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.     

Spatial water quality assessment of Langat River Basin (Malaysia) using environmetric techniques

This study investigates the spatial water quality pattern of seven stations located along the main Langat River. Environmetric methods, namely, the hierarchical agglomerative cluster analysis (HACA), the discriminant analysis (DA), the principal component analysis (PCA), and the factor analysis (FA), were used to study the spatial variations of the most significant water quality variables and to determine the origin of pollution sources. Twenty-three water quality parameters were initially selected and analyzed. Three spatial clusters were formed based on HACA. These clusters are designated as downstream of Langat river, middle stream of Langat river, and upstream of Langat River regions. Forward and backward stepwise DA managed to discriminate six and seven water quality variables, respectively, from the original 23 variables. PCA and FA (varimax functionality) were used to investigate the origin of each water quality variable due to land use activities based on the three clustered regions. Seven principal components (PCs) were obtained with 81% total variation for the high-pollution source (HPS) region, while six PCs with 71% and 79% total variances were obtained for the moderate-pollution source (MPS) and low-pollution source (LPS) regions, respectively. The pollution sources for the HPS and MPS are of anthropogenic sources (industrial, municipal waste, and agricultural runoff). For the LPS region, the domestic and agricultural runoffs are the main sources of pollution. From this study, we can conclude that the application of environmetric methods can reveal meaningful information on the spatial variability of a large and complex river water quality data.     

Characterization and source apportionment of water pollution in Jinjiang River, China

Characterizing water quality and identifying potential pollution sources could greatly improve our knowledge about human impacts on the river ecosystem. In this study, fuzzy comprehensive assessment (FCA), pollution index (PI), principal component analysis (PCA), and absolute principal component score–multiple linear regression (APCS–MLR) were combined to obtain a deeper understanding of temporal–spatial characterization and sources of water pollution with a case study of the Jinjiang River, China. Measurement data were obtained with 17 water quality variables from 20 sampling sites in the December 2010 (withered water period) and June 2011 (high flow period). FCA and PI were used to comprehensively estimate the water quality variables and compare temporal–spatial variations, respectively. Rotated PCA and receptor model (APCS–MLR) revealed potential pollution sources and their corresponding contributions. Application results showed that comprehensive application of various multivariate methods were effective for water quality assessment and management. In the withered water period, most sampling sites were assessed as low or moderate pollution with characteristics pollutants of permanganate index and total nitrogen (TN), whereas 90 % sites were classified as high pollution in the high flow period with higher TN and total phosphorus. Agricultural non-point sources, industrial wastewater discharge, and domestic sewage were identified as major pollution sources. Apportionment results revealed that most variables were complicatedly influenced by industrial wastewater discharge and agricultural activities in withered water period and primarily dominated by agricultural runoff in high flow period.     

Water quality of a tributary of the Pearl River, the Beijiang, Southern China: implications from multivariate statistical analyses

Water quality information of Beijiang River, a tributary of Pearl River in Guangdong, China, was analyzed to provide an overview of the hydrochemical functioning of a major agricultural/rural area and an industrial/urban area. Eighteen water quality parameters were surveyed at 13 sites from 2005 to 2006 on a monthly basis. A bivariate correlation analysis was carried out to evaluate the regional correlations of the water quality parameters, while the principal component analysis (PCA) technique was used to extract the most influential variables for regional variations of river water quality. Six principal components were extracted in PCA which explained more than 78% and 84% of the total variance for agricultural/rural and industrial/urban areas, respectively. Physicochemical factor, organic pollution, sewage pollution, geogenic factor, agricultural nonpoint source pollution, and accumulated pesticide usage were identified as potential pollution sources for agricultural/rural area, whereas industrial wastewaters pollution, mineral pollution, geogenic factor, urban sewage pollution, chemical industrial pollution, and water traffic pollution were the latent pollution sources for industrial/urban area. A multivariate linear regression of absolute principal component scores (MLR-APCS) technique was used to estimate contributions of all identified pollution sources to each water quality parameter. High coefficients of determination of the regression equations suggested that the MLR-APCS model was applicable for estimation of sources of most water quality parameters in the Beijiang River Basin.     

Application of Water Quality Indices and Dissolved Oxygen as Indicators for River Water Classification and Urban Impact Assessment

The usefulness of water quality indices, as the indicators of water pollution, for assessment of spatial-temporal changes and classification of river water qualities was verified. Four water quality indices were investigated: WQI (considering 18 water quality parameters), WQI(min) and WQI(m) (considering five water quality parameters: temperature, pH, DO, EC and TSS) and WQI(DO) (considering a single parameter, DO). The water quality indices WQI(min), WQI(m) and WQI(DO) could be of particular interest for the developing countries because of the minimum analytical cost involved. As a case study, water quality indices were used to evaluate spatial and temporal changes of the water quality in the Bagmati river basin (Nepal) for the study period 1999-2003. The results allowed us to determine the serious negative effects of the city urban activity on the river water quality. In the studied section of the river, the water quality index (WQI) was 71 units (classified as good) at the entry station and 47.6 units (classified as bad) at the outlet station. For the studied period, a significant decrease in water quality (mean WQI decrease = 11.6%, p = 0.042) was observed in the rural areas. A comparative analysis revealed that the urban water quality was significantly bad as compared with rural. The analysis enabled to classify the water quality stations into three groups: good water quality, medium water quality and bad water quality. WQI(min) resulted in overestimation of the water quality but with similar trend as with WQI and is useful for the periodic routine monitoring program. The correlation of WQI with WQI(min) and DO resulted two new indices WQI(m) and WQI(DO), respectively. The classification of waters based on WQI(m) and WQI(DO) coincided in 90 and 93% of the samples, respectively.     

Evaluation of water quality index for drinking purposes for river Netravathi, Mangalore, South India

An attempt has been made to develop water quality index (WQI), using six water quality parameters Dissolved oxygen (DO), Biochemical oxygen Demand (BOD), Most Probable Number (MPN), Turbidity, Total Dissolved Solids (TDS) and pH measured at eight different stations along the river basin. Rating curves were drawn based on the tolerance limits of inland waters and health point of view. Bhargava WQI method and Harmonic Mean WQI method were used to find overall WQI along the stretch of the river basin. Five point rating scale was used to classify water quality in each of the study areas. It was found that the water quality of Netravathi varied from Excellent to Marginal range by Bhargava WQI method and Excellent to Poor range by Harmonic Mean WQI method. It was observed that the impact of human activity was severe on most of the parameters. The MPN values exceeded the tolerable limits at almost all the stations. It was observed that the main cause of deterioration in water quality was due to the lack of proper sanitation, unprotected river sites and high anthropogenic activities.     

Spatial and seasonal characteristics of river water chemistry in the Taizi River in Northeast China

Anthropogenic activities have led to water quality deterioration in many parts of the world, especially in Northeast China. The current work investigated the spatiotemporal variations of water quality in the Taizi River by multivariate statistical analysis of data from the 67 sampling sites in the mainstream and major tributaries of the river during dry and rainy seasons. One-way analysis of variance indicated that the 20 measured variables (except pH, 5-day biological oxygen demand, permanganate index, and chloride, orthophosphate, and total phosphorus concentrations) showed significant seasonal (p?≤?0.05) and spatial (p?<?0.05) variations among the mainstream and major tributaries of the river. Hierarchical cluster analysis of data from the different seasons classified the mainstream and tributaries of the river into three clusters, namely, less, moderately, and highly polluted clusters. Factor analysis extracted five factors from data in the different seasons, which accounted for the high percentage of the total variance and reflected the integrated characteristics of water chemistry, organic pollution, phosphorous pollution, denitrification effect, and nitrogen pollution. The results indicate that river pollution in Northeast China was mainly from natural and/or anthropogenic sources, e.g., rainfall, domestic wastewater, agricultural runoff, and industrial discharge.     

River water quality and pollution sources in the Pearl River Delta, China

Some physicochemical parameters were determined for thirty field water samples collected from different water channels in the Pearl River Delta Economic Zone river system. The analytical results were compared with the environmental quality standards for surface water. Using the SPSS software, statistical analyses were performed to determine the main pollutants of the river water. The main purpose of the present research is to investigate the river water quality and to determine the main pollutants and pollution sources. Furthermore, the research provides some approaches for protecting and improving river water quality. The results indicate that the predominant pollutants are ammonium, phosphorus, and organic compounds. The wastewater discharged from households in urban and rural areas, industrial facilities, and non-point sources from agricultural areas are the main sources of pollution in river water in the Pearl River Delta Economic Zone.     

Performance of biotic indices in comparison to chemical-based Water Quality Index (WQI) in evaluating the water quality of urban river

In order to evaluate the water quality of one of the most polluted urban river in Malaysia, the Penchala River, performance of eight biotic indices, Biomonitoring Working Party (BMWP), BMWP^Thai, BMWP^Viet, Average Score Per Taxon (ASPT), ASPT^Thai, BMWP^Viet, Family Biotic Index (FBI), and Singapore Biotic Index (SingScore), was compared. The water quality categorization based on these biotic indices was then compared with the categorization of Malaysian Water Quality Index (WQI) derived from measurements of six water physicochemical parameters (pH, BOD, COD, NH₃-N, DO, and TSS). The river was divided into four sections: upstream section (recreational area), middle stream 1 (residential area), middle stream 2 (commercial area), and downstream. Abundance and diversity of the macroinvertebrates were the highest in the upstream section (407 individual and H′?=?1.56, respectively), followed by the middle stream 1 (356 individual and H′?=?0.82). The least abundance was recorded in the downstream section (214 individual). Among all biotic indices, BMWP was the most reliable in evaluating the water quality of this urban river as their classifications were comparable to the WQI. BMWPs in this study have strong relationships with dissolved oxygen (DO) content. Our results demonstrated that the biotic indices were more sensitive towards organic pollution than the WQI. BMWP indices especially BMWP^Viet were the most reliable and could be adopted along with the WQI for assessment of water quality in urban rivers.     

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.     

Groundwater quality and water quality index at Bhandara District

The present investigation reports the results of a monitoring study focusing on groundwater quality of Bhandara District of central India. Since, remediation of groundwater is very difficult, knowledge of the existing nature, magnitude, and sources of the various pollution loads is a prerequisite to assessing groundwater quality. The water quality index (WQI) value as a function of various physicochemical and bacteriological parameters was determined for groundwater obtained from a total of 21 locations. The WQI during pre-monsoon season varied from 68 to 83, while for post-monsoon, it was between 56 and 76. Significantly (P < 0.01) lower WQI for the post-monsoon season was observed, indicating deterioration of the groundwater overall in corresponding season. The study revealed that groundwater from only 19% locations was fit for domestic use, thus indicating the need of proper treatment before use.     

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.     

Indexing method for assessment of pollution potential of leachate from non-engineered landfill sites and its effect on ground water quality

Dumping of solid waste in a non-engineered landfill site often leads to contamination of ground water due to leachate percolation into ground water. The present paper assesses the pollution potential of leachate generated from three non-engineered landfill sites located in the Tricity region (one each in cities of Chandigarh, Mohali and Panchkula) of Northern India and its possible effects of contamination of groundwater. Analysis of physico-chemical properties of leachate from all the three landfill sites and the surrounding groundwater samples from five different downwind distances from each of the landfill sites were collected and tested to determine the leachate pollution index (LPI) and the water quality index (WQI). The Leachate Pollution Index values of 26.1, 27 and 27.8 respectively for landfill sites of Chandigarh (CHD), Mohali (MOH) and Panchkula (PKL) cities showed that the leachate generated are contaminated. The average pH values of the leachate samples over the sampling period (9.2 for CHD, 8.97 for MOH and 8.9 for PKL) show an alkaline nature indicating that all the three landfill sites could be classified as mature to old stage. The WQI calculated over the different downwind distances from the contamination sites showed that the quality of the groundwater improved with an increase in the downwind distance. Principal component analysis (PCA) carried out established major components mainly from natural and anthropogenic sources with cumulative variance of 88% for Chandigarh, 87.1% for Mohali and 87.8% for Panchkula. Hierarchical cluster analysis (HCA) identifies three distinct cluster types for the groundwater samples. These clusters corresponds to a relatively low pollution, moderate pollution and high pollution regions. It is suggested that all the three non-engineered landfill sites be converted to engineered landfill sites to prevent groundwater contamination and also new sites be considered for construction of these engineered landfill sites as the present dumpsites are nearing the end of their lifespan capacity.     

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.     

Surface water quality evaluation and modeling of Ghataprabha River,Karnataka, India

Belgaum city is a developmental hub of Karnataka State in India. In the recent time, the Government of Karnataka has planned to set up many processing industries in the vicinity of Belgaum to meet the growing needs of the region and to ease out the pressure on the already existing industrial hubs in Karnataka State. Ghataprabha, a tributary of river Krishna, is one of the major sources of water supply to Belgaum city and adjoining areas. During the last decade, a lot of anthropogenic activities such as unplanned agricultural activities are ongoing in many parts of the catchment. Therefore, people of Belgaum are more concerned about the quality of water in Ghataprabha river. Considering the significance of water quality of the river, surface water samples were collected during Pre- and Post-monsoon season from selected locations and analyzed for both physical and chemical constituents in the laboratory. The results indicate that the chemical parameters such as bicarbonates, sulphates, chlorides, sodium, potassium, calcium and magnesium are within the permissible limits. QUAL2E model was applied to assess the impact of point and non-point sources of pollution on the river water quality. Results show that the water quality conditions are highly acceptable all along the river stretch. Further, the variation of DO–BOD₅ with river discharge was also estimated. Also, a significant variations in DO (decrease in DO) with the increase in river flow was observed. However, at the downstream end, considerable improvement in DO was noticed which is attributed to the damming effect of the reservoir.     

Assessing the water quality index of water treatment plant and bore wells, in Delhi, India

Water quality monitoring exercise was carried out with water quality index (WQI) method by using water characteristics data for bore wells and a water treatment plant in Delhi city from December 2006 to August 2007. The water treatment plant received surface water as raw water, and product water is supplied after treatment. The WQI is used to classify water quality as excellent, good, medium, bad, and very bad. The National Sanitation Foundation WQI procedure was used to calculate the WQI. The index ranges from 0 to 100, where 100 represents an excellent water quality condition. Water samples were collected monthly from a bore well in Nehru Camp (site 1), a bore well in Sanjay Gandhi pumping station (site 2), and water treatment plant in Haiderpur (site 3). Five parameters were analyzed, namely, nitrate, pH, total dissolved solids, turbidity, and temperature. We found that the WQI was around 73–80 in site 3, which corresponds to “good,” and it decreased to 54.32–60.19 and 59.93–70.63 in site 1 and site 2, respectively, indicating that these bore wells were classified as “medium” quality.     

Chemometric application in identifying sources of organic contaminants in Langat river basin

Increasing urbanization and changes in land use in Langat river basin lead to adverse impacts on the environment compartment. One of the major challenges is in identifying sources of organic contaminants. This study presented the application of selected chemometric techniques: cluster analysis (CA), discriminant analysis (DA), and principal component analysis (PCA) to classify the pollution sources in Langat river basin based on the analysis of water and sediment samples collected from 24 stations, monitored for 14 organic contaminants from polycyclic aromatic hydrocarbons (PAHs), sterols, and pesticides groups. The CA and DA enabled to group 24 monitoring sites into three groups of pollution source (industry and urban socioeconomic, agricultural activity, and urban/domestic sewage) with five major discriminating variables: naphthalene, pyrene, benzo[a]pyrene, coprostanol, and cholesterol. PCA analysis, applied to water data sets, resulted in four latent factors explaining 79.0% of the total variance while sediment samples gave five latent factors with 77.6% explained variance. The varifactors (VFs) obtained from PCA indicated that sterols (coprostanol, cholesterol, stigmasterol, β-sitosterol, and stigmastanol) are strongly correlated to domestic and urban sewage, PAHs (naphthalene, acenaphthene, pyrene, benzo[a]anthracene, and benzo[a]pyrene) from industrial and urban activities and chlorpyrifos correlated to samples nearby agricultural sites. The results demonstrated that chemometric techniques can be used for rapid assessment of water and sediment contaminations.     

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.     

Environmental impact of coal mining and coal seam gas production on surface water quality in the Sydney basin,Australia

The extraction of coal and coal seam gas (CSG) will generate produced water that, if not adequately treated, will pollute surface and groundwater systems. In Australia, the discharge of produced water from coal mining and related activities is regulated by the state environment agency through a pollution licence. This licence sets the discharge limits for a range of analytes to protect the environment into which the produced water is discharged. This study reports on the impact of produced water from coal mine activities located within or discharging into high conservation environments, such as National Parks, in the outer region of Sydney, Australia. The water samples upstream and downstream from the discharge points from six mines were taken, and 110 parameters were tested. The results were assessed against a water quality index (WQI) which accounts for pH, turbidity, dissolved oxygen, biochemical oxygen demand, total dissolved solids, total phosphorus, nitrate nitrogen and E .coli. The water quality assessment based on the trace metal contents against various national maximum admissible concentration (MAC) and their corresponding environmental impacts was also included in the study which also established a base value of water quality for further study. The study revealed that impacted water downstream of the mine discharge points contained higher metal content than the upstream reference locations. In many cases, the downstream water was above the Australia and New Zealand Environment Conservation Council and international water quality guidelines for freshwater stream. The major outliers to the guidelines were aluminium (Al), iron (Fe), manganese (Mn), nickel (Ni) and zinc (Zn). The WQI of surface water at and downstream of the discharge point was lower when compared to upstream or reference conditions in the majority of cases. Toxicology indices of metals present in industrial discharges were used as an additional tool to assess water quality, and the newly proposed environmental water quality index (EWQI) lead to better trend in the impact of coal and coal seam gas mining activities on surface water quality when compared to the upstream reference water samples. Metal content limits were based on the impact points assigned by the Agency for Toxic Substances and Disease Registry, USA. For environmental and health impact assessment, the approach used in this study can be applied as a model to provide a basis to assess the anthropogenic contribution from the industrial and mining activities on the environment.     

Assessment of water quality using cluster analysis in coastal region of Mumbai, India

The coastal water quality of Mumbai is deteriorating due to various point and non-point wastewater sources. Hence, it is desirable to monitor coastal water quality for various water-related activities like bathing, contact water sports, recreation, and commercial fishing. The objective of this paper is to assess the seasonal water quality on the basis of seawater standards. Based on water-quality analysis of 17 seafronts and beaches, most of the parameters were exceeding the standards. The statistical cluster analysis was carried out for evaluating impact of wastewater and sewage discharges. The hierarchical cluster analysis resulted into three clustered groups, namely less polluted, moderately polluted, and highly polluted sites with similar characteristics of water quality. Mahim was found to be worst-affected beach due to incoming organic load from the Mithi river in comparison to other seafronts and beaches. Unaccounted sources of sewage and wastewater should be identified and rerouted through sewerage system by improving collection efficiency, treatment, and proper disposal for achieving designated receiving water quality standards.