep group of islands—A multivariate statistical analysis

An assessment on heavy metal (Al, Cd, Co, Cr, Cu, Fe, Mg, Mn, Ni, Pb and Zn) accumulation by seven seagrass species of Lakshadweep group of islands was carried out using multivariate statistical tools like principal component analysis (PCA) and cluster analysis (CA). Among all the metals, Mg and Al were determined in higher concentration in all the seagrasses, and their values varied with respect to different seagrass species. The concentration of the four toxic heavy metals (Cd, Pb, Zn and Cu) was found higher in all the seagrasses when compared with the background values of seagrasses from Flores Sea, Indonesia. The contamination factor of these four heavy metals ranged as Cd (1.97–12.5), Cu (0.73–4.40), Pb (2.3–8.89) and Zn (1.27–2.787). In general, the Pollution Load Index (PLI) calculated was found to be maximum for Halophila decipiens (58.2). Results revealed that Halophila decipiens is a strong accumulator of heavy metals, followed by Halodule uninervis and Halodule pinifolia, among all the tested seagrasses. Interestingly, the small-leaved seagrasses were found to be efficient in heavy metal accumulation than the large-leaved seagrass species. Thus, seagrasses can better be used for biomonitoring, and seagrasses can be used as the heavy metal sink as the biomass take usually long term to get remineralize in nature.     

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.     

Seasonal monitoring of heavy metals and physicochemical characteristics in a lentic ecosystem of subtropical industrial region, India

Assessment of seasonal changes in surface water quality is an important aspect for evaluating temporal variations of lentic ecosystem (lakes and reservoirs) pollution due to industrial effluent discharge. In this study, nine metals and 15 physicochemical parameters, collected from four sampling sites in a tropical lake receiving the discharge from thermal power plant, coal mine, and chloralkali industry, during the years from 2004 to 2005, were analyzed. For greater efficacy in monitoring of heavy metals, particle-induced X-ray emission has been used during present investigation. Different statistical techniques like analysis of variance, Pearson correlation, principal component analysis, and factor analysis were employed to evaluate the seasonal correlations of physicochemical parameters. Most of the metals and physicochemical parameters monitored in the present study exhibited high spatial and temporal variability. Pertaining to metal pollution, the most polluted site was Belwadah, i.e., waters and sediments had the highest concentration of all the relevant metals. The reference site was characterized by the presence of low concentrations of metals in waters and in sediments. Based on the high metal concentration recorded in lake ambient, drinking, bathing, and irrigation water should not be used by the local people at the effluent discharge points.     

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 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.     

Assessment of anthropogenic sources of water pollution using multivariate statistical techniques: a case study of the Alqueva’s reservoir, Portugal

Multivariate statistical techniques were applied to evaluate spatial/temporal variations, and to interpret water quality data set obtained at Alqueva reservoir (south of Portugal). The water quality was monitored at nine different sites, along the water line, over a period of 18 months (from January 2006 to May 2007) using 26 water quality parameters. The cluster analysis allowed the formation of five different similarity groups between sampling sites, reflecting differences on the water quality at different locations of the Alqueva reservoir system. The PCA/FA identified six varifactors, which were responsible for 64% of total variance in water quality data set. The principal parameters, which explained the variability of quality water, were total phosphorus, oxidability, iron, parameters that at high concentrations indicate pollution from anthropogenic sources, and herbicides indicative of an intensive agricultural activity. The spatial analysis showed that the water quality was worse in the north of the reservoir.     

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.     

Chemometric Analysis of Surface Water Quality Data: Case Study of the Gorganrud River Basin, Iran

Canonical correlation analysis (CCA), principal component analysis (PCA), and principal factor analysis (PFA) have been adopted to provide ease of understanding: interpretation of a large complex data set in the Gorganrud River monitoring networks, evaluation of the temporal and spatial variations of water quality, and finally identification of monitoring stations and parameters which are most important in assessing annual variations of water quality in the river. In accomplishing the research, 11 surface water quality data related to both of physical and chemical parameters have been collected from seven monitoring stations from 1996 to 2002. In general, our results from CCA method indicated strong relationship between physical and chemical parameters in the Gorganrud River. In addition, analyzing data through the PCA and PFA techniques revealed that all monitoring stations are important in explaining the annual variation of data set. From the point of view of the degree of importance of parameters contributing to water quality variations, further investigations by running two scenarios (rotated factor correlation coefficient value equal to 0.95 and 0.90 for the first and second scenarios, respectively) showed that the important parameters in one season may not be important for another season. For example, unlike in summer, water temperature, total suspended solids, total phosphorous, and nitrate parameters were important, electrical conductivity, and turbidity parameters had been realized as important parameters in spring through the first scenario.     

On the use of multivariate statistical methods for combining in-stream monitoring data and spatial analysis to characterize water quality conditions in the White River Basin,Indiana, USA

Mechanistic hydrologic and water quality models provide useful alternatives for estimating water quality in unmonitored streams. However, developing these elaborate models for large watersheds can be time-consuming and expensive, in addition to challenges that arise during calibration when there is limited spatial and/or temporal monitored in-stream water quality data. The main objective of this research was to investigate different approaches for developing multivariate analysis models as alternative methods for rapidly assessing relationships between spatio-temporal physical attributes of the watershed and water quality conditions in monitored streams, and then using the developed relationships for estimating water quality conditions in unmonitored streams. The study compares the use of various statistical estimates (mean, geometric mean, trimmed mean, and median) of monitored water quality variables to represent annual and seasonal water quality conditions. The relationship between these estimates and the spatial data is then modeled via linear and non-linear multivariate methods. Overall, the non-linear techniques for classification outperformed the linear techniques with an average cross-validation accuracy of 79.7%. Additionally, the geometric mean based models outperformed models based on other statistical indicators with an average cross-validation accuracy of 80.2%. Dividing the data into annual and quarterly datasets also offered important insights into the behavior of certain water quality variables impacted by seasonal variations. The research provides useful guidance on the use and interpretation of the various statistical estimates and statistical models for multivariate water quality analyses.     

Analysis of heavy metal concentrations in sediments of selected estuaries of Malaysia—a statistical assessment

Statistical analysis of heavy metal concentrations in sediment was studied to understand the interrelationship between different parameters and also to identify probable source component in order to explain the pollution status of selected estuaries. Concentrations of heavy metals (Cu, Zn, Cd, Fe, Pb, Cr, Hg and Mn) were analyzed in sediments from Juru and Jejawi Estuaries in Malaysia with ten sampling points of each estuary. The results of multivariate statistical techniques showed that the two regions have different characteristics in terms of heavy metals selected and indicates that each region receives pollution from different sources. The results also showed that Fe, Mn, Cd, Hg, and Cu are responsible for large spatial variations explaining 51.15% of the total variance, whilst Zn and Pb explain only 18.93 of the total variance. This study illustrates the usefulness of multivariate statistical techniques for evaluation and interpretation of large complex data sets to get better information about the heavy metal concentrations and design of monitoring network.     

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.     

Water quality assessment, by statistical analysis, on rural and urban areas of Chocancharava River (Río Cuarto), Córdoba, Argentina

Water quality has degraded dramatically in the Chocancharava River (Río Cuarto, Córdoba, Argentina) due to point and non-point sources. This paper aims to assess spatial and temporal variations of physical and chemical parameters of the river. Six sampling sites and six sampling campaigns were developed. During the period 2007–2008, wet and dry seasons were included. A statistical analysis was carried out with 23 physical and chemical variables. Then, a new statistical analysis was carried out including the Riparian Corridors Quality Index and the physical and chemical variables (24 variables). Considering a multivariate system, analysis of variance, principal component analysis and cluster analysis were used. From the statistical analysis, the river was divided into two zones with different degrees of contamination. The most polluted zone is due to pollution inputs of urban, industrial and agricultural sources. This area showed a remarkable deterioration in water quality, mainly due to wastewater discharges. According to Riparian Quality, better results were found in sections of poor water quality, due to the fact that the river bank forest was less degraded downstream of the sewage discharge.     

Groundwater levels time series sensitivity to pluviometry and air temperature: a geostatistical approach to Sfax region,Tunisia

In this paper, the pattern of groundwater level fluctuations is investigated by statistical techniques for 24 monitoring wells located in an unconfined coastal aquifer in Sfax (Tunisia) for a time period from 1997 to 2006. Firstly, a geostatistical study is performed to characterize the temporal behaviors of data sets in terms of variograms and to make predictions about the value of the groundwater level at unsampled times. Secondly, multivariate statistical methods, i.e., principal component analysis (PCA) and cluster analysis (CA) of time series of groundwater levels are used to classify groundwater hydrographs regard to identical fluctuation pattern. Three groundwater groups (A, B, and C) were identified. In group “A,” water level decreases continuously throughout the study periods with rapid annual cyclic variation, whereas in group “B,” the water level contains much less high-frequency variation. The wells of group “C” represents a steady and gradual increase of groundwater levels caused by the aquifer artificial recharge. Furthermore, a cross-correlation analysis is used to investigate the aquifer response to local rainfall and temperature records. The result revealed that the temperature is more affecting the variation of the groundwater level of group A wells than the rainfall. However, the second and the third groups are less affected by rainfall or temperature.