Integrated approach of heavy metal pollution indices and complexity quantification using chemometric models in the Sirsa Basin，Nalagarh valley，Himachal Pradesh，India

Rajkumar Herojeet·Madhuri S.Rishi·Naval Kishore

Integrated approach of heavy metal pollution indices and complexity quantification using chemometric models in the Sirsa Basin，Nalagarh valley，Himachal Pradesh，India

Rajkumar Herojeet1·Madhuri S.Rishi1·Naval Kishore2

Chemometric techniques and pollution assessment indices were applied to determine the source and intensity of pollution in the Sirsa River，Himachal Pradesh，India.Results show EC，Cr，F(xiàn)e，Mn，and Ni were above the permissible limit as per the Bureau of Indian Standards. The heavy metal pollution index（HPI）and contamination index（Cd）provided contrasting outcome and poor correlation was observed.A heavy metal evaluation index（HEI）method was developed using a multiple of the mean and correlation coefficient values to provide an alternative pollution classification.The criteria of HEI adopted for reclassification of HPI and Cdproduced comparable results；40%samples were labeled as low contamination，50%as medium contamination，and 10%as high contamination for all indices.Principal component analysis along with cluster analysis was used to identify the main factors responsible for degradation of water quality，namely discharge of industrial effluent，river bed mining，agricultural runoff，and minor natural or geogenic input. The methods and chemometric study proposed here can be used as effective tools to gather information about water quality and water resource management.

Heavy metal pollution index·Degree of contamination·Heavy pollution evaluation index·

1 Introduction

Water is important for our existence on this blue planet（Venu and Rishi 2011）.The benefit of natural freshwater reservoirs to mankind cannot be overemphasized（Mahapatra et al.2012；Monjerezi and Ngongondo 2012）.The United Nations Environmental Program（2000）concluded that there is worldwide deterioration of water quality and other studies suggest it is primarily attributable to growing human populations and corresponding economical and industrial development causing eutrophication and heavy metal pollution in the aquatic environment（Peierls et al. 1998；Pekey et al.2004；Li et al.2008；Krishna et al. 2009）.Natural sources of trace metals include volcanism，bedrock erosion，and atmospheric transport；in recent centuries，anthropogenic activities—particularly mining and industrial processing—have demonstrated significant influence on the biogeochemical cycles of trace metals（Nriagu 1989，1996）.The amount of heavy metals present in water determine its use for domestic，irrigation，and industrial purposes.Surface waters are especially vulnerable to heavy metal pollution due to their accessibility for disposal of wastewaters.

In the twenty first century，heavy metal pollution is a major threat and core concern to India's rivers owing to fast growing cities，the expansion of industry，and lack of proper sanitation.Trace metal contamination is important due to its potential toxicity for the environment and human beings（Gueu et al.2007；Adams et al.2008）.Some metals，like Cu，F(xiàn)e，Mn，Ni，and Zn，are essential micronutrients for the life processes in animals and plants while others，such as Cd，Cr，Pb and，Co，have no known physiological activities（Kar et al.2008；Aktar et al.2010）.Metals are non-degradable and can accumulate in the human body system，damaging the nervous system and internal organs（Lohani et al.2008）.Various researchers have studied heavy metal contamination in river water quality with respect to industrial，municipal，and domestic pollution（Table 1）.Monitoring is required on an ongoing basis to assess water quality and sustainable management.This is due to the increasing concentration of heavy metals in potable water，which increases the threat to human health and to the environment.

The Nalagarh valley represents a portion of the southernmost expanse of the Solan district and belongs to the rapidly growing industrial belt of Baddi，Barotiwala，and Nalagarh（BBN）（Kamaldeep et al.2011）.The valley has the fastest industrial growth in the last decade owing to the special packages of incentives granted by the Central government which act as a catalyst in boosting industrial development in the state，and particularly in the BBN area（Herojeet et al.2013）.The study area has the largest share of approved industries in the state of Himachal Pradesh.As per the BBN Development Authority report in 2007，the 72%of industries in Nalagarh operating without effluent treatment plants（ETP's）have aggravated the pollution in the Sirsa River.This has resulted in a high cumulative industrial and domestic load in the Sirsa watershed. Quarrying of the river bed for construction material and encroachment on the river bed are some of the recent problems.Besides industrialization，an increase in human population and economic activity in this region have led to an increase in demand for freshwater.Hence，it is necessary to assess the declining water quality and quantity of the river.

Severalwaterqualityindicesandcorresponding applications of index methods have been proposed for water quality assessment（Nishidia et al.1982；Tiwary and Mishra 1985；Edet and Offiong 2002；Bhuiyan et al. 2010）.Pollution indices are simple，useful，and easy-tounderstand tools for water quality executives，environmental managers，decision makers，and potential users of a given water system.The water quality index（WQI）was initially developed in the USA by Horton（1965）and has been widely used in Africa and Asia（Li et al.2009）.WQI is one of the most effective techniques of rating，taking into consideration the composite influence of individual water quality parameters on the overall quality of water（Mohanta and Patra 2000；Lermontov et al.2009）.WQI is calculated from the view point of human consumption and public health.Multivariate statistical techniques such as principal component analysis（PCA）and cluster analysis（CA）are other methods that have been used by many researchers around the world for the assessment of water quality（Swanson et al.2001；Arora and Mehra 2009；Vieira et al.2012）.The application of multivariate statistical techniques facilitates interpretation of complex data matrices for better understanding of water quality and variability of environmental factors，filling in gaps associated with WQI.Through comparative evaluations and multivariate analyses of heavy metal pollution index（HPI），heavy metal evaluation index（HEI），and contamination index（Cd），which have been successfully used by many researchers（Mohan et al.1996；Offiong and Edet 1998a，b；Edet and Offiong 2002；Prasad and Mondal 2008；Venkatramanan et al.2015），the present investigation focused on assessing the prevailing water quality to identify the pollution status and probable sources of pollutants in the Sirsa River.

Table 1 Comparison of dissolved metal concentrations with other Indian rivers as well as world river average

2 Study area

The study area lies between 30°52′and 31°04′N and between 76°40′and 76°55′E，forming the lower part of the Siwaliks.The Nalagarh valley is the southeastern most Himalayan intermountain valley and is about 230 km2.The valley is delimited by the Siwalik Hills to the northeast and Sirsa Nadi to the southwest.The valley borders Haryana to the southeast（i.e.Kalka-Pinjore area）and Punjab to the southwest（i.e.Ropar district）.The Sirsa is the main river that flows through the central part of the Nalagarh valley.It is a major tributary of the Sutluj River which originates from the southwest shoulder of Kasauli Dhar and flows across the valley（Fig.1）.The Sirsa watershed occupies the whole Nalagarh valley，as well as parts of Solan and Kasauli in Himachal Pradesh，Pinjore in Haryana，and Ropar in Punjab.Numerous perennial and ephemeral streams emerge from the northeast and pass through the BBN industrial belt，often becoming loaded with industrial and sewage discharges prior to joining the Sirsa Nadi（CGWB 1975）.Of these streams，the most important are the Chikni Nadi，Phula Nadi，Ratta Nadi，Balad Nadi，and Surajpur Chao.The climate of the valley is sub-tropical. The rainfall in the study area is governed by southwest monsoons from late June to early September.The winter rains are well-distributed from December to March by western disturbances.The average annual rainfall is about 1046 mm，with an average of 56 rainy days.The discharge in the streams fluctuates in accordance with the climatic conditions.During the monsoon，the streams are flooded and carry large amounts of sediment and deposit it in the flood plain of the valley.The drainage pattern in the study area is controlled by the Sirsa River along with its tributaries.The Sirsa River is a major source of water for the region as well as an effluent sink for the nearby industries. The industrial activity has ecological and environmental impacts，including heavy metal pollution in the river water.

3 Geology

Stratigraphically，the Nalagarh valley and its flanks are bounded by tertiary formations；structurally，they are highly disturbed.The rock types of the area can be broadly grouped into two tectonic zones striking and trending NW—SE.From north to south the two zones can be described as follows（Fig.2）；

（A） Belt of lower and middle tertiary occurring along the northeast flank of the valley（para-autochthonous）.

（B） Belt of upper tertiary confined to the valley and along its southwest flank（autochthonous）.

The contact of these zones is marked by a major fault—the Nalagarh thrust.A second major NE-SW trending thrust is known as the Sirsa thrust.The Nalagarh thrust is between Kasauli and the middle Siwaliks，while the Sirsa thrust separates the upper and middle Siwaliks.The majority of the Sirsa River basin is covered by alluvium soil consisting of Holocene and Pre-Holocene deposits. The alluvium varies from 10 to 20 m thick and is mostly granular.The upper and middle parts of the river basin are dominated by beds of clay alternating with cobbles，pebbles，gravel，and sand.The sediments get finer and finer until they become clay in the downstream part of the basin.The stratigraphic sequence of the basin is given in Table 2.

4 Methodology

Water samples were collected from ten different locations along the Sirsa River and its major tributaries at about 1.9 km intervals.Sampling stations were chosen to facilitate quantification of the impacts of industrial activity and land use patterns on heavy metal concentrations.The samples were taken from 10 to 15 cm below the water surface using plastic bottles（1000 mL）with locking lids，preserved by acidifying to pH～2 with HNO3，and kept at 4°C until analysis as per standard procedures（APHA 2002）.Temperature，pH，EC，and TDS were measured on site at the time of sample collection using a mercury thermometer and portable water analysis kit.The collected water samples were filtered for further analysis using Whatman filter paper no.42 with a pore size 2.5 μm.The concentrations of heavy metals（As，Cd，Cr，Cu，F(xiàn)e，Mn，Ni，Pb，and Zn）were determined using atomic absorption spectrophotometer with Perkin Elmer Science Elan 5000. Appropriate quality control/quality assurance samples were collected to provide confidence in the data regarding bias and variability.No replicates were analyzed for these samples.A drift blank（distilled water）was taken before the analysis of samples.This was to verify that decontamination procedures and laboratory protocols were adequate（Koterba et al.1995）.

The method of Ficklin et al.（1992），modified by Caboi et al.（1999），was applied for classification of surface water. Various pollution indices，namely HPI，HEI，and Cdwere used to understand the contamination level of the river with respect to heavy metals.

4.1 Heavy metal pollution index（HPI）

Fig.1 Location map of the study area and sampling points in parts of the Sirsa watershed

HPI is a rating method that considers the composite influence of individual heavy metals on overall water quality.The rating is a value between 0 and 1，and reflects the relative importance of an individual parameter.It can be defined as the weight for each selected water quality parameter and is inversely proportional to the standard permissible value（Si）for the corresponding parameter（Horton 1965；Mohan et al.1996；Reddy 1995；Prasad and Kumari 2008）.For computing HPI，the World Health Organization（1993）water standard for each chemical parameter in μg/L was considered.The index used was developed by Mohan et al.（1996）and proceeded as follows：The first step involved computing the relative weight（Wi）of each parameter using Eq.1.The unit weight（wi）for various water quality parameters is assumed to be inversely proportional to the maximum admissible concentration（MAC）forthecorrespondingparameter（Table 3）.

Fig.2 Geologic map of the study area

Table 2 Geological succession of the study area　

where K=constant of proportionality.

In the second step，an individual quality rating（qi）was computed for each parameter using Eq.2. where Mi=actual value present in the water sample，Ii=ideal value，and Si=standard value（Table 3）.The sign（-）indicates the numerical difference of the two values，ignoring the algebraic sign.

Third，summing these sub-indices resulted in the overall index，as in Eq.3.

where，Qiis the sub index of ith parameter，Wiis the unit weightage for the ith parameter，and n is the number of parameters considered.Generally，the critical value is 100 for drinking water.

Table 3 Standard values for the indices computation

4.2 Contamination index（Cd）

Cdsummarizes the combined effects or degree of contamination of several parameters considered potentially harmful to domestic water（Backman et al.1997）.Cdis a sum of the contamination factors of the individual parameters that exceed their respective permissible values，as presented in Eq.4：

Cdhas been used in previous studies to estimate the degree of metal pollution（Al-Ami et al.1987；Mustafa 2008）.The components considered include As，Cd，Cr，Cu，F(xiàn)e，Mn，Ni，Pb and Zn.Cdmay be classified into three categories（Backman et al.1997；Edet and Offiong 2002）as follows：low（Cd＜1），medium（Cd=1-3），and high（Cd＞3）.

4.3 Heavy metal evaluation index（HEI）

Similar to HPI，HEI assigns an overall water quality with respect to heavy metals（Edet and Offiong 2002）.HEI is computed as in Eq.5.

where Hciis the monitored value of the ith parameter and Hmacithe MAC of the ith parameter.

HEI was used for easy interpretation of the pollution index and level of pollution（Edet and Offiong 2002；Prasanna et al.2012）.

4.4 Chemometric techniques

Chemometric，or multivariate，techniques（PCA and CA）are used to identify the heavy metal pollution sources affecting water chemistry.The statistical software Microsoft Excel 2007 and Minitab v16 were employed for the present data calculation and statistical analysis.

4.5 Principal component analysis（PCA）

PCA is one of the best multivariate statistical techniques for extracting linear relationships among a set of variables（Simeonovetal.2003）.PCAisasetofwidelyusedanalytical techniques whereby a complex dataset containing variables is transformed to a smaller set of new variables，which maximize the variance of the original dataset.PCA provides informationonthesignificantparameterswithminimumloss of original information（Singh et al.2004）.This is achieved by transforming to a new set of variables which are uncorrelated，andwhichareorderedsothatthefirstfewretainmost of the variation present in all of the original variables. Standardization（z-scale）rendered each chemical parameter dimensionless prior to statistical analysis（Simeonov et al. 2004）inordertoeliminatepotentialbiastowardaparameter of different units with high concentration.The principal components are generated in a sequentially ordered manner with decreasing contributions to the variance，i.e.the first principal component（PC1）explains most of the variations present in the original data，and successive principal components account for decreasing proportions of the variance（Pires et al.2009；Vieira et al.2012）.

4.6 Cluster analysis（CA）

CA（hierarchical clustering）is a usefulmethod of objectively organizing a large dataset into groups on the basis of a given set of characteristics.The primary objective of CA is to identify relatively homogenous groups or clusters of objects based on their similarities/dissimilarities（Wai et al.2010）. Thegroupingofsimilarobjectsoccursfirstandeventually，as thesimilaritydecreases，allsubgroupsaremergedintoasingle cluster.In the clustering procedure z-transformation of the raw data was performed with squared Euclideandistanceasa similaritymeasureandWard'smethodoflinkage.Thecluster significance was checked by the Sneath's test of significance.

5 Analytical results

5.1 Water quality characterization

The physical characteristics and heavy metal concentrations of ten water samples from the Sirsa River elucidatethe existing surface water quality（Table 4）.Measured concentrations of heavy metal parameters decrease as follows：Fe＞Mn＞Zn＞Ni＞Cr＞Cu＞Pb＞As＞Cd（Fig.3）.Thestatisticaldescriptionofwaterquality parameters include mean，median，and standard deviation and also show the critical parameters exceeding permissible and desirable limits of the Bureau of Indian Standards（BIS）（1991）（Table 5）.The water temperature ranged from 31 to 36°C with mean±standard deviation（SD）of 34.3±1.494.Values for pH ranged between 7.05 and 8.25 with mean±SD 7.8±0.336，which is slightly alkaline in nature.EC values varied between 340 and 2300 μS/cm with mean±SD 1047.7±612.06 μS/cm.EC was above the permissible and desirable limits for 10%and 50%of samples，respectively，indicating a high solubility of ions. TDS was above the desirable limit in 60%of samples，rangingfrom221to1476 mg/Lwithmean±SD 680.4±396.168.Higher values of TDS，EC，and temperature were recorded in Sandholi nala which may be attributed to direct mixing of heated industrial effluent and domestic sewage.The concentrations of As，Cd，Cu，Pb，and Zn were well below the permissible limits for drinking water（BIS 1991）.The concentration of Cr ranged from 0 to 260 μg/L，with samples exceeding the permissible limit of 50 μg/L only at Nariayanwala（Balad nadi）.Fe exceeded the desirable（300 μg/L）and permissible（1000 μg/L）s in 40%and 50%of samples，respectively.The concentration of Mn exceeded both the desirable（100 μg/L）and permissible limits（300 μg/L）.Ni concentrations were above the permissible limit of 20 μg/L in 80%of samples.Theexcess concentration of heavy metals like Cr，F(xiàn)e，Mn，and Ni in the Sirsa River may be due to anthropogenic activities like industrial effluent；river bed mining of limestone，sandstone，and boulders；and minor geogenic input.

Table 4 Physical and heavy metals analysis of surface water

Fig.3 Heavy metal concentrations in the Sirsa River

Table 5 Statistical description of surface water exceeding permissible and desirable limits（BIS 1991）

Fig.4 Calculation of water samples based on the plot of metal load and pH

The relationship between the water pH and metal load（As+Cd+Cr+Cu+Fe+Mn+Ni+Pb+Zn mg/L）was computed（Ficklin et al.1992；Caboi et al.1999）. Figure 4 shows that a majority of samples（80%）areclassified as near neutral—high metal，with the remaining two sample locations（HM 7 and HM 9）plotting as near neutral—low metal.

Table 6 Results of different indices evaluation　

Table 7 Correlation analysis of different metal concentration and index values

6 Discussion

6.1 Integrated pollution indices

Pollution evaluation indices（HPI，HEI，and Cd）were computed individually using international methods（Edet and Offiong 2002）and are presented in Table 6.HPI ranged from 0.61 to 41.24 with a mean of 16.78，indicating that the selected sampling locations are not critically polluted with respect to heavy metals（Prasad and Bose 2001）. Cdvalues in the study area ranged from 0.23 to 45.29 with a mean value of 19.43.Cdexceeded 3 at eight sampling locations；the remaining two sampling locations—from a tributary of the Sirsa River where minimum industrial activities were encountered—returned values in the medium（HM7）and low（HM9）ranges.HEI was used for easy interpretation of the pollution index（Edet and Offiong，2002）.HEI varied from 9.45 to 55.53 with a mean value of 29.08.

In order to identify the main contributing parameters to the pollution indices，a correlation was carried out between pollution indices and heavy metal parameters as shown in Table 7.This suggests that Fe，Mn，As，and Cd were the key contributing parameters.Cdand HEI show high positive correlations with Fe（0.5943，0.5584）and Mn（0.7277，0.6933）.Offiong and Edet（1998a，b）concluded that high suspended sediment results in the elevation of Fe and Mn concentrations in water.In the case of the Sirsa，this suggests weathering of minerals from extensive river bed mining.Further，HPI shows negative correlation with As（-0.5894）and Cd（-0.5744）.The correlationbetweenCdandHEIissignificant（r=0.9938）and their results show similar trends at various sampling locations（Fig.5）.However，HPI is poorly correlated with Cdand HEI due to some differences between their respective results regarding water quality of the analyzed samples.

Fig.5 Comparative study of pollution evaluation indices

Table 8 Statistical evaluation of water pollution indices　

Fig.6 Distribution of contamination level based on HEI values

Table 9 Classification of surface water quality based on modified categories of Cd，HPI，and HEI

Fig.7 PCA scree plot of the eigen values

Since the measured Cdvalues were too high and the HPI method alone is inconclusive despite the fact that all the sampling locations are well below the critical limit，the HEI method was used to integrate the criteria for various pollution indices.The mean deviation and percent deviation for all indices were enumerated for each sampling point（Table 8）.Half of the HEI and Cdvalues and 40%of the HPI values were below their respective mean values and the percent deviation denotes relatively better quality as supported by Prasad and Bose（2001）.

Edet and Offiong（2002）and Prasanna et al.（2012）classified the HEI values in terms of pollution levels as low，medium，and high using their mean values；the different levels of contamination are demarcated by a multiple of the mean values.The proposed HEI values are reclassified for the samples as follows：low（HEI＜27），medium（27≤HEI≤54），and high（HEI＞54）.By these values，40%of samples were classified as having low pollution levels with 50%and 10%classified as medium and high，respectively（Fig.6）.The multiple mean approach of HEI is also applied to the existing water quality scales for HPI and Cd.Interestingly，the modified scale of HPI and Cdalso resulted in 40%，50%，and 10%of the samples being categorized as having low，medium，and high contamination，respectively（Table 9）.The HEI and reclassification schemes of HPI and Cdpresent comparable results but the HEI method may be preferred to assess the quality of surface water with respect to heavy metals contamination due to its simplicity.

6.2 Factor affecting pollution

PCA was executed in this study for 13 variables from ten sampling locations to identify metal pollution and confirm the sources of pollution.Principal components with eigen values＞1 were considered significant and loading values＞0.40 were taken into consideration for the interpretation（Liu et al.2003；Shrestha and Kazama 2007）.The PCA data obtained for surface water showed five principal components extracted by scree plot explained 91.6%of the total variance（Fig.7）.The calculated factors loadings，cumulativepercentage，andpercentageofvariance explained by each factor are listed in Table 10.

Table 10 Principal component analysis for the surface water sample

PC1 explained 36.4%of the total variance and had moderate negative correlation with EC and TDS reflecting the physicochemical characteristics of water quality.PC2 was loaded Cd，Ni，and Pb and accounted for 22.7%of the total variance，indicating a significant water quality impact from industrial effluent and agricultural runoff（pesticides and fertilizer）（Nriagu 1989；Wu et al.2008）.PC3 was responsible for 13.4%of the total variance，positively considered Cr，Pb，and inversely to As，metals which may be attributed to atmospheric deposition associated with smelting operations，automobile exhaust，and industrial effluent connected with electroplating paints and battery manufacturing.An additional 10.6%of the total variance was explained in PC4，due to strong negative loading on Cu and Fe representing the enhanced geogenic weathering resulting from river bed mining.Lastly，PC5 was dominated by a strong positive correlation with Mn.The main sources of Mn are leaching from local bedrock.The solubility of Mn minerals in water depends directly on anions and oxidation reduction potential particularly at near-neutral pH（ATSDR 2000；Lorite-Herrera et al.2008）.

CA was used to sort the ten sampling locations into a dendrogram （Fig.8）with three clusters having similar water quality characteristics［significance（Dlink/Dmax）× 100＜40］.The patterns formed by each cluster cannot be explained by only considering certain parameters.However，the specific tracers for each pattern representing individualclusterswereclarifiedbycalculatingthe average values of each chemical parameter belonging to each individual cluster.The results are presented in Table 11.The patterns of each cluster were compared with principal components to confirm the identified pollution sources.

Fig.8 Dendrogram generated from physical and heavy metal values of different sampling locations

Table 11 Average value for the water quality parameters for each cluster

Cluster1（C1）wasthebiggestgroupofsamplinglocations with the highest levels of As，Cd，Cr，Cu，Ni，and Zn and an elevated level of Pb which corresponds to PC2 and PC3.C1 containsthelocationsalongtheriverwiththemostsignificant pollution by anthropogenic activities.The intermediate cluster（C2）embodiednaturalwaterqualitywithnospecialtracers and where the averages for all analyzed parameters are less than or lie between the C1 and C3 values.The sampling locations in C2 are the Sirsa River tributaries，representing natural water quality（background level）as PC1.Finally，the third cluster（C3）was characterized by special tracers at the highest levels of temperature，EC，TDS，F(xiàn)e，Mn，and Pb corresponding to PC4 and PC5.These sampling locations were significantly affected by river mining activities and geogenic weathering of the altered river bed.

Further，the sampling locations grouped at each cluster were compared with HEI classifications to verify the extent of pollution.C1（samples 1，2，3，4，6，and 8）is categorized as the medium and low classes of pollution，C2（7 and 9）as the low class，and C3（5 and 10）as the medium and high classes.A good correlation between the chemometric techniques and indexing approach was elucidated in all analyzed datasets even though there are some differences between the results of CA and HEI.

7 Conclusion

In this study，integrated pollution evaluation indices and chemometric models were used to assess the impact and probable sources of pollution in surface water.Statistical description revealed that Cr，F(xiàn)e，Mn，and Ni were above the permissible BIS limits and 80%of samples were classified as near neutral—high metal load.The analyses of HPI and Cdprovided extreme results and were very poorly correlated.The variation may be attributed to the different evaluation process methods and concentration of heavy metals.A proposed HEI method was developed to providemeaningful pollution classification of the water samples and to bridge HPI and Cd.In the HEI scheme，samples were divided into three classes，namely low，medium，and high pollution.Using multiple mean criteria of HEI，reclassification of HPI and Cdlend a commensurate conclusion.PCA with the support of CA was used to identify the factors，revealing possible sources of water pollution and concentration of each water quality parameter contributed by identified pollution factors.The results identified the dominant role of industrial effluent and river bed mining in water pollution.The sampling locations at C1 fall in the medium and low classes，those in C2 belong to the medium class，and those in C3 to the medium and high classes as per HEI classification.The application of integrated heavy metal indices and chemometric models of surface water quality assessment has been demonstrated in this study.

AcknowledgmentsThe authors are thankful to the Chairperson，Department of Environment Studies and Department of Geology（CAS），Panjab University，Chandigarh，for providing necessary research facilities.The authors extend their sincere gratitude to editors and reviewers for reviewing the manuscript and providing important suggestions and comments to improve this paper.

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10.1007/s11631-015-0075-1

3 November 2014/Revised：10 August 2015/Accepted：25 September 2015/Published online：8 October 2015

? Rajkumar Herojeet

herojeet.rk@gmail.com

1Department of Environment Studies，Panjab University，Chandigarh，India

2Department of Geology（CAS），Panjab University，Chandigarh，India

?Science Press，Institute of Geochemistry，CAS and Springer-Verlag Berlin Heidelberg 2015

Principal component analysis·Cluster analysis·Industrial effluent·Sirsa River