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
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
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
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
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
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
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
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
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
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
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
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
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,
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.
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
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
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).
7
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.
8
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.
9
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.
10
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
11
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
12
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
13
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,
14
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-
15
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
16
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
17
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)
18
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
19
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).
20
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
21
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
22
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.
23
Fig. 5 Map of the study site
24
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.
25
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
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
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
28
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
29
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
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
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.
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.
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.
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
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
36
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
37
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
38
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
39
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
40
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
41
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
42
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
43
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
44
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.
45
(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
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)
47
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
48
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)
49
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.
50
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
51
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
51
52
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
53
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
54
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
55
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].
56
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).
57
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.
58
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
58
59
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
59
60
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
60
61
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
61
62
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
62
63
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
64
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
65
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
66
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
67
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
68
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
68
69
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
69
70
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
70
71
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
71
72
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
72
73
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
73
74
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
74
75
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
75
76
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
76
77
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
77
78
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
78
79
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
79
80
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
80
81
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
81
82
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
82
83
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
84
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
85
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
86
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
86
87
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
88
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
89
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
89
90
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
91
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
92
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.
93
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
94
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
95
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
96
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
97
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
98
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).
.
99
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