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Assessment of groundwater quality, toxicity and health risk in an industrial area using multivariate statistical methods

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Abstract

Background

This study investigates the common and anthropogenic activities that impact the science of groundwater in and around an industrial zone and exhibits the utilization of multivariate statistical methods for groundwater quality, toxicity and health risk associated with contaminated industrial sites for proficient administration of water assets. A total of 120 groundwater samples were collected during summer and winter season, and analyzed for their twenty physicochemical constituents including seven toxic heavy metals (pH, EC, total dissolved solids (TDS), F, K, Na, Ca, Mg, Cl, CO3, HCO3, NO3, SO4, As, Cd, Cr, Cu, Ni, Pb and Zn). Data obtained was treated using principal component analysis (PCA)/factor analysis (FA), hierarchical cluster analysis (HCA), Correlation coefficient and health risk analysis to find the common pollution source.

Results

The results for mean abundance during two seasons for cations and anions were 7 and 6.9 for pH; 1875 and 1527 for TDS; 3 and 3.3 (µs/cm) for EC; 655 and 569 for Ca2+; 59 and 56 for Mg2+; 340 and 211 for Na+; 5 and 4 mg/L for K+; 148 and 126 for CO32− 301 and 228 for HCO3; 289 and 223 for Cl 0.5 and 0.85 for F; 99 and 86 for SO42− 28 and 23 mg/L for NO3. While for heavy metals 18 and 4 for As; 2 and 0.4 for Cd; 29 and 5 for Cr; 17 and 4 for Cu; 25 and 6 for Ni; 82 and 3 for Pb; 953 and 989 µg/L for Zn, respectively. FA identified six dominant factors for each during summer and winter seasons that explained 70.43% and 71.06% of the variance in the dataset. Health risk assessment of chronic daily intake (CDI) and hazard quotient (HQ) during both seasons were in the order Ca > Na > HCO3 > Cl > CO3 > SO4 > Mg > NO3 > K > F and was as well computed.

Conclusion

The significant reasons for water quality degrading in the study area were associated with various natural and anthropogenic sources and their unsystematic apportionment, show that proper land uses, industrial planning, design some remedial techniques and implementation of existing laws to have active groundwater resource management.

Background

Water quality evaluation and administration are issues profoundly affecting human life. Particularly, the groundwater quality in a district is to a great extent influenced both by common procedures (geological interventions, weathering and soil disintegration) and by anthropogenic source (man-made, industrial and civil waste release). The industrial waste release constitutes a steady contaminating source, while surface overflow is a regular phenomenon (Kazi et al. 2009; Singh et al. 2004; Vega et al. 1996). Regular seasonal precipitation, surface run-off, groundwater stream and deliberation emphatically influence groundwater quality and subsequently on the concentration of toxins in water.

Broad analyses have been done on anthropogenic pollution of biological system by Niemi et al. 1990; Szymanowska et al. 1999; Krishna et al. 2009 and Issa et al. 1996. They have reported human activities are a major deciding factor in determining the nature of surface and groundwater through atmospheric contamination, effluent releases, utilization of farming chemicals like pesticides, dissolved soils and land utilize. Additionally, several recent studies on groundwater quality have been conducted (e.g., Chen et al. 2016; Cao et al. 2016) wherein they have concluded that agriculture activities, unplanned municipal development and insufficient hydrochemical knowledge are some factors responsible for poor groundwater quality. In recent years, overexploitation and irresponsible management of groundwater has resulted in many environmental problems such as groundwater table decline, and subsidence, and groundwater pollution (Xia 2002). Particularly, the small nations have been enduring this effect because of cluttered economic development related with the exploitation of natural resources (Kazi et al. 2009).

In various parts of India, especially in the dry and semi-dry areas, due to the driving forces of cyclones and deficiency of surface water, dependence on groundwater resources has extended gigantically in the progressing years. Furthermore, rapid growth in urban population, development of agriculture and industrial activities cause an intense increase in water consumption. In spite of the fact that the industrial utilization of water is small when contrasted with farming purposes, the transfer of modern effluents ashore/or surface water bodies and presence of micropollutants in the aquatic environment at different time scales makes water assets inadmissible for different purposes (Ghosh 2005; Buechler and Mekala 2005; Andreas et al. 2009). Nonetheless, because of spatial and temporal variations in water quality which gives a proxy and solid estimation of the groundwater quality is important (Dixon and Chiswell 1996). One such approach would be hydrochemical investigations of groundwater frameworks which have set overwhelming attention on variations in the physical and chemical qualities of groundwater in time and space. Similar research by Igibah and Tanko (2019) has been studied carried out in assessment of urban groundwater quality using piper trilinear and multivariate techniques, where agriculture is the most significant commercial activity affecting the changes in groundwater quality by anthropogenic activity.

A standard approach in groundwater hydrochemistry to interpret hydrochemical processes is to make scatter plots between parameters and to classify hydrochemical variables using various diagrams (ex., Piper, Wilcox diagram). Second is a high-level approach, and a valuable tool which incorporates the utilization of various multivariate statistical procedures (principal component analysis (PCA), factor analysis (FA), cluster analysis (CA) and correlation analysis) which help in understanding the complex information of water quality and regular status of the investigation zone. In this context various water quality monitoring programs based on statistical tools using large dataset have also been applied for better understanding of quality and hydrochemistry of rivers (Renato et al. 2018; Christopher et al. 2019; Mrazovac and Miloradov-Vojinovi 2011). These methods further, permit the distinguishing proof of the possible sources that impact water systems and offer a significant tool for contamination issues and risk assessment-oriented characterisation of contaminated sites (Shrestha and Kazama 2007; Simeonov et al. 2003; Reghunath et al. 2002; Ammar et al. 2014; Carlon et al. 2001; Howladar et al. 2017).

The topography of the study area, a more seasoned alluvium makes it more immobilized to draining. Hence, more attention is needed to understand the processes happening in and around this particular industrial area. Hence, this systematic study was carried out with four primary objectives. (i) Of studying the impact of the industries on groundwater quality, (ii) recognizing the hydrochemical forms identified with groundwater quality, (iii) to decide and portray the fundamental procedures influencing, groundwater quality utilizing an assortment of multivariate statistical methods and (iv) risk assessment due to physicochemical constituents utilizing health risk parameters like chronic daily intake (CDI) and hazard quotient (HQ) were evaluated to study their impact on human health.

Materials and methods

Study area

The proposed study area known as Katedan Industrial Development Area (KIDA) is located south of Hyderabad city on Hyderabad—Bangalore National Highway (NH 7). Around 300 industries are producing, edible oil, battery fabricating, metal plating, metal amalgams, plastic items, synthetic substances, and so forth, are situated in the area. These industries were arranged under small, medium and extensive scale industries. It is seen that most of the industrial parts, chiefly release their effluents into the streams and the solid waste created is discretionarily dumped on open land along lanes and lakes (Govil et al. 2012; Krishna and Mohan 2014).

The study area under examination falls in the semi-arid-dry zone, and the event of the initial spell of rainfall is amid June. Figure 1 demonstrates the magnitude of the study area (KIDA) encompassing residential and industrial zones separated. The industrial zone is isolated from the downstream residential locations by the railroad and an interstate expressway. The soil cover is an all-around well-developed persistent soil of weathered granite with porous and the invasion rate that can assimilate the vast majority of the rain aside from more extreme downpours. The lithological units comprise of granites and pegmatite of volcanic source having a place with the Archaean age. The granites are pink and dark, hard huge to foliated and very much jointed. Epidote and Quartz veins crosscut the granites at different spots. These rocks have minute porosity however are rendered with a porosity and penetrability because of secondary porosity by profound fractures and weathering, which locally shape potential aquifers. Water level varies every year in all the bore wells and usually rise in winter season with the water table fluctuating between 1 and 15 m below ground level and during summer season water levels often decline, and the water table fluctuates between 10 and 25 m.

Fig. 1
figure1

Study area and sample location map

Sampling and preparation

A total of one hundred and twenty (120) groundwater samples were collected from the study area out of which 60 samples during summer and 60 samples during the winter season (Fig. 1), which covers groundwater samples from bore wells, hand pumps and dug wells comprising of 98 bored and furnished with electric submersible pumps, 15 drilled and outfitted with hand pumps and 7 dug wells. The greater part of the wells outfitted with submersible pumps are essentially utilized for industrial purpose but yet many are additionally utilized for domestic reason. The capability of the wells isn’t known yet the electric wells keep running for 3 to 8 h every day and the hand pumps are consistently being utilized. Location and list of well inventory data are shown in Table 1.

Table 1 Summary of well inventory data in the study area

Water samples were collected in one litre size polythene bottles from demonstrative bore wells/burrowed wells/hand pump spread throughout the study area which are under use amid both summer and winter seasons. The sample containers were altogether washed with diluted acid and after that with distilled water in the lab. Before filling the samples, the container was flushed to keep away from any possible contamination. On location observations like area, source, pH, TDS and depth of the bore well were noted in the field notes. The water sample was then sifted and acidified (2 mL of HNO3) to each 100 mL of the sample and was measured for heavy metals by ICP-MS.

Analytical procedures and instrumentation

In light of broad analysis, 20 parameters were measured utilizing prescribed strategies for investigation (APHA 1995) for physicochemical parameters and significant metals which was incorporated into the list of criteria concerning the quality of water (Table 2). Measurements were done for pH, electrical conductivity (EC), total dissolved solids (TDS), total hardness (TH), calcium (Ca2+), magnesium (Mg2+), sodium (Na+), potassium (K+), carbonate (CO32−), hydrogen carbonate (HCO3), chloride (Cl), sulfate (SO42−) and nitrate (NO3) according to standard strategies (APHA 1995). Electrical conductivity measurements are expressed in micro siemens/cm at 25 °C, the concentrations of cations and anions are expressed in mg/L and for heavy metals in µg/L. Sodium and potassium by inductively plasma mass spectrometer (ICP-MS), calcium, magnesium, total dissolved solids, and alkalinity by titrimetric methods, sulphates, and nitrates by ion-selective electrodes. Heavy metals (As, Cd, Cr, Cu, Ni, Pb and Zn) were examined by ICP-MS. The instrument used for heavy metal measurements was Plasma Quad (VG Elemental Ltd., Winsford, Cheshine, UK). Calibration curves were prepared using multielement standard solution after dilution to micrograms per litre levels. The accuracy and precision (QC/QA) of the analysis was compared with reference water samples 1643b from National Institute of Standards and Technology (NIST, USA) which was used to check the reliability of calibration curve. All heavy metal results were obtained in the multielement mode and the samples were prepared in triplicates and analysed twice. The values obtained by ICP-MS are in close agreement with recommended values, the precision are better than 2% and show comparable accuracy (Table 3).

Table 2 Standards and method of analysis
Table 3 Heavy metal data for water reference sample NIST 1643b by ICP-MS (Krishna and Mohan 2014)

Data treatment and multivariate statistical methods

Groundwater data for summer and winter season was subjected to multivariate statistical methods regarding distribution and correlation among the studied parameters. The location of water sampling was recorded utilizing an Atrax display global positioning system (GPS) receiver framework. SPSS 10.0 programming platform (SPSS 1995) was utilized for statistical analysis of the information. Essential descriptive statistical parameters, for example, range, mean, standard deviation, kurtosis, and skewness were handled for both seasons (Table 4) correlation coefficient relationship analysis (Table 5), while multivariate measurements such as PCA and CA were also carried out. PCA was performed utilizing the varimax standardized rotation on the data, and the CA was linked to the sample concentrations utilizing dendrogram strategy (Liu et al. 2003; McKenna 2003; Omo-Irabor et al. 2008). These principal components give data on the most important parameters which depict the entire dataset bearing information on data reduction with least loss of unique data. PCA is an effective method for pattern recognition that attempts to explain the difference of an expansive arrangement between related factors and changing into a smaller arrangement of autonomous (uncorrelated) variables (principal components). In this study, hierarchical agglomerative CA was performed on the normalized dataset employing Ward’s method, using square Euclidean distances as a measure of similarity.

Table 4 Descriptive Statistics of groundwater quality during summer and winter seasons
Table 5 Correlation coefficient (r) matrix of cations, anions and selected metals in groundwater during summer season (above the diagonal) and winter season (below the diagonal; n = 120)

Human health risk assessment

The identification and characterization of associated human health risks is based on integrating factors such as ecotoxicology and physico-chemical analysis, of the dangerous metals on individuals through direct ingestion, inward breath through mouth and nose, dermal assimilation through skin introduction have been considered (Donkor et al. 2015; Sophie et al. 2011).

In the present study, we have adopted the same equations and calculated based on the concentrations of cations and anions to calculate the health risk assessment utilizing chronic daily intake (CDI) and hazard quotient (HQ) indices. The CDI through water ingestion was figured utilizing the condition by USEPA (1992) underneath:

$${\text{CDI}} = {\text{C}} \times {\text{DI/BW}}$$
(1)

where C, BW represents the concentration of cation/anion in groundwater (mg/L), average daily intake rate (2 L/day) and body weight (72 kg), respectively (USEPA 2005). On the other hand, the chronic risk level was calculated (HQ) for non-carcinogenic risk using following equation by USEPA (1999):

$${\text{HQ}} = {\text{CDI/RfD}}$$
(2)

where according to USEPA, the oral toxicity reference dose values (RfD) are 41.4 mg/kg-day for Ca, 11.0 mg/kg-day for Mg; 1.0 for K, 0.067 mg/kg-day for Cl 0.06 mg/kg-day for F, and 1.6 mg/kg-day for NO3 respectively (USEPA 1989). The scale of chronic risk level (HQ) based on average daily intake (CDI) and reference dose (mg/kg-day) is classified based on the ratio of CDI/RfD indicating ≤ 1 (no risk) if > 1 ≤ 5 (low risk), if > 5 ≤ 10 (medium risk), if > 10 (high risk).

Results and discussion

Descriptive statistics of 20 physicochemical parameters of groundwater in the study area during summer and winter seasons are summarized in Table 4. In summer groundwater samples indicated, pH (6.4–8.1) moderately acidic to alkaline while TDS and conductivity vary from 620 to 4361 mg/L and 1.0 to 7.6 µs/cm. Mean cation occur in the order of K < Mg < Na < Ca, anion concentrations arise in the order of F < NO3 < SO4 < CO3 < Cl < HCO3 and heavy metals occur in the order Cd < As < Ni < Cu < Cr < Pb < Zn. In winter, groundwater showed, pH (6.4–7.8) acidic to mild alkaline while TDS and conductivity vary from 445 to 2829 mg/L and 1.0 to 7.3 µs/cm. Mean cation and anion concentrations were found to be abundant in the order K < Mg < Na < Ca and F < NO3 < SO4 < CO3 < Cl < HCO3, whereas heavy metals occurred in the order Cd < Pb < As < Cu < Cr < Ni < Zn. The chemical composition of analyzed groundwater samples of the study area is represented by plotting them in the Piper tri-linear diagram for summer and winter seasons (Fig. 2a, b). These diagrams reveal the distribution of the groundwater samples in different subdivisions of the diamond-shaped field of the piper diagram, the analogies, and dissimilarities. The results obtained in this study were compared with the studies by Igibah and Tanko (2019) where results revealed that the water quality parameters showed wide spatial variations in the order Na+ > SO42− > EC > Mg2+ > TDS > Fe2+ > HCO3− > F > TH > Cl, ensuing groundwater contamination from weathering, agriculture and anthropogenic activities

Fig. 2
figure2

Piper trilinear diagram representing the chemical analysis during summer (a) and winter (b) season

Water quality assessment

The ion concentration distribution as displayed on the piper-diagram, where the trilinear diagrams illustrated the relative concentrations of cations and anions. During summer, it is found that 95% of the groundwater is falling in the field 1, 5% in the field 2, 13% in the field 3, 88% in the field 4, whereas 10% of the groundwater in the field 5 including secondary alkalinity. 25% of the area falls in the field 6, under secondary salinity. 11% of the groundwater falls in the field 7 indicating primary salinity and nil, in the field 8 indicating no primary alkalinity. It is witnessed that the entire area is devoid of alkaline earth and secondary salinity. During winter, it is found that 95% of the groundwater is falling in the field 1 and 5% and 10% of the groundwater is falling in the fields 2 and 3. 90% falls in the field 4 of strong acids exceed weak acids, whereas 10% of the groundwater in the field 5 including secondary alkalinity. 28% of the groundwater samples fall in the field 6 of secondary salinity. 4% of the groundwater falls in the field 7 indicating primary salinity and 57% in the area stating that no cation and anion exceeds 50%. The cations, particularly Ca, Mg and Na, concentrations during winter are almost same as during summer, due to which there is no major difference in the percentage of samples falling in field 1 in piper diagram. It is presumed that there is no significant dilution of cationic and anionic concentrations of almost all samples during winter compared to summer. The geochemistry of subsurface waters is affected by reactions with host rocks. Bi-carbonate (HCO3) is the dominant ion in most subsurface waters. The well water may be the principle source of drinking water for the majority of communities in the third world countries, as well as for small, remote communities or homesteads in the industrialized countries. It is necessary to understand the relationship between the rock type and chemical characteristics of water. The nature of water is of indispensable concern for humankind since it is specifically connected with human welfare. It is presently for the most part perceived that the nature of groundwater accessible in a region is as essential as the quantity.

Correlation of physicochemical parameters

Correlation coefficients between 20 representative chemical parameters were calculated for both summer and winter seasons and displayed in Table 5. The importance of the linear relationship between variables is determined by coefficients in the (− 1, 1) interval. The connection between two factors is the relationship coefficient (‘r’) which indicates how one variable predicts the other. A high correlation coefficient (near 1) means a good relationship between two variables, and a correlation coefficient around zero indicates no relationship. Positive values of ‘r’ indicate a +ve relationship while −ve values indicate an inverse relationship. The results in this study show different types of correlation, stronger positive, weak positive and negative type of correlation coefficient. The highest correlation (r > 8.0) is noticed during summer, Ca–As; Cl–As; Cr–Cl; Cu–Ca; EC–Cr; Mg–As, Cu; Na–K; Ni–CO3, Mg; SO4–Cd, Cr, HCO3, Na, Ni; and during winter Ca–As; Cd–As; CO3–Cd; Cr–As, CO3, K; NO3–Cd; Ni–CO3, K, Mg; Pb–As, CO3; SO4–Na, Ni; TDS–Ni, SO4; Zn–Cl, Pb.

High and significant correlations between cations, anions and metals indicate that contaminants in the study area (KIDA) waters have a similar source which originates from industrial activities.

Principal component/factor analysis

Factor analysis was performed for the samples during summer and winter by the extraction method (principal component analysis). The rotation of the principal components was executed by the Varimax method with Kaiser normalization. The outcome of the PCA based on the correlation matrix of chemical components for summer and winter seasons are expressed in Table 6. Six components of PCA analysis showed 70.43% of the variance in the summer data set of the study area. The eigenvectors classified the 20 physicochemical parameters including heavy metals into six groups. The first component (VF1) is loaded with cations and anions; the second component (VF2) is loaded with trace and toxic metals, while third, fourth and fifth components (VF3, VF4, VF5) shows F, Cu and SO4 and sixth component (VF6) was not significant. Whereas, for winter data set the six components of PCA analysis showed 71.06% of the variance for 20 physicochemical parameters into six groups. The first component (VF1) is loaded with major elements (Ca, Cl, CO3, Mg, and TDS)’ the second component (VF2) is loaded with trace and toxic metals. While third, fourth, fifth components (VF3, VF4, VF5, V6) are loaded with major and toxic metals.

Table 6 Loading for varimax rotated factor matrix during summer and winter of six-factor model explaining 70.43% of the total variance for groundwater during summer season

To select wells with a natural content of heavy metals, free of anthropogenic contamination, filled with uncertainties especially when some wells of little contamination were also inside the industrial area. A statistical interpretation of all the summer chemical well data improved the understanding of the chemical well characteristics. The results of the factor analysis as shown in Fig. 3 clearly indicate the increasing anthropogenic contamination. From a small cluster to the left of uncontaminated wells the contamination is increasing to the right, first in wells with contamination of individual heavy metals and further to the right wells, multi-contamination of heavy metals.

Fig. 3
figure3

Distribution of variables given by the factor analysis

Factor 1 during summer and winter seasons represented 25.35% and 23.38% of the aggregate difference and was essentially made out of positive loading (Ca2+, Cl CO32−, EC, TDS and HCO3). The positive content of Ca demonstrated the factors relationship with water–rock interaction as Ca2+ in groundwater essentially originates from the disintegration of carbonate. The Cl-might be derived from the contamination sources, for example, effluents of industrial and domestic composts and septic tanks (Bohlke and Horan 2000; Widory et al. 2004; Valdes et al. 2007) and common sources, for example, precipitation, the suspension of liquid considerations and Cl-bearing minerals. The high positive stacking of EC and negative stacking of pH supported the hypothesis of water–rock interaction. The electrical conductance imitates the measure of material dissolved in groundwater and pH estimation of the groundwater imitates the H+ particle focus. The higher positive stacking of EC and TDS esteems are a marker of higher ionic concentrations, likely because of the high anthropogenic events in the study area and geological weathering condition.

The study area is most densely populated with both industrial and residential area and consequently witness higher groundwater abstraction. The nearby anthropogenic activities could be released from intensive and drawn out farming activities which present ions and metals from composts and different agrochemicals (Laar et al. 2011; Dinka et al. 2015). TDS estimate of > 500 mg/kg during the two seasons shows the nearness of marginally hoisted groupings of salts and is identified with different issues, for example, hardness (Herojeet et al. 2013). The dominance and source of CO3 and HCO3 in the study area may be attributed to the dissolved CO2 in rainwater which dissolves as both CO3 and HCO3 ions when entered into the soil. Bouwer (1978) indicated that HCO3 is mainly formed due to the action of CO2 from the atmosphere and that, released from organic decomposition, accumulation of solid waste in industrial and sewage effluents. Therefore, factor 1 is assumed to be indicative of the contamination source related to human activity. Factor 2 explains during summer and winter seasons accounted for 15.21% and 17.33% of the total variance and was primarily composed of positive loading (As, Cr, Zn, Cd, Ni, and Pb). The data uncovers that these heavy metals have transported from surface water. As the study area is enveloped by granitic rocks, the spread of trace elements in granitic landscape is through fractures and joints which is extensively rapid than that in sedimentary arrangement.

The high loadings of Ni in winter may have impacted by nickel discharge into the air by scrap incinerators situated in a portion of the industries of the study area, which may have settled on surface water when it turns out to be a part of waste water streams as industrial effluents. The vast piece of all Ni aggravates that are released to the earth will ingest to deposits and end up stable (Krishna and Mohan 2014). Factor’s 3, 4 and 5 during summer season accounted for 8.72%, 8.04% and 6.68% of the total variance with loadings of F, Cu, and SO4. The high scores of SO4 recorded explain the dissolution of sulfides such as pyrite from the interstratified materials by percolating into the water which produces SO4 ions in water. Further, the salt water intrusion due to high TDS values in the study area is also probable source of the high SO4 values. The Cu may be attributed to the anthropogenic activity due to industrial pollution. The positive loadings of factor’s 3, 4, 5 and 6 with As, K, Cr, F, and pH during winter season accounted for 9.5%, 7.89%, 7.35%, 5.58% and 5.58% of the total variance. These loadings of K, F, pH and heavy metals can be attributed to the agriculture pollution and high salinity in the study area due to deposition of pesticides on to the surface soil, water and percolation into the groundwater aquifer system. Further, in a nutshell the results of PCA/FA can be presumed that in the study area the contamination of groundwater is mainly from agriculture run-off, soil weathering and run-off from solid waste, domestic and industrial wastewater disposal.

Cluster analysis

Cluster analysis involves a progression of multivariate strategies which are utilized to find right groups of information or stations. In clustering, the objects are grouped with the end goal that comparative articles fall into a similar class (Danielsson et al. 1999; Mrazovac et al. 2013). The hierarchical cluster analysis (CA) was applied utilizing Ward’s strategy (linkage between groups), Euclidian separation as a similarity measure and synthesised in dendrograms. CA was performed on groundwater samples for both summer and winter seasons. The results are illustrated by dendrograms (Fig. 4a, b).

Fig. 4
figure4

Dendrogram of hierarchical cluster analysis in groundwater during Summer (a) and winter (b) season

Dendrogram obtained for groundwater during summer (Fig. 4a) showed five clusters with Ca, TDS, Cl (Cluster I), CO3, HCO3, EC (Cluster II), Mg, Na, Cu, K (Cluster III), As, Ni, Cr, Zn, Cd, Pb (Cluster IV), NO3, F, pH, SO4 (Cluster V). Cluster I and II indicate the similar activity of factor 1; Cluster IV and V represent the same activity of factor’s 2, 3, 4, 5 obtained by factor analysis during both summer and winter seasons. The following multielement factors were divided into factors with strong anthropogenic influence. Whereas, Cluster III represents the combined activity of factor 1 and factor 2. Similarly for groundwater, dendrogram obtained during winter (Fig. 4b) also showed five clusters with Pb, Zn, Ni (Cluster I), Cd, Cu, As, K, Na (Cluster II), Cr, F, NO3 (Cluster III), pH, SO4, EC, Mg, CO3, HCO3 (Cluster IV), Ca, TDS, Cl (Cluster V). The cluster’s I, II and III show the dominance of SO4, Ca, Na, K, Mg, pH, and NO3. TDS and moderate loadings on Na and K basically represents the solids group. This clustering points to common sources of natural process of disintegration of soil constituents primarily carbonates. It also represents the nutrients group of contaminants which points to some source of wastewater run-off. The level of Nitrates in water suggests human health and is a marker of the level of natural contamination of the water source (Donkor et al. 2015; Eletta et al. 2010; Gopalkrushna 2011; Mahananda et al. 2010). Therefore, the dominant source may be attributed to anthropogenic contamination from surrounding industries and release of effluents and domestic waste.

Human health risk assessment

It was observed that inhabitants in the study area were utilizing groundwater for different local and drinking purposes. Accordingly, encompassing water drinking sources from bore-wells and hand pumps which were utilized generally for local intentions were additionally chosen for substance parameters (cations/anions) risk evaluation like chronic daily intake (CDI) and hazard quotient (HQ) indices. The results of which are briefed in Table 7. The results in the study area recommend that, where individuals have used groundwater for residential utilization is gradually heading with expanded levels of hazardous elements and groundwater in a few sections that are not reasonable for drinking.

Table 7 Chronic daily intake (CDI) and hazard quotient (HQ) indices for major cations and anions

The CDI values for major cations and anions (Table 7) in groundwater during summer ranged from 2.44 to 68.9 for Ca, 0.06 to 7.94 for Mg, 0.86 to 28.1 for Na, 0.02 to 0.57 for K, 0.89 to 8.92 for CO3, 0.86 to 21.2 for HCO3, 0.32 to 32.8 µg/kg per day for Cl−, 0.00 to 0.03 for F, 0.26 to 13.7 for SO4 and 0.17 to 6.17 for NO3 respectively. Whereas, during winter CDI values were ranging from 0.11 to 2.19 for Ca, 0.005 to 0.585 for Mg, 0.033 to 0.680 for Na, 0.001 to 0.006 for K, 0.024 to 0.249 for CO3, 0.014 to 0.348 for HCO3, 0.009 to 0.702 µg/kg per day for Cl−, 0.00 to 0.034 for F, 0.004 to 0.177 for SO4 and 0.002 to 0.052 for NO3 respectively. Therefore, the order of toxicity in the form of CDI indices for major cations and anions based on mean concentrations for groundwater during summer and winter were found in the order of Ca > Na > HCO3 > Cl > CO3 > SO4 > Mg > NO3 > K > F. The high CDI values during summer may be attributed to anthropogenic activity the pesticide usage in agriculture fields wherein it is left into the streams as run-off from the fields.

Hazard quotient (HQ) indices

Table 7 also summarizes the HQ indices of selected major cations and anions in the study area through regular consumption of groundwater for various purposes in the study area. The mean HQ index values for Ca2+, Mg2+, K+, Cl, F and NO3− for groundwater water during summer were 0.43, 0.14, 0.13, 0.11, 0.21 and 0.71 respectively. Similarly, for groundwater winter samples the mean HQ index values were 0.019, 0.011, 0.003, 2.32, 0.018 and 0.009. The HQ value for chloride indicates higher with 2.32 when compared to other parameters. Cl is one of the major inorganic anions in water and consumable water, the salty taste is delivered by the chloride ions. There is no known evidence that chlorides constitute any human health hazard and for this reason, chlorides are limited to 250 mg/L in supplies intended for public use (WHO 2014). Therefore, the order of distribution of cations and anions based on their mean concentration values during summer season is of the order Cl > K+ > Mg2+ > F > Ca2+ and NO3 for groundwater and during winter seasons the order of distribution was Cl > K+ > NO3 > Mg2+ > F > Ca2+.

Conclusions

This study reported the groundwater quality, toxicity and health risk in an industrial area by using various multivariate statistical and health risk methods for twenty physiochemical constituents of 120 groundwater samples collected during summer and winter seasons. The study area results demonstrated that both natural and anthropogenic processes were the two major factors for the chemical compositions of groundwater. The water quality results revealed that Ca–Na–Mg–HCO3 type with dominant concentrations during both summer and winter seasons respectively contributing to the groundwater salinity. The multiple regression analysis for physicochemical constituents exhibited the highest correlation (r > 8.0) during summer and winter. Results from factor analysis indicated that Ca–Cl–CO3–HCO3–EC–TDS were dominating in factor 1 which were primarily from water–rock interaction like granite rock and slightly from anthropogenic inputs. Whereas, factor 2 is dominated by toxic heavy metals As–Cr–Cd–Ni–Pb–Zn combined during summer and winter seasons in groundwater from sources related to industrial waste, effluents release and human activities. The health risk hazard evaluation like CDI and HQ records exhibited that the groundwater is safe to drink given some water treatment systems are involved. Overall, the multivariate and risk assessment approach suggests goodness of these statistical techniques in the source apportionment of industrial waters and can be ranked in the order of mean cation values K < Mg < Na < Ca, anion concentrations arise in the order of F < NO3 < SO4 < CO3 < Cl < HCO3 and heavy metals occur in the order Cd < As < Ni < Cu < Cr < Pb < Zn.

Recommendations

The present study suggests that regular monitoring of the quality of groundwater should be undertaken temporally and spatially to identify the source of toxic pollutants and other inhibitory chemicals which affect the water around industries and design some remedial techniques to prevent the pollution caused by hazardous toxic elements in future.

Availability of data and materials

Not applicable.

Abbreviations

PCA:

principal component analysis

HCA:

hierarchical cluster analysis

CA:

cluster analysis

FA:

factor analysis

CDI:

chronic daily intake

HQ:

hazard quotient

KIDA:

Katedan Industrial Development Area

ICP-MS:

inductively coupled plasma mass spectrometry

GPS:

global positioning system

References

  1. Ammar FH, Chkir N, Zouari K, Hamelin B, Deschamps P, Aigoun A (2014) Hydro-geochemical processes in the Complexe Terminal aquifer of southern Tunisia: an integrated investigation based on geochemical and multivariate statistical methods. J Afr Earth Sci 100:81–95

  2. Andreas M, Sebastian L, Monika M, Gerhard S, Frido R, Mario S (2009) Temporal and spatial patterns of micropollutants in urban receiving waters. Environ Pollut 157:3069–3077

  3. APHA (American Public health Association) (1995) Standard methods for the examination of water and waste water, 19th edn. The American Water Works Association (AWWA) and the Water Environment Federation (WEF), Washington, D.C

  4. Boehlke JK, Horan M (2000) Strontium isotope geochemistry of groundwater and streams affected by agriculture, Locust Grove, MD. Appl Geochem 15:599–609

  5. Bouwer H (1978) Groundwater hydrology. McGraw-Hill, New York

  6. Buechler S, Mekala GD (2005) Local responses to water resource degradation in India: groundwater farmer innovations and the reversal of knowledge flows. J Environ Dev 14(4):410–438

  7. Cao Y, Tang C, Song X, Liu C, Zhang Y (2016) Identifying the hydro chemical characteristics of rivers and groundwater by multivariate statistical analysis in the Sanjiang Plain, China. Appl Water Sci 6:169–178

  8. Carlon C, Critto A, Marcomini A, Nathanail P (2001) Risk based characterisation of contaminated industrial site using multivariate and geostatistical tools. Environ Pollut 111:417–427

  9. Chen X, Zhou WQ, Pickett STA, Li WF, Han LJ, Ren YF (2016) Diotoms are better indicators of urbanstream conditions: a case study in Beijing, China. Ecol Ind 60:265–274

  10. Danielsson A, Cato I, Carman R, Rahm L (1999) Spatial clustering of metals in the sediments of the Skagerrak/Kattegat. Appl Geochem 14:689–706

  11. Dinka MO, Loiskandl W, Ndambuki JM (2015) Hydrochemical characterization of various surface water and groundwater resources available in Matahara areas, Fantalle Woreda of Oromiya region. J Hydrol Reg Stud 3:444–456

  12. Dixon W, Chiswell B (1996) Review of aquatic monitoring program design. Water Res 30:1935–1948

  13. Donkor NKA, Ansah EEK, Opoku F, Adimado AA (2015) Concentrations, hydrochemistry and risk evaluation of selected heavy metals along the Jimi River and its tributaries at Obuasi a mining enclave in Ghana. Environ Syst Res 4:12

  14. Eletta O, Adeniyi A, Dolapo A (2010) Physico-chemical characterization of some groundwater supply in a school environment in Ilorin, Nigeria. Afr J Biotechnol 9(22):3293–3297

  15. Ghosh P (2005) Drug abuse: ranbaxy, Dutch Pharma put paid to groundwater. Down Earth 14(17):7–8

  16. Gopalkrushna MH (2011) Assessment of the physico-chemical status of groundwater samples in Akot city. Res J Chem Sci 1(4):117–124

  17. Govil PK, Sorlie JE, Sujatha D, Krishna AK, Murthy NN, Mohan KR (2012) Assessment of heavy metal pollution in lake sediments of Katedan Industrial Development Area, Hyderabad, India. Environ Earth Sci 66:121–128

  18. Herojeet RK, Rishi MS, Sidhu N (2013) Hydrochemical characterization, classification, and evaluation of groundwater Regimein Sirsa Watershed, Nalagarh Valley, Himachal Pradesh, India. Civil Environ Res 3(7):47–57

  19. Howladar MF, Al Numanbakth MA, Faruque MO (2017) An application of Water Quality Index (WQI) and multivariate statistics to evaluate the water quality around Maddhapara Granite Mining Industrial Area, Dinajpur, Bangladesh. Environ Syst Res 6:13

  20. Igibah CE, Tanko JA (2019) Assessment of urban groundwater quality using Piper trilinear and multivariate techniques: a case study in the Abuja, North-central, Nigeria. Environ Syst Res 8:14

  21. Issa YM, Elewa AA, Rizk MS, Hassouna AFA (1996) Distribution of some heavy metals in Qaroun Lake and river Nile, Egypt, Menofiya. J Agric Res 21:733–746

  22. Kazi TG, Arain MB, Jamali MK, Jalbani N, Afridi HI, Sarfraz RA, Baig JA, Shah Abdul Q (2009) Assessment of water quality of polluted lake using multivariate statistical techniques: a case study. Ecotoxicol Environ Saf 72(2009):301–309

  23. Krishna AK, Mohan KR (2014) Risk assessment of heavy metals and their source distribution in waters of a contaminated industrial site. Environ Sci Pollut Res 21:3653–3669

  24. Krishna AK, Satyanarayanan M, Govil PK (2009) Assessment of heavy metal pollution in water using multivariate statistical techniques in an industrial area: a case study from Patancheru, Medak District, Andhra Pradesh, India. J Hazard Mater 167:366–373

  25. Laar C, Akiti TT, Brimah AK, Fianko JR, Osae S, Osei J (2011) Hydrochemistry and isotopic composition of the Sakumo Ramsar Site. Res J Environ Earth Sci 3(2):146–152

  26. Liu CW, Lin KH, Kuo YM (2003) Application of factor analysis in the assessment of ground water quality in a blackfoot disease area in Taiwan. Sci Total Environ 313:77–89

  27. Mahananda M, Mohanty B, Behera N (2010) Physico-chemical analysis of surface and ground water of Bargarh District, Orissa, India. Int J Res Rev Appl Sci 2(3):284–295

  28. McKenna JE Jr (2003) An enhanced cluster analysis program with bootstrap Significance testing for ecological community analysis. Environ Model Softw 18:205–220

  29. Mrazovac S, Miloradov-Vojinovi M (2011) Correlation of main physicochemical parameters of some groundwater in northern Serbia. J Geochem Explor 108:176–182

  30. Mrazovac S, Vojinović-Miloradov M, Matić I, Marić N (2013) Multivariate statistical analyzing of chemical parameters of groundwater in Vojvodina. Chem Erde 73:217–225

  31. Niemi GJ, Devore P, Detenbeck N, Taylor D, Lima A (1990) Overview of case studies on recovery of aquatic systems from disturbance. Environ Manag 14:571–587

  32. Omo-Irabor OO, Olobaniyi SB, Oduyemi K, Akunna J (2008) Surface and groundwater water quality assessment using multivariate analytical methods: a case study of the Western Niger Delta, Nigeria. Phys Chem Earth 33:666–673

  33. Reghunath R, Murthy TRS, Raghavan BR (2002) The utility of multivariate statistical techniques in hydrogeochemical studies: an example from Karnataka, India. Water Res 36:2437–2442

  34. Renato ISA, Carolina SM, Cassio FB, Brisa MF, Nadal Martí, Sierra Jordi, Josep LD, Segura-Muñoz Susana I (2018) Water quality assessment of the Pardo River Basin, Brazil: a multivariate approach using limnological parameters, metal concentrations and indicator bacteria. Arch Environ Contam Toxicol. https://doi.org/10.1007/s00244-017-0493-7

  35. Shrestha S, Kazama F (2007) Assessment of surface water quality using multivariate statistical techniques: a case study of the Fuji river basin, Japan. Environ Model Softw 22:464–475

  36. Simeonov V, Stratis JA, Samara C, Zachariadis G, Voutsa D, Anthemidis A, Sofoniou M, Kouimtzis TH (2003) Assessment of the surface water quality in Northern Greece. Water Res 37:4119–4124

  37. Singh KP, Malik A, Mohan D, Sinha S (2004) Multivariate statistical techniques for the evaluation of spatial and temporal variations in water quality of Gomti River (India): a case study. Water Res 38:3980–3992

  38. Sophie C, Virginie D, Joaquim-Justo C, Sylvie G, Marie-Louise S, Winnie D, Patrick M, Jean-Pierre T (2011) Groundwater quality assessment of one former industrial site in Belgium using a TRIAD-like approach. Environ Pollut 159:2461–2466

  39. SPSS® (Statistical Package for Social Studies) version 6.1, USA. (1995). Professional Statistics 6.1, 385, Marija J. Norusis/SPSS Inc., Chicago

  40. Szymanowska A, Samecka-Cymerman A, Kempers AJ (1999) Heavy metals in three lakes in West Poland. Ecotoxicol Environ Saf 43:21–29

  41. USEPA (1989) Risk assessment guidance for superfund (RAGS): volume I- Human health evaluation manual (HHEM) part A: baseline risk assessment, Interim Final (EPA/540/1-89/002). United States Environmental Protection Agency, Office of Emergency and Remedial Response, Washington, DC

  42. USEPA (1992) Guidelines for exposure assessment, EPA/600/Z-92/001. Risk Assessment Forum, Washington, DC

  43. USEPA (1999) Guidance for performing aggregate exposure and risk assessment office of pesticide programs, Washington, DC

  44. USEPA (2005) Guidelines for carcinogen risk assessment. United States Environmental Protection Agency, Risk Assessment Forum, Washington, DC (EPA/630/P-03/001F)

  45. Valdes D, Dupont JP, Laignel B, Ogier S, Leboulanger T, Mahler BJ (2007) A spatial analysis of structural controls on Karst groundwater geochemistry at a regional scale. J Hydrol 340(1–4):244–255

  46. Vega M, Pardo R, Barrado E, Deban L (1996) Assessment of seasonal and polluting effects on the quality of river water by exploratory data analysis. Water Res 32:3581–3592

  47. WHO, UNICEF (2014) Progress on sanitation and drinking water—2014 update. Monitoring programme for water supply and sanitation. WHO, UNICEF, Geneva. ISBN 978-9-24-150724-0

  48. Widory D, Kloppmann W, Chery L, Bonnin J, Rochdi H, Guinamant JL (2004) Nitrate in groundwater: an isotopic multi-tracer approach. J Contam Hydrol 72(1–4):165–188

  49. Xia J (2002) A perspective on hydrological base of water security problem and its application in North China. Prog Geogr 21(6):51–526

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Acknowledgements

The three anonymous reviewers are profusely thanked for their intellectual comments and constructive suggestions that significantly improved the scientific quality of the manuscript. The present work is a part of authors Ph.D. work and thanks are due to Dr. P. K. Govil for his support, encouragement and scientific inputs. Thanks are also due to Drs. V. Balaram and M. Satyanarayanan for providing the ICP-MS analytical facility. Our sincere thanks are to Dr. V. M. Tiwari, Director, CSIR-National Geophysical Research Institute, for his permission to publish this paper. Institutes reference No: NGRI/Pub/2017/PME.

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In this manuscript AKK has contributed towards initiation of the idea, filed sampling, and interpretation of the data using multivariate statistical tools towards groundwater quality, contamination and health risk assessment. KRM has carried out filedwork and analysed physicochemical parameters and their interpretation. BD has assisted in carrying out filed sampling and sample preparation. All authors read and approved the final manuscript.

Correspondence to A. Keshav Krishna.

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Krishna, A.K., Mohan, K.R. & Dasaram, B. Assessment of groundwater quality, toxicity and health risk in an industrial area using multivariate statistical methods. Environ Syst Res 8, 26 (2019) doi:10.1186/s40068-019-0154-0

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Keywords

  • Water quality
  • Groundwater
  • Contamination
  • Multivariate analysis
  • Risk assessment