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

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

Application of 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.     

Assessment of temporal variation in water quality of some important rivers in middle Gangetic plains, India

The study explains water quality of three important tributaries of the Ganga River in the middle Gangetic plains in India. Seasonal changes in the water quality of the studied rivers: Gandak, Ghaghra, and Sone were observed. During monsoon, several water quality parameters show considerable changes due to increased runoff from the catchments and other seasonal factors. Multivariate discriminant analysis delineated a few parameters responsible for temporal variation in water quality. Seasonal variation in water quality of the Gandak River was rendered by seven parameters??turbidity, sulfate, pH, phosphate, water temperature, total alkalinity, and sodium, while total alkalinity and water temperature were responsible for seasonal discrimination in water quality of Ghaghra River. Water temperature, turbidity, total dissolved solids, total suspended solids, calcium, and phosphate were important for seasonal discrimination in water quality of Sone River. The seasonal changes in water quality of the rivers were due to seasonal effects and catchment characteristics. The discriminant functions classified most of the cases correctly.     

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.     

基于熵权的WQI法在鄱阳湖水质评价中的应用

基于2009—2018年鄱阳湖15个监测点位的水质监测数据,采用基于熵权改进的综合水质指数（WQI）法研究10年间该湖水质年际、季节演变特征及空间分布情况。结果表明：鄱阳湖WQI多年平均值为78.41,水质总体处于差等级,其中氮、磷污染较为严重;该湖水质空间分布差异较小,各湖区WQI较为接近,湖区西北部和东南部污染相对较为严重;该湖夏季水质较好,水质与降水量及湖面风浪强度紧密相关;该湖污染物主要来自入湖河流周边的工、矿、企业的废水及采砂造成的污染物释放,建议控制、治理入湖河流水质,提高鄱阳湖较重污染区水质。     

Water quality observations in the marine aquaculture complex of the Deeba Triangle,Lake Manzala,Egyptian Mediterranean coast

Coastal aquaculture is faced with extreme variation in water quality. The Deeba Triangle on Lake Manzala is the largest marine coastal aquaculture-producing area on the Egyptian Mediterranean. Samples from 16 ponds were taken during four seasons (2014–2015), to investigate the variation of 12 water quality parameters at that region. We tested the hypothesis that there is no spatial or temporal variation in water quality of the fish ponds. Fish ponds were statistically clustered into three groups (p?=?0.0005) coincident with their geographical location. Hypersaline and transparent waters characterized the western ponds; higher dissolved oxygen and higher nutrients characterized the central region. These spatial differences were principally due to variations in salinity and nutrients of the water sources used for irrigation of the ponds and to differences in the aeration management styles. Strong seasonality was seen in water temperature (following air temperature), nutrients, and turbidity (following the seasonal cycles of various water sources from the Lake Manzala and the seasonality of the petrochemical plants effluents close to these ponds). We conclude that municipal effluents significantly affected, spatially and temporally, the quality of the irrigation water used for coastal aquaculture purposes, which consequently might affect fish yield.     

Evaluation of surface water quality by using Canadian Council of Ministers of the Environment Water Quality Index (CCME WQI) method and discriminant analysis method: a case study Coruh River Basin

In this study, the water quality of the Coruh River Basin, which is located in the Eastern Black Sea Region of Turkey, was evaluated. The water quality data measurement results obtained by the State Hydraulic Works 26th Regional Directorate from four different sites over a course of 4 years between the years 2011 and 2014 in the Coruh River Basin were used as the data. In this study, the water quality was evaluated by using the Canadian Council of Ministers of the Environmental Water Quality Index (CCME WQI) method and discriminant analysis (DA). The water quality of the Coruh River Basin was calculated as 30.4 and 71.35 by using the CCME WQI and classified as “poor,” “marginal,” and “fair”. These values show that the water of the Coruh River Basin is degraded and under threat and its overall quality is not close to natural or desired levels. The monitoring sites were divided into two groups by the cluster analysis (CA). DA is a multivariate analysis technique used to divide individuals or objects into different groups and assign them into predetermined groups. As a result of DA, calcium (Ca) and sulfate (SO₄) were determined to be significant parameters in the determination of the water quality of the Coruh River Basin. The success of DA depends on the percentage of correct classification. As a result of the analysis, 23% of the parameters in the first measurement point, 69.2% of the parameters in the second and third measurement points, and 76.9% of the parameters in the fourth measurement point were classified correctly. Since the second measurement point is the discharge point of a copper mine, it can be said that the water quality parameters measured may provide accurate results in detecting pollution at this point.     

Integrated survey of water pollution in the Suquía River basin (Córdoba, Argentina)

We report a combined two-year seasonal monitoring of Suquía River basin using both chemical parameters and biomarkers measured in Jenynsia multidentata, aiming to correlate external levels of contaminants with the response of oxidative stress biomarkers in this fish. Identified pollution sources correspond to city sewage as well as agricultural and small industry activities downstream from Córdoba city. Physicochemical parameters integrated into a water quality index (WQI) were measured in Suquía River during dry and wet seasons. Ag, Mn, Cu, Cr, Ni, Fe, Pb and Zn were also monitored in water and sediment samples. Biomarkers include detoxication and antioxidant enzymes: catalase (CAT), glutathione peroxidase (GPX), glutathione S-transferase (GST) and glutathione reductase (GR). Enzymes showed a pollution dependent response, with increased activities in fish collected close to the sewage exit and progressive drop further downstream, matching changes in the Water Quality index. The combined use of biomarkers with water quality parameters allowed both the identification of pollution sources and the evaluation of effects of contaminants on the aquatic biota.     

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.     

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.     

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.     

Quantitative assessment of eutrophication: A scoring system for characterising water quality in coastal marine ecosystems

A scoring system based on nutrient concentrations was developed to assess coastal water quality according to the trophic level. Three nutrient data sets from eutrophic, mesotrophic and oligotrophic waters were used as the reference information for setting up a semi quantitative water quality scale (from 0 to 5) to express different nutrient loadings. The validity and sensitivity of the method was applied to a number of stations spaced out along the coastal area of Rhodes (Greece). A score for each nutrient/sampling site was calculated and the scorecard formed, was the data matrix used for numerical classification of the stations. The results showed (a) good discrimination between cutrophic, mesotrophic and oligotrophic waters (b) nitrate among the nutrients showed the maximum sensitivity in characterising pollution levels. The reference data sets used for assessing eutrophication levels ensured the objectivity of the method. The proposed method is described step-by-step and it is suggested that the method can be further adapted to describe other forms of pollution becoming a useful quantitative technique in coastal management practices.     

Detection of seasonal variations in surface water quality using discriminant analysis

In the study, discriminant analysis (DA) was applied to water quality data set monitored in winter and summer months along 3 years to investigate seasonal variations of Tahtali Reservoir water quality. The variables were divided into two categories as “parameters naturally found in surface waters—Al, Fe, Mn, F, Ba, Cl, and SO₄” and “pollutants—biochemical oxygen demand, chemical oxygen demand, and NO₃”. Linear discriminant functions were calculated to distinguish between winter and summer samples. Results revealed that water quality variables (naturally found in surface water and also pollutants) were distinctly different between two seasons. Seasonal changes of water quality caused by anthropogenic activities should be taken into consideration by the water managers especially in summer seasons in which higher concentrations were observed. The study showed that DA can be applied successfully to establish the seasonal variation in surface water quality and make it possible to design a future temporal sampling strategy in an optimal manner.     

Water Quality during Development and Apportionment of Pollution from Rivers in Tapeng Lagoon, Taiwan

This work investigated water samples collected from Tapeng Lagoon, Taiwan. Factor analysis was conducted to explain the characteristics and the variation in the quality of water during the disassembly of oyster frames and fishery boxes. The result shows that the most important latent factors in Tapeng Lagoon are the ocean factor, primary productivity factor, and the fishery pollution factor. Canonical discriminant analysis is applied to identify the source of pollution in neighboring rivers outside Tapeng Lagoon. The two constructed discriminant functions (CDFs) showed markedly contribution to all discriminant variables, and that total nitrogen, algae, dissolved oxygen and total phosphate, combined in the nutrient effect factor. The recognition capacities in these two CDFs were 95.6%, 4.4%, respectively. The water quality in the Kaoping river most strongly determined that in the Tapeng Lagoon the best is. And disassembling the oyster frames and fishery boxes improves the water quality markedly. However, environmental topographic conditions indicate that strengthening stream pollution prevention and to constructing another entrance to the ocean are the best approaches for improving the quality of water in Tapeng Lagoon, especially by reducing eutrophication. These approaches and results yield useful information concerning habitat recovery and water resource management.     

Design of sampling locations for river water quality monitoring considering seasonal variation of point and diffuse pollution loads

The design of a water quality monitoring network (WQMN) is a complicated decision-making process because each sampling involves high installation, operational, and maintenance costs. Therefore, data with the highest information content should be collected. The effect of seasonal variation in point and diffuse pollution loadings on river water quality may have a significant impact on the optimal selection of sampling locations, but this possible effect has never been addressed in the evaluation and design of monitoring networks. The present study proposes a systematic approach for siting an optimal number and location of river water quality sampling stations based on seasonal or monsoonal variations in both point and diffuse pollution loadings. The proposed approach conceptualizes water quality monitoring as a two-stage process; the first stage of which is to consider all potential water quality sampling sites, selected based on the existing guidelines or frameworks, and the locations of both point and diffuse pollution sources. The monitoring at all sampling sites thus identified should be continued for an adequate period of time to account for the effect of the monsoon season. In the second stage, the monitoring network is then designed separately for monsoon and non-monsoon periods by optimizing the number and locations of sampling sites, using a modified Sanders approach. The impacts of human interventions on the design of the sampling net are quantified geospatially by estimating diffuse pollution loads and verified with land use map. To demonstrate the proposed methodology, the Kali River basin in the western Uttar Pradesh state of India was selected as a study area. The final design suggests consequential pre- and post-monsoonal changes in the location and priority of water quality monitoring stations based on the seasonal variation of point and diffuse pollution loadings.     

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.     

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.     

The assessment and prediction of temporal variations in surface water quality—a case study

In order to optimize the processes of sampling, monitoring, and management, the initial aim of this paper was to develop a model for the definition and prediction of temporal changes of water quality. In the case of the Morava River Basin (Serbia), the patterns of temporal changes have been recognized by applying different multivariate statistical techniques. The results of the conducted cluster analysis are the indicators of the existence of the three monitoring periods: the low-water, transitional, and high-water periods, which is in accordance with changes in the water flow in the analyzed river basin. A possibility of reducing the initial data set and recognizing the main pollution sources was examined by carrying out the principal component/factor analysis. The results indicate that the natural factor has a dominant influence in temporal groups. In order to recognize the discriminatory water quality parameters, a discriminant analysis (DA) was carried out. Conducting the DA enabled a significant reduction in the data set by the extraction of two parameters (the water temperature and electrical conductivity). Furthermore, the artificial neural network technique was used for testing the possibility of predicting changes in the values of the discriminant factors in the monitoring periods. The reliability of this method for the prediction of temporal variations of both extracted parameters within all temporal clusters has been proven.     

Evaluation of environmental impacts of Integrated Industrial Estate��Pantnagar through application of air and water quality indices

Large-scale industrialization, population inflow, and rapid urbanization coupled with unfavorable meteorological conditions often induce significant degradation of urban environment. In order to assess the extent of environmental impacts due to establishment of the Integrated Industrial Estate??Pantnagar (IIE-Pantnagar), ambient air and groundwater were monitored from June 2007 to May 2008. Collected baseline information was normalized and interpreted with the application of air (AQI) and water quality indices (WQI). Among the pre-identified air pollutants, suspended particulate matter was found to be the principal culprit to deteriorate ambient air quality, with a maximum annual concentration of 418.5 ??g/m³. Monthly average concentrations of respirable particulate matter (aerodynamic diameter < 10 ??m) also persist at a critical level with an annual maximum of 207.3 ??g/m³. A segmented linear function with maximum operator concept was used to compute AQI, and the developed index was found well suitable to demonstrate temporal variations of ambient air quality. The computed AQI value for the selected study region varied from moderate (97.0) to very poor pollution level (309.2) in respect to developed air quality standards. Furthermore, an integrated WQI was developed comprising 9 parameters, and among all the 10 pre-identified locations, the average groundwater quality was found acceptable in terms of Indian drinking water standards. The maximum WQI (70.6) was found at the Kichha Railway Station during summer months, revealing moderate pollution load. Industrial discharge from IIE-Pantnagar coupled with other industrial setup may hold responsible for such kind of degradation of water quality. In contrast, WQI computed at Rudrapur City demonstrate minimum (15.0?C22.1) pollution load. For 95% of the monitoring period, the computed WQI was found acceptable for all selected locations with few exceptions. The application of WQI to assess temporal variations in groundwater quality was therefore found satisfactory.     

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.