Int. J. Water, Vol. 12, No. 3, 2018
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Groundwater quality assessment and mapping using multivariate statistics and analytic hierarchy process in Bhubaneswar city, Odisha, India Madhumita Das* and Ashok K. Nayak ICAR – Indian Institute of Water Management, Chandrasekharpur, Bhubaneswar 751-023, Odisha, India Email:
[email protected] Email:
[email protected] *Corresponding author
Bishnupriya Das Department of Botany, Utkal University, Vanivihar, Bhubaneswar, India Email:
[email protected]
O.P. Verma ICAR – Indian Institute of Water Management, Chandrasekharpur, Bhubaneswar 751-023, Odisha, India Email:
[email protected] Abstract: Groundwater, the major drinking water source in an urban area, is vulnerable to deteriorate by its quality due to population pressure and developmental activities. Assessment of and understanding the groundwater chemistry is therefore imperative. Using multivariate statistics and analytic hierarchy process, the groundwater quality of Bhubaneswar, an ever-growing city in eastern India is assessed and elaborated in this paper. Samples collected from the city were found to be dominated by Na-Cl-HCO3, Na-Ca-Cl-HCO3, Na-Cl and mixed hydrochemical facies through Piper trilinear diagram. Silicate weathering had come up as a dominant process for influencing ionic constituents in bore well (> 20 m depth), while anthropogenic intervention was responsible for excess nitrate, K, sulphate and chloride contents in dug well (≤ 3–10 m depth) water. Samples were classified to four clusters using hierarchical cluster analyses and cluster-wise discriminating variables were identified through discriminant function. The discriminating variables (turbidity, Fe, Mn, NO3–, K and pH) which determine the drinkability of water were then ranked through analytic hierarchy process (AHP), A hierarchy was prepared and used to generate the vulnerability map distinguishing low to high quality groundwater endowed localities in the study area. Keywords: groundwater quality; hydrochemical facies; hierarchical cluster analyses; HCAs; discriminant functions; analytic hierarchy process; AHP; vulnerability map; India. Copyright © 2018 Inderscience Enterprises Ltd.
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M. Das et al. Reference to this paper should be made as follows: Das, M., Nayak, A.K., Das, B. and Verma, O.P. (2018) ‘Groundwater quality assessment and mapping using multivariate statistics and analytic hierarchy process in Bhubaneswar city, Odisha, India’, Int. J. Water, Vol. 12, No. 3, pp.195–207. Biographical notes: Madhumita Das is a Principal Scientist in Soil Physics at the ICAR-IIWM, Bhubaneswar, India with research focuses on water quality and wastewater utilisation in agriculture. She developed approaches for wastewater reuse for different soil and land uses, strategies for supplying irrigation based on soil hydro–physical parameters and techniques for wastewater use in crop production. She is the recipient of 2008–2009 Fulbright Senior Research Fellowship given by the US Department of State. Ashok K. Nayak is a Senior Scientist in Computer Application in Agriculture at the ICAR-IIWM, Bhubaneswar, Odisha, India working on the development of a web-based information system for on-farm water management technologies with interactive interface for different stakeholders. Bishnupriya Das completed her MSc dissertation on the impact of wastewater use on carbon sequestration under environmental Science (2010–2012) from the Department of Botany, Utkal University, Vanivihar, Bhubaneswar. O.P. Verma is a Scientist in Agronomy at the ICAR-IIWM, Bhubaneswar, Odisha, India working in conjunctive use of surface and groundwater for increasing water use efficiency and crop yield under irrigated production system.
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Introduction
Quality management is a challenge to sustainable development of groundwater resources. It is an important source for drinking and domestic activities in the world, and a major drinking water source in both urban and rural India (Singhal, 2002). Fast growth in urban and industrial sectors reduces the areas for groundwater recharge, increases the volume of domestic, commercial and industrial wastes, which along with changing lifestyle largely interferes the quality of underground aquifer (Rahman, 2008). Leachates from urban landfills and seepage from untreated sewage dumping areas, sewerages and sometimes poor sanitation practices result in nitrate and bacteriological contamination in shallow groundwater aquifers (Chakraborty et al., 2011). With the progress of urbanisation and industrialisation, the source can become vulnerable to water quality deterioration that often leads to generate poor quality groundwater across the urban-conglomerates. Degraded water renders it unavailable for drinking, irrigation and other important activities of the ecosystem, and leads to water scarcity. Understanding and assessment of groundwater quality is therefore crucial for sustainable development of society. The quality of groundwater depends upon multiple factors based on geogenic processes and anthropogenic intervention. For water quality assessment each parameter has been controlled by separate regulation and standard. A comparison of water quality parameters with their corresponding thresholds that are set according to those standards leads to a conclusion as to whether the water is fit for its intended purpose. Making such an assessment for each and every parameter may reveal that the tested water sample is
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usable according to some parameters and but not others. A shift from conventional approach where parameters are compared one after another to multi-parameters comparison at once, would furnish appropriate results in most of the cases. Such an assessment is essential particularly for classification of utilisable water sources (Baris and Karadag, 2007). The classical and multivariate statistics however provide a platform to analyse variability in the data, explore and establish the relations among the parameters and aid to understand the underlying facts from the data structure (Das et al., 2010). Of the multivariate statistics, discriminant, principal component and cluster analyses were applied to understand the spatial variability of groundwater quality and identify the source of pollution in Terengganu (Usman et al., 2014), and for evaluation and interpretation of large data sets of Fuji River Basin in Japan (Shrestha and Kazama, 2007). Such type of information is hardly available from all the growing cities in urban India. Estimates of water quality variables reflect the status of water in terms of ‘suitable’ or ‘unsuitable’ by some variables for a specific use. At what proportion a water sample is suitable for a specific use is difficult to ascertain from the water quality assessment process. The weighing up of water quality parameters may help in water quality quantification, provided the technique adopted for the weighing is unbiased and justified. The analytic hierarchical process (AHP) is a multi-criteria based decision analyses method, used to determine relative weights of the alternatives and prioritise the choices among the alternatives (Saaty, 1980, 2008). The process has been successfully applied in a large number of diverse areas such as energy (Ishizaka et al., 2016), fishery (Jennings et al., 2016), food (Sun, 2015), deprivation indices to analyse health inequalities (Cabrera-Barona et al., 2015), legislative budgetary institution index (Chunsoon, 2014), and environmental, soil, water and forest resources management (Thungngern et al., 2015). These also include the evaluating groundwater potential and vulnerability in numerous locations (Jha et al., 2010; Sener and Davraz, 2013), estimated weights of individual water quality parameters (EC, sulphate, Cl– and hardness) that along with threshold level of respective parameters were used for generating groundwater quality map for drinking in Iran (Jeihouni et al., 2014). In terms of different aspects of water quality, few studies focused to employ AHP to identify critical water quality parameters for domestic use in Yamuna River Basin (Singh and Shrivastava, 2017), established the weights of water quality parameters for developing a WQI for river water in West Java (Sutadian et al., 2017), utilised for identifying water pollution levels of Cibin River at four locations in Romania (Serbu et al., 2016), estimating weights to the wastes, discharged from irrigation canal for studying the mass mortality of Sea Bass in Bang Pakong Basin, Thailand (Rungsupa, 2009) and utilised after modification to assess the water quality along with DSQ method for specific use of water body (Mukherjee et al., 2017). The worldwide adoptability of AHP is its sound theory, easily understandable methodology involving simple mathematics and ready applicability in divergent situations (Forman and Gass, 2001; Oddershede et al., 2007; Saaty, 1989). Fresh water is a receding resource, monitoring water quality where purity of water will not be compromised, is crucial such as drinking and domestic purpose, industrial use, etc. Sample collection, analyses and determination of water quality are time taking and cost-intensive exercise. Data analyses, interpretation and depiction are however imperative for vast and real time use of water quality information for a larger perspective.
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Statistical analyses of the data reveal the fact underlies with the data structure, which along with the objective assessment helps to generate the realistic information and enhance its utility in diverse situations. AHP provides such tool to reduce subjectivity, and enhance conciseness in water quality assessment. The advantage of using AHP is its pair-wise comparison, to decompose a complex problem using hierarchical process (Saaty, 1980; Shen et al., 2015) and enables to convert subjective judgment into quantitative estimate and would help to prioritise in planning and management (Lewis et al., 2006; Oddershede et al., 2007). Bhubaneswar, the capital city of Odisha in eastern India has been progressed fast by widening road, augmenting connectivity, establishing numerous institutions/colleges/ medicals/commercial complexes and high rises that eventually intensified the activities per unit land mass. These in consequence enhance the vulnerability of underground aquifer. Moreover rapid population growth, infrastructural expansion, urbanisation, industrialisation and commercialisation intensify the groundwater consumption, reduce the area of recharge, and consequently threaten the groundwater quality. It is a matter of serious concern in groundwater management (CGWB, 2010). Maintaining groundwater quality is indispensible for safe use, especially under urban settlement. The aim of the paper is to understand and assess the quality of groundwater, determine water quality indicating parameters and their relative effectiveness to interfere water quality in a bid to generate substantial information for planning, in the present study in urban India.
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Methods
2.1 Description of the study area Bhubaneswar, the state capital of Odisha in India, is located between 20º12’ to 20º25’N latitude and 85º44’ to 85º45’E longitude with an altitude of 40–45 m from mean sea level (msl), belongs to the Gondwana landmass, and the rocks range from Archean to the recent formation. It is underlain by Athagarh sandstones which are compact and hard at south western part but weathered and friable at the north western part of the city. These laterised, fractured and weathered Athagarh sandstones formed the pheratic aquifer zone (Das, 1988). Groundwater occurs both under water table conditions in shallow aquifers and confined to semi confined conditions in deeper aquifer. The major part of the city is covered with the quaternary alluvium and laterite soils. The older and the younger alluviums occur to the east of the city are the unconsolidated formations. The depth to the water table is shallow, ranges from 5–12 m in the laterites and the weathered sandstones; and as deep as 40–150 m in the fractured and friable sandstones under semi-confined to confined conditions.
2.2 Collection and analyses of water samples The investigation was carried out in areas where ground water was primarily used for drinking and domestic purposes. Fifty-five locations were chosen for sample collection in such a way to cover all the areas (135 sq km) of the capital city, Bhubaneswar (Figure 1).
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The water samples were collected from open dug and bore wells in white polythene sample bottles pre-washed with diluted acid, rinsed with distilled water, and air-dried during pre-monsoon period (April–May 2012). Each sample was collected in three separate containers for analysis of Cd, Zn, Fe, Mn, Pb, Cr and Hg (100 ml water in 0.3 ml ultra pure HNO3), NO3 (100 ml water in 2 ml of ultra pure H2SO4), and 500 ml water for estimation of other relevant parameters. The samples were analysed for electrical conductivity (EC), pH, sodium, potassium, calcium, magnesium, bicarbonate, chloride, fluoride, sulphate, nitrate, total dissolved solids (TDS), turbidity, iron, copper, zinc, manganese, cadmium, lead, chromium and mercury by adopting the standard methods (APHA, 1995). EC and pH were measured on spot after collection of the samples.
2.3 Characterisation of water quality Piper trilinear diagram was drawn from Na, K, Ca, Mg, Cl–, HCO3– and SO4–2 data using AQUACHEM software, and hydrochemical species in the water samples were identified (Figure 2). Scatter diagrams of Na versus Cl–, and Ca + Mg versus HCO3– + SO4 were subsequently drawn to recognise the major processes for releasing major ions in the samples (Figure 3). The quality of groundwater for use in drinking was assessed through comparing parameter by parameter with standard water quality guidelines given in Table 1 (WHO, 2011; BIS, 2012), and the percent impairment of samples by corresponding parameters was recorded.
2.4 Application of multivariate statistics Descriptive statistics of the water quality parameters have been worked out and presented in Table 1. Hierarchical cluster analysis (HCA) following Ward’s method and squared Euclidean distance interval was performed with 55 cases. Based on similarity in water quality data structure, the HCA classified the 55 sampling locations into four distinct clusters (Figure 4). Then for each cluster, the step-wise discriminant analysis classifying the cases within group covariance matrix with ‘potability of water’ as grouping variable was carried out. Based on level of significance (Wilks’ lambda), the cluster-wise discriminating functions were selected and discriminating variables were listed. Variance analysis then performed to realise the level of variation in discriminating parameters and eventually reflect the sensitiveness towards variability (Table 2). These along with the findings of water quality assessment, water quality indicating variables were specified. All statistical analyses were performed using SPSS software, version 16.0.
2.5 Application of AHP The AHP was applied to establish the weights of the water quality interfering parameters for developing the extent of water quality deterioration for drinking in Bhubaneswar city area. The procedure used to establish the weights using the steps (Saaty, 1980) as: 1
Construct a hierarchy; according to AHP, the water quality analyst experts’ views were considered to rate the relative importance of water quality variables for drinking purpose. The weights for water quality limiting parameters were then assigned in Saaty’s scale (Table 3).
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Formation of the pair-wise comparison matrix; following Saaty’s (1980) assumption that if ‘A’ is more important than ‘B’ by nine then ‘B’ must be less important than ‘A’ by 1/9 times, relative weight was allocated to particular variable and a square matrix was formed (Table 4).
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Calculating the weights (i.e., the priority eigenvector); AHP then derived principal eigenvector by taking the weights of a square reciprocal matrix in pair-wise comparisons.
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Evaluation of the consistency; computed the consistency ratio (CR), which indicates the probability that the matrix ratings were randomly generated. If CR is < 0.1 indicates the consistency is satisfactory otherwise serious inconsistency may involved, judgment will not be proper and the ratio should be corrected until CR < 0.1 eventually.
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Aggregate individual weights to obtain the group weights. Eigenvector, which computed for each water quality interfering variable was considered as relative score of degrading water quality for use in drinking, addition of scores of respective location specific water quality interfering variables then provided the quantitative estimate of water quality impairment in the study area.
The AHP score ranged from 0.0387 to 0.798 for 21 combinations of water quality deteriorating parameters (pH, K, Fe, Mn, NO3 and turbidity), and the number of deteriorating parameters varied from one to five per sample, the score was therefore divided into lowest to highest such as c1 (< 0.25), c2 (0.25–0.50), c3 (0.50–0.75) and c4 (> 0.75). These were then used to produce groundwater quality contours of the study area using SURFER 6.0.
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Results and discussion
In the study area, dug wells were mostly uncovered and water was drawn either manually or through water lifting devices, while bore wells were fitted with either manual or electric pumps. The groundwater table ranged from ≤ 3 to 75 m below the ground surface. Water samples were generally acidic to neutral in reaction, with low salinity, and moderate to high turbidity, Fe and Mn levels. No Cd, Cr, Pb or Hg ions was detected in the samples.
3.1 Hydrochemical facies identification The piper trilinear diagram reveals that Na-Cl-HCO3 (29%) superseded Na-Ca-Cl-HCO3 (25%), Na-Cl (9%) and mixed types (Figure 2). Besides, the level of Na was greater than that of Ca + Mg, and Cl– was present at higher concentrations than HCO3–. Of the total hydrochemical facies types, 76% was headed by Na (alkali metal), and 23% by Ca or Mg (alkaline earth). Bhubaneswar belongs to east and south eastern coastal plain, the dominance of Na in groundwater is primarily from chemical decomposition of feldspar and its allied minerals and secondarily from agricultural and industrial influence (Hem, 1989) while the primary sources of chloride are evaporates, salty connate water and marine water. However, no well was found to have Na and Cl– above the desirable limit of WHO (2011) and BIS (2012). Preponderance of Na+ and Cl– over Ca, Mg and HCO3–
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has also reported by Paul and Panigrahi (2016). The occurrence of Ca, Mg and HCO3– is mainly attributed to the presence of carbonate and dolomite limestone in aquifer minerals as the study area is underlain by sandstone, shale, clay and siltstones of the Athgarh formation of upper Gondwana that lateralised at the top with an average thickness of 10 m (Das, 1988). Scatter diagram of Na versus Cl (Figure 3) indicates that 53% samples are at or near to 1:1 equiline and presumably suggests that halite dissolution is a dominating process, while Na concentration was in excess of Cl in 31% samples, and typically indicated silicate weathering process has also occurred for releasing Na in groundwater samples (Gibbs, 1970; Stallard and Edmond, 1983). The Ca and Mg of the groundwater could be due to dissolution of calcite and dolomite and silicate weathering (Mayo and Loucks, 1995; Katz et al., 1998). A scatter plot of Ca + Mg against SO4 + HCO3 in Figure 3 however indicates that carbonate weathering is dominating in 73%, and in 27% samples silicate weathering is prevailing for releasing Ca and Mg in groundwater. Datta and Tyagi (1996) also showed that carbonate weathering was predominant than silicate weathering because most of the samples felling above the equiline for maintaining Ca and Mg in groundwater system.
3.2 Water quality evaluation The results of groundwater analyses in Table 1 show that the waters are acidic in reaction (mean pH 5.95) with Na, K, Ca, Mg and other elements at different concentrations. A comparison of water quality parameter with its respective drinking water standards (WHO, 2011; BIS, 2012) revealed that 73% samples were constrained by pH, 58% by turbidity, 34% by Fe and 22% by Mn. Few water samples collected mainly from shallow depth aquifers, were found to impair by excess K (1.82%) and NO3– (7.5%) concentrations. Among the water quality degrading parameters, NO3– is considered most harmful because it interrupts oxygen transportation mechanism and causes methemoglobinemia in infants. Of the others, the pH has mostly found lower than its level recommended for drinking purpose. Bhubaneswar is in laterite zone, where natural acidification is evident as it formed from removal of base cations under humid tropics. The high concentration of iron in groundwater also contributes acidity through redox reaction of ferrous and ferric ions (Srivastava and Bhargav, 2014). However pH is one of the operational water quality parameters and usually has no direct effect on water quality. Iron and manganese are the first and third most abundant transition metals in the earth’s crust respectively; they impart colour, unpleasant appearance and taste at high concentration in water, and may disrupt the aesthetic quality of water (Foster, 1985; Livingstone, 1963, Forstner and Wittmann, 1979). Turbidity showed a significant positive correlation with Fe (r = 0.62; P < 0.001) and Mn (r = 0.53; P < 0.001). It may be attributed to aqueous Fe(II) and Mn(II), which are significant in natural waters only in the absence of oxygen. Insoluble Fe(III) and Mn(III/IV) oxides form under oxic conditions and their solubility limit the aqueous concentrations of Fe and Mn species. The significant correlation between Fe and Mn with turbidity has also observed from the groundwater of mountainous areas in Ghana (Amfo-out et al., 2014). Potassium has been reported to be non-toxic, but to act as a cathartic agent at high concentration; accordingly, an admissible level of K (10–12 mg/L) for drinking water (Zuane, 1997). Ion like NO3– in groundwater is known to have anthropogenic origins such as sewage waste and decomposition of organic materials. NO3–, SO4–2 and K ions were however, prevalent in
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shallow groundwater (≤ 3 to 10 m from ground surface). A significant correlation between NO3– and K (r = 0.36; P < 0.001), Ca (r = 0.339; P < 0.01) and SO4–2 (r = 0.30; P < 0.01) ions further indicates that all those ions might have come from the same source in groundwater samples (Rajmohan et al., 2003). Deterioration of water quality by excess NO3–, Cl– and SO4–2 ions in shallow aquifers due to seepage from sewage as well as domestic and industrial wastes has been reported from several other urban areas (Raju et al., 2011; Naik et al., 2008).
3.3 Application of multivariate statistics Four clusters were identified from HCA, where cluster I (CI) formed with a membership of 29, 18 for cluster II (CII), 4 and 3 for cluster III (CIII) and cluster IV (CIV) respectively. Thus 55 sampling locations have come down to four based on spatial similarity within water quality variables. The step-wise DA then selected discriminant function (DF) with high level of significance for each cluster and based on loading of a variable, which is the correlation of this variable with the function, the relative significance of discriminating variables were identified. The DF values for CI, CII, CIII, and CIV are: γ CI
1.82* EC 0.1* K 0.98*C1 2.014* NO3 1.584* Mn
γ CII
0.98* Mn 0.9 * Turbidity
γ CIII
5.942 * F 5.942 * Fe
γ CIV
1.0*C1
EC, K, Cl–, NO3–, Mn, Turbidity, F– and Fe are thus found as discriminating variables in the study area. Variance analysis then reflects the range of variation (based on CV%) present in the discriminating variables in an order of 42 to 290% under CI, 122 to 187% in CII, 51 to 73% in CIII and 17% in CIV (Table 2). Amount of variation indicates the sensitiveness of the variable to change. Thus low variation occurred under CIV may due to small size of sample and was discarded.
3.4 Identification of water quality indicating parameters Evaluation of water quality indicates that the groundwater samples were impaired by pH, Fe, Mn, turbidity, NO3– and K, and consequently deserved to be as indicating parameters for water quality determination process irrespective of their variability present in the samples. Based on pH, 73% samples were found not suitable, therefore despite its low variability (CV% 11.95) it was considered for being an indicating parameter in water quality monitoring while EC (mean 0.2 dS/m), Cl– (mean 89.27 mg/L) and F– (mean 0.099 mg/L) could be avoided as they were well below the range recommended for use in drinking (Table 1). In view of that pH, K, NO3–, Mn, turbidity and Fe were considered as indicating parameters for water quality monitoring process.
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3.5 Application of AHP for degradability of water quality mapping The parameters primarily emphasised by BIS (2012) and the WHO (2011) are those that directly or indirectly influence the healthiness, and then aesthetic quality of water. Noncompliance of a single parameter however affects water quality in reality. The AHP was therefore, applied to quantify the deteriorating influence of the parameters with respect to others; Application of it is not preferable for > 9 numbers of criteria/alternatives to get an appropriate precision of obtained results (Saaty, 1980). The eigenvector derived from the matrix is 0.0387 for pH, 0.0619 for turbidity, 0.1122 for K, 0.1629 for Fe, 0.2268 for Mn and 0.3974 for NO3– with a consistency ratio of 0.031. These are the relative scores of respective water quality deteriorating parameters for a particular context and will be altered in presence or absence of parameter/s other than those mentioned in this investigation. The highest eigenvector of NO3– reflects the gravity of its adverse effects if found in excess in water with respect to other impairing parameters. pH is an operational water quality parameter, while excess K, Fe, turbidity and Mn affects the acceptability of water for drinking. Aggregation of eigenvector of respective parameters, the AHP scores for degrading water quality of the samples were determined. The AHP scores varied from 0.0387 to 0.798 for 21 combinations of water quality deteriorating parameters (pH, K, Fe, Mn, NO3 and turbidity), and the NO3– associated combinations of these parameters generated relatively higher scores (0.498 to 0.798) as compared to the scores of the combination without NO3–. The number of impairing variables per water sample varied from one to as high as five, therefore based on appearance of these variables the AHP scores were divided into four categories c1 (< 0.25), c2 (0.25–0.50), c3 (0.50–0.75) and c4 (> 0.75). Water samples were impaired mostly by pH-turbidity with 0.037 to 0.224 as AHP score in c1, pH-Fe-turbidity and Fe-Mn-turbidity with AHP score 0.263 to 0.498 in c2, NO3– other parameters with AHP scores 0.548 to 0.61 in c3 and 0.798 in c4. This damaging intensity of water quality parameter ultimately determines the vulnerability of sample at a particular context. Distribution of samples in the study area reveals that majority of the samples belonged to c1 and c2 where impairment of water quality is primarily due to natural rock formation, i.e., laterites that may caused for low pH, high Fe and turbidity except 1.8% of samples by NO3– in c2. The iron enriched groundwater under laterites formation in Bhubaneswar had also reported by Achary (2014). Locations belong to c3 and c4 are Laxmipur slum area, Raghunathpur, Patia and Chakeishuni, densely inhabited, where water was mainly collected from open dug well and was found to contain toxic amount of NO3, Fe, Mn, turbidity and K. Discharge of solid and liquid wastes in open space and drains may attributed to the migration of the contaminants through macro pore flow in laterite soil. High level of NO3 due to progress of urbanisation in pheratic shallow aquifer was observed in some localities of Bhubaneswar city (Srivastava and Bhargav, 2014). The effect of population density on drinking water quality in Bhubaneswar had also reported by Das (2013). The AHP (modified) and DSQ method were successfully utilised for evaluating the level of acceptance of water body for a specific use (Mukherjee et al., 2017). However application of AHP for generating water quality deteriorating score of the parameter is rare. AHP is a decision making technique thus its application provides the level of vulnerability of groundwater quality and the degree of treatments required for remedial purpose in the study area.
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Conclusions
The paper illustrates an integrated approach for evaluating water quality with the understanding of cation-anion distributions processes in groundwater samples; use of multivariate statistics for classification of samples and identification of water quality discriminating parameters for drinking purpose in a city area. The water quality discriminating parameters were then processed through AHP to generate quantifiable estimate of their capacity to interfere with water quality in presence of other variables. The AHP scores of respective individuals were further aggregated as per the presence of water quality interfering parameters and water quality contours of the area was generated that eventually show the gravity of water quality problem and enable prioritisation of management options for supplying potable water in an ever-growing city in urban India. This could be pursued to other areas in the country.
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