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GEOCHEMICAL FRACTIONATION AND
PHYTOREMEDIATION OF HEAVY METALS AROUND
YAMUNA RIVER IN DELHI
Thesis
Submitted to the
G. B. Pant University of Agriculture & Technology
Pantnagar – 263 145, Uttarakhand, India
By Shobhika Parmar
M.Sc. (Environmental Science),
P.M.D. (Natural Resource Management)
IN PARTIAL FULFILLMENT OF THE REQUIREMENTS
FOR THE DEGREE OF
Doctor of Philosophy (Environmental Science)
November, 2015
ACKNOWLEDGEMENT
First and foremost, I would like to express my deep sense of gratitude and indebtedness to my
advisor Prof. Vir Singh for his invaluable encouragement, guidance, suggestions and support from an
early stage of this research and sharing his extraordinary experiences throughout the work. Above all, his
priceless and meticulous supervision at each and every phase of work inspired me in innumerable ways.
His involvement with originality has triggered and nourished my intellectual maturity that will help me
for a long time to come.
I extend my thanks to Prof. J. P. N. Rai, Head, Department of Environmental Science for
proving necessary facilities, critical suggestions and guidance during the research work. I am also
thankful to Prof. Uma Melkania Dean, College of Basic Science and Humanities (Former Head,
Department of Environmental Science) for her guidance, needful suggestions and advised me to go in the
right direction whenever required. Thanks are due to Prof. Ajit Singh Nain, Head, Department of
Agrometerology for his supervision and support. Words are not enough to thank all my teachers for their
blessings and teachings, whatever little I have accomplished today I own it you all.
I would like to acknowledge AIRF, JNU, New Delhi, for X-ray Crystallography analysis;
SAIF, Cochin University, Cochin for SEM-EDX analysis; SAIF, Panjab University, Chandigarh for
WD-XRF analysis and Dept. of Chemistry, BHU, Varanasi for FTIR analysis.
This venture would have been incomplete without the diligent efforts of all the staff members
and laboratory assistants, Department of Environmental Science for their support and help during the
course of study.
I would like to thank Prof. A. K. Sharma, Department of Biological Science for extending his
glasshouse facility for the pot experiments. I am also grateful to Dr. Pushpesh Joshi (Research
Associate), Botanical Survey of India, NRC Dehradun, for providing the plants of P. vittata for the
experiments. I am also thankful to Dr. Bhagwan Kheredia, MMV, BHU, Varanasi for his help with
the FTIR analysis. I would also like to thanks Dr. V. K. Srivastava, Senior Chemist, GSI, Lucknow
for carrying AAS of some samples.
I am elated to avail this wonderful opportunity to record my gratitude to all my seniors, for
their guidance, cooperation, healthy criticisms and time to time help. How can I forget the love,
affection and assistance received from my juniors. I wish all the success and happiness for them. No
mission is complete unless your friends are with you. I will always cherish the friendship of Anil,
Shivani Uniyal, Shakuli Kashyap and Niki Nautiyal, their unwavering enthusiasm never failed to lift
me out of my blues.
I express profound sense of reverence to my Mummy and Papa who have always been a source
of inspiration and encouragement to me, their affection, blessing, constant support, great sacrifices,
instilled sense of responsibility and confidence into me, was driving force in completion of this research
work. A special mention is required to thanks my brother and sisters, Deepak, Rashmi and Kusum for
their moral support and constant encouragement throughout the progress of this work.
My heartfelt acknowledgement to my fiancée for his, motivation, encouragement, unconditional
support, care and continual love that, he served throughout my research work. I would also like to
thank my extended family, mother in law and father in law for their care and support.
I cannot forget to acknowledge the financial support in the form of assistantship by the
university. At last but not least, I am also grateful to those who could not find separate name in this
sheet but have helped me directly or indirectly during this research work.
Pantnagar (Shobhika Parmar)
November, 2015 Author
CERTIFICATE
This is to certify that the thesis entitled, ―GEOCHEMICAL FRACTIONATION
AND PHYTOREMEDIATION OF HEAVY METALS AROUND YAMUNA RIVER
IN DELHI‖ submitted in partial fulfillment of the requirements for the degree of Doctor
of Philosophy with major in Environmental Science and minor in Agrometeorology of
the College of Post Graduate Studies, G.B. Pant University of Agriculture and Technology,
Pantnagar, is a record of bonafide research carried out by Ms. Shobhika Parmar, Id. No.
41354, under my supervision and no part of the thesis has been submitted for any degree or
diploma.
The assistance and help received during the course of this investigation have been
acknowledged.
Pantnagar (Vir Singh)
November, 2015 Chairman
Advisory Committee
CERTIFICATE II
We, the undersigned, members of the Advisory Committee of Ms. Shobhika Parmar,
Id. No. 41354, a candidate for the degree of Doctor of Philosophy with major in
Environmental Science and minor in Agrometeorology, agree that the thesis entitled,
―GEOCHEMICAL FRACTIONATION AND PHYTOREMEDIATION OF HEAVY
METALS AROUND YAMUNA RIVER IN DELHI‖ may be submitted in partial
fulfillment of the requirements for the degree.
(Vir Singh) Professor
Dept. of Environmental
Science
Chairman
Advisory Committee
(Uma Melkania) Professor & Dean
C. B. S. H.
Member
(J. P. N. Rai) Professor & Head
Dept. of Environmental
Science
Member
(A. S. Nain) Professor & Head
Dept. of Agrometerology
Member
(J. P. N. Rai) Professor & Head
Dept. of Environmental
Science
Ex-Officio Member
CONTENTS
TOPIC PAGE
Chapter 1. INTRODUCTION
Chapter 2 REVIEW OF LITERATURE
2.1 Metals
2.2 Terms commonly used to specify groups of metals
2.3 Heavy metal pollution and its sources
2.4 Effect of heavy metals on plants
2.5 Effect of heavy metals on aquatic life
2.6 Effect of heavy metals on humans
2.7 Occurrence of heavy metals in river sediments
2.8 Requirement for chemical speciation and geochemical fractionation
study
2.8.1 Defining Chemical Speciation
2.8.2 Basics of the sequential extraction
2.8.3 Risk assessment code (RAC)
2.9 Remediation
2.10 Phytoremediation approaches and Hyperaccumulation of metals in
plants
2.10.1 Phytoremediation categories
2.10.1.1 Phytostabilization
2.10.1.2 Phytofiltration
2.10.1.3 Phytovolatilization
2.10.1.4 Phytoextraction
2.11 P. vittata a hyperaccumulator plant
2.12 Heavy metal remediation by immobilization using natural sorbents
Chapter 3 MATERIALS AND METHODS
3.1 Description of the investigated sites
3.2 Field sampling
3.3 Chemicals and reagents
3.4 Glass and plastic wares
3.5 Instruments used
3.6 Water Quality Analysis of the water samples
3.7 Determination of metal concentration
3.8 Geochemical fractionation or chemical speciation of potentially toxic
heavy metals
3.10 Statistical analysis
3.11 Chemical characterization of soil and sediments samples
3.11.1 SEM-EDX (Scanning Electron Microscope-Energry
Dispersive X-Ray
3.11.2 POWDER XRD –X ray Diffraction for solid phase
characterization
3.11.3 FTIR
3.12 Chelant induced phytoextraction of heavy metals by Pteris vittata
3.12.1 Experimental design
3.12.2 WDXRF analysis
3.13 Heavy metal immobilization potential of the vermiculite in the soil
3.13.1 Experimental design
3.13.2 FTIR analysis
Chapter 4 RESULTS AND DISCUSSION
4.1 Water quality parameters of the river Yamuna along the Delhi
segment
4.1.1 Variation of surface water pH
4.1.2 Variation in DO of surface water
4.1.3 Variation in BOD of surface water
4.1.4 Variation in COD of surface water
4.1.5 Correlation between different water quality parameters
surface water
4.2 Heavy metal contamination in river Yamuna along the Delhi segment
4.2.1 Correlation between concentrations of metals in surface water
at different sampling sites of river Yamuna along the Delhi
stretch
4.2.3 Hierarchical cluster analysis
4.2.3 Principal components analysis
4.2.4 Correlation between different metals studied
4.3 Spatial variation sediment and agriculture soil pH along river
Yamuna in Delhi segment
4.4Heavy metal load of the sediment and agriculture soil along river
Yamuna in Delhi segment
4.4.1 Heavy metal concentration in the freshly deposited sediments
of river Yamuna in Delhi
4.4.1.1 Correlation between concentrations of metals in freshly
deposited sediments at different sampling sites
4.4.1.2 Hierarchical cluster analysis
4.4.1.3 Principal components analysis
4.4.1.4 Correlation between different metals studied
4.4.2 Heavy metal concentration in the agriculture soil along river
Yamuna in Delhi
4.4.2.1 Correlation between concentrations of metals in
agriculture soil along river Yamuna in Delhi
4.4.2.2 Hierarchical cluster analysis
4.4.2.3 Principal components analysis
4.4.2.4 Correlation between different metals studied
4.5 Sequential extraction of sediments and agricultural soil samples of
selected sites
4.5.1 Mobility Factor of Metals
4.6 Geo-chemical analysis of sediments and agricultural soil of selected
sites
4.6.1 Detailed geochemical characterization of the agricultural soil
of site 5
4.7 Chelant induced phytoextraction of heavy metals by Pteris vittata
4.7.1 Dry biomass of Pteris vittata
4.7.2 Heavy metal concentration
4.7.2 Bioaccumulation factor
4.7.3 Translocation Factor
4.8 Heavy metal immobilization potential of the vermiculite in the soil
4.8.1 Total metal content of the experimental soil
4.8.2 Biomass production
4.8.3 Post harvest metal concentration in plant parts and soil
4.8.4 Translocation factor
4.8.5 Chemical composition of soil
Chapter 5 SUMMARY AND CONCLUSION
LITERATURE CITED
APPENDICES
BIO-DATA
ABSTRACT
LIST OF TABLES
Table 2.1: Terms often used to classify metals in biological and environmental
studies (Duffus, 2001)
Table 2.2: Maximum permissible limits of water quality parameters
Table 2.3: Maximum permissible limits of heavy metals in water and sediments
Table 2.4: Maximum Allowable Limits of Heavy Metal in Irrigation Water, Soils
and Vegetables (μg/g)
Table 2.5: Target values and soil remediation intervention values and background
concentrations soil/sediment and groundwater for metals.
Table 2.6: Brief methodology of different sequential extraction techniques
Table 2.7: Risk assessment code
Table 2.8: Cost of different remediation technologies (Glass, 1999)
Table 2.9: Overview of phytoremediation applications
Table 2.10: Effect of typical levels for heavy metals in plants
Table 3.1: Locations of the sampling sites
Table 3.2: Instruments used in the study
Table 3.3: Associations of geochemical fraction of heavy metals in soil and
sediments
Table 4.1: Water quality criteria according to CPCB
Table 4.2 : Temperature, humidity and rainfall of Delhi during the study period
(Jun-2013 to Feb-2014)
Table 4.3: Mobility factors of heavy metals for sediments and agricultural soil of
selected sites river Yamuna in Delhi
Table 4.4: Chemical analysis (wt%) of samples using EDX.
Table 4.5: Chemical analysis (wt%) of whole soil agriculture soil of site 5 and its
different residue samples using EDX.
Table 4.6: Metal/Mineral identified in the agricultural soil of the site 5 of the
river Yamuna and its residues
Table 4.7: Dry biomass yield of Pteris vittata grown in the control and treated
soil
Table 4.8: Qualitative results of the WDXRF showing the concentration of
different elements in soil; roots and fronds of the Pteris vittata
Table 4.9: Bioaccumulation factor (BAF) of different elements in control and
treated Pteris vittata
Table 4.10: Translocation factor (TF) of different elements in control and treated
Pteris vitata
Table 4.11: Levels of heavy metals in the control soil, polluted soil and standard
values of different agencies.
LIST OF FIGURES
Figure 2.1 Sources and sink of heavy metals
Figure 2.2 Schematic representation of phytoremediation approaches.
Figure 3.1 River Yamuna and water channels in Delhi and the sampling
sites.
Figure 3.2 Picturesque views of the sampling sites
Figure 3.3 Picturesque views of the various crops grown in agriculture fields
along the river Yamuna in Delhi
Figure 3.4 Picturesque views of the major power plants along the river
Yamuna in Delhi
Figure 3.5 Scheme of the selective sequential extraction (Tessier et al.,
1979)
Figure 4.1 Spatial variation of the pH of river Yamuna River at different
locations along the Delhi stretch during different seasons
Figure 4.2 Spatial variation of the DO (mg/l) of river Yamuna River at
different locations along the Delhi stretch during different
seasons
Figure 4.3 Spatial variation of the BOD (mg/l) of river Yamuna River at
different locations along the Delhi stretch during different
seasons
Figure 4.4 Spatial variation of the COD (mg/l) of river Yamuna River at
different locations along the Delhi stretch during different
seasons
Figure 4.5 Correlation matrix with scatter plot and histogram of the studied
parameters of water quality of Yamuna River along the Delhi
stretch during different seasons
Figure 4.6 Concentration of different metals in surface water at selected
sites of river Yamuna in Delhi segment, during different
sampling periods
Figure 4.7 Correlation matrix with scatter plot and histogram of the different
sites studied for the concentration of metals in surface water of
river Yamuna along the Delhi stretch
Figure 4.8 Dendrogram produced using the Ward algorithm showing the
variation of the metal concentration with the sampling sites in the
surface water of river Yamuna along the Delhi stretch
Figure 4.9 Dendrogram produced using the Ward algorithm showing the
variation of the metal concentration with the sampling sites and
period in the surface water of river Yamuna along the Delhi
stretch
Figure 4.10 Biplot depicting the variation of metal concentrations of surface
water of river Yamuna in Delhi with the sampling period
Figure 4.11 Correlation matrix with scatter plot and histogram of different
metals assessed in the surface water of river Yamuna along the
Delhi stretch
Figure 4.12 Spatial variation of the pH of sediments along river Yamuna in
Delhi stretch
Figure 4.13 Spatial variation of the pH of river-side agriculture soil along
river Yamuna in Delhi stretch
Figure 4.14 Concentration of different metals in the sediments of river
Yamuna in Delhi segment, during different sampling periods
Figure 4.15 Correlation matrix with scatter plot and histogram of the different
sites studied for the concentration of metals in sediments of river
Yamuna along the Delhi stretch
Figure 4.16 Dendrogram produced using the Ward algorithm showing the
variation of the metal concentration with the sampling sites in the
sediments of river Yamuna along the Delhi stretch
Figure 4.17 Dendrogram produced using the Ward algorithm showing the
variation of the metal concentration with the sampling sites and
period in the sediments of river Yamuna along the Delhi stretch
Figure 4.18 Biplot depicting the variation of metal concentrations of
sediments along river Yamuna in Delhi with the sampling period
Figure 4.19 Correlation matrix with scatter plot and histogram of different
metals assessed in the sediments along river Yamuna in Delhi
stretch
Figure 4.20 Concentration of different metals in the river-side agriculture soil
of river Yamuna in Delhi segment, during different sampling
periods
Figure 4.21 Correlation matrix with scatter plot and histogram of the selected
sites studied for the concentration of metals in the river-side
agriculture soil in Delhi
Figure 4.22 Dendrogram produced using the Ward algorithm showing the
variation of the metal concentrations of river-side agriculture soil
in different sites
Figure 4.23 Dendrogram produced using the Ward algorithm showing the
variation of the metal concentrations of river-side agriculture soil
in different sites and sampling periods
Figure 4.24 Biplot depicting the variation of metal concentrations of selected
agriculture sites along river Yamuna in Delhi with the sampling
period
Figure 4.25 Correlation matrix with scatter plot and histogram of different
metals assessed in the river-side agriculture soil along river
Yamuna in Delhi
Figure 4.26 Heavy metal distributions in different fractions of the sediments
and agricultural soil samples of selected sites
Figure 4.27 (a) SEM image and EDS of the sediments of Site 2
Figure 4.27 (b) SEM image and EDS of the agricultural soil of Site 2
Figure 4.27 (c) SEM image and EDS of the sediments of Site 7
Figure 4.27 (d) SEM image and EDS of the agricultural soil of Site 7
Figure 4.27 (e) SEM image and EDS of the sediments of Site 8
Figure 4.27 (f) SEM image and EDS of the agricultural soil of Site 8
Figure 4.27 (g) SEM image and EDS of the sediments of Site 9
Figure 4.27 (h) SEM image and EDS of the sediments of Site 12
Figure 4.28 Elemental composition (weight %) of sediment (Sed) and
agriculture soil (AgSo) samples of selected sites.
Figure 4.29 (a) FTIR spectrum of sediment of the site 2
Figure 4.29 (b) FTIR spectrum of agricultural soil of the site 2
Figure 4.29 (c) FTIR spectrum of sediment of the site 7
Figure 4.29 (d) FTIR spectrum of agricultural soil of the site 7
Figure 4.29 (e) FTIR spectrum of sediment of the site 8
Figure 4.29 (f) FTIR spectrum of agricultural soil of the site 8
Figure 4.29 (g) FTIR spectrum of sediment of the site 9
Figure 4.29 (h) FTIR spectrum of sediment of the site 12
Figure 4.30 (a) SEM image and EDS of the whole agriculture soil of site 5
Figure 4.30 (b) SEM image and EDS of residue 1 of agriculture soil of site 5
Figure 4.30 (c) SEM image and EDS of residue 2 of agriculture soil of site 5
Figure 4.30 (d) SEM image and EDS of residue 3 of agriculture soil of site 5
Figure 4.30 (e) SEM image and EDS of residue 4 of agriculture soil of site 5
Figure 4.31 Elemental composition (weight %) of whole agriculture soil of
site 5 and its different residues
Figure 4.32 XRD pattern of the agricultural soil of site 5 (a) whole soil, (b)
residue 1, (c) residue 2, (d) residue 3 (e) residue 4
Figure 4.33 (a) FTIR spectrum of whole agriculture soil of site 5
Figure 4.33 (b) FTIR spectrum of residue 1 agriculture soil of site 5
Figure 4.33 (c) FTIR spectrum of residue 2 agriculture soil of site 5
Figure 4.33 (d) FTIR spectrum of residue 3 agriculture soil of site 5
Figure 4.33 (e) FTIR spectrum of residue 4 agriculture soil of site 5
Figure 4.34 Control and treated plant of Pteris vittata in the pot experiment
Figure 4.35 WDXRF spectra of the soil; roots and fronds of the Pteris vittata
Figure 4.36 Bioaccumulation factor (BAF) of different elements in roots of
control and treated Pteris vittata
Figure 4.37 Bioaccumulation factor (BAF) of different elements in fronds of
control and treated Pteris vittata
Figure 4.38 Percentage change in the bioaccumulation factor (BAF) roots of
different elements in Pteris vitata after treatment
Figure 4.39 Percentage change in the bioaccumulation factor (BAF) fronds of
different elements in Pteris vitata after treatment
Figure 4.40 Translocation factor (TF) of different elements in control and
treated Pteris vitata
Figure 4.41 Percentage change in the translocation factor (TF) of different
elements in Pteris vitata after treatment
Figure 4.36 Bioaccumulation factor (BAF) of different elements in roots of
control and treated Pteris vittata
Figure 4.37 Bioaccumulation factor (BAF) of different elements in fronds of
control and treated Pteris vittata
Figure 4.38 Percentage change in the bioaccumulation factor (BAF) roots of
different elements in Pteris vitata after treatment
Figure 4.39 Percentage change in the bioaccumulation factor (BAF) fronds of
different elements in Pteris vitata after treatment
Figure 4.40 Translocation factor (TF) of different elements in control and
treated Pteris vitata
Figure 4.41 Percentage change in the translocation factor (TF) of different
elements in Pteris vitata after treatment
Figure 4.42 Maize plants grown in control soil and polluted soil
Figure 4.43 Metal concentrations in control soil and polluted soil
Figure 4.44 Comparisons of the maize biomass grown in the control
(uncontaminated) soil (C) and polluted soil (P) without (W) and
with (V) vermiculite amendments into the soil
Figure 4.45 Metal concentrations in leaf, stalk and roots of Maize and grown
in the control soil (Uncontaminated) without vermiculite (CW),
control soil with vermiculite (CV), polluted soil without
vermiculite (PW) and polluted soil with vermiculite (PV); (a) Pb;
(b) Cu; (c) Zn
Figure 4.46 Percentage decrease of the metals concentration in the different
plant parts of maize plant grown on control and polluted soil after
the vermiculite treatment
Figure 4.47 Translocation factor (TF) of the metals form soil to different
plant parts of maize plant grown on various soil; L= leaf;
S=stalk; R=roots; C=control (uncontaminated) soil; P= polluted
soil; W=without vermiculite; V= with vermiculite
Figure 4.48 FTIR spectrum of the soil samples after harvesting of maize (a)
control soil without vermiculite (b) control soil with vermiculite
(c) polluted soil without vermiculite (d) polluted soil with
vermiculite
ABBREVATIONS AND ACRONYMS
BOD Biochemical Oxygen Demand
COD Chemical Oxygen Demand
0C Temperature
rpm Rotation per minute
FT-IR Fourier transform infrared spectroscopy
SEM Scanning electron microscopy
EDS Energry Dispersive X-Ray Spectrum
WD-XRF Wavelength Dispersive X-ray Fluorescence
AAS Atomic absorption spectrophotometer
% Percent
BAF Bioaccumulation Factor
TF Translocation Factor
WHO World Health Organization
CPCB Central Pollution Control Board
STP Sewage Treatment Plant
JCPDS Joint Committee on Powder Diffraction Standards
*other abbreviations are defined in the text
Chapter 1
Introduction
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Chapter 1. INTRODUCTION
River Yamuna, the largest tributary of river Ganga in India, originates from
Yamunotri glacier in the Himalayas. Starting from Yamunotri in Uttarakhand, the Yamuna
crosses the Indian States of Himachal Pradesh, Haryana, Delhi and Uttar Pradesh and after
travelling about 1380 km, it finally merges into river Ganga at Allahabad. Almost 57
million people of our country depend directly or indirectly on this river for various needs.
The total catchment basin of the Yamuna river is 3,66,223 km2 which is 42.5% of the total
Ganga basin area and around 11% of the total landmass of the country. The Yamuna has a
great significance as far as Indian population is concerned. In Hindu mythology, she is
worshipped as goddess Yamuna and it is believed that bathing in the water of this holy
river eradicates all the sins. However, the Yamuna has gone polluted to an extent that
taking a dip into the river might invite several risks to one‘s health.
Since the last decade the water quality of the Yamuna river, despite huge
expenditure and efforts put by the government and other concerned bodies, has
deteriorated to a considerable extent. One of the compelling effects of the water pollution
is the presence of heavy metals, which at high concentrations are detrimental to health and
even toxic, affecting aquatic flora and fauna as well as human life.
Sources related to the environmental exposure of heavy metals are household dust,
ceramic pottery, soldered cans, herbal medicine, lead paint, peeling paint, surface soil,
plumbing system, batteries, municipal wastes, silver foil in foods and so on. Use of
aluminium utensils for cooking is also a source of high levels of aluminium in food.
Pesticides and fertilizers have also been linked to higher levels of cadmium and arsenic in
agricultural fields. Impacts of contamination of heavy metals on animal and human health
include muscular weakness, lower score in psychometric tests and symptoms of peripheral
neuropathy. Breathing problems and motor nerve conductivity have been noted in
occupationally exposed populations. Some heavy metals are also considered as human
carcinogens. Environmental exposure to these heavy metals over an extended period of
time may lead to adverse effects, and intensive efforts are needed to explore this
relationship as well as to maintain the levels.
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Presence of heavy metals in water is a major burning issue because of their
importunate and bio-accumulative nature. Origin of these metals can be geological,
entering the river system by weathering and erosion (Zhang and Huang, 1993) or
anthropogenic in nature due to mining, industrial processing, agricultural run-off and
sewage disposal (Abbasi et al., 1998). In the aquatic system, removal of heavy metals
from the water to sediments may occur by settling particles; while some of these pollutants
can be mobilized by accumulating into the biota from the sediments sink (Lo and Fung,
1992).
Heavy metal content of soil is of major significance in relation to their fertility and
nutrient status. Many metals, such as Ca, Co, Cr, Cu, Fe, K, Mg, Mn, Na, Ni, Se and Zn,
are essential; serve as micronutrients for normal growth of plants and living organisms.
However, high concentrations of these metals become toxic. Plants may also accumulate
heavy metals existing in soils, such as Ag, Al, Cd, Au, Hg, Ni, Cr and Pb, which are not
essential for plant growth, but may cause serious problems to the environment in higher
concentrations. Soluble metal compounds and metals held in exchange complexes are
considered to be available to vegetation uptake. The bioavailability of metal compounds is
influenced by the pH, temperature, redox potential, cation exchange capacity of the solid
phase, competition with other metal ions, ligation by anions and composition and quality
of the soil solution.
Industrialization is accelerating the deposition of heavy metals in soil and water
bodies. In some ecosystems these metals can be easily incorporated by organic and
inorganic fractions of the soil and by sediments. The extent of this incorporation depends
on the concentration of metals and on characteristic biotic and abiotic factors.
Nevertheless, in water bodies or soil, metals can be remobilized, acting as toxic elements.
This way, it is essential to minimize deleterious effects of dispersion in natural waters,
through the use of suitable technology-based techniques.
The pollution and its potent negative impact on our health have become
progressively more prevalent in our daily life. Amongst all the types of pollutants, heavy
metals make a significant part which cannot be neglected. Although some of them are very
important as trace elements, but at higher concentrations most of them can be toxic to all
forms of life due to formation of complex compounds within the cell. They cannot be
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biodegraded once introduced into the environment. Heavy metals are noteworthy for their
worldwide distribution with their polluting effect that can be seen in air, water and soil
which persist indefinitely.
Heavy metals are known to have adverse effects on human physiology. They have
the tendency to accumulate in selected tissues of the human body and their toxic effects are
evident even at relatively minor levels of exposure. Some metals, such as copper and iron,
are essential to life and play irreplaceable roles in the functioning of critical enzyme
systems. Other metals are xenobiotics, i.e., they have no useful role in human physiology
(and other living organisms) and some metals are even worse, as in the case of lead and
mercury that are toxic even at trace levels of exposure. Even those metals that are essential,
may turn harmful at very high levels of exposure, a reflection of a very basic tenet of
toxicology—―the dose makes the poison.‖ One reflection of the importance of metals
relative to other potential hazards is their ranking by the U.S. Agency for Toxic Substances
and Disease Registry (ATSDR), which lists all hazards present in toxic waste sites
according to their prevalence and the severity of their toxicity. The first, second, third, and
sixth hazards present on the list are heavy metals: lead, mercury, arsenic, and cadmium,
respectively.
Bioremediation which utilizes the microorganism or plant metabolism is excellent
tool to remove pollutants. Bioremediation technologies can be generally classified as in
situ or ex situ. In situ bioremediation involves treating the contaminated material at the site,
while ex situ involves the removal of the contaminated material to be treated elsewhere.
Some examples of bioremediation technologies are phytoremediation, bioventing,
bioleaching, landfarming, bioreactor, composting, bioaugmentation, rhizofiltration, and
biostimulation.
Controlling heavy metal pollution includes approaches such as reducing the
bioavailability, mobility and toxicity of metals. To be precise, remediation of heavy metal-
contaminated environments includes physical removal, detoxification, bioleaching, and
phytoremediation. Natural and industrial processes gradually increased the heavy metals in
microbial habitats. Microorganisms, over the course of evolution, have evolved several
mechanisms to tolerate the presence of heavy metals by adsorption, complexation, or
chemical reduction of metal ions or by using them as terminal electron acceptors in
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anaerobic respiration. Abating heavy metal pollution through microbial transformations are
getting increased focus because of high efficiency and cost effectiveness but this
technology is confined to the water system. Use of hyperaccumulator plants for the
phytoextraction of heavy metals from both water and soil is key area of focus. Heavy metal
immobilization in the soil is also on spotlight.
However, another issue comes to the fore: how to accurately interpret these sample
data and how to estimate the contaminant source? In this work, the data has been
statistically interpreted with the use of latest technologies available. Also a possible
remediation method has been evaluated for the removal of the heavy metal pollution.
Toxicity of a metal predominantly depends not only on the metal itself and its
concentration level but also on its chemical form (Hill, 1997). Thus it becomes more and
more important to first characterize the metallic contaminants into different chemical states
if a steadfast management strategy is to be best selected for achieving a result-oriented
remediation technique. Thus speciation techniques should be appropriately used to expand
the information and subsequently proper interpretation should be done for considering
suitable remediation technique.
At some places photoreduction may be important, and pH changes can shift the
acid-base equilibrium and redox conditions (Hill, 1997). Chromium most commonly exists
as Cr4+
and Cr3+
, contributing to its properties and also it geochemical nature. Cr4+
is
generally found as a mobile component in surface environments and toxic in nature, while
Cr3+
is relatively immobile and an essential nutrient (Miller, 1991). If we have this
knowledge about the chemical speciation of chromium, then the simplest strategy for
remediation should be to reduce Cr4+
to Cr3+
following by precipitation of Cr3+
(Ozer et al.,
1997). Fe2+
and Mn2+
are soluble in natural waters deficient in oxygen but precipitate out at
their higher oxidation states. It is now well established that the distribution, transport,
bioavailability of metallic pollutants with their physiology and toxic nature depend on the
actual chemical state in which it is present and not on the total concentration (Brummer et
al., 1986; Ge et al., 2000). Mn3+
is more toxic than Mn2+
, Mn4
+, Mn
6+ and Mn
7+.
Organometallic compounds of Hg, Pb and Sn are more toxic than their inorganic forms
while Cu, As and Al are more toxic in inorganic form than their organic ones (Chutia et
al., 2009; Tangahu et al., 2011; Hill, 1997).
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As mentioned before, heavy metals are responsible for several well-known disasters of
public health, an aspect of the problem that immediately needs to be resorted to mitigation
measures. Remediation of the polluted site is particularly of grave concern.
It is now quite evident that without having full information about the local
interaction of the contaminating metals and their potential ecological impact, the derivation
of total concentration levels for contaminants is worthless (Peter and Shem, 1995;
Wuana and Okieimen, 2011) although it gives a general idea about the present level of
pollution and creates a path for the future study. Realizing the matter, the concerned
governing bodies are now promoting process based on total assessment with its full
implications for contaminated sites (Lai et al., 2010; Fergusen et al., 1998).
Averting pollution or reducing it to safe limits is imperative for happy and healthy
living. To reduce the pollution load, a fine knowledge of the pollution degree in the areas
we live in is required. To acquire the situation of pollution, we need to collect information
on pollutant concentrations (gravity of pollution) and analyse the collected data. This study
was hence designed to evaluate the present situation of heavy metals in the river Yamuna.
Since it is extremely hard and tedious to obtain every single datum from a big river like
Yamuna, a sample survey was found to be only a feasible approach.
This study was carried out to understand the distribution of heavy metals and their
geochemical fractions in soil and sediments of selected sites along river Yamuna in Delhi
region, India. Tessier sequential extraction scheme was employed to study the chemical
states or speciation of the heavy metals that are associated to ―Exchangeable geochemical
fraction‖, ―Carbonate geochemical fraction‖, ―Iron and Manganese Oxides geochemical
fraction‖, ―Organic Matter fraction‖ and ―Residual geochemical fraction‖. This study also
attempts to investigate the phytoremediation potential of Pteris vittata and heavy metal
immobilization potential of vermiculite in pot experiment with Zea mays .
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Objectives
1. Determination of heavy metals concentrations in the water, sediments and river side
soils and the most important physico-chemical water and soil factors influencing
concentrations of these metals;
2. Study of chemical speciation and determination of geochemical fractionation of
heavy metals present in soil and sediment samples;
3. Evaluation of phytoremediation potential of Pteris vittata for heavy metal polluted
soil and immobilization potential of vermiculite in soil.
Chapter 2
Review
of literature
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Chapter 2. REVIEW OF LITERATURE
2.1 Metals
Atkins and Jones (1997) defines metals as the elements that conduct electricity, have a
metallic lustre, are malleable and ductile, form cations, and have basic oxides. Going by
this definition, we would describe most elements as metals. Thus, in order to ascertain their
individual properties and safe use, we need to subdivide the metals into their different
classes.
2.2 Terms commonly used to specify groups of metals
Conventionally, the word ―Metal‖ refers to the pure element or an alloy of metallic
elements. In conventional terms, the term ―Heavy‖ implies high density. There are various
terms often used for specifying groups of metals in biological and in environmental
studies. Table 2.1 presents these terms. However, there are certain limitations associated
with these terms; e.g., i) they are arbitrary and imprecise; ii) several categories overlap,
making them inexact; iii) the term ―heavy metal‖, because it is often used with
connotations of pollution and toxicity, is probably the least satisfactory of all the terms
quoted as it leads to the greatest confusion.
Table 2.1: Terms often used to classify metals in biological and environmental studies
(Duffus, 2001)
Term Comments
Metal Metals may be defined by the physical properties of the elemental
state as elements with metallic lustre, the capacity to lose electrons to
form positive ions and the ability to conduct heat and electricity, but
they are better identified by consideration of their chemical properties.
The term is used indiscriminately by nonchemists to refer to both the
element and compounds (for example, reference by biologists to ―the
uptake of copper by...‖ does not distinguish the form in which the
metal is absorbed).
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Metalloid See ―semimetal‖.
Semimetal An element that has the physical appearance and properties of a metal
but behaves chemically like a nonmetal.
Light metal A very imprecise term used loosely to refer to both the element and its
compounds. It has rarely been defined, but the originator of the term,
Bjerrum, applied it to metals of density less than 4 g/cm–3
.
Heavy metal A very imprecise term used loosely to refer to both the element and its
compounds. It is based on categorization by density, which is rarely a
biologically significant property.
Essential metal Broadly, one which is required for the complete life cycle of an
organism, whose absence produces specific deficiency symptoms
relieved only by that metal, and whose effect should be referred to a
dose–response curve. The term is often used misleadingly since it
should be accompanied by a statement of which organisms show a
requirement for the element. Again, it is used loosely to refer to both
the element and its compounds.
Beneficial metal An old term, now largely disused, which implied that a nonessential
metal could improve health. Another term that has been used loosely
to refer to both the element and its compounds.
Toxic metal An imprecise term. The fundamental rule of toxicology is that all
substances, including carbon and all other elements and their
derivatives, are toxic given a high enough dose. The degree of toxicity
of metals varies greatly from metal to metal and from organism to
organism. Pure metals are rarely, if ever, very toxic (except as very
fine powders, which may be harmful to the lungs from whatever
substance they may originate). Toxicity, like essentiality, should be
defined by reference to a dose–response curve for the species under
consideration. This is another term that has been used loosely to refer
to both the element and its compounds.
Abundant metal Usually refers to the proportion of the element in the earth‘s crust,
though it may be defined in terms of other regions, e.g., oceans, ―fresh
water‖, etc.
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Available metal One that is found in a form which is easily assimilated by living
organisms (or by a specified organism).
Trace metal A metal found in low concentration, in mass fractions of ppm or less,
in some specified source, e.g., soil, plant, tissue, ground water, etc.
Sometimes this term has confusing overtones of low nutritional
requirement (by a specified organism).
Micronutrient More recent term to describe more accurately the second of the
meanings of trace metal, above.
2.3 Heavy metal pollution and its sources
Heavy metals derived from anthropogenic activities contaminate the soil is a major
global demanding issue. Anthropogenic activities, including chemical industry, traffic and
transportation, iron and steel industry, smelting and mining, domestic activities and
agricultural practices, along with chemical and metallurgical industries are the major
contributors of the heavy metal load to the environment (Suciu et al., 2008; Chopin and
Alloway, 2007; Stihi et al., 2006; Garcia and Millán, 1998; Li et al., 2001; Sezgin et al.,
2004; Viard et al., 2004; Nabulo et al., 2006; Oliva and Espinosa, 2007; Kampa and
Castanas, 2008;Liao et al., 2008). The various sources and sink of heavy metals are
illustrated in figure 2.1. Although urban agriculture is the source of income and rural
employment but growing crops and vegetables through wastewater irrigation is a
worrisome matter in reality, especially in developing countries like ours. Thus major and
serious concern which arises is the contamination of the crops and vegetables due to uptake
of heavy metals (Muchuweti et al., 2006). Consumption of food crops contaminated with
heavy metals is a major food chain route for human exposure (Khan et al., 2008). Heavy
metal accumulation in plants varies from species to species, and the efficiency of absorbing
metals can be estimated by either plant uptake or soil-to-plant transfer factors of the metals
(Rattan et al., 2005). Crops raised on the metal-contaminated soils collect metals in
enough quantities, which can be clinically fatal to both animals and human beings
consuming these metal rich plants (Tiller, 1986).
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Heavy metals
Sediments
Storage in riverbed
VolatilizationUptake by
organisms
Settling and resuspension
Biological and chemical transformation
Attachment And release from
sediments
Industries
Agriculture
Mining
Waste water
treatment Urban
Fertilization &
Erosion
Figure 2.1 Sources and sink of heavy metals
Zhuang et al. (2009) evaluated heavy metals of food crops in the vicinity of
Dabaoshan mine, South China and found that the heavy metal load exceeded the
permissible limit thus concluded that there was a potential health risk for the local
inhabitants through consumption of contaminated food crops. Cement and printing industry
release toxic heavy metals such as cadmium, lead and zinc (Al-Khashman and
Shawabkeh, 2006; Thornton, 1991) and leather tanning industry are source of chromium
and arsenic in the ecosystem (Tiller, 1992; Rao et al., 2010a). River acts as the largest
carrier of these toxic elements, however they are significant environmental contaminates in
riverine network (Miller et al., 2003; Harikumar et al., 2009). These elements are
transported along hydrologic gradients, covering hundreds of kilometres in a relatively
short time period (Van Griethuysen et al., 2004, 2005; Wen and Allen, 1999; Huang and
Lin 2003).
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2.4 Effect of heavy metals on plants
Although a number of heavy metals are required in trace amounts for the proper
growth and development of plants but the excess amount causes toxicity further, as ions
such as Cd, Hg, As, etc are strongly poisonous to the metabolic activities. Occurrence and
toxicity of heavy metals in plants have been studied by many workers (Nagajyoti et al.,
2010; Yadav, 2010; Stankovic et al., 2014).
On excess level of heavy metal exposure the primary response of plants comes in
the form of generation of reactive oxygen species (ROS) (Yadav, 2010). High levels of Cd
cause a number of toxic symptoms in plants, e.g. growth retardation, inhibition of
photosynthesis, induction and inhibition of enzymes, altered stomatal action, water
relations, efflux of cations and generation of free radicals (Prasad, 1995). Surplus amount
of Pb in soil causes various toxicity symptoms in plants like stunted growth, chlorosis and
blackening of root system (Sharma and Dubey, 2005). Lead is known to inhibit
photosynthesis, disturbs mineral nutrition and water balance, changes hormonal status and
affects membrane structure and permeability while its uptake in plants is regulated by pH,
particle size and cation exchange capacity of the soils as well as by root exudation and
other physico-chemical parameters (Sharma and Dubey, 2005).
Cr is toxic to most higher plants at 100 μM·kg-1
dry weight (Davies et al., 2002), it
affects plant growth and development by altering the germination process as well the
growth of roots, stems and leaves, which may affect total dry matter production and yield
(Shanker et al., 2005). Physiologically Cr affects processes such as photosynthesis, water
relations and mineral nutrition, in addition inhibition of assimilatory enzymes, increases
activity of ROS scavenging enzymes, changes in glutathione pool, no production of
phytochelatins (Shanker et al., 2005).
Out of four heavy metals Zn, Cu, Cd and Pb, Zn is least toxic whereas the
phytotoxicity of Pb is low (Påhlsson, 1989). The level of toxicity of a heavy metal
depends on factors like time of exposure, amount and available chemical form in which the
metal is present in the soil. For example in plants the toxicity of Cr depends on its valence:
Cr (VI) is highly toxic and mobile whereas Cr (III) is less toxic (Shanker et al., 2005).
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2.5 Effect of heavy metals on aquatic life
The pollution of heavy metals in the aquatic ecosystem is a major concern because
of their toxicity and threat to plant and animal life, thus disturbing the natural ecological
balance (Bhattacharya et al., 2008). Heavy metals are entering in the aquatic environment
at an alarming rate. Heavy metals are known to bioaccumulate in aquatic biota (USEPA,
1991) and biomagnify in food chains. As a result of enhanced uptake and slow elimination
of heavy metals from water, bioaccumulation of heavy metals happens in an aquatic
organism (Bhattacharya et al., 2008).
In general heavy metals are released into aquatic systems bound to particulate
matters, which in due course settle down into sediments (reservoir or sink of metals).
These sediment-bound metals enter the food chain by the uptake of rooted aquatic
macrophytes and other aquatic organisms (Peng et al., 2008). As the heavy metals are
bioaccumulated by an aquatic organism, it keeps on passing to the upper classes of the
food chain, subsequently to the carnivores at the top of the food chain including humans
(Cumbie, 1975; Mance, 1987; Govind and Madhuri, 2014). If the majority of diet
includes fishes then the portion of the heavy metal intake from the aquatic ecosystem by
way of their food can be high (Zaza, et al., 2015). Diatom community structure and
macroinvertebrates can be sensitive to high levels of metals, found in rivers (Morin et al.,
2007; Jongea et al., 2009). The mode and site of accumulation may vary from organism to
organism. Effect and accumulation of heavy metals varies in fishes, depending on their
age, development and other physiological factors (Govind and Madhuri, 2014) with
different organs at different level of exposure (Irfan, 2014). Among all animal species, the
fishes are highly affected by these toxic pollutants due to direct contact (Govind and
Madhuri, 2014). Zinc accumulates in the gills of fish and this indicates a depressive effect
on tissue respiration leading to death by hypoxia (Crespso et al., 1979).
Blood of Tilapia nilotica fingerlings when exposed to sublethal concentrations of
zinc a marked reduction in hemoglobin values was observed suggesting the development
of some degree of anemia which is also supported by the observations of anisocytosis and
poikilocytosis (Caring, 1992). Sublethal haematological effects like decrease in total white
blood cell counts and the differential white blood cell counts due zinc was also observed
on the freshwater fish, Heteroclarias sp. (Kori-Siakpere and Ubogu, 2008).
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Genotoxic and cytotoxic damage in both gill and fin epithelia cells by Pb was
observed in Auratus auratus (Cavas, 2007). In a fresh-water fish Oreochromis
mossambicus drop of proteins was recorded due to the impact on the protein synthetic
pathway by the cadmium (Muthukumaravel et al., 2007), while in Cyprinus carpio
enhance susceptibility to disease was observed due to decrease in innate immune response
as a result of cadmium (Ghiasi et al., 2010). In Salmo gardnerii zinc caused toxic changes
in ventilatory and heart physiology (Hughes and Tort, 1975).
Brenner and co-workers (2004) reported that the biotic indexes and the number of
individuals and taxa was reduced with increasing Fe concentration in aquatic ecosystem.
Increased concentrations of Fe and Pb stimulate variation in the total lipid and cause
histological changes in gills, kidney, and liver of fish (Mohamed and Gad, 2005).
Ibemenuga (2013) reviewed the effects of heavy metals in freshwater fishes and
discussed that in fresh fishes such as Gasterosteus aculeatus, Fundulus heteroclutus,
Oreochromis mossambicus, Oreochromis niloticus, Cyprinus carpio, Clarias ischeriensis,
Salmo gardnerii, Clarias gariepinus, Tilapia galillilaeus, Clarias lazera, Salvelinus
namaycush and Poecilia latipinna heavy metals bioaccumulate (including cadmium, zinc,
lead and copper) through various organs such as gills, liver, stomach and intestine.
In a study of edible fishes located in Bhadra river, Karnataka an inverse
relationship was observed between physicochemical properties of river water and metals
accumulated in fish species (Shivakumar et al., 2014). Intestine and gills were found to be
greater accumulation site of metal (Shivakumar et al., 2014). High content of Fe, Zn and
Cu was observed in fish species, in contrast, Cd and Pb concentration were found near to
permissible limit of the World Health Organization standard pointing to the need of proper
majors to be taken to avoid these metals in the aquatic system (Shivakumar et al., 2014).
Cadmium, mercury and silver were found to have variable effect on oxygen
consumption, osmoregulation, and enzyme activity on marine animals (Calabrese et al.,
1977). Mercury was found to be highly toxic while lead was the resistant metal to marine
molluscs C. cingulated and M. philippinarum (Ramakritinan et al., 2012). The decreased
level of hemoglobin, hematocrit and RBC showed the hematotoxic effect of combined
heavy metals on Cyprinus carpio (Vinodhini and Narayanan, 2009).
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Heavy metals are the key aquatic environmental factors that affect the ecosystem
health of streams which are usually used to indicate aquatic biodiversity of the lake
ecosystem (Huang et al., 2010).
2.6 Effect of heavy metals on humans
As mentioned in the previous sections heavy metals enters the food chain by the
plant uptake from soils at high concentrations and fishes and others from polluted river,
lake, etc. These contaminated foods with heavy metals when consumed by human beings
become a major source for human exposure. The food plants that have great capacity of
extracting elements from soil, cultivation of such plants in contaminated soil represents a
potential risk due to heavy metal accumulation in the vegetative parts (Sharma et al.,
2008). In a study of wastewater irrigated site of a dry tropical area of India, heavy metal
concentrations were found several times higher in foodstuffs from the wastewater irrigated
site compared to clean water irrigated ones (Singh et al., 2010). The study also suggested
that even at low concentrations of heavy metals in irrigation water, its long term use cause
accumulation of heavy metals in food stuff causing potential health risks to consumers
(Singh et al., 2010).
Apart from the above mode of exposure, there is drinking contaminated water and
air in the areas having surface dumping or mining areas.
Iron is one of the essential elements but at higher level above the permissible limit,
it adversely affects human health and aquatic organisms. High concentrations of Fe causes
an unnecessary increase in Fe contents of blood because high Fe content damages cells of
the gastrointestinal tract and stops them from regulating Fe absorption (Sieliechi et al.,
2010).
Excessive iron affects human health and causes iron toxicity because free ferrous
iron reacts with peroxides to produce free radicals, which are highly reactive and can
damage DNA, protein, lipid, and other cellular components. Some of the problems due to
iron toxicity are anorexia, hypothermia, and cellular death.
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Table 2.2: Maximum permissible limits of water quality parameters
Parameter WHO NEQS
Temperature 40 °C 40 °C
pH 6.5–9.0 6–10
EC 500 μs/cm
Alkalinity 50–500 mg/l
Hardness 50–500 mg/l
Ca hardness 1,000 mg/l 500–1,000 mg/l
Mg hardness
DO 5 mg/l
TDS 1,000 mg/l 500–3,500 mg/l
TSS 50–150 mg/l
SO4 200–400 mg/l 600 mg/l
Na 200 mg/l
NO2 3 mg/l 3 mg/l
NO3 50 mg/l
Cl 250 mg/l 1,000 mg/l
Ca 200 mg/l
Mg 150 mg/l
Source: WHO NEQS (National Environmental Quality Standard for industrial effluents)
Table 2.3: Maximum permissible limits of heavy metals in water and sediments
Water Sediments
Metals WHO
(mg/l) NEQS (mg/l) CEQG (μg/g) WHO (μg/g)
USEPA
(μg/g)
Fe 0.3 2 – – 30
Cu 2.0 1.0 35.7 25 –
Pb 0.01 0.5 35 – 40
Zn 3.0 5.0 123 123 –
Ni 0.02 1.0 42.8 20 –
Cr 0.05 1.0 52.3 25 25
Mn 0.1 1.5 460 – 30
Cd 0.003 0.01 0.7 6 –
Source: WHO, NEQS, CEQG (Canadian Environmental Quality Guidelines), USEPA
(United States Environmental Protection Agency)
The maximum allowable limits of heavy metals in soils and vegetables have been
established by standard regulatory bodies such as World Health Organization (WHO),
Food and Agricultural Organization (FAO) and Ewers U, Standard Guidelines in Europe as
shown in Table 2.4.
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Table 2.4: Maximum Allowable Limits of Heavy Metal in Irrigation Water, Soils and
Vegetables (μg/g)
Chemical
element
Maximum permissible
level in irrigation
water (µg/ml)
Maximum permissible
level in soil (µg/g)
Maximum permissible
level in vegetables
(µg/g)
As 0.10 20 -
Cd 0.01 3 0.10
Co 0.05 50 50.00
Cr 0.55 100 -
Cu 0.017 100 73.00
Fe 0.50 50000 425.00
Mn 0.20 2000 500.00
Ni 1.40 50 67.00
Pb 0.065 100 0.30
Se 0.02 10 -
Zn 0.20 300 100
Source: Chiroma et al., 2014
The Dutch intervention values and the accompanying target values for soil/sediment and
groundwater are given in table 2.5. These soil remediation intervention values are based on
extensive studies of the National Institute for Public Health and Environmental Protection
of both human and ecotoxicological effects of soil contaminants.
Table 2.5: Target values and soil remediation intervention values and background
concentrations soil/sediment and groundwater for metals.
EARTH/SEDIMENT
(mg/kg dry matter)
GROUNDWATER
(mg/l in solution)
Metals national
background
concentration
(BC)
target
value
(incl.
BC)
intervention
value
target
value
shallow
national
background
concentration
deep (BC)
target
value
deep
(incl.
BC)
intervention
value
antimony 3 3 15 - 0.09 0.15 20
arsenic 29 29 55 10 7 7.2 60
barium 160 160 625 50 200 200 625
cadmium 0.8 0.8 12 0.4 0.06 0.06 6
chromium 100 100 380 1 2.4 2.5 30
cobalt 9 9 240 20 0.6 0.7 100
copper 36 36 190 15 1.3 1.3 75
mercury 0.3 0.3 10 0.05 - 0.01 0.3
lead 85 85 530 15 1.6 1.7 75
molybdenum 0.5 3 200 5 0.7 3.6 300
nickel 35 35 210 15 2.1 2.1 75
zinc 140 140 720 65 24 24 800
Values for soil/sediment have been expressed as the concentration in a standard soil (10% organic matter and
25% clay). Source: Dutch Environmental Guidelines & Standards, 2000.
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2.7 Occurrence of heavy metals in river sediments
Because a major fraction of the trace metals introduced into the aquatic
environment eventually become associated with the bottom sediments, environmental
degradation by metals can occur in areas where water quality criteria are not exceeded, yet
organisms in or near the sediments are adversely affected (Salomons and Forstner, 1984).
Tessier and Campbell (1987) have shown that trace metal levels in various benthic
organisms are best related not to total metal concentrations in the adjacent sediments, but
to the easily extracted fractions
Sediments constitute a dynamic component of river network channel. Majority of
the river channel sediments have high concentration of heavy metals (Kelepertzis et al.,
2010, Singare et al., 2012). Besides anthropogenic activities, weathering phenomena of
minerals deposits are the natural cause of increasing metals concentration in sediments (Qu
and Yan 1990,Chen et al., 2000). Sediments act as both carriers and sinks for
contaminants, which all depends on the hydraulic conditions of the river (Li et al., 2013).
Thus presence of the metals in the sediments is the indicator of the health of the aquatic
flora and fauna of the river and the anthropogenic activities of the area through which it
flows.
2.8 Requirement for chemical speciation and geochemical fractionation study
The potential mobility and distribution with the level of toxicity and bioavailability
of metals to the biological forms in natural waters particularly depends on the chemical
form in which the metal is present (Ahlf et al., 2009; Arnason and Fletcher, 2003; Rao et
al., 2010a; Powell et al., 2015). The credit to the overall behavioural changes to the metal
in the aquatic system goes to the geochemistry and composition of substrate and suspended
sediments and the water chemistry (Morillo et al., 2004). The heavy metals undergo a lot
of changes in their chemical speciation during their course due to dissolution, precipitation,
sorption and complexation phenomena (Akcay et al., 2003; Abdel-Ghani and
Elchaghaby, 2007) affecting their behaviour and bioavailability (Nicolau et al., 2006;
Nouri et al., 2011).
Therefore the investigation of total metal content in these media does not provide
enough information about contamination of the surrounding environment (Filgueiras et
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al., 2002; Abollino et al., 2005; Wang and Qin, 2007). Thus need arises among the
researchers to study and find the association of metals with geochemical fractions (Rauret,
1998; Saleem et al., 2015). Although sequential extraction scheme is time taking but it
provides sufficient relevant information related to mobility, fate and transport, occurrence,
origin, physicochemical or biological aspects (Passos et al., 2010). The modus operandi of
the sequential extraction involves series of steps each using different chemical reagents
that sequentially extract different target elements of the sample. Thus we get more detailed
and appropriate information about their availability forms. The last residual fractions are
mostly silicate bound metals, therefore are biologically unavailable (Tüzen, 2003).
More complex process of geoaccumulation, bioaccumulation, biomagnifications
and environmental transformation into more harmful phases may arise when these metals
enter into the ecosystem (Singare et al., 2012). In aquatic environment these metals are
adsorbed onto particulate matter, however they can also be converted into free metal ions
and soluble inorganic complexes that are available for uptake by biological organisms or
may also get accumulated in sediments (Lee et al., 2000; Weston and Maraya, 2002).
Physical changes like pH and temperature or chemical process such as redox potential,
leaching, ion-exchange or biological process of organic matter decomposition and
microbial activity are attributed to the mobilization of the metal ions associated with solid
phase into solution phase in the environment (Kennedy et al., 1997). Currently sequential
extraction scheme is used commonly to study the type of metal bonding behaviour in soil
(Kabala and Singh, 2001; Ettler et al., 2005; Tongtavee et al., 2005; Sungur et al.,
2015). Most commonly three extraction techniques given by Tessier et al. (1979); Kersten
and Förstner (1986); and the Bureau Communautaire de Référence (BCR) have been used
to study the different metal bound fractions. Comparative studies have also been conducted
to study the different extraction procedures (Usero et al., 1998; Nemati et al., 2011).
Oyeyiola et al. (2011) compared three sequential extraction procedures for the
fractionation of Cd, Cr, Cu, Pb, and Zn. The results obtained by the three methods (A
modified 5-step Tessier‘s procedure, 3-step original Community Bureau of Reference
(BCR) and the modified BCR techniques (4-steps)) were compared, and the modified BCR
and Tessier SEP were found to extract more Cu, Cr, Pb, and Zn in the reducible phase and
therefore a decrease in the oxidizable phase than the original BCR SEP.
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2.8.1 Defining Chemical Speciation
According to IUPAC, speciation is the ―process yielding evidence of atomic or
molecular form of an analyte‖ (Lobinski and Szpunar, 1999). The investigation of
distinct chemical species can be defined as speciation study and according to Hill (1997) it
is widely acknowledged for playing a key role in environmental chemistry. Functionally,
chemical speciation refers to the determination of species that are, for example, either
available to plants or present as exchangeable forms and operationally which refers to the
determination of extractable geochemical fraction of an element (Hill, 1997).
According to Powell et al., (2015) chemical speciation entails a distribution of the
metal ions between different complex (metal-ligand) species, colloid-adsorbed species and
insoluble phases, each of which may be kinetically labile or inert. For example, in fresh
water the metal ions are distributed among organic complexes (e.g., humates), colloids
(e.g., as surface-adsorbed species on colloidal phases such as FeOOH), solid phases (e.g.,
hydroxide, oxide, carbonate mineral phases), and labile complexes with the simple
inorganic anionic ligands commonly present in natural waters (e.g., for ZnII
, the aqueous
species, Zn2+
, ZnOH+, Zn(OH)2(aq), Zn2OH
3+, ZnSO4(aq), ZnCO3(aq), and so on).
Speciation of metals has a direct relation to the toxicity, mobility and
bioavailability in polluted soils and water. The metal pollutants should be characterized
into different chemical states, to evaluate the chemical properties and to precisely establish
the impact on environment and human health. Research and scientific studies related to
chemistry, biology, toxicology and ecology of chemical speciation associated to various
states provide a better knowledge, solving many previously unsolved queries.
Recently computer simulations have also been used for the speciation of metal ions;
however, the significance of such computation is critically dependent on some factors, like
on the equilibrium model used to define the system, the rigor of the computer modelling
program and the reliability of the equilibrium constants used in the calculations (Powell et
al., 2015).
In the sediments through speciation, it was found that metals exit into two major
geochemical phases, namely lithogenous (immobile) and nonlithogenous (mobile)
(Ladigbolu et al., 2014). Strong bond that exists between lithogenous or residual fraction
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metals and crystal lattice of silicate of the sediments and soils, thus are not available in
long term for biological uptake. There sources are natural source, i.e. rock or soils
weathering while nonlithognous fraction metals are readily available in short term (Badri
and Anston, 1983). Nonlithogenous fractions can be further categorized into exchangeable
fraction, carbonate bound fraction, reducible (Fe-Mn oxides/ hydroxides) fraction and
oxidisable (organic matter/ sulphide) fraction (Neill, et al., 1985). Mostly metals associated
with these four mobile fractions originate from anthropogenic sources and are readily
available for uptake and bioaccumulation in aquatic biota, though availability is pH, redox-
potential and temperature dependent (Gambrell, 1994; Schlinder, 1991).
Fractionation is defined as process of classification of an analyte from a certain
environmental medium on the basis of their physical (size, solubility) or chemical
properties (bonding, reactivity). In terms of the soil or sediment chemistry it is the
distribution of an element into different chemical species in a given system such as isotopic
composition, organic or inorganic complexes, organometallic complexes.
In contrast to the speciation which is the analytical process that identifies and
quantifies one or more individual chemical states (Templeton et al., 2000) the
fractionation, uses the concept of subdividing a total content of the element (Tack, et al.,
1996).
Regardless of the difficult procedural nature and some loopholes as criticized by
many researchers (Wallman et al., 1993; Lim and Kiu, 1995), sequential extraction
techniques are the most widely used approach to differentiate geochemical associations of
many heavy metals. Over the past few years different sequential extraction techniques have
been developed and used for the fractionation of sediment metals of different river
systems. Tessier et al. (1980) was among the few initial workers who started the sequential
extraction of trace metals (Cd, Co, Cu, Ni, Pb, Zn, Fe, and Mn) to investigate the
suspended sediment levels as well as total soluble and particulate trace metal in Yamaska
and St. François Rivers of Quebec, Canada. In Spain two different groups in a quest to
establish the level of pollution and their capacity to remobilization studied the chemical
forms of copper and lead in the sediments of river Tenes (Rauret et al., 1988) while the
speciation of zinc, cadmium, lead, copper nickel and cobalt in the sediments of river
Pisuerga (Pardo et al., 1990). In Turkey Akcay et al. (2003) studied the different phases
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| P a g e
of the trace metals of the two major economically important rivers Gediz and Buyuk
Menderes to determine their environmental pollution levels. While Baruah et al. (1996)
determined chemical states of selected heavy metals using the fractionation scheme of
Tessier et al. (1979) in bed sediments of Jhanji River, Assam (India), Singh et al. (2005b)
determined distribution and geochemical phases of bed sediments at ten selected sites of
river Gomti, a tributary of the river Ganges (India). In the last decade to determine the eco-
toxic potential of metal ions in the water of river Yamuna (India), Jain (2004) performed
the metal fractionation of bed sediments of ten selected site of the river. Modified Tessier
et al. (1979) protocol was also used in another study in which speciation of heavy metals
(Fe, Cu, Mn, Zn, Pb and As) in a red mud sample and a river sediment from abandoned
Italian pyrite mine site was done (Pagnanelli et al., 2004).
In a recent study to characterize the physico-chemical property of wastewater and
assessment of its impact on river water and sediments, sequential extraction procedure
coupled with SEM–EDS was done on sediments of river Ganga at Varanasi (India)
(Pandey et al., 2015). Major finding of the study was that the geo-accumulation index
(GAI) was highest for Cd and Pb (Pandey et al., 2015). In another recent study six trace
metals were measured in sediments and soft tissues of three commonly consumed fish
species of three urban rivers around the city Dhaka (Bangladesh) using the sequential
extraction techniques (Islam et al., 2015). The study reported that abundance of total
metals in sediments varied in the decreasing order of Cr > Ni > Pb > Cu > As > Cd, while
the level of biota-sediment accumulation factor for fish species were in the decreasing
order of Cu > As > Pb > Ni > Cr > Cd. Sequential extraction results showed metals studied
were associated with the residual fraction followed by the organically bound phase while
metal concentrations in fish were found above the international permissible standards
suggesting unsafe for human consumption (Islam et al., 2015).
In addition, these techniques have also been used for the speciation of metals in
other system like agriculture, coastal, estuarine and marine. To explore the geochemical
properties and metal contaminations of sediments from different aquatic environments and
their relationships, selected major elements and trace metals of sediments from a river, an
estuary and a lake were examined in the Yangtze River delta region of China (Yuan et al.,
2014). To study the metal pollution levels based on human activities, chemical speciation
of Pb, Cd, and Ni in surface sediments of the Hara Biosphere Reserve of Southern Iran was
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done using the 4-step sequential extraction technique (Nowrouzi et al., 2014). To
investigate the geochemical background of the bottom sediments of Goreckie Lake
localized in the central part of Wielkopolski National Park (protected area) fractionation
was done by a modified protocol by Tessier et al. (1979) and Zerbe et al. (1999). It was
reported that high fraction of chromium, nickel and lead is bound to organic material and
sulphides, while cadmium is bound to carbonate fraction, in a study of metal speciation in
sediments of the two branches of the Nile delta, Egypt (Elsokkary and Muller, 1990).
A number of comparative studies have also been carried out by some researchers.
Usero et al. (1998) used three different sequential extraction methods for metals in marine
sediments. Oyeyiola et al. (2011) also compared different sequential extraction protocols
for fractionation of Cd, Cr, Cu, Pb, and Zn in coastal sediments. Sequential extraction
methods have also been compared for the fractionation of heavy metals in shrimp
aquaculture sludge (Nemati et al., 2011). The modified BCR method was used to
determine the relationship between soil properties and heavy metal fractions in agricultural
soils from Çanakkale, Turkey (Sungur et al., 2015). Speciation of Fe, Mn, Zn and Cr in
selected agricultural soils of the central Ebro river valley, Spain was done using the well
adapted Tessier et al. protocol (1979) to study the geochemistry and function to the
ecosystem (Navas and Lindhorfer, 2003).
Understanding the urgency of perfect extraction schemes, the EC Measurement and
Testing Programme (formerly BCR) has organized a workshop and project to discuss and
develop superior and improved strategies to determine extractable trace metals through
extraction schemes for environmental risk assessment (Quevauviller et al. 1996a, 1996b).
Considering the above facts the developed countries have accepted the significance
of metal speciation and fractionation but in our country only comparatively few reports are
available on the speciation of metals in Indian rivers (Roy and Upadhyaya, 1985; Iyer
and Sarin, 1989; Jha et al., 1990; Baruah et al., 1996; Jain, 2004; Singh et al., 2005b;
Pandey et al., 2015).
2.8.2 Basics of the sequential extraction
Sequential extraction involves a series of steps in which harsh chemical reagents
consecutively attack the specific soil or sediment fractions, releasing specific metals
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| P a g e
associated with these fractions in their respective chemical states in each step. These
respective chemical states can be detected by other available techniques such as AAS,
SEM-EDS, FTIR and XRD etc. In order of their increasing strength, different reagents
such as weak acids, inert electrolytes strong mineral acids, oxidizing or reducing agents are
used to sequentially extract the different geochemical fractions (Passos et al., 2010).
Briefly sequential extraction gives five geochemical fractions of soil or sediments
(Tessier et al., 1979; Zimmerman and Weindorf, 2010):
I. Exchangeable geochemical fraction. Major portion of the sediments like clay,
hydrated oxides of iron and manganese, humic acid etc. adsorb metals.
Changing the ionic composition of water affects the sorption-desorption
process, thus allows the metals adsorbed to the exposed surface of soil or
sediments to be removed easily. A salt solution, eg. MgCl2 or CaCl2,is
commonly used to remove this exchangeable geochemical fraction.
II. Bound to Carbonates geochemical fraction. A significant concentration of the
metals is associated with the sediment carbonates. This metal bound to
carbonate is very susceptible to changes in pH; hence an acid solution is used,
e.g. a buffered acetic acid/ sodium acetate is commonly used. Metal release of
this fraction is achieved through dissolution of the fraction of solid material at
pH close to 5.
III. Bound to Iron and Manganese Oxides geochemical fraction. Iron and
manganese oxides exist as nodules, concretions, cement between particles, or
simply as a coating on particles. These oxides are brilliant scavengers of metals
and are thermodynamically unstable under anoxic (reducing) conditions thus a
solution capable of dissolving insoluble sulphide salts is generally used, e.g.
Hydroxylamine hydrochloride.
IV. Bound to Organic Matter fraction. Metals are also bound to different forms of
organic matter like living organism, detritus etc. Under the oxidizing conditions
in natural waters, organic matter can be degraded, releasing the soluble metal.
This oxidation can be achieved by the treatment of HNO3 and H2O2 or a
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| P a g e
sequence of HCl, NaOH and HNO3 (humic matter bound extracted by HCl and
NaOH, while those bound to sulphide inorganic form leach out by HNO3).
V. Residual geochemical fraction. This last residual solid fraction consists of metals
incorporated into the crystal structures of primary and secondary minerals.
These geochemical fraction metals very hardest to remove and require a
considerable much time and use of strong acids e.g. Aqua regia-Hydrofluoric
acid to breakdown to silicate structures.
Some of the variations of the protocol are available: for instance, modified versions of
the widely used Tessier et al. (1979) procedure and BCR procedure transformed by the
different researchers based on the nature of the samples and need of interest. Table 2.6
presents the operating conditions for different sequential extraction procedures.
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Tab
le 6
: B
rief
met
hod
olo
gy o
f d
iffe
ren
t se
qu
enti
al
extr
act
ion
tech
niq
ues
Fra
ctio
n
Tim
e
Tem
pera
ture
an
d o
ther
con
dit
ion
Q
uan
tity
R
eagen
t
Tes
sier
Pro
ced
ure
1 g
m
Ex
chan
gea
ble
1h
r co
nti
nu
ous
agit
atio
n
8 m
l 1m
olM
gC
l 2 p
H 7
.0
8 m
l or
1m
ol
NaO
Ac
pH
8.2
Bou
nd
to C
arbo
nat
es
5h
r co
nti
nu
ous
agit
atio
n-l
each
ed a
t ro
om
tem
p.
8 m
l 1m
ol
NaO
Ac
pH
5.0
w/a
ceti
c ac
id
Bou
nd
to
Iro
n a
nd
Man
gan
ese
Ox
ides
6h
r
20 m
l 0.3
mol
Na2
S2O
4+
0.1
75
mol
Na-
citr
ate
+ 0
.02
5 m
ol
H-c
itra
te
o
r 96
ºC ±
3 o
ccas
ion
al a
git
atio
n
20 m
l 0.0
4 m
ol
NH
2O
H∗H
Cl
in 2
5%
(v/v
)
HO
Ac
Bo
und
to
Org
anic
Mat
ter
2h
r 8
5 º
C ±
2 w
ith
occ
asio
nal
ag
itat
ion
5 m
l 0.0
2m
ol
HN
O3
5 m
l 30
% H
2O
2 p
H 2
wit
h H
NO
3
3h
r 85 º
C ±
2 w
ith
in
term
itte
nt
agit
atio
3 m
l 30
% H
2O
2 p
H 2
wit
h H
NO
3
30
min
co
nti
nuou
s ag
itat
ion
5 m
l 3.2
mol
NH
4O
Ac
in 2
0%
(v/v
) H
NO
3-
dil
ute
to 2
0 m
l
Res
idu
al
1 m
l
Unknow
n
HF
-HC
lO4 (
5:1
)
HF
-HC
lO4 (
10
:1)
HC
lO4 1
2N
HC
l
Co
mm
un
ity B
ure
au
of
Ref
eren
ce (
BC
R)
Pro
ced
ure
. 1 g
m
Ex
chan
gea
ble
16
hr
22ºC
± 5
wit
h c
onst
ant
agit
atio
n
40 m
l 0.1
1 m
ol
CH
3C
OO
H
Bou
nd
to
Car
bon
ates
Bou
nd
to
Iro
n a
nd
Man
gan
ese
Oxid
es
16
hr
22ºC
± 5
wit
h c
onst
ant
agit
atio
n
40 m
l 0.1
mol
NH
2O
H∗H
Cl
pH
2 w
ith H
NO
3
Bo
un
d t
o O
rgan
ic
Mat
ter
1h
r ro
om
tem
p.
wit
h occ
asio
nal
agit
atio
n
10 m
l 8.8
mol
H2O
2 p
H 2
-3
1h
r 85ºC
± 3
10 m
l re
duce
vol.
to l
ess
than
3m
L H
2O
2 p
H
2-3
red
uce
vol.
to 1
mL
16
hr
22ºC
± 5
wit
h c
onst
ant
agit
atio
n
50m
l 1m
ol
NH
4O
Ac
pH
2w
/HN
O3
Res
idu
al
H
F, H
NO
3, H
ClO
4
Tab
le 2
.6
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| P a g e
Sh
ort
Ex
tra
ctio
n P
roced
ure
by
Ma
iz.
3g
m
Ex
chan
gea
ble
2
hr
roo
m t
emp
. su
spen
d u
nd
er a
git
atio
n
10
ml
0.0
1 m
ol
CaC
l 2
Bo
un
d t
o C
arb
on
ates
4
hr
0.0
05
mo
l D
TP
A +
0.0
1m
ol
CaC
l2 +
0.1
mo
l T
EA
pH
7.3
Bo
un
d t
o I
ron
an
d
Man
gan
ese
Ox
ides
roo
m t
emp
.
Bo
un
d t
o O
rgan
ic
Mat
ter
Res
idu
al
a
qu
a r
egia
-HF
aci
d
Ga
lán
Pro
ced
ure
0
.5 g
m
Ex
chan
gea
ble
1
hr
20
ºC w
ith
co
nst
ant
agit
atio
n
35
ml
1M
NH
4O
Ac,
pH
5
Bo
un
d t
o C
arb
on
ates
Bo
un
d t
o I
ron
an
d
Man
gan
ese
Ox
ides
6
hr
96
ºC w
ith
man
ual
ag
itat
ion
ev
ery 3
0 m
in
20
ml
0.4
M N
H2O
H∗H
Cl
in C
H3C
OO
H
(25
%)
Bo
un
d t
o O
rgan
ic
Mat
ter
2h
r 8
5ºC
wit
h m
anu
al a
git
atio
n e
ver
y 3
0 m
in
3 m
l 0
.2M
HN
O3
5 m
l 3
0%
H2O
2,
pH
2
3h
r
3 m
l 3
0%
H2O
2
30
min
C
on
tin
uo
us
agit
atio
n
5 m
l 3
0%
H2O
2
Res
idu
al
2h
r
1
0 m
l H
F,H
NO
3,H
Cl
(10
:3:1
)
Geo
log
ica
l S
oci
ety
of
Ca
na
da
(G
CS
) P
roce
du
re.
1 g
m
Ex
chan
gea
ble
6
hr
2
0 m
l 1
.0m
ol
CH
3C
O2N
a p
H 5
6
hr
2
0 m
l 1
.0m
ol
CH
3C
O2N
a p
H 5
Bo
un
d t
o C
arb
on
ates
2
hr
60
ºC
vo
rtex
ev
ery 3
0m
in
20
ml
20
ml
0.2
5 m
ol
NH
2O
H∗H
Cl
in 0
.05
mo
l H
Cl
Bo
un
d t
o I
ron
an
d
Man
gan
ese
Ox
ides
3
0 m
in
60
ºC
2
0 m
l 2
0m
l 0
.25
mo
l N
H2O
H∗H
Cl
in 0
.05
mo
l H
Cl
3
hr
90
ºC
vo
rtex
ev
ery 2
0m
in
30
ml
1.0
mo
l N
H2O
H∗H
Cl
in 2
5%
CH
3C
O2H
Bo
un
d t
o O
rgan
ic
Mat
ter
1.5
hr
90
ºC
3
0 m
l 1
.0m
ol
NH
2O
H∗H
Cl
in 2
5%
CH
3C
O2H
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| P a g e
Bound t
o O
rgan
ic
Mat
ter
1.5
hr
90 º
C
30 m
l 1.0
mol
NH
2O
H∗H
Cl
in 2
5%
CH
3C
O2H
30 m
in
750 m
g K
ClO
3 a
nd 5
ml
12 m
ol
HC
l
vort
ex a
nd a
dd 1
0m
l H
Cl
more
15m
l
H2O
20 m
in
90 º
C
10 m
l 4m
ol
HN
O3
Res
idual
Unknow
n
200 º
C
2 m
l 16 m
ol
HN
O3∼
reduce
to 0
.5m
L
20 m
in
90 º
C
2 m
l 12 m
ol
HC
l
1hr
90 º
C
10 m
l ac
id m
ix H
(5m
l H
F, H
ClO
4 3
ml,
HN
O3 2
ml)
Over
nig
ht
1 m
l 12m
ol
HC
l
5-1
0 m
in
3 m
l 16m
ol
HN
O3
3 m
l 3 m
l H
2O
and w
arm
then
bri
ng u
p t
o
20m
l
Mod
ifie
d G
CS
) P
roce
du
re.
1 g
m
Exch
angea
ble
1.5
hr
25 º
C
30 m
l 0.1
mol
NaN
O3
1.5
hr
25 º
C
30 m
l 0.1
mol
NaN
O3
Adso
rbed
1.5
hr
25 º
C
30 m
l 1 m
ol
Na
OA
c pH
5.0
w/C
H3C
OO
H
1.5
hr
25 º
C
30 m
l 1M
Na
OA
c p
H5.0
w/C
H3C
OO
H
Org
anic
1.5
hr
25 º
C
30 m
l 0.1
mol
Na 4
P2O
7
1.5
hr
25 º
C
30 m
l 0.1
mol
Na 4
P2O
7
Am
orp
hous
Ox
yh
yd
roxid
es
1.5
hr
60 º
C
30 m
l 0.2
5m
ol
NH
2O
H∗H
Cl
in 0
.5 m
ol
HC
l
1.5
hr
60 º
C
30 m
l 0.2
5m
ol
NH
2O
H∗H
Cl
in 0
.5 m
ol
HC
l
Cry
stal
line
Oxid
es
1.5
hr
60 º
C
30 m
l 1m
ol
NH
2O
H∗H
Cl
in 2
5%
HO
ac
1.5
hr
90 º
C
30 m
l 1m
ol
NH
2O
H∗H
Cl
in 2
5%
HO
ac
Res
idual
U
nknow
n
30 m
l H
F, H
NO
3, H
ClO
4
Sourc
e: Z
imm
erm
an
an
d W
ein
dorf
(2010)
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| P a g e
In sequential extraction applying a series of successive reagents to attack specific
solid phase fraction of sediment or soil and releasing metals associated with these fraction
into solution has been reported in literature (Mossop and Davidson, 2003; Tuzen, 2003).
Fe and Mn oxides and organic matter occur as bulk phases or as coatings of mineral
particles which are the main binders in sediments (Tessier et al., 1980). Krupadam et al.
(2006) found in their sequential extraction study that Zn, Ni, and Co in top sediments were
mainly associated with the residual and Fe-Mn oxide fractions. In another study on the
sediments of Tirumalairajan river estuary, southeast coast of India, it was concluded that
that heavy metal can be considered immobile because of their high concentration in the
residual fraction and observed that they are strongly bound to mineral and resistant
components (Venkatramanan et al., 2015). It was also found that Fe-Mn phase and
organic matter can be more effective scavengers for selected heavy metals
(Venkatramanan et al., 2015).
2.8.3 Risk assessment code (RAC)
In the sediments, the metals are bound to the fractions with different strengths. The
RAC measures the availability of metals in solution by giving a scale to the percentage of
sediments that can reduce metals in the exchangeable and carbonate fractions. This
categorization is tabulated in Table 2.7 (Perin et al., 1985).
Table 2.7: Risk assessment code
Risk Metal in carbonate and exchangeable fractions (%)
No risk <1
Low risk 1–10
Medium risk 11–30
High risk 31–50
Very high risk 75
Singh et al. (2005b) assessed the distribution of metals in the water and bed
sediments both in the mobile and bound phases and found that most of the fractions of the
river Gomti (India) are associated with the carbonate and the exchangeable fractions (11%
and 30%). They found that the sediments having 11–30% carbonate and exchangeable
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fractions are at medium risk as per the Risk Assessment Code (RAC), while the
concentrations of cadmium and lead at some site were between 31 and 50%, where they
were posing high risk to the environment. Site Neemsar where the concentrations of
cadmium and lead were even higher than 50%, it was reported at very high risk.
Sequential extraction procedure have also been applied in the soil remediation
experiments to test the efficacy of the experiment in reducing the exchangeable and
carbonate bound fractions of Cu, Pb, Ni and Zn in soil (Malandrino et al., 2011).
Amendment of vermiculite was found to appreciably reducing the uptake of metal
pollutants in two plants, Lactuca sativa and Spinacia oleracea, in pot experiments
(Malandrino et al., 2011).
2.9 Remediation
Soils contaminated with heavy metals are often poor in nutrients and microbial
diversity and the over concentrations impart the plants to accumulate these metals or
affects the growth and development (White et al., 2006; Carlson et al., 1991).
Anthropogenic activities with lack of awareness of health and environmental effects
related to the production, use, and disposal of these metals into the soil add to the problem
(Vidali, 2001). Therefore it becomes important to remove the heavy metals from the
environment by using the remediation techniques.
Remediation in terms of environmental science refers to a course of action to the
source of contamination for reducing or removing the pollutants with the sole aim of
protecting the environment and humans of the harmful effects of the contaminants. Ever
since man has learnt about the influence of the pollutants on the nature, he realized the
need of conserving and protecting its environment. Returning the contaminated site to its
natural state is always not possible and necessary. Remediation activities should always be
reasonable and optimized and outcome should be balanced amid benefits, risks,
expenditure and feasibility. Therefore any acceptable remediation measures can be aptly
planned by understanding the source and nature of contamination, the site and remediation
technologies to be adopted.
Natural organisms, either indigenous or extraneous, are the prime agents used for
bioremediation. The organisms that are utilized vary depending on the chemical nature of
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the polluting agents and are to be selected carefully as they only survive within a limited
range of chemical contaminants. Since numerous types of pollutants are to be encountered
in a contaminated site, diverse types of microorganisms are likely to be required for
effective mediation. The first patent for a biological remediation agent was registered in
1974, being a strain of Pseudomonas putida (Prescott et al., 2002) that was able to
degrade petroleum. In 1991, about 70 microbial genera were reported to degrade petroleum
compounds (U.S Congress, 1991) and almost an equal number has been added to the list
in the successive two decades. Bioremediation can occur naturally or through intervention
processes (Agarwal, 1998). Natural degradation of pollutants relies on indigenous
microflora that is effective against specific contaminants and it usually occurs at a slow
rate. The rate of biodegradation is aided by encouraging growth of microorganisms under
optimized physico-chemical conditions with intervention processes (Blackburn and
Hafker, 1993; Bouwer et al., 1998; Smith et al., 1998).
Various techniques are available for the remediation. Simplest way to proceed is to
remove the uppermost contaminated soil by digging and remove it to landfill or cap the
contaminated site. But this method has its own disadvantages and risk as the contaminant
can leak out while excavation, handling and transport or in the cap and it may leak further
contaminating the ground water. In addition, it is very expensive and laborious. Different
techniques are available to remediate the metal contaminated soil, viz. chemical, physical
and biological techniques (McEldowney et al., 1993). The chemical method includes the
chemical wash and others using harsh chemicals like leaching of the heavy metals by the
chelating agents (Sun et al., 2001). Therefore, the researchers developed the
bioremediation techniques which are defined as a process whereby organic wastes are
biologically degraded under controlled conditions to an innocuous state, or to levels below
concentration limits established by regulatory authorities (Mueller et al., 1996).
Chemical and physical remediation can be costly. Table 8 illustrates the details of
different remediation techniques available. Among them, phytoextraction is one of the
effective low cost technique for enhanced remediation for metal contaminated soil.
Phytoremediation can provide sustainable measures for remediation of metal contaminated
soil.
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Table 2.8: Cost of different remediation technologies (Glass, 1999)
Process Cost
(US$/ton)
Other factors
Vitrification 75–425 Long-term monitoring
Land filling 100–500 Transport/excavation/monitoring
Chemical
treatment
100–500 Recycling of contaminants
Electrokinetics 20–200 Monitoring
Phytoextraction 5–40 Disposal of phytomass
2.10 Phytoremediation approaches and Hyperaccumulation of metals in plants
Phytoremediation can be defined as use of plants to remove, transfer and degrade
contamination in soil, sediment or water (Hughes et al., 1997). This uses living organisms,
especially plants and microorganisms, to reduce, eliminate, transform, and detoxify the
benign products present in soils, sediments, water, and air. Phytoremediation technology,
one of the bioremediation approaches, uses plants as filters for accumulating,
immobilizing, and transforming the contaminants to less harmful form (Vidali, 2001).
The term ―phytoremediation‖ came into existence combining Greek word ―phyto‖
meaning plant and Latin word ―remedium‖ meaning to restore or clean. Phytoremediation
includes a variety of remediation techniques which include many treatment strategies
leading to contaminant degradation, removal (through accumulation or dissipation), or
immobilization (Padmavathiamma and Li, 2007).
These remediation techniques may engage either the use of genetically engineered
or naturally occurring plants for removal of contamination in the surrounding environment
(Cunningham, et al. 1997; Flathman and Lanza, 1998). Utsunamyia (1980) and
Chaney (1983) reintroduced and developed the idea of using hyperaccumulating plants to
extract metal from contaminated soil. Baker et al. (1991) reportedly conducted the first
field trial on phytoextraction of zinc and cadmium.
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2.10.1 Phytoremediation categories
Based on contaminants, field conditions, clean-up level required and plant‘s type,
phytoremediation measures can be used for contaminants i.e. phytostabilization/
phytoimmobilization or for removal, i.e. phytovolatization/ phytoextraction purpose
(Thangavel and Subhuram, 2004).
Phytoremediation approaches involve different plant-based technologies with each
having different mode of action and mechanism. An overview of some of the
phytoremediation approaches is given in Table 2.9.
Table 2.9: Overview of phytoremediation applications
Technique Plant mechanism Surface medium
Phytoextraction Uptake and concentration of metal via direct
uptake into the plant tissue with subsequent
removal of the plants
Soils
Phytotransformation Plant uptake and degradation of organic
compounds
Surface water,
groundwater
Phytostabilization Root exudates cause metal to precipitate and
become less available
Soils, groundwater,
mine tailing
Phytodegradation Enhances microbial degradation in
rhizosphere
Soils, groundwater
within rhizosphere
Rhizofiltration Uptake of metals into plant roots Surface water and
water pumped
Phytovolatilization Plants evaportranspirate selenium, mercury,
and volatile hydrocarbons
Soils and
groundwater
Vegetative cap Rainwater is evaportranspirated by plants to
prevent leaching contaminants from disposal
sites
Soils
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2.10.1.1 Phytostabilization
It involves the use of plants to eliminate the bioavailability of toxic metals in soils
(Salt et al., 1995). The contaminants in soil are immobilized by certain hyper-
accumulating plants, through absorption and accumulation by roots, adsorption onto roots
or precipitation within the root zone and physical stabilization of soils. The schematic
representation of phytostabilization mechanism is shown in the Figure 2.1.
Figure 2.2 Schematic representation of phytoremediation approaches.
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Phytostabilization techniques reduce the metal contaminants in environment and
prevent its migration into air or groundwater (Padmavathiamma and Li, 2007). Green
vegetation is very helpful in controlling the soil erosion as tplants‘ roots effectively bind
the soil. The root of the vegetation also helps in holding a good amount of rain water,
which is returned to the atmosphere through transpiration, their presence reduces the
amount of heavy metals entering the water table and other water bodies (Tordoff et al.,
2000). To re-establish vegetation at the sites where flora has been destroyed or it has
disappeared due to presence of high metal concentration, metal-tolerant plant species can
be planted thereby reducing the effective migration of contaminants via soil leaching,
groundwater contamination, wind and transport of exposed surface soils (Tordoff et al.,
2000; Stoltz and Greger, 2002). Metal tolerance in some plants can be developed during
the course of evolution while others may have this ability inherently (Wu, 1990).
Plants selected for phytostabilization preferably should hold the contaminants in
roots and should resist the accumulation of heavy metals in their above-ground exposed
parts to prevent the entry of heavy metals into the food web (Padmavathiamma and Li,
2007; Gómez-Sagasti et al., 2012). Other characteristics of plants suitable for
phytostabilization are high levels of tolerance to the concerned contaminant(s) and ability
to immobilize these through uptake, precipitation or reduction by the high root biomass
produced as compared to the shoot (Padmavathiamma and Li, 2007). Metal
accumulation in plants is measured and expressed in terms of bioconcentration factor (BF)
or accumulation factor (AF) and translocation factor (TF) or shoot:root (S:R) ratio
(Mendez and Maier, 2008).
Ideally these values would be << 1, but if exceed a ratio of 1, it indicates that the
plant is useful for phytoextraction (accumulation of metals in shoot tissue) but should not
be used in phytostabilization (Brooks, 1998).
Translocation factor (TF) Total element concentration in shoot tissue
or shoot:root (S:R) ratio Total element concentration in the root tissue =
Bioconcentration factor (BF) Total element concentration in shoot tissue
or accumulation factor (AF ) Total element concentration in mine tailings =
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In a recent study Agrostis castellana was found to be a good plant to be used in
phytostabilization of abandoned mine sites of Spain that are heavily polluted with heavy
metals like Zn, Cu, Pb, Cd and As. However, close monitoring was suggested for the metal
concentrations in the above-ground mass of this plant and recommendation of no hunting
or grazing in areas under restoration (Pastor et al., 2015). In another study 36 plants
belonging to 17 species were assessed with the prospective of growing on a contaminated
site and reported that plants having high bio-concentration factor and low translocation
factor have the potential for phytostabilization (Yoon et al., 2006). Of all the plants
studied, Phyla nodiflora was -the most efficient in accumulating Cu and Zn in its shoots
finding place for the phytoextraction while Gentiana pennelliana was most suitable for
phytostabilization of sites contaminated with Pb, Cu and Zn (Yoon et al., 2006).
In order to improve physical and biological characteristics of the soil of
contaminated site, added natural and synthetic supplements was put into practice with the
phytostabilization processes. Thus the term of ―aided phytostabilization‖ or
―chemophytostabilization‖ came into existence. Changing the pH, increasing organic
matter content by adding compost, adding essential growth nutrients, increasing water
holding capacity, and reducing heavy metal bioavailability helps in the phytostabilization.
Five times reduction was observed in the concentration of Pb and Zn in aerial parts
and in roots of Lolium italicum and Festuca arundinacea while their growth was greatly
improved by the added compost (Rizzi et al., 2004). Decreased phytotoxicity index was
recorded on addition of compost, cyclonic ashes and steel shots to an industrial
contaminated sandy soil (Ruttens et al., 2006). In some studies complexing agents such as
citric acid, ethylenediaminetetraacetic acid (EDTA) etc. were shown to influence the
phytostabilization capacity (Vázquez et al., 2006). Addition of a synthetic (Calcinit + urea
+ PK14% + calcium carbonate) or organic (cow slurry) had positive response on soil
properties, growth and remediation potential of Lolium perenne while decreased root-to-
shoot translocation factors were observed compared to control plants (Epelde et al., 2009).
In a aided phytostabilization approach soil of ore dust-contaminated site of northern
Sweden was amended with alkaline fly ashes and peat to reduce mobility of trace elements
and vegetated with a mixture of consisting of six grass and thirteen herb species. Obtained
results show that the proposed approach significantly increased microbial biomass and
respiration, decreased microbial stress and increased key soil enzyme activities
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(Kumpiene et al., 2009). Plant growth-promoting bacteria (PGPB) was also reported to
improve the revegetation of two native species, quailbush and buffalo grass, of mine
tailings minimizing the need for compost amendment however the results were plant
specific (Grandlic et al., 2008). In a phytostabilization study of mine soils of France, use
of a legume species such as Anthyllis vulneraria in mixture with non-legume species
increased the biomass of the other species and consequently the biomass production of the
plant community (Frérot et al., 2006)
Care should be taken so that phytostabilized metals remain in the soil ecosystem.
Due to change in the soil condition and the degradation of organic matter there is always a
possibility of partial and gradual release and possibly leaching, resulting into dispersion of
phytostabilized metals to surrounding areas via soil erosion (Gómez-Sagasti et al., 2012).
Therefore, long-term monitoring or ―follow-up‖ programs are necessary in
phytostabilization processes to keep an eye on heavy metal mobilization, bioavailability,
toxicity and ecological impact (Gómez-Sagasti et al., 2012).
2.10.1.2 Phytofiltration
It involves utilizing plants to removal of pollutants from contaminated surface
waters or waste waters, cleaning the various aquatic environments. Phytofileration when
uses plant roots or seedlings or excised plant shoots to adsorb or absorb contaminants from
aqueous environment is termed as rhizofiltration, blastofiltration and caulofiltration
respectively (Prasad and Freitas, 2003; Mesjasz-Przybyłowicz et al., 2004). According
to Gardea-Torresdey and coworkers (2004) mechanisms involved in biosorption include
chemisorption, complexation, ion exchange, micro precipitation, hydroxide condensation
onto the biosurface, and surface adsorption. Young plants of Berkheya coddii growing in
pots on ultramafic soil enriched with Cd, Ni, Zn or Pb significantly accumulated a good
amount of theses metals, while excised shoots in solutions containing the same heavy
metals accumulated large amounts of these metals in the leaves (Mesjasz-Przybyłowicz et
al., 2004).
In rhizofiltration terrestrial plants are used in place of aquatic plants because the
former forms much larger fibrous root systems covered with root hairs, therefore has more
surface area compared to the others (Padmavathiamma and Li, 2007). Ideally a plant to
be used for rhizofiltration should be able to accumulate and tolerate significant
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concentrations of metals together with abilities of easy handling, low maintenance cost and
least amount of secondary waste needing disposal. It is also desirable of plants to produce
significant amounts of root biomass or root surface area (Dushenkov and Kapulnik,
2000).
Various aquatic plants have the potential to remove heavy metals from water, for
example Eichhornia crassipes (Zhu et al., 1999), Hydrocotyle umbellata L. (Dierberg et
al., 1987) and Lemna minor L. (Mo et al., 1989) but these plants have limited capacity for
rhizofiltration because they are inefficient owing to their small, slow growing roots
(Dushenkov et al., 1995). The higher water content of aquatic plants adds to the problem
of drying, composting and incineration. Despite of limitations, Zhu et al., (1999) found
Eichhornia crassipes (water hyacinth) effective in removing trace elements in waste
streams. Recently, Micranthemum umbrosum was found to be effective phytofiltrator of As
and moderate accumulator for Cd without any phytotoxic effect (Islam et al., 2015). The
aquatic plants Callitriche stagnalis Scop., Potamogeton natans L. and Potamogeton
pectinatus L. tested in the uranium phytofiltration experiments showed reduction of
uranium concentration, in the water, from 500 to 72.3 μg/L of uranium, emphasizing the
effectiveness of the selected plants to remove uranium from the water (Pratas et al., 2014).
The bryophyte Fontinalis antipyretica and Callitrichaceae members are found to
accumulate uranium with preferential partitioning in rhizome/roots, emerging as a
promising candidates for the development of phytofiltration (Favas et al., 2014).
Phytofiltration studies have also been performed for accumulation of arsenic by
aquatic plants. In a study out of 18 representative aquatic plant species Ranunculus
trichophyllus, Ranunculus peltatus sub sp. saniculifolius, Lemna minor, Azolla caroliniana
and the leaves of Juncus effusus showed a very high potential for phytofiltration of arsenic
through constructed treatment wetlands or introduction of these plant species into natural
water bodies (Favas et al., 2012).
Terrestrial plants like sunflower, Indian mustard, tobacco, rye, spinach and corn
have been studied for their ability to remove lead from effluent, with sunflower having the
greatest ability (Raskin and Ensley, 2000). The roots of Indian mustard (Brassica juncea
Czern.) are effective in the removal of Cd, Cr, Cu, Ni, Pb, and Zn (Dushenkov et al.,
1995) while sunflower (Helianthus annus L.) removes Pb (Dushenkov et al., 1995), U
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(Dushenkov et al., 1997a), 137
Cs and 90
Sr (Dushenkov et al., 1997b) from hydroponic
solutions. Cassava (Manihot sculenta Cranz) waste biomass was found effective in
removing two divalent metal ions, Cd (II) and Zn (II), from aqueous solutions (Horsfall
and Abia, 2003).
Sharp dock (Polygonum amphibium), duckweed (Lemna minor), water hyacinth
(Eichhornia crassipes), water dropwort (Oenathe javanica) and calamus (Lepironia
articulata) are found to good for phytoremediation of polluted waters, as follows: sharp
dock through accumulation of N and P in its shoots, water hyacinth and duckweed as
hyperaccumulators of Cd, water dropwort as an hyperaccumulator of Hg and calamus as an
hyperaccumulator of Pb (Wang et al., 2002).
2.10.1.3 Phytovolatilization
It utilizes plants which uptakes metals from soil, biologically converts into volatile
form and then release them into the atmosphere by volatilization. And this process is called
phytovolatilization. Some metal contaminants such as As, Hg, and Se exists in gaseous
form naturally in the environment.
Phytovolatilization can be applied for organic pollutants and these heavy metals. It
has its own limitation that it does not remove the pollutant completely, only it is transferred
from one form (soil) to another (atmosphere) from where it can redeposit. Therefore it is
most controversial of all the phytoremediation technologies (Prasad and Freitas, 2003).
Whether the volatilization of these elements into the atmosphere is safe or harmful is still a
question mark for the researchers (Watanabe, 1997). Selenium phytovolatilization has
received the most attention to date, the release of volatile Se compounds from higher plants
was first reported by Lewis and co-workers (1966) who showed that both selenium non
accumulator and accumulator species volatilize selenium. The Brassicaceae members are
capable of releasing as much as 40 gm Se ha−1
day −1
as various gaseous compounds
(Terry et al., 1992).
Moreno and co-workers (2008) in investigation of phytofiltration potential of Hg
in solution by B. juncea plant effectively removed up to 95% of Hg from the contaminated
solutions by both volatilisation and plant accumulation (Phytofiltration). Most Hg
volatilisation occurred from the roots which may have unforeseen environmental effects
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(Moreno et al., 2008). Uptake and evaporation of Hg is achieved by some bacteria.
Researchers are trying to develop transgenic plant by transferring the capable genes
through biotechnology for environmental restoration. Methyl-mercury is a strong
neurotoxic agent which is biosynthesized in Hg-contaminated soils. The bacterial genes
responsible such as Hg reductase have already been successfully transferred to Brassica,
tobacco and yellow poplar trees (Meagher et al., 2000).
2.10.1.4 Phytoextraction
It is most commonly recognized of all phytoremediation technologies, also known
as phytoaccumulation, phytoabsorption or phytosequestration, uses plants, which absorb
metals from soil and translocate them to harvestable shoots where they accumulate. The
effect of typical levels of heavy metals is summarized in Table 2.10
Table 2.10: Effect of typical levels for heavy metals in plants
Status Metal conc
Cd Cu Pb Zn
Deficient - <1-5 - <10
Normal 0.05-2 3-30 0.5-10 10-150
Phytotoxic 5-700 20-100 30-300 >100
Phytoextraction cannot be confused with the term phytoremediation which is a
concept while former is a specific clean-up technology (Prasad and Freitas, 2003). A
number of plants that may belong to distantly related families, but have common capability
to grow on metalliferous soils and to accumulate extremely large amount of heavy metals
in the aerial organs, far in excess of the levels found in other plants, without suffering
phytotoxic effects are termed as ―hyperaccumulator‖ (Rascio and Navari-Izzo, 2011).
These hyperaccumulator plants form the basis of phytoextraction technologies. Baker and
Brooks (1989) reported that hyperaccumulators should have a metal accumulation
exceeding a threshold value of shoot metal concentration of 1% (Zn, Mn), 0.1% (Ni, Co,
Cr, Cu, Pb and Al), 0.01% (Cd and Se) or 0.001% (Hg) of the dry weight shoot biomass.
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Phytoextraction is generally grouped into two types based on its methodology. The
first type is called continuous phytoextraction and uses hyperaccumulating plants while the
second type is called chelate-induced phytoextraction and uses high-biomass crop plants
and chelating agents (Padmavathiamma and Li, 2007; Gómez-Sagasti et al., 2012).
In continuous phytoextraction, metal accumulating plants are seeded or
transplanted into metal contaminated soil and are cultivated using established agricultural
practices. The roots of growing plants absorb metal elements from the soil and translocate
them to the aerial shoots where they get accumulated. According to an estimate, about 450
angiosperm species belonging to the members of Asteraceae, Brassicaceae,
Caryophyllaceae, Cyperaceae, Cunouniaceae, Fabaceae, Flacourtiaceae, Lamiaceae,
Poaceae, Violaceae, and Euphobiaceae (Padmavathiamma and Li, 2007) have been
identified so far as heavy metal (As, Cd, Co, Cu, Mn, Ni, Pb, Sb, Se, Tl, Zn,)
hyperaccumulators, accounting for less than 0.2% of all known species (Rascio and
Navari-Izzo, 2011).
Scientists are in continuous search to find new hyperaccumulators in nature, which
remain unidentified and new reports of this kind of plants continue to accrue (Lin e al.,
2015). Cadmium, which is one of the most toxic heavy metals, very low number of
hyperaccumulators (only 5 species to date) has been available for this metal (Rascio and
Navari-Izzo, 2011). Recently a new cadmium hyperaccumulator plant Youngia
erythrocarpa a farmland weed was discovered (Lin e al., 2015). Ni is the metal which is
hyperaccumulated by the maximum number of taxa, more than 75% while about 25% of
discovered hyperaccumulators are found to belong to the family of Brassicaceae and, in
particular, to genera Thlaspi and Alyssum (Rascio and Navari-Izzo, 2011).
To reduce the contamination at a particular site, planting and harvesting of the
hyperaccumulators must be repeated; while time required depends on the target metal,
plant selected and its efficacy, the duration of the process can vary from 1 to 20 years
(Kumar et al., 1995; Blaylock and Huang, 2000). A success of phytoextraction depends
on the high biomass production capability and ability to accumulate large quantities of
environmentally critical metals in the shoot tissue (Kumar et al., 1995; Blaylock et al.,
1997; Prasad and Freitas, 2003). For example, Ebbs et al. (1997) found that B. juncea is
more effective for Zn and Cd removal from soil than T. caerulescens (a known
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hyperaccumulator of Zn) although T. caerulescens achieved 10 times and 2.5 times more
concentration of Cd and Zn respectively in its shoot. B. juncea exhibited this property since
it produced ten-times more shoot biomass than T. caerulescens. In addition to the high
biomass production capability the plant must also be tolerant to the targeted metal(s)
efficient in translocating them from roots to the harvestable aerial parts of the plant
(Blaylock and Huang, 2000). Recently role of symbiotic bacterial sp. in helping the plant
to grow in poor soils and metal accumulation came into notice. A novel species of
Rhizobium metallidurans sp. nov., a symbiotic heavy metal resistant bacterium has been
isolated from Zn hyperaccumulating Anthyllis vulneraria legume (Grison et al., 2015).
When this bacterium was inoculated in A. vulneraria the concentration of Zn in shoots
increased up to 36% (Grison et al., 2014).
Chelate-induced phytoextraction gets into practice when metals do not exist in
available form in the soil for sufficient plant uptake, adding chelates or acidifying agents
help them to liberate into the soil solution, improving the metal accumulation capacities
and uptake speed of non hyperaccumulating plants (Evangelou et al., 2007). Over the past
decades the use of persistent aminopolycarboxylic acids (APCAs) such as ethylene
diamine tetraacetic acid (EDTA), biodegradable APCAs, ethylene diamine disuccinate
(EDDS) and nitrilo triacetic acid (NTA) as an alternative to EDTA and other persistent
APCAs and low molecular weight organic acids (LMWOA) have been used in the various
phytoextraction experiments (Evangelou et al., 2007). The degree of chelant induced
extraction depends upon a number of factors like fractionation of metals retained in soil,
types of chelating agents used and concentrations of chelating agents employed (Yeh and
Pan, 2012). Limitation of the addition of the chelating agents to the soil during the induced
phytoextraction process is that it is toxic to the plants and has negative effect on the soil
microbial health (Mühlbachová, 2011). There is always a potential risk of leaching of
metals to groundwater and presence of non-degradable metal-chelating agent complexes in
contaminated soils for long period (Lombi et al., 2001a, b). EDTA is a strong chelating
agent, having strong complexe-forming ability, has been most extensively studied but
presently interest is shifted on the usage of biodegradable chelating agents such as EDDS
which is a biodegradable isomer of EDTA (Yeh and Pan, 2012). EDDS is a naturally
occurring substance in soil where it is easily decomposed into less detrimental byproducts.
EDDS, less harmful to the environment, readily solubilizes metals from soils and more
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efficient in inducing metal accumulation in Brachiaria decumbens shoots (Santos et al.,
2006; Yeh and Pan, 2012).
2.11 P. vittata a hyperaccumulator plant
P. vittata, also known as brake fern, is aperennial, evergreen fern native to China
and was first discovered arsenic hyperaccumulator as well as the first fern found to
function as a hyperaccumulator (Ma et al., 2001). This fern possesses extraordinary ability
for As hyperaccumulation (up to 22,600 mg. As kg-1
in its fronds) (Ma et al., 2001), which
is far greater than most plant species (<10 mg As kg-1
) (Matschullat, 2000). Though at
reduced rate yet P. vittata is effective in taking up arsenic in the presence of other metals
(Ni, Zn, Pb and Cd) but it had a limited capability to take up other metals (Fayiga et al.,
2004). About a dozen of ferns belonging to genus Pteris are reported as As
hyperaccumulator and few from others such as Pityrogramma calomelanos, but not all
members of the genus Pteris are able to hyperaccumulate arsenic (Xie et al., 2009). It was
reported that plasma membranes of the root cells of Pteris vittata have a higher density of
phosphate/ arsenate transporters than non-hyperaccumulator P. tremula, may be as a result
of constitutive gene overexpression (Caille et al., 2005). As hyperaccumulation by fern
depends on the high affinity to arsenate by the phosphate/ arsenate transport systems
(Poynton et al., 2004) and the plant's capability to increase As bioavailability in the
rhizosphere through reducing pH by root exudation of large amounts of dissolved organic
carbon (Gonzaga et al., 2009). The decrease in pH increases water soluble As that can be
readily taken up by the roots (Fitz and Wenzel, 2002; Gonzaga et al., 2009).
2.12 Heavy metal remediation by immobilization using natural sorbents
Beside phytoextraction the remediation of heavy metal is mainly done using techniques
like chemical coagulation and precipitation, membrane filtration, reverse osmosis,
immobilization and chelant induced extraction (Charerntanyarak, 1999; Blöcher et al.,
2003, Bakalár et al., 2009; Qdais and Moussa, 2004; Yan and Viraraghavan, 2001). In
recent years many researchers studied different low-cost natural sorbents like bauxite
waste red muds, coal fly ashes, bark/tannin-rich materials, lignin, chitin/chitosan, dead
biomass, seaweed/algae/alginate, xanthate, zeolite, clay, peat moss, bone gelatin beads,
leaf mould, moss, iron-oxide-coated sand, modified wool, modified cotton, quartz,
aluminosilicates, calcite, dolomite, biogenic iron oxides and many more (Apak et al.,
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1998; Bailey et al., 1999; Al-Degs et al. 2006; Rentz and Ullman, 2012; Djukić et al.,
2013; Zhou et al., 2013).
Natural mineral clays have been used in many studies for the removal of heavy metals but
most of them are focused on the aqueous system. Sepiolite was found to have high
adsorption capacity for Cd(II), Cu(II), and Zn(II) from industrial waste water (Sanchez et
al., 1999). Palygorskite clay was used as an adsorbent for the removal of metal ions such
as Pb, Ni, Cr and Cu from aqueous solution, adsorption potential from the single-metal
solutions was highest for Pb followed by Cr, Ni and Cu (Potgieter et al., 2006). Acid-
activated montmorillonite increased adsorption of Cd(II), Co(II), Cu(II), Ni(II), and Pb(II)
from the aqueous medium (Bhattacharyya and Gupta, 2007). Raw kaolinite and
manganese oxide-modified kaolinite was effective for the removal of Cd(II) ions from
aqueous solution and waste water with the Langmuir adsorption capacities to be 14.11 and
36.47 mg g-1
respectively for raw and modified kaolinite (Sari and Tuzen, 2014). Zeolite
and vermiculite were having more Cd adsorption as compared to pumice while at the
lowest Cd levels the sorption percentage was higher (Panuccio, et al., 2009). In addition to
that the use of vermiculite is gaining enormous popularity in heavy metal remediation. The
use of vermiculite in heavy metal adsorption in aqueous solutions is well established in
some previous works (Das and Bandyopadhyay, 1992; Mathialagan and
Viraraghavan, 2003; Malandrino et al., 2006; Abollino et al., 2008). In a fixed bed and
batch reactor, exfoliated vermiculite was found to be the more effective over granular
clinoptilolite for Cu2+
removal in aqueous solutions. The percent removal of copper was
found in the following order: vermiculite > clinoptilolite dust > clinoptilolite of 2.5-5.0
mm grain size (Stylianou et al. 2007). In two separate studies vermiculite was found to be
a good sorbent for metal cations extracted from the soil (Malandrino et al. 2006, Abollino
et al. 2007) but also highly resistant to mechanical abrasions when used under column
conditions (Malandrino et al. 2006).
Vermiculite is a bioctahedral or trioctahedral layered aluminum silicate mineral with 2:1
layers structure having water molecules and exchangeable cations in the interlayer spacing.
The silicate layer of vermiculite is composed of one [MgO6] and/or [FeO6] octahedral
sheet bounded in between two opposing tetrahedral [SiO4] sheets and the structure is
frequently referred to as 2:1 phyllosilicate (Brigatti et al., 2006). Furthermore negative
charge arises in the vermiculite platelets due to isomorphic substitution of Al3+
in place of
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Si4+
which is balanced by some interlayer cations (Brigatti et al., 2006; Slade and Gates,
2004). There are two different mechanisms of heavy metals adsorption in vermiculite first
the interactions occurs between metal ions and negative permanent charge (outer-sphere
complexes) as a result exchange of cations takes place at the planar sites, second inner-
sphere complexes are made through Si–O– and Al–O– groups at the clay particle edges
(Stylianou et al., 2007). It also has the property of exfoliation, expands on heating to form
ultra lightweight aggregate due to rapid production of steam during flash-heating that
forces interlayers apart as the steam escapes from the structure (Hillier et al., 2013).
Exfoliation is also attributed to mosaic distribution of the different mineral phases within
the particles (Hillier et al., 2013). Malandrino et al. (2006) considered vermiculite as a
cost effective natural sorbents that can be used for the treatment of various types of
wastewaters to avoid pollutant release. Other than environmental importance vermiculite is
used extensively for fire protection, acoustic and thermal insulator, additive in concrete and
plaster, packing material, etc. in agricultural and industries (Malandrino et al. 2006).
Since most of the present remediation technologies requires high capital costs and are
ineffective therefore there is an urgent need for developing a cost effective and efficient
technology to address the crisis of heavy metal pollution. The high ion exchange and
adsorption capacities in the interlayer space of vermiculite make it suitable material in
remediation studies. While most of the studies of heavy metal removal by vermiculite are
primarily focused on the liquid waste water but few demonstrated the effectiveness of
vermiculite in soil remediation (Malandrino et al., 2011).
Chapter 3
Materials
and Methods
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Chapter 3. MATERIALS AND METHODS
3.1 Description of the investigated sites
The study included about 32.5 km of the river Yamuna stretch through Delhi, the national
capital of India. The study area varies from latitude of 28°46'17.30"N to 28°32'9.84"N and
longitude of 77°13'25.16"E to 77°19'29.16"E. A total of 12 sampling sites were selected
approximately 2.5 to 3.5 km apart from each other. Table 1 summarizes the details of the
investigated sites.
Table 3.1: Locations of the sampling sites
Site no. Latitude Longitude Location
Site 1 28°46'17.30"N 77°13'25.16"E 9 km upstream from Wazirabad barrage
Site 2 28°45'46.98"N 77°14'12.12"E 6.5 km upstream from Wazirabad barrage
Site 3 28°44'16.61"N 77°13'53.43"E 3.5 km upstream from Wazirabad barrage,
opposite Jagarpur kadar village
Site 4 28°43'8.88"N 77°14'27.36"E 1 km upstream from Wazirabad barrage
Site 5 28°41'55.44"N 77°13'46.62"E Majnu ka Tila, at a distance of about 0.9 km
downstream from Najafgarh drain
Site 6 28°40'13.26"N 77°14'1.44"E Near ISBT bridge
Site 7 28°39'1.92"N 77°15'51.00"E Near Geeta colony
Site 8 28°37'39.18"N 77°15'30.00"E Near ITO flyover and Delhi Jal Board
Site 9 28°35'59.70"N 77°15'44.82"E Near Nizamuudin bridge
Site 10 28°34'37.62"N 77°17'14.94"E Near Delhi Noida flyover
Site 11 28°32'54.28"N 77°18'23.53"E Okhla
Site 12 28°32'9.84"N 77°19'29.16"E 1.6 km downstream to Okhla
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INDIA
D E L H I
1 kmapproximately
N
E
S
W
Site 1
Site 2
Site 3
Site 4
Site 5
Site 6
Site 7
Site 9
Site 10
Site 11Site 12
Wazirabad barrage
Okhla barrage
Scale
Bhalswalake
ISBTKashmiri gate
Pragati Thermal Power Station
Gas Turbine Power Station
ITO bridge
Site 8
Majnuka Tila
U.P.
Haryana
Okhla Bird Sanctury
Noida Flyover
Qutub Minar
Dwarka
Noida
Greater Noida
Red Fort
Geeta colony
Nizamuddin bridge
Bahadurgarh
Rohini
Narela
Najafgarh
Figure 3.1 River Yamuna and water channels in Delhi and the sampling sites.
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Figure 3.2 Picturesque views of the sampling sites
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Site 1 Site 1
Site 2 Site 2
Site 3 Site 3
Site 7 Site 8
Site 5 Site 5
Figure 3.3 Picturesque views of the various crops grown in agriculture fields along
the river Yamuna in Delhi
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Figure 3.4 Picturesque views of the major power plants along the river Yamuna in
Delhi
Preliminary observation of the sampling sites indicated that the Site 1, 2, 3 and 4
upstream to the Wazirabad barrage were comparatively less polluted but some waste
deposits like plastic bag were deposited at the bank of the river. After the falling of the
Najafgarh drain downstream to the Wazirabad barrage into the river the natural water
turned into sewage waste at the site 5. Plastic bags and water bottles, non-biodegradable
religious offerings, papers and other house hold wastes were found at the bank of the river
at most of the sites. The flow of the river after the Okhla barrage at the site 12 was very
less and quiet polluted especially at the premonsoon period. Major power plants located at
the bank of the river were Pragati power plant and Gas turbine power plant. The major
drains that falls into the river are the Najafgarh drain, Shahadra drain, Ghazipur drain and
Hindon cut canal. According to estimated data about 22 drains falls into the river (CPCB,
2006; MOEF, 2013; Paul et al., 2014). Agriculture activity by the local farmers was
common in the flood plains of the river. The major crops and vegetables grown at the site
1, 2, 3, 5, 7 and 8 are wheat, Jawar, bajra, paddy, mustard, water melons, cucumber, chilis,
brinjal, lady finger, spinach, gourd, bitter gourd, onion, tomato, radish, carrot, cabbage,
cauliflower and pumpkin.
3.2 Field sampling
The samples were collected in triplicate from all the sampling sites in the month of June
(pre-monsoon), October (post-monsoon) and February (Spring) in the year 2013 to 2014.
The water samples were collected from the bank of the river to the highest possible depth
in high grade polythene bottles and labelled properly. The samples were brought to the
laboratory with necessary precautions and further processed within 24hrs of the sampling.
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To see the effect of river water on the associated bottom sediments, the upper 5 cm
layer of the freshly deposited sediments was collected from the bank of river at each
sampling location with the help of a clean plastic trowel. Agricultural soils and crops or
vegetables growing on it were also collected at the same time at the locations, where they
were available. The agricultural soil was collected from the depth of 5 to 15 cm with the
help of clean iron spade. The agricultural soil and crop growing there were collected to see
the level of contamination, since sediments particles were dispersed in agricultural soils
due to flooding or use of river water as a source of irrigation in the study region. The
sediment, soil and crop samples were kept in clean virgin poly bags, sealed and labelled
properly. The samples were taken to the laboratory and processed within 48 hours
3.3 Chemicals and reagents
The chemical and reagents used in this study were of analytical grade and brought from
standard manufactures, viz; Merck India Ltd; Mumbai; Himedia laboratories Ltd; Mumbai;
S.D. Fine Chem. Ltd; U.S.A; Sigma-Aldrich Corporation, U.S.A.
3.4 Glass and plastic wares
All the glass and plastic wares used in the current study were supplied by Borossil, Tarsons
and Schott Duran.
3.5 Instruments used
The instruments used in various experimental analyses in the current study along with their
make are given in the following table 3.2.
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Table 3.2: Instruments used in the study
S. No. Name of Instrument Make
1 Electronic balance Docbel, Braun
2 pH meter Decibel, India
3 Refrigerator Zenith, India
4 Hot air oven Labex, India
5 Centrifuge Remi Equipments, India
6 Hot plate Mettex, India
7 UV-VIS Spectrophotometer Barian, India
8 Atomic Absorption Spectrophotometer (AAS) Perkin-Elmer atomic
absorption spectrometer
(Model 3110)
9 Scanning Electron Microscope (SEM) JEOL Model JSM - 6390LV
10 Energry Dispersive X-Ray Spectroscopy (EDS)
coupled with SEM
JEOL Model JED - 2300
11 X-ray diffractometer (XRD) PANalytical X‘Pert Pro
12 FTIR Perkin-Elmer
14 Shaker Incubator Orbitek Scieneenics India
Pvt.Ltd
15 BOD Remi Instruments Ltd; India
16 Wavelength Dispersive X-ray Fluorescence-S8
Tiger (WD-XRF)
Bruker (Germany)
3.6 Water Quality Analysis of the water samples
The pH was measured on the site itself with the help of portable pH meter (Hanna). All the
reagents used for the analysis were of analytical reagent grade. The dissolved oxygen (DO)
was calculated by Winkler‘s titration. Biochemical oxygen demand (BOD) was calculated
by the 5-day BOD test while chemical oxygen demand (COD) was calculated by using
open reflux method. The detailed methodology adopted was according to the standard
methods of APHA (1995, 2005).
3.7 Determination of metal concentration
The river water samples (50 mL) were digested with 10 ml of concentrated HNO3 at 80°C
until the solution became transparent (APHA, 1985). The solution was filtered through
Whatman No. 42 filter paper and the solution was diluted to 50 mL with distilled water.
For the sediment, soil and vegetable samples, 0.5 g of dried samples was digested with
15ml of HNO3, H2SO4, and HClO4 in 5:1:1 ratio at 80°C until a transparent solution was
obtained (Allen et al., 1986). The solution was filtered through Whatman No. 42 filter
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paper and diluted to 50mL with distilled water. Metal concentrations in different extracts
were determined by flame atomic absorption spectrometry using Perkin-Elmer atomic
absorption spectrometer (Model 3110) using air-acetylene flame.
3.8 Geochemical fractionation or chemical speciation of potentially toxic heavy metals
Geochemical fractionation studies of heavy metals on collected samples are done by
employing Tessier Sequential Extraction Experiment. Sequential extraction procedure
applied on soil and sediments samples to determine their chemical state in the samples. Air
dried samples were used for this procedures. Tessier sequential extraction a five step
scheme is widely used for detailed investigation of the soil and bottom sediments samples.
It provides information on metal partionioning fractions, their mobility, bioavailability,
toxicity and their fate of transport .The use of Tessier sequential extraction gives detailed
information on contaminated samples and helps to understand their behavior. However in
previous studies and literature, there other sequential extraction procedures are alos
available that can predict metal distribution in contaminated samples. Among them tessier
sequential extraction procedure is one the most widely applied in scientific studies. The
behavior of potential toxic metals in the solid phase of samples depend not only on their
total metal content but also on the metal binding behavior in the chemical forms present
(Rao et al. 2010b). The association of the different geochemical fractions of Tessier
sequential extraction scheme was shown in table 3.3.
Procedure
The sequential extraction was performed on three sub samples of both soil and sediments
from severely polluted selected contaminated sites. One gram soil and sediments sample
placed in 50 ml polypropylene centrifuged tube for metal fractionation and sequential
extraction studies. At each extraction step, samples were centrifuged at 4000rpm for 50
min. At the end of centrifuge supernatant taken using pipette and decanted into clean
plastic vial. After that samples were two times wash with distilled water and again
centrifuged for 10 min.
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Table 3.3: Associations of geochemical fraction of heavy metals in soil and sediments
Geochemical
fraction Association
Fraction 1 Exchangeable (salt-
displaceable)
The exchangeable fraction means the mobile
and bio- available parts.
Fraction 2 Acid Extractable
(Bound to Carbonate)
The acid soluble or carbonate associated
means the metals precipitated or co-
precipitated with carbonate constituent.
Fraction 3 Reducible (Bound to Fe
and Mn oxides)
The fraction bound to metal oxides such as
Fe, Al, and Mn oxide which can trap the
metals. These oxide compounds are
thermodynamically instable in anoxic
conditions induced by the decreasing of redox
potential.
Fraction 4
Oxidisable (Bound to
Organic matter and
sulphides)
The organic fraction means the part of
elements bound to organic matter.
Fraction 5 Residual (Bound to
Silicates)
The residual fraction means the part of
elements bound to the elements that cannot
be extracted by the previous reagents.
3.10 Statistical analysis
To study the inter-relationships between various parameters the Karl Pearson's coefficient
of correlation was calculated and correlation matrix with distribution histogram and scatter
plot was constructed with the statistical software R. Hierarchical cluster analysis (HACA)
based on agglomerative statistics using Ward‘s Method was done for different variables
using PAST software (Hammer et al., 2001). To obtain more reliable multivariate
relationship among the variables, PCA was done using using PAST software (Hammer et
al., 2001).
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Figure 3.5 Scheme of the selective sequential extraction (Tessier et al., 1979)
3.11 Chemical characterization of soil and sediments samples
The chemical composition of the soil and river sediments was so complicated that it was
impossible to distinguish individual components by single instrument analysis (Hochella et
al. 2005). To determine the primary and secondary components of the samples, a
comprehensive instrumental analysis was conducted including scanning TEM
(transmission electron microscopy), XRD and Fourier transform infrared analysis (FT-IR).
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3.11.1 SEM-EDX (Scanning Electron Microscope-Energry Dispersive X-Ray
Spectroscopy)
A SEM/EDX equipment model JEOL JSM-6380- LA was used to analyze the selected soil
and sediment samples in the powder form. Scanning electron microscopy (SEM) and
energy dispersive X-ray spectroscopy (EDS) were used to determine the texture and
composition of the surface of the samples. The system operates at 20 kV and 10,000 x
magnification power for image clarification.EDX is an x-ray technique used to identify the
elemental composition of a sample. EDX systems are attachments to SEM instruments
where the imaging capability of the microscope is used to identify the specimen of interest.
3.11.2 POWDER XRD –X ray Diffraction for solid phase characterization
XRD studies were performed by preparing the sample by pressing some of the powder
material in a cylindrical standard sample holder of 16mmof diameter and 2.5mmof height.
The instrumental and experimental conditions employed were: Panalytical X‘Pert PRO
MPD Alpha1 powder diffractometer instrument using the Cu K_1 radiation (_ = 1.5406
Å). The qualitative phase analysis determination was carried out by means of the PDF
(Powder Diffraction File) data base, ICDD-JCPDS (International Centre for Diffraction
Data – Joint Committee of Powder Diffraction Standards, 2002).
3.11.3 FTIR
The infrared spectra were recorded range 4000 to 400cm-1 on a Perkin Elmer
spectrophotometer model Spectrum 2000 and 20 scans were performed with spectral
resolution of 4cm-1. The pellets containing about 1.0mg of fraction 50μm and 100mg of
KBr were dried at 100oC for 24 hours to eliminate any existing moisture.
3.12 Chelant induced phytoextraction of heavy metals by Pteris vittata
3.12.1 Experimental design
The soil samples were collected from an area with naturally growing P. vittata, located at
Ranibagh, Distt.-Nainital (Uttarakhand) India. Soil was air-dried and sieved through 5 mm
sieve followed by filling in 5 kg plastic pots (25 cm diameter and 30 cm height). Two P.
vittata plants of about 15 cm height were planted in each pot and no additives were added.
The plants were allowed to grow under greenhouse conditions for 3 months. In the
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greenhouse the temperature of 28±2ºC with 12 hr day/night light cycle was maintained. To
maintain moisture the pots were irrigated with tap water twice per week. After 3 months
the soil in the pots was treated with chelating agent (EDTA) at the time of watering at a
rate of 10 mM/kg soil. Control pots without any treatment of chelating agents were also
included in the experiment.
After adding chelating agent the plants were further allowed to grow for one more
month. The pot experiment was carried out in triplicate. Thereafter the plants were
harvested. The harvested plants were separated into two parts: aboveground (roots) and
belowground parts (fronds) and washed thoroughly with deionised water 2-3 times to
remove soil particles attached. Collected plant samples were oven-dried at 65°C to
complete dryness and ground to fine powder using motor pestle, weighed and stored in air
tight jars for further analysis.
3.12.2 WDXRF analysis
Samples were analysed using a commercial Wavelength Dispersive X-ray Fluorescence-S8
Tiger from Bruker (Germany), equipped with 4KWatt Rh anode X-ray tube with
proportional flow counter and scintillation counter detectors. The instrument was capable
of analyzing elements from carbon to uranium in the concentration range from PPM level
to 100% in any form, i.e. liquid, solid or powder samples. Fine grounded samples of <100
mess size were taken for the analysis, which were then pelletized under 15 tons pressure
using hydraulic press into pellets of 34 mm and a minimum thickness of ~ 3 mm. All the
samples were analysed to record a whole spectrum for the identification of the elements in
the samples. Quantitative analysis was done by the software provided with the instrument.
3.13 Heavy metal immobilization potential of the vermiculite in the soil
3.13.1 Experimental design
The present study was conducted in a glasshouse under suitable maintained condition. Soil
for the experiment was collected from CRC (Crop Research Centre) agricultural field, GB
Pant University of Agriculture and Technology, Pantnagar, India. The upper 0-25 cm soil
was collected using an auger. Soil was air dried and sieved using 2 mm sieve for further
experimental work. Seeds of maize (Zea mays) plant were also obtained from CRC. The
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half of the soil was subjected to the overages of heavy metal salts CuCl2.2H2O,
Pb(CH3COO)2, ZnSO4.7H2O, to a concentration of 0.7gm kg-1
,0.43gm kg-1
and 1.8gm kg-1
to make it polluted artificially. The polluted and control (unpolluted) soil was then placed
in polyethylene pots (2 kg in each). For the vermiculite treatments 200 gm of vermiculite
was added to 1.8 kg of each soil individually and filled into the pots. There were 3
replicates of each pot. Thus in total there were 12 pots (3 pots filled with control soil
without vermiculite, 3 pots filled with control soil with vermiculite, 3 pots filled with
polluted soil without vermiculite and 3 pots filled with polluted soil with vermiculite). 3
seeds of maize were sown in each pot. The pots were then placed in glasshouse in random
arrangements with 12 hours day light cycle, humidity maintained between 50-90%,
temperature was between 25ºC to 35 ºC and they were watered two times a week to
maintain moisture. The maize crop was allowed to grow in glasshouse for four months
from February to May, 2014 under suitable conditions.
After four months of the growth, plants were harvested and separated into leaves,
stalk and roots. All plant parts were washed with tap water followed by washing with
deionised water and air drying in an oven at 70ºC till a constant weight was achieved. After
harvesting soil samples were also collected from each pot, sieved through 2mm sieve and
air dried.= Fine powered plant-material and air dried soil samples (0.5g) were digested
with 15 ml of HNO3, H2SO4 and HClO4 (5:1:1 ratio) at 80ºC on a hot plate, after cooling
the samples were diluted to 50 ml with distilled water and filtered using Whatman no. 42
filter paper. Metals concentrations were determined by AAS (Atomic Absorption
Spectrophotometer).
3.13.2 FTIR analysis
To determine changes in the chemical composition of soil organic matter FTIR analysis
was done. The air dried soil samples were sieved with 2 mm sieve. The results were
recorded on a Perkin-Elmer spectrum version 10.03.05 FT-IT spectrometer employing KBr
disc in the range of 400 to 4000 cm-1
.
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Chapter 4
Results
and Discussion
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Chapter 4. RESULTS AND DISCUSSION
4.1 Water quality parameters of the river Yamuna along the Delhi segment
4.1.1 Variation of surface water pH
The pH was found to be in the range of 7.17 to 8.3 in June (pre-monsoon), 7.30 to 8.02 in
October (post-monsoon) and 7.42 to 8.28 in February (spring) (Figure 4.1). In general the
pH was higher in June followed by February and October at all locations except for the site
4 in June. The pH of the upstream sites was more alkaline than the downstream of the site
4. An abrupt downfall in the pH was observed after the site 3 and 4 during all the seasons
which might be due to the discharge of the wastewater to the river by Najafgarh drain
before the site 5. Overall, the pH recorded was in the range of different classes of the water
quality criteria described by CPCB. The variation of the temperature, humidity and rainfall
during the study period at the selected area is shown in the Table 4.1.
6.90
7.10
7.30
7.50
7.70
7.90
8.10
8.30
8.50
site 1
site 2
site 3
site 4
site 5
site 6
site 7
site 8
site 9
site 10
site 11
site 12
pH JUNE
OCT
FEB
Figure 4.1 Spatial variation of the pH of river Yamuna River at different locations
along the Delhi stretch during different seasons
The increased surface pH at some locations can be related to more metabolic
activities of the autotrophs present, which in general utilize CO2 and liberate O2 thus
reducing H+ ion concentration while the liberation of acids from decomposing organic
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matter under low O2 concentration result in low pH (Kaul and Handoo, 1980). The pH of
all the sites throughout the sampling period was in the prescribed range of the class A-D of
CPCB (Table 2).
Table 4.1: Water quality criteria according to CPCB
Designated-Best-Use Class of
water Criteria
Drinking Water Source
without conventional
treatment but after
disinfection
A
Total Coliforms Organism MPN/100ml shall be 50
or less
pH between 6.5 and 8.5
Dissolved Oxygen 6mg/l or more
Biochemical Oxygen Demand 5 days 20°C 2mg/l
or less
Outdoor bathing
(Organised) B
Total Coliforms Organism MPN/100ml shall be
500 or less pH between 6.5 and 8.5 Dissolved
Oxygen 5mg/l or more
Biochemical Oxygen Demand 5 days 20°C 3mg/l
or less
Drinking water source
after conventional
treatment and disinfection
C
Total Coliforms Organism MPN/100ml shall be
5000 or less pH between 6 to 9 Dissolved Oxygen
4mg/l or more
Biochemical Oxygen Demand 5 days 20°C 3mg/l
or less
Propagation of Wild life
and Fisheries D
pH between 6.5 to 8.5 Dissolved Oxygen 4mg/l or
more
Free Ammonia (as N) 1.2 mg/l or less
Irrigation, Industrial
Cooling, Controlled Waste
disposal
E
pH betwwn 6.0 to 8.5
Electrical Conductivity at 25°C micro mhos/cm
Max.2250
Sodium absorption Ratio Max. 26
Boron Max. 2mg/l
Below-E Not Meeting A, B, C, D & E Criteria
* Source http://www.cpcb.nic.in/Water_Quality_Criteria.php (assessed on 12/09/2015)
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Table 4.2: Temperature, humidity and rainfall of Delhi during the study period (Jun-
2013 to Feb-2014)
Mar
13
Apr
-13
May
-13
Jun
-13
Jul
-13
Aug
-13
Sep
-13
Oct
-13
Nov
-13
Dec
-13
Jan
-14
Feb
-14
Max.
Temp.
(ºC)*
30.7 36.1 41.5 37.9 35.4 33.6 35.1 32.4 27.2 22.7 22# 26
#
Min.
Temp.
(ºC)*
16 21.2 26.1 27.7 26.8 25.9 25.1 20.6 12.2 9 6# 6
#
Humidity
(%)* 77 53 40 70 82 85 75 84 81 94 NA NA
Rainfall
(mm)** 12.6 11.6 0.0 151.0 459.8 521.9 108.1 109 0.4 6.8 18.6 63.5
Source *Statistical abstract of Delhi 2014, Directorate of Economics & Statistics, New Delhi
**http://www.iari.res.in/?option=com_content&id=402&Itemid=322 Accessed on 11-09-2015
#http://www.accuweather.com
4.1.2 Variation in DO of surface water
The DO dropped at an alarming level after the site 3 during all the study periods (Figure 2).
The maximum values of the DO were observed at the Site 1 followed by Site 2 and 3 in the
February. In general DO of these three sites have higher values than the other locations
with an increasing order form June to February. Increase in DO can be related to the
decreasing temperature in months of October and February (Table 3). Almost all DO
values of the sampling locations after site 4 were nil through all the sampling periods
except for few locations in October. An increase in the DO was observed after the
monsoon period in October when it was recorded 7.09mg/l, 6.55 mg/l, and 6.73 mg/l for
the site 1, site 2, and site 3 respectively and 1.82 mg/l, 0.55 mg/l, and 0.55 mg/l, for the site
9, site 10, and site 11 respectively. Higher DO from site 1 to 2 indicated that the water was
comparatively clean and had less microbial activity. When water contains high amounts of
oxidizable matter, in particular organic pollutants, microorganisms utilize the dissolved
oxygen to oxidize the organic matter resulting into low DO.
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0.00
1.00
2.00
3.00
4.00
5.00
6.00
7.00
8.00
9.00
site 1
site 2
site 3
site 4
site 5
site 6
site 7
site 8
site 9
site 10
site 11
site 12
DO
mg/
l
JUNE
OCT
FEB
Figure 4.2 Spatial variation of the DO (mg/l) of river Yamuna River at different
locations along the Delhi stretch during different seasons
The availability of dissolved oxygen in water depends on the exchange across the air
and water interface, subjected to the conditions such as temperature, partial pressure of
gases, solubility, photosynthetic activity of the aquatic plants and respiration by
microorganisms, plants and animals in the water (Krishnaram et al., 2007). Increased
surface DO in winter and early spring and decreased DO in summer was also observed in
an estuary in a previous report (Yin et al., 2004). Comparatively high DO concentrations
that were observed during monsoon season can be related to the mixing of the fresh water
and high rainfall in the preceding months.
4.1.3 Variation in BOD of surface water
BOD gives the quantity of oxygen needed for the microbiological oxidation or
decomposition of organic matter present in water. Thus, lower the BOD, lesser is the
presence of organic contaminants and microorganisms flourishing on these contaminants
while higher the BOD, high will be the quantity of microorganisms and organic
contaminants. Maximum BOD (58.2 mg/l) was observed at site 5 during February while
minimum (2 mg/l) at site 1 during October. The BOD was found to be in the range of 2.5
to 7.3 mg/l in June, 2.0 to 5.5 mg/l in October and 2.4 to 7.3 mg/l in February for the site 1
to site 4. After site 4 the BOD increased sharply to 52.7 mg/l, 49.1 mg/l and 58.2 mg/l at
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site 5 for the June, October and February respectively. After the site 5 a little drop was
observed; from site 6 to site 12 the BOD was in the range of 32.7 mg/l to 43.6 mg/l for
June, 27.3 mg/l to 32.7 mg/l for October and 29.1 mg/l to 40.0 mg/l for February. In
general, the BOD was low in October shortly after monsoon than in June and February.
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
site 1
site 2
site 3
site 4
site 5
site 6
site 7
site 8
site 9
site 10
site 11
site 12
BO
D m
g/l
JUNE
OCT
FEB
Figure 4.3 Spatial variation of the BOD (mg/l) of river Yamuna River at different
locations along the Delhi stretch during different seasons
The high BOD at site 5 is consistent with the fact of Najafgarh drain falling into the
river before the site. The BOD at this site and sites thereafter also indicates the improper
treatment of the wastewater of the drains prior to release into the river. Thus BOD can also
be used to determine the effectiveness of current water treatment plants that discharge the
water into the river to ensure proper treatment processes.
4.1.4 Variation in COD of surface water
Lower COD (20mg/l to 24 mg/l) was observed from site 1 to site 3 that are upstream to
Wazirabad barrage during all the sampling periods. Exceptional high increase in COD was
observed after the site 3 which continued downstream to Wazirabad barrage up to site 5
where the COD was maximum, i.e., 260 mg/l, 172 mg/l and 244 mg/l for June, October
and February respectively. COD decreased slightly downstream at site 5, with little
increase at the last sampling locations (site 10 to 12). In general, the values of COD were
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in the range of 140 mg/l to 260 mg/l for June, 80 mg/l to 172 mg/l for October and 80 mg/l
to 244 mg/l for February through the segment of river Yamuna from the site 5 to site 12.
0
50
100
150
200
250
300
site 1
site 2
site 3
site 4
site 5
site 6
site 7
site 8
site 9
site 10
site 11
site 12
CO
D m
g/l
JUNE
OCT
FEB
Figure 4.4 Spatial variation of the COD (mg/l) of river Yamuna River at different
locations along the Delhi stretch during different seasons
COD gives an idea about the total amount required for the total oxidation of the
organic matter chemically. High COD is related to the high amount of the organic
pollutants present in the water. COD is a useful indicator of organic pollution in surface
water and deterioration of the water quality caused by the discharge of industrial effluent
(Mamais et al., 1993). Considering the findings, the surface water quality of river Yamuna
in Delhi except site 1, did not meet the requirements of the Class C and was not suitable to
be used as drinking water source after conventional treatment and disinfection. Excluding
site 3 and upstream, water quality did not even fulfil the requirements of the Class D and
was not suitable for propagation of wild life and fisheries (Table 2).
4.1.5 Correlation between different water quality parameters surface water
To study the inter-relationships between various parameters at different sampling period
the Karl Pearson's coefficient of correlation was calculated and correlation matrix with
distribution histogram and scatter plot was constructed with the statistical software R
(Table 3) (Figure 5). Strong correlation was observed between most of the parameters with
each other indicating close association of these parameters with each other. Within
Results and discussion
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different period of the sampling the strong relationship was observed for the DO, BOD and
COD respectively having correlation coefficient (r) larger than 0.8829 and p-value less
than 0.001 for all the sampling periods. While the pH of October and February were
strongly related with each other (r=0.8829, p< 0.001), pH values of June were having weak
correlation with October and February (r=0.4934, p=0.103 and r=0.4556, p=0.136,
respectively). In general, DO-BOD, DO-COD, pH-BOD and pH-COD were negatively
correlated to each other. The relationship was very strong among DO and BOD with r
value from -0.934 to -0.8322 and p< 0.001 in all the sampling periods. Although the
correlation of DO with COD was also found to be strong but the relationship was stronger
within DO of all three sampling period with the COD of June and October (r = -0.9185 to -
0.8512, p < 0.001) as compared to the correlation of DO with COD of February (r= -
0.7806 to -0.7307, p≤0.007). The pH values of October and February sampling were
having stronger relationship with the BOD and COD of all sampling periods (r= -0.9264 to
-0.822, p≤0.001) as compared to the relationship among pH readings of June with COD of
all sampling period (r= -0.6349 to -0.6278, p=0.027 to 0.029), while pH readings of June
were found to have weak relationship with BOD of all sampling periods (= -0.4246 to -
0.3818, p=0.169 to 0.221). The DO for all sampling period was having positive and high
correlation with pH of October and February (r= , p=) while DO was having moderate to
strong relationship with the pH of the June (r= 0.5823 to 0.7215, p=0.047 to 0.008). The
BOD and the COD was also having positive and strong correlation with each other (r=
0.9364 to 0.7688, p≤0.001 to 0.003). Considering all the correlation results, strong
correlation was observed between pH, DO, BOD and COD with an exception of pH
recorded in the June.
The downfall of water quality in the recent years, upstream of Wazirabad barrage,
has been due to release of pollutants from upstream towns. Major portion of the river water
is collected for drinking water at Wazirabad. Thus, the 22 km urban stretch of the river in
Delhi between Wazirabad barrage and Okhla barrage is left with the sewage from drains
and fresh water from Wazirabad barrage during monsoon (CPCB, 2006; MOEF, 2013).
The natural flow of the river in this stretch is quite restricted. At site 12 again an increase
in the pollution level was observed that can be related to the discharge of Hindon cut canal
from Hindon river before the Okhla barrage. The Hindon is also a highly polluted river, it
receives the discharge for the upstream districts of Ghaziabad, effluents and wastes from
Results and discussion
| P a g e
industrial estates located in Ghaziabad, Noida and Sahibadad (Suthar et al., 2010). The
DO, BOD, COD and TDS, were several times higher than the prescribed standards for
inland water bodies while he geoaccumulation index indicates that Hindon is moderately
polluted with Cu, Cr, Fe, unpolluted to moderately polluted with Mn, Pb and Zn and very
strong polluted with Cd (Suthar et al., 2009, 2010). Thus, Hindon also contributes to the
pollution load of Yamuna.
Despite of continuous efforts since last few decades, river water quality in India is
not improving. Yamuna Action Plan (YAP) was launched in 1993, with subsequent YAP
phase II in the year 2001with an aim to rejuvenate the river but Yamuna has not been able
to achieve the desired river standards after completion of two phases of the plan, leading to
another extension of second phase. The current finding tells the different side of the story
that the plan was a complete failure. In a study it was reported that out of 80 districts in the
Yamuna river basin, 20 districts face high water stress caused either due to depletion in
water quantity or deterioration in water quality (Narula et al., 2001). Large difference
between sewage generation and treatment capacity, improper allocation of sewage
treatment plants (STPs) and mixing of treated and raw sewage due to far positioning, are
identified as the major reasons for poor water quality of Yamuna in Delhi stretch
(Upadhyay et al., 2011). Based on the already available facilities, implementation of the
corrective measures such as proper sewerage planning, efficient STPs, regulatory
guidelines for operation and maintenance of STPs, strong water management plan,
controlling industrial pollution, awareness through community participation, maintaining
the minimum ecological flow and a sustainable management plan are needed to control the
pollution in river Yamuna (Upadhyay et al., 2011). Upflow anaerobic sludge blanket
(UASB) reactors used for treatment of sewage discharged into the river are either of under
capacity or not good enough to get the desired results within the limits of Indian discharge
standards (Von Sperling et al., 2004; Walia et al., 2014).
Results and discussion
| P a g e
JU
NE
.pH
7.3
7.6
7.9
r= 0
.4934
p= 0
.103
r= 0
.4566
p= 0
.136
02
46
r= 0
.5823
p= 0
.047
r= 0
.6998
p= 0
.011
04
8
r= 0
.7215
p= 0
.008
r= -
0.3
818
p= 0
.221
10
30
50
r= -
0.3
847
p= 0
.217
r= -
0.4
246
p= 0
.169
50
150
r= -
0.6
349
p= 0
.027
r= -
0.6
278
p= 0
.029
50
150
7.28.2
r= -
0.6
300
p= 0
.028
7.37.9
OC
T.p
H
r= 0
.8932
p<0.0
01
r= 0
.8305
p= 0
.001
r= 0
.8207
p= 0
.001
r= 0
.7789
p= 0
.003
r= -
0.8
768
p<0.0
01
r= -
0.9
004
p<0.0
01
r= -
0.9
238
p<0.0
01
r= -
0.9
264
p<0.0
01
r= -
0.8
220
p= 0
.001
r= -
0.8
731
p<0.0
01
FE
B.p
H
r= 0
.8883
p<0.0
01
r= 0
.8784
p<0.0
01
r= 0
.8486
p<0.0
01
r= -
0.9
229
p<0.0
01
r= -
0.9
200
p<0.0
01
r= -
0.9
165
p<0.0
01
r= -
0.9
202
p<0.0
01
r= -
0.8
731
p<0.0
01
7.48.2
r= -
0.8
396
p= 0
.001
036
JU
NE
.DO
r= 0
.9703
p<0.0
01
r= 0
.9791
p<0.0
01
r= -
0.9
340
p<0.0
01
r= -
0.9
101
p<0.0
01
r= -
0.8
940
p<0.0
01
r= -
0.9
001
p<0.0
01
r= -
0.8
512
p<0.0
01
r= -
0.7
307
p= 0
.007
OC
T.D
O
r= 0
.9834
p<0.0
01
r= -
0.8
701
p<0.0
01
r= -
0.8
608
p<0.0
01
r= -
0.8
517
p<0.0
01
r= -
0.9
185
p<0.0
01
r= -
0.8
733
p<0.0
01
04
r= -
0.7
806
p= 0
.003
048
FE
B.D
O
r= -
0.8
697
p<0.0
01
r= -
0.8
437
p= 0
.001
r= -
0.8
322
p= 0
.001
r= -
0.8
845
p<0.0
01
r= -
0.8
625
p<0.0
01
r= -
0.7
339
p= 0
.007
JU
NE
.BO
D
r= 0
.9814
p<0.0
01
r= 0
.9555
p<0.0
01
r= 0
.8810
p<0.0
01
r= 0
.8876
p<0.0
01
1050
r= 0
.7688
p= 0
.003
1050
OC
T.B
OD
r= 0
.9733
p<0.0
01
r= 0
.9033
p<0.0
01
r= 0
.9089
p<0.0
01
r= 0
.8161
p= 0
.001
FE
B.B
OD
r= 0
.9364
p<0.0
01
r= 0
.8818
p<0.0
01
1050
r= 0
.8739
p<0.0
01
50250
JU
NE
.CO
D
r= 0
.8906
p<0.0
01
r= 0
.9433
p<0.0
01
OC
T.C
OD
50
r= 0
.8829
p<0.0
01
7.2
7.8
50250
7.4
7.8
8.2
02
46
10
30
50
10
40
50
150
FE
B.C
OD
Fig
ure
4.5
Corr
elati
on
matr
ix w
ith
sca
tter
plo
t an
d h
isto
gra
m o
f th
e st
ud
ied
para
met
ers
of
wa
ter
qu
ali
ty o
f Y
am
un
a R
iver
alo
ng t
he
Del
hi
stre
tch
du
rin
g d
iffe
ren
t se
aso
ns
Results and discussion
| P a g e
4.2 Heavy metal contamination in river Yamuna along the Delhi segment
The concentration of the heavy metals in the surface water of the river Yamuna at different
sampling sites are shown in the figure 4.6. The average concentration of Chromium (Cr)
was in range of below detection limit (BDL) to 0.791 mg l-1
(Figure 4.6). The maximum
concentration of Cr was 0.791 mg l-1
while minimum concentration was 0.069 mg l-1
at site
10 and site 4 respectively during June sampling. During October sampling the
concentration of Cr was BDL at site 3 to 7 while maximum values for Cr concentration
was 0.179 mg l-1
at site 10. During February sampling Cr concentration was BDL at site 6
while maximum value 0.394 mg l-1
was observed at site 11. There was several times
difference between maximum and minimum concentrations. The concentration of Cr was
above the WHO permissible limits of Cr in water at most of the sampling sites (Figure 4.6,
Table 2.3) therefore the water is unsuitable for domestic use and drinking. The average
concentration of the Lead (Pb) was in the range of below detection limit (BDL) to 0.308
mg l-1
(Figure 4.6). The maximum value of Pb concentration was 0.308 mg l-1
while
minimum was 0.05 mg l-1
at site 7 and site 2 respectively during June sampling. During
October sampling the concentration of Pb was BDL at site 3 to 7 while highest value for
Pb concentration was 0.188 mg l-1
at site 12. The concentration of Pb was minimum (0.028
mg l-1
) at site 4 while maximum (0.261 mg l-1
) at site 12 during February sampling. There
was several fold difference between observed maximum and minimum concentrations of
Pb. The concentration of Pb observed in this study was higher than the recommended limit
of 0.01 mg l-1
Pb in water at all sampling sites except site 1, 7, 8 and 9 during October
(Table 2.3). The concentration of Pb was even higher than the maximum permissible level
in irrigation water at some sampling sites (Table 2.4). The concentration of lead may be
due to lead battery-based industries or vehicular use in these areas. The average
concentration of the Mercury (Hg) was in the range of below detection limit (BDL) to
0.008 mg l-1
(Figure 4.6). The concentration of Hg was BDL at site 1, 2 and 3 during all
sampling period. The maximum value of Hg concentration was 0.008 mg l-1
, 0.005mg l-1
and 0.006mg l-1
respectively during June, October and February sampling. The
concentrations of Hg was observed within the range of Dutch Target and Intervention
Values, (2000) for ground water (Table 2.5), however at most of the sampling sites (Site 4
to 12), during all sampling period it was higher than the 0.001 mg l-1
stipulated as per the
Criteria maximum concentration (CMC) which is an estimate of the highest concentration
Results and discussion
| P a g e
of a material in surface water to which an aquatic community can be exposed briefly
without resulting in an unacceptable effect (US EPA, 2005).
The average values of the Zinc (Zn) concentration were in the range of 0.171 mg l-1
to 1.084 mg l-1
in the surface water of river Yamuna along Delhi segment (Figure 4.6). The
maximum value of Zn (0.552 mg l-1
) was recorded at site 7 while minimum value (0.247
mg l-1
) was recorded at the site 5 during June sampling. The maximum value of Zn (0.573
mg l-1
) was recorded at site 7 while minimum value (0.257 mg l-1
) was recorded at the site
10 during October sampling. The maximum value of Zn (1.084 mg l-1
) was recorded at site
9 while minimum value (0.171 mg l-1
) was recorded at the site 11 during February
sampling. The observed values of Zn in current study much below then the WHO
permissible limits and Dutch intervention values of the groundwater (Table 2.3 and 2.5).
The average values of the Manganese (Mn) concentrations were in the range of BDL to
1.066 mg l-1
(Figure 4.6). The maximum value of Mn (0.0.667 mg l-1
) was recorded at site
5 while minimum value (0.387 mg l-1
) was recorded at the site 8 during June sampling. The
maximum value of Mn (0.483 mg l-1
) was recorded at site 6 while BDL at the site 3 and 4
during October sampling. The maximum value of Mn (1.066 mg l-1
) was recorded at site 7
while BDL at the site 4 during February sampling. The average concentration of Mn
exceeds the stipulated maximum permissible limit of 0.1 mg l-1
by WHO (Table 2.3) at
most of studied sites except site 3 and 4 during October and February sampling.
The average values of the Magnesium (Mg) concentration were in the range of
2.171 mg l-1
to 31.217 mg l-1
in the surface water of river Yamuna along Delhi segment
(Figure 4.6). The Mg concentration was recorded highest (31.217 mg l-1
) at site 5 while
lowest (2.174 mg l-1
) at the site 4 in June. The maximum value of Mg (13.903 mg l-1
) was
recorded at site 7 while minimum value (5.609 mg l-1
) was recorded at the site 2 in
October. Again in February the maximum value of Mg (25.145 mg l-1
) was recorded at site
5 while minimum value (2.192 mg l-1
) was recorded at the site 4. The observed values of
Mg were below the NEQS (National Environmental Quality Standard for industrial
effluents) limits (Table 2.2).
The average values of the Iron (Fe) concentrations were in the range of 0.148 mg l-1
to 2.847 mg l-1
(Figure 4.6). The highest concentration of Fe (2.847 mg l-1
) was recorded at
site 9 while lowest (0.279 mg l-1
) was recorded at the site 3 during June sampling. The
Results and discussion
| P a g e
maximum Fe concentration (1.271 mg l-1
) was recorded at site 11 while minimum (0.233
mg l-1
) at the site 3 during October sampling. During February sampling Fe was highest
(2.031 mg l-1
) at site 9 while lowest (0.148 mg l-1
) at the site 4. The average concentration
of Fe exceeds the stipulated maximum permissible limit of 0.3 mg l-1
by WHO (Table 2.3)
at most of the sampling locations after sites 4.
The recorded concentrations of Cr, Pb, Zn, Mn and Fe in present study were much
higher than the Cr (0.0013 – 0.0057 mg l-1
), Pb (0.019 – 0.039 mg l-1
), Zn (mg l-1
), Mn
(0.0013 – 0.0053 mg l-1
) and Fe (0.34 – 0.117 mg l-1
) in the water of river Gomti, another
major polluted river of India (Singh et al. 2005b). Kaushik et al., (2009) assessed
concentration of Cd, Cr, Fe, Ni in water, plants and sediments at 14 selected sites of river
Yamuna flowing in Haryana through Delhi covering the upstream and downstream sites of
major industrial complexes of the State. They observed that the river was significantly
contaminated with Ni and Cd, while Cr contamination was moderate except two or three
sites which are the downstream stations of dyeing, paint industries and anthropogenic
contamination of Fe was negligible. But the observed high concentrations of metals in
present study need particular attention for identifying and rectification of the source.
In a previous study on river Yamuna in Dehradun district of Uttarakhand, India, the
concentration range for Fe (1.3143 – 2.0989 mg l-1
) and Zn (1.2509 - 1.4506 mg l-1
) was
recorded high as compared to Cd (0.004 – 0.0084 mg l-1
), Co (0.0043 – 0.0055 mg l-1
), Cr
(0.0049 – 0.0064 mg l-1
) and Ni (0.0041 – 0.0069 mg l-1
) (Ishaq and Khan, 2013). Jain et
al., (2005) observed wide temporal variation in metal concentrations in water with bed
sediment because of variability in water discharge and variations in suspended solid
loadings in river Hindon which one of the important rivers in western Uttar Pradesh (India)
and joins river Yamuna at Tilwara. Rawat et al., (2003) reported high concentration of
heavy metals such as Fe (2-212 mg l-1
), Mn (0.3-39 mg l-1
), Cu (0.2-20 mg l-1
), Zn (0.2-5
mg l-1
), Ni (0.6-6 mg l-1
), Cr (0.2-53 mg l-1
), Cd (0.08-0.2 mg l-1
), Co (0.013-0.55 mg l-1
)
and Pb (0.3-0.7 mg l-1
) mg L(-1) in wastewater from small-scale industrial areas of Delhi
(India). Rawat et al., (2003) also pointed out that rules for the treatment of waste in small-
scale industries are less strict due to less waste generation within each individual industry.
Therefore small-scale industries commonly dispose their wastewater untreated into drains
and subsequently into the river Yamuna, adding to the pollution load of the river and
Results and discussion
| P a g e
posing a potential health and environmental risk to the people living in Delhi and
downstream.
0.000
0.100
0.200
0.300
0.400
0.500
0.600
0.700
0.800
0.900
1.000
Site 1 Site 2 Site 3 Site 4 Site 5 Site 6 Site 7 Site 8 Site 9 Site 10 Site 11 Site 12
June Oct Feb
Cr
mg
l-1
Location
0.000
0.050
0.100
0.150
0.200
0.250
0.300
0.350
Site 1 Site 2 Site 3 Site 4 Site 5 Site 6 Site 7 Site 8 Site 9 Site 10 Site 11 Site 12
June Oct Feb
Pb
Co
nce
ntr
atio
n m
g l-1
Location
0.000
0.001
0.002
0.003
0.004
0.005
0.006
0.007
0.008
0.009
0.010
Site 1 Site 2 Site 3 Site 4 Site 5 Site 6 Site 7 Site 8 Site 9 Site 10 Site 11 Site 12
June Oct Feb
Hg
Co
nce
ntr
atio
n m
g l-1
Location
Results and discussion
| P a g e
0.000
0.200
0.400
0.600
0.800
1.000
1.200
Site 1 Site 2 Site 3 Site 4 Site 5 Site 6 Site 7 Site 8 Site 9 Site 10 Site 11 Site 12
June Oct Feb
Zn
Co
nce
ntr
atio
n m
g l-1
Location
Mn
Co
nce
ntr
atio
n m
g l-1
Location
0.000
0.200
0.400
0.600
0.800
1.000
1.200
Site 1 Site 2 Site 3 Site 4 Site 5 Site 6 Site 7 Site 8 Site 9 Site 10 Site 11 Site 12
June Oct Feb
Co
nce
ntr
atio
n m
g l-1
Location
0.000
5.000
10.000
15.000
20.000
25.000
30.000
35.000
Site 1 Site 2 Site 3 Site 4 Site 5 Site 6 Site 7 Site 8 Site 9 Site 10 Site 11 Site 12
June Oct Feb
Mg
Results and discussion
| P a g e
FeC
on
cen
trat
ion
mg
l-1
Location
0.000
0.500
1.000
1.500
2.000
2.500
3.000
3.500
Site 1 Site 2 Site 3 Site 4 Site 5 Site 6 Site 7 Site 8 Site 9 Site 10 Site 11 Site 12
June Oct Feb
Figure 4.6 Concentration of different metals in surface water at selected sites of river
Yamuna in Delhi segment, during different sampling periods
4.2.1 Correlation between concentrations of metals in surface water at different
sampling sites of river Yamuna along the Delhi stretch
To study the inter-relationships between concentrations of metals in surface water at
different sampling sites of river Yamuna along the Delhi stretch the Karl Pearson's
coefficient of correlation was calculated and correlation matrix with distribution histogram
and scatter plot was constructed with the statistical software R (Figure 4.7). Strong positive
correlation (r> 0.9, p<0.001) was observed between heavy metals at most of the sites with
each other indicating close association of these with each other except site 3 and 4. Metal
concentration at the site 3 was also strongly (r= 0.7083 to 0.8846, p<0.001) correlated to
the same at other sites but relationship was not as strong as others. Metal concentration at
the site 4 had varied relationship with that at other sites having r value from 0.488 to
0.8842 and p< 0.001 to 0.025. The metal concentrations at site 4 was most weakly (r=
0.488, p=0.025) related to that at the site 5, this can related to sudden increase in the heavy
metal load of the river downstream to the Wazirabad barrage after falling of Najafgarh
drain.
Results and discussion
| P a g e
Site
1
02
4
r= 0
.8851
p<0.0
01
r= 0
.8312
p<0.0
01
04
8
r= 0
.5759
p= 0
.006
r= 0
.9703
p<0.0
01
010
20
r= 0
.8914
p<0.0
01
r= 0
.9865
p<0.0
01
05
15
r= 0
.9216
p<0.0
01
r= 0
.9336
p<0.0
01
010
20
r= 0
.9513
p<0.0
01
r= 0
.9409
p<0.0
01
05
15
015
r= 0
.9894
p<0.0
01
03
Site
2
r= 0
.8563
p<0.0
01
r= 0
.8172
p<0.0
01
r= 0
.8842
p<0.0
01
r= 0
.9605
p<0.0
01
r= 0
.9146
p<0.0
01
r= 0
.9765
p<0.0
01
r= 0
.9607
p<0.0
01
r= 0
.9276
p<0.0
01
r= 0
.9609
p<0.0
01
r= 0
.9160
p<0.0
01
Site
3
r= 0
.8846
p<0.0
01
r= 0
.7083
p<0.0
01
r= 0
.7169
p<0.0
01
r= 0
.7780
p<0.0
01
r= 0
.7780
p<0.0
01
r= 0
.7684
p<0.0
01
r= 0
.7210
p<0.0
01
r= 0
.7709
p<0.0
01
06
r= 0
.7919
p<0.0
01
06
Site
4
r= 0
.4880
p= 0
.025
r= 0
.6460
p= 0
.002
r= 0
.5648
p= 0
.008
r= 0
.6879
p= 0
.001
r= 0
.6513
p= 0
.001
r= 0
.5587
p= 0
.008
r= 0
.6480
p= 0
.001
r= 0
.5779
p= 0
.006
Site
5
r= 0
.9461
p<0.0
01
r= 0
.9940
p<0.0
01
r= 0
.9529
p<0.0
01
r= 0
.9662
p<0.0
01
r= 0
.9907
p<0.0
01
r= 0
.9715
p<0.0
01
020
r= 0
.9915
p<0.0
01
015
Site
6
r= 0
.9472
p<0.0
01
r= 0
.9945
p<0.0
01
r= 0
.9897
p<0.0
01
r= 0
.9796
p<0.0
01
r= 0
.9907
p<0.0
01
r= 0
.9441
p<0.0
01
Site
7
r= 0
.9630
p<0.0
01
r= 0
.9731
p<0.0
01
r= 0
.9881
p<0.0
01
r= 0
.9780
p<0.0
01
015
r= 0
.9992
p<0.0
01
015
Site
8
r= 0
.9943
p<0.0
01
r= 0
.9827
p<0.0
01
r= 0
.9956
p<0.0
01
r= 0
.9619
p<0.0
01
Site
9
r= 0
.9876
p<0.0
01
r= 0
.9972
p<0.0
01
015
r= 0
.9709
p<0.0
01
020
Site
10
r= 0
.9920
p<0.0
01
r= 0
.9855
p<0.0
01
Site
11
015
r= 0
.9772
p<0.0
01
05
15
015
04
80
10
25
010
20
05
15
05
15
Site
12
Fig
ure
4.7
. C
orr
elati
on
matr
ix w
ith
sca
tter
plo
t an
d h
isto
gra
m o
f th
e d
iffe
ren
t si
tes
stu
die
d f
or
the
con
cen
tra
tion
of
met
als
in
su
rface
wa
ter
of
river
Ya
mu
na a
lon
g t
he
Del
hi
stre
tch
Results and discussion
| P a g e
4.2.3 Hierarchical cluster analysis
Hierarchical cluster analysis (HACA) based on agglomerative statistics using Ward‘s
Method was done for concentration of heavy metals at each of the sampling sites and
period using PAST software (Hammer et al., 2001). In the first dendrogram, representing
the metal concentrations at different sites irrespective of the sampling period, the samples
were classified into three clusters using a criteria value of rescaled distance between 0-40.
There were 3 sites in cluster-1, 4 sites in cluster-2 and 5 sites in clusters-3 (Figure 4.8). Site
2, 3 and 4 were in the cluster-1, indicates similarity in the pattern of the metal
concentration at these sites. Site 10, 7, 12 and 5 were in the cluster-2, indicates that metal
concentrations at these sites have similarities. Site 1, 9, 11, 6 and 8 were in the cluster-3,
indicates that metal concentration at these sites have similarities. The cluster analysis
reveals that heavy metal concentrations in the river water vary greatly with the location of
the sampling sites.
To get the detailed insight of the variation and similarities of metal concentrations at each
sampling site with the sampling period second dendrogram was constructed (Figure 4.9),
the samples were classified into five major clusters using a criteria value of rescaled
distance between 0-45. There were eight samples in cluster-1, four samples in cluster-2, six
samples in cluster-3, five samples in cluster-4 and 12 samples in cluster-5. In cluster-1 and
cluster-2 most of the samples were of downstream sites (site 6, 11, 12, 9, 5, 7 and 10) of
June sampling. In the cluster-3 site 2, 3 and 4 were present representing the all three
sampling (June, October and February). In the cluster-4 Site 6, 3, 1, 8 and 4 were present
representing mostly October and February sampling. In the cluster-5 most of the samples
were of downstream sites (6 to 12) representing October and February sampling except site
1 representing June sampling. The observations of the cluster analysis depict that heavy
metal concentrations in the river water considerably vary with the location and period of
the sampling.
4.2.3 Principal components analysis
Principal components analysis (PCA) was done using PAST software (Hammer et al.,
2001) to examine multivariate relationship within the concentration of different metals and
sampling site and variance in sampling period.
Results and discussion
| P a g e
Cluster 1
Cluster 2
Cluster 3
40
36
32
28
24
20
16
12 8 4
Distance
Site2
Site3
Site4
Site10
Site7
Site12
Site5
Site1
Site9
Site11
Site6
Site8
Figure 4.8 Dendrogram produced using the Ward algorithm showing the variation of
the metal concentration with the sampling sites in the surface water of river
Yamuna along the Delhi stretch
Cluster 1
Cluster 2
Cluster 3
45
40
35
30
25
20
15
10 5
Distance
S6JuneS7FebruaryS1FebruaryS8JuneS12FebruaryS11JuneS12JuneS9JuneS5JuneS7JuneS10JuneS5FebruaryS2JuneS2OctoberS3JuneS2FebruaryS4JuneS4FebruaryS4OctoberS8FebruaryS1OctoberS3OctoberS3FebruaryS6FebruaryS8OctoberS5OctoberS7OctoberS1JuneS10FebruaryS9FebruaryS11FebruaryS10OctoberS12OctoberS6OctoberS9OctoberS11October
Cluster 4
Cluster 5
Figure 4.9 Dendrogram produced using the Ward algorithm showing the variation of
the metal concentration with the sampling sites and period in the surface water of
river Yamuna along the Delhi stretch
Results and discussion
| P a g e
The biplot depicting the variation of metal concentrations of surface water of river Yamuna
in Delhi with the sampling period is presented in figure 4.10. Principal component analysis
identified two discrete groups of metals. In the first group Mg was present having high
concentrations at most of the sampling sites and second group have Cr, Zn, Hg, Pb, Fe and
Mn having low concentrations as compared to Mg. Significant variations of metal
concentration in different sampling period observed suggests that metal concentrations in
the river Yamuna in Delhi have seasonal variations with June and February having similar
trend while October had a different trend.
June
October
February
-10 -5 5 10 15 20 25 30 35
Component 1
-4.8
-3.2
-1.6
1.6
3.2
4.8
6.4
8
Com
ponen
t 2
Mg
Cr Zn Hg PbFe Mn
Figure 4.10 Biplot depicting the variation of metal concentrations of surface water of
river Yamuna in Delhi with the sampling period
4.2.4 Correlation between different metals studied
To study the inter-relationships between different metals studied in surface water at
different sampling sites of river Yamuna along the Delhi stretch the Karl Pearson's
coefficient of correlation was calculated and correlation matrix with distribution histogram
and scatter plot was constructed with the statistical software R (Figure 4.7). No correlation
was observed between different metals studied (r= -0.2342 to 0.5866, p>0.001) except
Results and discussion
| P a g e
between Mg and Fe having r=0.6122 and p<0.001. This suggests that concentration varies
with the metal and sampling site that can be related to different source for each metal.
Cr
0.00 0.20
r= 0.2625
p= 0.122
r= 0.1296
p= 0.451
0.2 0.6 1.0
r= -0.2149
p= 0.208
r= 0.3002
p= 0.075
5 15 30
r= 0.4086
p= 0.013
0.0
0.4
0.8
r= 0.4257
p= 0.01
0.0
00.2
0 Pb
r= 0.5866
p<0.001
r= -0.1394
p= 0.417
r= 0.3823
p= 0.021
r= 0.5146
p= 0.001
r= 0.3404
p= 0.042
Hg
r= -0.2019
p= 0.238
r= 0.1417
p= 0.41
r= 0.5578
p<0.001
0.0
00
0.0
06
r= 0.5110
p= 0.001
0.2
0.6
1.0
Zn
r= 0.3626
p= 0.03
r= -0.2342
p= 0.169
r= 0.04967
p= 0.774
Mn
r= 0.3456
p= 0.039
0.0
0.6
r= 0.4236
p= 0.01
515
30
Mg
r= 0.6122
p<0.001
0.0 0.4 0.8 0.000 0.006 0.0 0.6 0.5 2.0
0.5
2.0Fe
Figure 4.11 Correlation matrix with scatter plot and histogram of different metals
assessed in the surface water of river Yamuna along the Delhi stretch
4.3 Spatial variation sediment and agriculture soil pH along river Yamuna in Delhi
segment
The pH of the sediments was found to be alkaline in the range of 7.51 to 8.6. The pH was
found to be in the range of 7.6 to 8.6 in June (pre-monsoon), 7.62 to 8.2 in October (post-
monsoon) and 7.51 to 8.32 in February (spring) (Figure 4.12). In general the pH was
higher in June followed by February and October at all locations except for the site 4 in
June and site 5, 9 in February. In general pH of the upstream sites was more alkaline than
the downstream of the site 4 with an exception of site 10. An abrupt downfall in the pH
Results and discussion
| P a g e
was observed after the site 3 and 4 during all the seasons which might be due to the
discharge of the wastewater to the river by Najafgarh drain before the site 5. Overall pH of
the sediments have similar trend as the pH of water at different sites (Figure 4.1)
7.20
7.40
7.60
7.80
8.00
8.20
8.40
8.60
8.80
site 1
site 2
site 3
site 4
site 5
site 6
site 7
site 8
site 9
site 10
site 11
site 12
pH JUNE
OCT
FEB
Figure 4.12 Spatial variation of the pH of sediments along river Yamuna in Delhi
stretch
7.60
7.80
8.00
8.20
8.40
8.60
8.80
9.00
Site 1 Site 2 Site 3 Site 5 Site 7 Site 8
pH JUNE
OCT
FEB
Figure 4.13 Spatial variation of the pH of river-side agriculture soil along river
Yamuna in Delhi stretch
The pH of the selected agriculture soil was also alkaline in the range of 8.00 to 8.9 (Figure
4.13). This range was higher than the recorded pH range of the water and sediments
(Figure 4.1 and 4.12) The pH was found to be in the range of 8.2 to 8.9 in June (pre-
Results and discussion
| P a g e
monsoon), 8.0 to 8.8 in October (post-monsoon) and 8.3 to 8.82 in February (spring)
(Figure 4.12). The observed values of the pH in the agriculture soil of site 5 were much
higher than the considerable low values of pH for water and sediments at site 5. This
observed high pH can be due to extensive use of chemical fertilizers and pesticides in the
vegetables grown at the site 5. Observed high pH is sometimes temporary e.g. .there is a
temporary effect around the fertilizer resulting into high pH from urea hydrolysis.
4.4Heavy metal load of the sediment and agriculture soil along river Yamuna in Delhi
segment
4.4.1 Heavy metal concentration in the freshly deposited sediments of river Yamuna
in Delhi
The concentrations of the heavy metals in the freshly deposited sediments of the river
Yamuna at different sampling sites are shown in the figure 4.14. The average concentration
of Chromium (Cr) was in range of 10.067 mg kg-1
to 249.433 mg kg-1
(Figure 4.14). The
maximum concentration of Cr was 249.433 mg kg-1
while minimum concentration was
51.5 mg kg-1
at site 12 and site 4 respectively in June. In October concentration of Cr was
minimum (10.067 mg kg-1
) at site 5 while maximum (67.733 mg kg-1
) at site 10. In
February Cr concentration was lowest (22.867 mg kg-1
) at site 7 while highest (84.467 mg
kg-1
) at site 10. There was several times difference between maximum and minimum
concentrations. The concentration of Cr was above the WHO permissible limits of Cr in
sediment at most of the sampling sites (Figure 4.14, Table 2.3) however concentration was
lower than the stipulated 380 mg kg-1
Dutch intervention value for sediments (Table 2.5).
The average concentration of the Lead (Pb) in the sediments was in the range of 4.267 mg
kg-1
to 97.233 mg kg-1
(Figure 4.14). The maximum Pb concentration (97.233 mg kg-1
) was
recorded at site 12 while minimum (21.6 mg kg-1
) was recorded at site 11 in June. In
October the concentration of Pb was highest (89.6 mg kg-1
) at site 12 while lowest (4.267
mg kg-1
) at site 9. The concentration Pb was minimum (20 mg kg-1
) at site 1 while
maximum (95.7 mg kg-1
) at site 12 in February. There was several fold difference between
observed maximum and minimum concentrations of Pb in sediments. The concentration of
Pb observed in this study was higher than the recommended limit of 40 mg kg-1
Pb in
sediments by USEPA (United States Environmental Protection Agency) at site 5, 6, 7, 8
and 12 in all sampling period (Table 2.3). But the recorded values of the Pb concentration
Results and discussion
| P a g e
was much lower than the recommended intervention value of 530 mg kg-1
for sediments by
Dutch Environmental Guidelines & Standards (2000) (Table 2.4). The high concentration
of lead can also be related to the high Pb concentration in the river water (Figure 4.6). The
average concentration of the Mercury (Hg) in the sediments was in the range of 1.733 mg
kg-1
to 77.9 mg kg-1
(Figure 4.14). The concentration of Hg was higher than 10 mg kg-1
recommended Dutch intervention value for sediments at sites 5, 6, 7, 8 and 9 at most of
sampling periods. The maximum value of Hg concentration was 77.9 mg kg-1
, 35.933 mg
kg-1
and 67.6 mg kg-1
respectively in June, October and February at site 5. The minimum
value of Hg concentration was 2.567 mg kg-1
at site 2, 1.733 mg kg-1
at site 1 and 2.367 mg
kg-1
at site 1 respectively in June, October and February.
The average values of the Zinc (Zn) concentration in freshly deposited sediments of
river Yamuna were in the range of 26.933 mg kg-1
to 202.2 mg kg-1
along the Delhi
segment (Figure 4.14). The maximum value of Zn (202.2 mg kg-1
) was recorded at site 7
while minimum value (87.833 mg kg-1
) was recorded at the site 5 in June. The maximum
value of Zn (146.033 mg kg-1
) was recorded at site 5 while minimum value (51.4 mg kg-1
)
was recorded at the site 12 in October. The maximum value of Zn (186.867 mg kg-1
) was
recorded at site 9 while minimum value (26.933 mg kg-1
) was recorded at the site 6 in
February. The observed values of Zn in the present study were higher than WHO
permissible limits (123 mg kg-1
) for sediments at all sites at some point of time or other
during the study period (Table 2.3). While the Zn concentrations in the water were within
in the recommended permissible limits of WHO for water, Zn concentrations in sediments
was slightly above than the recommended permissible limits of WHO for sediments. As
compared to Dutch intervention values of Zn (720 mg kg-1
) for sediments, the recorded Zn
concentration was much lower in current study (Table 2.5). The average values of the
Manganese (Mn) concentrations in the sediments were in the range of 157.167 mg kg-1
to
581.533 mg kg-1
(Figure 4.14). The maximum value of Mn (581.533 mg kg-1
) was
recorded at site 11 while minimum value (241.233 mg kg-1
) was recorded at the site 8 in
June. The maximum value of Mn (467.6 mg kg-1
) was recorded at site 7 while minimum
value (208.333 mg kg-1
) at the site 8 in October. The maximum value of Mn (556.867 mg
kg-1
) was recorded at site 7 while minimum value (157.167 mg kg-1
) at the site 5 in
February. The concentration of Mn observed in this study was much higher than the
Results and discussion
| P a g e
recommended limit of 30 mg kg-1
in sediments by USEPA (United States Environmental
Protection Agency) at all sites and sampling period (Table 2.3).
The average values of the Magnesium (Mg) concentration in freshly deposited
sediments of river Yamuna were in the range of 814.333 mg kg-1
to 21520.667 mg kg-1
along Delhi segment (Figure 4.14). The Mg concentration was highest (21520.667 mg kg-
1) at site 9 while lowest (839.333 mg kg
-1) at the site 2 in June. The maximum value of Mg
(19796.333 mg kg-1
) was recorded at site 9 while minimum value (877 mg kg-1
) was
recorded at the site 2 in October. The maximum value of Mg (19710.333 mg kg-1
) was
recorded at site 1 while minimum value (814.333 mg kg-1
) was recorded at the site 2. The
average values of the Iron (Fe) concentrations in sediments were in the range of 23168.333
mg kg-1
to 43580 mg kg-1
(Figure 4.14). The highest concentration of Fe (43580 mg kg-1
)
was recorded at site 9 while lowest (24545.333 mg l-1
) was recorded at the site 11 in June.
The maximum Fe concentration (40718.667 mg kg-1
) was recorded at site 9 while
minimum (23168.333 mg kg-1
) at the site 11 in October. In February Fe was highest
(41910.333 mg kg-1
) at site 9 while lowest (23175 mg kg-1
) at the site 11. The average
concentration of Fe at a particular site exceeds the stipulated maximum permissible limit of
30 mg kg-1
in sediments by USEPA at all sites and sampling period (Table2.3).
The recorded concentrations of Zn, Fe, Mn, Pb and Hg in present study were higher
than the Zn (31.9 to 136.85 mg kg-1
), Fe (4431.5 to 4915.3 mg kg-1
) and Mn (277 to 543
mg kg-1
), lower than Pb (11.5 to 114.65 mg kg-1
) and Hg (0.425 to 82.06 mg kg-1
) observed
in the soil samples along river Yamuna at Delhi (Sehgal et al., 2012). In a previous study,
Singh et al., (2002) analysed freshly deposited stream sediments from six urban centres
including Kanpur, Allahabad, Varanasi, Lucknow, Delhi and Agra of the Ganga Plain for
heavy metals and observed that concentrations of heavy metals varied within a wide range
for Cr (115–817), Mn (440–1 750), Fe (28 700–61 100), Co (11.7–29.0), Ni (35–538), Cu
(33–1 204), Zn (90–1 974), Pb (14–856) and Cd (0.14–114.8) in mg kg-1
. Kaushik et al.,
(2009) observed high Ni and Cd in the sediments of river Yamuna flowing through
Haryana and Delhi.
Results and discussion
| P a g e
CrC
on
cen
trat
ion
mg
kg-1
Location
0.000
50.000
100.000
150.000
200.000
250.000
300.000
Site 1 Site 2 Site 3 Site 4 Site 5 Site 6 Site 7 Site 8 Site 9 Site 10 Site 11 Site 12
June Oct Feb
Pb
Location
0.000
20.000
40.000
60.000
80.000
100.000
120.000
Site 1 Site 2 Site 3 Site 4 Site 5 Site 6 Site 7 Site 8 Site 9 Site 10 Site 11 Site 12
June Oct Feb
Co
nce
ntr
atio
n m
g kg
-1
Hg
Location
0.000
10.000
20.000
30.000
40.000
50.000
60.000
70.000
80.000
90.000
100.000
Site 1 Site 2 Site 3 Site 4 Site 5 Site 6 Site 7 Site 8 Site 9 Site 10 Site 11 Site 12
June Oct Feb
Co
nce
ntr
atio
n m
g kg
-1
Results and discussion
| P a g e
Zn
Location
0.000
50.000
100.000
150.000
200.000
250.000
Site 1 Site 2 Site 3 Site 4 Site 5 Site 6 Site 7 Site 8 Site 9 Site 10 Site 11 Site 12
June Oct Feb
Co
nce
ntr
atio
n m
g kg
-1
Mn
Location
0.000
100.000
200.000
300.000
400.000
500.000
600.000
700.000
Site 1 Site 2 Site 3 Site 4 Site 5 Site 6 Site 7 Site 8 Site 9 Site 10 Site 11 Site 12
June Oct Feb
Co
nce
ntr
atio
n m
g kg
-1
Mg
Location
0.000
5000.000
10000.000
15000.000
20000.000
25000.000
30000.000
Site 1 Site 2 Site 3 Site 4 Site 5 Site 6 Site 7 Site 8 Site 9 Site 10 Site 11 Site 12
June Oct Feb
Co
nce
ntr
atio
n m
g kg
-1
Results and discussion
| P a g e
Fe
Location
0.000
10000.000
20000.000
30000.000
40000.000
50000.000
60000.000
Site 1 Site 2 Site 3 Site 4 Site 5 Site 6 Site 7 Site 8 Site 9 Site 10 Site 11 Site 12
June Oct FebC
on
cen
trat
ion
mg
kg-1
Figure 4.14 Concentration of different metals in the sediments of river Yamuna in
Delhi segment, during different sampling periods
Rawat et al., (2003) reported high concentrations of Fe (5842-78000 mg kg-1
), Mn
(585-10889 mg kg-1
), Cu (206-7201 mg kg-1
), Zn (406-9000 mg kg-1
), Ni (22-3621 mg kg-
1), Cr (178-10533 mg kg
-1), Co (17-114 mg kg
-1), Cd (13-141 mg kg
-1), Pb (67-50171 mg
kg-1
) in suspended material and Fe (3000-84000 mg kg-1
), Mn (479-1230 mg kg-1
), Cu
(378-8127 mg kg-1
), Zn (647-4010 mg kg-1
), Ni (164-1582 mg kg-1
), Cr (139-3281 mg kg-
1), Co (20-54 mg kg
-1), Cd (37-65 mg kg
-1), Pb (228-293 mg kg
-1) in bed residues of the
wastewater from small-scale industrial areas of Delhi. This suggests that if the wastewater
from small-scale industries is released into the river without proper treatment, it can be
responsible for the high concentrations of metals in the river.
4.4.1.1 Correlation between concentrations of metals in freshly deposited sediments at
different sampling sites
To study the inter-relationships between concentrations of metals in freshly deposited
sediments at different sampling sites of river Yamuna along the Delhi stretch the Karl
Pearson's coefficient of correlation was calculated and correlation matrix with distribution
histogram and scatter plot was constructed with the statistical software R (Figure 4.15).
Strong positive correlation (r> 0.7, p<0.001) was observed between heavy metals at most
of the sites with each other indicating close association of these with each other. In
addition to that high correlation coefficient (r>0.99, p<0.001) was observed between many
sites.
Results and discussion
| P a g e
Fig
ure
4.1
5 C
orr
elati
on
matr
ix w
ith
sca
tter
plo
t an
d h
isto
gra
m o
f th
e d
iffe
ren
t si
tes
stu
die
d f
or
the
con
cen
trati
on
of
met
als
in
sed
imen
ts o
f riv
er Y
am
un
a a
lon
g t
he
Del
hi
stre
tch
Site
1
020000
r= 0
.7554
p<0.0
01
r= 0
.7650
p<0.0
01
015000
r= 0
.7681
p<0.0
01
r= 0
.7653
p<0.0
01
020000
r= 0
.8212
p<0.0
01
r= 0
.8499
p<0.0
01
020000
r= 0
.9126
p<0.0
01
r= 0
.9655
p<0.0
01
020000
r= 0
.9702
p<0.0
01
r= 0
.9859
p<0.0
01
015000
0
r= 0
.9310
p<0.0
01
0
Site
2
r= 0
.9995
p<0.0
01
r= 0
.9992
p<0.0
01
r= 0
.9986
p<0.0
01
r= 0
.9938
p<0.0
01
r= 0
.9866
p<0.0
01
r= 0
.9502
p<0.0
01
r= 0
.8989
p<0.0
01
r= 0
.8900
p<0.0
01
r= 0
.8485
p<0.0
01
r= 0
.9420
p<0.0
01
Site
3
r= 0
.9999
p<0.0
01
r= 0
.9992
p<0.0
01
r= 0
.9955
p<0.0
01
r= 0
.9877
p<0.0
01
r= 0
.9521
p<0.0
01
r= 0
.9051
p<0.0
01
r= 0
.8957
p<0.0
01
r= 0
.8560
p<0.0
01
0
r= 0
.9471
p<0.0
01
0
Site
4
r= 0
.9990
p<0.0
01
r= 0
.9958
p<0.0
01
r= 0
.9878
p<0.0
01
r= 0
.9523
p<0.0
01
r= 0
.9070
p<0.0
01
r= 0
.8974
p<0.0
01
r= 0
.8584
p<0.0
01
r= 0
.9485
p<0.0
01
Site
5
r= 0
.9955
p<0.0
01
r= 0
.9873
p<0.0
01
r= 0
.9528
p<0.0
01
r= 0
.9052
p<0.0
01
r= 0
.8961
p<0.0
01
r= 0
.8558
p<0.0
01
0
r= 0
.9472
p<0.0
01
0
Site
6
r= 0
.9971
p<0.0
01
r= 0
.9751
p<0.0
01
r= 0
.9406
p<0.0
01
r= 0
.9330
p<0.0
01
r= 0
.8999
p<0.0
01
r= 0
.9727
p<0.0
01
Site
7
r= 0
.9881
p<0.0
01
r= 0
.9568
p<0.0
01
r= 0
.9517
p<0.0
01
r= 0
.9214
p<0.0
01
0
r= 0
.9824
p<0.0
01
0
Site
8
r= 0
.9848
p<0.0
01
r= 0
.9840
p<0.0
01
r= 0
.9634
p<0.0
01
r= 0
.9939
p<0.0
01
Site
9
r= 0
.9991
p<0.0
01
r= 0
.9937
p<0.0
01
0
r= 0
.9935
p<0.0
01
0
Site
10
r= 0
.9951
p<0.0
01
r= 0
.9905
p<0.0
01
Site
11
0
r= 0
.9758
p<0.0
01
015000
0
015000
015000
020000
030000
015000
Site
12
Results and discussion
| P a g e
4.4.1.2 Hierarchical cluster analysis
Hierarchical cluster analysis (HACA) based on agglomerative statistics using Ward‘s
Method was done for concentration of heavy metals at each of the sampling sites and
period using PAST software (Hammer et al., 2001). In the first dendrogram, representing
the metal concentrations at different sites irrespective of the sampling period, the samples
were classified into five clusters using a criteria value of rescaled distance between 0-
6E04. There was only one site in cluster-1, three sites in cluster-2, four sites in clusters-3,
only one site in cluster-4 and three sites in cluster-5 (Figure 4.16). There were only one
sites in clusters-1 and cluster-4, therefore no comparison could be conducted between these
and other clusters. Site 3, 4 and 5 were in the cluster-2, indicates similarity in the trend of
the metal concentration at these sites. In cluster-3 site 6, 7, 8 and 12 were present,
indicating similar trend of metal concentrations at these sites, however within cluster-3,
site 6 and 7, site 8 and 12 were more closely related to each other respectively. Site 1, 10,
and 11 were in the cluster-5, indicates that metal concentration at these sites have
similarities. The variation in the heavy metal concentrations in the sediments at different
sites was related to separation of sites into different clusters.
To get the detailed insight of the variation and similarities of metal concentrations at each
sampling site with the sampling period second dendrogram was constructed (Figure 4.17),
the samples were classified into seven major clusters using a criteria value of rescaled
distance between 0-6E04. There were three samples in cluster-1, six samples in cluster-2,
nine samples in cluster-3, three samples in cluster-4, five samples in cluster-5, four
samples in cluster-6 and six samples in cluster-7. Each sample represents a sampling site
and period. In cluster-1 only site 2 was present representing all three sampling period
(June, October and February) indicating that site 2 has similar trend of metal
concentrations throughout all sampling periods but varies from metal concentration at
other sampling sites and period. In cluster-2 only site 6 and 7 was present representing all
three sampling period (June, October and February) indicating that these sites has similar
trend of metal concentrations throughout all sampling periods. In cluster-3 sites 3, 4 and 5
were present representing all three sampling period (June, October and February)
indicating that these has similar trend of metal concentrations throughout all sampling
periods but the trend varies from other sampling sites and period.
Results and discussion
| P a g e
Cluster 1
Cluster 2
Cluster 3
6E
04
5.4
E04
4.8
E04
4.2
E04
3.6
E04
3E
04
2.4
E04
1.8
E04
1.2
E04
6000
Distance
Site2
Site5
Site3
Site4
Site6
Site7
Site8
Site12
Site9
Site1
Site10
Site11
Cluster 4
Cluster 5
Figure 4.16 Dendrogram produced using the Ward algorithm showing the variation
of the metal concentration with the sampling sites in the sediments of river
Yamuna along the Delhi stretch
6E
04
5.4
E04
4.8
E04
4.2
E04
3.6
E04
3E
04
2.4
E04
1.8
E04
1.2
E04
6000
Distance
S2JuneS2OctoberS2FebruaryS6OctoberS7OctoberS7FebruaryS6JuneS6FebruaryS7JuneS5JuneS5FebruaryS3OctoberS5OctoberS4OctoberS3FebruaryS4FebruaryS3JuneS4June
S9JuneS9OctoberS9FebruaryS8OctoberS12OctoberS8FebruaryS12JuneS12FebruaryS8JuneS10JuneS10OctoberS10FebruaryS11OctoberS11JuneS11FebruaryS1OctoberS1JuneS1February
Cluster 1
Cluster 2
Cluster 3
Cluster 4
Cluster 5
Cluster 6
Cluster 7
Figure 4.17 Dendrogram produced using the Ward algorithm showing the variation
of the metal concentration with the sampling sites and period in the sediments of
river Yamuna along the Delhi stretch
Results and discussion
| P a g e
Similarly all three samples of site 9 were present in a separate cluster-4, showing variation
in the metal concentrations with other sites. All the samples of sites 8 and 12 were present
in a separate cluster-5 except one sample of June sampling of site 8. In cluster-6 three
samples of site 10 representing all three sampling period and one sample of June sampling
of site 8 were present showing that they have similar trend of metal concentration at these
samplings location sand period. In last cluster-7 sites 1 and 11 were present representing
all three sampling period (June, October and February) indicating they have similar trend
of metal concentrations throughout all sampling periods. The observations of the cluster
analysis depict that heavy metal concentrations in the river water vary considerably with
the sampling location but have similarities with the period of the sampling at each location.
4.4.1.3 Principal components analysis
Principal components analysis (PCA) was done using PAST software (Hammer et al.,
2001) to examine multivariate relationship within the concentration of different metals and
sampling site and variance in sampling period.
June
October
February
-8000 8000 16000 24000 32000 40000 48000 56000 64000
Component 1
-3600
-3000
-2400
-1800
-1200
-600
600
1200
Com
ponen
t 2
Mg
Fe
Cr Zn Hg Pb
Mn
Figure 4.18 Biplot depicting the variation of metal concentrations of sediments along
river Yamuna in Delhi with the sampling period
Results and discussion
| P a g e
The biplot depicting the variation of metal concentrations in the freshly deposited
sediments of river Yamuna in Delhi with the sampling period is presented in figure 4.16.
Principal component analysis identified two discrete groups of metals. In the first group Fe
and Mg was present having high concentrations at most of the sampling sites and in the
second group Cr, Zn, Hg, Pb, and Mn having low concentrations as compared to Fe and
Mg. Significant variations of metal concentration in different sampling period observed
suggests that metal concentrations in the river Yamuna in Delhi have seasonal variations
with October and February having similar trend while June had different trend.
4.4.1.4 Correlation between different metals studied
To study the inter-relationships between different metals studied in freshly deposited
sediments at different sampling sites of river Yamuna along the Delhi stretch the Karl
Pearson's coefficient of correlation was calculated and correlation matrix with distribution
histogram and scatter plot was constructed with the statistical software R (Figure 4.19). No
correlation was observed between different metals studied (r= -0.2728 to <0.5207,
p>0.001). The highest correlation was between Pb and Hg with r=0.5207 and p=0.001.
This suggests that concentration in the sediments varies with the metal and sampling site
that can be related to different source for each metal.
4.4.2 Heavy metal concentration in the agriculture soil along river Yamuna in Delhi
The concentrations of the heavy metals in the agriculture soil along river Yamuna at
selected sampling sites are shown in the figure 4.20. The average concentration of
Chromium (Cr) was in range of 0.2 mg kg-1
to 3.133 mg kg-1
(Figure 4.20). The maximum
concentration of Cr was 2.767 mg kg-1
while minimum concentration was 0.167 mg kg-1
at
site 7 and site 2 respectively in June. In October concentration of Cr was minimum (0.2 mg
kg-1
) at site 2 and 3 while maximum (2.9 mg kg-1
) at site 7. In February Cr concentration
was lowest (0.3 mg kg-1
) at site 2 while highest (3.133 mg kg-1
) at site 8. There was several
times difference between maximum and minimum concentrations. The concentration of Cr
was below the WHO permissible limits and Dutch intervention value of Cr in sediment at
all sampling sites (Figure 4.20, Table 2.3 and 2.5).
Results and discussion
| P a g e
Cr
20 60
r= 0.1448
p= 0.399
r= -0.1647
p= 0.337
50 150
r= 0.2062
p= 0.228
r= 0.2649
p= 0.119
0 15000
r= 0.2726
p= 0.108
50
200
r= 0.05431
p= 0.753
20
60 Pb
r= 0.5207
p= 0.001
r= -0.1068
p= 0.535
r= -0.01691
p= 0.922
r= -0.2323
p= 0.173
r= -0.1408
p= 0.413
Hg
r= 0.02486
p= 0.886
r= -0.04764
p= 0.783
r= -0.2728
p= 0.107
040
80
r= 0.02644
p= 0.878
50
150 Zn
r= 0.3633
p= 0.029
r= 0.1647
p= 0.337
r= 0.4177
p= 0.011
Mn
r= 0.1691
p= 0.324
200
500
r= 0.06717
p= 0.697
015000 Mg
r= 0.1675
p= 0.329
50 200 0 40 80 200 500 25000 40000
25000
40000
Fe
Figure 4.19 Correlation matrix with scatter plot and histogram of different metals
assessed in the sediments along river Yamuna in Delhi stretch
The average concentration of the Lead (Pb) in the agriculture soil along river
Yamuna in Delhi was in the range of 9.6 mg kg-1
to 51.433 mg kg-1
(Figure 4.20). The
maximum Pb concentration (51.433 mg kg-1
) was recorded at site 7 while minimum
(10.067 mg kg-1
) was recorded at site 1 in June. In October the concentration of Pb was
highest (42.167 mg kg-1
) at site 8 while lowest (9.6 mg kg-1
) at site 1. The concentration Pb
was minimum (10.367 mg kg-1
) at site 1 while maximum (45.7 mg kg-1
) at site 8 in
February. There was several fold difference between observed maximum and minimum
concentrations of Pb in agriculture soil along river. The concentration of Pb observed in
this study was higher than the recommended limit of 40 mg kg-1
Pb in sediments by
USEPA (United States Environmental Protection Agency) at site 5, 7 and 8 in June, Site 8
Results and discussion
| P a g e
in October, site 5 and 8 in February (Table 2.3). But the recorded values of the Pb
concentration in soil was much lower than the recommended intervention value of 530 mg
kg-1
for sediments by Dutch Environmental Guidelines & Standards (2000) (Table 2.5).
The average concentration of the Mercury (Hg) in the agriculture soil along river Yamuna
in Delhi was in the range of below detection limit (BDL) to 25.333 mg kg-1
(Figure 4.20).
The concentration of Hg was higher than 10 mg kg-1
recommended Dutch intervention
value for sediments at sites 5 in all sampling periods (Table 2.4). The maximum value of
Hg concentration was 25.333 mg kg-1
, 20.6 mg kg-1
and 21.167 mg kg-1
respectively in
June, October and February at site 5. The minimum value of Hg concentration was 2.833
mg kg-1
at site 1 and 2.867 mg kg-1
at site 1 respectively in June and February while in
October Hg concentration was BDL at site 1, 2 and 3.
The average values of the Zinc (Zn) concentration in agriculture soil along river Yamuna
in Delhi were in the range of 51.267 mg kg-1
to 158.633 mg kg-1
along the Delhi segment
(Figure 4.20). The maximum value of Zn (158.633 mg kg-1
) was recorded at site 7 while
minimum value (108.467 mg kg-1
) was recorded at the site 5 in June. The maximum value
of Zn (130.533 mg kg-1
) was recorded at site 5 while minimum value (66.233 mg kg-1
)
was recorded at the site 2 in October. The maximum value of Zn (129.267 mg kg-1
) was
recorded at site 7 while minimum value (51.267 mg kg-1
) was recorded at the site 8 in
February. The observed values of Zn in the present study were higher than WHO
permissible limits (123 mg kg-1
) for sediments at site 7 and 8 in June, site 5 and 7 October
and site 7 in February (Table 2.3). The recorded Zn concentration was much lower in
current study as compared to Dutch intervention values of Zn (720 mg kg-1
) for sediments
(Table 2.5). The average values of the Manganese (Mn) concentrations in the agriculture
soil along river Yamuna in Delhi were in the range of 156.533 mg kg-1
to 725.567 mg kg-1
(Figure 4.20). The maximum value of Mn (725.567 mg kg-1
) was recorded at site 7 while
minimum value (307.233 mg kg-1
) was recorded at the site 8 in June. The maximum value
of Mn (629.233 mg kg-1
) was recorded at site 7 while minimum value (156.533 mg kg-1
) at
the site 8 in October. The maximum value of Mn (647.967 mg kg-1
) was recorded at site 7
while minimum value (313.567 mg kg-1
) at the site 8 in February. The concentration of Mn
in soil observed in this study was much higher than the recommended limit of 30 mg kg-1
in sediments by USEPA (United States Environmental Protection Agency) at all sites and
sampling period (Table 2.3).
Results and discussion
| P a g e
Cr
Location
0.000
0.500
1.000
1.500
2.000
2.500
3.000
3.500
4.000
Site 1 Site 2 Site 3 Site 5 Site 7 Site 8
June Oct Feb
Co
nce
ntr
atio
n m
g k
g-1
Pb
Location
0.000
10.000
20.000
30.000
40.000
50.000
60.000
70.000
Site 1 Site 2 Site 3 Site 5 Site 7 Site 8
June Oct Feb
Co
nce
ntr
atio
n m
g kg
-1
Hg
Location
0.000
5.000
10.000
15.000
20.000
25.000
30.000
Site 1 Site 2 Site 3 Site 5 Site 7 Site 8
June Oct Feb
Co
nce
ntr
atio
n m
g kg
-1
Zn
Location
0.000
20.000
40.000
60.000
80.000
100.000
120.000
140.000
160.000
180.000
200.000
Site 1 Site 2 Site 3 Site 5 Site 7 Site 8
June Oct Feb
Co
nce
ntr
atio
n m
g k
g-1
Mn
Location
0.000
100.000
200.000
300.000
400.000
500.000
600.000
700.000
800.000
900.000
Site 1 Site 2 Site 3 Site 5 Site 7 Site 8
June Oct Feb
Co
nce
ntr
atio
n m
g k
g-1
Mg
Location
0.000
5000.000
10000.000
15000.000
20000.000
25000.000
Site 1 Site 2 Site 3 Site 5 Site 7 Site 8
June Oct Feb
Co
nce
ntr
atio
n m
g k
g-1
Fe
Location
0.000
5000.000
10000.000
15000.000
20000.000
25000.000
30000.000
35000.000
Site 1 Site 2 Site 3 Site 5 Site 7 Site 8
June Oct Feb
Co
nce
ntr
atio
n m
g kg
-1
Figure 4.20 Concentration of different metals in the river-side agriculture soil of river
Yamuna in Delhi segment, during different sampling periods
Results and discussion
| P a g e
While these values were much lower than the maximum permissible level of Mn in soil
(2000 mg kg-1
) reported by Chiroma et al., (2014) (Table 2.4).
The average values of the Magnesium (Mg) concentration in agriculture soil along
river Yamuna in Delhi were in the range of 1048.33 mg kg-1
to 20664 mg kg-1
along Delhi
segment (Figure 4.20). The Mg concentration was highest (20664 mg kg-1
) at site 8 while
lowest (1048.33 mg kg-1
) at the site 5 in June. The maximum value of Mg (18198.667 mg
kg-1
) was recorded at site 8 while minimum value (9211 mg kg-1
) was recorded at the site 5
in October. The maximum value of Mg (19819 mg kg-1
) was recorded at site 8 while
minimum value (4739 mg kg-1
) was recorded at the site 5. The average values of the Iron
(Fe) concentrations in agriculture soil along river Yamuna in Delhi were in the range of
19578 mg kg-1
to 26411 mg kg-1
(Figure 4.20). The highest concentration of Fe (24969 mg
kg-1
) was recorded at site 7 while lowest (19578 mg l-1
) was recorded at the site 8 in June.
The maximum Fe concentration (26373.333 mg kg-1
) was recorded at site 8 while
minimum (20254.667 mg kg-1
) at the site 2 in October. In February Fe was highest (26411
mg kg-1
) at site 8 while lowest (20109 mg kg-1
) at the site 2. The average concentration of
Fe in soil at a particular site exceeds the stipulated maximum permissible limit of 30 mg
kg-1
in sediments by USEPA at all sites and sampling period (Table2.3). While these
values were much lower than the maximum permissible level of Fe in soil (50000 mg kg-1
)
reported by Chiroma et al., (2014) (Table 2.4).
Puttaih, (2012) reported high concentrations of metals in the soil irrigated with
polluted water as compared to the unpolluted soil. Yadav et al., (2013) reported that the
heavy metal concentration in waste water polluted agriculture soil of Allahabad ranged
from 1345-1920 mg kg-1
for Fe, 38.34-38.78 mg kg-1
for Zn, 31.23-31.24 mg kg-1
for Cd,
32.54-35.26 mg kg-1
for Cu, 18.21-18.32 mg kg-1
for Pb and 117.2-117.6 mg kg-1
for Ni. In
addition to that they observed beside Fe, the mean highest concentrations recorded in the
soil was for Ni followed by Zn, Cu and Cd and the minimum concentration was observed
for Pb. Pathak et al., (2010) compared heavy metal concentration in waste water irrigated
agricultural soil, near Bindal river Haridwar bypass road and natural water irrigated
agricultural soil, Guler ghati in District Dehradun and reported that the percent
concentration in wastewater irrigated soil was in the order of Zn (48%)>Pb (20%)>Ni
(13%)=Cu (13%)>Cr (5%)>Cd (1%). They also showed that the concentrations of Zn, Cd
and Cr were found to be significantly (P<0.05) more while the concentrations of Pb, Cu
Results and discussion
| P a g e
and Ni were insignificantly (P>0.05) higher in wastewater irrigated soil than that in natural
water irrigated soil. Singh and Kumar (2006) observed that while heavy metal load of the
peri-urban Delhi soils were below the maximum allowable limit prescribed by the World
Health Organization (WHO), it was higher in irrigation water and vegetable samples,
spinach and okra samples showed Zn, Pb and Cd levels higher than the WHO limits. The
above finding suggest that heavily polluted water of river Yamuna used for the irrigation of
the river-side agriculture land is the major source of heavy metal pollution of the soil and
the crops grown on such soils can also be contaminated with heavy metals.
4.4.2.1 Correlation between concentrations of metals in agriculture soil along river
Yamuna in Delhi
To study the inter-relationships between concentrations of metals in agriculture soil at
different sampling sites along river Yamuna along the Delhi stretch, the Karl Pearson's
coefficient of correlation was calculated and correlation matrix with distribution histogram
and scatter plot was constructed with the statistical software R (Figure 4.21). Strong
positive correlation (r> 0.8, p<0.001) was observed between heavy metals at most of the
sites with each other indicating close association of these with each other.
Site1
0 10000
r= 0.9994
p<0.001
r= 0.9980
p<0.001
0 10000
r= 0.8944
p<0.001
r= 0.9176
p<0.001
0 15000
010000
r= 0.9866
p<0.001
010000
Site2
r= 0.9967
p<0.001
r= 0.8874
p<0.001
r= 0.9116
p<0.001
r= 0.9870
p<0.001
Site3
r= 0.9164
p<0.001
r= 0.9387
p<0.001
010000
r= 0.9808
p<0.001
010000
Site5
r= 0.9961
p<0.001
r= 0.8374
p<0.001
Site7
015000
r= 0.8669
p<0.001
0 10000
015000
0 10000 0 15000
Site8
Figure 4.21 Correlation matrix with scatter plot and histogram of the selected sites
studied for the concentration of metals in the river-side agriculture soil in Delhi
Results and discussion
| P a g e
4.4.2.2 Hierarchical cluster analysis
Hierarchical cluster analysis (HACA) based on agglomerative statistics using Ward‘s
Method was done for concentration of heavy metals at each of the sampling sites and
period using PAST software (Hammer et al., 2001). In the first dendrogram, representing
the metal concentrations at different sites irrespective of the sampling period, the samples
were classified into three clusters using a criteria value of rescaled distance between 0-
3E04. There were two sites in each cluster (Figure 4.22). Site 5 and 7 were in the cluster-1,
indicates similarity in the trend of the metal concentration at these sites. Site 1 and 2 were
in the cluster-2, indicates similarity in the trend of the metal concentration at these sites. In
cluster-3 site 3 and 8 were present, indicating similar trend of metal concentrations at these
sites. The cluster 2 and 3 were more closely related to each other than cluster 1. The sites
lying in separate clusters indicate the variation in the heavy metal concentrations in the
sediments in these sites.
To get the detailed insight of the variation and similarities of metal concentrations
at each sampling site with the sampling period second dendrogram was constructed (Figure
4.23), the samples were classified into six clusters using a criteria value of rescaled
distance between 0-27E04. There were three samples in cluster-1 and 2, two samples in
cluster-3, five samples in cluster-4, four samples in cluster-5 and only one sample in
cluster-6. Each sample represents a sampling site and period. Site 5 and 7 were present in
cluster-1 and 2 representing all three sampling period (June, October and February)
indicating they similarity in metal concentrations but samples of site 7 (October and
February) and site 5 (October) were present in cluster-1 and samples of site 7 (June) and
site 5 (June and February) were having more similarity with each other respectively.
Samples of October and February of Site 8 were present in cluster-3 while June sample of
site 8 was present separately in cluster-6. Since sample of site 8 representing June sample
was present separately therefore no comparison could be conducted between site and
others, however closest clusters to it was cluster 5 indicating they have some similarity in
trend of metal concentrations. Samples of site 3 (June, October and February) and site 1
(June and February) were present in cluster-4, while samples of site 2 (June, October and
February) and site 1 (October) were present in cluster-5 indicating similar trend of metal
concentration in the samples of these sites respectively.
Results and discussion
| P a g e
Cluster 1
Cluster 2
Cluster 3
3E04
2.7E04
2.4E04
2.1E04
1.8E04
1.5E04
1.2E04
9000
6000
3000
Distance
Site5
Site7
Site1
Site2
Site3
Site8
Figure 4.22 Dendrogram produced using the Ward algorithm showing the variation
of the metal concentrations of river-side agriculture soil in different sites
Cluster 1
Cluster 2
Cluster 3
2.7E04
2.4E04
2.1E04
1.8E04
1.5E04
1.2E04
9000
6000
3000
Distance
S7October
S5October
S7February
S5February
S5June
S7June
S8October
S8February
S3June
S1June
S1February
S3October
S3February
S2February
S2October
S2June
S1October
S8June
Cluster 4
Cluster 5
Cluster 6
Figure 4.23 Dendrogram produced using the Ward algorithm showing the variation
of the metal concentrations of river-side agriculture soil in different sites and
sampling periods
Results and discussion
| P a g e
The observations of the cluster analysis depict that heavy metal concentrations in
the agriculture soil vary considerably with the sampling location but have some similarities
with the period of the sampling at each location. The sites lying in separate clusters
indicate the variation in the heavy metal concentrations in soil at these sites.
4.4.2.3 Principal components analysis
Principal components analysis (PCA) was done using PAST software (Hammer et al.,
2001) to examine multivariate relationship within the concentration of different metals and
sampling site and variance in sampling period. The biplot depicting the variation of metal
concentrations in the agriculture soil along river Yamuna in Delhi with the sampling period
is presented in figure 4.24.
June
October
February
-10000 -5000 5000 10000 15000 20000 25000 30000 35000
Component 1
-6400
-4800
-3200
-1600
1600
3200
4800
Com
ponen
t 2
Mg
FeCr Zn Hg Pb
Mn
Figure 4.24 Biplot depicting the variation of metal concentrations of selected
agriculture sites along river Yamuna in Delhi with the sampling period
Principal component analysis identified two discrete groups of metals. In the first
group Fe and Mg was present having high concentrations at most of the sampling sites and
in the second group Cr, Zn, Hg, Pb, and Mn having low concentrations as compared to Fe
and Mg. Significant variations of metal concentration in different sampling period suggests
Results and discussion
| P a g e
that metal concentrations in the river Yamuna in Delhi have seasonal variations with
October and February having similar trend while June had different trend.
4.4.2.4 Correlation between different metals studied
To study the inter-relationships between different metals studied in agriculture soil at
different sampling sites along river Yamuna in Delhi stretch, the Karl Pearson's coefficient
of correlation was calculated and correlation matrix with distribution histogram and scatter
plot was constructed with the statistical software R (Figure 4.25). No correlation was
observed between most of the metals studied except some exceptions. High positive
correlation was recorded between Cr and Pb (r= 8.032, p<0.001), Fe and Cr (r= 0.6311,
p=0.005) while high negative correlation was observed between Hg and Mg (r= -0.6331,
p=0.005), Mn and Mg (r= -0.6567, p=0.003).This suggests that concentration in the
agriculture soil mostly varies with the metal and sampling site.
Cr
10 30 50
r= 0.8032
p<0.001
r= 0.4643
p= 0.052
60 120
r= 0.3857
p= 0.114
r= 0.3660
p= 0.135
5000 20000
r= -0.3554
p= 0.148
0.5
2.0
r= 0.6311
p= 0.005
10
30
50
Pb
r= 0.4501
p= 0.061
r= 0.3825
p= 0.117
r= 0.1931
p= 0.443
r= -0.3089
p= 0.212
r= 0.5476
p= 0.019
Hg
r= 0.2346
p= 0.349
r= 0.1489
p= 0.556
r= -0.6331
p= 0.005
010
20
r= 0.1717
p= 0.496
60
120 Zn
r= 0.5231
p= 0.026
r= -0.4469
p= 0.063
r= 0.1438
p= 0.569
Mn
r= -0.6567
p= 0.003
200
500
r= 0.3371
p= 0.171
5000
20000
Mg
r= -0.2477
p= 0.322
0.5 2.0 0 10 20 200 500 20000 25000
20000
25000
Fe
Figure 4.25 Correlation matrix with scatter plot and histogram of different metals
assessed in the river-side agriculture soil along river Yamuna in Delhi
Results and discussion
| P a g e
4.5 Sequential extraction of sediments and agricultural soil samples of selected sites
In sediments and soil heavy metals can be present in a number of chemical forms and
generally exhibit different physical and chemical behaviour in terms of chemical
interaction, mobility, biological availability and potential toxicity (Singh et al., 2005c).
Selected sediment samples and its respective nearest agricultural field soil samples
collected in June, 2013 were evaluated for specific geochemical form by chemical
partitioning using sequential extraction procedure (Tessier et al., 1979). Overall 5 fractions
were made: 1) exchangeable, 2) bound to carbonates, 3) bound to Fe and Mn oxides, 4)
bound to organic matter and 5) residue. The percentage distributions of heavy metals in
various fractions are given in the figure 2.6. In the first exchangeable fraction there are
comparatively lower percentages of Cr (0 –5.69%), Pb (0 – 18.52%), Zn (1.9 – 7.41%) and
Mn (13.60 – 20.21%). In the second bound to carbonate fraction the percentages of metals
are as follows: Cr (0 – 11.58%), Pb (2.13 – 27.59%), Zn (7.02 – 15.82%) and Mn (9.03 –
11.53%). In the third bound to Fe and Mn oxides fraction there are comparatively lower
percentages of Cr (0 – 27.78%), Pb (10 – 37.66%), Zn (21.72 – 27.72%) and Mn (7.18 –
9.19%). In the fourth bound to organic matter fraction the percentages of metals are as
follows: Cr (24.07 – 50%), Pb (8.62 – 27.66%), Zn (15.69 – 22%) and Mn (14.54 –
20.9%). In the fifth residue fraction the percentages of metals are comparatively higher, Cr
(35.19 – 53.85%), Pb (29.63 – 53%), Zn (34.67 – 50%) and Mn (45.07 – 49.37%). The
fractions introduced by anthropogenic sources are mostly adsorptive and exchangeable and
bound to carbonates that are considerably weakly bounded and may equilibrate with
aqueous phase therefore are rapidly bioavailable (Gibbs, 1977). The fractional profile of Cr
shows that major portion of Cr is present in the residual fraction and bound to organic
matter fraction. In exchangeable fraction Cr is either below detection limit or very less, this
suggests that Cr is considerably immobile. The fractional profile of Pb shows that major
portion of Pb is present in the residual fraction. In exchangeable fraction Pb concentration
increased in the downstream sites. The fractional profile of Zn shows that major portion of
Zn is present in the residual fraction and bound to Fe and Mn oxides fraction. The Fe–Mn
oxide and the organic matter have a scavenging effect and may provide a sink for heavy
metals (Jain, 2004). The release of the metals from this matrix will most likely affected by
the redox potential and pH (Jain, 2004). The fractional profile of Mn shows that major
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portion of Mn is present in the residual fraction and bound to organic matter fraction. Mn
was also present considerably in exchangeable fraction as compared to other metals.
4.5.1 Mobility Factor of Metals
The fate of metal ions in sediment of the overlying water column is dependent on its
mobility factor. Mobility factors (MF) of metals provide an indication of the bio-
availability or non-bioavailability of the metal. This may be assessed as a ratio of the
concentrations of metal in easily remobilizable fractions to the combine concentrations in
all the geochemical fractions. Mobility factor (MF), corresponding to the potentially
mobile amount of metallic contaminants, was calculated using the sequential extraction
results according to the equation of Kabala and Singh (2001): MF = [(F1 + F2)/(F1 + F2
+ F3 + F4 + F5)] × 100 (%), where F1 to F5 are the individual fractions of sequential
extraction analysis. The exchangeable (F1) and acid-extractable (F2) fractions are
considered to be easily available.
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Site 2 Sediment
Site 2 Ag.Soil
Site 5 Sediment
Site 5 Ag.Soil
Site 7 Sediment
Site 7 Ag.Soil
Site 12 Sediment
fraction 5
fraction 4
fraction 3
fraction 2
fraction 1
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Site 2 Sediment
Site 2 Ag.Soil
Site 5 Sediment
Site 5 Ag.Soil
Site 7 Sediment
Site 7 Ag.Soil
Site 12 Sediment
fraction 5
fraction 4
fraction 3
fraction 2
fraction 1
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Site 2 Sediment
Site 2 Ag.Soil
Site 5 Sediment
Site 5 Ag.Soil
Site 7 Sediment
Site 7 Ag.Soil
Site 12 Sediment
fraction 5
fraction 4
fraction 3
fraction 2
fraction 1
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Site 2 Sediment
Site 2 Ag.Soil
Site 5 Sediment
Site 5 Ag.Soil
Site 7 Sediment
Site 7 Ag.Soil
Site 12 Sediment
fraction 5
fraction 4
fraction 3
fraction 2
fraction 1
Cr
Zn
Pb
Mn
Figure 4.26 Heavy metal distributions in different fractions of the sediments and
agricultural soil samples of selected sites
MF of the metal ions in the sediments and soil samples of selected sites of river Yamuna is
present in the table 4.3. The MF values of <1 indicates no risk, 1–10 indicates low risk,
11–30 medium risk, 31–50 high risk and >50 very high risk according to Risk assessment
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code (Jain, 2004). In the present study the evaluated metals were of medium risk except Cr
in agriculture soil at all sites and Zn in agriculture soil of site 2.
Table 4.3: Mobility factors of heavy metals for sediments and agricultural soil of
selected sites river Yamuna in Delhi
Cr Pb Mn Zn
Site 2 Sediment 13.1 11.9 30.3 17.7
Site 2 Agriculture Soil 0.0 10.0 28.8 9.6
Site 5 Sediment 13.0 32.8 24.6 22.2
Site 5 Agriculture Soil 0.0 22.5 23.9 16.8
Site 7 Sediment 10.5 10.4 26.1 20.4
Site 7 Agriculture Soil 3.8 14.9 25.0 18.7
Site 12 Sediment 10.9 30.6 26.0 21.6
4.6 Geo-chemical analysis of sediments and agricultural soil of selected sites
The powdered sediment samples and the agricultural soil samples of the respective nearest
agriculture field of selected sites were analysed by SEM-EDX (equipment model JEOL
JSM-6380- LA) and FTIR (Perkin-Elmer) to determine the texture and geo-chemical
compositions. The samples collected during the June sampling were selected for this study
based on the previous observations of comparatively high contaminations during this
period. Locations that were selected are site 2, site 7, site 8, site 9 and site 12. While at the
first three sampling locations both sediment and agricultural soil samples were available
but at the latter two locations agricultural soil samples were not available as agricultural
field was not present within 200 meters of range, therefore at these locations only sediment
samples were analysed. . The surface morphologies of selected samples are exhibited in in
the SEM image given in figure 4.27. The Energry Dispersive X-Ray Spectrum (EDS) of
the samples analysed were given in the figure 4.27. The elemental composition of the
various samples (weight %) is presented in the table 4.4 and figure 4.28. Selected soil and
sediments particles in the study sites had rough surface and irregular shapes and sometimes
formed aggregates with irregular sizes and shapes. SEM image of sediments and
agriculture soil of site 2 displayed comparatively dense and smooth surface whereas SEM
image of sediments and agriculture soil of site 7 exhibited very high amount of patches and
the solid portion enriched with Si and Al. The sediments of site 12 have flakes with porous
aggregation and tiny clusters of fine flakes. The sediments of site 9 have irregular, tubular
as well as low crystalline shapes. The agricultural soil of site 8 exhibited particles that are
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irregular, tubular and have dense surface that were associated in larger aggregates while
the sediments of site 8 exhibited dense rough texture, triangular, irregular, high crystalline
shapes.
Table 4.4: Chemical analysis (wt%) of samples using EDX.
Element Site 2 Site 7 Site 8 Site 9 Site 12
Sediment Agriculture
soil
Sediment Agriculture
soil
Sediment Agriculture
soil
Sediment Sediment
C 0 1.85 0 1.84 0 8.51 2.64
O 29.23 19.56 11.76 20.99 26.14 24.62 20.44 15.41
Na 0 0.7 0 0 0 0 0 0
Mg 0 1.69 0.59 0.16 1.05 2.32 2.38 1.01
Al 12.22 29.85 27.58 28 23.34 3.7 19.83 19.17
Si 49.49 31.05 32.92 32.25 30.23 69.36 31.35 48.88
K 3.99 14.66 21.16 15.49 11.74 0 10.52 9.53
Fe 5.07 1.58 4.13 3.09 4.19 0 6.97 3.35
Ti 0.91 0 0 1.49 0 0 0
Si was the abundant element present in all the samples analysed. In the sediment of
the site 2 highest percentage was of Si followed by O, Al, Fe and K. In the sediment of the
site 7 highest percentage was of Si followed by Al, K, O, Fe, C and Mg. In the sediment of
the site 8 highest percentage was of Si followed by O, Al, K, Fe, C, Ti and Mg. In the
sediment of the site 9 highest percentage was of Si followed by O, Al, K, C, Fe and Mg. In
the sediment of the site 12 highest percentage was of Si followed by Al, O, K, Fe, C, and
Mg. In the agricultural soil of the site 2 highest percentage was of Si followed by Al, O, K,
Mg, Fe, Ti and Na. In the agricultural soil of the site 7 highest percentage was of Si
followed by Al, O, K, Fe and Mg. In the agricultural soil of the site 8 highest percentage
was of Si followed by O, Al, and Mg. In general, the elemental composition of the
sediments increased from upstream site 2 to downstream site 12. The number of elements
was five at the site 2, seven at the site 7, eight at the site 8 and seven at site 9 and 12. The
over trend of the elemental composition of the agricultural soil is decreasing from the
upstream site 2 to downstream site 7 and 8. The number of elements was eight at the site 2,
six at the site 7 and four at the site 8.
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Figure 4.27 (a) SEM image and EDS of the sediments of Site 2
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Figure 4.27 (b) SEM image and EDS of the agricultural soil of Site 2
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Figure 4.27 (c) SEM image and EDS of the sediments of Site 7
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Figure 4.27 (d) SEM image and EDS of the agricultural soil of Site 7
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Figure 4.27 (e) SEM image and EDS of the sediments of Site 8
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Figure 4.27 (f) SEM image and EDS of the agricultural soil of Site 8
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Figure 4.27 (g) SEM image and EDS of the sediments of Site 9
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Figure 4.27 (h) SEM image and EDS of the sediments of Site 12
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0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Site 2 (Sed) Site 2 (AgSo) Site 7 (Sed) Site 7 (AgSo) Site 8 (Sed) Site 8 (AgSo) Site 9 (Sed) Site 12 (Sed)
Pe
rce
nta
ge w
eig
ht Ti
Fe
K
Si
Al
Mg
Na
O
C
Figure 4.28. Elemental composition (weight %) of sediment (Sed) and agriculture soil
(AgSo) samples of selected sites.
In the sediments samples the percentage of Si decreased from the site 2 to 7,
thereafter increased up to site 12; C was not detected at the site 2, increased from site 7 to
site 9, then decreased again at the site 12; O decreased from site 2 to site 7, increased at the
site 8, thereafter decreased till site 12; Mg was having a increasing trend till site 9,
decreased thereafter at the site 12; Al and K was having a higher values at the site 7 as
compared to the site 2, thereafter the values were having decreasing trend; Fe was not
uniform; Ti was found only at the site 8. In the agriculture soil samples, O and Si increased
from site 2 up to site 8; Al, K and Fe increased from site 2 to site 7, but decreased or absent
in the subsequent site 8; Na and Ti were only present at the site 2; Mg was having lower
values at the site 7 as compared to the others.
Opposite to what was expected, the percentage weight of the respective elements
did not showed any correlation (pearson coefficient not greater than 0.5 except for Si,
having r= -0.62 [data not shown here]) between the values observed in the sediments and
agriculture soil. The possible reason for this could be either the agricultural soil
composition was changed due to extensive agricultural practices such as use of chemical
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fertilizers and pesticides or there is no sediment deposition due to lower river current and
less water in the downstream sites.
FTIR spectroscopy has been frequently used by the scientific community as a tool
to identify the presence of certain functional groups or chemical bonds of a compound or a
mixture because each specific chemical bond often has a unique energy absorption band
(Li and Bai, 2005). The spectrum of the sediment and agricultural soil samples analysed is
presented in the figure 4.29. The band corresponding to OH group was present in all the
samples.
Band at 3406 – 3439 cm-1
indicative of primary OH group was present in both
sediment as well as agriculture soil except at the site 8. In the sediments except at site 2
and 9 band around 3625 cm-1
was present that correspond to secondary OH group. In
agriculture soil samples also secondary OH group was present except at the site 7. Bands
corresponding to the halogenated compounds were also common in all the samples. The
band around 1082 – 1107 cm-1
was indicative of aliphatic fluoro compounds (C–F stretch)
and sulphate ions. The band around 1082 – 1107 cm-1
indicative of aliphatic fluoro
compounds (C–F stretch), phosphate and silicate ions was also present in most of the
samples except in the sediments at the site 7 and in agriculture soil at site 8. The presence
of aliphatic chloro and bromo compounds (C–Cl stretch and C–Br stretch) was reflected by
the bands at 778 – 779 cm-1
and 694 cm-1
respectively. Band at 513 – 518 cm-1
indicative
of aliphatic iodo compounds (C–I stretch) was also present in most of the sediment and
agriculture soil samples except in the sediment of site 8 and 9.
Band (463 – 472 cm-1
) corresponding to Aryl disulfides (S–S stretch) was present in both
sediment and agricultures soil but in the sediments the band shifted to 482 cm-1
while band
corresponding to Polysulfide (S–S stretch) was present only in the agriculture soil of the
site 8. Band (2924 – 2928 cm-1
) indicative of methylene asymmetrical C–H bend was
present in the sediment of site 7 and 12 and agriculture soil of site 7, while band (2825 –
2868 cm-1
) indicative of methylene symmetrical C–H bend was present in the sediment of
site 12 and agriculture soil of site 7 and 8. Transition metal carbonyls (band at 1871 – 1883
cm-1
) were present in most of the sediment samples analysed except in the sediments of
site 8 where a small band was present but was not prominent. In the agriculture soil the
transition metal carbonyls was only present at the site 7. The open chain imino (–C=N–
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stretch) and secondary amine (NH bend) (bands at 1621 – 1629 cm-1
) was present in most
of the sediment samples except at site 12, while in the agriculture soil at site 2 and 7. The
aliphatic nitro compounds (bands at 1530 cm-1
) and cyanide ions, thiocyanide ions, related
ions (bands at 2004 cm-1) were only present in the sediment of site 7. Band (1261 cm-1
)
indicative of aromatic primary amine (CN stretch) was only present in the sediment of site
8 while band (1164 cm-1
) indicative of secondary amine (CN stretch) was present in
sediments of site 2, 8 and 12, in agriculture soil of site 7 only. . Band (1433 – 1445 cm-1
)
indicative of methyl (asymmetrical C–H bend) was present in all the sediments except of
site 7 and in the agriculture soil of site 2 and 7, while band (1383 - 1385 cm-1
) indicative of
gem–Dimethyl was present in sediments and agriculture soil of site 8 only.
The presence of methylene, methyl and dimethyl groups indicates the presence of organic
carbon in the respective sediment and soil samples. Thus it can be concluded that the FTIR
results point towards the low organic carbon in the sediment and agriculture soil of the
upstream site 2. This observation was similar to the SEM-EDS observations (Table 4.4)
Aceves et al., (1999) observed that the high percentage of soil organic carbon and litter
accumulation was related to the high concentration of heavy metals in the sandy soils.
Jenkinson and Ladd, (1981) reported that sandy soils contains less organic pollution. The
number functional groups observed were more in the downstream sites as compared to the
upstream site 2, signify that the downstream site are rich of compounds.
Figure 4.29 (a) FTIR spectrum of sediment of the site 2
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Figure 4.29 (b) FTIR spectrum of agricultural soil of the site 2
Figure 4.29 (c) FTIR spectrum of sediment of the site 7
Figure 4.29 (d) FTIR spectrum of agricultural soil of the site 7
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Figure 4.29 (e) FTIR spectrum of sediment of the site 8
Figure 4.29 (f) FTIR spectrum of agricultural soil of the site 8
Figure 4.29 (g) FTIR spectrum of sediment of the site 9
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Figure 4.29 (h) FTIR spectrum of sediment of the site 12
The functional groups such as open chain imino (–C=N– stretch), secondary amine (NH
bend), aliphatic nitro compounds, aromatic primary and secondary amine (CN stretch),
cyanide ions, thiocyanide ions, related ions observed mostly in the downstream sites are
common in number of chemical pesticides such as atrazine, simazine, cyanazine,
bentazone, metamitron, metribuzin and vinclozolin. These findings suggest low level of
pollution at the upstream site 2 as compared to the downstream sites 7, 8, 9 and 12.
The bands around 1032 cm-1
and 1634 cm-1
correspond to the occurrence of clay mineral
kaolinite, bands around 3420 cm-1
correspond to the occurrence of another clay mineral
montmorillonite, bands around 779 cm-1
, 692 cm-1
and 464 cm-1
correspond to the
occurrence of silicate mineral quartz, bands around 520 cm-1
correspond to the occurrence
hematite in the analysed sediment and soil samples as reported in previous studies by
Sivakumar et al., (2012) and Cannane et al., (2013). While no correlation was observed
between the percentage weight of the respective elements in the sediments and agriculture
soil in the SEM-EDS results whereas the findings of FTIR analysis specify that the change
in functional groups or chemical structure of sediments and agriculture soil are site
specific.
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4.6.1 Detailed geochemical characterization of the agricultural soil of site 5
The agricultural soil sample of site 5 was selected for detailed analysis as this site was
located just after the Najafgarh drain and receives maximum amount of pollution load.
Furthermore it was observed that lot of vegetables and other food crops were grown by the
local farmers at the river side at this site. Therefore to detailed investigation of the
agriculture soil of this site was undertaken to know the structure and geochemical forms of
the soil. The powdered agriculture soil sample of site 5 and its different solid residues
obtained at each step of sequential extraction were analysed by SEM-EDX (equipment
model JEOL JSM-6380- LA), X-ray diffractometer (XRD) (PANalytical X‗Pert Pro) and
FTIR (Perkin-Elmer). The SEM image and the Energry Dispersive X-Ray Spectrum (EDS)
of the samples analysed were given in the figure 4.30. The elemental composition of the
various samples (weight %) is presented in the table 4.5 and figure 4.31.
Table 4.5: Chemical analysis (wt%) of whole soil agriculture soil of site 5 and its
different residue samples using EDX.
Element Whole soil (Unfractionated) Residue 1 Residue 2 Residue 3 Residue 4
C 2.32 0 8.13 0 0
O 31.01 19.42 21.46 19.61 30.03
Na 0 0 7.57 0 0
Mg 0 2.2 1.31 6.18 0
Al 25.23 26.39 7.57 11.68 8.23
Si 26.95 31.43 36.17 23.15 57.77
K 12.22 16.3 2.03 12.99 3.96
Ti 0 0 0 2.25 0
Fe 2.26 4.26 15.77 24.14 0
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Figure 4.30 (a) SEM image and EDS of the whole agriculture soil of site 5
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Figure 4.30 (b) SEM image and EDS of residue 1 of agriculture soil of site 5
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Figure 4.30 (c) SEM image and EDS of residue 2 of agriculture soil of site 5
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Figure 4.30 (d) SEM image and EDS of residue 3 of agriculture soil of site 5
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Figure 4.30 (e) SEM image and EDS of residue 4 of agriculture soil of site 5
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0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Unfractionated Fraction 1 Fraction 2 Fraction 3 Fraction 4
Fe
Ti
K
Si
Al
Mg
Na
O
C
Figure 4.31 Elemental composition (weight %) of whole agriculture soil of site 5 and
its different residues
Si was the abundant element present in all the samples analysed. In the whole
agriculture soil sample Si was the most abundant followed by Al, O, K, Fe and C. In the
residue 1 Si was the most abundant followed by Al, O, K, Fe and Mg. In the residue 2 Si
was the most abundant followed by O, Fe, C, Al, Na, K and Mg. In the residue 3 Fe was
the most abundant followed by Si, O, K, Al, Mg and Ti. In the residue 4 Si was the most
abundant followed by O, Al and K.
The agricultural soil of the site 5 and its residues was also characterized by XRD.
Peaks at 2θ in the XRD pattern was matched with the PDF2 database of the International
Centre for Diffraction Data (ICDD) by the XPowder12 (Ver. 2014.04.36) by J. D. Martin
(2012). PCPDFWIN (Ver. 1.30) by ICDD (1997) was used to view individual JCPDS card.
The XRD pattern of the agricultural soil of site 5 and its residues is shown in figure 4.32
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(a) (b)
(c) (d)
(e)
Figure 4.32 XRD pattern of the agricultural soil of site 5 (a) whole soil, (b) residue 1,
(c) residue 2, (d) residue 3 (e) residue 4
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The XRD pattern of the agricultural soil of site 5 exhibited characteristic peaks at 2θ =
20.8°, 26.5°, 39.39°, 40.2°, 42.3°, 45.7°, 50.0°, 54.7° and 59.8 that can be indexed to (100),
(101), (102), (111), (200), (201), (112), (202), and (211) planes of quartz respectively
(JCPDS No: 46-1045). The characteristic peaks at 2θ = 20.8°, 24.1°, 26.5°, 36.4°, 39.39°,
40.2°, 45.7°, 54.7° and 59.8 with minor shifting can be indexed to (100), (003), (102),
(110), (104), (200), (202), (106), and (212) planes of berlinite respectively (JCPDS No: 10-
0423) was observed. The characteristic peaks at 2θ = 20.8°, 26.59°, 27.9°, 29.8°, 36.47°,
39.39°, 40.2°, 45.7°, 54.78°, 59.8°, 64.0°, 67.6°, 68.2°, 75.66°, 79.8°, 81.3° and 83.78°
with minor shifting can be indexed to (121), (300), (310), (311), (004), (142), (313), (333),
(352), (254), (623), (525), (255), (274), (075), (128) and (228) planes of gismondine
respectively (JCPDS No: 20-0452) was observed. The characteristic peaks at 2θ = 26.59°,
27.9°, 29.8°, 34.77° and 36.4° can be indexed to (113), (023), (114), (025) and (115)
planes of illite respectively (JCPDS No: 26-0911) was observed. Characteristic peaks of
Lipscombite at 2θ = 26.59°, 50.03°, and 54.7° that can be indexed to (113), (040) and
(226) planes respectively (JCPDS No: 45-1454) was observed. Characteristic peaks of
Gadolinium at 2θ = 20.9°, 24.5°, 34.4°, 36.6°, 42.5°, 65.9° and 83.7° that can be indexed to
(110), (102), (103), (300), (220), (330) and (424) planes respectively with minor
adjustment (JCPDS No: 25-1096) was observed. Characteristic peaks of Copper Zinc
Telluride at 2θ = 42.3°, 45.8° and 50.0° that can be indexed to (2 3 13), (0 9 11) and (4 1 1)
planes respectively with minor adjustment (JCPDS No: 45-1297) was observed. The
presence of lead was confirmed by the characteristic peaks at 2θ = 10.7°, 12.5°, 20.8°,
23.5° and 28.5°, although the peaks were very small which can be related to very low
concentrations (JCPDS No: 26-1588). The presence of characteristic peaks (slightly
shifted) of Magnesium Hydrogen Phosphate at 2θ = 20.8°, 24.1°, 26.5°, 27.9°, and 39.39°
(JCPDS No: 40-0090) confirmed the presence of magnesium.
The XRD pattern of the 1st residue of agricultural soil of site 5 exhibited
characteristic peaks at 2θ = 20.7°, 26.5°, 36.5°, 39.4°, 40.1°, 42.3°, 45.7°, 50.0°, 54.7°,
55.2°, 59.8°, 68.2°, 73.4°, 75.5°, 81.0° and 83.7° that can be indexed with minor shifting to
(100), (101), (110), (102), (111), (200), (201), (112), (103), (211), (203), (104), (302),
(221) and (311) planes of quartz respectively (JCPDS No: 46-1045). The characteristic
peaks at 2θ = 20.7°, 26.5°, 35.0°, 36.5°, 39.4°, 40.1°, 42.3°, 45.7°, 50.0°, 51.2°, 54.7°,
59.8°, 63.8°, 68.2°, 73.4°, 75.5° and 79.8° with minor shifting can be indexed to (121),
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(300), (041), (004), (142), (313), (242), (333), (440), (053), (352), (254), (623), (255),
(615), (274) and (075) planes of gismondine respectively (JCPDS No: 20-0452) was
observed. The characteristic peaks at 2θ = 25.4°, 26.5°, 27.8°, 29.8° and 35.0° can be
indexed to (114), (024), (114), (025) and (131) planes of muscovite respectively (JCPDS
No: 19-0814) was observed. The characteristic peaks at 2θ = 20.7°, 26.5°, 36.5°, 39.4°,
40.1°, 42.3°, 45.7°, 54.7° and 59.8 with minor shifting can be indexed to (100), (102),
(110), (104), (112), (200), (202), (106), and (212) planes of berlinite respectively (JCPDS
No: 10-0423) was observed. The characteristic peaks at 2θ = 22.9°, 26.5°, 27.8°, 29.8°,
35.0°, 36.4° and 40.1° can be indexed to (110), (113), (023), (114), (025), (115) and (131)
planes of illite respectively (JCPDS No: 26-0911) was observed. Characteristic peaks of
Lipscombite at 2θ = 26.5°, 27.8°, 39.4° and 55.2° that can be indexed to (210), (211), (311)
and (420) planes respectively (JCPDS No: 14-0310) was observed. Characteristic peaks of
Gadolinium at 2θ = 36.5° and 42.39° can be indexed to (300) and (220) planes respectively
(JCPDS No: 25-1096) was observed. Characteristic peaks of Copper Zinc Telluride at 2θ =
42.3°, 45.8° and 50.0° that can be indexed to (2 3 13), (0 9 11) and (4 1 1) planes
respectively with minor adjustment (JCPDS No: 45-1297) was observed. The presence of
lead was confirmed by the characteristic peaks at 2θ = 10.3°, 12.8°, 20.8°, 26.6°, 27.8°,
39.4°, 45.7° and 50.0° that can be indexed to (001), (201), (002), (112), (221), (603), (604)
and (040) planes of Lead acetate hydrate respectively, although the peaks were very small
which can be related to very low concentrations (JCPDS No: 14-0829).
The XRD pattern of the 2nd
residue of agricultural soil of site 5 exhibited
characteristic peaks at 2θ = 20.8°, 26.5°, 36.5°, 39.4°, 40.2°, 42.3°, 45.7°, 50.0°, 50.6°,
54.8° and 59.9 with minor shifting can be indexed to (100), (101), (110), (102), (111),
(200), (201), (112), (003), (202), and (211) planes of quartz respectively (JCPDS No: 46-
1045). The characteristic peaks at 2θ = 20.8°, 26.5°, 27.7°, 31.2°, 36.5°, 39.4°, 40.2°,
42.3°, 45.7°, 50.0°, 54.8° and 59.9° with minor shifting can be indexed to (121), (300),
(310), (132), (004), (142), (313), (242), (333), (440), (352) and (254) planes of gismondine
respectively (JCPDS No: 20-0452) was observed. The characteristic peaks at 2θ = 20.8°,
26.5°, 36.5°, 39.4°, 40.2°, 42.3°, 45.7°, 50.0° 54.8° and 59.9 with minor shifting can be
indexed to (100), (101), (110), (104), (112), (200), (202), (114), 106) and (212) planes of
berlinite respectively (JCPDS No: 10-0423) was observed. The characteristic peaks at 2θ =
23.5°, 27.7°, 36.5°, 39.4°, 42.3°, 45.7° and 50.07° can be indexed to (111), (002), (221),
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(113), (060), (061), and (043) planes of albite respectively (JCPDS No: 09-0466) was
observed. The characteristic peaks at 2θ = 20.7°, 23.5°, 25.5°, 26.59°, 27.8°, 29.7, 31.2°
and 42.39° can be indexed to (111), (023), (114), (024), (114), (025), (115) and (135)
planes of muscovite respectively (JCPDS No: 19-0814) was observed. Characteristic peaks
of Gadolinium at 2θ = 20.0°, 36.5°, 42.5°, 79.7° and 83.4° that can be indexed to (110),
(300), (220), (306) and (424) planes respectively with minor adjustment (JCPDS No: 25-
1096) was observed. The presence of iron was also confirmed by characteristic peaks of
iron carbonate hydroxide at 27.7°, 39.2°, 42.3° and 75.6 that can be indexed (006), (012),
(104) and (116) planes respectively (JCPDS No: 46-0098). The presence of copper and
zinc was confirmed by the observed characteristic peaks of Copper Zinc Telluride at 2θ =
42.3°, 45.7° and 50.0° that can be indexed to (2 3 13), (0 9 11) and (4 1 1) planes
respectively (JCPDS No: 45-1297). The presence of copper and selenide was confirmed by
the observed characteristic peaks of Copper Selenide Sulfide at 2θ = 36.5°, 42.39°, 45.7°
and 50.07° (JCPDS No: 24-0377). The presence of lead was confirmed by the observed
characteristic peaks of lead carbonate hydroxide hydrate at 2θ = 26.5°, 34.5°, 39.4°, 42.3°,
45.7°, 50.07° and 54.8° that can be indexed to (204), (300), (221), (224), (226), (2 0 12)
and (2 2 10) planes respectively (JCPDS No: 09-0356) although some other peaks was not
observed, may be due to very low concentrations.
The XRD pattern of the 3rd
residue of agricultural soil of site 5 exhibited
characteristic peaks at 2θ = 20.8°, 26.59°, 36.4°, 39.4°, 40.2°, 42.4°, 45.4°, 50.09°, 54.8°,
55.3° and 59.9° that can be indexed with minor shifting to (100), (101), (110), (102), (111),
(200), (201), (112), (202), (103) and (211) planes of quartz respectively (JCPDS No: 46-
1045). The characteristic peaks at 2θ = 8.8°, 17.8°, 22.8°, 26.8°, 36.04°, 36.49°, 40.2°,
45.46°, 55.3° and 59.9° can be indexed to (003), (006), (104), (009), (0 0 12), (114), (200),
(0 0 15), (1 1 14) and (218) planes of muscovite respectively (JCPDS No: 07-0042) was
observed. The characteristic peaks at 2θ = 8.8°, 17.8°, 20.8°, 22.8°, 25.5°, 27.8°, 29.9°,
31.2°, 32.09°, 34.9°, 36.04°, 36.4°, 45.4°, 55.28° and 59.9° can be indexed to (002), (004),
(111), (113), (114), (114), (025), (115), (116), (131), (008), (113), (0 0 10), (2 0 10) and
(156) planes of Potassium magnesium aluminium silicate hydroxide respectively (JCPDS
No: 40-0020) was observed. Presence of aluminium was also confirmed by the observed
peaks at 2θ = 33.2°, 37.2°, 39.4° and 59.9° of aluminium oxide (JCPDS No: 46-1215). The
characteristic peaks at 2θ = 39.4° and 45.5° can be indexed to (111) and (200) planes of
Results and discussion
| P a g e
Aluminium iron titanium respectively (JCPDS No: 43-1156) was observed. Presence of
lithium and manganese was confirmed by the observed peaks at 2θ = 22.8° and 42.4° that
can be indexed to (011) and (020) planes of Lithium manganese zirconium oxide
respectively (JCPDS No: 40-0360) was observed.
The XRD pattern of the 4th
residue of agricultural soil of site 5 exhibited
characteristic peaks at 2θ = 20.8°, 26.65°, 36.55°, 40.29°, 42.38°, 45.8°, 50.1°, 59.9°, and
67.7° that can be indexed with minor shifting to (100), (101), (110), (111), (200), (201),
(112), (211) and (212) planes of quartz respectively (JCPDS No: 46-1045). The
characteristic peaks at 2θ = 17.8°, 19.8°, 20.8°, 26.6°, 27.8°, 36.5°, 39.48°, 40.29°, 42.38°
and 45.4° that can be indexed to (200), (012), (121), (300), (310), (004), (142), (313), (242)
and (333) planes of gismondine respectively (JCPDS No: 20-0452) was observed. The
characteristic peaks at 2θ = 20.8°, 26.6°, 27.8°, 36.5°, 39.48°, 40.29°, 42.38°, 45.81°,
50.12°, 54.8°, 59.9° and 68.3° indicating the presence of silicon sulfide (JCPDS No: 47-
1376). The characteristic peaks at 2θ = 8.8°, 17.8°, 19.7°, 19.9°, 26.65°, 28.7°, 36.06°,
36.55°, 42.3° and 45.48° that can be indexed to (003), (006), (100), (101), (006), (107), (0
0 12), (114), (118) and (0 0 15) planes of muscovite respectively (JCPDS No: 07-0042)
was observed, although some are very small or shifted. The major minerals or metals
identified in the original sample agricultural soil of the site 5 of the river Yamuna and its
residues is summarised in table 4.6.
To identify the presence of certain functional groups or chemical bonds of a
compounds present in the soil and its residues FTIR spectroscopy was carried The
spectrum of agriculture soil of site 5 and its different residues samples is presented in the
figure 4.33. The band corresponding to OH group was present in all the samples. The
primary OH group (3401 – 3417 cm-1
) was recorded in all the residues and original soil
sample except in residue 1. The secondary OH group (3620 – 3625 cm-1
) was recorded in
all the residues and original soil sample. Methylene (C-H asymmetrical (2921 – 2924 cm-1
)
and symmetrical (2852 – 2855 cm-1
) bend) was recorded only in the residues bound to Fe
and Mn oxides and organics. Bands corresponding to the halogenated compounds were
also recorded in most of the residues. The band around 1082 – 1085 cm-1
indicative of
aliphatic fluoro compounds (C–F stretch) and sulphate ions was recorded in all the samples
except bound residue 3. The band around 1033 cm-1
indicative of phosphate and silicate
ions was recorded only in residue 1.
Results and discussion
| P a g e
Table 4.6: Metal/Mineral identified in the agricultural soil of the site 5 of the river
Yamuna and its residues
Original agricultural
soil of site 5
Residue 1 Residue 2 Residue 3 Residue 4
Metal
/Mineral
JCPDS
No
Metal
/Mineral
JCPDS
No
Metal
/Mineral
JCPDS
No
Metal
/Mineral
JCPDS
No
Metal
/Mineral
JCPDS
No
Quartz 46-
1045
Quartz 46-
1045
Quartz 46-
1045
Quartz 46-
1045
Quartz 46-
1045 Berlinite 10-
0423
Gismondine 20-
0452
Gismondine 20-
0452
Muscovite 07-
0042
Gismondine 20-
0452
Gismondine 20-0452
Muscovite 19-0814
Berlinite 10-0423
Potassium magnesium
aluminium silicate
hydroxide
40-0020
Silicon sulphide
47-1376
Illite 26-0911
Berlinite 10-0423
Albite 09-0466
Aluminium oxide
46-1215
Muscovite 07-0042
Lipscombite 45-
1454
Illite 26-
0911
Muscovite 19-
0814
Aluminium
iron titanium
43-
1156
Gadolinium 25-
1096
Lipscombite 14-
0310
Gadolinium 25-
1096
Lithium
manganese
zirconium oxide
40-
0360
Copper
Zinc Telluride
45-
1297
Gadolinium 25-
1096
Iron 46-
0098
Lead 26-
1588
Copper Zinc
Telluride
45-
1297
Copper, zinc 45-
1297
Magnesium 40-
0090
lead 14-
0829
Copper,
selenide
24-
0377
Lead 09-0356
The presence of aliphatic chloro and bromo compounds (C–Cl stretch and C–Br
stretch) was reflected by the bands at 778 – 779 cm-1
and 694 cm-1
respectively. The
aliphatic iodo compounds (C–I stretch) (511 – 518 cm-1
) was recorded in the
unfractionated original soil and residue 3. Band (461 – 482 cm-1
) corresponding to Aryl
disulfides (S–S stretch) was recorded in soil and its all residues. Transition metal carbonyls
(band at 1871 – 1883 cm-1
) were recorded in most of the samples analysed except residue
1. The aliphatic nitro compounds (1537 cm-1
) were recorded in the soil and residue 3. The
open chain imino (–C=N– stretch) and secondary amine (NH bend) (1626 – 1628 cm-1
)
was recorded in all the samples but it was absent in residue 1. The band around 1626 –
1628 cm-1
also indicates the presence of organic nitrates in all the samples except residue
1. Secondary amine, CN stretch (1164 – 1172 cm-1
) was recorded in all the residues but it
was absent in the unfractionated soil. The organic siloxane or silicone (Si-O-Si) (1082 –
1085 cm-1
) was also recorded in all the samples excluding residue 3 in which a broad band
was recorded at 1166 cm-1
.
Results and discussion
| P a g e
Figure 4.33 (a) FTIR spectrum of whole agriculture soil of site 5
Figure 4.33 (b) FTIR spectrum of residue 1 agriculture soil of site 5
Figure 4.33 (c) FTIR spectrum of residue 2 agriculture soil of site 5
Results and discussion
| P a g e
Figure 4.33 (d) FTIR spectrum of residue 3 agriculture soil of site 5
Figure 4.33 (e) FTIR spectrum of residue 4 agriculture soil of site 5
Results and discussion
| P a g e
4.7 Chelant induced phytoextraction of heavy metals by Pteris vittata
4.7.1 Dry biomass of Pteris vittata
The dry weight of the control and treated, roots and fronds did not show any consistent
pattern (Table 4.7).
Table 4.7: Dry biomass yield of Pteris vittata grown in the control and treated soil
Dry biomass of roots Dry biomass of fronds Total dry biomass
(gm pot-1) (gm pot-1) (gm pot-1)
Control 5.0597±0.2731 12.3293±0.1412 17.3890±0.4079
Treated 5.1670±0.2409 12.1637±0.2497 17.3307±0.0904
*Mean ± standard error
Figure 4.34 Control and treated plant of Pteris vittata in the pot experiment
4.7.2 Heavy metal concentration
WDXRF spectra of the soil taken for experiment; roots and fronds of the Pteris vittata are
shown in figure 4.35. From the WDXRF multi-element spectral data concentrations of 22
elements (Si, Al, Fe, Ca, K, Mg, Na, Ti, P, S, Mn, Ba, Cl, Zr, Cr, Zn, Rb, Cu, Sr, Ni, As,
Re) in experiment soil, roots and fronds (both control and treated) were determined (Table
4.8). In control plant, the roots recorded higher concentration of Si, Al, Fe, Ca, Mg, Na, Ti,
Mn, Zr, Cr, Rb, Cu, Sr, Ni, As, Re while the fronds recorded higher concentrations of K, P,
Results and discussion
| P a g e
S, Ba, Cl. In treated plant Si, Al, Fe, Ca, Mg, Na, Ti, Mn, Ba, Zr, Cr, Zn, Cu, Sr, Ni, Re
concentrations were observed more in roots while K, P, S, Cl, Rb, As concentrations were
observed higher in the fronds. In roots of P. vittata increase in the accumulation was
recorded for As, Cu, Zn, Re, Cr, S, Ca, Sr, Na, Mg and Al as compared to the control.
Although increase in absorption of these 11 elements was observed but decreased
absorption of some elements was also observed. Reduced absorption was noted in Zr, Si,
Mn, Ni, Rb, Cl, Fe, K and Ti. After the treatment there was no change in the Al, Fe and Na
absorption in fronds of P. vittata as compared to the control (Table 4.8). In the treated
fronds increased absorption was observed in As, K, P, Mg, Cu, Re, S, Cl, Ni, Cr and Rb
while a drop in absorption was recorded for Ba, Si, Ti, Sr, Ca and Mn.
01
24
68
10
20
30
40
50
60
70
80
90
10
02
00
30
04
00
50
0
KC
ps
P
KA
1 Cu K
A1
Na K
A1
Ca K
A1
Zr
KA
1
Rb K
A1
Ni K
A1
Mn K
A1
O
KA
1
S
KA
1
Sr
KA
1
Zn K
A1
Mg K
A1
Ba K
A1
Ti K
A1
Si K
A1
Fe K
A1
C
KA
1
Y
KA
1
K
KA
1
As K
A1
Al K
A1
Cr
KA
1
0,2 0,4 0,6 1 2 3 4 5 6 7 8 9 10 20 30 40 50 52 54 56
KeV (a) Soil
Results and discussion
| P a g e
01
23
51
02
03
04
05
06
07
08
09
01
00
20
03
00
40
0
KC
ps
Zn K
A1
O
KA
1
Ca K
A1
Sr
KA
1
P
KA
1
Ni K
A1
Ba K
A1
Mg K
A1
As K
A1
Al K
A1
Mn K
A1
Y
KA
1
S
KA
1
Cu K
A1
C
KA
1
K
KA
1
Rb K
A1
Si K
A1
Fe K
A1
Zr
KA
1
Na K
A1
0,2 0,4 0,6 1 2 3 4 5 6 7 8 9 10 20 30 40 50 52 54 56
KeV (b) Control roots
01
23
51
02
03
04
05
06
07
08
09
01
00
20
03
00
40
0
KC
ps
As K
A1
Si K
A1
Mn K
A1
Sr
KA
1
C
KA
1
Cl K
A1
Cu K
A1
Mg K
A1
Ti K
A1
Rb K
A1
P
KA
1
Fe K
A1
O
KA
1
Zr
KA
1
K
KA
1
Zn K
A1
Al K
A1
Cr
KA
1
Y
KA
1S
KA
1
Ni K
A1
Ba K
A1
Na K
A1
Ca K
A1
0,2 0,4 0,6 1 2 3 4 5 6 7 8 9 10 20 30 40 50 52 54 56
KeV (c) Treated roots
Results and discussion
| P a g e
01
24
68
10
20
30
40
50
60
70
80
90
10
02
00
30
04
00
50
06
00
KC
ps
As K
A1
Mn K
A1
Si K
A1
Cu K
A1
C
KA
1
K
KA
1
Rb K
A1
Mg K
A1
Fe K
A1
P
KA
1
Zn K
A1
O
KA
1
Ca K
A1
Sr
KA
1
Al K
A1
Ni K
A1
S
KA
1
0,2 0,4 0,6 1 2 3 4 5 6 7 8 9 10 20 30 40 50 52 54 56
KeV (d) Control fronds
01
02
03
04
05
06
07
08
01
00
20
03
00
40
05
00
60
07
00
80
0
KC
ps
Sr
KA
1
Na K
A1
Fe K
A1
P
KA
1
As K
A1
O
KA
1
Ca K
A1
Y
KA
1
Al K
A1
Cu K
A1
S
KA
1
Rb K
A1
Mg K
A1
Mn K
A1
Si K
A1
Zn K
A1C
K
A1
K
KA
1
0,2 0,4 0,6 1 2 3 4 5 6 7 8 9 10 20 30 40 50 52 54 56
KeV (e) Treated fronds
Figure 4.35 WDXRF spectra of the soil; roots and fronds of the Pteris vittata
Results and discussion
| P a g e
Table 4.8: Qualitative results of the WDXRF showing the concentration of different
elements in soil; roots and fronds of the Pteris vittata
Elements Soil Control roots Control fronds Treated roots Treated fronds
Si 245500 109000 37100 55500 23700
Al 61400 20300 900 20500 900
Fe 25300 10800 700 8700 700
Ca 26100 11100 10800 15000 10400
K 19000 13400 16800 11200 37200
Mg 16900 8300 4400 9200 6800
Na 4500 2400 500 3000 500
Ti 2900 1100 60 1000 56
P 2000 1600 3400 1400 5300
S 1200 1400 2000 2700 2900
Mn 700 300 100 200 99
Ba 400 - 83 100 -
Cl 300 2200 5400 1600 7300
Zr 200 200 - 100 -
Cr 300 50 15 100 16
Zn 400 1 - 3 1
Rb 80 48 35 33 36
Cu 300 27 16 200 24
Sr 35 28 17 34 16
Ni 26 21 6 14 7
As 200 38 26 700 3500
Re - 200 68 600 100
*- below detectable limit, all the values are in mg kg-1
4.7.2 Bioaccumulation factor
To evaluate the ability of roots and fronds of P. vittata with respect to the element
concentration in the soil, Bioaccumulation factor (BAF) was calculated separately for roots
and fronds. BAF was calculated as follows: BAF(r)= R(c)/S(c) and BAF(f)=F(c)/S(c), where,
Results and discussion
| P a g e
BAF(r) is the bioaccumulation factor of roots, BAF(f) is the bioaccumulation factor of
fronds, R(c) is concentration of element in the roots, F(c) is concentration of element in the
fronds and S(c) is concentration of element in the soil.
Table 4.9: Bioaccumulation factor (BAF) of different elements in control and treated
Pteris vittata
Elements BAF(r) control BAF(r) treated BAF(f) control BAF(f) treated
Si 0.444 0.226 0.151 0.097
Al 0.331 0.334 0.015 0.015
Fe 0.427 0.344 0.028 0.028
Ca 0.425 0.575 0.414 0.398
K 0.705 0.589 0.884 1.958
Mg 0.491 0.544 0.260 0.402
Na 0.533 0.667 0.111 0.111
Ti 0.379 0.345 0.021 0.019
P 0.800 0.700 1.700 2.650
S 1.167 2.250 1.667 2.417
Mn 0.429 0.286 0.143 0.141
Ba 0.000 0.250 0.208 0.000
Cl 7.333 5.333 18.000 24.333
Zr 1.000 0.500 0.000 0.000
Cr 0.167 0.333 0.050 0.053
Zn 0.003 0.008 0.000 0.003
Rb 0.600 0.413 0.438 0.450
Cu 0.090 0.667 0.053 0.080
Sr 0.800 0.971 0.486 0.457
Ni 0.808 0.538 0.231 0.269
As 0.190 3.500 0.130 17.500
Results and discussion
| P a g e
Different elements
BA
F (r)
Si Al Fe Ca K Mg Na Ti P S Mn Cl Zr Cr Zn Rb Cu Sr Ni As0
1
2
3
5
6
7 BAF(r) controlBAF(r) treated
Figure 4.36 Bioaccumulation factor (BAF) of different elements in roots of control
and treated Pteris vittata
Si Al Fe Ca K Mg Na Ti P S Mn Ba Cl Zr Cr Zn Rb Cu Sr Ni As0.0
0.5
1.0
1.5
2.0
2.5
161820222426 BAF(f) control
BAF(f) treated
Different elements
BA
F (f)
Figure 4.37 Bioaccumulation factor (BAF) of different elements in fronds of control
and treated Pteris vittata
Results and discussion
| P a g e
Pe
rce
nta
ge c
han
ge
Different elements
-49.08
0.99
-19.44
35.14
-16.42
10.84
25
-9.09
-12.5
92.86
-33.33
-27.27
-50
100
200
-31.25
640.74
21.43
-33.33
1742.11
-75
-50
-25
0
25
50
75
100
125
150
175
200
4008001200160020002400
Si Al Fe Ca K Mg Na Ti P S Mn Cl Zr Cr Zn Rb Cu Sr Ni As
Figure 4.38 Percentage change in the bioaccumulation factor (BAF) roots of different
elements in Pteris vitata after treatment
Pe
rce
nta
ge c
han
ge
Different elements
-36.12
0 0
-3.7
121.43
54.55
0
-6.67
55.88
45
-1
-100
35.19
6.67
2.86
50
-5.88
16.67
13361.54
-100
-75
-50
-25
0
25
50
75
100
125130001320013400136001380014000
Si Al Fe Ca K Mg Na Ti P S Mn Ba Cl Cr Rb Cu Sr Ni As
Figure 4.39 Percentage change in the bioaccumulation factor (BAF) fronds of
different elements in Pteris vitata after treatment
Results and discussion
| P a g e
The result of the BAF for roots and fronds was presented in the table 4.9. It was found that
BAF was highest for the Cl for roots and fronds in both conditions, when no treatment was
given and when treatment with chelating agent was done prior to harvesting. In the roots
BAF was highest for Cl, 7.333 in control but after treatment it decreased to 5.333 while for
As BAF was as low as 0.19 but it increased to 3.5 after treatment (Table 4.9, Figure 4.36).
In fronds BAF was highest for Cl, 18 in control that after treatment increased to 24.333,
followed by a BAF of 1.7 for P which increased to 2.65. In fronds the BAF for As was
increased to 17.5 after treatment from 0.13 (Table 4.9, Figure 4.37). Percentage change in
the BAF was calculated to estimate the increase or decrease after the treatment. It was
observed that for roots after treatment BAF increase was highest for As (1742.11%)
followed by Cu (640.74%), Zn (200%), Cr (100%), S (92.86%), Ca (35.14%), Na (25%),
Sr (21.43%), Mg (10.84%), and Al (0.99%). It was found that decrease in BAF of roots
was highest for Zr (50%) followed by Si (49.08%), Mn (33.33%), Ni (33.33%), Rb
(31.25%), Cl (27.27%), Fe (19.44%), K (16.42%), P (12.5%) and Ti (9.09%) (Figure 4.38).
No change was observed after treatment in the BAF of fronds for Al, Fe and Na while
decrease was found highest for Ba (100%) followed by Si (36.12%), Ti (6.67%), Sr
(5.88%), Ca (3.70%) and Mn (1%). Highest increase in BAF was observed for As
(13361.54%) followed by K (121.43%), P (55.88%), Mg (54.55%), Cu (50%), S (45%), Cl
(35.19%), Ni (16.67%), Cr (6.67%) and Rb (2.86%) after the treatment (Figure 4.39).
4.7.3 Translocation Factor
Translocation Factor (TF) or mobilization ratio of metals from roots to fronds has been
estimated to determine relative translocation of elements from roots to fronds of P. vittata.
TF was calculated as follows: TF= F(c)/R(c) where F(c) is concentration of element in the
fronds and R(c) is concentration of element in the roots.
Results and discussion
| P a g e
Table 4.10: Translocation factor (TF) of different elements in control and treated
Pteris vitata
Elements TF control TF treated
Si 0.340 0.427
Al 0.044 0.044
Fe 0.065 0.080
Ca 0.973 0.693
K 1.254 3.321
Mg 0.530 0.739
Na 0.208 0.167
Ti 0.055 0.056
P 2.125 3.786
S 1.429 1.074
Mn 0.333 0.495
Cl 2.455 4.563
Cr 0.300 0.160
Rb 0.729 1.091
Cu 0.593 0.120
Sr 0.607 0.471
Ni 0.286 0.500
As 0.684 5.000
Re 0.340 0.167
Results and discussion
| P a g e
Si Al Fe Ca K Mg Na Ti P S Mn Cl Cr Rb Cu Sr Ni As Re
0.0
0.5
1.0
1.5
2.0
2.5
3.54.04.55.0
TF control
TF treated
Different elements
T F
Figure 4.40 Translocation factor (TF) of different elements in control and treated
Pteris vitata
The result of the TF was presented in table 4.10 and Figure 4.40. In the control the TF was
highest for Cl (2.454) followed by P (2.125) and lowest for Al (0.044) while after
treatment it recorded highest value for As (5) followed by Cl (4.562) and lowest for Al
(0.043). Percentage change in the TF was calculated to estimate the increase or decrease
after the treatment. It was found that after the treatment the TF increase was maximum for
As (630.77%), followed by K (164.92%), Cl (85.88%), P (78.15%), Ni (75%), Rb
(49.61%), Mn (48.5%), Mg (39.43%), Si (25.46%), Fe (24.14%) and Ti (2.67%) while
decrease was maximum for Cu (79.75%), followed by Re (50.98%), Cr (46.67%), Ca
(28.74%), S (24.81%), Sr (22.49%), Na (20%) and Al (0.98%) (Figure 4.41).
Results and discussion
| P a g e
Pe
rce
nta
ge c
han
geDifferent elements
25.46
-0.98
24.14
-28.74
164.92
39.43
-20
2.67
78.15
-24.81
48.5
85.88
-46.67
49.61
-79.75
-22.49
75
630.77
-50.98
-80-60-40-20020406080100120140160180
600
650
700Si Al Fe Ca K Mg Na Ti P S Mn Cl Cr Rb Cu Sr Ni As Re
Figure 4.41 Percentage change in the translocation factor (TF) of different elements
in Pteris vitata after treatment
Various natural and synthetic enhancers are known to increase metal uptake. For
example, sulphate and glutathione enhanced accumulation of arsenic in Pteris vittata (Wei
et al. 2010). Enhanced Cu and Zn uptake by sunflowers were via citric acid addition (Yen
and Pan, 2012). In the present study although no significant different was observed in the
dry biomass of roots and fronds (Table 4.7) but it was found that use of chelating agent
before the harvesting has a significant effect on the absorption of different elements in the
roots and fronds of P. vittata (Table 4.8). It was found that the concentration of the metal
in the soil has the following trend: Si>Al>Ca>Fe>K>Mg>Na>Ti>P>
S>Mn>Ba>Zn>Cl>Cr>Cu>Zr>As>Rb>Sr>Ni, while in the control roots it was
Si>Al>K>Ca>Fe>Mg>Na>Cl>P> S>Ti>Mn>Zr>Re>Cr>Rb>As> Sr>Cu> Ni>Zn>Ba and
in Control fronds it was Si>K>Ca>Cl>Mg>P>S>Al>Fe>Na>Mn>Ba>Re>Ti>Rb>
As>Sr>Cu>Cr>Ni>Zr>Zn. After the treatment with the chelating agent we recorded the
following trend: Si>Al>Ca>K>Mg>Fe>Na>S>Cl>P>Ti>As>Re>Mn>Cu>Ba>Cr>Zr>
Results and discussion
| P a g e
Sr>Rb>Ni>Zn for roots and K>Si>Ca>Cl>Mg>P>As>S>Al>Fe>Na>Re>Mn>Ti>
Rb>Cu>Cr>Sr>Ni>Zn>Ba>Zr for fronds (Table 4.8).
Accumulation of selected elements varied greatly among different plant species and
uptake of a particular element by a plant is primarily dependent on the plant species, its
inherent controls and the soil quality (Chunilall et al. 2005). The presence of elements in
the bioavailable form in the vicinity of the plant roots has a great impact on the
bioabsorption of an element. When elements do not exist in available form in the soil for
sufficient plant uptake, adding chelates or acidifying agents helps them to liberate into the
soil solution, improving the metal accumulation capacities (Blaylock et al. 1997).
Synthetic chelates such as EDTA have been shown to enhance phytoextraction of some
heavy metals from polluted soil in previous studies (Grčman et al. 2001). In our study, we
used EDTA as a chelating agent for treating the soil; it was found that increased
accumulation was recorded for 11 elements compared to the control in roots of P. vittata.
In fronds of P. Vittata, after the treatment, while there was no change in the Al, Fe and Na
absorption and decrease in absorption of 6 elements was observed as compared to the
control, but increased absorption was observed for 11 elements (Table 4.8, Figure 4.38 and
4.39). The degree of chelant induced extraction depends upon a number of factors like
fractionation of metals retained in soil, types of chelating agents used and concentrations of
chelating agents employed (Yeh and Pan 2012).
N, P and K are the primary macronutrients required by the plants for their growth
and survival. We found that in the roots BAF of P and K was slightly decreased after the
treatment but it increased considerably in fronds after the treatment (Figure 4.36 and 4.37).
For secondary macronutrients S and Mg, after the treatment, BAF, both in roots and
fronds, increased while for Ca it increased in roots and decreased slightly in fronds. This
suggests non-significant effect on the health of the plant after treatment. Elements like B,
Cu, Fe, Cl, Mn, Mo and Zn are also essential and are needed in only very small (micro)
quantities, therefore called as micronutrients. Increase in the BAF was observed for Cu and
Zn in roots and fronds, for Cl in fronds, while no change was observed for Fe in fronds
(Figure 4.36 and 4.37).
Heavy metals like Pb, Cr, As, Zn, Cd, Cu, Hg, Al, and Ni when present in excess
amount have well known associated environmental and health risks. In this study, Fe
Results and discussion
| P a g e
absorption decreased in the roots while it remained unchanged after the treatment. The
absorption of Cr increased after the treatment in both above and underground part of the
plant while it was more in the latter. Almost no change was observed in the absorption of
Al, it increased slightly for Zn while increase was higher in the Cu in both the plant parts
considered. Lou et al. (2007) found that chelating agents (EDTA, HEDTA) enhanced the
Cu, Zn and Pb accumulation in three plant species including Chinese brake fern. But in our
study elements like Pb, Cd and Hg were absent in both plant parts as they were also not
present in the soil (Table 4.8). Other reason for this can be as result of the limited
sensitivity of XRF instrumentation to Cd and Pb (Marguí and Hidalgo 2009).
Highest change was recorded for As for which this fern is well known. After the
treatment the BAF for As increased by 1742.11% for roots and 13361.54% for fronds
while TF increased by 630.77% (Figure 4, 5 and 7). Previously it was reported that EDTA
and HEDTA lowered the As accumulation in this plant (Lou et al. 2007). This contrasting
finding could be attributable to lower As level in the soil taken for the study as compared
to theirs or may be due to chemical state in which it is present in the soil. It is believed that
P. vittata has considerable ability to adjust As absorbing capacity under different soil As
levels (Liao et al. 2004). In a previous study P. vittata was effective in taking up As (up to
4100 mg kg-1
) and transporting it to the fronds, but little in other metals (Fayiga et al.
2004). Our study revealed similar results. The BAF of fronds for As can be as low as 0.06-
7.4 with a total 3-704 mg kg-1
As accumulation in the frond when soil As level was 51-261
mg kg-1
(Wei and Chen 2006). We recorded a BAF of 0.13 in the control while it was
high (17.5) in the treated fronds. Ma et al. (2001) reported total 3-704 mg kg-1
As in plant
after 6 weeks when 6-1500 mg kg-1
As was present in the soil. In the present study, after
treating with chelating agent, we recorded 700 mg kg-1
As in roots and 3500 mg kg-1
As in
fronds when 200 mg kg-1
As was present in the soil.
4.8 Heavy metal immobilization potential of the vermiculite in the soil
4.8.1 Total metal content of the experimental soil
The total metal concentrations in the control (unpolluted) soil and experimental soil are
presented in the figure 4.43. In the control soil the highest concentration was recorded for
the Zn followed by Pb and Cu. In the polluted soil also the same trend was observed for the
levels of heavy metals. All the metals in the control soil was within the permissible levels
Results and discussion
| P a g e
for the agriculture soil while in the polluted soil all the metals were above the critical levels
for the agriculture soil (Table 4.11). The levels of metals in the control soil are below the
levels mentioned in Canadian Soil Quality Guidelines while in the polluted soil are above.
In both soils Cu and Zn are with the normal range found in the soil however Pb in the
control soil was in the range, slightly above the normal range in the polluted soil.
According to Dutch Environmental Guidelines & Standards (2000) all the metals in the
control soil were within the target levels for soils while the levels in polluted soil are much
higher (Table 4.11).
Uncontaminated soil without vermiculite
Uncontaminated soil amended with
vermiculite
Contaminated soil without vermiculite
Contaminated soil amended with
vermiculite
Figure 4.42 Maize plants grown in control soil and polluted soil
Figure 4.43 Metal concentrations in control soil and polluted soil
Results and discussion
| P a g e
Table 4.11: Levels of heavy metals in the control soil, polluted soil and standard
values of different agencies.
Heavy metal Control Soil
(mg·kg-1)
Polluted Soil
(mg·kg-1)
Canadian Soil
Quality
Guidelines α (mg·kg-1)
Normal
range in
soil β
(mg·kg-1)
Critical soil
concentrationsβ
(mg·kg-1)
Dutch
Environmental
Guidelines &
Standards
Target values
for soilγ
(mg·kg-1)
Dutch
Environmental
Guidelines &
Standards
intervention
values for soilγ
(mg·kg-1)
Pb 38.667 412.333 70 2-300 100-400 85 530
Cu 25.200 183.000 63 2-250 60-125 36 190
Zn 88.133 495.200 200 1-900 70-400 140 720
(αCanadian Council of Ministers of the Environment, 2007;
βAlloway, 1995;
γDutch
Environmental Guidelines & Standards, 2000)
4.8.2 Biomass production
Increased dry biomass of the maize plants were observed for the soil amended with the
vermiculite as compared to the soil which was without vermiculite for both the control and
polluted soil (Figure 4.44). Dry biomass of the plants that were grown in the polluted soil
was lower as compared to the control soil irrespective of the vermiculite amendments. The
biomass of the stalk was highest followed by the leaf and lowest of the root for all the
maize plants. The relatively low biomass yield in the polluted soil can be related to the
presence of the heavy metals in comparatively high concentrations. Heavy metals such as
Pb, Cd, Ni, and Tl even at low concentrations inhibited transpiration and up to 50%
decrease of photosynthesis in detached leaves of sunflower, primarily due to interference
with stomatal function (Bazzaz et al., 1974). In another experiment in hydroponic system
corn and sunflower plants when treated with various dose of Pb, Cd, Ni, and Tl caused
toxicity and decreased growth of both plants (Carlson et al., 1975). Cabbage plants when
treated to Co, Ni and Cd at 500 μM concentrations in sand culture led to increased
accumulation of the metals with the inhibition of growth and appearance of visible
symptoms of metal toxicity like chlorosis, black spots and reddish purple coloration near
leaf margins (Pandey and Sharma, 2002).
Results and discussion
| P a g e
Leaf Stalk Root Total
-20
-10
0
10
20
30
40
175
200
225
250
275
300
325
350
375
Bio
ma
ss (
gm
)
CW
CV
PW
PV
Figure 4.44 Comparisons of the maize biomass grown in the control
(uncontaminated) soil (C) and polluted soil (P) without (W) and with (V)
vermiculite amendments into the soil
Different heavy metals have variable effect on the different plants at different
concentrations. In a study application of 800 mg Pb(NO3)2 kg-1
soil that is equivalent to
500 mg kg-1
Pb in soil did not affected the germination rate of the maize seeds and did not
produced any visible toxic symptoms in the young seedlings (Hadi et al., 2010). However
reduction in the root length and plant height was observed in the plants treated with Pb as
compared to the control, same trend was also observed for the root and shoot dry biomass.
In the same study EDTA in combination with GA3 or IAA considerably increased the Pb
accumulation in plant (Hadi et al., 2010). In hydroponic culture of chinese cabbage
(Brassica pekinensis Rupr) with variable Cu and N concentrations, root, shoot and total
biomass was significantly decreased by Cu treatment, while root biomass was not affected
by N concentration but shoot and total biomass decreased with N deficiency (Xiong, et al.,
2006). Excess Cu exposure lowered total chlorophyll content, increased Cu concentration
and decreased nitrate reductase (NR) activity in the roots and shoots. Reduced root length
and fewer leaves was observed but total free amino acid content in the leaves increased
which demonstrated the adverse effects of Cu on N metabolism and plant growth (Xiong,
et al., 2006). Maize root biomass under Cu stress decreased while the Cu accumulated in
the roots increased with the increase in the Cu concentrations (Ouzounidou et al., 1995).
Results and discussion
| P a g e
The Cu alters the ultrastructure of the roots cells of maize but the response was not uniform
to stress conditions, indicating the development of a resistance strategy of maize roots to
Cu-toxicity (Ouzounidou et al., 1995). Under field conditions in the sewage sludge
irrigated maize plants, no visible damaging effects on plants was observed with the soil
treatments, although the roots and stalks dry matter of plants grown with higher sludge was
considerably decreased. Increase in the Cu accumulation was increased in plants grown
with higher sludge (Jarausch-Wehrheim et al., 1996). The effects of heavy metals on
germination, growth and accumulation of metals are also evident in the plants found in
extreme habitat like grey mangrove, Avicennia marina (Forsk.) Vierh. Reduced seedling
height and biomass was observed, with the increase in the concentrations Cu and Zn while
Pb did showed little effect on the seedling height and no effect on the final biomass of the
seedling of Avicennia marina (MacFarlane and Burchett, 2002). In a long-term field trial
on annually cropped maize plots where Zn-contaminated sludge was used in two different
amounts per 2 years, 15 to 25% reduction in shoot yield was observed in plants grown in
the fields where sludge was applied (Jarausch-Wehrheim et al., 1999). Arsenic at very
low concentrations can have favourable effect on the plant growth. In a study on Spartina
patens growing in hydroponic conditions, addition of arsenate and monomethyl arsonic
acid, significantly increased total dry biomass production at low As concentrations (0.2 to
0.8 mg lt-1
) however on increasing the concentration (2 mg lt-1
) the dry biomass decreased
as compared to the control (Carbonell et al., 1998). The As was also found to decrease the
growth, leaf area and biomass accumulation, with induced lipid peroxidation and increased
peroxidise activity in maize (Stoeva et al., 2003). The maize plants that were grown on
soils amended with the vermiculite were found to have increase biomass as compared to
the soil without vermiculite. This suggests the decrease in the available heavy metals
concentrations possibly by the adsorption of metals by the vermiculite.
4.8.3 Post harvest metal concentration in plant parts and soil
The Pb and Cu accumulation was more in the roots followed by stalk and leaf among all
the experiments (Figure 4.45) while the Zn accumulation was more in leaf followed by
stalk and roots. In all the plant parts observed Pb, Cu, and Zn accumulation was more in
the polluted soil as compared to the control soil. The Pb, Cu and Zn accumulation was
more in the maize plants grown in the soils without vermiculite as compared to the soils
amended with vermiculite. In the soils post harvest the amount of metals were almost same
Results and discussion
| P a g e
in all the pots with or without vermiculite. Overall accumulation of Pb, Cu and Zn
decreased in all the plant parts of the maize plant by the vermiculite amendment
irrespective of the concentration of metals in the soil (Figure 4.45 and 4.46). The decrease
of metal accumulation by the amended vermiculite was highest, for Pb in the roots of
maize plant followed by Cu in leaf of maize plant grown in control soil (Figure 4.46). In
the polluted soil the decrease in accumulation by amended vermiculite was highest for Pb
followed by Cu in the leaf. The decrease in the Pb accumulation by amended vermiculite
was more in control soil then polluted soil in all plant parts. The effect of amended
vermiculite to the soil on Cu accumulation in roots was same in both the soils however
decrease in accumulation was more in the leaf in the control soil and in stalk in polluted
soil. The decrease in the Zn accumulation by amended vermiculite was more in leaf and
roots in the control soil while more in stalk in polluted soil.
CW CV PW PV
0
50
100
350
400
450
Co
nce
ntr
atio
n o
f P
b (
mg k
g-1)
Leaf
Stalk
Roots
Soil
(a)
Results and discussion
| P a g e
CW CV PW PV
0
10
20
30
40
50
60
160
180
200
Co
nce
ntr
atio
n o
f C
u (
mg
kg
-1)
Leaf
Stalk
Roots
Soil
(b)
CW CV PW PV
0
10
20
30
40
50
60
70
80
90
200
250
300
350
400
450
500
Co
nce
ntr
atio
n o
f Z
n (
mg
kg
-1)
Leaf
Stalk
Roots
Soil
(c)
Figure 4.45 Metal concentrations in leaf, stalk and roots of Maize and grown in the
control soil (Uncontaminated) without vermiculite (CW), control soil with
vermiculite (CV), polluted soil without vermiculite (PW) and polluted soil
with vermiculite (PV); (a) Pb; (b) Cu; (c) Zn
Results and discussion
| P a g e
Leaf Stalk Root Leaf Stalk Root
0
5
10
15
20
25
30
55
60
65
70
75
% d
ecre
ase
in
me
tal co
nce
ntr
atio
n
Pb
Cu
Zn
Control (Uncontaminated soil) Polluted soil
Figure 4.46 Percentage decrease of the metals concentration in the different plant
parts of maize plant grown on control and polluted soil after the
vermiculite treatment
If we compare the heavy metal accumulation in the above ground parts Zn was highest
followed by Pb and Cu in both control and polluted soil (Figure 4.44). The level of the
metals in the above ground plant parts are directly related to the level of the respective
metal in the soil (Figure 4.43 and 4.44). The metal concentration of two vegetable plants,
lettuce and spinach showed different trend in the polluted soil as compared to levels found
in the natural conditions however the high concentrations of some metals in these plants
had a correlation with the high concentrations of those metals in the contaminated soil
(Malandrino et al., 2011). Variability of heavy metals concentrations can also be due to
the presence of some other metal that sometimes interfere the uptake of another metal or
nutrient. Exposure of cabbage plants to surplus amount of the heavy metals decreased the
uptake of Fe and its translocation to leaves (Pandey and Sharma, 2002). Model
simulations suggested the role of saturable uptake rate of Pb, effective root mass and Pb
precipitation in the form of Pb-phosphate in the translocation and accumulation mechanism
in the maize plant (Brennan et al., 1999). In Avicennia marina accumulation of Cu was
highest followed by Zn and lowest for Pb, while the accumulation ratio of Cu and Pb in
roots to sediment increased with the increase in the sediments, the accumulation ratio of Zn
Results and discussion
| P a g e
in roots to sediment tend to decrease with the increase in the sediments (MacFarlane and
Burchett, 2002).
In general the accumulation in the roots was more as compared to the above ground
parts across all the experiments. Ouzounidou et al., (1995) found that the roots
accumulated significantly higher amounts of Cu than the above ground parts, at the
treatment of 80 µM Cu the accumulation in roots was about 99.5% of the total Cu in the
whole. In a similar study where maize plants were grown on a Cd and Zn enriched soil and
calcium silicate (CaSiO3) was used as amendment with different amounts of Si (0, 50, 100,
150, and 200 mg kg-1
) showed that the plants treated with Si had not only higher biomass
but also higher metal accumulation (da Cunha and do Nascimento, 2009). But in our
study where vermiculite was used as an amendment to the soil, there was an increase in the
biomass of the plants whereas the accumulation of the metals decreased. The observed Zn
accumulation in the leaf and stalk as compared to the roots in our pot experiments was
consistent with the previous observation of the long-term field trial on maize plots for
application of Zn-contaminated sludge where increased Zn concentrations were found in
all plant parts over the whole growing season. The upper leaves and stalk parts were the
storage sites of Zn and had higher Zn accumulation. In young maize plant Zn accumulation
was above 400 and 500 mg kg-1
in the roots and lower stalks whereas at silage stage Zn
acuumulation was between 300 and 500 mg kg-1
in the upper leaf and stalk parts treated
plants (Jarausch-Wehrheim et al., 1999).
The decreased level of heavy metals in the various plants parts of the maize grown
in the soils amended with vermiculite indicates the successful immobilization of metals by
vermiculite in the soil, thus decreasing the bioavailability of metals to the plant. The
immobilization of various heavy metals by vermiculite had been demonstrated in the
aqueous system by many workers in the past (Das and Bandyopadhyay, 1992;
Mathialagan and Viraraghavan, 2003; Malandrino et al., 2006; Stylianou et al. 2007;
Abollino et al., 2008). Malandrino et al., (2011) studied the effectiveness of vermiculite
treatment on the uptake of metal pollutants present in the soil by two vegetable plants,
Lactuca sativa and Spinacia oleracea. Significant reduction in the uptake of metal
pollutants by lettuce and spinach was found by the chemical stabilization of polluted soils
by vermiculite amendment, having increasing efficacy with contact time with the polluted
soil (Malandrino et al., 2011).
Results and discussion
| P a g e
4.8.4 Translocation factor
The effect of the vermiculite amendment to the soils on the ratio of metal concentration in
plant parts to soil calculated as translocation factor (TF) was shown in the figure 4.47. The
highest TF was observed for Pb in the roots of plant grown in control soil without
vermiculite followed by Pb in the roots of plant grown in polluted soil without vermiculite.
The TF for all the metals decreased for all the plant parts in maize plants grown in soils
amended with vermiculite as compared to the non-amended soils in both control as well
polluted soils. This means that the available metals to the plants are successfully
immobilized in the soil by the amended vermiculite decreasing the translocation of metals
to the plant. In general the TF of Cu and Zn for all parts of the plants grown in polluted soil
was higher than the control soil except an exception of Cu for roots where TF was equal
for polluted and control soil. This indicates that with the increase in the concentration of
metals in the soil, the translocation of metals to the plant is also increasing irrespective of
the presence or absence vermiculite in the soil.
Pb Cu Zn
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
1.1
1.2
1.3
1.4
1.5
1.6
1.7
Tra
nslo
ca
tio
n fa
cto
r
LCW
LCV
LPW
LPV
SCW
SCV
SPW
SPV
RCW
RCV
RPW
RPV
Figure 4.47 Translocation factor (TF) of the metals form soil to different plant parts
of maize plant grown on various soil; L= leaf; S=stalk; R=roots; C=control
(uncontaminated) soil; P= polluted soil; W=without vermiculite; V= with
vermiculite
Results and discussion
| P a g e
The TF of Pb for above ground part of the plants grown in control soil was higher
than the polluted soil, for the roots it was higher in the control soil than the polluted soil
without amended vermiculite while it was lower in the control soil than the polluted soil
with amended vermiculite. This indicates that either there is increased phytotoxicity of Pb
with the increased level of Pb in soil thus lower translocation in the polluted soil or more
immobilization of Pb even at higher level in the soil. Factors in the reaction medium such
as pH and ionic strength influenced the adsorption process (Sanchez, 1999; Malandrino,
et al., 2006). In batch mode adsorption studies, removal increased with an increase of
contact time, adsorbent amount and solution pH (Potgieter et al., 2006). More than 90%
adsorption was observed for Zn, Pb, Co, Al and Fe cations extracted from the soil with the
extractant used in the third fraction of Tessier by vermiculite at pH 6.50 with NaOH
(Abollino et al., 2007). Silicate and carbonate containing natural Jordanian sorbent was
found to be effective sorbent for removing Zn(II), Pb(II), and Co(II) ions from solution
with equilibrium sorption capacities of the metals were: 2.860, 0.320, 0.076 mM cation g-1
for Zn(II), Pb(II) and Co(II) at pH 6.5, 4.5 and 7.0, respectively (Al-Degs et al., 2006). The
affinity orders of the studied metal ions was different at different pH but mean adsorption
percentages of Pb(II) and Cu(II) were 76 and 75%, respectively by nonexpanded
vermiculite in sample from coatings industry (dos Santos and Masini, 2006).
4.8.5 Chemical composition of soil
To find the changes in the chemical composition of soil organic matter FTIR analysis was
done. The FTIR spectra of the soils showed characteristics peaks at 3418 – 3426, 1875 –
1879, 1082 – 1088, 1036 – 1039, 796 – 797, 693 – 694, 461 – 466 cm-1
in all the samples
(Figure 4.48). The band around 3418 – 3426 correspond to the OH stretching, 1875 – 1879
corresponds to the presence of transition metal carbonyls, 1082 – 1088 corresponds to the
presence of sulphate ions, 1036 – 1039 are indicative of alkyl-substituted ether (C–O
stretch) and aliphatic fluoro compounds (C–F stretch), 796 – 797 are indicative of aliphatic
chloro compounds (C–Cl stretch), 693 – 694 are indicative of aliphatic bromo compounds
(C–Br stretch), 461 – 466 cm-1
are indicative of aryl disulfides (S–S stretch). The bands at
2924 and 2928 cm-1
correspond to the methylene C-H stretch. In the polluted soil band was
observed around 1625 cm-1
are indicative of aryl-substituted C=C, while in the unpolluted
soil the band shifted to 1628 cm-1
are indicative of alkenyl C=C stretch.
Results and discussion
| P a g e
(a)
(b)
(c)
Results and discussion
| P a g e
(d)
Figure 4.48 FTIR spectrum of the soil samples after harvesting of maize (a) control
soil without vermiculite (b) control soil with vermiculite (c) polluted soil
without vermiculite (d) polluted soil with vermiculite
In the polluted soil that was amended with vermiculite a band was observed around
1384 cm-1
that corresponds to the presence of gem-Methyl or ―iso‖ (doublet). In the
unpolluted soil that was amended with vermiculite a band was observed around 1436 cm-1
which correspond to the presence of Methyl C-H bend and carbonate ions. The band
around 1172 cm-1
and 1163 cm-1
are indicative of secondary amine (CN stretch) observed
in the soils that are not amended with the vermiculite while in the unpolluted soil it was at
1172 cm-1
and in the polluted soil it shifted to 1163 cm-1
. The bands around 513 cm-1
and
516 cm-1
indicative of aliphatic iodo compounds (C–I stretch) are observed only in the
unpolluted soils while in the soil that was not amended with vermiculite the band was at
516 cm-1
and in the soil amended with vermiculite the band shifted to 513 cm-1
. The bands
around 1036 – 1039 also correspond to the presence of phosphate and silicate ion. Thus the
FTIR results specify that there are no major chemical changes in the structure of the soil by
vermiculite amendment while there are some minor changes in the soil with the increase in
the level of heavy metal pollution.
Chapter 5
Summary
and Conclusion
Summary and conclusion
| P a g e
Chapter 5. SUMMARY AND CONCLUSION
The pH of the surface water was found to be in the range of 7.17 to 8.3 in June (pre-
monsoon), 7.30 to 8.02 in October (post-monsoon) and 7.42 to 8.28 in February (spring).
The DO dropped at an alarming level after the site 3 during all the study periods. Almost
all DO values of the sampling locations after site 4 were nil through all the sampling
periods except for few locations in October. In general, the BOD was low in October
shortly after monsoon than in June and February. Lower COD (20mg/l to 24 mg/l) was
observed from site 1 to site 3 that are upstream to Wazirabad barrage during all the
sampling periods. Strong correlation was observed between most of the water quality
parameters with each other indicating close association of these parameters with each
other.
Form the above study it can be concluded that the Yamuna in Delhi is not in good
condition. While the water quality before entering the Delhi segment was much better, it
deteriorates considerably after the river passes through the national capital of Delhi.
Though the study has not evaluated water quality of the wastewater discharged into the
river but the highest impact observed was of the Najafgarh drain as downstream to it the
water was highly polluted. It can also be concluded that except pH, all parameters crossed
the prescribed limits of CPCB and water is not safe for drinking and for agriculture and
industrial use at most of the locations. Results indicate that the increase in pollution is
indicative of alarming situation and the preventive measures are not good enough to
control the same. Domestic sewage treatment plants or small community sewage treatment
plants should be set up to reduce the pressure on the existing STPs.
The concentration of the seven heavy metals (Cr, Pb, Hg, Zn, Mn, Mg and Fe) was
evaluated in the surface water, sediments and river-side agriculture soil of the river
Yamuna at selected sampling sites (12 site for water and sediments; 6 sites for agriculture
soil). The concentration of Cr was above the WHO permissible limits of Cr in water at
most of the sampling sites therefore the water is unsuitable for domestic use and drinking.
The concentration of Pb observed in this study was higher than the recommended limit of
0.01 mg l-1
Pb in water at all sampling sites except at few sites during October.
Summary and conclusion
| P a g e
Furthermore concentration of Pb was even higher than the maximum permissible level in
irrigation water at some sampling sites. However concentrations of Hg was recorded
within the range of Dutch Target and Intervention Values, (2000) for ground water it was
higher than the 0.001 mg l-1
stipulated as per the Criteria maximum concentration (CMC)
to which an aquatic community can be exposed briefly without resulting in an
unacceptable effect (US EPA, 2005). Zn concentrations in water in current study were
much below then the WHO permissible limits and Dutch intervention values of the
groundwater The average concentration of Mn exceeds the maximum permissible limit by
WHO at most of studied sites except site 3 and 4 during October and February sampling.
The observed values of Mg in water were recorded below the NEQS (National
Environmental Quality Standard for industrial effluents) limits. The average concentration
of Fe was crossed the stipulated maximum permissible limit of 0.3 mg l-1
by WHO at most
of the sampling locations after sites 4. Strong positive correlation (r> 0.9, p<0.001) was
observed between heavy metals at most of the sites with each other indicating close
association of these with each other except site 3 and 4. The observations of the cluster
analysis depict that heavy metal concentrations in the river water considerably vary with
the location and period of the sampling while some locations did have similarity in the
trend. Seasonal variations of metal concentration in river water of Yamuna in Delhi, with
June and February having similar trend while October having different trend was observed
in principle component analysis. No correlation was observed between different metals
studied (r= -0.2342 to 0.5866, p>0.001) except between Mg and Fe having r=0.6122 and
p<0.001, suggesting that concentration varies with the metal and sampling site that can be
related to different source for each metal.
The pH of the sediments was found to be alkaline in the range of 7.51 to 8.6.
Overall pH of the sediments have similar trend as the pH of water at different sites. The pH
of the selected agriculture soil was also recorded alkaline in the range of 8.00 to 8.9. The
average concentration of Cr at each site was above the WHO permissible limits of Cr in
sediment at most of the sampling sites however concentration was lower than the stipulated
380 mg kg-1
Dutch intervention value for sediments. The concentration of Pb observed in
this study was higher than the recommended limit of 40 mg kg-1
Pb in sediments by
USEPA (United States Environmental Protection Agency) at site 5, 6, 7, 8 and 12 in all
sampling period. The concentration of Hg was higher than recommended Dutch
Summary and conclusion
| P a g e
intervention value for sediments at sites 5, 6, 7, 8 and 9 at most of sampling periods. The
observed values of Zn in the present study were higher than WHO permissible limits for
sediments at all sites at some point of time during the study period. Compared to Dutch
intervention values of Zn for sediments, the recorded Zn concentration was much lower in
current study. The concentration of Mn observed in this study was much higher than the
recommended limit in sediments by USEPA at all sites and sampling period. The
maximum value of Mg in sediments was recorded at site 1 while minimum at the site 2.
The average concentration of Fe at a particular site crossed the stipulated maximum
permissible limit of 30 mg kg-1
in sediments by USEPA at all sites and sampling period.
Strong positive correlation (r> 0.7, p<0.001) was observed between heavy metals in
sediments at most of the sites with each other indicating close association of these with
each other. In addition to that high correlation coefficient (r>0.99, p<0.001) was observed
between many sites. The observations of the cluster analysis depict that heavy metal
concentrations in the river water vary considerably with the sampling location but have
similarities with the period of the sampling at each location. Seasonal variations of metal
concentrations in sediments in different sampling period with October and February having
similar trend while June having different trend was observed in the principle component
analysis. No correlation was observed between different metals studied (r= -0.2728 to
<0.5207, p>0.001) suggesting that concentration in the sediments varies with the metal and
sampling site that can be related to different source for each metal. October and February
having similar trend while June had different trend.
The average concentration of Cr in agriculture soil was below the WHO
permissible limits and Dutch intervention value of Cr in sediment at all sampling sites The
concentration of Pb observed in this study was higher than the recommended limit of Pb in
sediments by USEPA at site 5, 7 and 8 in June, Site 8 in October, site 5 and 8 in February.
The concentration of Hg was higher than recommended Dutch intervention value for
sediments at site 5 in all sampling periods. The observed values of Zn in the present study
were higher than WHO permissible limits (123 mg kg-1
) for sediments at site 7 and 8 in
June, site 5 and 7 October and site 7 in February but was much lower compared to Dutch
intervention values of Zn (720 mg kg-1
) for sediments. The concentration of Mn in soil
observed was much higher than the recommended limit of in sediments by USEPA. The
average concentration of Fe in soil at a particular site exceeds the stipulated maximum
Summary and conclusion
| P a g e
permissible limit of 30 mg kg-1
in sediments by USEPA at all sites and sampling period.
Strong positive correlation (r> 0.8, p<0.001) was observed between heavy metals at most
of the sites with each other indicating close association of these with each other. The
observations of the cluster analysis depict that heavy metal concentrations in the river
water vary considerably with the sampling location but have some similarities with the
period of the sampling at each location. The sites lying in separate clusters indicated the
variation in the heavy metal concentrations in soil at these sites. Principal component
analysis demonstrated seasonal variations with October and February showing similar
trend while June different. No correlation was observed between most of the metals studied
except some exceptions. This suggests that concentration in the agriculture soil mostly
varies with the metal and sampling site. The recorded concentrations of heavy metals in
water, sediments and agriculture soil in this study was higher than some previous reports
therefore needs immediate attention.
Sequential extraction of selected sediment samples and its respective nearest
agricultural field soil was also carried out. Heavy metals are demonstrated to be present in
a number of chemical forms in sediments and soil in varying amount. Based on the results
of the sequential extraction results mobility factors (MF) of metals was calculated. It was
observed that the evaluated metals were of medium risk in sediments and soil except Cr in
agriculture soil at all sites and Zn in agriculture soil of site 2 where the respective metals
were of low risk. SEM-EDX results gave the brief idea of surface morphology of texture
and geo-chemical compositions of the sediment and agricultural soil samples. Si was the
most abundant element present in all the samples analysed. In general, the elemental
composition in terms of the percentage for sediments increased from upstream site 2 to
downstream site 12. The number of elements detected in EDS was five at the site 2, seven
at the site 7, eight at the site 8 and seven at site 9 and 12. The over trend of the elemental
composition in terms of the percentage for agricultural soil is decreasing from the upstream
site 2 to downstream site 7 and 8. The number of elements detected in EDS was eight at
the site 2, six at the site 7 and four at the site 8. The findings of FTIR analysis identified
the changes in functional groups or chemical structure of sediments and agriculture soil
that are site specific.
Detailed investigation of the agriculture soil of site 5 by SEM-EDX revealed Si as
the most abundant element present in the soil and its all residues analysed. The varying
Summary and conclusion
| P a g e
percentage of the different elements detected in the EDS results specifies their different
geochemical forms present in the soil. XRD characterization of the soil of site 5, its
residues and their respective JCPDS match in the PDF2 database of the International
Centre for Diffraction Data (ICDD) predicted major minerals or metals. Functional groups
or chemical bonds of a compounds present in the soil and its residues were detected by
FTIR spectroscopy. The current extensive investigation has precisely assessed the present
status of heavy metal pollution in the river and river side soil. The extensive study will
help the researchers and the concerned authorities to take control and remediation
measures more appropriately.
Detrimental effects of heavy metals on the environment are well-revealing. Pteris
vittata can be used as a valid tool for the effective remediation of the soil. The use of the
chelant EDTA, was effective for enhancing the As absorption in the pot experiments. The
application of chelant reorganized the bioaccumulation capability of P. vittata. Further
study in relation to the use of chelating agents with the hyperaccumulator plants is needed
to find practical feasibility at field level. Special care should be taken in the selection of
suitable approach depending on the health attributes of the contamination site, target
contaminant and efficacy of the plant selected. X-ray fluorescence-based techniques can be
very useful for multi-element analysis, qualitatively and distribution in different plant parts
with accuracy and reproducibility in less time.
The adding vermiculite to the soil in pot experiments increased the dry biomass
while decreased the TF and accumulation of Pb, Cu and Zn in maize plants, showing
effective immobilization of metals in the soil amended. The FTIR analysis of the post
harvest soil samples showed vermiculite amendment to the soil in this experiment had no
major effect on the chemical structure of the soil while there were few observed changes
with the level of heavy metal pollution in soil. The current study determined the
applicability of vermiculite as a suitable sorbent that can be added to the soil to reduce the
phytoaccumulation of heavy metals to the plants and decrease phytotoxicity in plants.
Vermiculite amendments can be used for remediation of agricultural soil having excess
amount of heavy metals, to reduce the risk of the heavy metal contamination of the food
crops.
Summary and conclusion
| P a g e
Future research should be focused on the combined use of more than one phytoremediation
approaches for the successful remediation of the polluted area at the field conditions. Field
study should be conducted in future to find suitable amount of vermiculite needed for
effective reduction of toxic metals to the safe limits, knowledge of factors influencing the
adsorption capacity in the soil like pH and ionic strength needs to be understood. Study of
the nature of interaction of vermiculite and soil components is another focus area to see the
effect on the soil health.
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7.
Literature cited
Appendices
Appendices
APPENDICES
JCPDS match of the original agricultural soil of site 5
Quartz
Berlinite
Illite
Gismondine
Appendices
Lipscombite
Gadolinium
Copper Zinc Telluride
Lead 2-9tetra-butyl ammonium) 2-(dicyano-ethylene 1, 2-di-thiolate)
Magnesium Hydrogen Phosphate
JCPDS match of the fraction 1 of agricultural soil of site 5
Quartz
Appendices
Gismondine
Muscovite
Berlinite
Illite
Lipscombite
Appendices
Gadolinium
Copper Zinc Telluride
Lead acetate hydrate
JCPDS match of the fraction 2 of agricultural soil of site 5
Quartz
Berlinite
Appendices
Gismondine
Albite
Muscovite
Gadolinium
Iron carbonate hydroxide
Appendices
Copper Zinc Telluride
Copper Selenide Sulfide
Lead Carbonate Hydroxide Hydrate
JCPDS match of the fraction 3 of agricultural soil of site 5
Quartz
Muscovite-3
Appendices
Potassium magnesium aluminium silicate hydroxide
Aluminium oxide
Aluminium iron titanium
Lithium manganese zirconium oxide
JCPDS match of the fraction 4 of agricultural soil of site 5
Quartz
Appendices
Gismondine
Silicon sulfide
Muscovite
About the author
Ms. Shobhika Parmar the author of this manuscript, was born on November 25,
1985 at Old Tehri City of Uttarakhand state of India.
She passed his Secondary School Examination in 2001 from NMV Inter College
Tehri Garhwal (Uttrakhand) from U.P. Board, Allahabad and Senior Secondary
examination in 2003 from the same school from Uttaranchal Board, Ramnagar.
She earned B.Sc. degree from SRT Campus, Badshai Thaul, New Tehri of H. N. B.
Garhwal University, Srinagar in 2006. She earned M.Sc. degree in Environmental Science
from H. N. B. Garhwal University, Srinagar in 2008. Same year she joined the F. R. I.
University, Dehradoon and completed Post Masters Diploma in Natural Resource
Management in 2009.
She has completed dissertation on the topic ―Aquatic Animal Diversity in Henval
Stream (Garhwal Himalayas)‖ under the supervision of Prof. R. C. Sharma while pursuing
M.Sc. and completed dissertation on the topic ―Environmental Impact Assessment of Kotli
Bhel Hydroelectic Project, Stage II (530 MW)‖ from NHPC‘, while doing P.M.D.N.R.M.
She has a keen interest in the environment issue and challenges. She had actively
participated and presented papers in 7 National Conference/Symposiums and 3
International Conference/Symposium. She has also completed a short term course on
Remote Sensing and GIS from B.H.U., Varanasi. She joined Ph.D. (Environmental
Science) in historic G. B. Pant University of Agriculture and Technology, Pantnagar,
Uttrakhand in 2011. The author has 2 research papers to her credit.
Address:
Shobihika Parmar
D/o Mr. M. S. Parmar (Geological Survey of India)
Lane no.-2, Chanakyapuri, near Doon University road,
Bengalikothi (Ajabpur Kala),
Dehradun, Uttarakhand (248121)
: 9452153790
ABSTRACT
Name : Shobhika Parmar Id. No. : 41354 Semester & Year of admission
: IInd
, 2010-2011 Degree : Ph.D.
Major : Environmental Science Department : Environmental Science
Minor : Agrometeorology :
Thesis Title : GEOCHEMICAL FRACTIONATION AND
PHYTOREMEDIATION OF HEAVY METALS AROUND
YAMUNA RIVER IN DELHI
Advisor : Prof. Vir Singh :
In the present study determination of heavy metals (Cr, Pb, Hg, Zn, Mn, Mg, Fe)
concentrations in the water, sediments and river side agriculture soils of river Yamuna at
12 selected locations in three different time period (June, October and Febuary) was done.
Some selected important physico-chemical water and soil factors were also assessed. It
was observed that the downstream sites were more polluted as compared to upstream sites.
The results also concluded that the Yamuna in Delhi is not in good condition. While the
water quality before entering the Delhi segment was much better, it deteriorated
considerably after the river passes through the national capital of Delhi. Sequential
extraction, XRD and SEM-EDS demonstrated that heavy metals are present in a number of
chemical forms in sediments and soil in varying amount. Differences in the chemical
composition of soil at various locations were also observed through FTIR analysis. The
potential chelant (EDTA) enhanced phytoextraction was evaluated in Pteris vittata in pot
experiments. In the current study although no considerable difference was observed in the
dry biomass of roots and fronds but it was found that use of chelating agent before the
harvesting has a considerable effect on the absorption of different elements in the roots and
fronds of P. vittata. Thus P. vittata was found instrumental for the effective remediation of
the soil. The use of the EDTA, was effective for enhancing the As absorption in the pot
experiments. The adding vermiculite to the soil in pot experiments increased the dry
biomass while decreased the TF and accumulation of Pb, Cu and Zn in maize plants,
showing effective immobilization of metals in the soil amended. Thus vermiculite can be
effectively used as a suitable sorbent that can be added to the soil to reduce the
phytoaccumulation of heavy metals to the plants and decrease phytotoxicity in plants.
Vir Singh Shobhika Parmar
(Advisor) (Author)
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