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A Multivariate Statistical Modeling of Geochemical Factors of Soils, Sediments and Ground Water By Shri Sabyasachi Rout Bhabha Atomic Research Centre, Mumbai A Dissertation Submitted to the Board of Studies in Engineering Sciences In Partial Fulfillment of Requirements For the Degree of Master of Technology Of Homi Bhabha National Institute February, 2011

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Page 1: A Multivariate Statistical Modeling of Geochemical Factors of Soils, Sediments and Ground Water M.tech Thesis_Sabyasachi Rout

A Multivariate Statistical Modeling of Geochemical Factors of Soils, Sediments and Ground Water

By

Shri Sabyasachi Rout

Bhabha Atomic Research Centre, Mumbai A Dissertation Submitted to the

Board of Studies in Engineering Sciences

In Partial Fulfillment of Requirements

For the Degree of

Master of Technology Of

Homi Bhabha National Institute

February, 2011

Page 2: A Multivariate Statistical Modeling of Geochemical Factors of Soils, Sediments and Ground Water M.tech Thesis_Sabyasachi Rout

Homi Bhabha National Institute Recommendations of the Thesis Examining Committee

As the members of Thesis examining Committee, we recommend that the dissertation prepared

by Shri Sabyasachi Rout entitled “A Multivariate Statistical Modeling of Geochemical Factors of

Soils, Sediments and Ground Water” be accepted as fulfilling the dissertation requirement for the

Degree of Master of Technology.

Final approval and acceptance of this dissertation is contingent upon the candidate‟s submission

of the final copies of the dissertation to HBNI.

Date: July26, 2011

Place: Mumbai

II

Page 3: A Multivariate Statistical Modeling of Geochemical Factors of Soils, Sediments and Ground Water M.tech Thesis_Sabyasachi Rout

DECLARATION

I, hereby declare that the investigation presented in the thesis has been carried out by me. The

work is original and has not been submitted earlier as a whole or in part for a degree/diploma at

this or any other Institution/University.

Sabyasachi Rout

III

Page 4: A Multivariate Statistical Modeling of Geochemical Factors of Soils, Sediments and Ground Water M.tech Thesis_Sabyasachi Rout

ACKNOWLEDGEMENTS

I had the unique privilege of working under guidance of Dr. P. K. Sarkar, Head, Health Physics

Division and Dr. A.G Hegde, Head Environmental studies section, Bhabha Atomic Research

Center, Mumbai.

I take this opportunity to express immense debt of gratitude for their relentless guidance and

supervision. I wish to express my deep sense of gratitude to Mr. Ajay Kumar, for his day to day

supervision at every stage of my work. It is indeed his constant encouragement and valuable

advice, which enabled to me complete this thesis in present form.

I also thankful to Mr. Manish Kumar Mishra, Dr. (Smt) Usha Narayanan and Smt. Rupali K

Health Physics Division for their valuable advice and help during my experimental works.

It would be incomplete if I do not acknowledge, Shri G.L Teli and Shri A.K.Kazi for giving me a

helping hand during collection of samples and experimental works.

Sabyasachi Rout

IV

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CONTENTS

Page. No.

Synopsis VIII

List of Figures X

List of Tables XI

CHAPTER-1 Introduction

1.1 Genesis 1

1.2 Multivariate statistical Analysis of Variance

1.2.1 Factor analysis (FA) or Principal component analysis (PCA) 3

1.2.2. Cluster Analysis 5

1.2.2.1 Clustering Observations or type 6

1.2.2.2 Distance Measures 8

1.2.2.3 Possible Data Problems in the Context of Cluster Analysis 8

1.3 Hydro geochemical Models and Computer programs 9

1.3.1 Trilinear Piper diagram 10

1.3.2 Gibbs- boomerang diagram 11

1.3.3 Stability diagram between solid-liquid phases in aquatic system 12

1.3.4 United states salinity laboratory classification for irrigation

water diagram

12

1.3.5 PHREEQC hydrochemical code 12

1.3.6 WATCLAST Hydrogeochemical model 13

1.3.7 Ionic Equilibrium model (MEDUSA) 14

1.4 Quality Assessment of Soil, Sediment and Groundwater in the

terrestrial environment

14

1.4.1 Enrichment Factor of heavy metals 16

1.4.2 Geo-accumulation Index (Igeo) of heavy metals 17

1.4.3 Pollution Load Index (PLI) for sampling sites 18

1.5 Objective

18

V

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CONTENTS

Page. No.

CHAPTER-2 Materials and Methods

2.1 Context of Study area

2.1.1 Geology and Hydrogeology 20

2.1.2 Sampling Sites and Samples Collection 22

2.2 Preliminary process of ground water, soil and sediment samples 24

2.3 Sample digestion and analysis preparation 24

2.4 Analysis Techniques

2.4.1. Field Measurements 24

2.4.2. Major cations and anions analysis 25

2.4.3 Analysis of heavy metals 25

2.4.3.1 Preparation of post column PAR 25

2.4.3.2 Detection and separation of Cu, Fe, Mn, Ni and Co using ion

chromatography

25

2.4.3.3

Determination of Pb and Cd using DPASV (differential pulse

adsorptive stripping voltammeter

27

CHAPTER-3 Results and Discussions

3.1 Basic Statistical Analyses 28

3.2 Hydro-geochemical evaluation of groundwater 31

3.3 Piper‟s groundwater‟s classification 32

3.4 Saturation Indices (SI) of minerals in ground water 40

3.5 Speciation study of major chemical species in ground water of

studied area

41

3.6 Gibbs-Boomerang diagram for ground water of samples of study

site

44

3.7 Stability diagrams of clay minerals in groundwater system 45

3.8 United State Salinity Laboratory (USSL) classification diagram 49

VI

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CONTENTS

Page. No.

3.9 Multivariate Statistical Analysis of water.

3.9.1 Factor analysis 49

3.9.2 Cluster analysis of ground water 55

3.10 Variation in distribution of Heavy metals in soil and sediments of

study area

56

3.11 Geochemical normalization and enrichment factors (EF) of heavy

metals in soil and sediments with respect to continental upper

crust

57

3.12 Geo-accumulation indices of heavy metals in soil and sediments

with respect to continental upper crust

57

3.13 Textural analysis of soil 63

3.14 Multivariate analysis of Soil

3.14.1 Factor analysis 90

3.14.2 Cluster analysis 92

3.15 Multivariate Analysis of soils sediments and water 93

CHAPTER-4 Conclusion

4.1 Water

4.1.1 Hydrogeochemical model study 96

4.1.2 Multivariate statistical analysis of ground water 97

4.2 Soil

4.2.1 Multivariate statistical analysis of soil. 97

4.2.2 Textural, enrichment factor and Geoaccumulation index study of

soil

98

4.3 Multivariate statistical study of soils, sediments and water 98

References 99

VII

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Synopsis

The present study exclusively focuses on the multivariate analysis of geochemical factors of

three matrices (i.e. soil, sediment and ground water) and their association for, a) evaluation of

site specific geochemical factors of soils, sediments and groundwater, b) identification and

assessment of the different factors influencing these matrices and d) identification of the

mineralogy of the study site. In this study special emphasis is given to estuarine area of Mumbai

(most populous city of India) due to fact that, monitoring the health of coastal and estuarine

ecosystems has become increasingly important over the past decade as human activities continue

to affect these systems, and as a result nation is becoming more aware of the need to take a more

comprehensive approach to protecting the freshwater and marine water resources.

Introductory part of the thesis consists of literature review, objective of the study and description

of various hydrochemical models and/or codes for ground water modeling. It also includes a

brief discussion of multivariate technique for geochemical data mining with associated

drawbacks. Second chapter of the thesis includes sampling, sample processing and sample

analysis using different analytical methods like ion chromatography and voltametry etc. An

extensive sampling was carried out at estuarine area (formed by Ulhas river) of Mumbai in the

month of March and April-2010. Total area covered was around 200 km2 along the both sides of

Ulhas River nearest to the creek. Whole study site was divided into 25 locations based upon grid

sampling method. Representative samples of soil, sediment (well) and ground water (well water)

collected from each location as per protocols.

In the third chapter of the thesis different multivariate statistical approaches like Cluster analysis

and factor analysis were used in combination with hydrogeochemical programming like

PHREEQC, WATCLAST and MEDUSA to access the geochemical parameters (Na+, K

+, Mg

2+,

Ca2+

, Fe, Cu, Ni, Cu, Cd and Mn) of soils, sediments and hydrogeochemical parameters (pH, EC,

TDS, SAR, Na+ ,K

+ ,Mg

2+ ,Ca

2+, Cl

-, NO3

- , SO4

2, HCO3

-, Fe, Cu, Si and Mn) of ground water

of estuarine area. The geochemical models or diagrams like Piper diagram, USSL diagram,

Gibbs boomerang diagram, stability diagram and saturation index value of different minerals

present generated using PHREEQC were used to identify hydrochemical facies of ground water,

mineralogy of the study area and ongoing geochemical processes etc.

VIII

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Q-mode cluster analysis of hydrogeochemical data of ground water of all 25 locations leads to

four distinct zones having similar type of hydrogeochemical evolution, where zone-1 having Ca-

Mg- SO42-

-Cl type, zone -2 is of Ca-Mg- HCO3- type, zone-3 is Na-K-Cl- SO4

type and zone -4 is

Ca-Mg- SO42-

-Cl type with external input of Cu, Mn and Pb.

Similarly Q-mode Cluster analysis of geochemical data of soils of the study area classify soils of

25 locations into four geochemically distinct zones, where zone-1 is controlled by anthropogenic

input of Cu and Cd, zone-2 is affected by weathering of dolomite minerals, zone-3 is almost

unaffected by any of the process and zone-4 is purely lithogenic (affected by weathering process)

in nature contaminated by external input of Co.

Factor analysis of soil revealed that natural weathering and anthropogenic input of Cu and Cd are

important factors controlling the soil type of the study area. Similarly factor analysis of ground

water conclude that, there are four factors controlling hydrogeochemical evolution of the ground

water, where first factor is lithogenic in origin, factor-2 is anthropogenic in nature, factor-3 is

mineralization of ground water by Jorasite-K dissolution and use of NPK fertilizers and the last

one is dissolution of sulphate and bicarbonates minerals.

Stability diagrams of ground water shows that the studied site is predominated with kaolinite

minerals, on the other hand geochemical diagrams like Gibbs-boomerang diagram, Piper

diagram etc. suggest that ground water chemistry of the study area is controlled by weathering as

well as dissolution of salts of marine origin. There is no evidence of saline water incursion to

local aquifer system.

Textural, enrichment factor and geoaccumulation index studies reveal that soils of study area is

practically uncontaminated w.r.t. Fe, Mn and Pb (except location 1, 2 and 21), moderately

contaminated by Co and Ni at few locations. All the locations are contaminated with Cu and Cd.

Multivariate statistical study of soils, sediments and water, revealed that Fe in soils, sediments

and water, Mn, in soils and sediments have common origin (i.e., soils and sediments have

common parent rock and chemical compositions of ground water is controlled by chemical

composition of nearby soil and sediment of the well. Similarly Cu in water and sediments, Pb in

soil and sediments have common in origin, Cu (soil) is isolated in last factor indicates its external

input to the different systems. The difference in association of Mn (soils and sediments) with Mn

(water), Cu (water and sediment) with Cu (soil), and Pb (soil and sediment) with Pb (water) may

be due to different migration rate of these species from one system to another.

IX

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List of Figures

Figure 1: Factor Analysis. Flowchart

Figure 2: Piper-tri-linear diagram

Figure 3: Output of simulation of groundwater data generated using PHREQC

Figure 4: Hydrogeology map of greater Mumbai

Figure 5: Map of Study Site

Figure 6: Chromatogram (mAu Vs Retention Time) of Fe, Cu, Ni, Co and Mn

Figure 7: Differential pulse voltammograms of Cd, Pb and Cu

Figure 8: Box and Whiskers plot of major elemental concentration of soil

Figure 9: Whiskers plot for major ionic concentration in ground water

Figure 10: Box and Whiskers plot of distribution of heavy metals in soil

Figure 11: Box and Whiskers plot of major elemental concentration in sediment

Figure 12: Whiskers plot of distribution of heavy metals in sediment

Figure 13: Trilinear Piper‟s plot for ground water‟s classification of study site

Figure 14: Hydrogeochemical facies in ground water

Figure 15: pH- dependent calculated species distribution of Na in the groundwater at the

various range of [SO4] & [Cl]

Figure 16: pH- dependent calculated species distribution of Mg and Ca in the groundwater at

the various range of [SO4] & [Cl]

Figure 17: pH- dependent calculated species distribution of Mg and Ca in the groundwater at

the various range of [CO3]

Figure 18: pH- dependent calculated species distribution of Fe and Mn in the groundwater at

the various range of [CO3]

Figure 19: Gibbs-Boomerang diagram for cations and anions in ground water of study site

Figure 20 (a, b, c, d):Stability diagram for K, Na, Ca and Mg system

Figure 21: Stability diagram in presence of sea water impact

Figure 22: USSL diagram for classification of ground water

Figure 23: Scree plot for ground water

Figure 24 -27: Factor score plot of F1 to F4 of ground water

Figure 28: Dendrogram of Q-Mode cluster analysis of water samples

Figure 29 -39:Bar graph of geoaccumulation indices of soil fraction-1 to11

Figure 40 -42: Factor score plot of F1 to F3 of soil

Figure 43: Dendrogram of Q-Mode cluster analysis of soil samples

X

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List of Tables

Table 1: Description of different section of diamond field of Piper diagram

Table 2: Ground Water Data Sheet

Table 3: Descriptive statistics of ground water samples

Table 4: Soil Data Sheet

Table 5: Descriptive statistics of soil samples

Table 6: Chemical characteristic data sheet of ground water

Table 7: Saturation level of different minerals in groundwater of all locations

Table 8: Standardized data set of water parameters

Table 9: Correlation matrix chart for different species in ground water

Table 10: Eigen value for factor analysis of ground water

Table 11: Factor loading matrix of ground water

Table 12: Sediment Data Sheet

Table 13: Soil enrichment factor Data sheet

Table 14 : Sediment enrichment factor data sheet

Table 15: Geoaccumulation Index of Soil

Table 16: Geoaccumulation Index of Sediment.

Table 17: Classification of geo-accumulation index based on sediment/soil quality

Table 18 -28: Soil data sheet fraction-1 to 11

Table 29-39: Soil enrichment factor Data sheet fraction-1 to 11

Table 40: Eigen value for factor analysis of soil

Table 41: Factor loading matrix of soil

Table 42: Correlation Matrix for soil parameters

Table 43: Eigen value spread sheet soil sediment and water

Table 44: Factor loading matrix of soils, sediments and water

Table 45: Correlation Matrix for soils, sediments and water.

XI

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1

CHAPTER-1

I N T R O D U C T I O N

1.1 Genesis

tudy of chemical and physical evolution of soils, sediments and ground water is very

complex due to multiple interactions between these matrices and various controlling

parameters. Water plays a major role in controlling the geochemistry of soils and sediments as it

is the interface between soils and sediments. To understand hydrochemistry and to analyze

natural as well as man-made impacts on aquatic systems, hydrogeochemical models have been

used since the 1960‟s and more frequently in recent times. Numerical groundwater flow,

transport and geochemical models are important tools besides classical deterministic and

analytical approaches. Solving complex linear or non-linear systems of equations, commonly

with hundreds of unknown parameters, is very complex and hectic task for researchers.

Modeling hydro-geochemical processes requires a detailed and accurate water analysis, as well

as thermodynamic and kinetic data as inputs with physical parameters of soil. Thermodynamic

data, such as complex formation constants and solubility-products are often provided as

databases within the respective programs. However, the description of surface-controlled

reactions (sorption, cation exchange, surface complexation) and kinetically controlled reactions

requires additional input data.

Unlike groundwater flow and transport models, thermodynamic models, in principal do not need

any calibration. Nevertheless, considering surface-controlled or kinetically controlled reaction

models might be subject to calibration, typical problems for the application of geochemical

models are as follows

a) Speciation

b) Determination of saturation indices

c) Adjustment of equilibria /disequilibria for minerals or gases

d) Mixing of different waters

e) Modeling the effects of temperature

S

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2

f) Stoichiometric reactions (e.g. titration)

g) Reactions with solids, fluids, and gaseous phases (in open and closed systems)

h) Sorption (cation exchange, surface complexation)

i) Inverse modelling

j) Kinetically controlled reactions

k) Reactive transport

Hydrogeochemical models depend on the quality of the chemical analysis, the boundary

conditions presumed by the program, theoretical concepts (e.g. calculation of activity

coefficients) and the thermodynamic data. For this, a basic knowledge about chemical and

thermodynamic processes is required.

Several models and methods of data analysis have been devised to simplify interpretation and

presentation such as trilinear diagram, Gibbs Boomerang diagram, Stability diagram, Duorv

diagrams etc. for hydrogeochemical studies [1]. The existing methods may provide some

information. Nevertheless, these conventional techniques are generally limited to major

constituent ions. They ignore many parameters which are otherwise important for studies. The

limitation that is coupled for using the traditional graphical methods has been discussed by

several authors [2]. Although enrichment factor and geoaccumulation indices [3] studies reveal

the extent of pollution of soil and sediments, it is unable to present source approximation. In

view of the limitation of the existing methods and increasing number of chemical and physical

variables measured in different systems (groundwater, soils and sediments) investigations,

multivariate analysis comes into play as a rather essential tool for explaining chemistry of soils,

sediments and water. A lot of work has been done to identify the sources of different chemical

species and geochemistry of ground water, soil and sediment, consequently to formulate the

conceptual models of geochemical parameters distribution and to identify the source of heavy

metal contents in different kinds of soil using multivariate analysis [4-8]. Some work has also

been done on application of multivariate analysis of chemical composition of soils, sediments

and ground water all together which is very important because all these three systems are inter

linked to each other, one of the work which highlights the application of multivariate analysis of

heavy metals contents in soils, sediments and water in the regions of Meknes (central Morocco)

reveals association of heavy metals in different systems have common origin [9]. Hence,

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3

multivariate statistical methods are found to be very useful tools for exploration of

hydrogeochemical evolution of ground water and geochemistry of soil and sediments.

1.2 Multivariate statistical Analysis of Variance

1.2.1 Factor analysis (FA) or Principal component analysis (PCA)

The main use of PCA/FA is to reduce the dimensionality of a data set by replacing the old

coordinate of the factor space. It computes a compact and optimal description of the data set. The

first step in factor analysis is computation of correlation coefficient matrix which requires

normal distribution of all variables. The correlation coefficient is computed by the eigen values

and percent of trace or the amount of variance which describes that all the variables are common

or shared. The following series of procedure are required for analysis of geochemical data using

factor analysis.

a) Data reduction using replacement of the large number of variables by small number of factors

in terms of the information content of data matrix.

b) Identify the structure underlying a set of variables by removing redundancy.

c) Develop a scale using several variables

d) Identify uncorrelated factors

e) Calculation of a correlation matrix

f) Extraction of initial factors

g) Rotation of factors

h) Interpretation

i) Conducting factor analysis

R-mode factor analysis gives the interrelationship between variables and Q-mode is devoted

exclusively to interrelationship between samples. In R-mode, the number of original variables is

reduced by detecting the variables. It provides several positive factors that allow interpretation of

the data set. By examining the factor loadings, communalities and eigen values of those variables

whose specific chemical process can be identified and the importance of major elements can be

evaluated in terms of factors. Communalities are an indicator of error term.

The equation for FA and /or PCA is as follows;

EPTX . …………………………………………………….. (1)

Where, X = original data matrix, T = Score matrix (sampling points), P =loading matrix

parameters), E = Errors.

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4

Fig-1. represents flow chart for factor analysis starting from problem formulation to prediction of

model for interpretation of data

Problem formulation

Construction of the correlation matrix (R-type and Q-type)

Method of factor analysis (PCA)

Determination of number of factors (eigen values >1)

Rotation of factors using varimax rotation for easy interpretation

Interpretation of factors by calculation of factor scores and selection of surrogate variables

Determination of model fit based on residuals

Fig. 1 Factor Analysis Flowchart

The new variables (X), being linear combinations of previous variables are called latent factors

or principal components. The interpretation of new factors gives the vital information about

latent relationships within the data set. The new principal components (latent factors) explain a

substantial part of total variance of the system for adequate statistical models. Usually, first

principal components (PC1/F1) explains the maximal part of the system variation and each

additional PC has a respective contribution to the variance explanation but less significant. In our

model, we have applied the Varimax rotation mode for FA that allows a better explanation of the

system in consideration since it strengthens the role of latent factors with higher impact on the

variation explanation and diminish the role of PCs with lower impact. The application of PCA to

the data set aimed the identification of latent factors responsible for the data structure and

possibly representing the emission of source. The results are indicated by two sets: factors

loading providing information on the relationship between the variables and factor. Whereas

factor scores giving the new coordinate of the factor space with the location of the objects. Only

statistically significant factor loadings (> 0.7) are important for the modeling procedure. But the

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5

some researchers consider the factor loading (> 0.6) as significant contributor and 0.3-0.5 as

possible contributor [2]. The significant factor loading may be positive (+) or negative (-). The

positive loadings indicate that the contribution of variables increases with increasing loading in a

dimension and negative loadings indicate a decrease [2]. Since the factor scores are calculated

for each sample and reflect the importance of a given factor at the sampling site, Dalton and

Upchurch have shown that factor scores can be related to intensity of the chemical process

described by each factor. Extreme negative number (≤ -1) reflect areas essentially unaffected by

the process and positive scores (≥+ 1) reflect areas most affected. Near 0 score approximate areas

affected to an average degree by the chemical process of that factor.

1.2.2 Cluster Analysis

The principal aim of cluster analysis is to partition observations into a number of groups. A good

outcome of cluster analysis will result in a number of clusters where the observations within a

cluster are as similar as possible while the differences between the clusters are as large as

possible. Cluster analysis must thus determine the number of classes as well as the memberships

of the observations to the groups. To determine the group membership most clustering methods

use a measure of similarity between the observations. The similarity is usually expressed by

distances between the observations in the n-dimensional space of the variables. Cluster analysis

is still a popular technique, in part because as a complicated statistical technique it appears to add

a scientific component to a publication. Readers of papers using cluster analysis should be very

aware of the problems – cluster analysis can be applied as an "exploratory data analysis tool" to

better understands the multivariate behaviour of a data set. It can, however, never be a "statistical

proof” of a certain relationship between the variables or observations.

Clustering methods also exist that are not based on distance measures, like model-based

clustering [10]. These techniques usually find the clusters by optimising a maximum likelihood

function. The implicit assumption is that the data points forming the single clusters are

multivariate normally distributed, and the algorithm tries to estimate the parameters from the

normal distribution as well as the membership of each observation to each cluster.

With geochemical data cluster analysis can be used in two different ways: it can be used to

cluster the variables (e.g. to detect geochemical relations between the variables) and it can be

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6

used to cluster the observations (e.g. to assign soil samples to certain parent materials) to come to

more homogenous data subsets for further data analysis.

1.2.2.1 Clustering Observations or Types

One of the main problems with cluster analysis is that a multitude of different clustering methods

exists. The observations need to be grouped into classes (clusters). If each observation is

allocated into only one (of several possible) cluster(s) this is called "partitioning". Partitioning

will result in a pre-defined (user defined) number of clusters. It is also possible to construct a

hierarchy of partitions, i.e. group the observations into 1 to n clusters (n = number of

observations). This is called hierarchical clustering. Hierarchical clustering always delivers n

cluster solutions, and based on these solutions the user has to decide which result is most

appropriate.

a) Hierarchical Methods Input to most hierarchical clustering algorithms is a distance matrix

(distances between the observations). The widely used agglomerative techniques start with single

object clusters (each observation forms an own cluster) and enlarge the clusters stepwise. The

computationally more intensive reverse procedure starts with one cluster containing all

observations and splits the groups step by step. This procedure is called divisive clustering. At

the beginning of an agglomerative algorithm each observation forms its own class, leading to n

single object clusters. The number of clusters is reduced by one by combining (linking) the most

similar classes at each step of the algorithm. The similarity of the combined pair, a new class,

can be measured to all other classes, and the next two most similar classes linked, and so on. At

the end of the process there is only one single cluster left, containing all the observations. A

number of different methods are available for linking two clusters and the best known are Wards

method, average linkage, complete linkage and single linkage. Ward‟s method is much

successful to form clusters that are more or less homogeneous and geochemical distinct from

other clusters, compared to other method, uniqueness of wards method is, it uses analysis of

variance approach to evaluate distance between cluster to perform CA. Because the cluster

solutions grow tree-like (starting with the roots and ending upwards with the trunk) results are

often displayed in a graphic called the dendrogram. Horizontal lines indicate the linkage of two

objects or clusters, and thus the vertical axis presents the associated height or similarity as a

measure of distance. The objects are arranged in such a way that the branches of the tree do not

overlap. Linking of two groups at a large height indicates strong dissimilarity (and vice versa).

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Therefore, a clear cluster structure would be indicated if observations are linked at a very low

height, and the distinct clusters are linked at a considerably higher value (long roots of the tree).

The dendrogram does not provide cluster assignments by itself, as the number of clusters to be

formed must be chosen by the user. This flexibility is one of the subjective points in CA, because

the user is free to achieve a certain desired result cutting the dendrogram at the height (phenon

line) corresponding to this visible number of clusters allows assigning the objects to the clusters.

Visual inspection of a dendrogram is often helpful in obtaining an initial idea of the number of

clusters to be generated by a partitioning method.

b) Partitioning Methods: In contrast to hierarchical clustering methods, partitioning methods

require the number of resulting clusters to be pre-determined. As noted above, when nothing is

known about the observations it can be useful to first carry out a hierarchical clustering. The

other possibility is to partition the data into different numbers of clusters and evaluate the results

for regionalised data a more subjective but still reasonable approach of evaluation is to visually

inspect the location of the resulting clusters in a map. This exploratory approach can often reveal

interesting data structures. A very popular partitioning algorithm is the k-means algorithm. It

attempts to minimise the average squared distance between the observations and their cluster

centres or centroids. Starting from k initial cluster centroids (e.g. random initialisation by k

observations) the algorithm assigns the observations to their closest centroids (using e.g.

Euclidean distances) recomputes the cluster centres, and iteratively reallocates the data points to

the closest centroid. Several algorithms exist for this purpose; those of Hartigan [11] and

MacQueen [12] are the most popular. There are also some modifications of the k-means

algorithm. Manhattan distances are used for k-medians and the centroids are the medians of each

cluster. Hard competitive learning works by randomly drawing an observation from the data and

moving the closest centre towards that point [13]. Martinetz et al. [14] have introduced

"neuralgas", this method is similar to hard competitive learning, but in addition to the closest

centroid also the second closest centroid is moved at iterations. Kaufmann and Rousseeuw

proposed several clustering methods which are implemented in a number of software packages.

The result of all these algorithms depends on the initial cluster centres, which are often a random

selection of k of the observations. If bad initial cluster centres are selected, the iterative

partitioning algorithms can lead to a local optimum that can be far away from the global

optimum.

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1.2.2.2 Distance Measures

A key issue in most cluster analysis techniques is how best to measure distance between the

observations (or variables). Note that "distance" in cluster analysis has nothing to do with

geographical distance between two observations but is rather a measure of similarity between

observations in the multivariate space defined by the entered variables. Many different distance

measures exist [15]. Modern software implementations of cluster algorithms can accommodate a

variety of different distance measures because the distances rather than the data matrix are taken

as input and the algorithm is applied to the given input. For clustering the observations the

Euclidean distance, correlation based distance measures and Manhattan distance is the most

frequent choice. Other distance measures like the Gower distance, Canberra distance and a

distance measure based on the random forest proximity measure [16] can give completely

different cluster results. To demonstrate the effect of the distance measure used for clustering

geochemical data the average linkage clustering algorithm has been applied to the present data.

1.2.2.3 Possible Data Problems in the Context of Cluster Analysis

a) Data Outliers: Regional geochemical data sets practically, always contain outliers. The

outliers should not simply be ignored but they have to be accommodated because they contain

important information about data quality and unexpected behaviour in the region of interest. In

fact, finding data outliers indicative of mineralisation (in exploration geochemistry) or of

contamination (in environmental geochemistry), are one of the major aims of geochemical

surveys. Outliers can have a severe influence on cluster analysis, because they can affect

proximity measures and obscure clustering tendencies. Outliers should thus be removed prior to

entering a cluster analysis or statistical clustering methods capable of handling outliers should be

used. This is rarely done. Finding data outliers is not a trivial task, especially in high dimensions.

One way of identifying such outliers is to compute robust Mahalanobis distances, i.e.

Mahalanobis distances on the basis of robust estimates of location and scatter.

b) Censored Data: A further problem that often occurs when working with geochemical data is

the detection limit problem. For some determinations a proportion of all results are below the

limit of detection of the analytical method, i.e. the data are censored. For statistical analysis,

these results are often set to a value of ½ the detection limit. However, a sizeable proportion of

all data with an identical value can seriously influence any cluster analysis procedure. It is very

questionable as to whether or not such elements should be included at all in a cluster analysis.

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The elements of greatest interest that contain the highest number of censored data, the temptation

to include these in a cluster analysis is thus high. In that case, the elements with below detection

can be omitted from cluster analysis.

c) Data Transformation and standardisation: Cluster analysis in general does not require that

the data be normally distributed. However, it is advisable that heavily skewed data are first

transformed to a more symmetric distribution. If a good cluster structure exists for a variable, we

can expect a distribution, which has two or more modes. A transformation to more symmetry

will preserve the modes but removes large skewness. Most geochemical textbooks still claim that

for geochemical data a log-transformation is most suitable. Recently Reimann and Filzmoser

have shown that very few geochemical variables will indeed follow a (log)-normal distribution.

Each single variable needs, unfortunately, to be considered for transformation and different

transformations, with the Box-Cox transformation [17] being the most universal choice, need to

be considered. The most practical decision guides whether to transform or not and how to

transform should be dependent upon the data distribution and it should be close to symmetry

prior to entering cluster analysis. Even Box-Cox transformations of all single variables do not

guarantee symmetry of the resulting multivariate data distribution, but more closeness to

symmetry (or removal of strong skewness) will in general improve the cluster results. An

additional standardisation is needed if the variables show a striking difference in the amount of

variability in major, minor and trace elements. Different methods, all having advantages and

disadvantages, exist to accommodate this requirement. The most universal method is the z-

transformation, which builds on the mean and standard deviation of the data. When working with

geochemical data, a robustified version, using median should be preferred.

1.3 Hydro geochemical Models and Computer programs

Hydrogeochemical models and diagrams are aimed at facilitating interpretation of evolutionary

trends, particularly in groundwater systems, when they are interpreted in conjunction with

distribution maps and hydrochemical sections. A trilinear diagram to describe water chemistry

was first attempted by Hill [18] and refined by Piper [19], Gibbs diagram, stability diagram etc.

used to assess the water type. But almost all the diagrams are based upon major cations/anions of

the aquatic system.

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1.3.1 Trilinear Piper diagram

In the Piper diagram, major ions are plotted in the two base triangles as cation and anion mill

equivalent percentages. Total cations and total anions are each considered as 100%. The

respective cations and anions locations for an analysis are projected into the diamond field,

which represents the total ion relationship. The Piper diagram has been widely used to study the

similarities and differences in the composition of waters and to classify them into certain

chemical types. The water types demonstrated by the Piper diagram, as described by Karanth

[20] shows the essential chemical character of different constituents in percentage reacting

values, expressed in milligrams equivalent. Piper diagram allow comparisons to be made among

numerous analyses, but this type of diagram has a drawback, as all trilinear diagrams do, in that

it does not portray actual ion concentration. The distribution of ions within the main field is

unsystematic in hydrochemical-process terms, so the diagram lacks certain logic. Piper suggested

the method of encircling the plotted points in the central diamond field with its area proportional

to the absolute concentration. This method is not very convenient when plotting a large volume

of data. Nevertheless, this shortcoming does not lessen the usefulness of the Piper diagram in the

representation of some geochemical processes. Fig.2 is the piper-tri-linear diagram which

represents different ionic composition of water. The classification based on chemical

characterization of aquatic system is presented in Table-1.

Fig. 2 Piper-tri-linear diagram

Legends

A- Calcium type

B- No Dominant type

C- Magnesium type

D-Sodium and

potassium type

E- Bicarbonate type

F- Sulphate type

G- Chloride type

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Table 1. Description of different section of diamond field of Piper diagram

Different divisions

Of piper Diagram Chemical Composition Type

1. Alkaline earth (Ca+Mg) Exceed alkalies (Na+K)

2. Alkalis exceeds alkaline earths

3. Weak acids (C03+HCO3) exceed Strong acids (SO4+Cl)

4. Strong acids exceeds weak acids

5. Magnesium bicarbonate type

6. Calcium-chloride type

7. Sodium-chloride type

8. Sodium-Bicarbonate type

9. Mixed type (No cation-anion exceed 50%)

1.3.2 Gibbs- boomerang diagram

Gibbs-boomerang diagram is an important tool to analyze geochemical processes [21]. Gibbs

studying the salinity of world surface water concluded that three natural mechanisms control the

chemistry of waters: atmospheric precipitation, rock dominance or rock weathering, and

evaporation–crystallization process. Gibbs diagram, a boomerang-shaped envelope, is obtained

when the weight ratio Na+/ (Na

++Ca

2+) on the X- axis is plotted versus TDS values on the Y-

axis (for cations). Similarly for anions of Cl-/ (Cl

-+HCO3

-) on X -axis versus TDS values on Y-

axis. When the dominant process is rock weathering, waters produce calcium and bicarbonate as

predominant ions, TDS values are moderate and sample data plot in the middle region of the

Gibbs boomerang. Low salinity waters of sodium chloride type are due to the atmospheric

precipitation process and sample data plot in the lower right corner of the boomerang. The

processes mentioned above do not exclude each other, and many water present chemical

compositions between the two extremes. It seems better to consider rock weathering and

atmospheric precipitation as the ends of a continuous series. The third mechanism that controls

the water chemistry is the evaporation–crystallization process, important in arid areas, where

evaporation is larger than precipitation. The evaporation process increases TDS and the relation

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Na+/ (Na

++Ca

2+), the latter principally due to calcite precipitation. The surface waters that

respond to this process are on the right upper side of the Gibbs boomerang in a continuous series

between those whose chemical composition is derived from rock weathering and seawater

composition.

1.3.3 Stability diagram between solid-liquid phases in aquatic system

Stability diagrams are graphical representations of equilibrium between minerals and aqueous

solution (water); it reveals which mineral is in equilibrium with water at ambient temperature.

Hence stability diagram gives idea about, what happens when water of various compositions

interact with solid phase (minerals), what will happen during chemical weathering of silica and

what will happen if there is change in the concentration of constituent ions by addition or

removal. It is a general practice to draw the stability diagram by plotting the graph between log

[M+] / [H+] versus log [H4SiO4], where M is metal of interest in most of the cases, Ca, Mg, K,

and Na is taken as these four metal ions are major constituent of water.

1.3.4 United states salinity laboratory classification for irrigation water diagram

The diagram (Fig.20) is the plot of specific conductance of water (micromho/cm) versus sodium

adsorb ratio (SAR ), consist of sixteen regions based upon SAR and specific conductance values

which revels the sodium hazard and salinity hazard of the water samples. Water with high low

value of SAR and sp.conductance has low salinity and sodium hazards than water with high SAR

and specific conductance which has high sodium hazard and salinity hazard.

1.3.5 PHREEQC hydrochemical code

PHREEQC (ver. 2) is a computer program written in the C programming language that is

designed to perform a wide variety of low-temperature aqueous geochemical calculations.

PHREEQC is based on an ion-association aqueous model and has capabilities for (1) Speciation

and saturation-index calculations; (2) Batch-reaction and one-dimensional (1D) Transport

calculations involving reversible reactions, which include aqueous, mineral, gas, solid-solution,

surface-complexation, and ion-exchange equilibria, and irreversible reactions, which include

specified mole transfers of reactants, kinetically controlled reactions, mixing of solutions, and

temperature changes; and (3) Inverse modeling, which finds sets of mineral and gas mole

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transfers that account for differences in composition between waters, within specified

uncertainty limits. Fig-3 represents the output file of the simulation data of this study.

Fig. 3 Output of simulation of groundwater data generated using PHREQC

It is specially used for simulating chemical reactions and transport processes in natural or

polluted water. PHREEQC uses ion-association and Debye Hückel expressions to account for the

non-ideality of aqueous solutions. This type of aqueous model is adequate at low ionic strength

but may break down at higher ionic strengths (in the range of seawater and above). An attempt

has been made to extend the range of applicability of the aqueous model through the use of an

ionic-strength term in the Debye Hückel expressions. These terms have been fit for the major

ions using chloride mean-salt activity-coefficient data [22]. Thus, in sodium chloride dominated

systems, the model may be reliable at higher ionic strengths. For high ionic strength waters, the

specific interaction approach to thermodynamic properties of aqueous solutions should be used

[23-25], but this approach is not incorporated in the other limitation of the aqueous model is lack

of internal consistency in the data in the databases.

1.3.6 WATCLAST hydrogeochemical model

It‟s a DOS based computer programming which is used for calculating ionic activity, saturation

indices and different physical parameters of the water like sodium adsorb ratio(SAR), residual

sodium carbonate (RSC) used to plot different hydrogeochemical graphs like, stability diagram,

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Gibbs boomerang diagram, USSL diagram etc. which in turn are helpful to characterise the water

type.

1.3.7 Ionic Equilibrium model (MEDUSA)

MEDUSA stands for Make Equilibrium Diagrams Using Sophisticated Algorithms. It is a

Windows‟ interface to the MS−DOS programs, which perform the calculations needed to create

chemical equilibrium diagrams. It can also call HYDRA (Hydrochemical Equilibrium Constant

Database) to make diagrams based on equilibrium constants retrieved from a database.

1.4 Quality Assessment of Soil, Sediment and Groundwater in the terrestrial environment

With the rapid industrialization, infrastructure development, tourism increase and economic

development in urban areas over the last few decades, heavy metals are continuing to be

introduced in the terrestrial environment through various routes. Heavy metals are natural

constituents of the earth‟s crust and are present in varying concentration in all ecosystems. These

metals can be transferred from soil to the other ecosystem components, such as underground

water or crops and can affect human health. The natural input of several heavy metals to soils

due to pedogenic processes has been exceeded in some local areas by human activity; even on

regional scale in particular agricultural soils can be a long-term sink for heavy metals. These

soils have also been influence by other pollutant activities such as the use of manures, sewage

sludge disposal or aerial fallout from industrial activities [26]. As a consequence, potentially

toxic elements have accumulated in the soil profile. This can result in loss of soil functions

concerning environmental quality protection, maintenance of human health and productivity,

which are relevant aspects of soil quality [27]. In some areas with heterogeneous lithology,

heavy metal contents can be highly variable, determined by the parent material and soil

properties. For example, organic matters, clay and carbonates play a crucial role in the

availability of heavy metals in calcareous soils [28] as the heavy metals cannot be degraded or

destroyed they tend to accumulate in different compartments of the systems like water, soil and

sediment .

Various studies have demonstrated for quality assessment for soil, sediments and ground water in

the terrestrial ecosystem, which are highly contaminated by heavy metals. Therefore, the

evaluation of metal distribution in soil and sediments is useful to assess pollution in the

terrestrial environment. These heavy metals participate in various biogeochemical mechanisms

that have significant mobility, which affects the ecosystems through bioaccumulation and bio-

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magnification processes and are potentially toxic for environment and human life. Metals such as

Fe, Ni, Cu, Co, Mn, Cd and Pb etc. are used in contamination studies in the systems due to their

relationship with anthropogenic activities.

Sediments are important carriers of trace metals in the hydrological cycle because metals are

partitioned with the surrounding waters; they reflect the quality of an aquatic system. Thus,

geochemical characteristics of the soil and sediments can be used to infer the weathering trends

and the sources of pollution. Because of their large adsorption capabilities, fine-grained

sediments represent a major repository for trace metal and a record of the temporal changes in

contamination. Over the last few decades, the study of soil and sediment quality has shown to be

an excellent tool for establishing the effects of anthropogenic and natural processes on

depositional environments. A number of recent pieces of work have used sediment profiles to

describe the contamination history of different environments. Metals enter the environment by

two means: natural processes (including erosion of ore-bearing rocks, wind-blown dust, volcanic

activity and forest fires) and processes derived from human activities by means of atmospheric

deposition, rivers and direct discharges or dumping. The composition of groundwater sediments

is dependent on local geology as well sediments actively interact with emerging groundwater.

All the above factors influence ground water quality. Additional anomalies of associations of

several ore-related elements are attractive targets for follow-up study. Single-element anomalies

may be caused by local pollution i.e. anthropic input. In most cases if the soil is contaminated the

sediment also is contaminated, as sediments are derived from soil.

Groundwater is the main source of irrigation water supply and drinking water for many

settlements. Hydro geochemical evaluation of ground water varies from place to place as water

derives its composition from the parent rock in the weathering region; sediments owe their

mineralogical composition partly to the chemical reactions between rock and water. When such

reactions reach thermodynamic equilibrium with certain mineral assemblages would coexist in

sediment phase, provided the chemistry of water remains same [28] with respect to the other

factors like quality of water, interaction with other water systems (lake, river and sea) and human

activities (agricultural, industry, urbanization and increasing exploitation) [29]. Groundwater

may be contaminated upon leaching of chemicals in the soil surface towards the aquifer. The

agricultural irrigation effluents, industrial wastewater discharge and domestic effluents have

largely contributed to groundwater. The changes in agricultural practices during the last fifty

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years (use of fertilizers, simplification of the landscape, mechanization, drainage etc.) have

significantly contributed to increase the concentrations of pollutants in surface and shallow

groundwater to such an extent that it has become detrimental to aquatic ecosystems which

present evident signs of eutrophication [30]. Non-point sources of pollution by agriculture

activities and livestock have appeared as major risks to the planet‟s groundwater resources [31,

32]. The main non-point source pollutants are agrochemicals, fertilizers and salts contained in

irrigation leaching. These are the major pollutants in the water that percolates through the root

zone into the shallow aquifer, limiting urban, industrial, agricultural and ecological uses [33].

Nowadays there is a great threat of saline incursion to the costal aquifers which will become

more problematic in future due to over exploration of ground water with rise in mean sea level.

The measurement of trace element concentrations and distribution in terrestrial environment

leads to better understanding of their behaviour in the aquatic environment and is important for

detecting sources of pollution. Hence, the pollution status of soil and sediments of any area can

be predicted by evaluating the enrichment factor (EF), Pollution Load Index (PLI) and Geo-

accumulation Index (Igeo) of heavy metals.

1.4.1 Enrichment Factor of heavy metals

The nature and relative importance of various sources of heavy metals and other species of

environmental interest are determined in number of ways. The most common discrimination

techniques use elemental, molecular and isotopic signatures to characterize anthropogenic,

crustal and marine sources. The enrichment factor represents the amount of a particular element

in excess of that expected from natural rock or soil source. It is often assumed that aluminium

content of a particulate is due solely to crustal sources. Iron, scandium, titanium and silicon may

also be reasonable choices for elements of totally crustal origin. Hence, Fe or Al can be chosen

as reference element. The crustal source can either be average crust or local rock or soil.

Normalizing elements relative to Al is widely used to compensate for variations in both grain

size and composition, since it represents the quantity of aluminosilicates, which is the

predominant carrier phase for adsorbed elements in soil and sediments. Therefore, this method is

a powerful tool for the regional comparison of trace metal content in sediments, soils and can

also be applied to determine enrichment factors [34]. When assessing metal sediment and/or soil

concentrations for environmental studies, one major problem is the choice of methods of data

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analyses. One may attempt to evaluate the data on the basis of absolute metal concentrations, or

choose between varieties of other methods, ranging from relatively simple ones like elemental

ratios to more sophisticated, such as discriminate analysis. Helz and others calculated enrichment

factors (EF) for the data using A1 as the reference metal and average crustal by the relations:

EF = (X/A1) sediment (or soil)/ (X/A1) continental upper crust ---------------- (Eq.1)

Where, X/A1 is the ratio of the concentration of element X to A1. Using Fe-based enrichment

factors, Helz and others compared their data pertaining to a particular environment with that of

similar environment in other places of the country/ world [35]. Formulae used for calculation of

enrichment factor for this study is as follows.

EF = (CX /Fe) sediment (or soil) / (CX / Fe) continental upper crust -------------- (Eq.2)

Where, Cx is the concentration of metal x in sediment or soil. When enrichment factors (EFs) of

heavy metals are close to unity then it is assumed that metals have originated from crustal origin

while those greater than 10 are considered to be non-crustal source. EF values lower than 0.5 can

reflect mobilisation and loss of these elements relative to Fe, or indicate an over estimation of the

reference metal contents. The world‟s continental upper crust value is considered as reference

element for bottom sediment and soil. But in general, the textural characteristic of the sediments

and soils in our present investigation was sandy, silty-sand type nature so the use of Al as a

normalisation element is not of much significance for the universal comparison of the sediments.

As stated by Forstner, Wittmann and Jenne [35] in the case of Fe, particularly the redox sensitive

iron-hydroxide and oxide under oxidation constitute significant sink of heavy metals in aquatic

systems. Even a low percentage of Fe (OH)3 has a controlling influence on the heavy metal

distribution in an aquatic system. Because of importance, Fe could be used as normalisation

element for the sedimentary and soil geochemistry, which would provide better result and also

help universal composition.

1.4.2 Geo-accumulation Index (Igeo) of heavy metals

Geo-accumulation index (Igeo) has been used widely to evaluate the degree of metal

contamination or pollution in terrestrial, aquatic and marine environment and to quantify the

metal accumulation, which compare the present status with the pre-civilized background values

proposed by Muller [3].

Igeo = log2 [Cx /1.5Bn] ------------------------------------------------ (Eq.3)

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Where, Cx = concentration of element and Bn = geochemical background value

Based on world‟s average continental upper crust value and world‟s average suspended sediment

value, the Igeo values are calculated. Igeo may be classified in seven grades. An Igeo of „6‟ indicates

a 100-fold enrichment of an element above the background [3]. Igeo value for 0-1 indicates slight

pollution and less than zero (0) means no pollution. Classes 1-2 and 2-3 indicate moderate to

strong pollution.

1.4.3 Pollution Load Index (PLI) for sampling sites

Pollution load index is used in order to find out the mutual effect of different studied metals.

Pollution load index (PLI), for a particular site, can be evaluated following the method proposed

by Tomilson et al. This parameter is expressed as:

PLI = (CF1x CF2 x CF3 x ……….. x CFn) 1/n

---------------- (Eq.4)

Where, „n’ is the number of metals and CF is the contamination factor. The contamination factor

can be calculated from the following relation:

CF (Contamination factor) = Metal concentration in the sediments/ Background value of the

metal ----------- (Eq.5)

1.5 Objective

The geochemical characteristics of soils, sediments and groundwater of any area are controlled

by many factors, which include local geology of the area, mineralogy of the area, precipitation,

meteorological changes and topography. All these factors combine to generate diversified types

of soils, sediments and groundwater in terrestrial ecosystem. In addition to various geochemical

processes (mineralization, weathering etc.) some anthropogenic activities like rapid

industrialisation; urbanisation etc. can also be responsible for changes in chemical characteristics

of all the three interlinked matrices. Due to complexity of chemical and physical evolution of

soils, sediments and water, interpretation of results are often insufficient to provide a clear

picture of the systems under study [36].

The purpose of present study is exclusively focused on application of multivariate analysis to

geochemical factors (major cations, anions, heavy metals and physical parameters of ground

water like, pH, EC and TDS etc.) of three matrices i.e. soils, sediments and ground water of

estuarine region (formed by Ulhas river) at north of Mumbai. In this study special emphasis was

given to estuarine area which is a semi-enclosed costal body of water that has free connection

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with the open sea (Arabian Sea) and within which sea water is measurably diluted with

freshwater derived from river (Ulhas River). Monitoring the health of coastal and estuarine

ecosystems has become increasingly important over the past decade [37]. The study involves

establishment of the relations, associations and causes for the interdependence among the various

chemical species present in the groundwater, sediment and soil profile and to identify and assess,

possible sources of their origin using different geochemical models like PRHEEQC,

WATCLAST, Trilinear Piper‟s diagram, Gibbs Boomerang diagram, etc. As the study of the

hydrogeochemistry of groundwater and geochemistry of soils and sediments requires handling of

a large data set the classification, modeling, and interpretation of the data are the most important

steps in the assessment of soil, sediment and water quality. Therefore the main purpose of data

analysis is to detect inter-elemental relationships of geochemical data which reflect the

mineralogy, chemical species interactions and different geological processes, then isolate a

typical observations or groups of observations that are potentially identified with processes of

interest (mineral deposits, hazardous environment) and finally recognize the pattern or trend in

data analysis. In order to achieve this objective, multivariate statistical technique such as factor

analysis (FA) as well as cluster analysis (CA) has been used successfully. These statistical

techniques can provide a powerful tool for analyzing the multivariate geochemical data of water,

sediment and soil. Since, multivariate data exists in multidimensional space which is clearly

impossible to visualize above 3D. Therefore, the factor analysis is used to simplify the complex

and diverse relationships which exist among a set of observed variables by revealing common

and unobservable factors. It also explains the correlations between the variables in terms of the

underlying factors, which are not apparent. Usually, CA is carried out to reveal specific links

between sampling points, while FA/PCA is used to identify the ecological aspects of pollutants

on environmental systems [1]. In this study, STATISTICA software package (Statsoft India,

version 7) has been used for the basic statistical analyses, Hierarchical cluster analysis (HCA),

correlation matrix and factor analysis (using Principal component extraction method).

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CHAPTER 2

M A T E R I A L S A N D M E T H O D S

2.1 Context of Study area

2.1.1 Geology and Hydrogeology

he physiographic feature of the study area is broad and flat terrain flanked by north – south

trending hill ranges. The hill ranges from almost parallel ridges in the eastern and western

part of the area. The site is located in the north-east part of Mumbai between latitude 18059

‟33

‟‟

N- 19001

‟12

‟‟ N and longitude 72

0 54‟45

‟‟ - 72

0 57‟11

‟‟E at an average elevation of 13 m from the

sea level. The northern part of Mumbai is hilly. Climate of Mumbai is fluctuating one as it is a

coastal area and the weather is highly influenced by Arabian Sea. Generally May is the hottest

month of the year and the average temperature ranges between 320C- 40

0C. January is the coldest

month in Mumbai and the average temperature remains about 180C. The average annual rainfall

in this region is 2170 mm. Because of the southwest monsoon winds, more than 95% of the

annual rainfall occurs during four months period of June to September. This city has a highly

humid climate with an annual average relative humidity of more than 60%. Two types of soils

have been observed in this area viz. medium to deep black and reddish colour soil. The soil type

is predominantly sandy due to its proximity to the sea. In the suburbs, the soil cover is largely

alluvial and loamy. The underlying rock of the region is composed of black Deccan basalt flows,

and their acid and basic variants dating back to the late Cretaceous and early Eocene eras. [38].

The „Pahoehoe‟ flow in the area consists of highly vesicular bottom layer having closely spaced

horizontal joints but the thickness is generally less. The vesicles are generally filled with

secondary minerals and green earths. In such cases, they do not serve as aquifer. Such vesicular

zones are weathered in most part of the area, thus, making them moderately permeable. But if,

vesicles are not filled, they act as highly permeable aquifers. The simple and compound

“Pahoehoe” flow comprises a basal vesicular zone, middle relatively massive portion followed

by a vesicular top. The vesicles of “Pahoehoe” flows are generally not interconnected and thus

there is a variation in water holding capacity from the base to the top of the flow. The ground

water exists in fractures, joints, vesicles and in weathered zone of Basalt. The occurrence and

T

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circulation of ground water is controlled by vesicular unit of lava flows and through secondary

porosity and permeability developed due to weathering, jointing, fracturing etc., of Basalt. The

ground water occurs under phreatic, semi confined and confined conditions. The leaky confined

conditions are also observed in deeper aquifers. Generally, the phreatic aquifer can be found in

the range down to a depth of 15 m bgl. The water bearing zone down to depth of 35 m bgl forms

the semi confined aquifer and below this deeper down to depth of 60 m bgl is observed. It is

expected that the potential of deeper aquifers would be much more limited as compared to the

unconfined/phreatic aquifer. River alluvium patches along the course of rivers and marine

alluvium in the coastal area, highly potential aquifer but with limited areal extent. The ground

water occurs under water table condition in sandy gritty layers. The alluvial fill of low lying

areas underlain by weathered basalt has relatively better ground water potential.

Fig. 4 Hydrogeology map of greater Mumbai

Source –Doc-1618/DB/2009.CGWB

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Fig. 4 is the hydrogeology map of greater Mumbai taken from document, groundwater

information greater Mumbai district, Govt. of India Ministry of water resource Central

groundwater board (CGWB) [38]. As per CGWB ground water is suitable for drinking purpose,

but there occur pollution of many of the places due to dumping of sewage and industrial

effluents. In addition to this various effluents from oil refineries, reactors, fertilizers have

polluted the ground water. As a result the concentration of heavy metals in ground water and soil

in the surrounding areas of creek has been observed beyond the prescribed limits. The entire

sampling site is underlain by basaltic lava flows of upper cretaceous to lower Eocene age. The

shallow alluvium formations of recent age also occur as narrow stretch along the river flowing in

the area.

2.1.2 Sampling Sites and Samples Collection

Since estuarine regions have their own importance due to its uniqueness in hydrogeology, as the

aquifer of estuarine area is affected by both sea water and river water which leads to a very

complex type of chemical evolution of ground water. Therefore, the ecology of the estuarine area

is totally different from that of others. In our study, sampling sites were selected near the creek,

where Ulhas River connects with an open Arabian sea of Mumbai area. This region receives the

sewerage and effluents discharged from the chemical industries and factories. An extensive

sampling was carried out at this area during the month of March and April, 2010. The total

sampling area along the Ulhas River at the creek was covered to be about 200 km2. A grid

sampling scheme was prepared with the help of topocity and a sub grid dimension of 2 km x 2

km was adequate for sampling. The sampling area was classified into 25 sub-locations. In these

locations, representative samples of soil, sediment(well sediments) and ground water (well

water) were collected as per standard protocols and proper care was taken to avoid the inter

contamination of samples. Fig-5 represents the studied site with the sampling locations coded

with L1 to L25.

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Fig. 5 Map of the study site

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2.2 Preliminary process of ground water, soil and sediment samples.

Ground water samples were collected in duplicate from each well and filtered through 0.45

micron filter paper, stored in, acid washed, 200 ml capacity polypropylene bottles one of the

sample was acidified with 0.01M of nitric acid (AR Grade, Merck, Mumbai, India) and kept for

heavy metal and major cations analysis.

The composite surface soil(S) samples (up to 15cm depth) from each location were collected

using soil sampler and packed in WHIRL-PAK®

Sterile sampling bags with code from S1 to S25

at each geo referenced location. These samples were dried at 110°C for 24 h, sieved through

2000 µm test sieve. The sieved soil samples again subjected to sieving with the help of

electromagnetic sieve shaker for textural analysis (i.e. 500, 355, 250, 188, 125, 106, 90, 75, 53,

25 and < 25 µm).

Sediment samples from each well were collected using Ekman Dredge sediment sampler. All the

samples were packed in WHIRL-PAK®

Sterile sampling bags and processed as soil samples.

2.3 Sample digestion and analysis preparation

Soil and sediment samples were digested in microwave digestion system (Milestone Srl, Model

Ethos 1, Italy with pro-24 rotor) as per manufacturer specification. About 0.5 g of powdered and

dried soil and sediment samples were placed in PTFE (Poly Tetra Fluoro ethylene) digestion

vessels with 5 mL of HNO3 (MERCK, high purity), 3 mL of HF (MERCK) and 2 mL of

hydrogen peroxide (H2O2). After that, samples were completely destructed in a closed

microwave digestion system. The resulting solutions were then evaporated to near dryness after

extracting with 0.25% HNO3. Finally, the aliquots were prepared in a 50 mL (by adding MilliQ

water) standard flask for instrumental analysis.

2.4 Analysis Techniques

2.4.1 Field Measurements

The measurement of the parameters like electrical conductivity (EC), pH and temperature for

each water sample during sample collection using standard procedures employing field meters by

Orion RDO meter. Measurements of pH were made with a glass sensor calibrated at the sample

temperature and using pH 4, pH 7 and pH 9.2 buffers. The deviation of pH measurements was

±0.2 pH units.

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2.4.2 Major cations and anions analysis

The determination of major cations (Na+, K

+, Mg

++ , Ca

++ ) and major anions ( F

-, Cl

- , SO4

- -,

NO3-) ground water samples and major cations in soils and sediments were estimated by

conductivity suppressed Ion Chromatography System (DIONEX600) using an Ion Pac AS17

(anion-exchange column) as a stationary phase with 12 mM of NaOH as a mobile phase for

anions and an Ion Pac CS17 (cation-exchange column) as a stationary phase with 6 mM of

methane sulphonic acid (MSA) as a mobile phase for cations. The unknown sample was

analyzed by measuring the peak area for the ions (identified by retention time), and comparing it

with the standard curve, the concentration of unknown ion in the solution was calculated. The

instrument was calibrated and standardized with the stock solution of ultra pure Fluka

(Switzerland) standards for the above cations and anions. The eluent flow rate was confined at

0.25 ml/min under isocratic flow. HCO3- was estimated titrimetrically using autotitrator

(Metrohm-798 MPT Titrino). Silica in ground water was determined by AAS (Atomic

absorption spectrophotometer) and finally dissolved silica was quantified stoichiometrically.

Quality assurance was made by spike recovery, replicate analysis and cross method checking.

The relative standard deviation was calculated to be 8–12%.

2.4.3 Analysis of heavy metals

2.4.3.1 Preparation of postcolumn PAR (4-(2-pyridylazo) resorcinol) reagent and

complexing agent (2, 6-Pyridyle dicarboxylic acid) for ion chromatography

In a well ventilated fume hood, 0.12 g of PAR was thoroughly dissolved in 185 mL of NH4OH

and 400 mL of Milli Q water. 58 ml of acetic acid was added in 600 mL of Mili Q water to

prepare 1.7MCH3COOH which was added slowly to the PAR solution. For preparation of eluent,

1 gm of PDCA (2, 6-Pyridyle Dicarboxylic Acid), 3.2g of 50% NaOH and 5.4mLof glacial

acetic acid were added in 1 L of Milli Q water and pH was maintained to 4.8. Fluka standards of

Fe, Cu, Ni, Co and Mn were used for the calibration of the instrument. Prepared reagents were

stored under an inert gas, such as nitrogen and used within two weeks of preparation.

2.4.3.2 Detection and separation of Cu, Fe, Mn, Ni and Co using ion chromatography

Most hydrated and weakly complexed metals will precipitate in a suppressor and therefore,

cannot be detected by conductivity. Also, with a few exceptions, transition metals cannot be

detected by direct UV absorbance. Therefore, the metal complexing agent 4-(2-pyridylazo)

resorcinol (PAR) is added post column to form a light-absorbing complex. Hydrated and weakly

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26

complexed transition metals can be separated as cations on a cation exchange column. By adding

a carboxylic acid chelating agent to the eluent, the net charge on the metal is reduced, since the

carboxylic acids are anionic in solutions. The selectivity of the separation is actually due to the

different degrees of association between the metals and the chelating agents producing different

net charges on the metal complexes. If strong enough chelating agents are used in high enough

concentration, the net charge of the metal complexes can be negative. These anionic metal

complexes are separated by anion exchange. The IonPac® CS5A column has, cation and anion

exchange capacity, allowing metals to be separated as cations or anions on a single column. This

is called a mixed mode separation. Finally, heavy metals were detected by measuring the

absorbance at 530 nm of the complex formed with the post column PAR reagent.

The concentration of Cu, Fe, Mn, Ni and Co in sediment and soil was analysed by Ion-

chromatography system (DIONEX-600) using UV-Visible detector under the following

conditions:

Columns: IonPac CS5A Analytical and CG5A Guard, Eluent: Met Pac PDCA eluent, Flow Rate:

0.35 mL/min, Injection Volume: 50 µL, Mixing Device: 375- µL knitted reaction coil, Post

column Reagent: 0.5 mM PAR(0.25Mm for water analysis ),Reagent Flow Rate: 0.45 mL/min,

Detector Wavelength: 530 nm. Fig.6 shows the Ion-chromatogram (mAu vs retention time) of

Fe, Cu, Ni, Co and Mn.

Fig. 6 Ion-chromatogram (mAu Vs Retention Time) of Fe, Cu, Ni, Co and Mn

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27

2.4.3.3 Determination of Pb and Cd and Cu using DPASV (differential pulse anodic

stripping voltametry)

Pb, Cd and Cu in soil, sediment and water samples were analyzed using DPASV. Sample size for

soil, sediment analysis has been fixed at 0.5 mL and for water 5mL. Sodium acetate buffer (pH-

4.75) was used as supporting electrolyte. Fig.7 shows the voltammogram of Cd, Pb and Cu in

soil.

DPASV measurements were made under following conditions

a) Potential ranges of −0.8 to 0.2 V

b) Mode-Differential

c) Electrode-HMDE(Hanging mercury drop electrode)

d) Calibration –Standard addition method to remove matrix interference

Fig.7 Differential Pulse Voltammograms of Cd, Pb and Cu

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CHAPTER-3

R E S U L T S A N D D I S C U S S I O N S

3.1 Basic Statistical Analyses

ig. 8, 10 and 11 represents box and whiskers plots with different percentile (1%-99%) of

ionic concentration in groundwater, major elements in soil and sediment. Fig-9 and 12

shows whiskers plots of concentration of heavy metals in soil and sediment. A Box plot consists

of a box, whiskers and outliers. Box covers middle half (50%) of the data, the bottom of the box

is at 1st quartile (Q1) at the 25

th percentile and the top is at the 3

rd quartile (Q3) value at 75

th

percentile. The median, the midpoint of the data set is shown as a solid point in the box. The

outer parts of the data set or “tails” are the Whiskers, which show the range of the data. The

whiskers are plotted by lines that extend from the top and bottom of the box to extreme data

values (maximum and minimum) that are not under taken to be outliers. An outlier is any values

that lay more than one and a half times the length of the box from either end of the box. That is,

if a data point is below Q1 – 1.5×IQR or above Q3 + 1.5×IQR, it is viewed as being too far from

the central values to be reasonable. Outliers are points outside the lower and upper limits and are

plotted with open dots whereas the extremes are the values that lies more than three times the

length of box from either end of the box, i.e., data point is below Q1 – 3×IQR or above Q3 +

3×IQR. The box, whiskers and location of the mean indicates the symmetry. Closer the mean is

to median, the more symmetrical the distribution. In case of skewed data, the box plot is not

symmetric. It is obvious from Table 3 and 5 that the mean and median of all the measured

concentration of elements in ground water and soil are significantly different indicating that their

values were highly positively skewed about mean.

F

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BOX Plot

Median; Box: 25%-75%; Whisker: 1%-99%

Median 25%-75% 1%-99% Outliers Extremes

Cl NO3 SO4 Na K Mg Ca HCO30

10

20

30

40

50

60

70

Ion

ic c

on

ce

tra

tio

n (

me

q/L

)

Fig. 8 Box and Whiskers plot for major ionic concentration in ground water

Median; Whisker: Min-Max

Median Min-Max Outliers Extremes

Pb Cu Cd Ni Co Mn0

50

100

150

200

250

300

350

400

co

nc

en

tra

tio

n o

f h

ea

y m

eta

ls (

mg

/kg

)

Fig. 9 Whiskers plot of distribution of heavy metals in soil

Box Plot

Median; Box: 25%-75%; Whisker: Min-Max

Median 25%-75% Min-Max Outliers Extremes

Na K Mg Ca Fe0

20000

40000

60000

80000

1E5

co

nc

en

tra

tio

n o

f e

lem

en

ts i

n s

oil

(m

g/k

g)

Fig. 10 Box and Whiskers plot of major elemental concentration in soil

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30

Median; Box: 25%-75%; Whisker: Min-Max

Median

25%-75%

Min-Max

Outliers

ExtremesNa K Mg Ca Fe

-10000

0

10000

20000

30000

40000

50000

60000

Co

nce

ntr

atio

n o

f e

lem

en

ts in

se

dim

en

t (m

g/k

g)

Median; Whisker: Min-Max

Median

Min-Max

Outliers

ExtremesPb Cd Cu Ni Co Mn

-20

0

20

40

60

80

100

120

140

160

180

200

Co

nce

ntr

atio

n o

f H

ea

vy m

eta

ls in

se

dim

en

t (m

g/k

g)

Fig. 11 Box and Whiskers plot of major elemental concentration in sediment

Fig. 12 Whiskers plot of distribution of heavy metals in sediment

Inter quartile range (Q3-Q1) of all the measured elements in soil and groundwater were also

evaluated and found to be very high indicating that, the data of all elements in both matrices was

highly dispersed. Because, it is well understood that larger the Inter quartile range (IQR), bigger

the length of the box and higher the dispersion of the data.

Box and whisker plot (Fig.8) shows that very large range of Na+ and Cl

- in ground water. In soil

among heavy metals (Fig.9) Cu and Mn have large ranges and among major elements (Fig.10)

Ca and Fe have very wide ranges of variation. Similarly for sediments almost all the major

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31

elements exhibits wide range except „K‟ (Fig.11) and among heavy metals Cu and Pb shows very

wide range of variation (Fig.12).

3.2 Hydro-geochemical evaluation of groundwater

The statistical parameters (minimum, maximum, mean, median, 1st quartile, 3

rd quartile) for

different geochemical parameters distribution in groundwater samples are presented in Table 2.

The pH of all groundwater samples ranged from 6.25 to 8.1 with an average of 7.37 (alkaline).

The measured pH was found to be within the permissible range (6.5 to 8.5) recommended by

Bureau of Indian standard guidelines [39] for drinking water. Some well water having higher

value of pH may be due to weathering of plagioclase feldspar by dissolved atmospheric carbon

dioxide that will release sodium and calcium which progressively increase the pH and alkalinity

kind of result observed.

Electrical conductivity and total dissolved solid are closely related to each other. Higher values

of electrical conductivities recorded in groundwater at few locations is likely due to seawater

influence. Although the studied site is nearby Arabian Sea the impact of saline water intrusion

may be ruled out due to following reasons, a) Low Mg/Ca ratio (Table-6) at all locations, if it is

due to saline incursion, Mg to Ca ratio in ground water should be high i.e. about five. b) Low Cl-/

HCO3− ratio (Table-6) at all locations of studied site except location 3, 7 and 24 which are at far

distance from waterline. The mean of EC (Electrical Conductivity) and TDS (Total Dissolved

Solids) of groundwater samples were in the range of 1174.9 µS/cm and 745.3 mg/L respectively.

The calculated ionic strength ranged from 0.004 to 0.105 with mean of 0.043. The possible

reason behind high EC value at some locations might be impact of dissolution of salt of marine

origin or formation of local saline pockets due to Base Exchange processes in weathered zone.

The groundwater were dominated by the major ions like Na+, K

+, Ca

2+, Mg

2+, Cl

−, NO3

-, HCO3

−,

CO32-

and SO42−

. These ionic species may be attributed to the leaching of salts from the soil,

chemical weathering process and also to anthropogenic activities. The excess content of ions in

ground water may be due to the presence of variety of lithological components of natural origin

in the sampling region. Cl- concentration is the most suitable parameter as a reference for the

water because it is conservative (i.e. not lost from solution by sorption or precipitation) and

shows the largest concentration range among other ions. Ions like K+ and NO3

- in water samples

have shown high enrichment, which might have originated from multi sources like leaching from

soil, decay of organic matter, sewerages etc.

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32

High content of „K‟ may be due to silicate minerals, orthoclase, microcline, hornblende,

muscovite and biotite in igneous and metamorphic rocks and evaporate deposits gypsum and

sulphate which releases considerable amount of potassium in to groundwater. Main reason for

increasing potassium content in groundwater is due to agricultural activities. The other source of

K+ may also be predicted from decomposition of primary minerals like K-feldspar and hydroxyl-

apatite respectively by the chemical weathering process. A high mean ratio (> 2) of Ca/Mg was

observed in groundwater indicating that there could be either high decomposition of calcium

bearing minerals like calcite, dolomite etc. or calcium containing silicate and carbonate minerals

might have dissolved congruently in the study area. Generally Ca and Mg do not behave equally

in ground water system because Mg deteriorates the mineral structure particularly when waters

are sodium dominated and highly saline due to high exchange with Na bearing minerals.

Intrusion of saline water through the soil and chemical weathering of halite minerals is one of the

important processes responsible for the higher concentration of Na+ and Cl

- ions in groundwater.

Some ground water showed the observed mean ratio (>1) of Na/Cl in ground water indicating

that not only the congruent dissolution of halite by water is responsible for sodium but also

incongruent dissolution of Na bearing silicate minerals like feldspar by acids. If halite dissolution

is responsible for sodium, the Na/Cl ratio should be approximately equal to one whereas the ratio

greater than one is typically interpreted as Na released from a silicate weathering reaction.

Abnormal concentrations of Cl may result from saline residues in the soil or contamination by

sewage. In this study, as the studied site is an estuarine area the ground water recharge also took

place from river water which contaminated by discharge from various sources. The ratio of

HCO3− to dissolved SiO2 in ground water was found to be greater than 10 indicates that

carbonate weathering due to major water–rock interaction was the dominant process. To study

the variability and homogeneity of ions in ground water of study area, coefficient of variation

(%CV) was calculated. The calculated %CV for all ions showed very wide variation (42.5 to

230.26%) indicates that there is non-homogeneity of the distribution of ions contents throughout

the studied area. A wide dispersion along the investigated area may be due to the consequences

of weathering processes, mineralogical composition, anthropic input etc.

3.3 Piper’s groundwater’s classification

Fig.13 shows a trilinear Piper‟s plot for ground water classification of studied site. In this

diagram, cations and anions were normalized to 100% and plotted in their appropriate triangle.

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33

The data plotted on each triangle are then projected in a central diamond-shaped field. In the

cations triangular diagram of Piper‟s plot of groundwater, about 25% of the groundwater were

strongly alkaline (Na++ K

+ >50 mg/L %), 50% were Ca

2+ type (Ca

2+>50 mgL

-1%) and remaining

was without dominant cation type (Na+, K

+, Mg

2+ and Ca

2+, < 50 mg/L %). Similarly for anions,

36% groundwater were bicarbonate type (HCO3-> 50 mg/L %), 36% were chloride type and

remaining were without dominant anion type (Cl-, HCO3

- and SO4

2 < 50 mg/L %). In addition to

this, groundwater were also classified into three dominant chemical facies of (Na+K) –(SO4+Cl)

type (32%), (Ca + Mg) – (SO4+Cl) type (40%), (Ca+Mg)–HCO3 type (28%). Hence, for Ca2+

-

Mg2+

type water there are 18 locations (1, 4, 5, 8, 9, 10, 12, 13, 14, 15, 16, 17, 19, 20, 21, 22, 23 and 24)

which can be divided as follows:

18= 10 (With anions SO42-

and Cl- ) + 8 ( With anion type C03

2- and HCO3

-)

And rest seven locations (2, 3, 6, 7, 11, 18, and 25) belongs to Na+ -K

+ water type with anions as follows:

7= 6 (With anions SO42-

and Cl- ) +1 ( With anion type CO3

2- and HCO3

-)

Fig. 13 Trilinear Piper‟s plot for ground water‟s classification of study site.

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34

This wide variation of water types reflects the local variation of geology and geochemistry

significantly which affects the groundwater compositions. Calcium-bicarbonate facies suggest

that rock weathering is the major factor for controlling the water chemistry. Na−HCO3 type

water can be formed by the dissolution of plagioclase minerals, increasing the Na-concentrations

in groundwater. Piper diagram revealed that, Ca and Mg are predominating cations in the ground

water of study area with SO4+Cl as predominating anions.

From the classification of hydrogeochemical facies in groundwater as shown in Fig.14 (Piper,

Lawarence and Balsubramanian diagram), it was observed that 32% of ground water was

predominated with gypsum, 32% were high content of Ca +Mg with SO4 and Cl and remaining

were the combination of these cations with bicarbonate and carbonate. This diagram is well

supported by simulated data (saturation index) generated using PHREQC hydrochemical code

presented in Table.7.

Fig. 14 Hydrogeochemical facies in ground water

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35

Table 2. Ground Water Data Sheet

Location

Cl-

(mg/L)

NO-3

-

(mg/L)

SO42--

(mg/L)

Na+-

(mg/L)

K+-

(mg/L)

Mg2+-

(mg/L)

Ca2+-

(mg/L)

pH

HCO-3-

(mg/L)

Si-

(mg/L)

Cu

(µg/L)

Fe

(µg/L)

Mn

(µg/L)

Pb

(µg/L)

TDS

(mg/L)

Ec

(µS/cm)

1

207.39

165.8

33.36

176.53

12.78

66.88

66.95

7.25

127.14

2.46

18.66

5.04

557.1

13.66

863.08

1530

2 182.78 190.84 34.48 273.66 21.27 18.71 111.65 7.6 130.27 1.74 1 34.6 11 BDL 985.47 810

3 1960.88 208.3 35.84 238.22 20.87 15.28 105.62 7.51 166.58 3.39 4.5 14 3.5 1 2776.52 4200

4 18.64 76.97 2.52 32.6 10.23 7.64 85.25 7.71 207.98 1.17 2.05 29.5 16.5 BDL 600.91 974

5 88.27 81.33 40.47 121.87 6.085 18.95 89.99 7.6 200.84 1.64 3.5 60 14.5 1.1 677.79 1450

6 387.86 51.75 543.59 412.72 19.92 32.56 71.31 8.1 323.4 1.44 16.53 1.36 14.33 0.46 1394 2370

7 1669.45 80.54 76.41 1493.84 11.88 76.01 138.47 6.87 217 4.25 2 116 2 1 3767.9 5890

8 192.06 10.7 67.13 15.06 0.64 4.49 21.5 7.26 120.9 5.73 24 57 467 1 484.07 756

9 17.37 1 40.14 25.08 3.54 7.24 62.75 7.92 131.45 3.73 4.75 61.25 7.2 0.6 279.14 488

10 80.7 0.53 31.62 21.93 1.94 10.31 49.01 7.11 66.66 3.85 2 61 15.2 BDL 451.32 446

11 180.94 9.2 63.31 86.32 6.43 15.85 41.09 7.84 72.2 8.67 1.55 56.1 22 0.85 234.34 769.5

12 30.25 5.35 9.8 14.7 1.62 3.99 18.99 7.92 133.66 3.41 1 14.2 42 BDL 204.06 361

13 49.71 5.16 27.3 15.87 1.93 5.68 22.86 6.95 76.78 3.86 5.55 60.8 18.2 0.7 283.3 1099

14 38.96 1.6 41.87 30.41 1.85 10.58 50.18 7.12 104.1 3.51 1 60 17 BDL 213.46 442

15 12.36 5.44 11.87 9.07 0.59 5.4 18.137 7.19 145.43 6.43 2 13.2 31 BDL 116.12 333

16 20.49 1.09 9.8 18.49 1.51 4.41 24.47 7.23 78.13 4.85 7 1.2 16.8 1 534.18 181

17 137.1 22.21 30.97 41.28 6.12 9.48 69.14 7.29 206.04 3.59 2 17.7 26.25 1.2 148.78 815

18 22.46 0.335 8.95 31.65 1.59 3.98 13.78 6.26 40.23 3.16 2.5 46.5 29.05 0.7 602.2 2401.5

19 183.98 0.1 35.82 74.21 9.11 15.66 87.35 7.53 194.56 3.52 2 23 17.3 BDL 362.13 940

20 57.76 36.96 8.09 25.76 2.86 6.4 26.9 6.25 177.33 2.59 1 34 17.2 1 362.26 566

21 62.53 23.46 35.7 32.55 3.16 14.85 71.38 7.39 128.25 6.28 2.42 53.2 26.24 1.33 357.08 577.86

22 84.36 2.87 14.2 28.325 2.17 16.82 64.91 7.85 139.89 10.17 1.4 46.5 18.8 1.85 210.13 557

23 23.72 0.35 14.57 25.55 1.27 5.4 45.68 7.73 72.3 3.92 2 47 17.1 BDL 1241.9 327

24 550.59 95.3 320.06 29.34 1.963 9.92 64.77 7.17 160.41 5.54 1 42 17.71 1.2 737.04 2011

25 260.55 58.24 76.64 132.46 9.59 19.97 65.57 7.48 150.8 3.96 5.65 61.5 37 1.35 745.33 1140 %CV 182.85 134.01 177.54 215.48 98.69 108.389 53.38 6.17 42.5 50.65 125.68 63.09 230.26 216.65 110.63 111.5

35

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Table 3. Descriptive statistics of ground water samples

Geochemical

parameters Mean Median Minimum Maximum Lower Upper Percentile Percentile Range Quartile Std.Dev. Skewness Kurtosis

Cl (mg/L)

260.85

84.36

12.36

1960.88

30.25

192.06

17.36

1669.45

1948.52

161.81

486.78

2.97

8.34

NO3- 45.417 10.70 0.10 208.3 1.6 76.97 0.33 190.84 208.20 75.37 62.12 1.58 1.67

SO42-

64.580 34.48 2.52 543.59 14.20 41.87 8.09 320.06 541.07 27.67 117.01 3.53 12.85

Na 136.29 31.64 9.07 1493.84 25.07 121.86 14.70 412.72 1484.77 96.79 299.76 4.21 19.16

K 6.436 3.16 0.59 21.27 1.85 9.59 0.64 20.87 20.68 7.74 6.48 1.29 0.66

Mg 16.258 10.31 3.98 76.01 5.68 16.82 3.99 66.88 72.03 11.14 17.98 2.59 6.57

Ca 59.508 64.77 13.785 138.47 26.9 71.38 18.14 111.65 124.68 44.48 32.42 0.50 -0.051

pH 7.36 7.39 6.25 8.10 7.175 7.71 6.26 7.92 1.85 0.535 0.464 0.86 0.94

HCO3 142.89 133.66 40.23 323.4 104.1 177.33 66.66 217.00 283.17 73.23 61.97 0.82 1.59

Si 4.115 3.73 1.169 10.17 3.16 4.85 1.44 8.67 9.0 1.69 2.13 1.24 1.94

Cu(µg/L) 4.682 2.0 1.0 24 1.55 4.75 1.0 18.66 23.00 3.20 6.00 2.33 4.75

Fe 40.66 46.5 1.2 116 17.7 60.0 1.36 61.50 114.80 42.30 26.18 0.61 1.34

Mn 58.48 17.3 2.0 557.1 15.20 26.25 3.5 467.00 555.10 11.05 137.44 3.32 10.04

Pb 33.520 1.2 0.46 101.0 1.0 101.0 0.6 101.00 100.54 100.00 47.31 0.81 -1.45

TDS 745.30 484.07 116.12 3767.9 279.14 745.33 148.78 2776.52 3651.78 466.19 841.53 2.65 7.42

EC 1257.39 810.0 181 5890 488.0 1450. 327 4200.00 5709.00 962.00 1314.65 2.42 6.35

36

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Table 4. Soil data Sheet

Location Pb

(µg/g) Cu

(µg/g)

Cd

(µg/g)

Fe

(µg/g)

Ni

(µg/g)

Co

(µg/g)

Mn

(µg/g)

Na

(µg/g)

K

(µg/g)

Mg

(µg/g)

Ca

(µg/g)

1 57.27 99.32 0.45 61968 32.12 2.85 270.47 23919.2 33019.8 3409.8 14749.67

2 58.54 106.92 0.49 62073 40.65 1.24 281.02 24025 33261.1 3465.28 14960.67

3 24.29 167.7 1.35 33677.21 37.85 62.68 117.22 20096.31 5781.31 3423.31 5804

4 23.63 167.04 2.27 33675.9 39.07 61.37 115.91 20095 5780 3422 5802

5 49.37 103.75 0.21 34060.9 41.87 63.41 116.06 20099.5 5782 3424 5803.84

6 57.38 345.47 2.86 61893.6 28.6 7.6 203 30568 15626 4834 12015

7 23.65 110.79 0.66 33742.64 40.12 66.94 117.89 11533.26 5470.58 30475.73 68061.35

8 16.66 106.43 0.24 33541.7 37.53 61.49 115.34 2002.04 1176.9 4986.17 8339.24

9 14.74 101.14 0.19 33152.28 34.08 59.14 115.26 1705.04 336.9 4420.17 7584.24

10 16.99 107.16 0.25 32912.28 31.54 61.03 117.11 1920.04 5743 3747.25 5869.87

11 14.54 102.04 0.22 32654.94 30.3 60.94 115.87 1865.69 5598.6 3725.67 5842.72

12 15.26 103.33 0.398 32760.74 31.19 61.04 116.31 2286.95 5741.1 3835.87 6077.22

13 22.85 110 0.238 33741.06 38.54 65.36 116.32 11531.68 5469 30474.15 68059.77

14 31.87 68.33 0.208 33875.26 40.08 65.24 116.89 20109.43 15557 11191.4 27717.75

15 42.84 159.53 0.482 61967.2 39.4 0.19 280.46 23919.34 33135.56 3330.33 14808.17

16 37.72 151.91 0.419 61841.53 37.25 0.11 277.92 23780.7 33010.36 3203.59 14594.22

17 18.77 111.54 0.285 61985 39.75 0.56 283.59 23781.78 33118.96 3218.79 14651.42

18 15.84 99.42 0.245 61771.55 38.24 0.5 279.72 23723.24 33020.42 3193.201 14626.01

19 16.62 103.57 0.26 61876.1 37.21 0.466 281.05 23239.78 32877.46 3133.42 14601.07

20 14.3 96.25 0.24 61736.9 36.77 0.402 280.9 23283.68 32023.96 2640.59 13963.48

21 44 81.76 0.429 61722.65 18.85 0.24 266.97 23678.98 32732.59 3164.6 14380.97

22 44.13 171.95 BDL 34265.36 40.95 68.59 118.24 26441.5 8130 29410.4 89994.27

23 15.7 144.05 2.26 33511.32 37.72 61.29 113.11 19940 5348.8 3318 7801.09

24 15.72 94.87 0.21 32702.23 30.57 60.08 116.14 1644.24 5848.76 3850.33 5895.32

25 11.95 89.61 0.18 32270.02 27.72 57.63 115.56 1430.24 5706.76 3692.8 5770.12

%CV 55.68 43.54 124.79 31.86 15.29 80.54 44.16 61.25 83.59 124.75 119.02

37

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Table 5. Descriptive statistic of soil samples

Valid N Mean Median Minimum Maximum Lower Upper Percentile

5th

Percentile

95th

Range Inter

Quartile

Std.

Dev.

Skewness Kurtosis

Pb 25 28.19 22.85 11.95 58.54 15.72 42.84 14.30 57.38 46.59 27.12 15.69 0.86 -0.76

Cu 25 124.16 106.43 68.33 345.47 99.42 144.05 81.76 171.95 277.14 44.63 54.05 3.08 11.83

Cd 25 0.601 0.26 0.18 2.86 0.22 0.482 0.19 2.86 100.82 0.262 0.75 2.16 3.66

Fe 25 44775.1 33875.26 32270.02 62073.00 33511.32 61841.53 32654.94 61985.00 29802.98 28330.21 14264.24 0.43 -1.97

Ni 25 35.52 37.53 18.85 41.87 31.54 39.40 27.72 40.95 23.02 7.86 5.43 -1.37 2.13

Co 25 38.02 60.08 0.11 68.59 0.56 61.49 0.19 66.94 68.48 60.93 30.62 -0.42 -1.95

Mn 25 177.93 117.22 113.11 283.59 116.06 277.92 115.26 281.05 170.48 161.86 78.57 0.52 -1.825

Na 25 16264.82 20099.50 1430.24 30568.00 2286.95 23780.70 1644.24 26441.50 29137.76 21493.75 9962.25 -0.61 -1.32

K 25 15971.88 5848.76 336.90 33261.10 5706.76 32877.46 1176.90 33135.56 32924.20 27170.70 13351.64 0.46 -1.76

Mg 25 7079.63 3465.28 2640.59 30475.73 3318.00 4420.17 3133.42 30474.15 27835.14 1102.17 8831.79 2.36 4.07

Ca 25 18710.94 13963.48 5770.12 89994.27 5895.32 14749.67 5802.00 68061.35 84224.15 8854.35 22270.44 2.38 4.84

38

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Table 6. Chemical characteristic data sheet of ground water (in meq/L)

LOCATIONS Mg2+

/ Ca2+

SAR Cl-/HCO3

- Cl

-/SO4

2-

1 1.66 3.65 2.84 8.53

2 0.28

6.31 2.45 7.27

3 0.24

5.74 20.52 75.03

4 0.15

0.91 0.15 10.14

5 0.35 3.05 0.76 2.99

6 0.76

10.16 2.09 0.98

7 0.91

25.33 13.40 29.96

8 0.35

0.77 2.76 3.92

9 0.19 0.79 0.23 0.59

10 0.35

0.74 2.10 3.50

11 0.64

2.9 4.36 3.92

12 0.35

0.80 0.39 4.23

13 0.41 0.77 1.13 2.49

14 0.35

1.02 0.65 1.27

15 0.49

0.48 0.15 1.43

16 0.30

0.9 0.46 2.87

17 0.22 1.24 1.16 6.07

18 0.48

1.94 0.97 3.44

19 0.29

1.92 1.65 7.04

20 0.39 1.16 0.57 9.79

21 0.34 0.92 0.85 2.40

22 0.43

0.81 1.05 8.15

23 0.19

0.95 0.57 2.23

24 0.25

0.89 5.98 2.35

25 0.51 3.67 3.01 4.66

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3.4 Saturation Indices (SI) of minerals in ground water

The level of saturation of different minerals in groundwater of all locations as tabulated in Table.

7, were calculated using PRHEEQC version 2 geochemical model The saturation indices (S.I

=log (IAP/KT).) of predominant minerals like chrysotile, chalcedony, sepiolite, gypsum and

halite in all locations showed negative values indicating that the groundwater were significantly

under saturated with respect to respective minerals. Groundwater of some locations were

oversaturated with respect to minerals like aragonite, anhydrite, calcite, dolomite, talc and silica

bearing minerals like quartz etc. because these minerals are dominated by soil and decomposed

quickly by silicate weathering processes except quartz, which decomposed slowly due to limited

buffering capacity of soil. The high SI of Ca and Mg bearing minerals indicates that there is

depletion of Ca2+

and Mg2+

content due to their precipitation in the ground water system.

However, the under saturation concluded that the elements were being removed from the mineral

surfaces in presence of other dominant species and consequently increase their concentration.

The dissolution of gypsum releases Ca2+

and increase in Ca2+

concentration leads to

oversaturation of the water in calcite due to common ion effect. The other ion SO42-

released

during gypsum dissolution does not participate in the calcite equilibrium reaction. Dolomite is

also present in the carbonate rocks and as calcite precipitates, dolomite dissolves. The

combination of three reactions-gypsum dissolution, calcite precipitation and dolomite dissolution

leads the observed increases in Mg2+

and SO42-

concentration. This model underestimated the

concentration of some ions like NO3- and K

+ indicating that these ions were being added to the

system and overestimated the concentration of other species.

Table 7. Saturation level of different minerals in groundwater of all locations

Mineral Chemical Formula Locations

Under saturation

Locations

oversaturation

Anglesite PbSO4 1,2,3,5,6,7,8,9,13,16,17,18,

20,21,22 and 25

Anhydrite CaSO4 2

Aragonite CaCO3 2,3,4,5,6,9,12,19,22

Calcite CaCO3 2,3,4,5,6,9,12,17,19,22

and 25

Cerrusite PbCO3 All

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3.5 Speciation study of major chemical species in ground water of studied area

The speciation study of major chemical constituents of ground water of study site was done

using the thermodynamic data base and calculations with HYDRA (Hydrochemical Equilibrium

Constant Database) speciation program. Fig. 15 -18 depicts the pH dependent calculated species

distribution of metals in ground water under the varying amount of complexing ions. Speciation

study revealed that most of the groundwater belonged to the predominant species like,

Na(SO4+Cl), (MgCa)SO4, MgCa(OH), CaCO3 and CaMg(CO3)2. However, among heavy metals,

Fe and Mn bearing minerals were dominating. Fe(OH)3, goethite [FeO(OH)], hematite (Fe2O3)

also indicates oversaturation, this is supported by the fact that Basalt (Deccan trap) contains

Mineral Chemical Formula Locations

Under saturation

Locations

oversaturation

Chalcedony SiO2 All

Chrysotile Mg3Si2O5(OH)4 All

Dolomite CaMg(CO3)2 2,4,5,9,19 and 22

Fe(OH)3(a) Fe(OH)3 16,18,20,23,24

Goethite FeOOH All

Gypsum CaSO4:2H2O All

Halite NaCl All

Hausmannite Mn3O4 All

Hematite Fe2O3 All

Jarosite-K KFe3(SO4)2(OH)6 All

Manganite MnOOH All

Melanterite FeSO4:7H2O All

Pb(OH)2 Pb(OH)2 All

Pyrochroite Mn(OH)2 All

Pyrolusite MnO2 All

Quartz SiO2 11,15,16,22

Rhodochrosite MnCO3 1 and 8

Sepiolite Mg2Si3O7.5OH:3H2O All

Sepiolite-d Mg2Si3O7.5OH:3H2O All

Siderite FeCO3 All

SiO2 SiO2 All

Talc Mg3Si4O10(OH)2 11 and 22

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ferromagnesian elements i.e., rich in Fe and Mg minerals. In this case rhodochrosite (MnCO3).

and mangnite [MnO(OH)] seems to be contributing to Mn.

Fig. 15 pH- dependent calculated species distribution of Na in the groundwater at the various

range of [SO4] = 2.6×10 -5

M- 5.67×10-3

M, [Cl] = 3.5×10 -4

M- 5.52×10-2

M and [Na] =4 ×10-4

-

6.5× 10 -2

M

Fig. 16 pH- dependent calculated species distribution of Mg and Ca in the groundwater at the

various range of [SO4] = 2.6×10 -5

M- 5.67×10-3

M, [Cl] = 3.5×10 -4

M- 5.52×10-2

M and [Mg]

=1.6 ×10-4

– 3.16× 10 -3

M and [Ca] =3.45 ×10-4

– 3.46× 10 -3

M

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Fig. 17 pH- dependent calculated species distribution of Mg and Ca in the groundwater at the

various range of [CO3] = 6.6×10 -8

M- 5.3×10-3

M, [Mg] =1.6 ×10-4

– 3.16× 10 -3

M and [Ca]

=3.45 ×10-4

– 3.46× 10 -3

M

Fig. 18 pH- dependent calculated species distribution of Fe and Mn in the groundwater at the

various range of [CO3] = 6.6×10 -8

M- 5.3×10-3

M, [Fe] =2.16 ×10-8

– 2.1× 10 -6

M and [Mn] =3.6

×10-8

– 1× 10 -5

M

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3.6 Gibbs-Boomerang diagram for ground water of samples of study site.

Gibbs-Boomerang diagram ( Fig.19.a & 19.b) of ground water of study site shows majority of

the water samples of study sites were fall in region-2 [e.g., Weathering] and water samples of

few locations falls outside the boomerang ( i.e., water samples having weight ratio of Na+/(Na

++

Ca2+

) >0.6, and TDS values > 800 mg/L ). From this result it can be concluded that, the chemical

composition in most of the ground water is largely controlled by rock weathering. However the

water samples which are out of the curve area, rock weathering and evaporation-crystallization

processes alone cannot explain the chemical characteristics of ground water of that locations.

Moreover, no data plot in the lower right side of the boomerang shows that atmospheric

precipitation is not an important process in determining the chemical composition of these water

samples. Since the average amount of Na+ (meq/L) in ground water is lower than that of Cl

-

(meq/L) and absence of Na-HCO3- hydrochemical facies, it may be predicted that dissolution of

ancient salts of marine origin is the predominate process, cause for deviation rather than cation

exchange process. However few locations also show cation exchange causing the deviation, as

when a sodium bearing clay minerals [e.g., Na-Montmorillonite and albite (NaAlSi3O8), in basalt

albite is less than Ca-plagioclase] interacts with calcium dominant ground water, each couple of

absorbed sodium ions is replaced by solubilised calcium, which enriches groundwater into

sodium, thus changing it from a calcium bicarbonate type, into a sodium bicarbonate type.

Therefore, the groundwater become enriched in sodium leading to increase the weight relation

Na+/ (Na

++Ca

2+) values and data plot outside the Gibbs boomerang. Study of Boomerang suggest

that location 6, 7 and 18 exhibits cation exchange as controlling process causing deviation from

general pattern.

On the other hand, the weight relation Cl- /(Cl

- +HCO3

-) (x axis) was plotted against TDS values

represented in Fig.19.b called as Gibb‟s anionic diagram shows that data plot inside the

boomerang shape envelope in the upper side, being rock weathering and evaporation–

crystallization processes controlling the hydrochemistry. No data plot in the lower-right side of

the boomerang, where chemical composition is determined by atmospheric precipitation process.

The evaporation–crystallization process increases TDS and promotes calcite precipitation which

is supported by oversaturation of ground water of study site w.r.t calcite (Table.7). Locations

affected by dissolution of ancient salt of marine origin are 1, 3, 8, 10, 13, 15, 11, 21, 22, 23 and

25.

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(a) (b)

Fig. 19 Gibbs-Boomerang diagram for cations and anions in ground water of all sampling

locations

3.7 Stability diagrams of clay minerals in groundwater system

Stability diagrams are graphical representations of equilibria between minerals and aqueous

solution. Such diagrams are very useful in inferring what will happen when waters of various

compositions interact with solid phases. Fig.20 (a, b, c and d) show the stability diagram of K,

Na, Ca, Mg-systems respectively. The process of constructing the diagrams from thermodynamic

data and then use the diagram to make interferences during silicate minerals weathering have

been carried out. Plagioclase and K-feldspar along with quartz are among the most abundant

minerals in the earth‟s crust. The stability diagrams can be used to understand the chemical

breakdown of Na-plagioclase to a variety of weathering products. The other stability diagrams of

interest involve weathering of Ca-plagioclase and K- feldspar. The phases of interest in aqueous

system are gibbsite, kaolinite, Na-montmorillonite and albite. In this study stability diagram was

plotted for all the four systems i.e., Na, K, Ca and Mg The stability diagram also helps to

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46

understand the chemical changes that occur during water-rock interactions. The conversion of

albite to gibbsite involves the consumption of hydrogen ions and release of silicic acid and

sodium ions. Thus both silicic acid activity and ratio of sodium/hydrogen ions increases. When

the gibbsite to kaolinite boundary is encountered, gibbsite is converted to kaolinite.

2Na Al Si3O8 ( albite) + 2H+ + 4 Al (OH)3(gibbsite) ↔ Al2Si2O5(OH)4 (kaolinite) + H2O + 2Na

+

As long as gibbsite and kaolinite are present, the reaction will occur at constant silicic acid

activity. After all the gibbsite is converted into kaolinite, the following reaction takes place:

2Na Al Si3O8 (albite) + 2H+ + 9H2O ↔ Al2Si2O5 (OH)4 (kaolinite) + 4 H4SiO4 (aq) + 2Na

+

During the reaction, hydrogen ions are consumed and silisic and sodium ions are released to

solution. The result is an increase in the Na+/H

+ ratio and increase in the activity of silicic acid.

For the open system, the important variable is the rate at which water moves through the

weathering environment. This is sometimes referred to as the flushing rate. In case of high

rainfall and good infiltration, the concentration of silicic acid and various ions in solution will be

low. Under this condition, albite will weather to gibbsite. This is the situation observed in

tropical settings where there is deep weathering and weathered materials largely consist of Al

(gibbsite) and iron hydroxides. In regions of lower rainfall and less rapid infiltration, the

concentration of ions in solution is greater and albite will weather to kaolinite or Montmorillonite

clays. This is the situation usually in temperature setting. The variation in clay minerals content

is related to climatic conditions in the continental source region and high kaolinite content

representing humid conditions. This inference is related to the flushing rates. In our study, most

of the groundwater were dominated by kaolinite mineral suggesting that the chemical weathering

is the predominate process controlling the chemistry of ground water. The possible reaction

controlling the mineral stability is as follows.

a) K-feldspar – Kaolinite- Gibbsite formation

2KAISi3O8 + H+

+ 9H20 ↔A12Si205(OH)4 + 2H4Si04 + 2K+

(k-feldspar/microcline) (Kaolinite)

The potassium can also be incorporated into the formation of more potassium feldspar, illitic

mica, or the mixed-layer clay illite/smectite [40]

A12Si205 (OH)4 +5H20 ↔ A12 (OH)6 + 2H4Si04

(Kaolinite) (Gibbsite)

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b) CaAl14Si22060 (OH) 12 + 2H+ + 23H20 = 7Al2Si2O5(OH)4, + 8H4SiO4 + Ca

2+ ……… .(5) [41]

(Ca-Montemorilonite) (Kaolinite)

The climatic condition of Mumbai (tropical wet climate-humid) supports the dominance of

kaolinite at sampling site. Absence of muscovite at sampling site revealed that there may not be

any interaction between ground water of studied aquifer system and sea water. As Clay minerals

(e.g., kaolinite) in contact to sea water tend to take up K+ and are converted to illite (muscovite.),

presented in fig.21. (Taken from Faure ch. 12, fig. 12.1, p. 174)

Fig. 20 a. Stability diagram for K-system

Fig. 20 b. Stability diagram for Na-system

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Fig. 20 c. Stability diagram for Ca-system

Fig.20 d. Stability diagram for Mg-system

Fig. 21 Stability diagram in presence of sea water impact (source-Faure ch. 12, fig. 12.1, p. 174)

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3.8 United State Salinity Laboratory (USSL) classification diagram of groundwater.

For the purpose of diagnosis and classification, the total concentration of soluble salts (salinity

hazard) in ground water can be expressed in terms of specific conductance. Based on salinity

hazard, as presented in Fig.22, the USSL classification revealed that, about 50% water samples

(dotted symbol in diagram) were found to be under high salinity with low sodium hazard and

45% were under moderate with low sodium hazard and 5% were under very high salinity with

high sodium hazard. High salinity of ground water may be due to sea water incursion,

evaporation process and uptake of water by hydration reaction. But in this study, chemical

weathering is the predominating process in the most of the ground water as explained earlier.

Fig. 22 USSL classification of ground water

3.9 Multivariate Statistical Analysis of water.

3.9.1 Factor analysis: In order to find most significant processes controlling chemistry of the

groundwater of study area, factor analysis was applied to standardized/transformed (e.g., mean=0

and standard deviation=1, shown in Table-8) values of thirteen chemical parameters of water.

Factors were extracted using principal components extraction method and subjected to varimax

normalization rotation. Factor analysis extract four factors (factors having Eigen value>1; Kaiser

criterion and loading >0.55) which accounts for 80% variance of total variance. The correlation

matrix chart which gives idea about the nature of association of different species and eigen value

of four factors are presented in Table -9 and 10 respectively.

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Table 8. Standardized data set of water parameters

Cl- NO

-3 SO4

2- Na

+ K

+ Mg

2+ Ca

2+ HCO

-3 Si Cu Fe Mn Pb

1 -0.10981 1.937888 -0.26683 0.13421 0.978379 2.814526 0.229515 -0.25418 -0.77776 2.327213 -1.3606 3.628058 4.69585

2 -0.16037 2.340974 -0.25722 0.458233 2.287807 0.136307 1.608173 -0.20367 -1.11622 -0.6131 -0.23167 -0.34547 -0.45225

3 3.492345 2.62204 -0.2456 0.340006 2.226115 -0.0544 1.422193 0.382204 -0.34058 -0.03037 -1.0184 -0.40004 -0.07537

4 -0.49756 0.507929 -0.53034 -0.34594 0.585088 -0.47918 0.793932 1.050211 -1.38463 -0.43828 -0.42644 -0.30545 -0.45225

5 -0.35451 0.578115 -0.20608 -0.04815 -0.0542 0.149651 0.940125 0.935004 -1.16322 -0.19686 0.738387 -0.32 -0.03769

6 0.260921 0.101945 4.093461 0.922134 2.079594 0.906358 0.363988 2.912564 -1.25724 1.972576 -1.50114 -0.32124 -0.27889

7 2.893666 0.565398 0.101096 4.528727 0.83957 3.322148 2.435369 1.195753 0.063687 -0.44661 2.877088 -0.41095 -0.07537

8 -0.14131 -0.55887 0.021792 -0.40445 -0.894 -0.65431 -1.17228 -0.35486 0.761287 3.2163 0.623813 2.972474 -0.07537

9 -0.50018 -0.71501 -0.2089 -0.37104 -0.44672 -0.50142 0.099976 -0.18472 -0.18076 0.011255 0.786126 -0.37312 -0.22612

10 -0.37007 -0.72258 -0.28166 -0.38153 -0.69349 -0.33073 -0.3238 -1.23005 -0.12435 -0.44661 0.776578 -0.31491 -0.45225

11 -0.16415 -0.58301 -0.01085 -0.16675 -0.00099 -0.02271 -0.56807 -1.14066 2.141444 -0.52153 0.589441 -0.26543 -0.13191

12 -0.47371 -0.64499 -0.46813 -0.40565 -0.74285 -0.68211 -1.24969 -0.14898 -0.33118 -0.6131 -1.01077 -0.11991 -0.45225

13 -0.43373 -0.64805 -0.31858 -0.40175 -0.69504 -0.58815 -1.13033 -1.06676 -0.11964 0.144452 0.76894 -0.29308 -0.18844

14 -0.45582 -0.70535 -0.19407 -0.35324 -0.70738 -0.31572 -0.28771 -0.62594 -0.28417 -0.6131 0.738387 -0.30181 -0.45225

15 -0.51046 -0.64354 -0.45044 -0.42443 -0.90171 -0.60372 -1.276 0.040939 1.088463 -0.44661 -1.04896 -0.19994 -0.45225

16 -0.49376 -0.71356 -0.46813 -0.39301 -0.75981 -0.65876 -1.08067 -1.04498 0.345735 0.385871 -1.50725 -0.30327 -0.07537

17 -0.25421 -0.37358 -0.28722 -0.31698 -0.04881 -0.37687 0.29706 1.018908 -0.24657 -0.44661 -0.8771 -0.23451 -1.7E-16

18 -0.48971 -0.72572 -0.47539 -0.34912 -0.74748 -0.68267 -1.41023 -1.65659 -0.4487 -0.36336 0.222807 -0.21413 -0.18844

19 -0.15791 -0.7295 -0.24577 -0.20713 0.412348 -0.03327 0.858701 0.833673 -0.27947 -0.44661 -0.67468 -0.29963 -0.45225

50

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Cl- NO

-3 SO4

2- Na

+ K

+ Mg

2+ Ca

2+ HCO

-3 Si Cu Fe Mn Pb

20 -0.4172 -0.13614 -0.48274 -0.36876 -0.5516 -0.54812 -1.00573 0.55566 -0.71665 -0.6131 -0.25458 -0.30036 -0.07537

21 -0.4074 -0.35346 -0.2468 -0.3461 -0.50533 -0.07831 0.366147 -0.23627 1.017951 -0.37668 0.478687 -0.23458 0.048994

22 -0.36255 -0.68488 -0.43051 -0.3602 -0.65848 0.031225 0.166596 -0.04845 2.846566 -0.54651 0.222807 -0.28871 0.244968

23 -0.48712 -0.72548 -0.42737 -0.36946 -0.79745 -0.60372 -0.4265 -1.13905 -0.09144 -0.44661 0.241902 -0.30108 -0.45225

24 0.595214 0.803 2.183248 -0.35681 -0.68995 -0.35241 0.162278 0.282648 0.670091 -0.6131 0.050947 -0.29664 -1.7E-16

25 -0.00061 0.206419 0.103061 -0.01281 0.486379 0.206362 0.186952 0.127586 -0.07264 0.161101 0.795673 -0.15629 0.056531

Table 9. Correlation matrix chart for different species in ground water

Cl- NO-3 SO4

2- Na+ K+ Mg2+ Ca2+ HCO-3 Si Cu Fe Mn Pb

Cl - 1.00

NO3- 0.58 1.00

SO42- 0.18 0.13 1.00

Na+ 0.69 0.32 0.22 1.00

K+ 0.56 0.79 0.35 0.46 1.00

Mg2+ 0.50 0.47 0.21 0.80 0.51 1.00

Ca2+ 0.62 0.64 0.15 0.65 0.72 0.60 1.00

HCO3- 0.33 0.30 0.58 0.42 0.55 0.37 0.55 1.00

Si -0.04 -0.39 -0.13 -0.12 -0.45 -0.13 -0.26 -0.36 1.00

Cu 0.02 0.14 0.35 0.03 0.19 0.29 -0.14 0.16 -0.12 1.00

Fe 0.20 -0.17 -0.16 0.46 -0.22 0.23 0.25 -0.19 0.20 -0.22 1.00

Mn -0.07 0.21 -0.05 -0.06 0.01 0.35 -0.16 -0.10 -0.01 0.81 -0.15 1.00

Pb 0.03 0.41 -0.04 0.05 0.18 0.61 0.07 -0.03 -0.06 0.50 -0.23 0.76 1.00

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Table 10. Eigen value for factor analysis of ground water

Factors Eigen

value

% Total

variance

Cumulative

Eigen value

Cumulative

%

1 4.692 36.096 4.692 36.096

2 2.657 20.442 7.349 56.537

3 1.824 14.03 9.174 70.568

4 1.268 9.759 10.443 80.328

Table-11 represents the factor loading matrix and Figure-23 represents the scree plot of the factor

analysis (plot of Eigen value versus factor). Table-10 and 11 indicates that these four factors are

hydrochemically meaningful, which seems to describe the existing conditions of groundwater

chemistry. For obtaining an interpretation of the nature of the retained factors, these factors are

discussed as follows.

Factor-1: (F1), it accounts for 36.09% of total variance it has high loading for Cl, Na, Mg, and

Ca and moderately loaded with Fe. F1 may be assumed to be due to two reasons one is lithogenic

in nature other one is saline incursion as the study area is a costal aquifer. But saline incursion is

rejected due to following reasons, a) locations from 1 to 5 are moderately affected by F1 and

locations 20 to 25 least affected by F1, although these locations are neared by sea. In the other

hand location 7 is highly affected by F1 (factor score-4.15 Fig.24) situated far way from water

line. b) Other indicators of saline water incursion are high Cl-/HCO

-3 ratio, high Mg

2+/Ca

2+ and

high Cl-/SO4

2-ratio are not followed (Table-6). Table-6 elucidate that, the Cl

-/HCO

-3 ratio varies

between 0.15 to 20.5 with mean value 2.8 and Mg2+

/Ca2+

ratio lies in the range of 0.15 to 1.66

with mean value of 0.43, if there occur sea water incursion then Mg2+

/Ca2+

ratio should be in the

range of 1 to 5 as Mg concentration in sea is about five times more than calcium concentration

c) The topographic condition of study area also does not supports the intrusion of sea water as

rocky structure of the area with high elevation cause low permeability and high fresh water

potential than sea water. Hence, F1 is purely lithogenic origin.

Factor-2: (F2), Its accounts for 20.44 % of total variance, which is highly loaded with Cu, Mn

and Pb the factor is anthropic in origin good correlation between Cu, Mn and Pb (from Table-9)

revealed that source of these heavy metals are vehicular activity. Factor score plot of factor-2

(Fig-25) shows that almost all the locations are unaffected by this factor except location-1, 6, 8

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and 16. All these locations situated nearby to road and agricultural fields hence most probably

affected by vehicular pollution.

Table 11. Factor loading matrix of ground water

Fig. 23 Scree Plot

Factor-3: (F3) its accounts for the 14.03% of total variance and loaded with NO3- , K

+ positively

and with Si, Fe negatively. The factor attributed due to combination of both anthropic as well as

natural. Good correlation between nitrate and potassium indicates leaching of fertilizer (NPK)

Variables Factor 1 Factor 2 Factor 3 Factor 4

Cl- 0.768 -0.042 0.233 0.088

NO-3 0.464 0.234 0.763 -0.085

SO42-

0.086 0.054 0.080 0.901

Na+ 0.914 0.004 -0.038 0.215

K+ 0.488 0.072 0.744 0.263

Mg2+

0.794 0.474 0.097 0.121

Ca2+

0.788 -0.144 0.440 0.082

HCO-3 0.314 -0.074 0.384 0.726

Cu -0.071 0.823 0.007 0.403

Fe 0.581 -0.202 -0.587 -0.194

Mn -0.051 0.957 -0.010 -0.062

Si -0.005 0.008 -0.671 -0.156

Pb 0.128 0.866 0.208 -0.185

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from soil. Factor score plot (fig-26) shows that location no 1 to 6, and 12,17,19 and 20 are

affected by factor-3.out of these location 1 to 6 are nearby agricultural field, due to high

precipitation at this area there is maximum chance of leaching of fertilizers from land to ground

water. Negative loading of Fe and Si revealed that there occurs incongruent weathering [42] of

jarosite-K. Jarosite-K mineral is the weathered product of basalt due to water rock interaction

[43]. As Jarosite-K is very unstable in humid condition it rapidly decomposes to produce ferric

oxihydroxide (Goethite) [44]. Other evidences in supports of the Jorasite-K decomposition are

a) All the locations are over saturated with goethite (Table-7).b) Predominance of kaolinite at

study area, as under most conditions jarosite is accompanied by kaolinite and gypsum [44].

Factor-4: (F4) loaded with sulphate and bicarbonate although these are produced by

mineralization product of ground water and sulphate act as source of oxygen for oxidation of

dissolved organic carbon (DOC) to carbon dioxide which in turn increases the concentration of

bicarbonates the reason of association in factor is still not clear.

Fig. 24 Factor-1 score plot Fig. 25 Factor-2 score plot

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Fig. 26 Factor-3 score plot Fig. 27 Factor-4 score plot

3.9.2 Cluster analysis of ground water

There are several clustering techniques are exists, but hierarchical clustering is the most widely

applied in earth science [45]. Q-mode Cluster analysis was performed on the data sets

considering Wards method as linkage rule and correlation distance as distance major. Q–mode

cluster analysis of chemical parameters of ground water classified all the 25 sampling locations

into four groups or cluster as shown in dendrogram (Fig-28) assuming similar type of ground

water evolution. The different clusters are as follows;

C1(Location no.-18,13,10,9,14 and 23) predominated with Ca-Mg cation and anions SO4+Cl-.all

these locations are less affected by four factors as shown in factor score plot (Fig-24 to27)

C2 (Locations-22, 21, 19, 17, 20, 16, 15, 12, 5 and 4) predominance of cations (Ca-Mg) with

majority of location is dominated by bicarbonate.

C3 (Locations 25, 11, 7, 24, 3, 6 and 2) is dominated by alkali metal ion (Na+K) and Cl+SO4

anions.

C4 (Location-1 and 8) predominated by Ca-Mg –SO4-Cl type water form a distinct group as it is

highly affected by factor 2,i.e., Cu, Mn and Pb. (factor score plot. Fig-25).

These spatial variation and grouping of locations suggest that, ground water of locations under

cluster C2 and C3 are natural in origin. Where C1 and C4 influenced by external factors which

may be natural (i.e., weathering) and anthropic (human activity) which is based upon fact that

free sodium-bicarbonate and chlorides of calcium and magnesium on the other hand do not exist

in natural water[46].

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Fig. 28 Dendrogram of Q-Mode cluster analysis of water samples

3.10 Variation in distribution of Heavy metals in soil and sediments of study area

From Table -4 the mean concentrations of Pb, Cu, Cd, Fe, Ni, Co and Mn in soils were found to

be 28.2mg/kg, 124.15 mg/kg, 0.6 mg/kg, 44775 mg/kg, 35.52 mg/kg, 38.01 mg/kg and 178

mg/kg respectively. However, in case of sediments, (Table-12) the mean concentration of these

metals were found to be 18.22 mg/kg, 76.4 mg/kg, 0.26 mg/kg, 40885 mg/kg, 8.62 mg/kg, 0.77

mg/kg and 107.25 mg/kg respectively. Akin to ground water (Table-2), soils (Table-4) and

sediments (Table-12) also shows high % CV for majority of species, this high value of %CV

revealed non homogenous distribution of species at studied sites. There exist a number of

reasons behind the non homogeneous distribution which might be due to anthropogenic and/or

natural input. The concentration of Pb, Cu, Cd, Fe, Ni, Co and Mn in soil differed from sediment

by a factor of 1.55, 1.63, 2.3, 1.1, 4.12, 49.4 and 1.66 respectively. Low content in sediment is

due to their distribution in both phases i.e., between sediment and groundwater system. Fe is

leached from the Fe-rich basalts of the Deccan Traps which could enrich the Fe content in the

samples. Organic matter is known to contain up to 100 ppm of Co and has high flux rates of

organic carbon, which apparently transfers high amounts of Co to the groundwater where Corg is

then re-mineralized and substantial proportions of Co and Ni can be retained by the sediments.

High Cu and Ni contents in clay-minerals (Illite) that are derived from soils and trapping of these

metals in strongly reducing sediments could increase the content in the sediment. Like Cu and

Co, Ni concentrations are highest in ultramafic rocks. A third factor affecting Ni concentrations

in sediments is its tendency to bind to metals, especially sulphides to Fe (pyrite).

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3.11 Geochemical normalization and enrichment factors (EF) of heavy metals in soil and

sediments with respect to continental upper crust

In an attempt to compensate for the natural variability of heavy metals in soil and sediments,

normalization was done so that any anthropogenic metal contributions could be detected and

quantified. The equation -2 was used to estimate the EF of metals for each sampling location

using Fe as a normalizer to correct for differences in sediments grain size and mineralogy.

Enrichment factor of Pb, Cu, Cd, Ni, Co and Mn in soils of various locations is given in Table-

13. The mean enrichment factor of Pb, Cu, Cd, Ni, Co and Mn in soil was calculated to be 1.12,

4.15, 52.1, 1.52, 3.95 and 0.22 respectively. However, in case of sediment, the mean enrichment

factor was worked out to be 0.84, 2.94, 25, 0.4, 0.07 and 0.16 for the same metals respectively.

The EF values in soil samples were observed in the order of Cd > Cu > Co > Ni > Pb > Mn

whereas in case of sediment the trend was found as follows: Cd > Cu > Pb > Ni > Co > Mn. In

soils and sediments Cd has the highest enrichment factor followed by Cu revealed that they were

from a common source.

3.12 Geo-accumulation indices of heavy metals in soil and sediments with respect to

continental upper crust

Table.17 represents the geo-accumulation indices based on soil and sediment quality. Geo-

accumulation indices of metals with respect to continental upper crust in soils and sediments of

various locations were calculated using equation-3. Using the geo-accumulation indices of heavy

metal, the predominant class in soil was found to be as follows presented in table-15.from

geoaccumulation indices value following conclusions can be derived: Cd was found to be in the

highest Igeo class (6) indicating extremely contaminated, Mn remains in class 0 (uncontaminated),

Fe and Ni are in class 1, Cu in class 3 (Moderately to strongly contaminated) and Co is in class 2.

However for sediment (Table-16) Fe, Ni, Co and Mn were within the Igeo class (0-1) indicating

uncontaminated sediment, whereas Cd belonged to the same class as soil. Geoaccumulation

index and enrichment factor of “Cd” indicates that soil is contaminated with “Cd”, it may be

contributed by fertilizer since phosphate fertilizers are “Cd” rich.

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Table 12. Sediment Data Sheet

Location Na

(mg/kg)

K

(mg/kg)

Mg

(mg/kg)

Ca

(mg/kg)

Pb

(mg/kg)

Cd

(mg/kg)

Cu

(mg/kg)

Fe

(mg/kg)

Ni

(mg/kg)

Co

(mg/kg)

Mn

(mg/kg) 1 25614.3 10589.5 12354 4261 67.54 1.59 189.76 27534.2 11.7 0.023 115.84

2 40122.3 11677.4 12444.9 8467.6 43.12 1.43 124.61 37614.3 12.6 0.02 117.23

3 22486 11358.4 13452 8431.5 3.45 0.004 53.87 36143.5 6.7 2.1 102.15

4 15671.16 10423.5 13482.71 6241.3 1.81 0.008 16.5 33163.8 6.8 1.3 102.06

5 23970.7 16926 2385.41 5567 87.51 0.002 82.54 48451 8.9 1.5 102.48

6 28216.54 9616.42 17537.53 4658.5 24.61 0.045 157.5 33145.12 5.3 0.056 110.3

7 26228.62 11434.72 25764.58 8315.5 2.12 0.042 77.26 52130.2 10.87 1.7 102.33

8 10073.89 6174.5 53112.04 2461.3 1.65 0.0061 97.82 31642.4 9.5 1.65 101.56

9 21541.51 7856.5 24541 4463 3.61 0.045 98.62 30121.3 7.58 0.97 101.06

10 24514.5 4586.4 45871.5 3846 2.51 0.054 67.87 41245 8.99 0.845 102.4

11 18547.5 8214.8 43512.5 3843.2 3.85 0.034 29.76 48457.2 7.65 1.56 102.43

12 35124.6 4254.5 32154.3 4523.2 5.61 0.0024 35.34 30431 7.5 0.827 101.86

13 29457.1 4682.4 45123.6 5426.5 7.56 0.0421 63.83 51757 8.85 0.7262 101.52

14 24512.8 4365.34 26781.6 5637 1.85 0.0046 36.42 56153.4 11.73 0.846 101.06

15 15435.5 3512.7 43512.2 3192 2.94 0.085 54.61 55345.2 12.61 0.0027 112.51

16 27451.8 4051 21341.3 3124.6 42.51 0.0021 92.34 52314.2 7.82 0.0028 113.42

17 21354.2 8651.5 12351.6 7594.9 26.51 0.0315 86.9 33419.2 8.81 0.002 117.5

18 22154.87 4361.8 24315.5 3049.5 11.61 0.054 77.87 31461 10.71 0.0062 117

19 21421.6 10246.8 13524.2 8564.5 2.51 0.053 97.8 34651 7.83 0.003 118.64

20 18679.2 5468.9 12453.6 2013.5 4.51 0.014 65.76 31536.1 7.66 0.0064 111.52

21 19854.3 6128.4 17543.6 4317.8 53.15 0.58 43.76 32134.84 5.61 0.0076 115.32

22 26142.2 5124.6 24316.2 4029.3 43.15 0.042 48.52 53821.1 11.83 0.978 103.12

23 35214.2 4982.7 22431.5 24351.2 1.95 2.15 56.75 51352.2 7.3 1.4 104

24 28645.6 5342.5 10542.3 23145.6 5.31 0.125 85.98 33481.6 6.22 1.8 102.15

25 25462.2 10845.3 36251.5 9861.6 4.59 0.025 67.76 54612 4.48 0.95 101.92

Mean 24315.89 7635.06 24284.05 6775.48 18.22 0.258 76.39 40884.71 8.62 0.77 107.25

SD 6583.14 3405.34 13494.6 5539.4 24.12 0.574 38.91 10085.58 2.32 0.71 6.65

%CV 27.07 44.6 55.6 81.75 132.31 221.66 50.9 24.66 26.88 92.32 6.19

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Table 13. Soil enrichment factor Data sheet

Location Pb Cu Cd Ni Co Mn

1 1.62 2.24 25.93 0.90 0.16 0.25

2 1.65 2.41 28.19 1.14 0.06 0.26

3 1.26 6.97 143.16 1.96 6.51 0.20

4 1.22 6.94 240.74 2.03 6.37 0.2

5 2.53 4.26 21.8 2.15 6.51 0.19

6 1.62 7.81 165.03 0.8 0.42 0.19

7 1.22 4.59 69.85 2.08 6.94 0.20

8 0.86 4.44 25.55 1.95 6.41 0.2

9 0.77 4.27 20.47 1.79 6.24 0.2

10 0.90 4.55 27.13 1.67 6.49 0.20

11 0.77 4.37 24.06 1.62 6.531 0.20

12 0.81 4.41 43.38 1.66 6.52 0.20

13 1.18 4.56 25.19 1.99 6.77 0.20

14 1.64 2.82 21.93 2.07 6.74 0.20

15 1.20 3.60 27.78 1.11 0.01 0.26

16 1.06 3.43 24.19 1.05 0.006 0.26

17 0.53 2.52 16.42 1.12 0.031 0.26

18 0.45 2.25 14.16 1.08 0.028 0.26

19 0.47 2.34 15.01 1.05 0.026 0.26

20 0.40 2.18 13.88 1.04 0.023 0.26

21 1.25 1.85 24.82 0.53 0.013 0.25

22 2.25 7.02 0 2.09 7.00 0.20

23 0.81 6.01 240.85 1.96 6.4 0.19

24 0.84 4.06 22.93 1.63 6.43 0.20

25 0.64 3.88 19.92 1.5 6.25 0.20

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Table 14. Sediment enrichment factor data sheet

Location Pb Cd Cu Ni Co Mn

1 4.3 206.24 9.65 0.74 0.002 0.24

2 2.00 135.77 4.63 0.58 0.0018 0.18

3 0.16 0.39 2.08 0.32 0.203 0.16

4 0.095 0.86 0.69 0.36 0.137 0.18

5 3.16 0.14 2.38 0.32 0.108 0.12

6 1.29 4.84 6.65 0.27 0.006 0.19

7 0.071 2.87 2.07 0.36 0.114 0.12

8 0.091 0.68 4.33 0.53 0.182 0.18

9 0.209 5.33 4.58 0.44 0.112 0.19

10 0.106 4.67 2.30 0.38 0.071 0.14

11 0.139 2.50 0.85 0.27 0.112 0.12

12 0.322 0.28 1.62 0.43 0.095 0.19

13 0.255 2.90 1.72 0.29 0.049 0.12

14 0.057 0.29 0.90 0.36 0.052 0.10

15 0.093 5.51 1.38 0.39 0.0001 0.12

16 1.42 0.14 2.47 0.26 0.0001 0.12

17 1.38 3.36 3.64 0.46 0.0002 0.20

18 0.64 6.13 3.46 0.59 0.0007 0.21

19 0.12 5.46 3.95 0.39 0.0003 0.19

20 0.25 1.58 2.91 0.43 0.0007 0.20

21 2.89 64.46 1.90 0.30 0.00083 0.20

22 1.40 2.77 1.26 0.38 0.0636 0.11

23 0.066 149.5 1.54 0.25 0.095 0.12

24 0.27 13.33 3.59 0.32 0.19 0.17

25 0.14 1.63 1.73 0.14 0.0608 0.10

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Table 15. Geoaccumulation Index of Soil

Location Pb Cu Cd Fe Ni Co Mn

1 0.93 1.40 4.94 0.24 0.098 -2.39 -1.73

2 0.96 1.51 5.05 0.24 0.44 -3.59 -1.67

3 -0.30 2.16 6.52 -0.64 0.34 2.063 -2.94

4 -0.34 2.15 7.27 -0.64 0.38 2.03 -2.95

5 0.72 1.46 3.82 -0.62 0.48 2.07 -2.95

6 0.94 3.20 7.60 0.24 -0.068 -0.98 -2.14

7 -0.34 1.56 5.48 -0.64 0.42 2.15 -2.93

8 -0.84 1.50 4.02 -0.65 0.32 2.03 -2.96

9 -1.02 1.43 3.69 -0.66 0.18 1.97 -2.96

10 -0.82 1.51 4.08 -0.67 0.07 2.02 -2.94

11 -1.04 1.44 3.90 -0.68 0.014 2.02 -2.95

12 -0.97 1.46 4.76 -0.68 0.05 2.02 -2.95

13 -0.39 1.55 4.02 -0.64 0.36 2.12 -2.95

14 0.087 0.86 3.82 -0.63 0.42 2.12 -2.94

15 0.52 2.08 5.04 0.24 0.39 -6.30 -1.68

16 0.33 2.01 4.83 0.24 0.31 -7.09 -1.69

17 -0.67 1.57 4.27 0.24 0.40 -4.74 -1.66

18 -0.92 1.40 4.06 0.23 0.35 -4.90 -1.68

19 -0.85 1.46 4.14 0.23 0.31 -5.0 -1.67

20 -1.07 1.35 4.03 0.23 0.29 -5.22 -1.67

21 0.55 1.12 4.87 0.23 -0.67 -5.96 -1.75

22 0.55 2.19 0 -0.61 0.45 2.19 -2.92

23 -0.93 1.9 7.26 -0.64 0.33 2.03 -2.992

24 -0.93 1.33 3.84 -0.68 0.027 2.00 -2.95

25 -1.33 1.25 3.61 -0.70 -0.11 1.94 -2.96

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Table 16. Geoaccumulation Index of Sediments

Location Pb Cd Cu Fe Ni Co Mn

1 1.17 6.76 2.34 -0.93 -1.35 -9.35 -2.95

2 0.52 6.60 1.73 -0.48 -1.25 -9.55 -2.94

3 -3.12 -1.87 0.52 -0.54 -2.16 -2.84 -3.14

4 -4.04 -0.87 -1.18 -0.66 -2.14 -3.53 -3.14

5 1.54 -2.87 1.13 -0.11 -1.75 -3.32 -3.13

6 -0.28 1.61 2.07 -0.66 -2.5 -8.06 -3.028

7 -3.82 1.51 1.04 -0.01 -1.46 -3.14 -3.13

8 -4.18 -1.26 1.38 -0.73 -1.65 -3.18 -3.14

9 -3.05 1.61 1.39 -0.80 -1.98 -3.95 -3.15

10 -3.57 1.87 0.85 -0.34 -1.73 -4.14 -3.13

11 -2.96 1.21 -0.33 -0.11 -1.97 -3.26 -3.13

12 -2.42 -2.61 -0.08 -0.78 -2 -4.18 -3.14

13 -1.98 1.52 0.76 -0.02 -1.76 -4.36 -3.15

14 -4.01 -1.68 -0.04 0.09 -1.35 -4.15 -3.15

15 -3.35 2.53 0.54 0.07 -1.25 -12.43 -2.99

16 0.5 -2.80 1.30 -0.005 -1.94 -12.38 -2.98

17 -0.17 1.09 1.21 -0.65 -1.76 -12.87 -2.93

18 -1.37 1.87 1.05 -0.74 -1.48 -11.24 -2.94

19 -3.57 1.85 1.38 -0.59 -1.93 -12.28 -2.92

20 -2.73 -0.07 0.81 -0.74 -1.97 -11.19 -3.01

21 0.82 5.30 0.22 -0.70 -2.42 -10.94 -2.96

22 0.52 1.51 0.37 0.03 -1.34 -3.94 -3.12

23 -3.94 7.19 0.59 -0.03 -2.03 -3.42 -3.11

24 -2.49 3.08 1.19 -0.64 -2.26 -3.05 -3.14

25 -2.7 0.76 0.85 0.05 -2.74 -3.98 -3.14

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Table 17. Classification of geo-accumulation index based on sediment/soil quality

Igeo Igeo class Description of sediment and soil quality

>5 6 Extremely contaminated

4-5 5 Strongly to extremely strongly contaminated

3-4 4 Strongly contaminated

2-3 3 Moderately to strongly contaminated

1-2 2 Moderately contaminated

0-1 1 Uncontaminated to moderately contaminated

<0 0 Uncontaminated

Source: Igeo classification (Muller, 1979)

3.13 Textural analysis of soil

Table-18 to 28 summarizes the concentration of different chemical species in different size

fraction of soils of 25 locations. All the data sheet shows that, the distribution of Fe is

homogenous in all the fractions of soil of all locations having CV>40%, except in soil texture

355 µm <X<500 µm. (Table-19). This elucidates that occurrence of iron is purely natural, which

is also supported from fact that geoaccumulation indices of iron in all texture of soils of study

area belongs to Igeo class „0‟ (i.e. uncontaminated) Other species shows high % CV in all the

fraction indicates there is non-homogeneous distribution which may be due to natural processes

or anthropogenic. Pb shows high enrichment in texture size 2mm>x>355µm, and <90µm suggest

both natural and anthropic input of Pb to the soil. Pb in higher fraction is due to weathering

basalt and in lower fraction is due to contamination of soils by bottom sediments of Ulhas River.

Higher %CV value of Cu and Cd is in all fractions of soils of all locations accounts for its

anthropogenic inputs to soil by mixing of excavated sediments of Ulhas River which contains

high level of Cd and Cu. Enrichment factor in 11 fractions (texture) of soil presented in Table-

29-39 and geoaccumulation indices of Pb, Cu, Cd, Ni, Co, Fe and Mn in different texture of soil

of all locations graphically presented in as follows (Fig-29-39). These graphs revealed that, all

the locations are extremely contaminated w.r.t Cd (Igeo class-6) followed by strongly

contaminated with Cu (Igeo class-3, which is moderately to strongly). Geochemical indices of

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remaining heavy metals showed that all locations are uncontaminated with respect to these

elements.

Fig. 29 Geoaccumulation indices of heavy metals in soil fraction-1

Fig. 30 Geoaccumulation indices of heavy metals in soil fraction-2

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Fig. 31 Geoaccumulation indices of heavy metals in soil fraction-3

Fig. 32 Geoaccumulation indices of heavy metals in soil fraction-4

Fig. 33 Geoaccumulation indices of heavy metals in soil fraction-5

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Fig. 34 Geoaccumulation indices of heavy metals in soil fraction-6

Fig. 35 Geoaccumulation indices of heavy metals in soil fraction-7

Fig. 36 Geoaccumulation indices of heavy metals in soil fraction-8

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Fig. 37 Geoaccumulation indices of heavy metals in soil fraction-9

Fig. 38 Geoaccumulation indices of heavy metals in soil fraction-10

Fig-39 Geoaccumulation indices of heavy metals in soil fraction-11

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Table 18. Soil data sheet fraction-1 (Soil texture 500 µm <X<2mm)

Location Pb

(µg/g)

Cu

(µg/g)

Cd

(µg/g)

Fe

(µg/g)

Ni

(µg/g)

Co

(µg/g)

Mn

(µg/g)

Na

(µg/g)

K

(µg/g)

Mg

(µg/g)

Ca

(µg/g)

1 133.75 75 2.75 42758.4 8.5 19.8 137.87 16904.94 25287.1 2929.72 3458.01

2 135.02 82.61 2.792 42863.4 61.71 21.04 148.42 17010.74 25528.4 2985.2 3669.01

3 31.37 164.70 2.18 36350.72 58.91 35.65 367.59 5332.31 7171.31 4155.31 1779

4 30.71 164.05 3.095 36349.41 60.13 34.34 366.28 5331 7170 4154 1777

5 43.86 108.65 0.454 36734.41 62.93 36.38 366.43 5335.5 7172 4156 1778.84

6 49.4 506.4 1.8 52123.6 81.8 52.19 2086.31 9909 21044 5596 12542.8

7 0.79 111.66 5.645 36256.64 61.56 36.94 451.007 1966.48 2312.89 757.43 7391.26

8 17.71 134.79 5 36215.21 58.59 34.46 365.707 6352.17 3121.53 15195.34 3039.21

9 15.78 129.51 4.96 35825.79 55.14 32.11 365.627 6055.17 2281.53 14629.34 3014.21

10 18.03 135.53 5.017 36845.58 52.6 34 367.477 6270.17 7008 4378.8 5819.15

11 15.13 130.79 4.495 36588.24 50.24 33.91 366.237 6215.82 6950.49 4352.22 1792

12 15.79 131.92 4.674 36694.04 51.13 34.01 366.677 6637.08 7092.99 4462.42 1785.58

13 BDL 110.87 5.217 36255.06 59.98 35.36 449.427 1964.9 2311.31 755.85 7389.68

14 39.76 93.09 0.238 36389.26 61.52 35.24 450 19705.48 16826.7 10620.6 9004.55

15 21.56 55.91 2.77 42757.6 60.46 19.99 147.86 16905.08 25402.86 2850.25 7516.51

16 16.44 48.28 2.715 42631.93 58.31 19.11 145.32 16766.44 25277.66 2723.51 7302.56

17 19.81 139.91 5.047 42775.4 60.81 19.56 150.99 16767.52 25386.26 2738.71 8359.76

18 16.88 127.78 5.007 42561.95 59.3 19.5 147.12 16708.98 25287.72 2713.121 8334.35

19 17.66 131.94 5.023 42666.5 58.27 19.46 148.45 16225.52 25144.76 2653.34 7309.41

20 15.34 124.62 5 42527.3 57.83 19.40 148.3 16269.42 24291.26 2160.51 8671.82

21 120.48 57.45 2.725 42513.05 4 19.71 134.37 16664.72 24999.89 2684.52 8089.31

22 22.86 68.33 BDL 36412.06 60 37 451 29506.7 6306.4 912.85 7546.68

23 22.78 141.06 3.085 36184.83 58.78 34.26 363.48 5176 6738.8 4050 8022.11

24 16.763 123.25 4.973 36635.53 51.63 33.05 366.507 5994.37 7113.57 4481.88 2844.6

25 12.99 117.97 4.944 36203.32 48.78 30.6 365.927 5780.37 6971.57 4324.35 2719.4

Mean 34.027 128.64 3.58 39284.77 54.52 29.88 368.97 11110.24 13767.96 4456.85 5638.27

SD 37.84 85.03 1.70 4053.93 15.80 8.63 377.81 6974.53 9586.78 3686.93 3071.44

%CV 111.22 66.09 47.60 10.32 28.98 28.88 102.39 62.77 69.63 82.73 54.47

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Table 19. Soil data sheet fraction-2 (Soil texture (355 µm <x<500 µm)

Location Pb

(µg/g)

Cu

(µg/g)

Cd

(µg/g)

Fe

(µg/g)

Ni

(µg/g)

Co

(µg/g)

Mn

(µg/g)

Na

(µg/g)

K

(µg/g)

Mg

(µg/g)

Ca

(µg/g)

1 25.23 55.45 4.77 19349 60.73 14.78 136.7 148254.6 10899.4 1082.01 8757.11

2 26.49 63.05 4.82 19454 19.38 16.02 147.25 148360.4 11140.7 1137.49 8968.11

3 45.92 381.97 4.6 55927.31 16.58 1.31 175 8841.55 9651.31 4437.31 7787

4 45.26 381.33 5.53 55926 17.8 BDL 173.69 8840.24 9650 4436 7785

5 50.65 122.83 0.87 56311 20.6 2.04 173.84 8844.74 9652 4438 7786.84

6 37.27 691.36 1.82 15897 39.9 BDL 149.2 3669 9044 2848 6978.8

7 18.95 98.42 0.69 54742.38 18.56 1.7 191.85 1815.5 3540.58 27573.28 13301.25

8 17.75 142.25 0.25 55791.8 16.26 0.12 173.12 7988.11 3674.8 21749.51 38498.7

9 15.82 136.96 0.21 55402.38 12.81 0.09 173.04 7691.11 2834.8 21183.51 37743.7

10 17.27 142.98 0.31 56542.5 10.27 1.98 174.88 7906.11 9808 4744 7828.24

11 14.23 137.87 0.26 56285.16 9.58 1.89 173.65 7851.76 9699.8 4723.42 7801.09

12 14.91 138.99 0.44 56390.96 10.47 1.985 174.08 8273.02 9842.3 4833.62 8035.59

13 18.16 97.63 0.26 54740.8 16.98 0.12 190.27 1813.92 3539 27571.7 11299.67

14 2.83 6.875 0.5 54875 18.52 BDL 190.85 17978.04 20273.7 21475.7 8200.21

15 39.76 108.89 4.8 19348.2 18.13 14.97 146.69 148254.8 11015.16 1002.54 8815.61

16 34.64 101.27 4.74 19222.53 15.98 14.09 144.15 148116.1 10889.96 875.8 8601.66

17 19.85 147.37 0.29 19366 18.48 14.54 149.82 148117.2 10998.56 891 8658.86

18 16.93 135.25 0.26 19152.55 16.97 14.48 145.95 148058.7 10900.02 865.411 8633.45

19 17.71 139.4 0.27 19257.1 15.94 14.446 147.28 147575.2 10757.06 805.63 8608.51

20 15.38 132.08 0.25 19117.9 15.5 14.382 147.13 147619.1 9903.56 506.4 7970.92

21 11.95 37.91 4.75 19103.65 56.23 14.69 133.2 148014.4 10612.19 836.81 8388.41

22 41.05 121.32 BDL 54897.8 12.51 1.56 198.52 26022.6 8715.01 29024.87 20622.05

23 37.33 358.33 5.52 55761.42 16.45 BDL 170.89 8685.24 9218.8 4332 7845.03

24 16.01 130.69 0.26 56332.45 9.3 1.03 173.92 7630.31 9913.78 4847.08 7853.69

25 12.24 125.44 0.232 55900.24 6.45 0.58 173.33 7416.31 9771.78 4689.55 7728.49

Mean 24.54 165.43 1.86 41003.81 19.61 6.11 165.13 58945.53 9437.85 8036.42 11397.92

SD 12.93 144.42 2.18 18415.64 13.21 6.84 18.65 68356.04 3433.38 9870.63 8497.23

%CV 52.69 87.29 117.04 44.91 67.35 111.94 11.29 115.96 36.378 122.82 74.66

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Table 20. Soil data sheet fraction-3 (Soil texture 250 µm <X< 355 µm)

Location Pb

(µg/g)

Cu

(µg/g)

Cd

(µg/g)

Fe

(µg/g)

Ni

(µg/g)

Co

(µg/g)

Mn

(µg/g)

Na

(µg/g)

K

(µg/g)

Mg

(µg/g)

Ca

(µg/g)

1 50.44 53.26 1.52 20512.92 17.5 3.2 145.65 16605.3 11498.56 1448.63 7489.06

2 51.72 60.86 1.56 20617.92 59.58 4.44 156.2 16711.1 11739.86 1504.11 7700.06

3 30.65 255.88 2.04 46767.11 56.78 60.02 107.39 1158609 10291.31 4255.31 8026.3

4 30 255.23 2.95 46765.8 58 58.71 106.08 1158608 10290 4254 8024.3

5 35.68 82.955 1.14 47150.8 60.8 60.75 106.23 1158613 10292 4256 8026.14

6 33.96 458.75 1.66 41114.8 206.08 696.47 100 23298 13051 3965 9649.6

7 4.19 0.79 0.65 46925.38 58 59.55 111.56 23588.48 15334.58 113924.7 116732.5

8 27.25 227.25 0.25 46631.6 56.46 58.83 105.51 8471.15 3457.5 18166.27 32527.5

9 25.32 221.96 0.21 46242.18 53.01 56.47 105.42 8174.15 2617.5 17600.27 31772.5

10 27.57 227.98 0.27 46002.18 50.47 58.36 107.27 8389.15 10602 4821.46 8049.26

11 24.93 223.41 0.24 45744.84 49.23 58.27 106.03 8334.8 10600.5 4799.88 8022.11

12 25.59 224.54 0.42 45850.64 50.12 58.37 106.47 8756.06 10743 4910.08 8256.61

13 3.41 BDL 0.23 46923.8 56.42 57.97 109.98 23586.9 15333 113923.1 116730.9

14 63.57 140.47 0.714 47058 57.96 57.85 110.56 21018.66 17950 25662.71 56757.6

15 34.62 111.66 1.55 20512.12 58.33 3.39 155.64 16605.44 11614.32 1369.16 7547.56

16 29.49 104.05 1.48 20386.45 56.18 3.31 153.1 16466.8 11489.12 1242.42 7333.61

17 29.35 232.37 0.29 20529.92 58.68 3.76 158.77 16467.88 11597.72 1257.62 7390.81

18 26.43 220.25 0.25 20316.47 57.17 3.7 154.9 16409.34 11499.18 1232.031 7365.4

19 27.21 224.4 0.27 20421.02 56.14 3.66 156.23 15925.88 11356.22 1172.25 7340.46

20 24.88 217.08 0.25 20281.82 55.7 3.60 156.08 15969.78 10502.72 679.42 6702.87

21 37.16 35.711 1.49 20267.57 13 3.11 142.15 16365.08 11211.35 1203.43 7120.36

22 35.91 124.09 BDL 47080.8 58 49.9 111.52 23340.78 8671.88 29399 75015.55

23 22.06 232.24 2.94 46601.22 56.65 58.63 103.28 1158453 9858.8 4150 7251.09

24 26.31 215.69 0.22 45792.13 49.5 57.42 106.31 8113.35 10707.41 4924.54 8074.71

25 22.52 210.44 0.19 45359.92 46.65 54.96 105.73 7899.35 10565.41 4767.01 7949.51

Mean 30.1 174.45 0.92 36874.3 58.26 63.78 123.523 198191.2 10915 14995.53 23074.25

SD 12.6 102.32 0.869 12641.73 32.94 134.41 23.056 427818.8 3084.73 30765.98 32918.76

%CV 41.95 58.65 95.2 34.28 56.55 210.71 18.67 215.86 28.61 205.1676 142.66

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Table 21. Soil data sheet fraction-4(Soil texture 250 µm>X>188 µm)

Location Pb

(µg/g)

Cu

(µg/g)

Cd

(µg/g)

Fe

(µg/g)

Ni

(µg/g)

Co

(µg/g)

Mn

(µg/g)

Na

(µg/g)

K

(µg/g)

Mg

(µg/g)

Ca

(µg/g)

1 39.13 64.56 0.435 22401 24.37 10.55 136.36 9976.8 14906.7 1888.81 5670.17

2 40.4 72.17 0.477 22506 58.15 11.79 146.91 10082.6 15148 1944.29 5881.17

3 25.42 255.89 2.42 51785.31 55.35 1.31 1190.38 7191.01 12177.31 4715.31 7988

4 24.76 255.24 3.33 51784 56.57 BDL 1189.07 7189.7 12176 4714 7986

5 39.52 98.09 0.476 52169 59.37 2.04 1189.22 7194.2 12178 4716 7987.84

6 46.95 260.44 1.95 38277.15 215.45 32.29 482.76 6948 17121 4955 17581.55

7 26.69 81.47 0.427 51853.38 56.12 1.7 1199.01 33866.58 16072.48 6918.78 23974.08

8 14.56 145.22 0.217 51649.8 55.03 0.12 1188.49 14989.02 4544.46 7224.48 37834.28

9 12.64 139.93 0.177 51260.38 51.58 1.29 1188.42 14692.02 3704.46 6658.48 37079.28

10 14.89 145.95 0.235 51020.38 49.04 3.18 1190.27 14907.02 12505 5017.23 7869.18

11 12.45 140.84 0.194 50763.04 48.24 3.09 1189.03 14852.67 11542.6 4995.65 7845.03

12 12.95 142.02 0.373 50868.84 49.13 3.185 1189.47 15274.12 11685.1 5105.85 8079.53

13 25.91 80.68 BDL 51851.8 54.54 0.12 1197.43 33865 16070.9 86917.2 23972.5

14 46.25 115 0.5 51986 56.08 BDL 1198 7309.56 8510.84 8122.87 19128.63

15 36.97 213.88 0.462 22400.2 56.9 10.74 146.35 9976.94 15022.46 1809.34 5728.67

16 31.85 206.26 0.399 22274.53 54.75 10.66 143.81 9838.3 14897.26 1682.6 5514.72

17 16.67 150.34 0.264 22418 57.25 11.11 149.48 9839.38 15005.86 1697.8 5571.92

18 13.75 138.21 0.224 22204.55 55.74 11.05 145.61 9780.84 14907.32 1672.211 5546.51

19 14.52 142.36 0.239 22309.1 54.71 11.02 146.94 9297.38 14764.36 1612.43 5521.57

20 12.2 135.05 0.217 22169.9 54.27 10.95 146.79 9341.28 13910.86 1119.6 4883.98

21 25.86 47.02 0.409 22155.65 19.87 10.46 132.86 9736.58 14619.49 1643.61 5301.47

22 38.26 226.30 BDL 52008.8 211.54 0.25 1354.43 13938.4 6656.6 28493.2 57342.6

23 16.83 232.25 3.323 51619.42 55.22 0.09 1186.27 7034.7 11744.8 4610 8061.85

24 13.62 133.66 0.189 50810.33 48.07 2.23 1189.29 14631.22 12610.77 5120.31 7894.63

25 9.85 128.40 0.16 50378.12 45.22 1.78 1188.72 14417.22 12468.77 4962.78 7769.43

Mean 24.52 150.05 0.684 40436.99 64.10 6.04 792.21 12646.82 12598.06 8332.71 13520.6

SD 12.09 62.3 0.964 14120.33 45.95 7.23 517.23 7050.24 3466.58 17207.51 13214.3

%CV 49.32 41.52 140.99 34.91 71.67 119.7 65.29 55.75 27.52 206.5 97.7

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Table 22. Soil data sheet fraction-5 (soil texture 188 µm>x>125µm)

Location Pb

(µg/g)

Cu

(µg/g)

Cd

(µg/g)

Fe

(µg/g)

Ni

(µg/g)

Co

(µg/g)

Mn

(µg/g)

Na

(µg/g)

K

(µg/g)

Mg

(µg/g)

Ca

(µg/g)

1 34.32 60 0.45 44209.5 20.81 4.2 933 19971.69 21798.48 2996.13 7041.53

2 35.58 67.61 0.49 44314.5 48.95 5.44 943.55 20077.49 22039.78 3051.61 7252.53

3 47.65 26.92 1.84 46947.23 46.15 1.31 922.73 6663.71 9897.31 3726.11 7184.9

4 47 26.25 2.75 46945.92 47.37 BDL 921.42 6662.4 9896 3724.8 7182.9

5 35.75 96.25 0.75 47330.92 50.17 3.75 921.57 6666.9 9898 3726.8 7184.74

6 45.68 357.04 1.59 31768.24 966.23 BDL 389.23 61387.65 10079 3914 10101

7 28.04 98.79 0.42 46936.38 47.87 1.7 991.53 32428.92 23734.58 15786.1 12390.5

8 10.35 95 0.173 46811.72 45.83 0.12 920.85 9216.54 2872.62 9798.35 2706.44

9 8.414 89.71 0.13 46422.3 42.38 0.12 920.77 8919.54 2032.62 9232.35 2951.44

10 11.57 95.73 0.18 46182.3 39.84 2.01 922.62 9134.54 10012 4038.17 7278.24

11 9.63 90.62 0.14 45924.96 38.6 1.92 921.34 9075 9865.54 4018.59 7251.09

12 10.29 91.75 0.32 46030.76 39.49 2.015 921.82 9496.26 10008.04 4128.79 7354.86

13 27.25 98 BDL 46934.8 46.29 0.12 989.95 32427.34 23733 15784.5 12388.9

14 56.25 120.5 BDL 47069 47.83 BDL 990.52 13609.7 14268 6554 16059.83

15 43.51 111.57 0.48 44208.7 47.7 4.39 942.99 19971.83 21914.24 2916.66 7100.03

16 38.38 103.95 0.41 44083.03 45.55 4.31 940.45 19833.19 21789.04 2789.92 6886.08

17 12.44 100.12 0.21 44226.5 48.05 4.76 946.12 19834.27 21897.64 2805.12 6943.28

18 9.525 87.99 0.17 44013.05 46.54 4.7 942.25 19775.73 21799.1 2779.531 6917.87

19 10.29 92.15 0.19 44117.6 45.51 4.66 943.58 19292.27 21656.14 2719.75 6892.93

20 7.979 84.83 0.17 43978.4 45.07 4.60 943.43 19336.17 20802.64 2226.92 6255.34

21 21.04 42.45 0.42 43964.15 16.31 4.11 929.5 19731.47 21511.27 2750.93 6672.83

22 44.8 124 BDL 47091.8 203.29 0.27 1146.95 20463.4 5482.8 19624.5 8998.86

23 39.06 3.26 2.74 46781.34 46.02 0.08 918.62 6507.4 9464.8 3620.8 6751.45

24 10.3 83.44 0.14 45972.25 38.87 1.06 921.65 8858.74 10117.66 4141.25 7303.69

25 6.53 78.18 0.116 45540.04 36.02 1.01 921.07 8644.74 9975.66 3983.72 7178.49

Mean 26.06 93.04 0.57 45112.22 85.87 2.26 924.3 17119.48 14661.84 5633.57 7689.19

SD 16.35 62.46 0.78 3054.95 186.378 1.97 121.07 11923.43 7124.22 4729.9 2723.8

%CV 62.70 67.13 137.04 6.77 217.05 87.28 13.1 69.65 48.59 83.9 35.42

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Table 23. Soil data sheet fraction-6 (Soil texture 125µm>X>106µm)

Location Pb

(µg/g)

Cu

(µg/g)

Cd

(µg/g)

Fe

(µg/g)

Ni

(µg/g)

Co

(µg/g)

Mn

(µg/g)

Na

(µg/g)

K

(µg/g)

Mg

(µg/g)

Ca

(µg/g)

1 43.16 87.368 0.53 31001.5 14.68 28.3 361 8245.2 502.5 425 515.53

2 44.43 94.973 0.57 31106.5 25.33 29.54 371.55 8351 743.8 480.48 726.53

3 28.15 182.93 2.72 42186.96 22.53 3.31 102.61 9940.21 11977.31 4670.01 8070

4 27.5 182.27 3.63 42185.65 23.75 2 101.29 9938.9 11976 4668.7 8067.2

5 44.25 113.75 1.25 42570.65 26.55 4.04 101.45 9943.4 11978 4670.7 8069.04

6 25 537.04 1.29 49675 437.23 36.9 100 20430.06 10651.34 422 2034.7

7 41.02 123.74 0.427 42293.98 24.13 3.7 111.26 5640.08 3676.58 26062.58 29028.58

8 21.66 111.11 0.185 42051.45 22.21 2.12 100.72 6342.78 1760.17 6945.73 18768.86

9 19.74 105.82 0.145 41662.03 18.76 2.04 100.64 6045.78 920.17 6379.73 18013.86

10 21.99 111.85 0.202 40586.8 16.22 3.93 102.49 6260.78 11908 4947.34 8089

11 19.25 105.29 0.162 40329.46 14.98 3.84 101.25 6195 11754 4925.76 8061.85

12 19.92 106.42 0.345 40435.26 15.87 3.94 101.69 6616.26 11896.5 5035.96 8296.35

13 40.23 122.95 BDL 42292.4 22.55 2.12 109.67 5638.5 3675 26061 29027

14 47 103.25 0.25 42426.6 24.09 2 110.25 13476.4 13520.37 7067.04 16077.6

15 38.71 189.57 0.553 31000.7 24.08 28.49 370.99 8245.34 618.26 345.53 574.03

16 33.58 181.95 0.491 30875.03 21.93 28.41 368.45 8106.7 493.06 218.79 360.08

17 23.77 116.23 0.233 31018.5 24.43 28.86 374.12 8107.78 601.66 233.99 417.28

18 20.85 104.11 0.193 30805.05 22.92 28.8 370.25 8049.24 503.12 208.41 391.87

19 21.62 108.26 0.207 30909.6 21.89 28.76 371.58 7565.78 360.16 148.62 366.93

20 19.3 100.94 0.185 30770.4 21.45 28.70 371.43 7609.68 .342.5 215.3 0.412

21 29.88 69.82 0.501 30756.15 10.18 28.21 357.5 8004.98 215.29 179.8 146.83

22 40 202 BDL 42449.4 179.55 2.98 110.55 19380.6 6372 26177.43 8432.88

23 19.56 159.28 3.626 42021.07 22.4 1.92 101.54 9783.9 11544.8 4564.7 10322.15

24 20.72 99.55 0.157 40376.75 15.25 2.98 101.53 5984.98 12013.87 5050.42 8114.45

25 16.95 94.29 0.128 39944.54 12.4 0.53 100.95 5770.98 11871.87 4892.89 7989.25

Mean 29.13 140.59 0.719 38069.26 43.41 13.46 198.99 8786.97 6313.9 6032.6 8445.63

SD 9.86 88.37 1.02 5634.29 86.32 13.21 127.24 3733.96 5337.23 7957.9 8224.9

%CV 33.86 62.86 142.19 14.8 198.83 98.19 63.94 42.49 84.53 131.92 97.4

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Table 24. Soil data sheet fraction-7(Soil texture 106µm>X>90µm)

Location Pb

(µg/g)

Cu

(µg/g)

Cd

(µg/g)

Fe

(µg/g)

Ni

(µg/g)

Co

(µg/g)

Mn

(µg/g)

Na

(µg/g)

K

(µg/g)

Mg

(µg/g)

Ca

(µg/g)

1 20.47 33.57 0.47 30549 BDL BDL 100 11612.9 16463.69 1338.04 3098.6

2 21.74 41.17 0.52 30654 12.62 1.24 110.55 11718.7 16704.99 1393.52 3309.6

3 31.15 166.15 2.34 48264.91 9.82 2.61 115.81 11757.81 16608.6 1482.95 3345

4 30.5 165.5 3.25 48263.6 11.04 1.3 114.5 11756.5 16607.29 1481.64 3242.2

5 39.75 214.5 2.5 48648.6 13.84 3.34 114.65 5374.66 8988 2946.6 6939.84

6 21.45 316.45 1.46 51115.55 BDL 237 2031 5370.16 8986 2944.6 6938

7 35.79 15.33 0.43 48130.98 11.72 3.55 118.85 6740.58 3484.28 29086.58 26556.98

8 17.17 102.60 0.25 48129.4 9.5 1.42 113.92 11373.3 2498.77 9348.1 26350.85

9 15.24 97.32 0.17 47739.98 6.05 0.97 113.84 11076.3 1658.77 8782.1 25595.85

10 16.27 103.34 0.23 47499.98 3.51 2.86 115.69 11291.3 9596 3288.51 6778.6

11 13.82 96.77 0.18 47242.64 2.3 2.77 114.45 11154 9584.49 3276.93 6751.45

12 14.49 97.89 0.36 47348.44 3.19 2.865 114.89 11575.36 9726.99 3387.13 6985.95

13 35 14.54 BDL 48129.4 10.14 1.97 117.27 6739 3482.7 29085 26555.4

14 52.4 88 BDL 48263.6 11.68 1.85 117.85 18839.6 21971.41 9644.08 25322.18

15 57.65 332.31 0.5 30548.2 11.37 0.19 109.99 11613.04 16579.45 1258.57 3157.1

16 52.53 324.69 0.44 30422.53 9.22 0.162 107.15 11474.4 16454.25 1131.83 2943.15

17 19.27 107.73 0.26 30566 11.72 0.612 112.82 11475.48 16562.85 1147.03 3000.35

18 16.35 95.6 0.22 30352.55 10.21 0.552 108.95 11416.94 16464.31 1121.44 2974.94

19 17.12 99.75 0.24 30457.1 9.18 0.518 110.28 10933.48 16321.35 1061.66 2950

20 14.80 92.44 0.22 30317.9 8.74 0.454 110.13 10977.38 15467.85 568.83 2312.41

21 7.21 16.02 0.451 30303.65 2.3 0.08 100.35 11372.68 16176.48 1092.84 2729.9

22 58.94 344.74 BDL 48286.4 167.14 2.09 120.56 25269.04 7853.7 21645.6 3382.8

23 22.56 142.51 3.24 48099.02 9.69 1.22 111.7 11601.5 16176.09 1377.64 7856.55

24 15 91.053 0.19 47289.93 2.54 1.91 114.72 11015.5 9701.75 3391.59 6804.05

25 11.23 85.793 0.16 46857.72 1.69 1.26 114.14 10801.5 9559.75 3234.06 6678.85

Mean 26.319 131.43 0.72 41739.24 13.96 10.91 189.36 11373.24 12147.19 5780.67 8902.42

SD 15.25 100.01 0.99 8664.05 32.19 47.11 383.71 3901.87 5538.51 8349.31 8946.14

%CV 57.95 76.09 124.60 20.76 219.55 422.77 202.63 34.32 45.59 144.43 103.28

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Table 25. Soil data sheet fraction-8 (Soil texture 90µm>X>75 µm)

Location Pb

(µg/g)

Cu

(µg/g)

Cd

(µg/g)

Fe

(µg/g)

Ni

(µg/g)

Co

(µg/g)

Mn

(µg/g)

Na

(µg/g)

K

(µg/g)

Mg

(µg/g)

Ca

(µg/g)

1 42.95 97.95 0.22 36795.08 BDL BDL 100 1254.5 29971.4 2994.2 8011.67

2 44.22 105.56 0.27 36900.08 3.28 1.24 110.55 1360.3 30212.7 3049.68 8222.67

3 33.98 268.51 5.037 44414.73 0.48 50.32 101.31 1314.71 30031.61 3054.41 8072.57

4 33.33 267.85 5.952 44413.42 1.7 49.01 100 1313.4 30030.3 3053.1 8070.57

5 34.54 78.64 6.59 44798.42 4.5 51.05 100.15 7866.85 15227 4062.7 10243.84

6 45.9 480.68 1.82 53407.04 150.26 42.67 610 7862.35 15225 4060.7 10242

7 88.54 111.29 0.42 44438.38 1.93 52.68 101.73 6589.38 2985.61 29574.48 23032.38

8 12.72 85.91 0.22 44279.22 0.16 49.13 100.15 10732.61 2555.75 8549.44 25251.6

9 10.79 80.62 0.18 43889.8 1.22 46.78 100.09 10435.61 1715.75 7983.44 24496.6

10 13.05 86.64 0.24 43649.8 1.04 48.67 101.94 10650.61 16039 4408.22 10349.3

11 10.61 81.52 0.2 43392.46 1.04 48.58 100.7 10652.58 15981.49 4386.64 10322.15

12 11.37 82.65 0.39 43498.26 1.93 48.675 101.14 11073.84 16123.99 4496.84 9865.52

13 87.75 110.5 BDL 44436.8 0.35 51.1 100.15 6587.8 2947.7 29572.9 23030.8

14 53.86 91.59 0.68 44571 1.89 50.98 100 11722.96 17609.9 8972.3 22968.53

15 103.02 385.30 0.254 36794.28 2.03 0.19 109.99 1254.64 30087.16 2914.73 8070.17

16 97.90 377.68 0.19 36668.61 0.79 0.11 107.45 1116 29961.96 2787.99 7856.22

17 14.83 91.02 0.27 36812.08 3.29 0.56 113.12 1117.08 30070.56 2803.19 7913.42

18 11.90 78.90 0.23 36598.63 1.78 0.5 109.25 1058.54 29972.02 2777.601 7888.01

19 12.68 83.05 0.25 36703.18 0.75 0.466 110.58 575.08 29829.06 2717.82 7863.07

20 10.36 75.73 0.23 36563.98 0.31 0.402 110.43 618.98 28975.56 2224.99 7225.48

21 29.68 80.40 0.202 36549.73 0.23 0.45 100.12 1014.28 29684.19 2749 7642.97

22 104.3 397.72 BDL 44593.8 157.35 57.55 100.31 35872.23 7675.5 27958.59 26572.6

23 25.39 244.86 5.94 44248.84 0.35 48.93 100.21 1158.4 29599.1 2949.1 6955.45

24 11.78 74.35 0.19 43439.75 0.35 47.72 100.97 10374.81 16144.14 4511.3 10374.75

25 8.012 69.09 0.17 43007.54 0.31 45.27 100.39 10160.81 16002.14 4353.77 10249.55

Mean 38.14 159.52 1.21 41794.6 13.49 31.72 123.63 6549.53 20186.34 7078.68 12431.68

SD 32.64 126.92 2.12 4328.28 42.26 24.08 101.43 7533.31 10545.2 8475.63 6882.2

%CV 85.57 79.56 175.52 10.36 313.17 75.93 82.04 115.02 52.24 119.73 55.4

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Table 26. Soil data sheet fraction-9 (Soil texture 75µm>x>53 µm)

Location Pb

(µg/g)

Cu

(µg/g)

Cd

(µg/g)

Fe

(µg/g)

Ni

(µg/g)

Co

(µg/g)

Mn

(µg/g)

Na

(µg/g)

K

(µg/g)

Mg

(µg/g)

Ca

(µg/g)

1 76.19 144.52 10 42794 BDL BDL 100 15255.88 27578.74 2629.09 8437.17

2 77.46 152.12 10.04 42899 13.98 1.24 110.55 15361.68 27820.04 2684.57 8648.17

3 45.18 391.13 1.70 60260.27 11.18 2.2 101.31 7889.91 7552.31 3851.31 7674.5

4 44.52 390.47 2.61 60258.96 12.4 0.89 100 7888.6 7551 3850 7672.5

5 3.86 102.27 0.90 60643.96 15.2 2.93 100.15 7893.1 7553 3852 7674.34

6 26.95 396.52 2.39 17037 BDL 99.65 707.53 5628.8 4667 4873 12029

7 16.79 86.04 13.17 60956.95 13.12 3.22 101.73 8162.88 2832 26468.98 27745.6

8 10.6 72.4 10.2 60124.76 10.86 1.01 100.15 5090.68 2750.06 21453.88 19661.3

9 8.67 67.11 10.16 59735.34 7.41 0.13 100.15 4793.68 1910.06 20887.88 18906.3

10 10.92 73.13 10.21 59495.34 4.87 2.02 102 5008.68 7718 4148.43 7878.7

11 8.48 68.02 9.77 59238 4.13 1.93 100.76 4954.33 7660.58 4126.85 7856.55

12 9.145 69.14 9.95 59343.8 5.02 2.025 101.2 5375.59 7803.08 4237.05 8091.05

13 16 85.25 12.75 60955.37 11.54 1.64 100.15 8161.3 2855 26467.4 27848.6

14 45.95 107.14 0.47 61089.57 13.08 1.52 100 14002.64 23855.8 10814.6 26505.6

15 80.44 322.35 10.03 42793.2 12.73 0.19 109.99 15256.02 27694.5 2549.62 8495.67

16 75.32 314.73 9.96 42667.53 10.58 0.182 109.15 15117.38 27569.3 2422.88 8281.72

17 12.70 77.52 10.24 42811 13.08 0.632 114.82 15118.46 27677.9 2438.08 8338.92

18 9.78 65.39 10.20 42597.55 11.57 0.572 110.95 15059.92 27579.36 2412.491 8313.51

19 10.55 69.55 10.22 42702.1 10.54 0.538 112.28 14576.46 27436.4 2352.71 8288.57

20 8.235 62.23 10.2 42562.9 10.1 0.474 112.13 14620.36 26582.9 1859.88 7650.98

21 62.92 126.97 9.97 42548.65 2.5 0.09 100.05 15015.66 27291.53 2383.89 8068.47

22 81.73 334.78 BDL 61112.37 168.54 1.96 100.36 25916.9 6446.27 22233.7 20753

23 36.58 367.4 2.60 60094.38 11.05 0.81 100.52 7733.6 7119.8 3746 6738.55

24 9.65 60.84 10.17 59285.29 3.9 1.07 101.03 4732.88 7823.77 4251.51 7904.15

25 5.88 55.58 10.14 58853.08 3.88 1.025 100.45 4518.88 7681.77 4093.98 7778.95

Mean 31.78 162.51 7.93 52114.41 15.25 5.12 127.89 10525.37 14440.41 7643.59 11889.7

SD 28.22 129.03 4.18 11162.35 32.25 19.71 120.86 5465.81 10724.49 8343.2 7027.5

%CV 88.8 79.40 47.76 21.42 201.47 377.19 94.49 51.93 74.26 109.15 59.1

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Table 27. Soil data sheet fraction-10 (Soil texture 53 µm>X>25µm)

Location Pb

(µg/g)

Cu

(µg/g)

Cd

(µg/g)

Fe

(µg/g)

Ni

(µg/g)

Co

(µg/g)

Mn

(µg/g)

Na

(µg/g)

K

(µg/g)

Mg

(µg/g)

Ca

(µg/g)

1 58.25 130.25 0.5 51086.87 BDL BDL 100 16693.67 36992.08 1857.82 6070.9

2 59.52 137.85 0.542 51191.87 12.71 1.24 110.55 16799.47 37233.38 1913.3 6281.9

3 43.75 255.89 2.418 67028.31 9.91 1.31 101.31 7352.39 9077.31 4206.31 6597

4 43.09 255.24 3.333 67027 11.13 BDL 100 7351.08 9076 4205 6590

5 4.13 10.65 0.217 67412 13.93 1.35 100.15 7355.58 9078 4207 6591.84

6 68.07 411.54 2.115 12496.06 BDL BDL 100 18513.4 13008 3106.4 8179

7 35.33 147.83 0.427 67381.91 12.34 1.7 101.73 5769.88 5489 43290.38 27850.18

8 11.96 79.46 0.535 66892.8 9.59 0.12 100.15 5976.03 23171.7 21925 11296

9 10.03 74.17 0.495 66503.38 6.14 0.16 100.1 5679.03 22331.7 21359 10541

10 12.28 80.19 0.553 65824 3.6 2.05 101.95 5894.03 9299 4788.45 6982.6

11 9.84 74 0.55 65566.66 3.08 1.96 100.69 5795 9241.49 4766.87 6955.45

12 10.94 168.12 0.456 69899.96 1116.92 2.045 101.26 7526.76 9793.28 4518.62 6973.05

13 34.54 147.04 BDL 67380.33 10.76 0.12 100.15 5768.3 2855 43288.8 6775.7

14 49.25 103.25 0.25 67514.53 12.3 BDL 100 16746.5 26722.3 10845.95 28505.5

15 80.07 161.89 0.527 51086.07 11.46 0.19 109.99 16693.81 37107.84 1778.35 6129.4

16 74.95 154.27 0.464 50960.4 9.31 0.172 108.45 16555.17 36982.64 1651.61 5915.45

17 14.07 84.58 0.583 51103.87 11.81 0.622 114.07 16556.25 37091.24 1666.81 5972.65

18 11.14 72.45 0.543 50890.42 10.3 0.562 110.2 16497.71 36992.7 1641.221 5947.24

19 11.92 76.61 0.558 50994.97 9.27 0.528 111.53 16014.25 36849.74 1581.44 5922.3

20 9.599 69.29 0.536 50855.77 8.83 0.464 111.38 16058.15 35996.24 1088.61 5284.71

21 44.98 112.7 0.475 50841.52 0.25 0.086 100.2 16453.45 36704.87 1612.62 5702.2

22 81.36 174.31 BDL 67537.33 167.76 0.236 100.28 32511.32 7603.44 24415.34 9536.5

23 35.16 232.24 3.323 66862.42 9.78 0.125 100.6 7196.08 8644.8 4101 BDL

24 11.01 67.90 0.508 65613.95 2.63 1.1 100.98 5618.23 9404.27 4891.53 7008.05

25 7.249 62.65 0.479 65181.74 2.545 1.055 100.4 5404.23 9262.27 4734 6882.85

Mean 33.30 133.78 0.82 59005.37 58.65 0.68 103.44 11951.19 20640.33 8937.65 8770.5

SD 25.62 85 0.92 12407.89 222.81 0.709 4.84 6734.97 13498.31 12278.54 6159.59

%CV 76.93 63.54 104.24 21.02 363.88 85.16 4.68 56.35 65.39 137.37 70.043

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Table 28. Soil data sheet fraction-11 (Soil texture X<25µm)

Location Pb

(µg/g)

Cu

(µg/g)

Cd

(µg/g)

Fe

(µg/g)

Ni

(µg/g)

Co

(µg/g)

Mn

(µg/g)

Na

(µg/g)

K

(µg/g)

Mg

(µg/g)

Ca

(µg/g)

1 67.5 134.77 0.68 47692.3 BDL BDL 100 19785.92 36207.5 3159.35 8992.17

2 68.77 142.37 0.72 47797.3 1126.38 1.24 110.55 19891.72 36448.8 3214.83 9203.17

3 58.83 400.88 2.26 70697.82 1123.58 1.31 101.31 7300.61 9406.81 4102.31 6767

4 58.18 400.23 3.18 70696.51 1124.8 BDL 100 7299.3 9405.5 4101 6765

5 51.52 115.44 0.65 71081.51 1127.6 2.04 100.15 7303.8 9407.5 4103 6766.84

6 78.79 BDL 1.89 70442.51 BDL BDL 100 3069 2631 638.5 1835

7 19.19 273.52 5.65 70852.94 1153.84 1.7 101.73 7822.58 2949.28 25769.72 6777.28

8 11.57 171.43 0.28 70562.31 1123.26 0.12 5410.59 53105 2671.17 23430.7 23529

9 9.641 166.14 0.24 70172.89 1119.81 0.15 100.18 52808 1831.17 22864.7 22774

10 12.42 172.16 0.30 70051.5 1117.27 2.04 102.03 7154.58 9708 4421 6775.7

11 10.28 166.9 0.26 69794.16 1116.03 1.95 100.82 7105.5 9650.78 4408.42 6738.55

12 0.665 1.13 0.18 105.8 0.89 0.095 100.44 421.26 142.5 110.2 234.5

13 18.4 272.73 5.23 70851.36 1152.26 0.12 100.15 7821 3953.9 25768.14 30768.7

14 58.09 83.81 BDL 70985.56 1153.8 BDL 100 19018.6 23220.4 10379.67 27877.43

15 56.66 255.53 0.71 47691.5 1125.13 0.19 109.99 19786.06 36323.26 3079.88 9050.67

16 51.54 247.91 0.64 47565.83 1122.98 0.183 109.45 19647.42 36198.06 2953.14 8836.72

17 13.67 176.55 0.33 47709.3 1125.48 0.633 115.12 19648.5 36306.66 2968.34 8893.92

18 10.75 164.42 0.29 47495.85 1123.97 0.573 111.25 19589.96 36208.12 2942.751 8868.51

19 11.53 168.57 0.31 47600.4 1122.94 0.539 112.58 19106.5 36065.16 2882.97 8843.57

20 9.206 161.25 0.28 47461.2 1122.5 0.475 112.43 19150.4 35211.66 2390.14 8205.98

21 54.23 117.22 0.65 47446.95 BDL BDL 100 19545.7 35920.29 2914.15 8623.47

22 57.95 267.95 0 71008.36 1309.26 0.28 100.31 29592.5 5566.71 23660.58 9956.1

23 50.24 377.24 3.17 70531.93 1123.45 0.13 100.17 7144.3 8974.3 3997 BDL

24 11.15 159.87 0.26 69841.45 1116.3 1.09 101.06 6878.78 9813.47 4524.08 6801.15

25 7.38 154.61 0.23 69409.24 1116.22 0.84 100.48 6664.78 9671.47 4366.55 6675.95

Mean 34.33 190.11 1.14 59421.86 953.91 0.63 316.03 16266.47 17755.74 7726.04 10002.42

SD 25.41 103.39 1.57 16592.98 426.48 0.70 1061.38 13253.88 14695.09 8657.93 7789.15

%CV 74.01 49.38 134.29 27.92 44.71 111.55 335.85 81.48 82.76 112.067 77.7

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Table 29. Enrichment factor in Soil fraction-1 (500 µm <X<2mm)

Location Pb Cu Cd Ni Co Mn

1 5.47 2.45 229.69 0.35 1.62 0.188

2 5.51 2.69 232.63 2.52 1.71 0.202

3 1.51 6.34 214.18 2.83 3.43 0.589

4 1.47 6.318 304.09 2.89 3.31 0.587

5 2.1 4.14 44.139 2.99 3.46 0.581

6 1.66 13.6 123.33 2.74 3.51 2.334

7 0.04 4.31 556.05 2.97 3.56 0.725

8 0.85 5.21 493.08 2.83 3.33 0.589

9 0.77 5.06 494.45 2.69 3.16 0.595

10 0.85 5.149 486.29 2.49 3.22 0.581

11 0.72 5.004 438.76 2.40 3.24 0.583

12 0.75 5.033 454.92 2.43 3.24 0.583

13 0 4.28 513.92 2.89 3.41 0.723

14 1.91 3.58 23.36 2.96 3.38 0.72

15 0.88 1.83 231.37 2.47 1.63 0.202

16 0.67 1.58 227.44 2.39 1.56 0.198

17 0.81 4.58 421.38 2.48 1.6 0.206

18 0.69 4.203 420.14 2.43 1.60 0.203

19 0.72 4.33 420.45 2.38 1.59 0.202

20 0.63 4.10 419.89 2.37 1.59 0.203

21 4.95 1.89 228.92 0.16 1.62 0.184

22 1.09 2.63 0 2.88 3.55 0.722

23 1.10 5.45 304.48 2.84 3.31 0.585

24 0.8 4.71 484.79 2.46 3.15 0.584

25 0.63 4.56 487.72 2.35 2.95 0.59

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Table 30. Enrichment factor in Soil fraction-2 (355 µm >X>500 µm )

Location Pb Cu Cd Ni Co Mn

1 2.28 4.01 880.81 5.49 2.67 0.41

2 2.38 4.53 883.95 1.74 2.88 0.44

3 1.43 9.56 294.47 0.52 0.082 0.18

4 1.41 9.55 352.90 0.55 0 0.18

5 1.57 3.053 55.15 0.64 0.12 0.18

6 4.10 60.88 408.43 4.39 0 0.54

7 0.60 2.517 45.055 0.59 0.108 0.20

8 0.55 3.569 16.003 0.51 0.007 0.18

9 0.49 3.46 13.537 0.40 0.005 0.18

10 0.53 3.54 19.328 0.31 0.122 0.18

11 0.44 3.42 16.497 0.29 0.117 0.18

12 0.43 3.45 27.48 0.325 0.123 0.18

13 0.58 2.49 17.15 0.54 0.007 0.2

14 0.09 0.17 32.54 0.59 0 0.20

15 3.5 7.87 886.01 1.64 2.708 0.44

16 3.15 7.37 880.10 1.45 2.56 0.43

17 1.79 10.65 54.771 1.669 2.627 0.45

18 1.55 9.88 47.92 1.55 2.646 0.44

19 1.60 10.13 50.63 1.44 2.625 0.45

20 1.40 9.67 46.70 1.41 2.63 0.45

21 1.09 2.77 887.45 5.15 2.691 0.41

22 1.3 3.093 0 0.39 0.099 0.21

23 1.17 8.99 353.29 0.55 0.005 0.17

24 0.49 3.24 16.55 0.288 0.064 0.18

25 0.38 3.14 14.82 0.20 0.03 0.18

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Table 31. Enrichment factor in Soil fraction-3 (250 µm <X< 355 µm)

Location Pb Cu Cd Ni Co Mn

1 4.30 3.63 264.98 1.49 0.54 0.41

2 4.38 4.13 270.91 5.05 0.75 0.44

3 1.14 7.65 155.71 2.12 4.49 0.13

4 1.12 7.64 225.66 2.17 4.39 0.13

5 1.32 2.46 86.04 2.25 4.50 0.13

6 1.44 15.62 144.71 8.77 59.28 0.14

7 0.15 0.023 49.62 2.16 4.44 0.14

8 1.02 6.82 19.14 2.11 4.41 0.13

9 0.95 6.71 16.21 2.00 4.27 0.13

10 1.04 6.93 21.11 1.92 4.44 0.14

11 0.95 6.83 18.73 1.88 4.45 0.13

12 0.97 6.85 32.63 1.91 4.45 0.13

13 0.12 0 17.12 2.10 4.32 0.14

14 2.36 4.17 54.18 2.15 4.30 0.14

15 2.95 7.62 269.7 4.97 0.57 0.44

16 2.53 7.1 260.32 4.82 0.56 0.44

17 2.50 15.84 51.66 5.00 0.64 0.45

18 2.27 15.17 45.17 4.92 0.63 0.44

19 2.33 15.38 47.57 4.81 0.62 0.45

20 2.14 14.98 44.02 4.80 0.62 0.45

21 3.20 2.46 263.79 1.12 0.53 0.41

22 1.33 3.68 0 2.15 3.70 0.14

23 0.82 6.97 225.69 2.12 4.40 0.13

24 1.00 6.59 17.70 1.89 4.38 0.13

25 0.86 6.49 15.58 1.79 4.24 0.14

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Table 32. Enrichment factor in Soil fraction-4 (250 µm >X>188 µm)

Location Pb Cu Cd Ni Co Mn

1 3.06 4.04 69.35 1.90 1.64 0.35

2 3.14 4.48 75.69 4.52 1.83 0.38

3 0.86 6.92 166.79 1.8 0.08 1.3

4 0.84 6.9 229.8 1.91 0 1.33

5 1.32 2.63 32.58 1.99 0.14 1.32

6 2.15 9.53 182.55 9.85 2.95 0.74

7 0.90 2.19 29.41 1.89 0.11 1.35

8 0.49 3.94 15.01 1.86 0.01 1.34

9 0.43 3.82 12.33 1.76 0.08 1.35

10 0.51 4.00 16.45 1.68 0.22 1.36

11 0.42 3.88 13.65 1.66 0.21 1.36

12 0.45 3.90 26.18 1.69 0.22 1.36

13 0.87 2.17 0 1.8 0.01 1.35

14 1.55 3.09 34.35 1.88 0 1.34

15 2.88 13.36 73.66 4.4 1.67 0.38

16 2.50 12.96 63.97 4.30 1.67 0.37

17 1.30 9.38 42.06 4.4 1.73 0.38

18 1.08 8.71 36.03 4.39 1.74 0.38

19 1.13 8.93 38.26 4.29 1.73 0.38

20 0.96 8.52 34.95 4.28 1.73 0.38

21 2 2.97 65.91 1.57 1.65 0.34

22 1.28 6.09 0 7 0.017 0

23 0.57 6.29 229.91 1.8 0.006 1.34

24 0.47 3.68 13.28 1.65 0.15 1.36

25 0.34 3.56 11.34 1.57 0.12 1.37

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Table 33. Enrichment factor in Soil fraction-5 (188 µm>X>125µm)

Location Pb Cu Cd Ni Co Mn

1 1.3 1.9 36.67 0.82 0.33 1.23

2 1.40 2.13 40.05 1.93 0.43 1.24

3 1.77 0.80 139.5 1.72 0.09 1.14

4 1.75 0.78 209.2 1.76 0 1.14

5 1.32 2.84 56.59 1.85 0.27 1.13

6 2.5 15.73 178.7 53.22 0 0.71

7 1.04 2.94 32.53 1.78 0.13 1.2

8 0.38 2.84 13.19 1.71 0.008 1.15

9 0.32 2.70 10.15 1.59 0.009 1.15

10 0.4 2.90 14.62 1.51 0.152 1.16

11 0.36 2.76 11.51 1.47 0.146 1.17

12 0.39 2.79 25.37 1.50 0.153 1.1

13 1.02 2.92 0 1.73 0.01 1.23

14 2.09 3.58 0 1.77 0 1.22

15 1.72 3.53 38.94 1.88 0.35 1.24

16 1.52 3.30 33.95 1.81 0.34 1.24

17 0.49 3.17 17.68 1.90 0.37 1.24

18 0.38 2.79 14.52 1.85 0.37 1.25

19 0.41 2.92 15.78 1.80 0.37 1.24

20 0.32 2.7 14.04 1.79 0.36 1.25

21 0.84 1.35 34.85 0.65 0.33 1.23

22 1.66 3.68 0 7.55 0.02 1.42

23 1.46 0.09 209.18 1.72 0.005 1.14

24 0.39 2.54 11.26 1.47 0.08 1.17

25 0.25 2.40 9.09 1.38 0.078 1.18

83

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Table 34. Enrichment factor in Soil fraction-6 (125µm>X>106µm)

Location Pb Cu Cd Ni Co Mn

1 2.44 3.95 60.59 0.83 3.19 0.67

2 2.49 4.27 65.21 1.43 3.32 0.69

3 1.16 6.07 230.35 0.93 0.27 0.14

4 1.14 6.05 307.82 0.98 0.1 0.14

5 1.82 3.74 104.86 1.091 0.3 0.13

6 0.88 15.14 93.17 15.40 2.59 0.12

7 1.69 4.09 36.06 0.99 0.31 0.15

8 0.90 3.69 15.71 0.92 0.17 0.14

9 0.83 3.55 12.43 0.78 0.17 0.14

10 0.95 3.85 17.77 0.69 0.34 0.15

11 0.83 3.65 14.34 0.65 0.33 0.15

12 0.86 3.68 30.47 0.68 0.3 0.15

13 1.66 4.07 0 0.93 0.17 0.15

14 1.94 3.41 21.04 0.99 0.16 0.15

15 2.18 8.56 63.71 1.35 3.21 0.7

16 1.90 8.25 56.79 1.24 3 0.69

17 1.34 5.24 26.82 1.37 3.25 0.70

18 1.18 4.73 22.37 1.30 3.2 0.7

19 1.22 4.90 23.92 1.24 3.25 0.70

20 1.09 4.59 21.47 1.22 3.26 0.70

21 1.7 3.17 58.17 0.58 3.21 0.68

22 1.65 6.66 0 7.40 0.2 0.15

23 0.81 5.31 308.17 0.93 0.159 0.14

24 0.89 3.45 13.88 0.66 0.26 0.14

25 0.74 3.30 11.44 0.54 0.05 0.15

84

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Table 35. Enrichment factor in Soil fraction-7 (106µm>X>90µm)

Location Pb Cu Cd Ni Co Mn

1 1.17 1.53 55.65 0 0 0.19

2 1.24 1.88 60.35 0.72 0.14 0.21

3 1.13 4.82 172.78 0.35 0.18 0.14

4 1.12 4.8 240.49 0.4 0.09 0.14

5 1.42 6.17 183.53 0.49 0.24 0.13

6 0.73 8.66 101.87 0 16.22 2.32

7 1.30 0.44 31.68 0.42 0.26 0.14

8 0.62 2.98 16.10 0.35 0.10 0.13

9 0.55 2.85 13.24 0.22 0.07 0.14

10 0.59 3.05 17.66 0.13 0.21 0.14

11 0.51 2.86 13.90 0.08 0.21 0.14

12 0.53 2.89 27.38 0.12 0.21 0.14

13 1.27 0.42 0 0.37 0.14 0.14

14 1.89 2.55 0 0.42 0.13 0.14

15 3.30 15.23 58.92 0.65 0.02 0.21

16 3.02 14.94 51.77 0.53 0.02 0.21

17 1.10 4.93 30.96 0.67 0.07 0.21

18 0.94 4.41 26.35 0.58 0.06 0.20

19 0.98 4.58 28.02 0.53 0.06 0.21

20 0.85 4.27 25.5 0.50 0.05 0.21

21 0.42 0.74 53.15 0.13 0.01 0.19

22 2.14 9.99 0 6.06 0.15 0.14

23 0.82 4.14 240.57 0.35 0.08 0.13

24 0.55 2.69 14.27 0.09 0.14 0.14

25 0.42 2.56 12.19 0.063 0.09 0.14

85

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Table 36. Enrichment factor in Soil fraction-8 (90µm>X>75 µm)

Location Pb Cu Cd Ni Co Mn

1 2.04 3.73 22.03 0 0 0.16

2 2.09 4.00 26.04 0.155 0.12 0.17

3 1.34 8.46 405.03 0.018 3.96 0.13

4 1.31 8.44 478.62 0.066 3.86 0.13

5 1.35 2.46 525.37 0.175 3.98 0.13

6 1.50 12.6 121.57 4.923 2.79 0.66

7 3.48 3.51 34.31 0.076 4.15 0.13

8 0.50 2.7 18.31 0.006 3.88 0.13

9 0.43 2.57 15.22 0.048 3.73 0.13

10 0.52 2.77 19.96 0.042 3.90 0.14

11 0.43 2.63 16.70 0.042 3.92 0.14

12 0.46 2.66 32.59 0.077 3.92 0.14

13 3.45 3.48 0 0.013 4.02 0.131

14 2.12 2.87 54.65 0.074 4.00 0.13

15 4.9 14.66 24.65 0.096 0.02 0.17

16 4.67 14.42 18.7 0.037 0.01 0.17

17 0.71 3.46 26.67 0.16 0.05 0.18

18 0.51 3.02 22.83 0.085 0.05 0.17

19 0.61 3.17 24.23 0.035 0.04 0.17

20 0.49 2.89 22.17 0.014 0.04 0.17

21 1.42 3.08 19.73 0.011 0.043 0.16

22 4.09 12.48 0 6.17 4.51 0.13

23 1.00 7.75 479.67 0.014 3.87 0.13

24 0.47 2.39 16.36 0.014 3.84 0.13

25 0.33 2.25 14.12 0.012 3.68 0.14

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Table 37. Enrichment factor in Soil fraction-9 (75µm>X>53 µm)

Location Pb Cu Cd Ni Co Mn

1 3.11 4.73 834.56 0 0 0.13

2 3.15 4.96 836.01 0.57 0.10 0.15

3 1.31 9.08 100.99 0.32 0.13 0.1

4 1.29 9.07 155.22 0.36 0.05 0.09

5 0.11 2.36 53.532 0.44 0.17 0.09

6 2.77 32.58 501.22 0 20.47 2.42

7 0.48 1.97 772.03 0.37 0.182 0.09

8 0.31 1.68 605.88 0.32 0.06 0.09

9 0.25 1.57 607.44 0.22 0.007 0.09

10 0.32 1.72 613.31 0.14 0.12 0.1

11 0.25 1.6 589.02 0.12 0.11 0.1

12 0.26 1.63 598.75 0.15 0.12 0.1

13 0.45 1.95 747.03 0.33 0.09 0.09

14 1.32 2.45 27.82 0.37 0.08 0.09

15 3.3 10.54 836.83 0.52 0.015 0.15

16 3.09 10.3 834.0 0.43 0.015 0.15

17 0.52 2.54 854.83 0.54 0.052 0.16

18 0.40 2.15 855.76 0.47 0.047 0.15

19 0.43 2.28 854.92 0.43 0.04 0.15

20 0.33 2.04 855.87 0.42 0.04 0.15

21 2.58 4.17 837.27 0.10 0.01 0.14

22 2.34 7.67 0 4.82 0.11 0.09

23 1.07 8.56 155.05 0.32 0.047 0.09

24 0.28 1.43 612.77 0.11 0.06 0.09

25 0.17 1.32 615.54 0.11 0.06 0.09

87

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Table 38. Enrichment factor in Soil fraction-10 (53 µm>X>25µm)

Location Pb Cu Cd Ni Co Mn

1 1.99 3.57 34.95 0 0 0.11

2 2.03 3.77 37.81 0.43 0.085 0.13

3 1.14 5.35 128.84 0.26 0.068 0.08

4 1.13 5.33 177.59 0.29 0 0.08

5 0.11 0.22 11.49 0.36 0.07 0.08

6 9.53 46.10 604.47 0 0 0.46

7 0.92 3.07 22.63 0.32 0.088 0.08

8 0.31 1.66 28.56 0.25 0.006 0.08

9 0.26 1.56 26.58 0.161 0.008 0.08

10 0.32 1.71 30.004 0.095 0.109 0.09

11 0.26 1.58 29.96 0.082 0.104 0.09

12 0.27 3.36 23.29 27.96 0.102 0.08

13 0.89 3.05 0 0.279 0.006 0.08

14 1.27 2.14 13.22 0.318 0 0.08

15 2.74 4.44 36.84 0.392 0.013 0.12

16 2.57 4.23 32.52 0.319 0.012 0.12

17 0.48 2.32 40.74 0.404 0.042 0.13

18 0.38 1.99 38.11 0.354 0.038 0.12

19 0.41 2.1 39.08 0.318 0.0362 0.12

20 0.33 1.91 37.64 0.303 0.0319 0.12

21 1.55 3.10 33.36 0.008 0.006 0.11

22 2.11 3.61 0 4.346 0.012 0.08

23 0.92 4.86 177.49 0.256 0.006 0.08

24 0.29 1.44 27.65 0.070 0.058 0.09

25 0.19 1.35 26.245 0.068 0.056 0.09

88

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Table 39. Enrichment factor in Soil fraction-11 (X<25µm)

Location Pb Cu Cd Ni Co Mn

1 2.47 3.95 51.07 0 0 0.12

2 2.52 4.17 54.09 41.24 0.090 0.13

3 1.45 7.94 114.47 27.81 0.065 0.08

4 1.44 7.93 160.69 27.84 0 0.08

5 1.26 2.27 32.76 27.76 0.1 0.08

6 1.95 0 96.13 0 0 0.08

7 0.47 5.4 284.99 28.49 0.083 0.08

8 0.28 3.40 14.43 27.85 0.0059 4.47

9 0.24 3.31 12.47 27.92 0.007 0.08

10 0.31 3.44 15.45 27.91 0.101 0.08

11 0.25 3.35 13.41 27.98 0.097 0.08

12 10.99 14.94 6042.3 14.72 3.142 55.37

13 0.45 5.38 263.47 28.46 0.006 0.08

14 1.43 1.65 0 28.44 0 0.08

15 2.08 7.50 53.094 41.28 0.013 0.13

16 1.89 7.29 48.50 41.31 0.0134 0.13

17 0.50 5.18 24.93 41.28 0.046 0.14

18 0.39 4.84 22.03 41.4 0.042 0.13

19 0.42 4.95 23.11 41.28 0.03 0.14

20 0.34 4.75 21.52 41.38 0.035 0.14

21 2 3.46 49.38 0 0 0.12

22 1.43 5.28 0 32.26 0.013 0.08

23 1.25 7.48 160.56 27.87 0.006 0.08

24 0.28 3.20 13.193 27.97 0.054 0.08

25 0.18 3.12 11.783 28.14 0.042 0.08

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3.14 Multivariate analysis of Soil

3.14.1 Factor analysis:-Factor analysis was applied to eleven chemical parameters of soil

(Table-4). Factors were extracted using principal components extraction method and subjected to

varimax normalization rotation to interpret factor loading.

Factor analysis extracts three factors (factors having Eigen value >1) which accounts for 83.62%

variance of total variance represented in Table.40 Therefore these three factors are assumed to

represent adequately the overall variance of data set.

Table 40. Eigen value for factor analysis of soil

Factor-1(F1), it accounts for 44.21% of total variance as shown in Table-40, summarizes that F1

is highly loaded with Fe, Mn K, Na, Co and moderately loaded with Pb (Table-41). Where Fe,

Mn K, Na and Pb show positive loading and Co shows negative loadings. Factor one is

considered to be lithogenic factor with anthropogenic input of Co. This fact is supported by high

enrichment and geoaccumulation index of Co. Co rapidly adsorbed on the oxides of Mn and Fe

[47] that‟s why it gets associated to this factor. But negative loading of Co indicates that,

continent of this metal either controlled by different geochemical mechanism (e.g., Mobilization

from the matrix. As in oxidizing environment sulfides of iron leads to oxidation and, acidic

solutions are created which tend to decrease adsorption and promote mobility of metals) or it has

other source of origin.

Factor-2 (F2), it accounts for 21.4% of total variance, highly loaded with Ca, Mg and moderately

with Ni. F2 is considered to be purely lithogenic. And high correlation (correlation coefficient

0.96 .table correlation matrix tables) between Ca and Mg revealed that, they have common origin

that is dolomite. The above interpretation is supported geomorphology of the sampling location

(Presence of dolomite rich soil in Mumbai) and association of Ni with the Ca and Mg in the F2.

Dolomite is an ore of Ni. [48].

Factor Eigen

value

% Total

Variance

Cumulative

Eigen value

Cumulative%

1 4.86 44.21 4.86 44.21

2 2.35 21.40 7.22 65.62

3 1.98 18.00 9.19 83.62

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Factor-3(F3), it accounts for 18% of total variance is loaded with copper and cadmium. F3 is

considered to be anthropogenic in origin. All the locations are highly enriched with copper and

cadmium. Geoaccumulation index (Table-15) shows that all the locations are highly

contaminated with Cd and moderately by copper. High Cu and Cd concentration is in soil may

be due to use of fertilizers and fungicides used in the agricultural field as most of fertilizers

contains elevated concentration of Cu, Cd and Pb [49]. Sediments and water of Ulhas estuary

posses an elevated concentration of Cd followed by Cu and Pb [38,49]and its regular practice to

excavate the bottom sediments from the Ulhas river and spared it to nearby land. So this

disturbance in natural pattern plays major role in soil contamination by Cd, Cu and Pb (to an

average level) of the study area.

Table 41. Factor loading matrix of soil

Variables

Factor 1

Factor 2

Factor 3

Pb 0.497 0.268 0.473

Cu 0.059 0.050 0.923

Cd -0.100 -0.191 0.908

Fe 0.972 -0.117 0.098

Ni 0.037 0.575 0.003

Co -0.962 0.205 -0.045

Mn 0.981 -0.113 -0.059

Na 0.744 0.291 0.481

K 0.983 -0.056 -0.104

Table 42. Correlation Matrix for soil parameters

Pb Cu Cd Fe Ni Co Mn Na K Mg Ca

Pb

1.00

Cu 0.40 1.00

Cd 0.19 0.76 1.00

Fe 0.45 0.18 0.02 1.00

Ni 0.03 -0.01 -0.05 -0.08 1.00

Co -0.38 -0.13 0.01 -0.99 0.12 1.00

Mn 0.36 0.02 -0.11 0.98 -0.02 -0.99 1.00

Na 0.64 0.43 0.34 0.70 0.29 -0.64 0.64 1.00

K 0.39 -0.05 -0.15 0.95 0.00 -0.95 0.98 0.68 1.00

Mg 0.05 0.03 -0.16 -0.34 0.32 0.41 -0.35 -0.01 -0.31 1.00

Ca 0.17 0.05 -0.20 -0.15 0.35 0.23 -0.15 0.18 -0.10 0.96 1.00

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Fig. 40 Factor-1 score plot Fig. 41 Factor-2 score plot

Fig. 42 Factor-3 score plot

3.14.2 Cluster analysis. The main result of HCA performed on soil samples of 25 locations is

the dendrogram (fig.41).The Q–mode cluster analysis (using Wards linkage rule and Euclidian

distance as distance measure and phenon line 10 classifies all the 25 sampling locations in to four

clusters. The dendrogram obtained was used to define four geochemical groups (that illustrate

the study area).

These clusters can be best explained with the help of factor score plot (Fig.38, 39 and 40). From

the factor score plot different clusters can be explained as follows.

C1 includes location: 22, 13 and 7, these locations are totally unaffected by F1 and F2 but highly

affected by F3.

C2 includes locations: 25, 12, 24, 11, 10, 9 and 8 affected by F2 but unaffected by F1 and

affected to an average extent by F3.

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C-3 includes locations: 14, 23, 5, 3 and 4.these locations are totally unaffected by F1 and F2 but

affected to an average level by F3.

C4 includes locations: 1, 2, 6, 15, 16, 17, 18, 19, 20 and 21.these locations are highly affected by

F1 but have mixed result for F2 and F3.

Fig. 43 Dendrogram of Q-Mode cluster analysis of soil samples

3.15 Multivariate Analysis of soils, sediments and water.

Factor analysis by means of principal factor method and varimax normalization rotation can shed

more light and help understanding these data. Table-43 and 44 represents factor analysis spread

sheets of the common chemical parameters of soil sediment and water samples simultaneously.

Table-43 Shows those four factors accounts for more than 84% of total variance.

Factor-1 (F1) highly loaded with Fe (soil), Mn (soil), Mn (sediment), moderately with Fe

(water), this revealed that they were from common parent material. This is supported by the fact

that, Fe and Mn are chemically associated and is usually found in the same geologic

environments in the form of Fe-Mn nodules. Hence the possible source may be mangniferous

minerals. A negative loading of Fe (water) indicates that, water soluble „Fe‟ is from soil and

sediment. F1 shows weak negative loading (-0.45) of Fe (sediment) may be shows its depletion

of Fe in sediment by any geochemical process.

Ward`s method

L22L13

L7L25

L24L11

L12L10

L9L8

L14L5

L23L4

L3L20

L19L18

L17L6

L21L16

L15L2

L10

5

10

15

20

25

30

35

Lin

ka

ge

Dis

tan

ce

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Table 43. Eigen value spread sheet soil sediment and water

Factors Eigen value % Total

variance

Cumulative

Eigen value

Cumulative

%

1 4.89 40.81 4.89 40.81

2 2.36 19.66 7.26 60.47

3 1.51 12.57 8.76 73.05

4 1.36 11.35 10.13 84.40

Table 44. Factor loading matrix of soils, sediments and water

Variables

Factor-1

Factor-2

Factor-3

Factor-4

Pb(Soil) 0.241 0.175 0.839 -0.333

Cu(Soil) 0.013 -0.012 0.120 -0.965

Fe(Soil) 0.944 0.082 0.197 -0.136

Mn(Soil) 0.966 0.046 0.159 0.015

Cu(Water) -0.070 0.858 0.048 -0.347

Fe(Water) -0.581 -0.182 0.010 0.541

Mn(Water) 0.008 0.941 0.056 0.119

Pb(Water) 0.153 0.778 0.357 0.189

Cu(Sediment) 0.356 0.680 0.287 -0.232

Fe(Sediment) -0.450 -0.496 0.431 0.067

Pb(Sediment) 0.235 0.242 0.834 0.061

Mn(Sediment) 0.960 0.073 0.177 0.013

Expl.Var 0.241 0.175 0.839 -0.333

Prp.Totl 0.013 -0.012 0.120 -0.965

Factor-2 is highly loaded with Cu (water and sediments), Mn (water), Pb (water) suggesting their

common source of origin which is already explained in factor analysis of water samples, i.e.,

vehicular pollution. Moderate negative loading (-0.496) of Fe (sediment) in F2 indicates sorption

and desorption mechanism playing main role at study site i.e., in the oxidation of sulphides of Fe,

acidic solutions are created which tend to decrease adsorption and promote mobility of metals.

i.e., desorption [50]. But at the same time significant correlation between Cu(sediment) and Cu

(water)(correlation coefficient 0.64; Table-45), positive loading of Cu(sediment) in F2 can be

justified on the basis of the fact that, Cu sorbed on organic matter in preference to other heavy

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metals [51,52], which is attributed to its high charge/radius ratio enabling it to form stable

complexes with humic substances [53,54] irrespective of other metal.

Factor-3 is loaded with Pb (soils and sediments), suggest theire common source of origin which

is purely natural because of very low geoaccumulation indices value. Factor-4 is loaded with Cu

(soil) revealed its external input due to human activity.

Table 45. Correlation Matrix for soils, sediments and water.

Variable Pb

(Soil)

Cu

(Soil)

Fe

(Soil)

Mn

(Soil)

Cu

(Water)

Fe

(Water)

Mn

(Water)

Pb

(Water)

Cu

(Sediment)

Fe

(Sediment)

Pb

(Sediment)

Mn

(Sediment)

Pb

(Soil) 1

Cu

(Soil) 0.40 1

Fe

(Soil) 0.45 0.18 1

Mn

(Soil) 0.36 0.02 0.98 1

Cu

(Water) 0.27 0.32 0.12 0.03 1

Fe

(Water) -0.33 -0.45 -0.56 -0.52 -0.22 1

Mn

(Water) 0.19 -0.13 0.10 0.10 0.81 -0.15 1

Pb

(Water) 0.38 -0.11 0.23 0.23 0.50 -0.23 0.76 1

Cu

(Sediment) 0.49 0.26 0.49 0.41 0.64 -0.31 0.53 0.60 1

Fe

(Sediment) 0.06 -0.02 -0.32 -0.30 -0.29 0.36 -0.34 -0.26 -0.37 1

Pb

(Sediment) 0.76 0.03 0.35 0.32 0.19 -0.20 0.24 0.49 0.42 -0.04 1

Mn

(Sediment) 0.37 0.04 0.96 0.97 0.03 -0.50 0.11 0.25 0.46 -0.34 0.36 1

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CHAPTER-4

C O N C L U S I O N S

he results obtained in this work after multivariate analysis (MVA), clearly reflects that

MVA is an important statistical tool to deal with the problems in environmental matrices as

they are very complex system (consisting of number of interlinked natural processes coupled

with anthropogenic activity). Multivariate statistical methods also allow the defining of

geochemical zonation of aquifer systems and soils of the studied area, which takes into account

effects of lithologies, anthropogenic contests and hydrogeology contest when dealing with

aquifer systems. The study leads to following conclusions.

4.1 Water

4.1.1 Hydrogeochemical model study

The non homogeneous distribution of different chemical species attributed due to differential

mineralization of ground water of the study area.

Most of the locations are affected by dissolution of gypsum and dolomite minerals

leading to the predominance of Ca-Mg cations and mixed HCO3--SO4

2-anions in

ground water except few locations.

Gibbs-Boomerang diagram study revealed that hydro geochemical evolution of

ground water of study site is mainly controlled by the weathering process and few

locations showing deviation from boomerang is due to dissolution of salts of marine

origin i.e. dissolution of salt pockets and cation exchange reaction.

Stability diagram suggests predominance of kaolinite mineral at the study site. From

this we can predict that the study area has a tropical wet (humid) climatic condition.

As in humid tropical conditions, kaolinite mineral is present as a result of plagioclase

weathering. Important result from study is that, there is no evidence of saline

incursion to the ground water system.

T

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4.1.2 Multivariate statistical analysis of ground water.

Factor analysis revealed that four factors controlling hydrogeochemical evolution of ground

water of the study area are, a) weathering process (lithogenic), b) anthropogenic inputs of

heavy metals by agricultural practice and vehicular pollution, c) a combined effect of

lithogenic as well as anthropogenic i.e., use of NPK fertilizer and weathering of Jarosite-K

and d) Overall mineralization of ground water of study area by gypsum and dolomite.

Cluster analysis spatially divided the study area in to four clusters (or zones), where zone -

1(C1) was having a Ca-Mg-SO4-Cl hydrochemical facies. Zone-2(C2) having a Ca-Mg-

HCO3 facies, zone-3 Na-K-SO4- Cl type water and zone-4 (C4) has predominant Ca-Mg –

SO4-Cl type but highly affected by factor-2 (anthropogenic input of heavy metals.). it shows

very good matching with Pipers classification.

.

4.2 Soil

4.2.1 Multivariate statistical analysis of soil.

Factor analysis shows that a) The positive association of Fe, Mn K, Na and Pb indicating

their lithogenic origin and negative loading of Co suggests that its depletion due to either

by any undergoing geochemical processes or its different source of origin. b) Soils of study

area are enriched with dolomite mineral and good association of Ni envisages that dolomite

is also an ore of Ni. c) Strong association of Cu, Cd in factor-3 suggests that, study site is

contaminated by heavy metal content sediments of Ulhas river coupled with application of

fertilizer used in agricultural land

Cluster analysis of soils of studying area divides it into four zones considering all the factors

in to account. C1 (Zone-1) soils were contaminated by Cu and Cd to an average level. These

locations are totally unaffected by factor 1 and 2 (i.e. soil is neither enriched with dolomite

mineral nor affected by weathering by process). C2 (zone -2), soil is enriched with dolomite

and affected by F3 to an average degree. C3 (Zone-3) soils were relatively not affected by

any of these factors except location-14(of zone 3) which is contaminated by Cu, and Cd to an

average degree. C4 (Zone-4) soils were although control by all the three factors (weathering

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process, enriched with dolomite as well as contaminated with Cu and Cd.) weathering is the

predominating process controlling chemical composition of soil.

4.2.2 Textural, enrichment factor and Geoaccumulation index study of soil

Textural ,enrichment factor and Geoaccumulation index study revealed that soils of study area is

practically uncontaminated w.r.t Fe, Mn and Pb (except location 1,2 and 21), moderately

contaminated by Co and Ni at few locations. All the locations are contaminated with Cu and Cd.

4.3 Multivariate statistical study of soils, sediments and water.

Simulations multivariate study of three matrices revealed that

Fe in soils, sediments and water, Mn, in soils and sediments has common origin (i.e., soils

and sediments have common parent rock and chemical compositions of ground water are

controlled by chemical composition of nearby soil and sediment of the well). Similarly the

Cu in water and sediments, Pb in soil and sediments has their common source of origin.

Cu (soil) isolated in last factor indicate external input of the metal into the system.

Complex geochemical process also controls the concentration of Fe (sediment), Cu

(sediments and soil).

.

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