Thesis - Semantic Scholar · 2018-12-30 · Thesis Submitted to the ... His involvement with originality has triggered and nourished my intellectual maturity that will help me for

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


Science

Member

(A. S. Nain) Professor & Head

Dept. of Agrometerology

Member

(J. P. N. Rai) Professor & Head


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


3.13.2 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




4.5 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


4.7.1 Dry biomass of Pteris vittata

4.7.2 Heavy metal concentration

4.7.2 Bioaccumulation factor

4.7.3 Translocation Factor


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


seasons

Figure 4.4 Spatial variation of the COD (mg/l) of river Yamuna River at


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


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


variation of the metal concentration with the sampling sites in the

sediments of river Yamuna along the Delhi stretch


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


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


variation of the metal concentrations of river-side agriculture soil

in different sites


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


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


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


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


Figure 4.37 Bioaccumulation factor (BAF) of different elements in fronds of


Figure 4.38 Percentage change in the bioaccumulation factor (BAF) roots of




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


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

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.

Introduction

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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

Introduction

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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

Introduction

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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).

Introduction

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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 .

Introduction

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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

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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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


| P a g e

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


| 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


| 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.


| P a g e

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


| 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


| 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)


| 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


| P a g e

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


| P a g e

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.


| P a g e

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

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 bioavailable 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.



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.



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

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


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


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


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


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


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


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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


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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).


| 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


| 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


| 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 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 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


| 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


| 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


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


June Oct Feb

Hg

Co

nce

ntr

atio

n m

g l-1

Location


| P a g e

0.000

0.200

0.400

0.600

0.800

1.000

1.200


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


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


June Oct Feb

Mg


| 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


June Oct Feb

Figure 4.6 Concentration of different metals in surface water at selected sites of river


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.


| 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


| 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.


| 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


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



| 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


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


| 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


| 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-


| 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


| 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


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


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



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


) at the site 5 in

February. The concentration of Mn observed in this study was much higher than the


| 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


) 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


) 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.


| 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


June Oct Feb

Pb

Location

0.000

20.000

40.000

60.000

80.000

100.000

120.000


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


June Oct Feb

Co

nce

ntr

atio

n m

g kg

-1


| P a g e

Zn

Location

0.000

50.000

100.000

150.000

200.000

250.000


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


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


June Oct Feb

Co

nce

ntr

atio

n m

g kg

-1


| P a g e

Fe

Location

0.000

10000.000

20000.000

30000.000

40000.000

50000.000

60000.000


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.


| 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


| P a g e






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.


| 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


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


of the metal concentration with the sampling sites and period in the sediments of



| 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.




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


| 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.


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).


| 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


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


| 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


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 the site 2 in October. The maximum value of Zn (129.267 mg kg-1

) was



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


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


) at

the site 8 in October. The maximum value of Mn (647.967 mg kg-1


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).


| P a g e

Cr

Location

0.000

0.500

1.000

1.500

2.000

2.500

3.000

3.500

4.000


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


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


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


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


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


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


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



| 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


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



) 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


| 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


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


| P a g e






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.


| 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


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


of the metal concentrations of river-side agriculture soil in different sites and

sampling periods


| 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.




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


| 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.


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


assessed in the river-side agriculture soil along river Yamuna in Delhi


| 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


| P a g e

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


| P a g e

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 7 Sediment 10.5 10.4 26.1 20.4


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


| P a g e

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


soil


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


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



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-


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


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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


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-


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.


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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

.


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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


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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


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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,


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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


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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


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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


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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,


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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


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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


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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


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




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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.


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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


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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).


| 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>


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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


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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.


4.8.1 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


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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


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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).


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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).


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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


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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)


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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


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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


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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).


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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


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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.


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(a)

(b)

(c)


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(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

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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.


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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


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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


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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


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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.


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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.

Literature

Cited

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Abollino, O., Giacomino, A., Malandrino, M., & Mentasti, E. (2007). The efficiency of

vermiculite as natural sorbent for heavy metals. Application to a contaminated

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Abollino, O., Giacomino, A., Malandrino, M., Mentasti, E., Aceto, M., & Barberis, R.

(2005). Assessment of metal availability in a contaminated soil by sequential

extraction. Water Air Soil Poll., 137: 315–338.

Aceves, M. B., Grace, C., Ansorena, J., Dendooven, L., & Brookes, P. C. (1999). Soil

microbial biomass and organic C in a gradient of zinc concentrations in soils

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Al-Degs, Y. S., El-Barghouthi, M. I., Issa, A. A., Khraisheh, M. A., & Walker, G. M.

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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


Lead acetate hydrate


Quartz

Berlinite

Appendices

Gismondine

Albite

Muscovite

Gadolinium

Iron carbonate hydroxide

Appendices


Copper Selenide Sulfide

Lead Carbonate Hydroxide Hydrate


Quartz

Muscovite-3

Appendices

Potassium magnesium aluminium silicate hydroxide

Aluminium oxide

Aluminium iron titanium

Lithium manganese zirconium oxide


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

✉:[email protected]

mailto:[email protected]

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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Thesis - Semantic Scholar · 2018-12-30 · Thesis Submitted to the ... His involvement with originality has triggered and nourished my intellectual maturity that will help me for

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