07list of abbreviation.pdf

Monitoring of Agricultural Soils of Amritsar for their Genotoxicity and Effect on Antioxidative

Defense System in Allium cepa L.

Thesis

Submitted to Faculty of Life Sciences

For the award of degree of

DOCTOR OF PHILOSOPHY

Supervisor Submitted by Prof. (Dr.) Avinash Kaur Nagpal Rajneet Kour Soodan Co-supervisor Dr. Jatinder Kaur

Department of Botanical and Environmental Sciences

Guru Nanak Dev University Amritsar-143005

Punjab, India 2014

Dedicated to

my family

CERTIFICATE

The work included in the thesis entitled “Monitoring of Agricultural Soils of

Amritsar for their Genotoxicity and Effect on Antioxidative Defense System in

Allium cepa L.” submitted to the Faculty of Life Sciences, Guru Nanak Dev University,

Amritsar for the degree of Doctor of Philosophy, was carried out by Ms. Rajneet

Kour Soodan at the Department of Botanical and Environmental Sciences, Guru Nanak

Dev University, Amritsar, under our supervision. This is an original work and has not

been submitted in part or full for any other degree/diploma at this or any other

university/ institute. This thesis is fit to be considered for the award of degree of Ph.D.

Dated: Supervisor

(Dr. (Mrs.) Avinash Kaur Nagpal)

Professor

Co-Supervisor

(Dr. Jatinder Kaur)

Assistant Professor

DECLARATION

The work embodied in the thesis entitled “Monitoring of Agricultural Soils of

Amritsar for their Genotoxicity and Effect on Antioxidative Defense System in

Allium cepa L.” has been done by me and not submitted elsewhere for the award of any

other degree. All the ideas and references have been duly acknowledged.

Dated: (RAJNEET KOUR SOODAN)

Supervisor

(Dr. (Mrs.) Avinash Kaur Nagpal)

Professor

Co-Supervisor

(Dr. Jatinder Kaur)

Assistant Professor

ACKNOWLEDGEMENTS

Words can never be enough in expressing how grateful I am to many people around me, who made my doctoral thesis possible. I would like to make an attempt to acknowledge them all for all their help and support and making my research period a time, I will always treasure. It is a privilege to express my heartfelt and deep sense of gratitude and indebtedness to the person who has been most instrumental in the present study, my mentor Prof. Dr. (Mrs.) Avinash Nagpal, for her humane sense of supervision which she employed in guiding me. I would like to thank her for her immense confidence and faith in me and my capabilities along with her knowledge, experience and perceptiveness without which I would never have been able to finish my thesis. She is the role model for me of a successful researcher and a good human being. I am thankful to her for her persistent encouragement, motivation, love and incomparable guidance. I will never find words to tell what I owe to her and if I start doing it, I would not know where to stop. Thanks for everything Mam, to me, you are “perfection personified”. I am delighted to express my thanks to Dr. Jatinder Kaur my Co- supervisor for her active involvement and continuous support at each step of my thesis. I would like to thank her for all her support, patience and enthusiasm throughout. My special thanks are due to Prof. Renu Bhardwaj for being kind to help me with all her support and guidance in carrying out biochemical analysis during my work. I sincerely thank Dr. P.S. Ahuja, Director, IHBT, Palampur for allowing me to carry out some of my Ph.D. work at his Institute. It is my prerogative to thank Dr. Y. B. Pakade, Scientist, Division of Hill area Tea Science for generously allowing me to work in his lab and providing all the necessary facilities to carry out heavy metal analysis embodied in this thesis. I shall be failing in my duties if I fail to acknowledge the co-operation provided by everyone in his lab especially Mr. Piar Chand. My heartfelt gratitude goes to Dr. Satwinderjeet Kaur, Head, Department of Botanical and Environmental Sciences, Guru Nanak Dev University Amritsar and former Heads of the Department, Dr. Saroj Arora and Dr. A.P.Vig for providing me necessary guidance and laboratory facilities. I have always had the awe and reverence for Dr. A.K. Thukral for his assistance in statistical analysis of the data, Dr. G.S. Virk Dr. A.S.Soodan and Dr. M.S. Bhatti for helping me in one or the other way with all their generosity and affection. I also avail this opportunity to acknowledge my sincerest thanks to Dr. Rajinder Kaur, Er. Bhupinder pal Singh, Dr. Rajbir Singh, Mr. Vaneet, Mr. Rajinder Singh and Mr. Baldev Singh for their whole hearted support and cooperation during soil sampling. I pay my gratitude to all the people who allowed me to collect soil samples from their agricultural fields, without which this work could not be possible. I am grateful to non teaching staff members of the Department for their help. I must acknowledge that without the support of my friends, junior and senior research scholars in

the department, it would have been hard to successfully conclude the present study. My special thanks to Dr. Gurveen, Manik, Savinder, Harpreet, Ashita, Mandeep and Manish. I acknowledge the help, inspiration and cheerful company of my elders Mr. Arun Agnihotri, Mr. Kashmir Singh, Dr. Arvind Ranade, Dr. Hardesh Arora, Dr. Jagmeet Singh Nagpal, Dr. Samson and Mrs. Jasbir Kaur for their whole hearted support during the course of time. There are, however, some other people at different universities of the world whose contributions demand my special mentioning even at the expense of time and space who were always ready to render me with the literature I needed. I am grateful to Dr. Ravneet Kaur, Dr. Sonia, Aditi and Shounak for their timely help. I am highly thankful to University Grants Commission (UGC) for supporting me with fellowship. My acknowledgement will never be complete without the special mention of Madhu who always stood by me in the need of hour. I am lucky enough to have her as my best friend, who was always there at several steps. I thank God for blessing me with such a great friend. Finally I fall short of words to describe the depth of gratitude towards my family especially my father, Mr. Tarlochan Singh Soodan and Mother, Mrs. Taranjeet Kour Soodan who mean world to me. Their love, blessings and all kinds of support moral as well as financial, backed me up to achieve what I desired. My heartfelt thanks to my younger sister Dr. Jasmeet Kour for her love and affection. I extend my respect to my elders, Nani maa, Dr. Joginder Singh, Mr. Paramjeet Singh, Mr. Sanjeev Singh, Er. Gurmeet Singh, Dr. Navdeep Singh and their families whom I owe everything. They have always supported and encouraged me to do my best in all matters of life. The warmest of thanks to my husband Mr. Ajeetpal Singh for all his love, understanding and patience while I was working on my Thesis. I would express my deep sense of gratitude to the affection and support shown to me by my parents-in-law Dr. Gurcharan Singh Rissam and Mrs. Joginder Kour. My sincere thanks and appreciation also goes to Dr. Rajinder Singh Bajwa, Dr. Amitpal Kaur, Er. Amarpal Singh, Er. Rituraj and our Princes Jaishnoor. Without their blessings and love this work, could not have taken material form. I consider myself the luckiest in the world to have such a supportive family, standing behind me with their love and support. Above all, I express my gratitude to God for the strength and protection HE shielded over me which saw me being steadfast in my line of thought throughout the research. Thank you all!

Rajneet Kour Soodan

ABBREVIATIONS

A. cepa Allium cepa

AAS Atomic absorption spectrometer

ABDTPA Ammonium bicarbonate diethylene triamine pentaacetic acid

AFS Atomic fluorescence spectrometry

Ag Silver

Al Aluminum

AlRCAA Allium cepa root chromosomal aberration assay

ANOVA Analysis of variance

APX/ APOX Ascorbate peroxidase

AsA Ascorbic acid or Ascorbate

AsA-GSH Ascorbate glutathione

ATSDR Agency for Toxic Substances and Disease Registry

Au Gold

Ba Barium

BSA Bovine serum albumin

C Carbon

Ca Calcium

CA Chromosomal aberrations

CAT Catalase

Cd Cadmium

CDNB 1-chloro, 2, 4- dinitro benzene

Ce Cesium

CEC Cation exchange capacity

Cl- Chloride ion

Co Cobalt

CO32+

Carbonate ion

Cr Chromium

Cr (III) or Cr3+

Chromium trivalent

Cr (VI) or Cr6+

Chromium hexavalent

Cu Copper

CuSO4 Copper sulphate

DCP-MAES Direct current argon plasma -Multielement atomic emission

spectrometry

DCM Dichloromethane

DHA Dehydroascorbate

DHAR Dehydroascorbate reductase

DMSO Dimethyl sulphoxide

DNP Dinitropyrene

DW Dry weight

Dy Dysprosium

E.coli Escherichia coli

EC Electrical conductivity

EDTA Ethylene diamine tetra acetic acid (disodium salt)

Fe Iron

Fig. Figure

FW Fresh weight

GPX /GPOX Glutathione peroxidase

GR Glutathione reductase

GSH Glutathione reduced

GSSG Glutathione disulphide/Oxidized glutathione

GST Glutathione-S-transferase

H+ Hydrogen ion

H2O2 Hydrogen peroxide

H2SO4 Sulphuric acid

HCO3- Bicarbonate ion

Hf Hafnium

Hg

Mercury

ICP-AES Inductively coupled plasma-Atomic emission spectroscopy

ICPEMC International Commission for Protection against Environmental

Mutagens and Carcinogens

ICP-MS Inductively coupled plasma-Mass spectroscopy

ICP-OES Inductively coupled plasma-Optical emission spectroscopy

IPCS International Programme on Chemical safety

K Potassium

K2CrO4 Potassium chromate

K2HPO4 Dipotassium hydrogen phosphate

KCl Potassium chloride

KH2PO4 Potassium dihydrogen ortho phosphate

KNO3 Potassium nitrate

La Lanthanum

MDA Malondialdehyde

MDHA Monodehydrcoascorbate

MDHAR Monodehydroascorbate reductase

Mg Magnesium

MgCl2 Magnesim chloride

Min. Minute

Mn Manganese

MN Micronuclei

Mo Molybdenum

Na Sodium

NAA Neutron activation analysis

NaCl Sodium chloride

NADH Nicotinamide adenine dinucleotide

NADPH Nicotinamide adenine dinucleotide phosphate

NaOH Sodium hydroxide

Nb Niobium

NBT Nitroblue tetrazolium

Nd Neodymium

NH2OH.HCl Hydroxylamine hydrochloride

Ni Nickel

NO3- Nitrate ion

O2 oxygen

O2-• Superoxide anion radical

O3 Ozone

OH. Hydroxyl radical

OM Organic matter

OSHA Occupational Safety and Health Administration

p Probability (level of significance)

P Phosphorus

PAH Poly aromatic hydrocarbon

Pb Lead

PCDD/Fs Polychlorinated dibenzo-p-dioxins and dibenzofurans

PCR Polymerase chain reaction

pH Negative log of hydrogen ion concentration

POD Guaiacol peroxidase

ROS Reactive oxygen species

S9 Supernatant, 9000xg

Sb Antimony

Sc Scandium

SD Standard deviation

SE Standard error

Se Selenium

Spp. Species

Sm Samarium

SO42-

Sulphate ion

SOD Superoxide dismutase

Sr Strontium

SA Specific activity

TBA Thiobarbituric acid

TCA Trichloroacetic acid

Tm Thulium

Trad/MCN Tradescantia micronucleus assay

Trad/SHM Tradescantia stamina hair mutation assay

U Uranium

UA Unit activity

UNEP United Nations Environment Programme

V Vanadium

V.faba Vicia faba

W Tungten

WHO World Health Organization

WRC Water retention capacity

XRFS X-ray fluorescence spectrometry

Yb Ytterbium

Zn Zinc

Zr Zirconium

UNITS

A Absorbance

% Percent

°C Degree celsius

µg Microgram

µM Micromolar

cm Centimeter

g Gram

h Hour

kg Kilogram

l Litre

M Molar

m Meter

mg Milligram

ml Milliliter

mM Millimolar

Min. Minute

nm Nanometer

rpm Revolutions per minute

ppm Parts per million

rpm Revolutions per minute

µl Micro litre

Cmol Centimoles

µS Microsiemens

g/cc Gram per cubic centimeter

dS DeciSiemens

ha Hectare

SYMBOL

γ Gamma

CONTENTS

S. No. Title Page No.

1. INTRODUCTION 1-4

2. REVIEW OF LITERATURE 5-44

2.1. Genotoxic/ mutagenic potential of soil 6

2.1.1. Ames assay 6

2.1.2. Plant assays 9

2.1.3. Allium cepa root chromosomal aberration assay 13

2.2. Antioxidative defense system in plants 14

2.2.1. Heavy metals and reactive oxygen species (ROS) 16

2.2.2. Reactive oxygen species and antioxidative enzymes 17

2.3. Physico-chemical analysis of soil 19

2.4. Analytical techniques for estimation of heavy metals in soil

ecosystem

35

2.4.1. Inductively Coupled Plasma Optical Emission Spectroscopy

(ICP-OES) 36

2.4.2. Inductively Coupled Atomic Emission Spectroscopy (ICP-AES) 37

2.4.3. Inductively Coupled Mass Spectroscopy (ICP-MS) 38

2.4.4. Atomic Fluorescence Spectrometer (AFS) 39

2.4.5 X-ray Fluorescence Spectrometer (XRFS) 40

2.4.6. Neutron Activation Analysis (NAA) 41

2.4.7. D.C. Argon Plasma Multielement Atomic Emission

Spectrometer (DCP-MAES) 42

2.4.8. Atomic Absorption Spectrometer (AAS) 42

3. MATERIALS AND METHODS 45-78

3.1. Collection of Soil Samples 45

3.1.1. Study area 45

3.1.2. Sites of sample collection 46

3.2. Physico chemical analysis 48

3.2.1. Soil texture 48

3.2.1.1. Procedure 48

3.2.2. Preparation of soil extracts 48

3.2.3. pH 49


3.2.4. Alkalinity 49

3.2.4.1. Preparation of reagents 49



3.2.5. Calcium (Ca) 51

3.2.5. Preparation of reagents 51


3.2.6. Magnesium (Mg) 52



3.2.7. Nitrates (NO3-) 53



3.2.8. Phosphates (PO43-

) 54



3.2.9. Potassium (K) 55

3.2.9.1. Principle of flame photometer 55

3.2.9.2. Preparation of standard solutions 55


3.2.10. Sodium (Na) 56

3.2.10.1. Preparation of standard solutions 56


3.2.11. Estimation of heavy metals 57

3.2.11.1. Principle of Atomic Absorption Spectrophotometer (AAS) 57

3.2.11.2. Chemicals make 57


3.3. Estimation of genotoxic potential 59

3.3.1. Preparation of stain 59

3.3.2. Allium cepa root chromosomal aberration assay 59

3.3.2.1. Treatment 59

3.3.2.1.1. In situ treatment 59

3.3.2.1.2. Root dip treatment 59

3.3.2.1.3. Procedure (In situ treatment) 60

3.3.2.1.4. Procedure (Root dip treatment) 61

3.3.2.2. Cytological investigations 62

3.3.2.2.1. Fixation 62

3.3.2.2.2. Squash preparation 62

3.3.2.2.3. Scoring 62

3.4. Estimation of protein content in A. cepa bulbs 62

3.4.1. Principle 63


3.4.2. Exposure of Allium cepa bulbs to different soil samples 63

3.4.3. Preparation of sample extract 63

3.4.4. Preparation of reagents 63

3.4.5. Procedure 65

3.4.6. Calculations 65

3.5. Estimation of different antioxidative enzymes 65

3.5.1. Preparation of extract 66

3.5.2. Ascorbate peroxidase (APOX, EC 1.11.1.11) 66

3.5.2.1. Principle 66



3.5.2.4. Calculations 67

3.5.3. Catalase (CAT, EC 1.11.1.6) 68





3.5.4. Dehydroascorbate reductase (DHAR, EC 1.8.5.1) 69





3.5.5. Glutathione reductase (GR, EC 1.6.4.2) 71





3.5.6. Glutathione-S-transferase (GST, EC 2.5.1.18) 73





3.5.7. Guaiacol peroxidase (POD, EC 1.11.1.7) 74






3.5.8. Superoxide dismutase (SOD, EC 1.15.1.1) 76





3.6. Statistical Analysis 78

4. RESULTS 79-106

4.1. Physico-chemical analysis 79

4.1.1. Soil texture 79

4.1.1.1. Soil texture of rice cultivated soils 79

4.1.1.2. Soil texture of wheat cultivated soils 79

4.1.2. pH 80

4.1.2.1. pH of rice cultivated soils 80

4.1.2.2. pH of wheat cultivated soils 80

4.1.3. Alkalinity 81

4.1.3.1. Alkalinity of rice cultivated soils 81

4.1.3.2. Alkalinity of wheat cultivated soils 81

4.1.4. Calcium 81

4.1.4.1. Calcium content of rice cultivated soils 81

4.1.4.2. Calcium content of wheat cultivated soils 82

4.1.5. Magnesium 82

4.1.5.1. Magnesium content of rice cultivated soils 82

4.1.5.2. Magnesium content of wheat cultivated soils 83

4.1.6. Nitrates 83

4.1.6.1. Nitrate content of rice cultivated soils 83

4.1.6.2. Nitrate content of wheat cultivated soils 83

4.1.7. Phosphates 84

4.1.7.1. Phosphate content of rice cultivated soils 84

4.1.7.2. Phosphate content of wheat cultivated soils 84

4.1.8. Potassium 84

4.1.8.1. Potassium content of rice cultivated soils 84

4.1.8.2. Potassium content of wheat cultivated soils 85

4.1.9. Sodium 85

4.1.9.1. Sodium content of rice cultivated soils 85

4.1.9.2. Sodium content of wheat cultivated soils 85

4.1.10. Heavy metals 86

4.1.10.1. Heavy metal content of rice cultivated soils 86


4.1.10.2. Heavy metal content of wheat cultivated soils 87

4.2. Genotoxicity of agricultural soils using Allium cepa root

chromosomal aberration assay (AlRCAA)

88

4.2.1. In situ treatment 88

4.2.1.1. Agricultural soils under rice cultivation 88

4.2.1.2. Agricultural soils under wheat cultivation 89

4.2.2. Root dip treatment 90

4.2.2.1. Agricultural soils under rice cultivation 90

4.2.2.1.1. Samples collected from North east zone during r1 sampling

(NEFIr1and NEFIIr1)

91

4.2.2.1.2. Samples collected from North east zone during r2 sampling (NEFIr2 and NEFIIr2)

91

4.2.2.1.3. Samples collected from South east zone during r1 sampling

(SEFIr1 and SEFIIr1)

91

4.2.2.1.4. Samples collected from South east zone during r2 sampling (SEFIr2 and SEFIIr2)

92

4.2.2.1.5. Samples collected from South West zone during r1 sampling

(SWFIr1 and SWFIIr1)

92

4.2.2.1.6. Samples collected from South West zone during r2 sampling (SWFIr2 and SWFIIr2)

93

4.2.2.1.7. Samples collected from North West zone during r1 sampling

(NWFIr1 and NWFIIr1)

93

4.2.2.1.8. Samples collected from North West zone during r2 sampling (NWFIr2 and NWFIIr2)

94

4.2.2.2. Agricultural soils under wheat cultivation 94

4.2.2.2.1. Samples collected from North east zone during w1 sampling (NEFIw1 and NEFIIw1)

94

4.2.2.2.2. Samples collected from North east zone during w2 sampling

(NEFIw2 and NEFIIw2)

95

4.2.2.2.3. Samples collected from South east zone during w1 sampling (SEFIw1 and SEFIIw1)

95

4.2.2.2.4. Samples collected from South east zone during w2 sampling

(SEFIw2 and SEFIIw2)

96

4.2.2.2.5. Samples collected from South West zone during w1 sampling (SWFIw1 and SWFIIw1)

96

4.2.2.2.6. Samples collected from South West zone during w2 sampling

(SWFIw2 and SWFIIw2)

96

4.2.2.2.7. Samples collected from North West zone during w1 sampling (NWFIw1 and NWFIIw1)

97


4.2.2.2.8. Samples collected from North West zone during w2 sampling (NWFIw2 and NWFIIw2)

97

4.3. Biochemical parameters 98

4.3.1. A. cepa bulbs exposed to rice cultivated soils 98

4.3.1.1. Protein content 98

4.3.1.2. Activities of antioxidative enzymes 99

4.3.1.2.1. Ascorbate peroxidase (APX) 99

4.3.1.2.2. Catalase (CAT) 99

4.3.1.2.3. Dehydroascorbate Reductase (DHAR) 100

4.3.1.2.4. Glutathione Reductase (GR) 100

4.3.1.2.5. Gluthione-S-Transferase (GST) 101

4.3.1.2.6. Guaicol Peroxidase (POD) 102

4.3.1.2.7. Superoxide dismutase (SOD) 102

4.3.2. A. cepa bulbs exposed to wheat cultivated soils 103

4.3.2.1. Protein content 103

4.3.2.2. Activities of antioxidative enzymes 103

4.3.2.2.1. Ascorbate peroxidase (APX) 103

4.3.2.2.2. Catalase (CAT) 104

4.3.2.2.3. Dehydroascorbate Reductase (DHAR) 104

4.3.2.2.4. Glutathione Reductase (GR) 105

4.3.2.2.5. Glutathione-S-Transferase (GST) 105

4.3.2.2.6. Guaicol Peroxidase (POD) 106

4.3.2.2.7. Superoxide dismutase (SOD) 106

5. DISCUSSION 107-144

5.1. Physico-chemical analysis of soil 109

5.2. Genotoxicity studies in A. cepa root chromosomal

aberration assay

132

5.3. Biochemical parameters 136

6. SUMMARY 145-152

7. REFERENCES 153-248

.

Tables Figures Plates

1-32

1-37

I-VIII

* * * * * * *

ABSTRACT

The present study pertains to analyze agricultural soil samples of Amritsar under

rice and wheat cultivation for various physico-chemical parameters like soil texture, pH,

alkalinity, calcium, magnesium, nitrates, phosphates, potassium, sodium and heavy

metals viz. copper (Cu), cadmium (Cd), chromium (Cr), iron (Fe), manganese (Mn),

nickel (Ni), lead (Pb) and zinc (Zn). The study also involved evaluation of genotoxic

potential in terms of chromosomal abnormalities induced by collected soil samples

using Allium cepa root chromosomal aberration assay following in situ and root dip

modes of treatment. The study also comprised the estimation of total protein content

and responses of certain antioxidative/detoxification enzymes viz. ascorbate peroxidase

(APX), catalase (CAT), dehydroascorbate reductase (DHAR), glutathione reductase

(GR), glutathione-S-transferase (GST), guaicol peroxidase (POD) and superoxide

dismutase (SOD), in A. cepa bulbs upon exposure to such soils.

All the physico-chemical parameters and content of heavy metals (Cd, Cr, Cu,

Fe, Mn, Ni, Pb and Zn) were found to vary among different soil samples studied. Metal

analysis has shown the presence of one or the other heavy metal in all the soil samples

tested. Spectrum of chromosomal aberrations observed included c-mitosis, stickiness,

delayed anaphases, laggards, vagrants, abnormal metaphases, abnormal anaphases

(physiological aberrations) and chromatin bridges, ring chromosomes and chromosomal

breaks (clastogenic aberrations). Activity of antioxidative enzymes in A. cepa bulbs

exposed to different soil samples was found to be higher (CAT, APX, GST, DHAR,

SOD) or lower (POD and GR) as compared to control bulbs. The increase in enzyme

activities may be attributed to heavy metal induced oxidative stress indicating response

of cells to cope up with increased level of reactive oxygen species (ROS). Our findings

suggest that A. cepa test model is a simple and sensitive system for monitoring the

toxicity of soil samples polluted with contaminants from various anthropogenic

activities. The study preludes the use of combination of physico-chemical analysis

along with plant bioassays to evaluate the potential toxicity of contaminated soils of

Amritsar. The data from genotoxicity and oxidative stress studies using chromosomal

aberration assay and antioxidative enzyme activities, respectively serve as useful

biomarkers and provide significant information for soil monitoring.

Chapter-1

Introduction

There is nothing more difficult to take in hand, more perilous to conduct, or more uncertain in

its success, than to take the lead in the introduction of a new order of things.

-Niccolo Machiavelli

Chapter-1

INTRODUCTION

Soil has a complex nature that directly depends on its physical, chemical and

biological composition. It is considered as one of the most dynamic ecological interface

between atmosphere, hydrosphere and biota (Pohren et al., 2013). It is well documented

that soil, all over the world, has been widely contaminated as a result of continuous

discharges from industries, domestic sewage, metallurgical activities, vehicular

emissions and over application of inorganic fertilizers and pesticides (Ping et al., 2011).

The reason of concern is that soil not only behaves as a sink to various contaminants but

also acts as a reservoir for other ecosystems. The contaminants once enter into the soil,

stay there for a long period and slowly percolate to other compartments of environment

viz., ground water as leachates, surface water bodies as run offs and atmosphere as dust

particles. Among different contaminants, heavy metals have attained a great deal of

attention amongst researchers involved in various sub disciplines of environmental

studies (Feng et al., 2013; Janeshwar et al., 2013; Ji et al., 2012; Wuana et al., 2012).

Accumulation of heavy metals in agricultural soils can lead to their entry into

human beings via consumption of contaminated food crops. Upon entering the human

body, heavy metals can cause serious health effects such as damaged or reduced central

nervous functions, lower energy levels, altered blood composition, as well as damaged

lungs, kidneys, liver and other vital organs. Long term exposure can also result in slow

progressing physical, muscular and neurological degenerative processes like

Alzheimer’s disease, Parkinson’s disease, muscular dystrophy and multiple sclerosis

(Aguilera et al., 2010; Zubero et al., 2010; Nair et al., 2013; Song et al., 2014;

Thompson et al., 2014). Moreover, heavy metals being water soluble can be readily

absorbed by living tissues and can form covalent/ion bonds with biomolecules viz.,

proteins, lipids, enzymes, coenzymes, cofactors and nucleic acids, ultimately impairing

their functions. Apart from being toxic, the subtle danger of availability of heavy metals

in the soil lies in their being mutagenic, genotoxic as well as carcinogenic. Therefore, it

becomes important to evaluate the pollutants in soil ecosystem with special

Introduction

2

consideration to the agricultural soils. In this pretext, many bioassays introducing

microorganisms, plants and animals have been formulated by various researchers

(Chakraborty and Mukherjee, 2009; Abu and Mba, 2011; Visioli et al., 2013;

Rodriguez-Ruiz et al., 2014).

Several organizations like International Commission for Protection against

Environmental Mutagens and Carcinogens (ICPEMC), United Nations Environmental

Protection (UNEP), Occupational Safety and Health Administration (OSHA) have been

constituted to frame the policies and programs regarding analysis of environmental

genotoxins using different protocols. The ultimate objective was to determine the

magnitude of health risk to man by environmental agents under a specified level of

exposure. Unfortunately, direct assessment in humans is not feasible due to ethnic,

logistic and practical considerations. Even the epidemiological approaches to detect

genotoxic and carcinogenic chemicals have limitations because detection is possible

only after the genetic damage has incurred (Gillespie et al., 2011). Therefore, it

becomes obvious that one relies on experimental data obtained from other systems. In

order to identify the harmful effects of substances at different concentrations and times

of exposure, a variety of tests are commonly used for biomonitoring the extent of

pollution and to evaluate the effects of toxic and mutagenic substances in the natural

environment (Matsumoto and Marin-Morales, 2004; Matsumoto et al., 2006; Kataeva et

al., 2012). Among various bioassays, plant bioassays have gained a great attention due

to the fact that plants are readily available and it is easier to handle the plant material as

compared to animals.

Chromosomal aberration assays using various plant systems including Allium

cepa, Tradescantia and Vicia faba were validated by International Programme on

Chemical safety (IPCS) under the auspices of World Health Organization (WHO) and

United Nations Environment Programme (UNEP) to estimate genotoxicity of various

compounds, wastewater samples, soil solutions and contaminated agricultural soil

(Cabrera and Rodriguez, 1999 a, b; Achazi, 2002; Chandra et al., 2005; Alam et al.,

2009). Among different higher plant assays, Allium cepa assay has been recommended

as it is simple, less expensive, requires least facilities, has large and less number of

Introduction

3

chromosomes with morphology similar to that of mammals as well as unique adaptation

for in situ studies. This assay was first introduced as a biological test system by Levan

(1938), when he used this system to evaluate the cytogenetic effects of colchicine, an

alkaloid isolated from Colchicum autumnale. Ever since then, Allium cepa root

chromosomal aberration assay has been widely used to assess genotoxic potential of a

wide range of environmental chemicals and mixtures (Lopane, 1950; Scholes, 1955;

Sawamura, 1965; Grant, 1979; Fiskesjo, 1983, 1988; Younis et al., 1989; Smaka-Kincl

et al., 1996; Kong and Ma, 1999; Amin, 2002; Chandra et al., 2005; Marcano et al.,

2006; Mouchet et al., 2006; Vujosevic et al., 2007; Yi et al., 2007; Asita and

Makhalemele, 2009; Asita and Matebesi, 2010; Herrero et al., 2012; Souza et al. 2013).

Apart from the genotoxic responses, biochemical changes/enzyme alterations

also play a significant role to indicate the magnitude of toxicity. Enzymes of the

detoxification machinery serve as important markers of environmental pollution (Filho

et al., 2001). The efficacy of antioxidant enzymes as biomarkers of contaminants

especially the heavy metals was established by several researchers (Ahmad et al., 2000;

Geret et al., 2002, 2003). Several studies indicated that heavy metals caused oxidative

stress by intervening the activities of antioxidative enzymes (Luna et al., 1994; Cuyers

et al., 2000, Fatima and Ahmed, 2005; Tabrez and Ahmed, 2009). It has been observed

that heavy metal stress led to sharp changes in the activities of certain enzymes like

ascorbate peroxidase (APX), catalase (CAT), dehydroascorbate reductase (DHAR),

glutathione reductase (GR) and superoxide dismutase (SOD) (Cossu et al., 1997;

Vitoria et al., 2001; Patisska et al., 2002; Shainberg et al., 2000; Panda, 2003; Gupta

and Ahmad, 2011).

Presently in India, ongoing rigorous agricultural practices are pulling out the

essential nutrients particularly from wheat and rice crop fields. The district Amritsar of

Punjab (India), an agricultural land, is under intensive cultivation of wheat, rice and

vegetable crops. In order to have high yield, vast varieties of organic and inorganic

pesticides as well as fertilizers containing different heavy metals, are being used by the

farmers which ultimately result in soil and water pollution. Apart from this, the direct

application of sewage sludge, industrial effluents and waste to agricultural lands as

Introduction

4

source of plant nutrients also aroused serious concern as they contain many toxic metals

along with useful nutrient elements. Considering the alarming consequences of

contaminated agricultural soils of Amritsar, Punjab (India), the present study was

planned to:

analyze physico-chemical parameters including soil texture, pH, alkalinity, calcium,

magnesium, nitrates, phosphates, potassium, sodium and heavy metals viz. copper

(Cu), cadmium (Cd), chromium (Cr), iron (Fe), manganese (Mn), nickel (Ni), lead

(Pb) and zinc (Zn) in agricultural soils of Amritsar under rice and wheat cultivation.

estimate the genotoxic potential of soil samples using Allium cepa root

chromosomal aberration assay following in situ and root dip treatment methods.

estimate the total protein content and responses of certain antioxidative/

detoxification enzymes viz. ascorbate peroxidase (APX), catalase (CAT),

dehydroascorbate reductase (DHAR), glutathione reductase (GR), glutathione-S-

transferase (GST), guaicol peroxidase (POD) and superoxide dismutase (SOD), in

Allium cepa bulbs exposed to collected soil samples.

Review

of

Literature

Chapter-2

The only things worth learning are the things you learn after you know it all

-Harry S Truman

Review of Literature

5

Chapter-2

REVIEW OF LITERATURE

Soil, an important environmental medium, sustains life on earth and serves as a

reservoir of nutrients, provides mechanical anchorage and favorable tilth. Apart from

these, it acts as a connecting link between inorganic, organic and living systems of the

world (Sandhu, 2002). In recent decades, soil has been exposed to a number of

pollutants including toxic heavy metals by various natural and anthropogenic activities.

Heavy metal contaminated soil has potential to pose severe health risk to humans

through various routes of exposure such as direct ingestion of contaminated drinking

ground water and food crops; dermal contact and through food chain. Soil ecosystems

are so complex that the impact of these pollutants causes acute toxicity to various soil

organisms, flora and fauna along with human beings (Edwards, 2002). The persistent

nature of heavy metals not only affects the status of cell by damaging DNA but also

triggers the metabolism of different cellular components like proteins, lipids and

antioxidative enzymes. The direct effect on these cellular components indicates the

level of environmental pollution (Fatima and Ahmad, 2005).

Historically, plants have been a choice of research and constitute an important

material for genetic tests to monitor environmental pollutants. Cytogenetic tests in

plants are well established systems for screening and monitoring of genotoxicity of

environmental pollutants and have been validated in a number of international

collaborative studies (Ma, 1999; Uhl et al., 2003; Turkoglu, 2007 and Yi et al., 2007).

Plant bioassays are relatively inexpensive; can be easily handled; more sensitive and

simpler than other methods used for detection of genotoxicity of environmental

pollutants. Plant roots are extremely useful in biological testing because root tips are the

first to be exposed to toxicants dispersed in soil or in water (Fiskesjo, 1988). Therefore,

the root tip chromosomal aberration assays constitute rapid and sensitive methods for

biomonitoring the extent of pollution and to evaluate the effects of toxic and mutagenic

substances in the natural environment (Matsumoto and Morales, 2004; Matsumoto et

al., 2006).


6

Since the present study involves monitoring of agricultural soils for their

genotoxicity and effect on antioxidative enzyme system in Allium cepa as well as their

physicochemical analysis including estimation of heavy metals, the review of studies on

these aspects is presented in following sections.

2.1. Genotoxic/mutagenic potential of soil

The contaminants present in the soil not only affect the general health of human

beings but also cause damage to their genetic material. Mutagenic/genotoxic effects of

different soil samples using number of bioassay have been well documented (Courty et

al., (2004; Watanabe et al., 2005; Goto et al., 2009; Leme et al., 2012; Man et al., 2013;

Souza et al., 2013). At present, risk assessment of contaminated soil of any area is

mainly based on chemical analyses of a priority list of toxic substances in the soil. This

analytical approach neither indicates mixture toxicity nor does it take into account the

bioavailability of other pollutants present in the soil. In this respect, bioassays provide

an alternative because they constitute a measure for environmentally relevant toxicity

i.e. the effects of bioavailable fractions of interacting pollutants present in a complex

environmental matrix (Lah et al., 2005). Among wide range of bioassays used, bacterial

and higher plant bioassays have obtained very good acceptability in genotoxicity

studies. The following part of review deals with the assessment of mutagenic or

genotoxic potential of soil of diverse areas of the world in a number of bioassays.

2.1.1. Ames assay

Ames assay is the short term bacterial reverse mutation assay which is used for

the evaluation of the mutagenic potential of wide range of chemical substances (White

and Claxon, 2004). Goggleman and Spitazaour (1982) and Smith (1982) were first who

independently reported the mutagenicity of agricultural soils in Salmonella typhimurium

plate incorporation assay both in the presence and absence of S9 mix.

Ehrlichmann et al. (2000) estimated the genotoxicity of soil samples collected

from a coal mine, cooking plant and a sand soil sample contaminated with heavy metals

using three bacterial assays (umu test with salmonella typhimurium TA 1535/Psk1002,


7

the NM2009 test with Escherichia coli PQ37). The samples were shown to be highly

genotoxic in all the three bioassays.

Watanabe and Hirayama, (2001) estimated the mutagenic potential of soil

samples collected from different sites viz. Hekinan, Kanto, Muroran, Chubu, Kobe,

Osaka and Hekinam of Japan using Ames/ Salmonella assay. The soil samples from

Hekinan, Kobe and Osaka were found to be highly mutagenic in both TA98 and TA100

strains of Salmonella typhimurium, where as the sample collected from Muroran were

found to have strong mutagenicity towards TA100 strain of Salmonella typhimurium. It

was found that the major mutagenic compounds present in soil were dinitropyrene

(DNP) isomers.

Aleem and Malik (2003) reported the genotoxic potential of agricultural soil

which was irrigated with the wastewater from industrial and domestic sources in the

city of Aligarh (India) in three biological assays viz., Ames Salmonella/mammalian

microsome test, survival of SOS defective E. coli K-12 mutants and bacteriophage

lamda systems. Different organic solvents (methanol, acetonitrile and acetone) were

used for the preparation of extracts. Methanol extracts exhibited significant degree of

mutagenicity in TA98 strains whereas acetonitrile and acetone extracts have shown

significant mutagenicity with TA98 strains both in presence and absence of S9

fractions. A significant decline was reported in survival of DNA repair defective E. coli

K-12 mutants than their isogenic wild type when treated with different soil extracts.

Courty et al. (2004) reported the influence of extraction parameters on the

mutagenicity of soil samples using TA98 and TA100 stain of Salmonella typhimurium.

Four soil samples were collected from urban sites and were analyzed. TA98 strain has

shown more number of revertant colonies as compared to TA100. It was found that high

mutagenic activity was obtained with solid mass/ solvent volume ratio of 1 : 10 at

temperature of 37ºC, with an extraction time of 24 h.

Watanabe et al. (2005) estimated the mutagenicity of surface soil samples

contaminated with aromatic nitro and hydroxyamino-mutagens and 1,3-DNPS from

urban areas of Aichi Prefecture, Japan and Bangkok, Thialand. 60 soil samples were

collected in Aichi and Bankok. Mutagenicity of soil extracts was examined in Ames


8

assay using two tester strains TA98 and TA100 of Salmonella typhimurium with and

without S9 fraction. The results revealed that most of the soil samples showed

mutagenicity in both the strains.

Alam et al. (2009) estimated the genotoxic and mutagenic potential of

agricultural soils irrigated with tannery effluents at Jajmau (Kanpur), India. Different

organic solvents (Dichloromethane, acetonitrile, acetone and methanol) were used for

the preparation of soil extracts. Dichloromethane- extract exhibited maximum

mutagenic potential in TA98 with and without S9 fraction. Acetonitrile extract was

weakly mutagenic in TA100 and acetone extract did not exhibit any mutagenicity in

TA102 and TA104 strains.

Goto et al. (2009) evaluated the mutagenic substances and PAH contents in 60

soil samples from forest and planted areas in Japan. Mutagenicity was estimated using

Ames test in which the tester strains TA98, TA100 and YG1024 of Salmonella

typhimurium were used. Mutgenicity of soil samples was tested with and without the

addition of S9 mix. It was observed that the surface soil did not exhibit mutagenicity in

TA100 without S9 but exhibited mutagenicity when S9 was added. The results of test

using TA98 and YG1024 showed that the mutagenicity of surface soil was higher than

the deeper soils

Katnoria et al. (2011) evaluated the mutagenic potential of soil samples

contaminated with effluent from zinc coating industry (SI) and copper manufacturing

industry (SII) of Amritsar, India. In Ames test, sample SII was found to be lethal to

Salmonella test strain at all the concentrations of soil extracts whereas soil sample SI

was found to be mutagenic in TA100 strain of Salmonella typhimurium .

Anjum and Malik (2012) reported mutagenicity of agricultural soils irrigated

with wastewaters discharged from industries and ground water irrigated soil of Chinhat,

Lucknow (India) using Ames Salmonella test with special reference to pesticide

pollution. Mutagenicity was observed in the test samples and TA98 was the most

responsive strain for all the soil extracts (irrigated with wastewater) in terms of

mutagenic index in the presence (+S9) and absence (−S9) of metabolic activation.

Hexane-extracted soil sample (wastewater) exhibited maximum mutagenicity in terms


9

of net revertants per gram of soil in the presence and absence of S9 mix as compared to

the other soil extracts. Ground water irrigated soil extracts displayed low level of

mutagenicity as compared to wastewater-irrigated soil.

Man et al. (2013) evaluated mutagenicity of soils from 12 different land use

types such as electronic waste dismantling workshop, open burning site and car

dismantling workshop (CDW), based on soil concentrations of polycyclic aromatic

hydrocarbons (PAHs) and polychlorinated dibenzo-p-dioxins/polychlorinated

dibenzofurans (PCDD/Fs) using Ames test. Soil of CDW was reported to have a

significantly higher mutagenic potency of 13.8 and 7.43 on both strains of TA98 and

TA100 with S9 mix, respectively,

Masood and Malik (2013) studied mutagenic potential of soil samples from

agricultural fields in the vicinity of industrial area of Jajmau, Kanpur (India) using

Ames Salmonella/mammalian microsome test. Samples were extracted using

dichloromethane and hexane solvents and it was found that maximum mutagenic

potential was induced by dichloromethane extracts of soil as compared to hexane

extracts for each strain of Salmonella typhimurium. DCM extracts showed maximum

response with TA98 strain in the presence and absence of S9 microsomal fraction and

exhibited maximum damage to the cells at a dose of 40 μl of soil extract/ml of culture

after a 6 h treatment and were more mutagenic as compared to hexane extracts for each

strain of S. typhimurium.

2.1.2. Plant assays

Historically, plants have been a choice of research and constitute an important

material for genetic tests to monitor environmental pollutants. Since 1970s, higher plant

bioassays have been recommended for use in mutation screening and monitoring for the

detection of genotoxins in emissions, effluents, or ambient environmental media by the

Royal Swedish Academy of Sciences (1973), Committee 17 of the Environmental

Mutagen Society (1975), the World Health Organization (1985) and the National

Swedish Environmental Protection Board (1989). Cytogenetic tests in plants are well

established and presently different plant test systems are available for evaluating the

genotoxicity of different environmental samples i.e air emissions, industrial effluents,


10

soil etc. using Tradescantia staminal hair mutation assay (Trad/SHM), Tradescantia/

micronucleus assay (Trad/MCN), Allium cepa root chromosomal aberrations assay

(AlRCAA) and Vicia faba root chromosomal aberration assay. Number of researchers

have successfully employed plant bioassays viz. Allium cepa, Allium sativum, Vicia

faba and Tradescantia spp. as sensitive and rapid bio-tools for genotoxicity screening of

environmental contaminatants in soil, sludges; surface and ground water (Steinkellner et

al., 1998; Cotelle et al., 1999 and Cabrera and Rodriguez, 1999 a,b; Katnoria and

Nagpal, 2004; Marciulioniene et al., 2004; Ferreira et al., 2007; Kontek et al., 2007;

Chakraborty and Mukherjee, 2009; Srivastva and Mishra, 2009; Liu et al., 2009; Saxena

et al., 2009; Abu and Mba, 2011). Many studies have been conducted worldwide to

estimate the genotoxicity/carcinogenicity of different environmental ecosystems using

plant bioassays. The use of plant bioassays is becoming a common practice because

plants are known to be direct recipient of agrotoxins and are simple, affordable and

applicabile for in situ monitoring.

Cotelle et al. (1999) evaluated the genotoxicity of contaminanted soil near Metz,

France using three bioassays viz. Allium, Vicia and Tradescantia-micronucleus assays.

Two samples were collected from different sites. Sample A was collected from the soil

contaminated with industrial waste and sample B soil from contaminated with waste

from cokeworks. The roots of Vicia faba, Allium cepa and cuttings of Tradescantia

were treated with soil extracts. Sample A was more toxic, contaminated with metals,

PCBs and solvents than sample B which was contaminated with metals and PAHs.

Kong and Ma (1999) reported the genotoxic response of contaminated soils and

shallow well water samples in three plant bioassays viz., Allium cepa root anaphase

aberration assay, Tradescantia stamen hair mutation assay (Trad/SHM) and

Tradescantia micronucleus assay (Trad/MCN). Allium cepa root anaphase aberration

assay showed similar response as shown by Tradescantia micronucleus assay. Cabrera

and Rodriguez (1999b) also employed three bioassays to determine the genotoxicity of

soil collected from farmland irrigated with waste water. Cuttings of Tradescantia and

roots of Allium were treated with different extracts (distilled water and organic solvents)

of soil samples. Both the extracts showed genotoxic responses in all the three bioassays.


11

Gichner and Veleminsky (1999) employed Tradescantia stamen hair mutation

and micronucleus assays for genotoxicity evaluation of soil samples collected from two

heavily polluted areas of Prague, the capital of Czech Repbulic. Misik and Micieta

(2002) also reported genotoxicity of six different soil samples collected from urban

areas of Brastilava, capital of Slovak Republic by employing Tradescantia

micronucleus assay (Trad./MCN assay) and Vicia ana-telophase chromosomal

aberration assay. The soil samples were extracted with distilled water or 5% dimethyl

sulphoxide (DMSO). One out of six soil samples showed significant increase in

frequency of chromosomal aberrations in Vicia faba and micronuclei in

Tradescantia/MCN assay. Chen et al. (2004) determined the genotoxic potential of

agricultural soil irrigated with wastewater using Vicia faba micronucleus assay. The soil

samples contained different PAHs and organo chlorine pesticides (OCPs) which

resulted in genotoxicity in the form of increase in the micronucleated root tip cells.

Aijun et al. (2005) evaluated the genotoxicity of soil artificially contaminated

with cadmium in different combinations with imidacloprid by using single cell gel

electrophoresis or Comet assay in Vicia faba root tip cells. The roots were exposed to

soil containing different concentrations of cadmium (0.0, 0.2, 0.5, 1.0, 2.0 mg/kg dry

soil) or cadmium (0.0, 0.2, 0.5, 1.0, 2.0 mg/kg dry soil) in combination with

imidacloprid (0.5 mg/kg dry soil) for 2 h. The results revealed an increase in

genotoxicity with increase in dose and showed further increase with addition of

imidacloprid.

Song et al. (2006) tested 23 different soil samples including 6 controls, 6 from

paddy rice fields irrigated with wastewater, 6 from bioremediated sites and 5 from

PAHs contaminated areas collected from Shenyang, China. The genotoxicity of soils

was evaluated by employing Vicia faba root tip micronucleus assay. Elevated levels of

PAHs especially benzo (a) pyrene and PCB were found in the soils. The micronuclei

frequency was found to be higher than obtained with control soils. No correlation was

observed between micronuclei (MN) frequencies and the content of pollutants present.

Lah et al. (2008) evaluated the genotoxicity of soil from six different sites of

agricultural and industrial areas having power plant in vicinity and its leachates by


12

using Tradescantia MCN assay. The assay revealed high genotoxicity of the samples in

terms of increased number of micronucleated cells which were resulted from

chromosomal breakage in the pollen mother cells during meiotic division. The soil

samples were found to be highly contaminated with heavy metals and sulphur

compounds.

Marcato-Romain et al. (2009) evaluated genotoxicity of soil samples, collected

from surface layer of forest and areas contaminated by industrial effluents and coke

works in France, using Vicia faba micronucleus test. A dose dependent increase in

genotoxicity was observed on exposing seeds of V. faba to aqueous soil extracts.

Cesniene et al. (2010) reported surface soil genotoxicity of military and urban

territories in Lithuania, employing Tradescantia micronucleus (Trad-MN) and stamen-

hair mutation (Trad-SHM) bioassays. Higher genotoxicity of soil samples was shown in

both bioassays. Both the assays showed high clastogenic effects of the soil samples.

Kataeva et al. (2012) used Allium root-micronucleus test to study the

genotoxicity of soil samples contaminated with heavy metals. Two types of

contaminated soils were collected, one near the copper-smelters (the Middle Urals) and

second from Ni-enriched soil from an area of a natural geochemical anomaly (the Polar

Urals). An increase of frequencies of total nuclear anomalies like micronuclei, anaphase

and telophase bridges, separated fragments of chromosomes, extrusions and pycnotic

degeradation of nuclei in meristematic cells as compared to control samples was found.

Khadra et al. (2012) assessed the genotoxicity of soil matrix contaminated with

quinolone and fluroquinolone using the micronucleus (MN) test on Vicia faba roots by

direct contact exposure. Plants were exposed to quinolones (nalidixic acid) and

fluoroquinolones (ciprofloxacin and enrofloxacin) alone or mixed with artificially

contaminated soils. Four different concentrations 0.01, 0.1, 1 and 10 mg/kg for nalidixic

acid and 0.005, 0.05, 0.5 and 5 mg/kg for ciprofloxacin and enrofloxacin were tested.

Exposure of V. faba plants to each antibiotic at the highest two concentrations showed

significant MN induction. The lowest two concentrations had no significant genotoxic

effect. A similar genotoxic effect was obtained with the mixture of all the three


13

antibiotics at 0.2 mg/kg in comparison with each molecule alone at 5-10 mg/kg. Data

revealed a clear synergistic effect of these molecules on V. faba.

2.1.3. Allium cepa root chromosomal aberration assay

Allium test was first introduced by Levan in 1938. Since then, it has been widely

used for monitoring the genotoxic potential of complex mixtures. This assay is

successfully used for determining the genotoxic potential because of its sensitivity,

simplicity and easy availability in all the seasons. Moreover, it has large size and small

number of chromosomes. Allium cepa root chromosomal aberration assay is used for

evaluating the presence of carcinogenic and mutagenic chemicals in air, water and soil

as it represents good correlation with mammalian test systems. Some of recent works on

genotoxicity studies of soil matrix carried out during last few years are mentioned

below while a tabulated review on the use of A. cepa root chromosomal aberration

assay for estimation of genotoxicity of different environmental chemicals/ mixtures is

given as Table 1.

Andrade et al. (2008) reported genotoxicity of spent /soil leachates (waste soil)

collected from the vicinity of aluminum industry of Brazil. High frequency of

chromosomal aberrations like bridges, stickiness, fragments, C-metaphase, multipolar

anaphases were observed in their study.

Katnoria et al. (2009) estimated genotoxicity potential of soil of Amritsar by

using Allium cepa root anaphasic chromosomal aberration assay. Soil samples were

collected from 10 different sites. 5 samples were collected from agricultural fields (A1,

A2, A3, A4 and A5), 2 from dumping yards (D1 and D2) and 3 samples from soils

contaminated with industrial effluents (I1, I2, I3). I2 showed the maximum of 9.66% of

aberrant anaphase cells. Katnoria et al. (2011) estimated the genotoxic potential of soil

samples contaminated with effluents from zinc coating industry (SI) and copper

manufacturing industry (SII) of Amritsar, India employing A. cepa root anaphasic

chromosomal aberration assay. Different concentrations of soil extracts (10, 25, 50, 75

and 100 %) were prepared by diluting with distilled water. The soil extracts were

prepared by adding water to soil in the ratio of 1 : 2 :: soil : water (w/v). The freshly

emerged roots (1-2 cm) of A. cepa were treated with different concentrations of extracts


14

for 3 h and fixed in Farmer’s fluid. It was found that maximum anaphasic aberrations

were obtained at 100% concentration in both SI (10.19%) and SII (4.83%) samples. The

genotoxicity of effluents was reported to be due to presence of heavy metals in soil

samples.

Leme et al. (2012) assessed genotoxic potential of soils contaminated with

biodiesel and their diesel blends by using A. cepa root chromosomal aberration assay. A.

cepa test was performed with the raw polluted soil (without extraction procedure) and

genotoxic effects were determined by observing chromosomal abnormalties (CA) as

well as micronucleated cells (MN). The evaluation of CA and MN frequencies in

merismatic cells of A. cepa exposed to the polluted soils revealed a significant induction

of chromosomal abnormalties indicating their genotoxic potential.

Masood and Malik (2013) assessed cytotoxic and genotoxic potential of soil

samples from agricultural fields in the vicinity of an industrial area of Jajmau, Kanpur

(India) heavily contaminated with various toxic heavy metals. In A. cepa assay, all the

test concentrations of soil extracts (5–100%) affected mitotic index in a dose-dependent

manner and resulted in several types of chromosomal abnormalities viz., c-mitosis,

anaphase bridges, laggards, binucleated cells, stickiness, broken and unequal

distributions of chromosomes at anaphase etc.

Souza et al. (2013) reported clastogenic/aneugenic potential of landfarming soil

collected from a petroleum refinery before and after addition of sugarcane vinasse using

the A. cepa bioassay. Addition of sugarcane vinasse to landfarming soil, potentiated the

clastogenic effects of the latter due to release of metals that were previously adsorbed

into the organic matter. Mitotic and chromosomal abnormalities observed included

anaphase with chromosome loss, micronuclei induction, chromosome fragmentation,

polyploidy and chromosomal adherence, multipolar anaphase, metaphase with

chromosomal breakage and chromosomal bridges.

2.2. Antioxidative defense system in plants

The contamination of environment with heavy metals (HMs) due to various

anthropogenic and geogenic activities is becoming a worldwide problem of concern


Table 1. Summary of literature on genotoxicity of different chemicals/

environmental mixtures in root tip cells of Allium cepa.

S.No. Agent/s studied Types of aberration/s Reference

Chemical compound/s

1. Indiumtin oxide Laggards, stickiness, disturbed

anaphase–telophase and anaphase

bridges, c-metaphase and binuclear

cells

Cigerci et al. (2014)

2. Bromoform and Chloroform

Disturbed anaphase and telophase,

laggards, stickiness and anaphase

bridges

Khallef et al. (2014)

3. Sodium selenite sodium

hydrogen selenite, sodium

selenate and sodium

ammonium selenate

Reduction of the mitotic index

Michalska-Kacymirow

et al. (2014)

4. Rhodamine B (Xanthene dye) Abnormal nuclei, bridged nuclei,

nuclear bud in an interphase cell,

single and double nuclear bud during

prophase, multiple nuclear buds,

nuclear bud with broken connection

to nuclei, chromosome break at

metaphase

Tan et al. (2014)

5. Zinc oxide eugenol (Dental

cement)

Anaphase bridges, chromosomal

breakages, micronucleus formation

Rezende et al. (2013)

6. Erythrosine, brilliant blue

and red 40 (food dye)

Anaphase and telophasic bridges,

micronucleated cells and significant

reduction in cell division

Aguiar de Oliveira

et al. (2013)

7. Di (2 ethyl hexyl) phthalate,

triclosan and propyl paraben

Micronucleus, sticky metaphase,

bridges, chromosome missegregation

Herrero et al. (2012)

8. Chlorpyrifos, mercuric chloride,

Ethyl methanesulphonate and

garden ripcord

Bridges, chromosomal fragments,

vagrants, c-anaphases, multipolarity

and sticky chromosomes

Asita and Matebesi

(2010)

9. Benzo(a)pyrene Stickiness, vagrant, laggards, acentric

fragments, anaphasic bridges

Cabaravdic (2010)

10. N-nitrosodiethylamine Laggards, Anaphasic bridges,

micronuclei, stickiness

Claudia et al. (2010)

11. Peracetic acid (PAA) Micronucleated anaphases, chromatin

bridges, laggards, fragments

Rathore et al. (2010)

12. Sodium chloride C-mitosis, anaphasic bridges,

decrease in mitotic index, stickiness

Teerarak et al. (2009)

13. Sodium benzoate, boric acid,

citric acid, potassium citrate

and sodium citrate

Chromosomal breaks, c-mitosis,

stickiness, lagging chromosomes

Turkoglu (2007)

14. Sodium metabisulfite (Food

preservative)

Laggards, chromatin bridges,

c-mitosis, abnormal cells

Rencuzogullari (2001)



15. N-methyl-N-nitrosourea,

maleic hydrazide, sodium

azide and ethylmethane

sulfonate

Fragments, chromatin bridges,

vagrant chromosomes

Rank and Nielsen

(1997)

16. Defomax, Idthin Chromosomal break, c-mitosis,

laggards, multipolarity

Zoldos et al. (1997)

17. Tetracycline Chromatin bridges, stickiness,

c-mitosis

Mann (1978)

18. 6-methylcoumarin Chromatin bridges, chromosomal

breaks

Ronchi and Areara

(1967)

Effluents

19. Effluent of water constructed

wetlands

Aberrations at metaphase, single

break chromatid, double break

chromatid, gaps and centromere

break

Firbas and Amon

(2013)

20. Hospital laundary waste

water

Micronucleated cells, metaphase with

spindle viscosity,anaphase with

adhesion and multiple chromosome

bridges, multipolar anaphase with

chromosome bridges, anaphase and

telophase with chromosome delay,

metaphase with chromosome loss

Kern et al. (2013)

21. Pharmaceutical effluents Chromatin bridge, c-mitosis,

multipolar nuclei

Abu and Mba (2011)

22. Effluent from processing unit

of cassava mill in Usela

Quarters, Benin city, Edo

state in Nigeria

Anaphasic bridges, fragments,

vagrant chromosomes, polar

deviation

Olorunfemi et al.

(2011a)

23. Three industrial wastewaters

(brewery, rubber and bottling

industry) collected from

Benin city metropolis,

Nigeria

Bridges, laggards, vagrant

chromosomes, fragments, stickiness,

micronuclei, c-mitosis, disturbed

spindle formation

Olorunfemi et al.

(2011b)

24. Raw effluent from a

pharmaceutical plant in

Lagos state, Nigeria

Sticky chromosomes, breaks, ring

chromosomes, dicentric

chromosomes

Bakare et al. (2009)

25. Effluent from hospital in the

municipality of Santa Maria,

Rio Grande do Sul State

(RS), Brazil

Vagrant chromosomes, stickiness,

breaks, anaphasic bridges, ring

chromosomes

Bagatini et al. (2009)

26. Waste water from eight

gasoline stations in Brasilia,

Brazil

Bridges, fragments, lagging

chromosomes

Cynthia et al. (2009)

27. Spent potliners/ soil leachates

from aluminum industry of

Brazil.

Stickiness, bridges, fragments,

c-metaphases, multipolar anaphases

Andrade et al. (2008)



28. Textile industry effluents C-mitosis, decrease in mitotic index,

chromatin bridges

Carita and Marin-

Morales (2008)

29. Leachates from highly

polluted lowland river

sediments polluted with

complex chemical mixture of pollutants

Vagrant chromosomes, chromatin

bridges

Magdaleno et al.

(2008)

30. Municipal wastewater

effuluents

Vagrant chromosomes, fragments,

chromatin bridges

Grisolia et al. (2005)

31. Sewage waste Binucleated cells, inhibition of root

growth, stickiness, contracted

chromosomes

Amin (2002)

32. Sewage and industrial

effluent

Chromosomal bridges, micronucleus

induction, chromosomal breaks

Grover and Kaur

(1999)

33. Non diluted wastewater

effluents sample from

slaughter house and dye

house

Fragments, vagrant chromosomes,

chromatin bridges, c-mitosis, stickiness

Rank and Nielson

(1993)

34. Drilling fluids from reserve

pit of oil and gas drilling

activity

C-mitosis, stickiness, multipolarity,

reduction in mitotic index,

micronuclei formation

Vidakovic and papes

(1993)

35. Municipal wastewater sludge C-mitosis, vagrants, bridges,

fragments

Rank and Nielsen

(1998)

Industrial by product

36. Vinnase a by product of

sugar cane industry

Micronuclei and chromosomal break,

metaphase with chromosome

adherence, breaks and polyploidy,

anaphase with chromosome bridges, chromosome loss and nuclear buds

Christofoletti et al.

(2013)

37. Crude oil from shell

petroleum Development

Company Port Harcourt,

Nigeria

Chromatin bridges, stickiness,

c-mitosis, vagrant chromosomes

Awodele et al. (2010)

38. Fly ash (Coal byproduct) Chromosomal gaps, chromosomal

breaks

Swamy et al. (2010)

39. Coal fly ash- by product of

coal-fired electricity generation plant

Micronuclei, binucleated cells,

anaphasic bridges

Chakraborty et al.

(2009)

Fluids

40. Metal working fluids

(mixture of oil and emulsifying agents)

c-mitosis, fragments, bridges,

vagrants and micronucleus

Pekol et al. (2013)

Metals

41. Chromium Formation of micronuclei and

induction of chromosomal

aberrations. Decrease in mitotic

activity.

Patnaik et al. (2013)



42. Aluminium DNA damage and cell death Achary et al. (2008)

43. Lead Stickiness, micronucleated cells Carruyo et al. (2008)

44. Boron Disturbed anaphases, c-metaphases,

telophase-anaphase bridges

Konuk et al. (2007)

45. Arsenic Micronuclei induction, decrease in

mitotic index.

Yi et al. (2007)

46. Vanadium C-mitosis, stickiness, decrease in

mitotic index, anaphasic bridges

Marcano et al. (2006)

47. Cadmium Cell vacuolization, decrease in

nucleoplasmic ratio, nucleus and

cytoplasmic condensation, nucleus

margination

Behboodi and Samadi

(2002)

48. Selenium and mercury Stickiness, Bridges, Delayed

anaphases, fragments, multipolarity,

c-mitosis

Fiskesjo (1979)

Nanoparticles

49. Titanium dioxide Chromosomal breaks, stickiness,

multipolarity, laggard chromosomes

and micronucleus formation

Pakrashi et al. (2014)

50. Bismuth (III) oxide nano

particles

Stickiness, laggards, disturbed

anaphase-telophase and anaphase

bridges, c- metaphase

Liman (2013)

51. Nano composites prepared

with organoclays Cloisite

20A (PLA/CLO20A) and

Cloisite 30B (PLA/

CLO30B)

Metaphase with adherence, polyploid

metaphase, anaphase with

chromosomal bridge, chromosomal

loss, telophase with chromosomal

loss, multipolar telophase with

chromosomal bridge and adherence,

lobulated nucleous, binucleated and

trinucleated cell

Souza et al. (2013)

52. Zinc oxide nanoparticles Disturbed anaphases and metaphases,

sticky chromosomes, laggards,

anaphasic bridges, multipolar and

binucleated cells

Kumari et al. (2011)

53. Silver nanoparticles Stickiness, chromosomal breaks and

disturbed metaphases

Kumari et al. (2009)

Pesticides

54. Dioxacarb Chromosome and chromatid break,

dicentric chromosomes, fragmentation,

polyploidy and chromatid exchange

Eren et al. (2014)

55. Anilofos C-metaphase, disturbed nucleus,

binuclear cells, laggard, stickiness,

anaphase bridge and decrease in

mitotic index

Ozkara et al. (2014)



56. Mercaptothion, permethrin,

Aluminium phosphide,

Dichlorvos

Sticky metaphase, chromosome

bridges at telophase, sticky telophase

bridge, c-mitosis, vagrant or laggard

chromosome, multipolar anaphase

Asita and Mokhobo

(2013)

57. Flusilazole Chromosomal bridges, stickiness and

laggards

Ozakca and Silah

(2013)

58. Diphenyl-ether C-mitosis, vagrant chromosomes,

fragments, multipolar anaphases,

bridges in anaphase and telophase

Dragoeva et al.

(2012)

59. Fenaminosulf Chromatin bridges, laggards,

pulverized and sticky chromosomes,

multipolarity

Liman et al. (2011)

60. Pendimethalin Bridges, fragments, multipolar

micronuclei, laggards, c-mitosis

Promkaew

et al. (2010)

61. Raxil C-mitosis, disturbed metaphases,

laggards

Fisun and Rasguele

(2009)

62. Carbofuran C-mitosis, multipolar anaphases,

lagging chromosomes, chromatin

bridges

Saxena et al. (2009)

63. 4,6-Dinitro-o-cresol (DNOC) Stickiness, c-mitosis, chromosomal

bridges

Aydemir et al. (2008)

64. Benomyl (Benlate) Breaks, bridges, stickiness, decrease

in mitotic index

Dane and Dalgic (2005)

65. Maleic hydrazide Chromatin bridges, stickiness,

micronuclei formation

Marcano et al. (2004)

66. Thimet, carbaryl Vagrants, fragments, bridges,

stickiness, c-mitosis

Nagpal and Grover

(1994)

67. Phosphine Laggards, chromatin bridges,

fragmentation

Younis et al. (1989)

68. Oxydemeton methyl C-mitosis, chromosomal breaks,

chromatin bridges

Pandita (1986)

69. 2-methoxy ethyl mercury

chloride, phenyl mercury

acetate

Chromosomal breaks, c-mitosis,

chromatin bridges

Nandi (1985)

70. TCA Chromatin bridges, chromosomal

breaks

Kurinnyi (1984)

71. Thimet Chromatin bridges, stickiness,

chromosomal breaks, micronuclei

Pandita and Khoshoo

(1984)

72. Carbetamide Stickiness, chromosomal breaks,

chromatin bridges, micronuclei

Badr (1983)

73. Vinclozolin Chromosomal break, c-mitosis,

chromatin bridges

Escelza et al. (1983)

74. Benomyl, carbendazim Chromatin bridges, chromosomal

breaks

Sahu et al. (1983)



75. Isoproturon C-mitosis, stickiness, chromosomal

breaks, chromatin bridges

Badr and Elkington

(1982)

76. Prometryne Micronuclei induction Mousa (1982)

77. Carboxin, oxycarbixin Micronuclei induction Sakamoto and

Takahashi (1981)

78. Thiophanate Chromosomal breaks, c-mitosis,

chromatin bridges

Kozera and Klein

(1981)

79. Chlorophenoxy acids Delayed anaphase, reduction in root

growth, c-mitosis, stickiness,

vagrants, multipolarity

Fiskesjo (1981)

80. 2,4,5-trichlorophenoxyacetic

acid

Mitotic inhibition, stickiness,

c-mitosis, laggards, multipolarity

Grant (1979)

81. Dithane Stickiness, Chromatin bridges,

c-mitosis

Mann (1977)

82. Asulam Chromatin bridges, chromosomal

breaks

Sterett and Fretz (1975)

83. Phenyl mercury acetate C-mitosis Bielecki (1974)

84. Parathion Chromatin bridges, chromosomal

breaks

Ravindran (1971)

85. Dichlorvos C-mitosis, chromosomal breaks Sax and Sax (1968)

86. Propham C-mitosis Mann and Storey (1966)

87. Dinoseb Stickiness, c-mitosis, chromosomal

bridges, chromosomal breaks

Sawamura (1965)

88. Endothal Chromosomal breaks Wilson et al. (1956)

89. Aldrin, DDT, Heptachlor Stickiness, chromosomal breaks,

micronuclei.

Scholes (1955)

90. 2,4-D, 2,4,5-T Chromatin bridges, c-mitosis,

micronuclei

Croker (1953)

91. γ - HCL Chromosomal breaks D’Amato (1950)

92. TCA (Chloroacetic acid) Stickiness Lopane (1950)

93. γ - HCH Chromosomal breaks Quidet and Hitier (1948)

Plant extracts

94. Spondias mombin,

Nymphea lotus and

Luffa cylindrica

Binucleated cell at prophase, sticky

chromosome, bi polar anaphase, vagrant

chromosome, disoriented chromosomes,

spindle disturbance at anaphase

Oyeyemi and Bakare

(2013)

95. Erythrina velutina Laggards, chromatin bridges, chromosome

fragments, disturbed metaphase and

anaphase

Silva et al. (2011)

96. Vernonia amygdalina Lagging chromosomes, C-mitosis, giant

chromosome

Adegbite and sanyaolu

(2009)



97. Ecballium elaterium Stickiness, chromosomal breaks, pole

deviation and micronuclei.

Celik and Aslanturk

(2009)

98. Amorphophallus

paeoniifolius

Reduction in mitotic index Angayarkanni et al.

(2007)

99. Azadirachta indica Stickiness, chromatin bridges, laggards Soliman (2001)

Soil samples

100. Soil from municipality

of Triunfo, state of Rio

Grande do Sul, Brazil.

Multipolar anaphases, metaphases with

adherence, binucleated cells, anaphases

with bridges, chromosomal losses

chromosomal breaks and micronucleated

cells.

Pohren et al. (2013)

101. Soil collected from

Nangli village of

Amritsar, Punjab

C-mitosis, delayed anaphases, stickiness,

chromosomal breaks, chromatin bridges

Chahal et al. (2012)

102. Soil collected from

outskirts of two

industries viz. zinc

coating industry and

copper sulphate

manufacturing industry,

Amritsar

Laggards, chromosomal breaks, chromatin

bridges, vagrants

Katnoria et al. (2011)

103. Agricultural soil Vagrant chromosomes, chromosomal

fragments at anaphase and telophase,

multipolar anaphases

Dragoeva et al. (2009)

104. Soil samples Chromatin bridges, decrease in mitotic

index, fragmentation

Kong and Ma (1999)

105. Soil sample from

Ukraine polluted with

radioactive pollutants

Chromatin bridges, decrease in mitotic

index, fragments, vagrant chromosomes,

stickiness

Kovalchuk et al.

(1998)

Water samples

106. Surface water from

Quatorze River, Francisco

Beltrao, Parana, Brazil

No chromosomal alterations Dusman et al. (2013)

107. Water samples from

Paraıba do Sul River at

the Brazilian cities of

Tremembe and

Aparecida

Micronuclei formation Barberio et al. (2011)

108. Polluted surface and

waste water from Sava

river, Crotia

Chromatin bridges, lagging chromosomes,

stickiness, C-mitosis, chromosomal

bridges

Radiac et al. (2010)

109. Water samples from the

river Paraiba do Sul, at

the cities of Tremembe

and Aparecida, (Sao

Paulo state), Brazil

Decrease in mitotic index, inhibition and

delay of root growth

Barberio et al. (2009)



110. River water Polluted with

Hexavalent chromium

Inhibition of root growth Espinoza-Quinones et

al. (2009)

111. Contaminated river

water from downstream

and upstream of river

Alamuyo

Chromatin bridges, chromosomal fragments,

lagging chromosomes

Fawole et al. (2008)

112. Water sample collected

from the region of

Panagjurishte,

southwest Bulgaria

Chromatin bridges, fragments, vagrant

chromosomes

Staykova et al. (2005)

113. Polluted water samples

of Slovenia river

Chromosomal breaks, decrease in mitotic

index, chromatin bridges

Glasencnik et al.

(2004)

114. Shallow well water Chromatin bridges, decrease in mitotic

index, fragmentation

Kong and Ma (1999)

115. Water samples of

various pollutant levels

like biological treatment

plant output water-

undiluted, industrial and

municipal wastewater

C-mitosis, stickiness, fragmentation Smaka-kincl et al.

(1996)


15

because of their persistent nature. It is a known fact that heavy metals cannot be

destroyed biologically but can only be transformed from one oxidation state to other

(Garbisu and Alkorta, 2001; Gisbert et al., 2003; Bhaduri and Fulekar, 2012). The

increase in heavy metal contamination of soil is widespread globally and can lead to

multiple toxic effects in plants which are expressed in terms of changes in the plant

metabolism, growth and general development. As a result of stress, there is an increase

in production of Reactive oxygen species (ROS) and successive reduction of ground

state molecular oxygen (O2). ROS like superoxide (O2•−), hydrogen peroxide (H2O2),

hydroxyl radicals (•OH) and singlet oxygen (1O2) are formed due to energy transfer or

by electron transfer reactions. Under stress, the equilibrium between production and

scavenging of ROS gets disturbed and results in rapid rise of intracellular levels of

ROS. This rapid increase in ROS concentration is known as oxidative burst that may

lead to the oxidation of proteins and/or lipids, ultimately causing DNA damage. Since

plants are sessile and cannot avoid climatic or adaphic stresses by moving away from

the particular site therefore, they have develop various ways to adapt to changing

environmental conditions. Plants have efficient mechanism for scavenging of ROS in

order to protect themselves from oxidative burst which comprises of non enzymatic and

enzymatic components. Non enzymatic scavengers include metabolites such as

ascorbate, glutathione, tocopherol, flavonoids, cartenoids and enzymatic scavengers

include ascorbate peroxidae (APX), catalase (CAT), glutathione peroxidase (GPX),

superoxide dismutase (SOD).

Alterations in activities of various enzymes, stress proteins, phytochelators have

been observed in different plants upon exposure to heavy metals (Van Assche and

Clijsters, 1990; Srivastava, 1999; Li et al., 2006). One of the ways by which heavy

metals damage plant tissues is the increased production of reactive oxygen species

(ROS) (Foyer et al., 1997). Reactive Oxygen Species (ROS) are a natural consequence

of the aerobic metabolism and plants have mechanisms to deal with them in normal

conditions by controlling their formation and removal rates. Increase in ROS production

under stress condition leads to disruption of cell homeostasis and thus causes heavy

burden on antioxidative mechanisms (Mittler et al., 2004; Mourato et al., 2012).


16

2.2.1. Heavy metals and reactive oxygen species (ROS)

Heavy metals are the metals with the density greater than 5 g/cc. Among 17

naturally occurring heavy metals of biological importance, Fe (iron), Mo (molybdenum)

and Mn (manganese) are important as micronutrients; Zn (zinc), Ni (nickel), Cu

(copper), V (vanadium), Co (cobalt), W (tungsten) and Cr (chromium) are important as

trace elements and toxic at high concentrations; As (arsenic), Hg (mercury), Ag (silver),

Sb (antimony), Cd (cadmium), Pb (lead) and U (uranum) have no known function as

nutrients and are highly toxic to plants and other living organisms (Godbold and

Huttermann, 1985, Breckle, 1991; Nies, 1999; Schutzendubel and Polle, 2002). Heavy

metal contamination of soil is widespread globally and can lead to multiple toxic effects

in plants by: i) production of ROS by autooxidation and Fenton reaction. (A reaction

that is typical for transition metals like Fe and Cu) (Stohs and Bagchi, 1995); ii)

blocking the essential functional groups in biomolecules (mainly reported for non-redox

reactive heavy metals such as Cd and Hg) (Goyer, 1997) and iii) replacing essential

metals in pigments/enzymes or disrupting their function (Leonard et al., 2004).

Evidences are accumulating to prove that metals possess the ability to generate reactive

radical species which include wide variety of oxygen-, carbon-, sulfur-, and nitrogen-

radicals, originating not only from superoxide radical, hydrogen peroxide and lipid

peroxides but also form chelates of amino acids, peptides and proteins complexed with

the toxic metals (Bhaduri and Fulekar, 2012).

Reactive oxygen species (ROS) are group of free radicals which are partially

reduced forms of atmospheric oxygen. The most common ROS include superoxide

(O2•), hydrogen peroxide (H2O2), hydroxyl radicals (

•OH) and singlet oxygen (

1O2). All

these ROS are chemically reactive and biologically toxic because when an ROS collides

with an organic molecule, it extracts an electron from it, converting it into a free radical

capable of attacking another stable molecule to convert that into a free radical, thus

initiating a chain reaction as follows:


17

Cascade of Free radical chain reaction

ROS have also been shown to play important roles in several metabolic processes of

plants for example H2O2 and O2 are involved in lignin formation in cell walls (Inze and

Montagu, 1995; Teichmann, 2001). A large increase in ROS (oxidative burst) is

observed rapidly upon infection of plants with pathogens and hence has also been

observed to involve in active defense against pathogens (Alvarez and Lamb, 1997;

Doke, 1997; Bolwell et al., 2002). At low concenteration, ROS also act as secondary

messagers in intracellular signaling cascades to regulate expression of several genes

(May et al., 1998; Karpinski et al., 1999; Vranova et al., 2002; Guan et al., 2014;

Smelalova et al., 2014). Because of their dual role (beneficial at low concentration and

deleterious at higher concentration), levels of ROS within plant cells should be tightly

regulated so as to avoid any oxidative damage to the cell but not eliminating them

completely. The increase in heavy metal contamination of soil is widespread globally

and can lead to multiple toxic effects in plants by inducing reactive oxygen species

(ROS), which inhibit most cellular processes at various levels of metabolism (Stanjer et

al., 2003; Yadav, 2010; Mithofer et al., 2004).

2.2.2. Reactive oxygen species and antioxidative enzymes

The exposure of plants to a variety of environmental stresses like soil salinity,

drought, extreme of temperature and heavy metals result in oxidative injury due to

production of increased levels of ROS or free radicals chain reaction (De Vos et al.,

1992; Gallego et al., 1996; Weckx and Clijsters, 1996; Chaoui et al., 1997; Yamamoto

et al., 1997; Shah et al., 2001). Under normal conditions, the antioxidative defense

system (comprising of non enzymatic and enzymatic antioxidants) provides protection


18

against ROS and equilibrium is maintained between different ROS producing and

scavenging mechanisms in the plants. The term antioxidant generally refers to a broad

class of compounds that protect cells from damage caused by highly reactive chemical

species (Poljsak et al., 2013). Non enzymatic antioxidants with in plant cells include

ascorbate (AsA), glutathione (GSH), carotenoids, tocopherols and phenolics where as

enzymatic antioxidants include superoxide dismutase (SOD), catalase (CAT), guaiacol

peroxidase (GPX), enzymes of ascorbate glutahione (AsA-GSH) cycle such as

ascorbate peroxidase (APX), monodehydroascorbate reductase (MDHAR),

dehydroascorbate reductase (DHAR) and glutathione reductase (GR) (Asada and

Takahashi, 1987; Allen, 1995; Willekens et al., 1997; Apel and Hirt, 2004; Sharma et

al., 2012).

SOD is found in almost all cellular compartments and it acts as first line of

defense against reactive oxygen species dismutating superoxide radical to H2O2.

O2 -

H2O2

CAT which is present only in peroxisomes catalyzes the conversion of H2O2 to water

H2O2 H2O + ½ O2

Enzymes of ascorbate- glutathione cycle (APX, MDHAR, DHAR, GR) present in

almost all compartments of plant cells viz. chloroplasts, cytosol, mitochondria, cytoplast

and peroxisomes, also participate in reduction of H2O2 to H2O. The first reaction in

ascorbate- glutathione cycle is catalyzed by APX where the reducing agent is ascorbate

and is oxidized to monodehydroascrobate (MDA).

H2O2 + Ascorbate H2O + monodehydroascrobate (MDA)

In the second reaction which is catalyzed by monodehydroascrobate reductase

(MDHAR), MDA is reduced to ascorbate with the help of NAD(P)H.

SOD

CAT

APX


19

MDA + NAD(P)H Ascorbate + NAD(P)+

The third reaction catalyzed by DHAR that involves reduction of dehydroascrobate

(DHA) produced spontaneously by dismutation of MDA to ascorbate with the help of

SGH which is oxidized to GSSG.

Dehydroascorbate (DHA) + GSH Ascorbate + GSSG

The last reaction of the cycle is catalyzed by GR which converts GSSG back to GSH

with reducing agent NAD(P)H

GSSG + NAD(P)H GSH + NAD(P)+

Like ascorbate-glutathione cycle, guaiacol peroxidase cycle is also involved in

reduction of H2O2 to H2O. The first reaction in this cycle is catalyzed by GPX which

detoxifies H2O2 to H2O but uses GSH as a reducing agent.

H2O2 + GSH H2O + GSSG

The cycle closes with conversion of GSSG back to GSH with the help of NAD(P)H.

The reaction is catalyzed by GR.

The alterations in the activities of antioxidative enzymes in plants in response to

heavy metal stress is well established. Table 2 summarizes the studies carried out

during last 15 years on responses of antioxidative enzymes in different plant species

under heavy metal stress.

2.3. Physico-chemical analysis of soil

As soil is a complex matrix, its quality can not be determined by evaluation of a

single factor (Ayuke et al., 2011; Salvia et al., 2013). The indicators of soil quality

include a combination of physical, chemical and biological properties (Aparicio and

Costa, 2007; Mowo et al., 2006). Emphasis has been given to determine physico-

chemical characteristics of soil like texture, pH, alkalinity, micro/macronutrients

content, electrical conductivity and solubility of different metals of soil samples along

MDHAR

DHAR

GR

GPX


20

with estimation of pesticides and heavy metals in a number of studies on soil pollution

(Sequaris and Lewandowski, 2003; Al-Khasman and Shawabkeh, 2006; Zaiad, 2010;

Pujar et al., 2012; Janeshwar et al., 2013). Bentham et al. (1992) analyzed physico-

chemical characteristics viz. pH, soil texture, total carbon content (TCC), total nitrogen

content (TNC) and bulk density (BD) of samples collected from 12 sites affected due to

opencast coal mining operations. Range of pH of different soil samples varied from 4.0

- 7.2; TCC was found to be in the range of 14 - 60 µg/ml; TNC (0.1-2.9 µg/ml); BD

(0.54 - 0.98 g/ml). Soil texture analysis revealed 4 - 40 w/w sand; 6 - 66 w/w silt; and 2

- 40 w/w clay. McKee (1993) reported that the pH of different soil samples collected

from mangrove forest in south-west Florida at Rookery Bay near Naples was in the

range of 6.7 -7.0 indicating the slightly acidic to neutral nature of the soil samples.

Ekundayo and Obuekwe (2000) analyzed physico-chemical parameters of soil

samples collected from an oil spill site in delta basin of Nigeria. The site was

subdivided into three broad zones viz heavy impact area, medium impact area and

unimpacted reference zone. Textural analysis showed that the soil samples for three

zones were: predominantly dark brown sand in the topsoil followed by loamy sand,

greyish brown sand, clay or light grey coarse sand at medium and greater depths

respectively. pH (3.8 - 6.6; 3.6 - 6.2; 4.2 - 6.8) and electrical conductivity (29 - 58

µhos/cm; 40.5 - 112 µhos/cm; 13.1 - 64.2 µhos/cm) was observed for heavy impact

zone, medium impact zone and unimpacted zone, respectively. Total nitrogen content

ranged from 0.021 % (w/w) - 0.074 % (w/w) in the impacted and unimpacted zones.

Total organic carbon values in the topsoil (0 - 30 cm) both within the bundwall (2.30 %)

and outside the bundwall (2.27 %) of heavy impact zone were found to be significantly

higher. Ammonium ion levels were generally high (8.6 - 76.4 ppm) while nitrite and

nitrate content were found to be low i.e. 0.2 - 1.6 and 0.3 - 7.3 ppm, respectively in all

the zones studied. The content of available phosphorus in soil samples ranged from 2.4 -

4.8 ppm, 2.6 - 4.5 ppm and 3.0 - 4.3 ppm in the heavy impact, medium impact and

unimpacted area, respectively.


Table 2. Summary of literature on responses of antioxidative enzymes in different plant species exposed to various toxic heavy

metals (HMs) and metalloids.

S. No. Family Name of Plant

species

Plant part

used

Metal stress Antioxidative enzymes studied

and response

Reference

1. Amaranthaceae

Amaranthus viridis Leaves Cr ↑ CAT and SOD Liu et al. (2008a)

2. Atriplex horetensis

Var. rubra

Seedlings Cu, Ni, Pb and

Zn

↑ CAT and GR

↓ APX and SOD

Kachout et al. (2009)

3. Atriplex rosea Seedlings Cu, Ni, Pb and

Zn

↑ CAT and GR

↓ APX and SOD

Kachout et al. (2009)

4. Halimione

portulacoides

Leaves/Roots Cr (VI) ↑ APX and GST in both leaves and

roots

↑ SOD in roots

↓ CAT in leaves and roots

↓ SOD in leaves

Duarte et al. (2012)

5. Amaryllidaceae

Allium cepa

Var. Agrogarium

Seedlings Cd ↑ CAT, POD and SOD Zou et al. (2012)

6. A. cepa Roots Al ↑ APX, GPX and SOD

↓ CAT

Achary and Panda

(2010)

7. A. cepa Bulbs Cd, Cr, Cu,

Hg, Pb and Zn

↑ APX, GPX, GST and MDHAR

↓ GR, SOD and CAT

DHAR – No effect

Fatima and Ahmad

(2005)

8. A. Sativum Leaves/roots Cu ↑ CAT, POD and SOD Meng et al. (2007)

9. 9

.

Araceae

Pistia stratiotes Roots Cr ↑ SOD Sinha et al. (2005)

10. Lemna minor Whole plant Cu ↑ SOD

↓ CAT

Kanoun-Boule

et al. (2009)

11. Lemna minor Whole plant Cu and Cd ↑ SOD Hou et al. (2007)

12. Spirodela polyrrhiza Whole Plant Cu ↑ SOD Tu et al. (2006)

13. Wolffia arrhiza Whole plant Cd and Pb ↑ CAT and APX Piotrowska et al.

(2010)


14. W. arrhiza Whole plant Pb ↑ CAT Piotrowska et al.

(2009)

15. Asteraceae

Helianthus annuus Seedlings Cd ↑ POD and SOD

↓ APX, CAT and GR

Saidi et al. (2014)

16. H. annuus Seedlings Cd ↓ APX, CAT, DHAR and GPOX

SOD – No effect

Gallego et al.

(1999)

17. Lactuca sativa Leaves Bo ↑ SOD

Eraslan et al. (2007)

18. Matricaria

chamomilla

Leaves/Roots Cu and Cd ↑ CAT and GR

Kovacik et al. (2008)

19. Sonchus

transcaspicus

Roots/shoots Cd, Co, Cr, Cu,

Mn, Ni, Pb and

Zn

↑ APX, CAT, POD and SOD Lu et al. (2013)

20. Brassicaceae Alyssum argenteum Seedlings Cd and Ni ↑APX, CAT and SOD Kumchai et al.

(2013)

21. A. maritimum Seedlings Cd and Ni ↑APX, CAT and SOD Kumchai et al.

(2013)

22. Cannabaceae Cannabis sativa Seed lings Cd ↑ GPOD and SOD

CAT – No effect

Shi et al. (2009)

23. Ceratophyllaceae

Ceratophyllum

demersum

Leaves Cd ↓ GR and GST Mishra et al. (2009)

24. C. demersum Whole plant Cd ↑ APX, GPX and SOD

↓ CAT and GR

Mishra et al. (2008)

25. C. demersum Leaves/roots Cu ↑ SOD Devi and Prasad

(1998)

26. Chlorellaceae Chlorella vulgaris Green alga Cr ↑ APX, CAT, GST and SOD Rai et al. (2013)

27. Crassulaceae

Sedum alfredii Roots/Shoots Pb ↑ APX, CAT, POX and SOD. Huang et al. (2012)

28. S. alfredii Leaf Pb ↓ SOD Liu et al. (2008b)

29. Cruciferae Arabidopsis

thaliana

Seedlings Cu and Cd ↓ APX, CAT, GR and SOD Anna et al. (2011)


30.

A. thaliana Leaves Cu and Cd ↑ APX and GPOD at lower

concentrations

↓ CAT, GR and SOD

Smeets et al. (2009)

31. A. thaliana Whole plant Cu and Cd ↑ POX under both Cd and Cu stress

↑ CAT and SOD under Cd stress

↓ CAT and SOD under Cu stress

Maksymiec and

Krupa (2006)

32. Brassica juncea

Var. Varuna and

RH-30

Seedlings Cd ↑ CAT, POX and SOD Irfan et al. (2014)

33. Brassica juncea Leaves Co ↑ DHAR, GR, MDHAR, POX and

SOD

↓ CAT

Karuppanapandian

and Kim (2013)

34. B. juncea Leaves Co ↑ APX, CAT, DHAR, GR, MDHAR

and SOD

↓ POD

Arora et al. (2012)

35. B. juncea Seedlings Ni ↓ CAT, POX and SOD Yusuf et al. (2012)

36. B. juncea Seedlings Cd ↑ APX, CAT, GR and SOD Iqbal et al. (2010)

37. B. juncea Roots/aerial

parts

Cd ↑ APX, CAT and SOD Hayat et al. (2007)

38. B. juncea Roots Cd ↑ CAT, APX, GR and SOD

↓ GR at high dose of Cd

Mobin and Khan

(2007)

39. B. juncea Leaves/Roots Cr ↑ SOD Pandey et al. (2005)

40. B. napus Leaves As ↑ GPOD

↓ APX, CAT and GR

Nouairi et al. (2009)

41. Brassica oleracea

var. capitata

Seedlings Mo ↑ GST Kumchai et al.

(2013)

42. B. oleracea Seedlings Cu ↑ APX, POX and SOD

↓ CAT and GR

Posmyk et al. (2009)

43. Raphanus sativus

cv. „Japanese white‟

Seedlings Zn ↑ CAT, GPX and SOD

↓ POD

Ramakrishna and

Rao, (2012)


44. R. sativus Shoots Cr ↑ APOX, CAT, DHAR, GR, MDAR

and SOD

↓ POD

Sharma et al. (2011a)

45. R. sativus Shoots Ni ↑ APOX and SOD

↓ CAT, DHAR, GR and POD

Sharma et al. (2011b)

46. Cucurbitaceae Cucumis sativus Seedlings Co ↑ APX and CAT Iseri et al. (2011)

47. C. sativus

(cv. Aodai).

Seedlings Al ↑ APX and CAT

↓ SOD

Pereiraa et al. (2010)

48. C. sativus

Leaves La ↑ CAT, POD and SOD

↓ APX, DHAR and GR

Gao et al. (2008)

49. C. sativus

Seedlings Hg ↑ CAT

At higher concentration (500 μM)-

marked inhibition

Cargnelutti et al.

(2006)

50. C. sativus Roots Mn ↑ POD and SOD Shi et al. (2006)

51. Luffa cylindrical LeaveS Ni ↑ APX, POD, POX and SOD

↓ CAT

Awasthi and Sinha

(2013)

52. Euphorbiaceae Jatropha curcas L. Leaves of

cuttings and

seedlings

Pb ↑ SOD and CAT in leaves of cuttings

↑ POD in leaves of both cuttings and

seedlings

↓ SOD and CAT in seedlings

Shu et al. (2012)

53. Fontinalaceae Fontinalis

antipyretica

Seedlings Cd, Cu Pb and

Zn

↑ APX, CAT, GR and SOD under

Cd, Cu and Zn stress

↓ APX under Pb stress

Dazy et al. (2009)

54. Hydrocharitaceae

Elodea canadensis Leaves Ni ↑ SOD and GR

↓ CAT

Maleva et al. (2009)

55. Hydrilla vericillata

i. Tokak cv. Hamidiye

Whole plant Cu ↑ SOD Srivastava et al.

(2006)

56. Lamiaceae Elsholtzia

haichowensis

Roots Cu ↑ APX ,CAT, POX and SOD Zhang et al. (2008)

57. E. splendens Leaves Cu ↑ APX, CAT and SOD Peng et al. (2006)


58. Mentha pulegium Leaves Ca, Cu, Mg,

Mn and Zn

↑ SOD Candan and Tarhan

(2003)

59. Ocimum tenuiflorum leaves Cr ↑ SOD Rai et al. (2004)

60. Malvaceae Hibiscus cannabinus Plant seedlings Cd ↑ CAT, GR, POD and SOD Feng-tao et al. (2007)

61. Fabaceae Arachis hypogaea Seedlings Cd ↑ CAT, POX, GR and SOD Dinakar et al. (2008)

62. Macrotyloma

uniflorum

(Lam.)Verdc

Leaves/Roots Pb ↑ CAT, GR, POX and SOD Reddy et al. (2005)

63. Onobrychis vicifolia Leaves Cu and Pb ↑ CAT, GPX and SOD Beladi et al. (2011)

64. Cajanus cajan cv.

LRG30 and ICPL87

Seedlings Ni and Zn ↑ APX, GR and SOD

↓ CAT

Rao and Sresty

(2000)

65. Cicer arietinum Seedlings Co ↑ APX, CAT and SOD Ali (2014)

66. C. arietinum Shoots B ↑ APX and SOD

↓ CAT, POX and GR

Ardic et al. (2009)

67. C. arietinum Leaves/Roots Pb ↑ CAT, GR, POX and SOD Reddy et al. (2005)

68. Glycine max Roots Cd ↑ CAT, GOX, GST and SOD Perez-chaca et al.

(2014)

69. G. max Roots/shoots Pb2+

↑ APX, CAT, GPOD, GR and SOD Mishra et al. (2013)

70. G. max Leaves/roots Cd ↑ GR in roots

GR – No effect in leaves

CAT and SOD – No effect in both

leaves and roots

Ferreira et al. (2002)

71. Lupinus albas

cv Marta

Seedlings Cu, ↑ CAT and SOD

APX- Nochange

Sanchez-Pardo

et al. (2014)

72. Lupinus luteus Seedlings Cu ↑ GPOD, PPO and SOD

CAT – No change

Mourato et al. (2009)

73. L. luteus L. cv. Ventus Seedlings Cd and Pb ↑ CAT, POX and SOD Kopyra and Gwozdz

(2003)

74. L. luteus cv. Ventus Roots Pb ↑ POX and SOD

↓ APOX and CAT

Rucinska et al. (1999)


75. Medicago sativa Roots

Hg ↑ POX and SOD

↓ APX and GR

Zhou et al. (2009)

76. Phaseolus vulgaris Roots Cu ↑ APX, DHAR, GR and MDHAR Gupta et al. (1999)

77. Pisum sativum Seedlings Cr ↑ APX and SOD

↓ CAT and GR

Gangwar et al. (2011)

78. P. sativum Seedlings

Cd

↑ SOD Romero-Puertas

et al. (2006)

79. P. sativum Leaves/roots Cd ↑ CAT, POD and SOD

GR – No effect

Sandalio et al. (2001)

80. Sesbania

drummondii

Seedlings Pb ↑ SOD Ruley et al. (2004)

81. Trifolium pratense

cv. Renova

Shoots As, Cd and Zn ↑ POX and SOD Mascher et al. (2002)

82. Vicia faba Leaves Pb ↑ APX, GPOX, GR and SOD

↓ CAT

Shahid et al. (2014)

83. V. faba Leaves/stems Cd, Cu,

Ni, Pb and Zn

↑ CAT and POD under Cd, Cu, Ni,

Pb and Zn stress.

Nadgorska-Socha et

al. (2013)

84. V. faba Roots Cd ↑ GR, POX and SOD

↓ CAT

Rosa et al. (2003)

85. Vigna radiata

cv. Wilczek

Leaves Cr ↑ SOD

↓ CAT and GR

Karruppanapandian

et al. (2009)

86. V. radiata Leaves Al ↑ SOD Ali et al. (2008)

87. Plantaginaceae Bacopa monnieri Roots/ shoots Cd ↑ APX, GPX, GR and SOD in

leaves and shoots

↓ CAT

Mishra et al. (2006)

88. Poaceae Arrhenatherum

elatius

Roots/shoots Cd, Co, Cr, Cu,

Mn, Ni, Pb and

Zn

↑ APX, POD and SOD

CAT - No effect

Lu et al. (2013)

89. Triticum aestivum

cvPBW- 373

Seedlings Cd ↑ CAT, POX and SOD Hayat et al. (2014)


90. T. aestivum Leaves Cd ↑ APX, CAT and SOD Li et al. (2014)

91. T. aestivum Seedlings Pb ↑ APX, CAT, GR, POD and SOD Lamhamdi et al.

(2013)

92. T. aestivum Seedlings Cd ↑ CAT and POD

↓ SOD

Zhao (2011)

93. Hordeum Vulgare Leaves Cd ↑ SOD Tiryakioglu et al.

(2006)

94. H. Vulgare leaves Cu and Mn ↑ SOD, POD Demirevska-Kepova

et al. (2004)

95. H. Vulgare . cv.

Triangle

Leaves/roots Cd ↑ APX and POD in leaves

↑ APX in roots

↓ POD in roots

CAT – No effect in both leaves and

roots

Hegedius et al.

(2001)

96. Hordeum vulgare

Seedlings Cd ↑ APX, CAT

↓ GR and SOD

Patra and Panda

(1998)

97. Oryza sativa Seedlings Fe ↑ CAT Chalmardi et al.

(2014)

98. O. sativa

variety IR- 64

Seedlings As (V)

Cr (VI) and

Cd

↑ APX and SOD under all stress

↑ GPX under Cr and Cd stress

↑ GPX for 3 days under As stress

↓ GPX during long exposure (7

days)

↑ GST under all stress

Dubey et al. (2014)

99. O. sativa

Seedlings Cd ↑ GPX and SOD

↑ CAT during early days of

exposure

↓ CAT by 16 days

Srivastava et al.

(2014)

100. O. sativa

Seedlings Cu ↑ CAT , GPX and SOD Thounaojam et al.

(2014)

101. O. sativa Roots/shoots Cd ↑ APX,CAT, POD and SOD Yu et al. (2013)


102. O. sativa

Seedlings Pb ↑ CAT, GR and POX

↓ APX and SOD

Panda et al. (2011)

103. O. sativa Roots Cd ↑ SOD Guo et al. (2007)

104. O. sativa

Leaves/roots Pb ↑ GR, POX and SOD

↓ APX and CAT

Verma and Dubey

(2003)

105. O. sativa

cvs. Ratna and Jaya

Seedlings Cd ↑ CAT, GPX and SOD

Shah et al. (2001)

106. T. aestivum cv

Sonalika

Roots/Shoots Cr ↑ POX in shoots

↓ POX in roots

↓ SOD and CAT in both roots and

shoots

Dey et al. (2009)

107. T. aestivum Seedlings Cd ↑ APX, CAT, GR and SOD Khan et al. (2007)

108. T. aestivum Leaves/roots Cd ↑ GR Yannarelli et al.

(2007)

109. Z. mays

Inbred line VA35

Seedlings Cu2+

and

Zn2+

↑ POD Vuletic et al. (2013)

110. Z. mays

Seedlings Zn

↑ POD and SOD

↓ CAT

Cui and Zhao (2011)

111. Z. mays

Seedlings Cd ↑ CAT and POD

↓ SOD

Zhao (2011)

112. Z. mays

Leaves Cd ↑ APX,CAT, DHAR, GPOD, GR,

MDHAR and SOD

Ekmekci et al. (2008)

113. Z. mays

Seedlings Cd ↑ SOD

Krantev et al. (2008)

114. Z. mays

Roots/shoots Cu ↑ APX, CAT, GPX and GR Pourakbar et al. (2007)

115. Z. mays cv. LG

23/01

Seedlings Ni

↑ APX, CAT and GR

Baccouch et al. (1998)

116. Pontederiaceae Eichhornia

crassipes (Mart)

solms

Roots/Petiole/

Leaf

Hg ↑ APX and DHAR in order: roots >

petioles > leaf

↑ GR in order: roots > leaf > petioles

↑ MDHAR in order: petioles > leaf

> root

Narang et al. (2008)


117.

Potamogetonaceae Potamogeton

crispus

Leaves Pb ↑ CAT and SOD

↓ POX

Hu et al. (2007)

118. Rhizophoraceae Kandelia candel Leaves/roots Cd, Hg and Pb ↑ CAT in leaves

↓ POD and SOD in leaves

↑ CAT, POD and SOD in roots

Zhang et al. (2007)

119. Salicaceae Salix viminalis

Roots/ shoots Cu, Cd and Zn ↑ APX, CAT, GPX and SOD

Landberg and Greger

(2002)

120. Popular hybrid 107

(Populus deltoides ×

Populus nigra)

Whole plant Cd ↑ CAT and SOD

APX and GPX – No effect

Zhang et al. (2014)

121. Solanaceae

Capsicum annuum

Leaves Cd ++

↑ GR, POD

↓ CAT and SOD

Leon et al. (2002)

122. Nicotiana tabacum Leaves Al ↑ SOD Devi et al. (2003)

123. Lycopersicum

esculentum

Seedlings Cu ↑ APX and CAT Darcansoy Iseri et al.

(2011)

124. L. esculentum

var. Rio Grande

Root /Leaves Zn ↑ APX, CAT and GST Sbartai et al. (2011)

125. L. esculentum Seedlings Cd ↑ SOD Dong et al. (2006)

126. L. esculentum cv.

Lukullus.

Leaves Cd ↑ CAT and SOD

↓ APX and GR

Unyayar et al. (2005)

127. L. esculentum

Leaves/

roots

Hg ↑ SOD Cho and Park (2000)

128. L. peruvianum Leaves Cd ↑ CAT and SOD

↓ APX and GR

Unyayar et al. (2005)

129. Solanum melongena Leaves/roots Cd ↓ POD and SOD

CAT – No effect

Sun et al. (2007)

130. S. nigrum Seedlings Cd ↑ CAT, POD and SOD Bao et al. (2011)

131. S.nigrum Leaves Cd ↑ APX, CAT and GPOD. Pinto et al. (2009)

132. S. nigrum. Leaves/roots Cd ↑ CAT, POD and SOD Sun et al. (2007)


133. Typhaceae Typha angustifolia Leaves Cd, Cr and Pb ↓ GPOD and SOD under Cd and Cr

stress

SOD – No effect under Pb stress

APX, GPX and CAT- no effect

under Cd, Cr and Pb

Bah et al. (2011)

134. Ulvaceae Ulva fasciata Whole plant Cd ↑ CAT, GR and SOD

↓ APX

Wu et al. (2009)

135. Vitaceae Vitis vinifera Leaves Bo ↑ SOD Gunes et al. (2006)

APX or APOX - ascorbate peroxidase; CAT – catalase; DHAR - dehydroascorbate reductase; GR - glutathione reductase; GST - glutathione-

S-transferase; MDHAR – monodehydro ascorbate reductase; POD or GPOD - guaicol peroxidase; SOD - superoxide dismutase


21

Roh et al. (2000) examined physico-chemical parameters of soil collected from

two Uranium contaminated sites (K311 and K1300) at the DOEK-25 site, located at

East Tennessee Technology Park, Oak Ridge, Tennessee. Soil pH was found to be slightly

acidic (5) to neutral (7). The site soils had moderate water-retention capacity (0.11 to 0.26

g/kg) with moisture contents ranging from 0.21 - 0.43%. In addition, electrical

conductivities ranged from 17- 68 dS/m for surface K311 soils and were below the

detection limits for K1300 soils. Majority of the soil samples from the K311 i.e.

uranium contaminated site have shown the sand fraction while that of K1300 site

samples showed the silt fraction. Cation exchange capacity of the soils varied between 9

- 14 meq and was observed to be saturated by base cations such as calcium (6.24 - 8.96

cmol/kg for K311; 5.42-7.07 cmol/kg for K1300 site samples) and magnesium (0.92 -

2.85 cmol/kg for K311; 1.05 - 8.13 cmol/kg for K1300). The study further indicated that

there was no relationship between the level of uranium and physico-chemical

parameters such as pH, soil texture, CEC, water-retention capacity and electrical

conductivity.

Srinivas and Kumar (2001) reported some physico-chemical characteristics viz.,

pH, alkalinity, electrical conductivity, organic carbon, chlorides, nitrates, phosphates,

calcium and magnesium in soils of Vishakhapatnam. Agricultural soil samples collected

were from different environmental backgrounds like rural/control area, semi-urban area,

urban area and industrial area.The samples were collected from C-Agraharam, Rajiv

Nagar, Sheela Nagar and Auto Nagar, respectively. pH of all soil samples ranged

between 7.34 - 8.25, conductivity (0.23 - 0.44 mMhos cm-1

), organic carbon (0.077 -

0.132 mg/g), chlorides (0.034 - 0.037 mg/g), nitrates (0.061 - 0.294 mg/g), phosphates

(0.513 - 3.340 mg/g), calcium (2.352 - 8.097 mg/g) and magnesium (0.753 - 2.53 mg/g).

Cai et al. (2002) analyzed physico-chemical parameters of arsenic contaminated

soils of some golf courses in Miami-Dade County, South Florida, contaminated by

application of arsenic-containing herbicides. Range of different parameters for fine (<

0.25 mm) and large (0.25 - 0.71 mm) sized soil particles varied as- pH (7.25 - 7.88; 7.15

- 8.86), total nitrogen (0.05 - 0.80 %; 0.17 - 0.62 %), total phosphorus (266 -3878 µg/g;


22

96-2990 µg/g), total carbon (3.55 - 14.45%; 0.17 - 16.40%), respectively. Textural

analysis showed the range of content of sand (30 - 75%; 15 - 86%), silt (9 - 86 %; 1 - 79

%) and clay (1 - 10 %; 1 - 10 %) for fine and large sized soil particles, respectively.

Nan et al. (2002) carried out analysis of soil samples from croplands near the

non ferrous metal mining and smelting base of Baiyin city, Gansu province, P.R. China

for pH, organic matter and available phosphorous. Soil pH varied from 7.7 - 7.9. The

organic matter content (0.14 % - 2.28%) was found to be low and the content of

available phosphorous (0.40 mg/kg- 55.80 mg/kg) varied widely in the soil studied.

Kelly-Quinn et al. (2003) studied physico-chemical characteristics of sediment

soil collected from different locations of Caher River. Range of various parameters

were reported to be pH (8.29 - 8.34); electrical conductivity (424-442 µS/cm); alkalinity

(7.4-8.75 mg/l CaCO3); temperature (11.2 - 11.4ºC); total hardness (105.18 - 142.38

mg/l CaCO3); chloride (12.16-17.83 mg/l); sulphate (2.29 - 2.67 mg/l); sodium (11.54 -

13.18 mg/l); potassium (0.84 mg/l); calcium (38.95 - 53.75 mg/g) and magnesium (1.92

- 2.15 mg/g).

He et al. (2004) determined the physico-chemical characteristics of soils from

the coastal plain in Changjiang Delta in Kanghai, China. The particle size distribution

and mineral composition of soil were analyzed. The results revealed that the texture of

soil was silt loam. The soil contained 16.64 - 35.34 % sand, 52.52 - 60.50 % silt and

10.56 - 17.91 % clay. The value of Si/Al ranged from 3.33 - 4.38 % and was found to

increase with depth of soil. The pH value of different soil samples ranged from 6.54 -

8.22 while cation exchange capacity ranged from 10.25 - 17.86 mol/kg.

Sam-Wobo and Mafiana (2005) examined surface soil collected from sites near

defecation areas of Ogun state, Nigeria for some physico-chemical parameters. Particle

size classification indicated that the soil textures were sandy (92.8 - 94.8%) with 2.0 -

2.8 % and 2.4 - 5.2 % of silt and clay content, respectively. Content of potassium (0.58 -

0.99 cmol/kg) and sodium (0.44 -1.3 cmol/kg) were found to be higher as compared to

magnesium (0.13 - 0.64 cmol/kg). Contents of calcium and manganese were reported to

vary from 0.06 - 4.61 cmol/kg and 0.03 - 1.65 cmol/kg, respectively.


23

Ali et al. (2006) reported that physico-chemical properties of soil were

influenced by surface erosion under different cropping systems on upland-sloping soil

land of Pakistan. The content of sand, silt and clay were found to be 18 %, 59 % and 23

% respectively. It was found that soil had pH - 8.25, organic matter content - 0.87 %,

lime - 22.33 %, Mineral N - 23 mg/kg soil, ammonium bicarbonate diethylene triamine

pentaacetic acid (ABDTPA) extractable phosphorus - 5.03 mg/kg soil and ABDTPA

extractable sodium - 131 mg/kg soil.

Singh and Kumar (2006) analyzed five major vegetable grown sites

viz.,Yamunapusta (contaminated through aerial deposition from a coal based plant),

Okhla (thermal plant and sewage water irrigation contamination), Najafgarh (sewage

water contamination), Alipur (agro chemicals pollution) and Ballabhgarh (industrial

emission pollution) in the peri-urban area at New Delhi. The soil samples from five

sites were analyzed for organic carbon content and pH which were found to be 1.0, 0.5,

0.6, 0.6, 0.9% and 7.5, 7.8, 7.6, 8.0, 8.1 respectively.

Maki et al. (2007) analyzed soils of steppe area of the Kherlen River basin in

northeastern Mongolia for some physico-chemical characteristics. Study area was

comprised of sites from villages of Baganuur (BGN), Jargalthaan (JGH), Kherlenbayan-

Ulaan (KBU), Underhaan (UDH), and Darhan (DH). The hardness (kPa) ranged from

617 - 4720, 342 - 4720, 838 - 1970, 617 - 2956 and 532 - 1970 for BGN, JGH, KBU,

UDH and DH, respectively. The organic carbon content (g/kg) varied from 3.0 - 21.4,

3.0 - 18.2, 3.0 - 27.5, 3 - 16.4, and 3 – 10.4 for BGN, JGH, KBU, UDH and DH,

respectively. Total nitrogen content also showed a similar trend for BGN, JGH, KBU,

UDH and DH ranging from 0.2 -2.0, 0.2 - 1.8, 0.2 - 2.7, 0.2 - 1.3 and 0.0 - 0.9 g/kg,

respectively. BGN, JGH, KBU, UDH and DH had bulk density (g/cc) in the range of

1.27 - 1.55, 1.31 - 1.51, 1.26 - 1.73, 1.27 - 1.52 and 1.13 - 1.46, respectively. The

electrical conductivity ranged from 0.29 to 0.67 dS/m in BGN, JGH, and KBU and was

found to be higher in UDH and DH with range from 0.45 to 0.95 dS/m. The pH ranged

from 6.6 - 8.9, 6.3 - 9.1, 6.4 -9.1, 6.6 - 10.0, 7.0 - 9.1 for BGN, JGH, KBU, UDH and

DH, respectively.


24

Weber et al. (2007) determined the physico-chemical characteristics in different

soil horizons from six sites of alluvial zone of national importance at Swiss Alps which

is a part of flood plain of the Sarine River, a tributary of the Aare River. It was found

that pH ranged from 7.4 - 8.6; total CaCO3 from 33.9 - 76.1 % and active CaCO3 from

7.5 - 20.0 %. The range of content of organic carbon and total nitrogen for different

sites was found to be 0.2 - 12.1 % and 0.03 - 0.9 %, respectively. Particle size

distribution analysis showed that soil contained 7.4 – 84.1 % coarse sand, 3.5 – 40.3 %

fine sand, 1.8 – 18.8 % coarse silt, 2.0 – 30.6 % fine silt and 0 – 23.1 % clay.

Van Gestel (2008) monitored heavy metal polluted soil of Biesbosch sites Lage

Hofen Petrusplaat Oost nearby the city of Dordrecht, Netherlands for some physico-

chemical parameters. It was observed that at different soil depths of 0 - 15 cm, 15 - 30

cm and 30 - 45 cm, the pH was 7.2, 7.4 and 7.4; organic matter content was 18.8, 15.4

and 14.9 %; percentage of clay (> 2µ m) was 35.1, 31.9 and 36 %, respectively.

Katnoria et al. (2008) estimated different physico-chemical parameters of four

soil samples collected from different agricultural fields of Amritsar (Punjab). pH,

alkalinity, water holding capacity, bulk density and moisture content of soil samples

ranged from 6.11 – 8.07, 0.13 - 0.96 mEq/100g, 4.55- 18.8%, 1.05-1.38 g/cc, 2.66-

6.10%, respectively. The concentration of nitrates (0.18- 0.45 mg/g), phosphates (0.75-

6.90 mg/g) and potassium (0.16 - 0.25 mg/g) were found to be in optimum range.

Chaudhuri et al. (2009) estimated the physico-chemical characteristics of soil of

mangrove areas of the Andaman affected by tsunami. Different physico-chemical

properties of soil viz. pH, electrical conductivity of saturated extract (EC), clay, cation

exchange capacity (CEC), organic carbon (OC), total N (TN), phosphorus (P),

exchangeable cations (Ca, Mg, K and Na) were determined. The range of different

parameters was observed as follows: EC (31.3 – 36.2 dS/m), clay content (11- 19 %),

cation exchange capacity (189 – 275 mmol/kg), total N (2.0 - 2.85 mg/g), organic

carbon (2.0 - 2.85 mg/g), Na (10.3 - 15.8 Cmolc/kg), K (0.81 – 1.06 Cmolc/kg) and Mg

(4.1 - 4.9 Cmolc/kg).


25

Gupta and Singh (2009) studied the effects of flyash on physico-chemical

parameters of soil from an Arpa irrigation project area of Bilaspur district of

Chhattisgarh. pH and electrical conductivity in the soil samples collected from different

villages under investigation ranged from 6.10 to 6.35 and 0.610 to 0.630 mhos/cm,

respectively. Different combinations of soil and flyash showed increase in parameters

like pH (6.50 - 7.10) and electrical conductivity (0.64 - 0.859 mhos/cm).

Joshi et al. (2009) conducted some physico-chemical analysis viz. pH, specific

gravity, bulk density, particle size, moisture content, organic matter, content of sodium,

potassium, magnesium, calcium, chloride, carbonate and bicarbonate of four farm site

soils in an area surrounding Rajkot, Gujarat, India. It was revealed that pH of the

samples varied from 7.07 - 8.38 and specific gravity was found in the range of 1.92 to

2.40 g/cc. Bulk density ranged from 0.698 to 0.970 g/cc and percentage porosity 53.22

to 68.38 %. Particle size and moisture content ranged from 453.1 to 651.9 µm and 7.89

to 15.54 %, respectively. It was found that magnesium ranged from 0.49 - 0.60 meq/l;

calcium from 0.72 - 1.04 meq/l; sodium 3.7 - 4.2 meq/l and potassium 0.21- 0.29 meq/l.

Study also showed the range of chloride and bicarbonate content of soil samples to be

4.7 - 6.0 meq/l and 1.6 - 3.4 meq/l, respectively.

Satheeshkumar and Khan (2009) studied the physico-chemical parameters of

soils collected from four different stations in Pondicherry mangroves, southeast coast of

India. Different parameters investigated showed the variation as follows: salinity (6.36 -

36.77ppt), dissolved oxygen (3.45 - 5.49 mg/l), pH (7.11 - 8.52), electrical conductivity

(26.65 - 52 ms), sulphide (2.76 - 47.16 mg/l), soil parameters sand (63.69 - 87.31%), silt

(9.89 - 29.32 %), clay (3.06 - 17.98 %) and organic matter (0.94 - 3.94 %).

Akan et al. (2010) studied the physico-chemical parameters in soil samples

collected from the agricultural sites of Gongulon located within Maiduguri Metropolis,

Borno State, Nigeria for studying the effects of sewage sludge and wastewater used by

the farmers to improve soil fertility for the growth of vegetables (spinach, carrot, lettuce

cabbage, tomato, waterleaf, and onion. The soil pH values ranged from acidic (5.98) to

moderately alkaline (7.26) and varied with incresing depth. Conductivity values varied


26

from 2.03 μS/cm to 2.54 μS/cm. Range of organic carbon content was found to be

1.03% to 2.11%. Cation exchange capacity values were 20.45 - 23.54 c mol/kg while

organic matter ranged from 8.56 - 10.55 %.

Akpoveta et al. (2010) determined some physico-chemical characteristics in soil

samples around metal scrap dumps at Abraka and Agbor vicinities of Delta State,

Nigeria, to determine the effects of dumps on the soil. It was found that the pH values in

all the sites ranged from 7.01 to 7.99. The total organic carbon of soil samples ranged

from 0.38 to 0.54 % indicating the presence of organic matter and microbiological

activities in the soil samples. The high conductivity values were found to vary from 165

to 201 μs/cm indicating significant presence of soluble inorganic salts in soils.

Attah (2010) estimated the physico-chemical characteristics of cereal cultivated

rhizospheric soils of Ambo, Woreda, Westshoa and Euthopia. The soil samples were

collected from different sites viz. Awaro, Senkele, Meja and Guder. The soils varied

texturally from clayey to sand. The pH was slightly acidic ranging from 6.2 to 6.7. The

content of phosphorus was found to be moderately low (1.4 - 2.4 mg/kg ) where as the

content of nitrogen was very high, ranging from 480 - 986 mg/kg. The potassium

content was found to be high in all the soil samples i.e. from 240 - 496 mg/kg except in

Awaro soil.

Nagole et al. (2010) determined the physico-chemical characteristics such as

texture, pH, electrical conductivity (EC), water retention capacity (WRC), organic

matter (OM) content and cation exchange capacity (CEC) of geophagic clayey soils

from South Africa and Swaziland. The study revealed that the color ranged from grey to

red while soils varied texturally from loam to clay. The pH ranged from 5.0 to 7.4. The

values obtained for EC (54.93 – 59.99 µS/cm) and OM content (0 – 1.7%) were low

while that for WRC of all soil samples ranged from 60 – 70 %.

Ololade et al. (2010) determined various physico-chemical parameters of soil

collected from largest cocoa- producing Ondo state in Nigeria. pH, OC and CEC ranged

from 5.1 - 7.5, 0.35 - 4.30 % and 2.10 - 6.05 meq 100 g, respectively which decreased


27

with depth. Ca, Mg, Na and K (cmol/kg) ranged from 0.7 - 2.9, 0.7 - 2.2, 0.15 - 0.24 and

0.12 - 0.4, respectively.

Rabah et al. (2010) determined physico-chemical parameters of soil

contaminated with abattoir effluents of Sokoto meteropolis, Nigeria. The soil samples

had mean pH of 7.36, mean temperature of 33 oC and nitrogen concentration of 0.23

mg/g. The organic matter content as well as the cation exchange capacity was reported

to be 12 % and 18.53 %, respectively while the total nitrogen was found to be 0.42 %.

The concenterations of magnesium, phosphorus, potassium and calcium were found to

be 2.67 mg/g, 5.60 mg/g, 1960 ppm and 76 ppm, respectively.

Zaiad (2010) studied soils collected from farms of Al-Khums region of Libya

for various physico-chemical parameters like soil pH, texture, moisture content, water

holding capacity, electrical conductivity, chloride content and total dissolved solids.

The soil samples were taken from four different places of the city and nearby areas. pH

of all soil samples were found to be alkaline in nature varying from 8.12 - 8.60 with

moisture content ranging from 0.02 to 3.64%. Textural analysis revealed that soil was

sandy in nature. Soil electrical conductivity, chloride content and total dissolved solids

ranged from 410 - 766 mS/cm, 0.43 - 1.42 mg/g and 246 - 455 ppm, respectively

Bahuguna et al. (2011) determined physico-chemical characteristics of

polycyclic aromatic hydrocarbons contaminated soils collected from Uttarakhand, India.

Soil samples were collected from seven subsurface automobile contaminated sites (110

cm below the surface) from different locations of nearby regions of Rishikesh

(Uttarakhand) and were analyzed for parameters such as temperature, pH, moisture

contents, inorganic phosphate, inorganic nitrate and total organic matter. Various

parameters for all the soil samples ranged as: temperature (38 o

C - 43 o

C); pH (6.80 -

8.10); moisture content (0.472 - 1.864 mg/g) and inorganic phosphate content (0.030 -

0.499 mg/l). The content of nitrates varied from 0.221 - 7.112 μg/g whereas total

organic matter content from 75.25 - 270.3 mg/g.

Bhat et al. (2011) analyzed the physico-chemical parameters of soils collected

from two sites viz. site 1 and site 2 situated at Chandur Bazar tehsil of Amravati district.


28

The range of pH of all soil samples was found to be 7.28 - 8.02 which indicated slight

alkalinity of soils and electrical conductivity of the samples ranged from 0.41 to 1.75

dsm/m. Site 1 was found to have greater content of nitrogen (179.2 mg/kg) than site 2

(153.0 mg/kg) and concentration of available phosphorus was recorded to be

comparatively higher for site 1 (151.80 mg/kg) than site 2 (43.90 mg/kg). Minimum

content of organic carbon was found to be 0.54 mg/kg and maximum 0.29 mg/kg.

Kebir and Bouhadjera (2011) analyzed physico-chemical parameters of

agricultural soils near dumpsite of Ghazaouel, Algeria. pH was found to be in the range

of 5.05 - 7.29. Other physico-chemical parameters like total organic carbon (TOC),

nitrogen (N) content, C/N ratio, Na, K, Ca, Mg and CEC were in the range of 1.25 -

3.48 g/kg, 0.24 - 1.23 g/kg, 1.86 - 2.82, 0.89 - 5.04 g/kg, 1.35 - 12.05 g/kg, 10 - 238.28

g/kg, 0.88 - 4.89 g/kg, and 21.87- 32.01 cmol/kg, respectively.

Khan et al. (2011) conducted a study on six agriculturally important, water

eroded soil series of Sharkul area of district Mansehra, Hazara Division, Khyber

Pakhtunkhwa in Pakistan. Soil samples were collected from surface (0 - 15 cm), sub-

surface (30 - 45 cm) and sub-strata (60 -75 cm) layers. Soil pH ranged from 7.0 - 8.58,

7.29 - 8.59 and 8.00 - 8.60, electrical conductivity ranged from 2.23 to 3.33 dS/m, 2.19

to 4.21 dS/m and 2.36 to 4.21 dS/m, available K content varied from 56.89 - 120.49,

50.89 - 105.90 and 71.79 - 82.29 mg/kg in surface, subsurface and substrata soils of

different soil series, respectively. The P content in surface and subsurface layers of

slighltly eroded soil samples (Dosera and Girari), moderately eroded soil series

(Nakholi and Sharkul) and severely eroded soil series (Ahl and Banser) was found to be

4.40, 4.6 mg/kg and 2.3, 2.7 mg/kg; 3.77, 3.45 mg/kg and 1.25, 1.34 mg/kg; 2.77, 2.84

mg/kg and 0.82, 0,96 mg/kg, respectively. Soils were found to be deficient of available

K with content ranging from 56.89 - 120.49, 50.89 -105.90 and 71.79 - 82.29 mg/kg in

surface, subsurface and substrata soils of different soil series, respectively.

Rai et al. (2011) estimated the heavy metal concentration and physio-chemical

parameters viz. temperatures, water holding capacity (WHC), bulk density (BD),

electrical conductivity (EC), available phosphorus, available calcium and magnesium,


29

total nitrogen of the soil at site I and site II which were irrigated with sewage water and

canal water of Dehradun city, India, respectively. The values for different parameters

for site I and Site II were found to be as follows: temperature (1.33 ºC, 14.50 ºC), WHC

(53.60%, 41.58%), BD (1.26 g/cm3), EC (0.122 dsm, 0.169 dsm), pH (7.5, 7.9),

Nitrogen (2.22 %, 1.61 %), phosphorus (108.44 mg/kg, 23.43 mg/kg), potassium

(121.66 mg/kg, 81.66 mg/kg), calcium (2.18 mg/kg, 2.75 mg/kg and magnesium (2.1%,

2.7 %), respectively.

Kumar et al. (2012) analyzed soils collected from upland, midland and lowland

pedons of three agro-climatic zones (Baliapur, Bagru and Mosabani) of Jharkhand for

various physico-chemical properties viz., organic carbon, pH, electrical conductivity,

cation exchange capacity, clay and silt content. Upland pedons of Baliapur, Bagru and

Mosabani revealed an increasing trends of soil pH which varied from 4.8 - 6.8, 5.3 -

6.25 and 4.74 - 5.86, respectively while in midland pedons, pH increased from 4.4 -

7.25, 5.0 - 5.4 and 4.3 4 - 5.7, respectively. Similarly, in lowland pedons, pH increased

from 6.8 - 7.6, 5.4 - 6.0 and 5.0 - 6.3 for Baliapur, Bagru and Mosabani, respectively.

EC in upland pedons of Baliapur, Bagru and Mosabani varied from 0.32 - 0.50, 0.34 -

0.51 and 0.57 - 1.40 dS/m; in midland pedons as 0.49 - 1.00, 0.32 - 0.66 and 1.60 - 2.29

dS/m while in lowland pedons, 0.60 - 1.49, 0.30 - 0.60 and 0.71 - 1.27 dS/m,

respectively. Organic carbon in Upland pedons of Baliapur, Bagru and Mosabani varied

from 3.4 - 0.5, 2 - 0.05 and 3.4 - 0.6 g/kg, in midland pedons 4.7 - 0.2, 2.1 - 0.2 and 5.9

- 0.5 g/kg and similarly in lowland pedons 7.4 - 0.2, 5.3 - 0.1 and 5.0 - 0.1 g/kg,

respectively. Cation exchange capacity of upland pedons of Baliapur, Bagru and

Mosabani varied from 5.12 to 10.39, 5.5 to 6.5 and 7.9 to 11.43 cmol(P+)/kg; of

midland pedons 5.32 - 7.77, 5.82 - 9.96 and 5.5 - 10.7 cmol(P+)/kg and similarly of

lowland pedons 2.67 - 9.6, 5.64 - 9.12 and 6.8 - 8.01 cmol(P+)/kg, respectively.

Ashraf et al. (2012) studied grassland soils of Yusmarg Hill Resort, Kashmir,

India for various physico-chemical parameters in the year 2010. For the month of May,

June, November and December, average pH and temperature (ºC) was reported to be

6.23, 6.07, 6.18, 5.90 and 14.55, 18.25 11.37 and 2, respectively. Soil texture analysis


30

revealed that the soils at all the study sites were with major proportion of sand fraction

and had a sandy silt nature. Organic carbon and organic matter content were found to

vary with average content of 2.68, 2.80, 2.66 and 2.62 %; 4.92, 4.83. 4.73 and 4.52 for

the months of May, June, November and December, respectively. The average content

of calcium, magnesium, sodium, potassium and total phosphorus for the months of

May, June, November and December was found to be 3.25, 3, 2.31 and 2.29 cmol/kg ;

1.05, 0.84, 0.46, 0.40 cmol/kg and 8.42, 7.52, 8.22 and 7.52 cmol/kg; 6.67, 6.40, 5.95

and 6.00 cmol/kg; 24, 26.5, 22.5, 21.75 µg/g, respectively.

Ibrahim et al. (2012) reported physico-chemical characteristics of affected soil

samples of South Korea. Three sites namely Chung-nam university field (Site-I),

Chung-buk Geosan (Site-II) and Yong-in (Site-III) were investigated for diversity in

physicochemical properties. The variation in different parameters for site I, site II and

site III was reported to range as follows: temperature - 10.9 - 13.6, 15.2 - 16.1, 11.1 -

11.4 ºC; pH - 6.11 - 7.59, 6.0 - 6.4, 6.8 - 9.1, conductivity - 0.15 - 0.28, 0 - 0.1, 0.12 -

10.7 dS/m; organic matter - 1.7 - 19.8, 0 - 4.9, 5.1 - 37 mg/kg, cation exchange capacity

(3.3 - 10.5, 0 - 40.7, 5.3 -8.0 cmol/kg), soil water content (25.2 - 31.3, 0 - 20.4, 16.5 -

37.8 %), bulk density (0 - 1.80, 0 - 1.65, 1.07 - 1.63 mg/m3, respectively.

Onojake and Osuji (2012) assessed physico-chemical properties of hydrocarbon

contaminated field soils of mangrove swamp forest of the Niger delta, South Nigeria.

Different parameters for surface, subsurface and reference samples were found to be pH

- 6.50, 6.48, 5.33, Conductivity - 2844.85, 2072,14.0 µS/cm, moisture content - 15.40

,12.78, 5 mg/kg, total acidity - 782.06, 4328, 85.60 mg/kg, chloride - 973.94, 366.06,

56.00 mg/kg, sulphate - 1.06, 0.25, 0.60 mg/kg, total organic carbon 2.84, 3.57, 3.56

mg/kg, total organic matter - 4.90, 6.15, 6.14 mg/kg, respectively.

Pandeeswari and Kalaiarasu (2012) reported physico-chemical properties of the

soil samples collected from different locations of tsunami affected soils of Cuddalore

district of Tamil Naidu. Soil samples analyzed belonged to five textural types viz.,

sandy loam, clay loam, sandy clay, clay and loam. Soil pH was found to vary from 7.25

to 8.71 and the electrical conductivity content ranged from 1.34 to 5.26 dS/m. Soil


31

organic carbon content was low and ranged from 0.32 to 0.64 %. Soil available

nutrients viz., nitrogen ranged from 6.00 to 14.53 kg/ha and value of available

phosphorus measured ranged from 6.00 to 14.53 kg/ha.

Pujar et al. (2012) conducted characterization of soil samples collected from

various localities of Bijapur taluka, Karnataka for parameters like pH, electrical

conductivity, nitrogen, phosphorous and potassium. Fluctuation in various parameters

were recorded as: soil pH (7.9 - 8.4); electrical (0.22 - 0.30 dS/m); available potassium

(295 - 355 kg/ha); available nitrogen (135 - 160 kg/ha) and available phosphorus (8.0 -

10.1 kg/ha).

Velmurugan et al. (2012) estimated soil samples collected from different (red

soil and sandy loam) sunflower fields with chemical fertilizer and organic manure

treatment. Various physico-chemical parameters of soil, like pH, EC, available N,

available P, available K and organic carbon were determined. pH ranged between 7.30 –

8.10 and 7.85 – 8.27; high organic carbon content varied from 0.36 % - 0.56 % and 0.26

% – 0.59 %; EC from 0.01 ds/m - 0.03 ds/mand 0.02 ds/m

- 0.05 ds/m

in red and sandy

loam soil, respectively. Potassium content was observed to vary from 27.74 kg/ha to

75.52 kg/ha for red soil and 23.28 kg/ha to 62.51 kg/ha for sandy loam soil. Range of

nitrogen content was 56.01 kg/ha to 66.37 kg/ha and 50.75 kg/ha to 69.70 kg/ha and

phosphorus content was 92.23 kg/ha to 184.731 kg/ha and 95.41 kg/ha to 186.61 kg/ha

for red and sandy sunflower growing soil, respectively.

Janeshwar et al. (2013) studied soils of Sadli reservoir region Kasrawad, district

Khargone, Madya Pradesh, India undergoing rapid urbanization and agricultural

activities for various physico-chemical parameters as pH, electrical conductivity (EC),

plasticity, elasticity, moisture content, specific gravity. Various physico-chemical

characteristics of soil showed that pH values ranged from 6.90- 6.95; temperature: 17-

18 ºC; specific gravity: 2.65-2.7%; moisture content: 25-30%; plasticity: 28-40%;

elasticity: 30-55% and electrical conductivity: 107.11-235.40 µs/cm.

Moghimi et al. (2013) studied the physico-chemical properties of arid soils in

southeastern Iran. Samples from eight pedons (different horizons) at different depths


32

ranging from 0 - 150 cm were selected. Soils were found to be alkaline and calcareous

in nature, containing 19.83 % - 61.45 % CaCO3 throughout the profiles. All soils had

pH values above 7.0. The average electrical conductivity (EC) values exhibited some

variations among the soils studied and thus soils were placed from slightly saline to

saline levels. In pedons 2, 4, 5, and 8, the EC values ranged from 0.4 - 2.64 dS/m while

soils in pedons 3, 6, and 7 had EC values ranging from 2.1 - 7 dS/m. Pedon 1 soil was

observed to be saline with EC values ranging from 18.11 - 30.5 dS/m. The content of

Na showed slight variability among the soils studied. In pedons 2, 5, and 8, the amount

of Na was between 0.4 and 6.8 cmol/kg as compared to pedons 1, 3, 4, 6, and 7, in

which Na ranged between 1.84 - 20 cmol/kg. The cation exchange capacity ranged from

1.7 - 41.4 cmol/kg. In pedons 1, 3, 6, and 7, the CEC was between 19.5 and 41.1

cmol/kg as compared to pedons 2, 4, 5, and 8, with values of 11.7 - 21.5. The organic

carbon content was found to be low in all soils.

Nenadovic et al. (2013) analyzed agricultural soils located a Rudovci table and

near Lazarevac, Serbia for physico-chemical characterstics. Soil pH was reported to

range from strongly acid (4.6) to near neutral (7.6). Total content of sand was 2.0 - 3.2

%. The soils were characterized as light clay based on the clay content which varied

from low to intermediate (55.48 – 65.03%). The organic matter of soil samples ranged

from 0.75- 1.60 %.

Oyeyiola and Agbaje (2013) analyzed physico-chemical parameters of soil

collected from microbiology laboratory from the main campus of university of Ilorin.

pH value was found to be 7.10 to 7.82, the range of water holding capacity was from

0.28 ml/g to 0.53 ml/g of soil. The organic matter content of the soil samples ranged

from 3.42 % to 4.70 %. The moisture content ranged from 2.10 % to 5.23 %. The

texture was discovered to be loamy sand with average composition of 89 % sand, 7 %

silt and 4 % clay.

Peng et al. (2013) studied the soil physical (bulk density and moisture content)

and chemical (pH, soil organic matter, nitrogen (N), phosphorus (P) and potassium (K)

contents) parameters of eroded rhizosphere soils under revegetation from the towns of


33

of Zuolin (gully bed) and Yuanma (slope area) in the Yuanmou dry-hot valley, China.

Range of bulk density (g/cm3) of soil samples from Yuanma and Zuolin region was 1.35

- 1.53 and 1.50 - 1.54, respectively and soil moisture (%) content was 1 - 2.2 and 0.8 -

2.8, respectively. Yuanma and Zuolin regions showed range of pH (5.41 - 6.85; 6.27 -

7.00); soil organic matter (9.7 - 14.2; 0.8 - 5.8 g/kg); content of total N (0.52 - 0.88;

0.19 - 0.44 g/kg); content of available N (31.6 - 43.1; 14.4 - 25.0 mg/kg); Total P (0.24 -

0.31; 0.15 - 0.24 g/kg); Available P (21.8 - 23.2; 19.0 - 21.5 mg/kg); Total K (10.3 -

11.4; 10.3 - 13.6 g/kg) and Available K (115.3 - 122.7; 52.8 -115.3 mg/kg),

respectively.

Wagh et al. (2013) studied physico-chemical characteristics of soils collected

from the sugarcane fields of Manjari, Hadapsa and Phursungi located in the south

eastern part of the Pune city, India which were affected due to solid wastes as well as

industrial effluents. Physical parameters like pH, electrical conductivity (EC), organic

carbon (%) and chemical parameters like phosphorus, potassium were analyzed. The

values for pH ranged from 7.32 to 8.52; electrical conductivity from 0.20 mS/cm to 3.02

mS/cm; organic carbon (%) ranged from 0.38 to 1.5 %. The content of phosphorus and

potassium were found to vary from 10 kg/ha to172.9 kg/ha and 112 kg/ha to 840 kg/ha,

respectively.

Waterlot et al. (2013) studied physico-chemical parameters in soil samples from

twenty seven urban sites highly contaminated by the past emissions of two lead and zinc

smelters located in the North of France. Two groups were formed depending on the

context and uses of urban soils. The first group was composed of kitchen soil (KG) used

for the production of food and the second one of lawns (L) soil commonly used for

private and public recreational activities. The mean content (g/kg ) of clay, silt and sand

in KG and L soils was found to be 225, 520, 255 and 226, 554, 270, respectively. pH

was found to be 7.5 for KG soils and 7.3 for L soils. Total CaCo3 (g/kg) was reported to

be higher (40.0) for KG soils than L soils (15.8). Mean content of organic matter was

100 g/kg for kitchen garden soils and 95.6 g/kg for lawn soils. KG and L soils were

reported to have cation exchange capacity of 18.0 and 16.2 cmol/kg) respectively.

Assimilated phosphorous was very low in both KG (1.3 g/kg) and L (0.5 g/kg) soils.


34

Essien and Hanson (2014) studied pollution effect of municipal solid waste

dumpsite site (WDS) at old stadium road, Uyo Akwa Ibom State on physico-chemical

properties of dumpsite and adjoining (CS) soils. pH in WDS ranged from 7.13 - 7.92

which was higher than that of CS that ranged from 5.59 - 5.78. Values of electrical

conductivity ranged from 0.15 - 0.80 and 0.03 - 0.06 ds/m for WDS and CS soils,

respectively. Total organic matter in terms of organic carbon was estimated and was

found to be high in WDS soils (2.10 - 5 76 %) as compared to CS soils (0.36 and

1.23%). Total nitrogen in the soil ranged from 0.05 - 0.14 % for WDS and 0.3 and 0.5

% for CS and was observed to be very low. The range of content for exchangeable Ca

was found to be higher (4.08-5.20 cmol/kg) in WDS than CS soils (2.0 - 4.0 cmol/kg).

Exchangeable Mg varied from 2.0 - 2.6 mol/kg and 1.20 - 2.70 cmol/kg for WDS and

CS, respectively. Values of exchangeable K ranged from 0.09 - 0.13 cmol/kg in WDS,

and from 0.09 - 0.12 cmol/ kg in CS whereas exchangeable Na content ranged from

0.06 - 0.09 cmol/kg in WDS and 0.02 - 0.2 cmol/kg in CS soils. Particle size

distribution showed clayey sand texture at both sites with mean sand : silt : clay ratio of

76 : 7 : 17 for WDS and 76 : 3 : 20 for CS soils. Sand fraction ranged from 70.00 -

86.00 % for WDS and 70.46 - 92.46 % for CS. The bulk density and total porosity of

the soil under WDS was found to be 1.20 - 1.30 g/cc and 53.2 - 56.20 g/cc and for WDS

and CS, respectively.

Liao et al. (2014) studied hexavalent chromium-contaminated soils collected

from an area around a steel alloy factory in Hunan province. Soil under study included

two types of soil samples i.e. lightly polluted soil (S1) and heavily polluted soil (S2) in

which the average Cr (VI) concentrations were 80 and 1,276 mg/kg, respectively. S1

was sandy loam (64 % sand, 15 % silt, 21 % clay) and S2 was yellow clay soil (17 %

sand, 22 % silt, 61 % clay). The pH value of S1 was 10.85 and S2 was 11.89. The

content of organic matter (g/kg) was higher at the S1 (21.17) than at S2 (7.54). The

content of total and available nitrogen (g/kg) was higher at the S1 (2.99, 71.84) than at

S2 (0.42, 48.10), respectively. The phosphorus content at S1 (108.44) was more than

that of S2 (23.43). The content of total and available phosphorus (g/kg) was found to be

0.42, 1.29 at S1 and 0.24, 0.92 at site II, respectively.


35

Ma et al. (2014) studied different physico-chemical parameters of petroleum

contaminated soil samples collected from the city of Daqing, an oil producing region of

China which accounts for nearly 25 % of China’s oil production. Surface soil samples

were collected randomly from four different sites viz., S0 (the control, no TPH-polluted

industrial soil), S1 (saline-alkali meadow), S2 (a secondary pollution area of oil pipeline

leakage), and S3 (an oil polluted central area with historical TPH-polluted industrial

soil). The contents of different parameters for S0, S1, S2 and S3 were reported as: pH

(7.85, 8.25, 8.65. 9.06); electrical conductivity (687, 916.5, 1043.25, 1352.32 (µS/cm);

soil organic matter (3.02, 2.9, 3.21, 5.28 g/kg); total nitrogen (1.84, 0.89, 0.65, 0.59

g/kg); total phosphorus (0.71, 0.37,0.44,0.51 g/kg), available nitrogen (146, 128, 56, 37

mg/kg), available phosphorus (84, 31, 15.50, 12.70 mg/kg); available potassium

(235.97, 137.24, 162.80, 112.40 mg/kg); salt content (0.58, 0.92,1.25,3.15 %),

respectively

Masakorala et al. (2014) reported physico-chemical parameters in soil samples

exposed to petroleum contamination from the DaGang oil field, southeast of Tianjin in

the northeast China. The range of content of different parameters reported were as

follows: pH - 7.72 - 8.80; organic matter - 4.17 % to7.63 %; total organic carbon - 2.42

% to 4.43 %; gravimetric water potential – 14.38 % to 26.60 %; nitrate- 6.60 mg/kg to

20.81 mg/kg; extractable phosphorus – 14.38 mg/kg to 19.67 mg/kg.

2.4. Analytical techniques for estimation of heavy metals in soil ecosystem

Over the past decades, industrialization and mining activities led to emergence

and dispersal of various pollutants in ecosystems. Among different contaminants, heavy

metals were categorized as the most hazardous class of anthropogenic environmental

pollutants due to their persistent nature and higher toxicity (Nyarko et al., 2008).

Furthermore, some heavy metals can enter the food chain thereby posing threat to the

life of various biological systems at different trophic levels (Koz et al., 2012).

Therefore, human exposure to toxic metals via inhalation, food chain or dermal contact

requires considerable attention (Granero and Dormingo, 2002). It is well established

that all metals including the micronutrient elements can turn out to be noxious at higher


36

concentrations (Ji et al., 2012). The most important anthropogenic source of heavy

metals for soils includes agrochemicals such as pesticides and fertilizers, sewage from

domestic sources, effluents from industrial activities as well as urban and vehicular

emissions (Senesi et al., 2009). As soil is one of the most important sink of heavy

metals, the emphasis has been given by many researchers to explore various techniques

for analysis of heavy metals.

There are many established analytical techniques, such as Inductively Coupled

Plasma Optical Emission Spectroscopy (ICP-OES), Inductively Coupled Atomic

emission Spectroscopy (ICP-AES), Inductively Coupled Mass Spectroscopy (ICP-MS),

Atomic fluorescence spectrometry (AFS), X-ray fluorescence spectrometry (XRF),

Neutron Activation Analysis (NAA), D. C. Argon Plasma Multielement Atomic

emission spectrometry (DCP-MAES) and Atomic Absorption Spectrometry (AAS) for

the purpose of quantification and are considered to be efficient with good detection

limits (Gauglitz and Vo-Dinh, 2003; Weseloh et al., 2006; Nicolas et al., 2010 and

Srungaram et al., 2013). The present study focuses on the use of some of the widely

used analytical techniques for estimation of heavy metal contents in different soil

samples. The Literature survey has revealed different analytical techniques, extraction

and quantification methods used by various scientist for estimation of heavy metals

along with their range of content in soils from different regions of the world (Table 3).

A brief introduction of different techniques is given below.

2.4.1. Inductively Coupled Plasma Optical Emission Spectroscopy (ICP-OES)

Inductively Coupled Plasma Optical Emission Spectroscopy is demonstrated to

be one of the most widely used techniques for estimation of heavy metal content in soils

samples (Reed, 1963). Moreover, this technique also has feasibility to analyze the

metals in traces. Though this technique was earlier considered as the non economical as

compared to other techniques and had its limitations but the recent technological

developments in ICP-OES has made it one of the important tool in field of heavy metal

pollution analysis (Greenfield et al., 1965; Dunken and Pffor, 1966; Morrison et al.,


37

1970). Major advantage of this technique is the increasing demand of performing

simultaneous multielement determination at the nanogram/milliliter level for many trace

constituents and limited volume of samples especially the biological samples. It was

reported that even the most widely accepted technique like Atomic Absorption

Spectroscopy also sometimes cannot meet these requirements (Greenfield et al., 1964).

Scott et al. (1974) described the use of ICP-OES for trace determination of

different metallic elements. They mentioned that detection limits were in range 0.1 - 10

ng/ml for most of the elements studied. The use of inductively coupled plasma as

excitation source for trace metal estimations was first investigated by Greenfield and his

associates in the year 1965. Since then, many authors demonstrated the feasibility of

this technique in emission spectrometry (Wendt and Fassei, 1965; Greenfield et al.,

1965; Dunken and Pforr, 1966; Hoare, 1967; Mermet and Robin, 1968; Veillon and

Margoshes, 1968a, b; Dickinson and Fassel, 1969; Barnett et al., 1970; Morrison and

Talmi, 1970; Truitt and Robinson,1970; Greenfield and Smith, 1972; Boumans and de

Boer, 1972; Kirkbright et al., 1972).

Various studies have been reported on the use of ICP-OES for the determination

of trace elements in soils from different regions of the world (Bettinelli et al., 2000;

Madrid et al., 2002; Martin et al., 2006; Doelsch, 2006a,b; Covelo et al., 2007; Ongley

et al., 2007; Valladares et al., 2009; Vega et al., 2009; Senesi et al., 2009; Zhang et al.,

2009; Aktas and Kocabas 2010; Sollitto et al., 2010; Soriano-Disla et al., 2010; Wu et

al., 2010; Cui and Du, 2011; Escarre et al., 2011; Lin et al., 2011; Rastmanesh et al.,

2011; Xia et al., 2011; Al-Farraj et al., 2012; Desogus et al., 2012; Afkhami et al.,

2013; Borgese et al., 2013; Cheng et al., 2013).

2.4.2. Inductively Coupled Atomic Emission Spectroscopy (ICP-AES)

ICP-AES spectroscopy was introduced for rock sample analysis by Govindaraju

and Mevelle (1987). Authors emphasized the use of AES for rock analysis over the use

of atomic absorption spectrometry and X-ray fluorescence spectrometry due to the fact

that the instrument was capable of determining several elements simultaneously.


38

Knasmuller et al. (1998) also studied heavy metals viz. Cu, Ni, Cr, Cd, Hg, Sb

and As in soils contaminated with emissions of smelters of Arnoldstein, Brixlegg,

Slovakian (Eastern Austria), Saualpe (Central Austria) and Alps Carinthia region. Kim

et al. (2002) reported high contents of heavy metals from agricultural (Paddy grown),

farmland soils and dressing plant soil in the vicinity of Duckum Au-Ag mine, Duckum

(Korea). Li et al. (2004) also reported the use of this technique for analyzing soils from

Urban areas (residential, commercial and industrial estates) and Hotspots (Volcanic

regions) of Kowloon Peninsula (Hong Kong ) for Cd, Co, Cr, Cu, Ni, Pb and Zn

estimation. Rodriguez et al. (2009) used ICP-AES technique for estimation of Pb, Zn,

Cu and Cd in Arable and Pasture soils (polluted soils) in the vicinity of Pb-Zn mine area

in Ciudad Real, Spain. Similarly, several workers through out the world have used

Inductively Coupled Atomic Emission Spectrometer for estimating the contents of

heavy metals in contaminated soil mixtures from different regions of the world (Bech et

al., 1997; Negoija and Ropota, 2000; Abollino et al., 2002; Sandroni et al., 2003;

Boularbah et al., 2006; Chen et al. 2006; Ljung and Otabbong, 2006; Ke-Lin et al.,

2006; Osher et al., 2006; Tume et al., 2008; Aelion et al., 2009; Neupane and Roberts,

2009; Sun et al., 2009; Bai et al., 2010; Zhang et al., 2010; Bhagure and Mirgane, 2011;

Luo et al. 2011; Baghdadi et al. 2012; Bai et al., 2012; Garcia-Salgado et al., 2012;

Malizia et al., 2012; Bo et al., 2013; Cai et al., 2013; Souza et al., 2013).

2.4.3. Inductively Coupled Mass Spectroscopy (ICP-MS)

The multielemental ultratrace detection capability of inductively coupled plasma

mass spectrometry (ICP-MS) makes it the most powerful technique (Beauchemin,

2008). Speciation analysis using inductively coupled plasma mass spectrometry (ICP-

MS) has become common practice in recent years for ultra-sensitive detection of trace

metal- and metalloid-containing compounds (Rosen and Hieftie, 2004). It was

prominent from survey of literature that this instrument has been used for industrial

analysis of metals, chemicals, advanced synthesized materials (Truscott et al., 2001;

Fischer et al, 2002), for environmental analysis (Meisel et al., 2001; Meisel et al., 2003;

Fritsche and Meisel, 2004) and clinical and biological materials, food and beverages

(Taylor et al., 2003). This technique has supersided other speciation techniques like


39

optical spectrometric, inductively coupled plasma atomic emission spectroscopy and

atomic absorption spectroscopy which are less sensitive and do not provide

simultaneous multi elemental detection respectively as compared to ICP-MS (Fritsche

and Meisel, 2004). ICP-MS can detect trace-element-containing species, even when a

particular trace element is distributed amongst a large number of species (Chyla and

Zyrnicki, 2000; McSheehy et al., 2002; Mataveli et al., 2010). Hernandez et al. (2003)

reported the use of ICP-MS for estimating Pb, Ni, Cu, Co and Cd in forest soils of

France. Ke-Lin et al. (2006) also studied agricultural soils of Daxing, Beijing (North

China Plain) for metals Cu, Zn, Pb, Cr, Cd, Ni, As, Se, Hg and Co using this multiple

element detection technique. Similarly, Zhang et al. (2009) analyzed different heavy

metals in soils under agricultural practices in Fuyang County, situated at the north of

Zhejiang Province, near Hangzhou city, China. Yaylal-Abanuz et al. (2011) monitored

the contents of Cd, As, Pb, Zn, Mn, Cu, Cr and Hg in agricultural soils located in the

vicinity of two large industrial zones i.e. the Gebze Organized Industrial Zone (GOIZ)

and Dilovası Organized Industrial Zone (DOIZ) of Kocaeli City (Turkey). Identification

and quantification of different heavy metals in soil mixture using ICP-MS has been

recognized by researchers for its with unique analytical capabilities through out the

world (Granero and Domingo, 2002; Fernandez et al., 2008; Ahsan et al., 2009; Rogan

et al., 2010; Ling-yu et al., 2010; Guo et al., 2012; Ji et al., 2012; Amuno, 2013; Kuang

et al., 2013).

2.4.4. Atomic Fluorescence Spectrometer (AFS)

Atomic-fluorescence spectroscopy (AFS) has undoubtedly become very

sensitive, selective and versatile method for the determination of number of

environmentally and biomedically important elements (Cai, 2000). This technique has

been used by several groups of workers as a complementary technique that may permit

higher analytical sensitivity than Atomic absorption spectroscopy (AAS) for chemical

analysis of various substances (Kirkbright, 1971).

Gomez-Ariza et al. (2005) made a comparative study of atomic fluorescence

spectroscopy and inductively coupled plasma mass spectrometry for mercury and

arsenic multispeciation and suggested that although multispeciation based of ICP-MS


40

exhibited better sensitivity than the coupling based on tandem atomic fluorescence but

AFS was observed to be a very robust system with low cost of acquisition and

maintenance. Due to its excellent sensitivity and selectivity, AFS was considered as one

of the most important analytical tool for the analysis of trace elements, including

mercury, arsenic and selenium.

Yan-Feng et al., (2007) analyzed heavy metals viz. As, Hg, Cu, Zn, Pb, Cr, and

Cd in soil of Wuxi City, Jiangsu Province, China under anthropogenic influence of

heavy metal distribution using AFS. Speciation of different metals in soil mixtures have

been explored by researchers through out the world applying this technique (Jones et

al., 1995; Ke-Lin et al., 2006; Martin et al., 2006; Li et al., 2009; Chao et al., 2010 ;

Liu et al., 2011; Ping et al., 2011; Bo et al., 2013; Xu et al., 2013).

2.4.5. X-ray Fluorescence Spectrometer (XRFS)

X-Ray fluorescence (XRF) spectroscopy is based on simple relation of the

fundamental physics comprising of atom–radiation interaction. It is highly sensitive

analytical tool used for the analysis of heavy metals (Gilfrich et al., 1990; Torok, 1996

and Anjos et al., 2000). Atsushi and Yoshiyuki (1996) conducted quantitative analysis

of rock samples by X-ray Fluorescence spectrometry (XRF) and emphasized the major

advantage of XRF was its ability to perform accurate quantitative analysis for a wide

range of elements. However, they also mentioned that like most other analytical

techniques, matrix effects (inter element effects) was required to be corrected while

performing quantitative analysis.

Kaminski and Landsberger (2000) reported Zn, Sb, Cd, Pb, Cu, Sn, As, Cr, Hg

and Ni in soils (Industrial and residential) with history of abundant industrial activities

like smelters of ferrous and nonferrous metals, a coal-fired power plant, organic and

inorganic chemical companies, municipal waste incinerators and petroleum refineries in

East St. Louis, IL (Illinois, Urbana). Bhuyian et al. (2010) reported Ti, Mn, Zn, Pb, As,

Fe, Rb, Sr, Nb and Zr in soils of mine drainage and surrounding agricultural fields in

Barapukuria coal basin located in Dinajpur District (Northern part of Bangladesh) using

X-ray Fluorescence spectrometry. Cheng et al. (2013) also estimated heavy metals Cu,


41

Ni, and Zn in sediment soils from agricultural area in Taiwan contaminated due to

waste pollution of industrial plants, such as electroplating, metalwork, textile, livestock,

dyeing pharmaceutical, chemical plants, tanneries, and papermaking. Few studies have

examined the utility of this technique for estimation of heavy metals in soils mixtures

(Orescanin et al., 2003; Bueno et al., 2009; Krishna et al., 2011; Machender et al.,

2011; Chen et al., 2012; Koz et al., 2012; Krishna et al., 2013).

2.4.6. Neutron Activation Analysis (NAA)

Neutron Activation Analysis (NAA) is a quantitative and qualitative method of

high efficiency for the precise determination of trace elements in different types of

samples. The mechanism of NAA was discovered in the year 1936 by Heves when he

observed that the samples containing certain rare earth elements became radioactive on

exposure to source of neutrons (Desote et al., 1972). The major difference between

NAA and other techniques is that the former is based on nuclear transitions while later

ones on electronic transitions. Heavy metals were detected using epithermal neutron

activation analysis by Alfassi (1985). He suggested that the important source for

determination by activation analysis was the nuclear reactor due to its high flux of

bombarding particles and radioactive capture reaction (n, γ) of thermal neutrons. During

activation analysis, although the samples were activated mainly by n and γ, yet the

whole spectrum of neutrons was used. He stated that the major drawback of this

technique was the Compton’s interference with the gamma ray of trace elements. The

author also stated that apart from this phenomenon, some elements couldnot be

determined either due to too long half life of produced radionuclide or due to the reason

that it did not emit gamma rays. However, epithermal neutron activation can overcome

these difficulties.

Determination of rare earth elements including La, Nd, Sm, Ti, Gd, Dy, Ho, Er,

Lu including Th was done using thermal and epithermal neutron activation analysis that

was carried out by Brunfelt and Roelandts (1974). The major advantage of thermal and

epithermal neutron activation analysis is that rare earth elements can be determined non

destructively by neutron activation using a short irradiation time. Neutron activation


42

analysis is a sensitive multielement analytical technique which is used for analysis of all

types of elements including rare elements both qualitatively and quantitatively. Stafilov

et al. (2010) used NAA for estimation of Fe, Ti, As, Sb, Zn As, Au, Br, Co, Cr, Cu, Cd,

Hg, Dy, Hf, In, Mn, Mo, Ni, Cd, Cu, Hg, In, Pb, Rb, Sb, Se, Sm, Sr, Tb, Th, U, V, W,

Yb and Zn in soils from urban area, industrial zone, cultivable land, uncultivable land

from polluted city of Veles region which is known for its lead and zinc industrial

activity. Few scientific studies have reported the use of this analytical technique for

determination of heavy metals in soil complexes (Kaminski and Landsberger 2000;

Karim et al., 2008; Daud et al., 2009; Sharma et al., 2009; Mireles et al., 2012;

Pantelica et al., 2013).

2.4.7. D.C.Argon Plasma Multielement Atomic Emission Spectrometer (DCP-

MAES)

It is also a validated analytical methods for determination of several elements

simultaneously. DCP-MAES system offers acceptable levels of selectivity, sensitivity,

speed and economy for the analysis of many trace elements. Johnson et al. (1979)

determined the trace elements in natural waters using d.c. argon plasma multielement

atomic emission spectrometer. Holmgren et al. (1993) reported the presence of Cd, Pb,

Zn, Cu, and Ni in agricultural soil samples which were collected from at least 8 km

downwind of the stack emitter, smelter and foundry, 200 m from U.S.A or state

highways, 100 m from rural road ways, 100 m from abandoned, or obliterated building

sites.

2.4.8. Atomic Absorption Spectrometer (AAS)

Atomic Absorption spectrometry (AAS) is the most common technique for

analyzing metals and some metalloids present in complex environmental matrices. AAS

dates back to 19th

century, when in 1950s Alan Walsh and his team developed this

technique for determination of particular metal concentration within the test sample.

This chemical technique can reliably analyze 62 different metals in a solution and has

proven to be less time consuming, accurate and more convenient than other

spectroscopic techniques for elemental analyses (Garcia-Salgado et al., 2012). The


43

concentration is determined from calibration curve obtained by standards of known

concentrations. The instrument uses flame and furnace spectroscopy for low detection

limits and trace metal analysis (Piccolo and Connor, 1968; Baez et al., 2007 and Garcia

et al., 2009). Although recent scientific advances in other analytical techniques like

ICP-OES, ICP-MS have left AAS behind but still its larger specificity, make it as a well

established, excellent, reliable and most widely used chemical technique for analyzing

any type of material (Gauglitz et al., 2003; Nicholas et al., 2010; Garcia-Salgado et al.,

2012)

It has been well documented that this technique has been widely used by several

workers from different regions of the world with an emphasis on soil heavy metal

analysis (Holmgren et al., 1993; Roberts et al., 1994; Motalib et al., 1997; Escrig and

Morell, 1998; Wilcke et al., 1998; Xiong, 1998; Kashem and Singh 1999; Palambo et

al., 2000; Tra and Egashira, 2001; Lee et al., 2002; Abollino et al., 2002; Adamo et al.,

2003; Banerjee, 2003; Imperato et al., 2003; Tuzen, 2003; Yilmaz et al., 2003; Cicek

and Koparal, 2004; Hobbelen et al., 2004; Ilg et al., 2004; Razo et al., 2004; Viard et

al., 2004; Aydinalp et al., 2005; Banat et al., 2005; Chen et al., 2005; Grigalaviciene et

al., 2005; Liao et al., 2005; Mapanda et al., 2005; Moller et al., 2005; Notten et al.,

2005; Rattan et al., 2005; Tariq et al., 2005; Bi et al., 2006; El-Arby et al., 2006; Kuo et

al., 2006; Liu et al., 2006; Mico et al., 2006; Song et al., 2006; Tariq et al., 2006;

Vasquez Murrieta et al., 2006; Banat et al., 2007; Chen et al., 2007; Kidd et al., 2007;

Sharma et al., 2007; Zglobicki and Rodzik, 2007; Kasassi et al., 2008; Khalil et al.,

2008; Achiba et al., 2009; Adie and Osibanjo 2009; Ahsan et al., 2009; Basar et al.,

2009; Christoforidis and Stamatis, 2009; Dragovic and Mihailovic, 2009; Iwegbue et

al., 2009; Jian-Hua et al., 2009; Lu et al., 2009; Mleczek et al., 2009; Moreno-Jimenez

et al., 2009; Zhang et al., 2009; Aktas and Kocabas, 2010; Baykara and Dogru, 2010;

Chao et al., 2010; Ikenaka et al., 2010; Jiang et al., 2010; Kien et al., 2010; Limei et al.,

2010; Markovic et al., 2010; Mushtaq and Khan, 2010; Nganje et al., 2010; Omwoma

et al., 2010; Parizanganeh et al., 2010; Stafilov et al., 2010; Zhao et al., 2010; Aslam et

al., 2011; Chanda et al. 2011; Evdokimova et al., 2011; Gjoka et al., 2011; Liu et al.,


44

2011; Khan et al., 2011; Khorasanipour and Aftabi, 2011; Kizilkaya et al., 2011;

Muhammad et al., 2011; Ping et al., 2011; Odigi et al., 2011; Ogbonna et al., 2011;

Olorundare et al., 2011; Singh et al., 2011; Ye et al., 2011; Alam et al., 2012; Al-

Khasman, 2012; Kanmani and Gandhimathi, 2012; Lee et al., 2012; Mahanta and

Bhattacharya, 2011; Reboredo et al., 2012; Sehgal et al., 2012;; Wuana et al., 2012;

Amin et al., 2013; Chopra and Pathak, 2013; Gamino-Gutierrez et al., 2013; Jalali and

Hemati, 2013; Aslam et al., 2013; Li et al., 2013; Marcin et al., 2013; Massas et al.,

2013; Nazzal et al., 2013; Novaes dos Santos and Alleoni, 2013; Silva et al., 2013;

Skrbic and Mladenovic, 2013; Sun et al., 2013; Szopka et al., 2013; Zhang et al. 2013).


Table 3. Summary of literature on different analytical techniques used for estimation of heavy metal (HM) content of soils from

different regions of the world.

S.No. HMs

studied

Study

region/

Country

Type of Soil/

Contamination Source

Digestion mixture

used

Analytical

equipment used

Results Reference

1. As, Cd,

Cr, Ni,

Cu, Zn,

Pb and

V

Iran Sediment samples collected

from the local soil, drilling

mud and the waste pit

nearby Ahwaz oil field

HNO3 +HClO4 ICP-OESd

(Ultima2C

model)

Heavy metal content (mg/kg) observed:

In Local soil: Cr (0.69), Cu (0.05), Ni

(1.26), Pb (0.24), Zn (0.79), As (25.70), Cd

(5.40), V (215.40) and Fe (7,630).

In Drilling mud: Cr (0.31), Cu (21.76), Ni

(0.18), Pb (18.88), Zn (43.58), As (160.30),

Cd (7.20), V (2,014) and Fe (21,000).

In waste pit soil: Cr (0.54), Cu (10.24), Ni

(0.27), Pb (0.27), Zn (7.65), As (92.60), Cd

(7.90), V (577.40) and Fe (16,000).

Afkhami

et al. (2013)

2. Cu, Ni,

Zn, Cr,

Fe, Mn,

Co and

Pb

Mardan

(Pakistan)

Crop cultivated soils

irrigated with waste water,

Tube well water, waste

water irrigated soil and

Tube well water irrigated

soil.

HNO3+ HClO4 AASa (PE

b model

700)

Heavy metal content (mg/g) observed:

Waste water irrigated soil: Cu (0.867), Ni

(0.633), Zn (1.867), Cr (0.493), Fe (3.54),

Mn (4.775), Co (0.124) and Pb (0.867).

Tube well irrigated soill: Cu (0.225), Ni

(0.123), Zn (1.001), Cr (0.121), Fe (2.562),

Mn (1.345), Co (0.102) and Pb (0.225).

Amin et al.

(2013)

3. As, Cu,

Cr, Pb,

and Zn

Kigali

(Rwanda)

Cemetery Soils from Gisozi

Memorial Centre where

genocide mass graves are

approximately 3 m deep,

with the about 50

individuals interred in each

coffin for burial

HNO3+HClO4 ICP-MSh Heavy metal content (mg/kg) observed:

As (21.9), Cr (77), Cu (19.57), Fe (5.04%),

Pb (28) and Zn (51)

Amuno (2013)


4. As, Cr,

Cu, Ni,

Pb, Zn

and Fe

Lanzhou

(China) Urban top soil from along

the roads in Lanzhou city

with factories like petroleum

processing, chemical

combination and machine

assembly. Large amount of

emissions from coal-fired

factories and vehicle

emissions have led to poor

soil quality.

HClO4+HNO3+ HF ICP-AESc for Cr,

Cu, Ni, Pb, Zn,

Fe

Double-way atom

fluorescence

AFSe-9800 for As

Range of heavy metal content (mg/kg)

observed:

As (5.40-46.30), Cr (29.80-549.40), Cu

(19.60-136.30), Ni (20.50-100.00), Pb

(27.00-77.10), Zn (68.40-509.30) and Fe

(24.85-44.58)

Bo et al.

(2013)

5. Cd,

Mn, Fe,

Cr, Zn

and Pb

Vallecamo

nica and

Garda

Lake

(Italy)

Surface soil samples from

areas where previously

operating Mn ferroalloy

plants were located.

HNO3 ICP-OESd Range of heavy metal content (mg/kg)

observed:

In Vallecamonica soils: Cr (3.8-139), Mn

(380-2,384), Fe (2,518-14,035), Zn (28-

456), Cd (0.84-4.39) and Pb (14-358).

In Garda Lake soils: Cr (3.8-216), Mn

(239-601), Fe (2,464-6,973), Zn (17-363),

Cd (0.66-2.9) and Pb (14-103).

Borgese et al.

(2013)

6. Cd, Cr,

Cu, Ni,

Pb, and

Zn

Guangzhou

(China)

Soil and dust samples from

roadside, residential areas,

parks, campus sport

grounds, and commercial

sites deteriorated due to

industrialization (included

electronic equipment

manufacturing, automobile

manufacturing, and

petrochemical industries)

HNO3+HClO4+HF ICP-AESc Heavy metal content (mg/kg) observed:

In park soils: Cd (0.26), Cr (22.8), Cu

(12.9), Ni (11.0), Pb (65.5) and Zn (266).

In roadside soils: Cd (0.25), Cr (23.1), Cu

(77.4), Ni (12.1), Pb (68.6) and Zn (422).

In residential soils: Cd (0.13), Cr (20.5),

Cu (15.2), Ni (8.7), Pb (43.9) and Zn (212).

In sport grounds soils: Cd (00.17), Cr

(15.3), Cu (60.9), Ni (8.7), Pb (83.4) and

Zn (231)

In urban soils: Cd (0.23), Cr (22.4), Cu

(41.6), Ni (11.1), Pb (65.4) and Zn (277)

In dusts: Cd (4.22), Cr (62.2), Cu (116.3),

Ni (31.9), Pb (72.6) and Zn (504)

Cai et al.

(2013)


7. Cr, Hg,

Cu, Ni

and Zn

Taiwan Sediment soils from

agricultural area with

paddy fields contaminated

due to waste pollution of

industrial plants, such as

electroplating, metalwork,

textile, livestock, dyeing

pharmaceutical, chemical

plants, tanneries, and

papermaking

HNO3 +HCl ICP-OESd

Field Portable

XRFr


Using ICP-OESg method:

Cr (1,444.2), Cu (2,462.9), Hg (0.9), Ni

91,860.8), Pb (154.4) and Zn (4,666.9).

Using Portable XRFr method: Cr (467.9),

Cu (374.5), Hg (9.9), Ni 91,(154.4), Pb

(151.5) and Zn (1,076.2).

Cheng et al.

(2013)

8. Zn, Ni,

Cr, Cd

and Fe

Gurukula

Kangri

University

, Haridwar

(India)

Soil from Spinacea

oleracea cultivated

microplots contaminated

due to irrigation with Sugar

Mill Effluent (SME)

collected from R.B.N.S.

Sugar Mill Ltd., Laksar

(Uttarakhand) as well as

control soil irrigated with

bore well water (BWW).

HNO3+ HClO4 AASa (Make-

ECIL, Model No.

4129)


In SME irrigated soil: Cr (22.31), Ni

(213.27), Cd (8.72), Zn (49.64) and Fe

(183.11).

In BWW irrigated soil: Cr (18.52), Ni

(63.02), Cd (6.50), Zn (43.44) and Fe

(178.84).

Chopra and

Pathak (2013)

9. As and

Pb

Villa de la

Paz

(Mexico)

Surface soil samples from

urban areas, school

playgrounds and backyard

houses contaminated due to

abandoned mine wastes

from historical mines

HNO3+HClO4 Flame AASa

(Varian Spectra

AA 220) for Pb

Flame AASa (PE

b

MHS-10 hydride

generation system

– AAS 2380) for

As


observed:

Pb (212-16,595) and As (22-2,544)

Gamino-

Gutierrez

et al. (2013)

10. As, Cd,

Cr, Cu,

Ni, Pb

and Zn

Baiyangdi

an Lake

(China)

Surface soils of reed

wetlands in raised fields of

the Baiyangdian Lake

receiveing nutrient inputs

from domestic and

industrial sewage and

aquaculture wastewater.

HClO4+HNO3+ HF ICP AESc


observed:

As (26.79-50.49), Cd (0.54-0.80), Cr

(54.36-132.37), Cu (30.00-72.75), Ni

(30.18-67.26), Pb (20.03-46.550) and Zn

(75.32-284.71)

Gao et al.

(2013)


11. Cd, Cu,

Fe, Mn,

Ni, Pb,

and Zn

Isfahan

Province

(Iran).

Top soils from three

agricultural areas (paddy

fields) affected due to

natural and anthropogenic

Inputs of metals

HNO3+HClO4 AASa Heavy metal content (mg/kg) observed:

Cd (2.3-3.2), Cu (2.8-13.5)Fe (583.2-

2686.9), Mn (47.0-140.2, Ni (10.8-18.4),

Pb (38.3- 66.0), Zn (12.3-44.7).

Jalali and

Hemati,

(2013)

12. As, Ba,

Co, Cr,

Cu,

Mo, Ni,

Pb, Sr,

V, Zn

and Zr

Nuggihalli

,

Karnataka

(India)

Soil around three different

active (Tagdur), abandoned

(Jambur) chromite mining

sites as well residential

zone around

Chikkondanahalli of the

Nuggihalli Schist

contaminated due to

Chromium metalliferous

mining

Soil pellets

XRFg (Philips

PW2440 model

MagiX PRO)


observed:

As (0.00-11.20), Ba (168.3- 602.3), Co

(2.1-74.2), Cr (64.1-4,863), Co (-4.64-

2.03), Mo (0-6.9), Ni (0.8-1,398.2). Pb

(0.6-84.1, Sr (14.4-470.7), V (4.6- 141.5),

Zn (57-655.2)

Krishna et al.

(2013)

13. Pb Dinghushan

(China)

Upper layers of soil from a

mixed forest affected due

to rapid urbanization and

industrialization

HNO3+HClO4-

Total digestion

method

HCl - dilute acid

extraction method

ICP-MSh (Agilent

7500CX, USA).

Range of heavy metal content (mg/kg) at

depth of 0–20 cm:

With total digestion method: Pb (52.0-

62.0).

With dilute acid extraction method: Pb

(9.0-13.0)

Kuang et al.

(2013)

14. Cu, Zn

and Pb

Zhejiang

(China) Soil from Zhejiang

province of China : on a

road near a steel factory in

Hangzhou city, soil from a

copper industrial zone in

Fuyang city, and soil from

a lead-zinc mine in Fuyang

city .

HCl+HNO3+HF+H

ClO4

AASa (AA 300,

Jena)


Roadside soils near Hangzhou: Cu (132),

Zn (1034) and Pb (140)

Cu industrial zone soils, Fuyang: Cu

(375), Zn (22) and Pb (2711).

Pb-Zn mine soils, Fuyang: Cu (105), Zn

(1281) and Pb (259).

Li et al.

(2013)


15. Cu , Pn

and Zn

Olkusz,

Legnica

and

Głogów (Poland).

Forested soils from Olkusz

(OLK) severely polluted

due to zinc and lead

industries and soils from

Legnica and Głogów

(LEG) affected due to

copper ore mines and

copper smelters in the area.

HNO3 AASa (Perkin

Elmer, AAnalyst 800).

Range of heavy metal content (mg/g)

observed:

In OLK soils: Cu (3-52), Pb (198-1877) and Zn (193-4792).

In LEG soils: Cu (85-1353), Pb (177-778)

and Zn (54-116)

Marcin et al.

(2013)

16. Cr, Zn,

Ni, Pb,

Co,

Mn, Ba,

Cu, and Fe

Thriassio

plain

(Greece)

Soils of plain (area with

mixed land uses (i.e.,

residential, agricultural,

and industrial) between

Elefsina and Aspropyrgos

two major towns affected

due to heavily

industrialization (steel

industries cement factories,

petroleum recycling units,

large warehouses, oil and many chemical industries)

HCl+ HNO3 AASa (Varian

spectra

A300 system).


observed:

Cr (42.5-141.3), Zn (76.5-690.8), Ni (36.4-

297.7), Pb (60.4-693.0), Mn (160.3-588.5), Cu (12.3- 261.6) and Fe 7.0-24.5 mg/g)

Massas et al.

(2013)

17. Pb, Zn,

Cd, Ni,

Vr, Cu

and Mn

Greater

Toronto

(Canada)

Road dust samples were

collected from four

highways (401, 400, 404

and the Don Valley

Parkway) polluted mainly

because of vehicle emissions

HNO3+HClO4+HF AASa (PYE

UNICAM SP9)

Heavy metal content (ppm) observed:

In 401, 400, 404 highway: Cd (0.492,

0.539, 0.508), Cr (148.9, 230.2, 187.8), Cu

(178.6, 186, 134.7), Fe (51,784.2, 52,558.5,

44,799.6), Zn (456.8, 221.6, 156.1), Mn

(1,330, 1,506.4, 1,391.9), Pb (170.1, 205.1,

152.1) and Ni (53, 80.2, 39.2).

Don Valley Parkway soils: Cd (0.50), Cr

(203.3), Cu (154.1), Fe (44,988.5), Zn

(183.7), Mn (1367.1), Pb (196) and Ni (58.7).

Nazzal et al.

(2013)


18. Cd, Co,

Cr, Cu,

Ni, Pb

and Zn

Southwest

ern

Amazon

(Brazil)

Soils from Brazilian

agricultural frontier of

southwestern Amazon with

naturally occurring heavy metal contentin soil.

HCl+HNO3 (aqua

regis method)

HNO3 (EPA 3051 method)

Flame AASa Range of heavy metal content (mg/kg)

observed:

By aqua regia method: Cd (nd-<0.050),

Co (7.2-38.9), Cr (20.4-142.1), Cu (6.5-

32.9 Ni (0.2-24.3), Pb (5.2-25.8) and Zn (1.2-100.9)

By EPA 3051 method: Cd (0.050-<0.050),

Co (16.6-39.0), Cr (19.2-98.8), Cu (9.7-

28.4), Ni (0.0-5.6), Pb (2.7-15.7) and Zn

(0.0-69.6)

Novaes dos

Santos and

Alleoni,

(2013)

19. Fe, Mn,

Cu, Zn,

Ni, Cr

and Pb

Galicia

(Spain) Treated soils from

greenhouse experiment:

Control soil (S), limed soil

(L) soil limed and fertilized

(LF), soil amended with

25g/kg granite powder

(SG1), soil amended with

50g/kg granite powder

(SG2), and soil amended

with 100g/kg granite

powder (SG3)

NH4AO +EDTA AASa (VARIAN

FS220).

Heavy metal content (mg/kg) in S, L, LF,

SG1, SG2 and SG3 soils observed:

Fe (244, 251, 274, 441, 597, 771), Mn (1.0,

nd, nd, 8.3, 15.2, 22.7, 350,0.5), Cu (nd,

nd, nd, 1.3, 2.1, 3.4, 46.7, 0.8), Zn (3.1, 2.7,

3.7, 2.5, 3.4, 2.9, 12.0, 0.2), Pb (4.8, 5.6,

5.7, 5.2, 4.9, 3.8, 11.1, 1.9), Cr (nd, nd, nd,

nd, nd, nd) and Ni (nd, nd, nd, nd, nd, 1.9),

respectively

Silva et al.

(2013)

20. Cd, Co,

Cr, Cu,

Fe, Hg,

Mn, Ni,

Pb and Zn

Novi Sad

(Serbia) Urban, suburban and rural

surface soils in and around

the city affected due to

industrial, commercial,

agricultural

activities, and automobile exhaust.

HNO3+H2O2 GFf AAS

a (Varian

240/GTA120

instrument


observed:

Cd (0.75-2.32), Co (6.07-22.4), Cr (2.08-

5.44), Cu (8.36-45.7), Fe (6637-18994), Hg

(0.16-0.55), Mn (286-836), Ni (16.6-41.1), Pb 912.7-55.9) and Zn (61.3-401)

Škrbic and

Mladenovic

(2013)


21. Fe, Mn,

Cu, B,

Ba, Cd,

Cr, Ni,

Pb and

Zn,

Sao Paulo

(Brazil)

Landfarming soil before

and after addition of sugar

cane vinasse a byproduct of

the sugar industry. L (land

farming soil), and LV

(vinnase treated soil) in the

beginning of the

experiment (T0) and after

33 days (T33).

HNO3+HCl

ICP-AESc Heavy metal content observed:

In L soils at T0 and T33: Units

mmolc/dm3-Fe (54.0, 46.0), Mn (7.8, 7.4),

Cu (1.5, 1.2), Zn (39.0, 33), B (2.7, 2.5),

Units mg/kg- Ba (658.1, 514.9), Cd (1.4,

1.4), Cr (731.7, 345.0), Ni (69.69, 68.6)

and Pb (94.38, 70.2)

In LV soils : Units mmolc/dm3-Fe (38.0),

Mn (7.7), Cu (1.2), Zn (56.1), B (2.4),

Units mg/kg- Ba (511.5), Cd (3.83), Cr

(380.1), Ni (62.7) and Pb (78.2)

Souza et al.

(2013)

22. Cd Jilin

(China).

Agricultural soil HCl+HNO3+HF+H

ClO4

AASa (Solaar

M6, Thermo

Fisher Scientific,

USA).

Heavy metal content (mg/g) observed:

Cd (0.16)

Sun et al.

(2013)

23. Pb Karkonos

ze

National

Park,

(Poland)

Mountain soils („O‟ layer,

0-10, 10-20 cm depth)

subjected to natural factors

like parent rock,

bioaccumulation, effects of

soil forming processes, and

external factors, such as

air-borne pollutants input,

changing chemistry of dry

and wet precipitation.

HNO3+HCl Flame AASa Range of heavy metal content (mg/g)

observed:

In ‘O’ layer soils: Pb (24-200)

In 0-10 cm depth soils: Pb (18.5-248)

In 10-20 cm depth soils: Pb (3.8-196)

Szopka et al.

(2013)

24. As Huludao

(China)

Street dust soil of Huludao

contaminated due to

Huludao Zinc Plant (HZP)

atmospheric deposition,

wastewater, and unknown

pollution due to metal

smelting activities at HZP.

HNO3+HClO4+HF AFS e (PF6-2) Range of heavy metal content (mg/kg)

observed:

As (3.33-185.1)

Xu et al.

(2013)


25. Cd, Cr,

Cu, Ni,

Pb and

Zn

Shanghai

(China) Street dust samples from

Fengjing town (FJ),

Songjiang New City (SJ,

Zhu Jiajiao (ZJJ) and urban

areas affected due to

industrialization and urbanization.

HClO4+HNO3+ HF AASa (PE

b

AANALYST 800)


In FJ soils: Cd (0.94), Cr (115), Cu (104),

Ni (90), Pb (163) and Zn (420).

In SJ soils: Cd (1.52), Cr (103), Cu (100),

Ni (62), Pb (119) and Zn (350).

In ZJJ soils: Cd (0.74), Cr (80), Cu (187), Ni (63), Pb (123) and Zn (494).

In Urban area soils: Cd (1.21), Cr (145),

Cu (216), Ni (115), Pb (259) and Zn (895)

Zhang et al.

(2013)

26. Cd, Pb,

Cu, Mn

and Zn

Chittagong,

Bangladesh

Soils from 0–15, 15–30 and

30–45-cm depths of three

industrial (Fertilizer

manufacturing complex

(FMC), Paper mill complex

(PMC) and Cement

manufacturing complex

(CMC), and two municipal

(Biosolids disposal site

(BSD) and Municipal open

pit landfill (MLF) waste

disposal sites around

Chittagong city

HCI+ HNO3 AASa (Varian

Spectra

AA-220)

Heavy metal content (mg/kg) at 0–15, 15–

30 and 30–45-cm depths:

In FMC soils: Cd (1.2,1.3,1.3), Pb

(82,86,90), Cu (42, 44, 45), Mn (420,

516,521) and Zn (317,347,325)

In PMC soils: Cd (0.5, 0.6, 0.3), Pb

(70,49,51), Cu (37,20, 19), Mn (248,

99,349) and Zn (256,178,183)

In CMC soils: Cd (1.1,0.9, 0.8), Pb

(70,64,56), Cu (39,34, 35), Mn

(679,677,517) and Zn (248, 198, 165)

In BSD soils: Cd (1.5, 1.5, 1.6), Pb (58, 51,

54), Cu (41, 35, 30), Mn (252, 200, 331)

and Zn (325, 255, 202)

In MLF soils: Cd (2.0, 1.9, 1.7), Pb (88,

65, 49), Cu (73, 49, 27), Mn (409, 458,

439) and Zn (325, 232, 153)

Alam et al.

(2012)


27. Fe, Zn,

Mn,

Cu, Co,

Cr, Cd,

Ni, and

Pb

Mahad

AD‟

Dahab

County,

south-east

part of

Medina

area

(Saudi

Arabia)

Contaminated soil from

area bounded by Mahad

AD‟ Dahab gold mine

Microwave, HNO3

(Hossner method)

HF+H2SO4+HClO4

(USEPA 3051)

ICP-OESd Heavy metal content (mg/kg) observed:

Using Hossner method: Cd (17.2), Co

(11.4), Cr (45.7), Cu (1030), Fe (33,300)

Mn (962), Ni (33.3), Pb (791) and Zn

(6320)

Using USEPA 3051 method: Cd (18.1),

Co (10.6), Cr (34.7), Cu (1100), Fe

(27,400) Mn (871), Ni (22.9), Pb (782) and

Zn (2860)

Al-Farraj

et al. (2012)

28. Cu, Fe,

Zn, Mn,

Pb, Cd,

and Cr.

The Dead

Sea

(Jordan)

Urban soil two depths, 0-10

cm and 10-20 cm around

Potash Industrial Site and

farms in the Ghore El-Safi

area on the east of the Dead

Sea (lowest place on earth

whose surface lies about

1385 feet below sea level.

HNO3 AASa (AA-6200,

Shimadzu)


observed:

At 0-10 cm depth: Cu (8.60-48.65), Fe

(45.6-77.9), Zn (25.32-74.35), Mn (29.31-

95.32), Pb (12.36-93.65), Cd (1.06-16.36)

and Cr (6.35-29.36).

At 0-10 cm depth: Cu (10.51-24.30), Fe

(28.6-66.30), Zn (19.65-49.65), Mn (24.95-

88.38), Pb (7.88-48.65), Cd (2.72-8.65) and

Cr (0.97-12.65).

Al-Khasman

(2012)

29. Pb, Cu,

Zn, Cd

and Fe

Beni

Mellal

City

(Morocco)

Urban Soils from olive

planted area, large engine

park, residential and

commercial place and

residential areas with olive

plantation polluted due to

emissions from road traffic

and anthropogenic

activities

HNO3+HF+HCl ICP-AESc

(ULTIMA2)


observed:

In Olive planted soils: Cd (0.218-2.154),

Cu (23.98-67.99), Pb (2.34-4.88), Zn

(60.43-186.92) and Fe (19.08-295.76%).

In Large engine park soils: Cd (0.342-

1.568), Cu (62.91-72.98), Pb (110.04-

559.65), Zn (167.88-267.07) and Fe (2.24-

4.88%)

In residential and commercial place

soils: Cd (0.456-1.943), Cu (21.07-81.76),

Pb (63.76-450.12), Zn (74.37-221.66) and

Fe (1.88-4.88%)

Baghdadi

et al. (2012)


In residential and Olive planted soils: Cd

(0.159-0.786), Cu (25.17-45.82), Pb

(19.53-342.74), Zn (65.78-137.25) and Fe

(1.74-4.35%)

30. As, Cd,

Cu, Pb

& Zn

Yellow

River

Delta

(YRD),

(Northern

China)

Wetland soils from Tidal

freshwater & salt marshes

polluted with heavy

sediment load due to

intensive anthropogenic

activities, rapid agricultural

development in upstream of

the YRD, heavy input of

agrochemicals & fertilizers.

HClO4+HNO3+HF ICP-AESc Heavy metal content (mg/kg) in tidal fresh

water marsh and salt marsh before and after

the flow-sediment regulation regime in the

Yellow River Delta observed:

As (27.54 and 41.52), Cd (0.59 and 1.00),

Cu (26.41 and 31.37), Pb (27.40 and

28.06), Zn (76.81 and 87.49).

Bai et al.

(2012)

31. Co, Cr,

Cu,

Mn, Ni,

Pb, Zn,

and V

Xi'an

Second

Ringroad

(China)

Urban topsoil contaminated

due to vechicular and

industrial pollution

Soil pellets XRFg Range of heavy metal content (mg/g)

observed:

Co (14.5-38.9), Cr (71.0-130.9), Cu (27.2-

792.0), Mn (518-780.3), Ni (23.7-39.1), Pb

(26.0-506.5), Zn (68.6-4964.8) and V

(63.8-97.5)

Chen et al.

(2012)

32. Ni, Sb,

Cu, Cr,

Se, As,

Ag, Be,

Co, Tl,

Cd, Zn

and Pb

Italy Natural soil samples

collected inside a

metallurgical plant (Pb and

Zn producing) from four

locations in the upper soil

layer about 1–2 m below

the surface (PZP4, PZP10,

PZE9 and PZP25).

HCl+ HNO3 ICP/OESd (PE

b

Optima 7000 DV

ICP/OES

spectrometer)

Heavy metal content (mg/kg) at PZP25,

PZP4, PZE9 and PZP10:

Ni (6.37, 19.15, 31.56, 5.56), Sb (4.90,

0.00, 2.34, 1.74), Cu (48.60, 50.49, 16.57,

11.68), Cr (13.89, 41.70, 12.49, 16.01), Se

(0.77, 0.00, 1.14, 1.14), As (25.33,

12.68,2.18, 4.89), Ag (1.07, 0.06, 0.20,

0.19), Be (0.71, 0.87, 0.86, 1.07), Co (2.51,

0.00, 2.56, 2.46), Tl (3.26, 7.01. 2.01,

3.65), Cd (11.16, 36.81, 2.20, 1.97), Zn

(2169, 2478, 206.3, 339.7) and Pb (2881,

20.33, 75.71, 155.6).

Desogus

et al. (2012)


33. As, Cd,

Cr, Cu,

Ni, Pb,

and Zn

Madrid

(Spain)

Vegetated soil from the

surroundings of the Mónica

mine (exploited first for Ag

(matildite) and then for As

(arsenopyrite) extraction, in

the northwestern part of the

village of Bustarviejo

HNO3+HCl ICP-AESc

(Varian model

Liberty Series II

Australia) for Cd,

Cr, Cu, Ni, Pb,

and Zn.

Hydride

generation AFSe

(PS Analytical

Millennium

Excalibur Kent,

UK)


observed:

Cd (1.44-53.5), Cr (12-28), As (3.0×102-

327×102), Cu (2.54× 10-309×10), Pb

(1.0×10-278×10) and Zn (0.92×102-

159×102)

García-

Salgado

et al. (2012)

34. Cu, Pb,

Zn and

Cd

Hainan

Island

(China)

Top and deep soils from

pepper grown unpolluted

areas with different soil

parent rocks (i.e. basalt

area (BA), granite area

(GA), sedimentary rock

area (SA), metamorphic

rock area (MA) and alluvial

deposits area (AA).

HNO3+HClO4

ICP-MSh (X7

ICP-MS, TMO,

USA)

Heavy metal content (mg/kg) in topsoil and

deepsoil:

In BA soil: Cu (97.15, 99.70), Pb (28.47,

26.25), Zn (144.94, 141.14) and Cd (0.05,

0.03) respectively.

In GA soil: Cu (50.38, 71.80), Pb (45.18,

77.06), Zn (71.93, 90.38) and Cd (0.07,

0.07) respectively.

In MA soil: Cu (10.09, 14.59), Pb (108.4,

89.97), Zn (54.6, 74.27) and Cd (0.26,

0.15) respectively.

In SA soil: Cu (13.92, 16.40), Pb (24.08,

23.64), Zn (47.26, 53.28) and Cd (0.05,

0.09) respectively.

In AA soil: Cu (13.32, 14.58), Pb (21.35,

22.90), Zn (39.5, 51.45) and Cd (0.07,

0.12) respectively.

Guo et al.

(2012)


35. As, Cd,

Cr, Cu,

Hg, Ni,

Pb and Zn

Hangzhou

, Zhejiang

Province

(China)

Soils from the site of a

retired paint/ink manufactory

HNO3+ HCl ICP-MS h

(Agilent 7500a)


observed:

In topsoils: As (24.8-4.1), Cd (0.29-1.21),

Cr (20.6-85.4), Cu (22.3-108.6), Hg (0.57-

11.25), Ni (23.6-66.2), Pb (26.6-86.8), and Zn (64.2-261.5).

In subsoils: As (20.5-45.7), Cd (0.25-

1.03), Cr (22.0-80.4), Cu (16.7-70.9), Hg

(0.68-18.11), Ni (22.1-58.6), Pb (24.5-

51.7) and Zn (72.5-215.0).

In basal soils: As (21.8-44.4), Cd (0.16-

1.75), Cr (20.7-84.9), Cu (18.5-86.3), Hg

(0.52-6.73), Ni (22.7-50.2), Pb (27.2-53.8), and Zn (60.7-298.1).

Ji et al.

(2012)

36. Cd, Cu,

Mn and

Pb

Tamilnad

u (India)

Soil samples from and

around the open dumpsite

Ariyamangalam municipal solid waste (MSW)

HCl+ HNO3 AASa (Thermo

Fisher

Scientific,USA)


observed:

Cu (bdl-75.52), Cd (bdl-47.72), Mn (42.07-

171.16) and Pb (bdl-291.29)

Kanman and

Gandhimathi,

(2012)

37. Al, V,

Cr, Mn, Fe, Ni,

Cu, Zn,

As and Pb

East of

Turkey‟s

eastern

Black Sea

Region (Turkey)

Vegetated soil samples in

the neighborhood of the

copper mining was

collected

Powdered form of

soil was used

EDXRFj

(Epsilon

5, PANalytical,

Almelo, The

Netherlands)


V (76.17), Cr (45.52), Mn (1018.78), Ni

(28.74), Cu (281.39), Zn (152.95), As

(17.56), Pb (5.00), Al (8.84 g/kg) and Fe

(5.41g/kg).

Koz et al.

(2012)

38. Cd and

Pb

Seosan-si,

Chungche

ognam-do

(Korea).

Agricultural soil (paddy)

from area adjacent to the

Seosung mine

HNO3 AASa (PE

b

AAnalystTM

700, USA).


Cd (11.27) and Pb (1233.21)

Lee et al.

(2012)


39. Cu,

Mn,Zn,

Pb, Cr

and Pd

Rome Vegetated soil under

Plantago major L.,

Taraxacum officinale L.,

Urtica dioica L. and

Trifolium pratense L.

cultivation exposed to

different level of Traffic

pollution from Muro Torto,

Olimpica, Ostiense, Eur

and Pamphili area

HNO3 ICP-AESc

(Varian Vista

MPX CCD)

Range of heavy metal content (ppm) in

soils during spring, summer and autumn in:

P. major cultivated soils: Cu (27-126, 39-

124, 43-195); Mn (730-820, 449-579, 534-

627); Zn (71-226, 122-303, 116-368); Pb

(137-578, 58-840, 82-686), Cr (22-43, 11-

29, 16-33); Pd (41-73, 41-74, 44-77)

respectively.

T. officinale cultivated soils: Cu (32-126,

104-155, 55-142); Mn (646-809, 93-426,

140-742); Zn (61-229, 93-426, 140-742);

Pb (148-627, 174-796, 89-730), Cr (33-45,

28-34, 21-35) respectively.

U. dioica cultivated soils: Cu (26-104, 47-

186, 87-169); Mn (553-651, 496-580, 554-

640); Zn (92-185, 147-255, 204-428); Pb

(136-528, 202-971, 198-854), Cr (21-28,

25-43, 21-30); Pd (55-76, 57-72, 59-72)

respectively.

T. pratense cultivated soils: Cu (36.1-93.1,

41.5-160.2, 49.3-126.4); Mn (510.7-640,

467.2-592, 548-597); Zn (126.5-196.2,

201.2-369, 255.1-492.2); Pb (126-698, 110-

1266, 80-1051), Cr (22-29, 23-37, 15-30);

Pd (37-70, 45-71, 47-68) respectively

Malizia et al.

(2012)

40. Cd, Cr,

Mn, Ni,

and Pb

Lisbon

(Portugal,

Spain)

Soils from three different

localities affected due to

anthropogenic activities

and mainly from car

exhaust: Serra de

Monsanto- a green park (4

sampling stations samples

viz. Station 1, Station 2,

Station 3 and Station 4),

Lisbon Botanical Garden,

and Cabo da Roca

(reference area)

HCI+

HNO3+HClO+

AASa (Unicam

939 model,

Cambridge, UK)

Heavy metal content (μg/g) observed:

Station 1, 2, 3 and 4 at Serra de

Monsanto park: Ni (36.9, 10.6, 36.1,

15.9), Pb (164, 62.2, 65.8, 67.9), Mn (402,

253, 347, 302), Cd (nd, 0.53, 0.43, 0.44),

Cr (28.4, 1.98, 15.7, 3.6)

Lisbon botanical garden: Ni (23.4), Pb

(275), Mn (142), Cd (nd), Cr (49.1).

In Cabo da Roca soils: Ni (9.7), Pb (15.4),

Mn (617), Cd (nd), Cr (2.2)

Reboredo

et al. (2012)


41.

Cd, Ni,

Zn, Fe,

Cu,

Mn, Pb,

Cr, Hg

and As

Waziraba

d- Okhla

barrage,

New

Delhi

(India)

Agricultural soil from

fields along the course of

River Yamuna which is

affected due to pollution of

anthropogenic, due to

mining, industrial

processing, agricultural

run-off and sewage

disposal

HNO3+H2O2 Flame AASa

(Analytic

Jena, 5FL model)

for Cd, Ni, Zn,

Fe, Cu, Mn, Pb,

and Cr.

Hydride system

AASa for As and

Hg


observed:

Cd (nd), Ni (17.45-40.25), Zn (31.9-

136.85), Fe (4431.5-4,915.3), Cu (9.3-

36.45), Mn (nd-543.8), Pb (nd-114.65), Cr

(17-145.95), As (2.03-7.07) and Hg (nd-

82.05)

Sehgal et al.

(2012)

42. Ni, Cu,

Zn, Cd,

and Pb

Makurdi (

North-

central

Nigeria.)

Surface soils were collected

from:

A major agricultural zone

nearby Benue Industrial

Layout (rapidly growing

city) and composited as the

parent soil (PS).

A pretreated composite

sample of poultry manure

(PM) from a poultry farm

in Makurdi

HNO3+ HCI AASa Heavy metal content (mg/kg) observed:

In PS soils: Ni (0.18), Cu (1.2), Zn (1.5),

Cd (0.34) and Pb (1.3).

In PM soils: Ni (11.8), Cu (372.7), Zn

(479.2), Cd (0.85), Pb (3.6)

Wuana et al.

(2012)

43. Cd, Pb,

Cu, Ni,

Fe, Mn

and Zn

Dubai

(United

Arab

Emirates)

Roadside soil near different

traffic signals affected by

automobile emissions

HNO3+ HClO4 GBC AASa

(model, 906)

Range of heavy metal (mg/kg) observed:

Sites with one traffic signal: Cd (nd–0.80),

Pb (145.95-308.09), Cu (0.82-18.04), Ni

(18.29-59.36), Fe (88.51–3649.42), Mn

(25.88-147.34) & Zn (8.97-106.11).

Sites with no traffic signals: Cd (nd-0.93),

Pb (8.34–113.26), Cu (nd–5.81), Ni (nd–

73.80), Fe (nd–332.81), Mn (nd–102.58)

and Zn (1.23–46.6).

Aslam et al.

(2011)


44. Cu, As,

Co, Cd,

Fe, Hg,

Ni, Zn,

Pb and

Cr

Thane district,

Maharashtra (India)

Soil from residential

industrial and agricultural

land

HNO3 ICP-AESc (Model

-9000, Shimadzu,

Japan).


observed:

In residential soils: Cu (94.6-185), As (nd-

6.54), Co (56.6-85.4), Cd (28.7-38.6), Fe

(45.70-74.8), Hg (nd-1.54), Ni (77.2-465),

Zn (73.1-88.9), Pb (35.9-49.2) and Cr

(66.2-269.4)

In Industrial soils: Cu (51.5-373), As (nd-

6.86), Co (51.1-97.5), Cd (23.2-36), Fe

(44.2-68.7), Hg (nd-1.3), Ni (68.9-347), Zn

(64.40-7,120), Pb (39.4-48.6) and Cr (97.5-

279.4)

In two Agricultural soils: Cu (161, 206),

As (nd), Co (41.7, 70.3), Cd (21.6, 33), Fe

(30.30, 51.20), Hg (nd), Ni (89,120), Zn

(124, 188), Pb (38.6, 42.9) and Cr (79.9-

109.9)

Bhagure and

Mirgane,

(2011)

45. Cr, Pb

& Hg

Kolkata

(West

Bengal)

Agricultural Soils under

rice cultivation utilizing

metropolitan sewage

effluent of Kolkata

metropolitan city, near East

Kolkata Wetland for

irrigation

HNO3+ HCl AASa

(AA50 –

VARIAN)


Cr (55.6-105.2), Pb (18.9-79.7), Hg (0.19-

9.65).

Chanda et al.

(2011)

46. Cu, Zn,

Cd and

Pb

Shangyu

City,

eastern

China

Soils of different types of

land uses, such as

forestland, agricultural land

and mining area from the

vicinity of an abandoned

mine area polluted by

heavy metals from the

tailings of the Pb–Zn mine.

HNO3+HCl+HClO4

ICP-OESd (PE

b

Optima-2000,

USA).

ICP-MSh (Agilent

7500i, USA)


observed:

In minning areas: Cu (73.5-430.4), Zn

(243.8-552.6), Cd (1.1-36.6) and Pb

(851.7-19,631.6).

In paddy field soils: Cu (25.6-26.0), Zn

(83.1), Cd (0.4) and Pb (415.8).

In Non-agricultural land: Cu (6.2-6.9),

Zn (26.0-33.7), Cd (<0.1-0.6) and Pb

(142.0-297.4).

Cui and Du,

(2011)


In forestry field soil: Cu (140.6), Zn

(516.9), Cd (14.3) and Pb (8,621.2)

In vineyard soil: Cu (40.3), Zn (85.1), Cd

(0.1) and Pb (76.9)

47. Zn, Pb,

Cd, As

and TI

Les

Malines

Mining

District

(Southern

France)

Topsoils from Zn- and Pb-

contaminated sites (Petra

Alba (PA), Les Avinières

mine (AM) and Les

Avinières tailing ponds

(AT) mine sites)

HCl+ HNO3 ICP-OESd

(Varian Vista

MPX)


observed:

In PA soils: Zn (4,690-6,107), Pb (3,967-

4,415), Cd (37.7-37.9) and Ti (0.82-1.16).

In AM soils: Zn (9,929-91,454), Pb

(4,135-84,130), Cd (35.2-108) and TI (6.4-

46.8).

In AT soils: Zn (129,032-131,365), Pb

936,354-38,305), Cd (700-745) and TI

(27.0-115.1).

Escarre et al.

(2011)

48. Cu and

Ni

Severonikel

(Moncheg

orsk)

Vegetated soils of the zone

affected by aerial emissions

from the Severonikel

Enterprise (pollution

source) dealing with ore

processing and smelting

HNO3 AASa (AAS-30) Heavy metal content (mg/kg) at 0.5, 1, 3, 5,

7, 13, 15, 18, 21, 25, 30, 35, 40 and 50 km

distance from pollution source :

Cu (3514,2148, 1331, 839, 585, 581, 423,

474, 596, 307, 195, 112, 139, 76) and Ni

(4197, 5214, 2469, 1462, 1030, 678, 586,

685, 598, 590, 254, 176, 176, 56)

respectively

Evdokimova

et al. (2011)

49. Cd, Cr,

Ni, Pb,

Zn, and

Cu

Tirana

(Albania)

Vegetated surface soils

from the Tirana area, not

subjected to point pollution

sources

HCl+HNO3 Flame AASa (PE

b

4100).


observed:

Cd (0.1-2.3), Cr (101.4-323.2), Ni (156.0-

559.2), Pb (7.8-57.2) Zn (68.2-178.4) and

Cu (34.7-56.8)

Gjoka et al.

(2011)


50. Cr, Co,

Ni, Cu,

Zn, As,

Cd, Pb,

Sr and

Hg

Zambia

(Africa)

Soils from road side,

industrial areas of various

cities and towns (Lusaka,

Kabwe, and the Eastern,

western, southern and

Northern areas). Potential

sources of HM‟s were the

mining activities

H2SO4+HNO3+

HClO4

Cr, Co, Ni, Cu,

Zn, As, Sr, Cd,

and Pb by

Analyst™ 800

AASa (PE

b, USA)

with either an

acetylene flame

(Cu and Zn) or an

argon non-flame

(Cr, Co, Ni, As,

Sr, Cd and Pb).


In soils of Lusaka: Cr (39), Co (11), Ni

(20), Cu (343), Zn (147), As (4), Sr (9), Cd

(0.11), Hg (0.02), Pb (48).

In soils of Kabwe: Cr (39), Co (46), Ni

(47), Cu (572), Zn (16991), As (32), Sr

(13), Cd (7.12), Hg (01), Pb (7076).

In soils of Eastern area: Cr (31), Co (11),

Ni (18), Cu (37), Zn (32), As (2), Sr (3), Cd

(0.05), Hg (0.00), Pb (13).

In soils of Western area: Cr (31), Co (11),

Ni (18), Cu (37), Zn (32), As (2), Sr (3), Cd

(0.05), Hg (0.00), Pb (13).

In soils of Northern area: Cr (33), Co

(94), Ni (29), Cu (1646), Zn (99), As (3),

Sr (23), Cd (0.14), Hg (0.02), Pb (52).

In soils of Southern area: Cr (17), Co (7),

Ni (15), Cu (39), Zn (42), As (3), Sr (3), Cd

(0.06), Hg (0.01), Pb (27).

Ikenaka

et al. (2010)

51. Co, Cu,

Fe, Mn,

Ni, Pb,

and Zn

Karachi

and

Lahore

(Pakistan)

Roadside soils from the

main highway National

Highway (N-5) between

Karachi and Lahore,

polluted due to vechicular

emmisions

HNO3+HCl Flame AASa

(PEb

AA2380)

Range of heavy metal content (μg/g)

observed:

Pb (12.30-176.05), Zn (13.83-180.00), Cu

(5.26-26.88), Ni (5.96-13.23), Cd (0.56-

1.25), Co (2.61-13.73), Mn (117-309.00),

Hg (61.50-144.05) and Fe (2.70-7.98 mg/g)

Khan et al.

(2011)

52. Pb, As,

Cu,

Mo, Sb,

Zn, Sn,

Cd and

Bi

Rafsanjan,

Kerman

(Iran)

Surface soil around

Sarcheshmeh copper mine

smelter plant which is one

of the biggest copper

producers in Iran and the

world‟s largest Miocene

porphyry copper deposits.

HF+ HClO4 Flame AASa (PE

b

4100).


observed

Pb (10.2-338), As (4.4-500), Cu (41.25-

8,430), Mo (0.6-61.1), Sb (0.5-56.2), Zn

(36-664), Sn (1.2-10.2), Cd (nd-17.2) and

Bi (nd-13.4)

Khorasanipo

ur and

Aftabi

(2011)


53. Ni, Cd,

Co, Cu,

Zn, and

Pb

Bafra

Plain

(Turkey)

Agricultural soils from

feilds with application of

high rates of phosphorus

fertilizers and intense soil

cultivation

HCl+HNO3 AASa (PE

b A400

spectrophotometer) Range of heavy metal content (mg/kg)

observed:

Cd 90.31-3.18), Co (6.52-32.94), Cu (9.57-

82.36), Ni (5.46-208.88), Pb (4.45-33.33)

and Zn (20.51-90.13)

Kizilkaya

et al. (2011)

54. As, Ba,

Co, Cr,

Cu,

Mo, Ni,

Pb, Sr,

V, Zn

and Zr

Karnataka

(India0

Soils around three different

active (Tagdur), abandoned

(Jambur) chromite mining

sites as well residential zone

around Chikkondanahalli

of the Nuggihalli Schist

Belt

Soil pellets XRFg (

Philips

PW2440 model

MagiX PRO)

Range of eavy metal content (mg/kg)

observed:

As (0.00-11.20), Ba (168.30-602.300, Co

(2.10-74.20), Cr (64.10-4863), Cu (1.50-

153.60), Mo (0.00-6.90), Ni (0.80-

1,398.20), Pb (0.60-84.10), Sr (14.40), V

(4.60-141.50), Zn (57.00-655.20) and Zr

(14.60-945.50)

Krishna et

al. (2011)

55. Cd, Hg,

As,

Pb, Cr,

Cu, Zn,

and Ni

Shandong

Province

(China)

Agricultural soils (grain-

vegetable cropping area).

Pollution is due to use of

agrochemicals

HCl+ HNO3+ HF GF AASa for Cd,

Pb, Cr, Cu, Zn,

and Ni

Cold-vapor AFSe

for Hg and As


Cd (0.15), Hg (0.09), As (9.02), Pb (16.18),

Cr (41.78), Cu (29.28), Zn (82.26) and Ni

(28.00).

Liu et al.

(2011)

56. Cr, Cu,

Ni, Zn,

Pb, As,

Cd, and

Hg

Changhua

County

(Taiwan)

Agricultural soils were

taken. Study area has plants

of metalwork, electroplating,

textile & metal surface

treatment industries & has

been suspected of

discharging wastewater into

irrigation channels used for

agricultural activities.

HNO3+HCl ICP-OESd Range of heavy metal content (mg/kg)

observed:

Cr (22.6-3070.0), Cu (11.0-3810.0), Ni

(21.3- 4020), Zn (60.5- 7850), Pb (6.11-

148.27), As (4.6-37.4), Cd (<0.116-18.0)

and Hg (0.038-5.35).

Lin et al.

(2011)

57. Cd, Hg,

As,

Pb, Cr,

Cu, Zn,

and Ni

Shandong

Province

(China)

Soils from greenhouses and

farmlands from four main

vegetable production areas

HNO3+HF+ HCIO4 Flame AASa for

Cu, Zn, Ni, and

Cr

GFf AAS

a for Pb

and Cd

Cold vapor AFSe

for Hg and As.

Heavy metal content (mg/kg) in soils of

vegetable production areas:

Cd (0.15), Hg (0.09), As (9.02), Pb (16.18),

Cr (41.78), Cu (29.28), Zn (82.26) and Ni

(28.00).

Liu et al.

(2011)


58. Cd, Cu,

Pb, and

Zn

Guangdon

g province

of South

China.

Soil from different sites

where e-waste is burned in

the open air (incineration

site), those of surrounding

paddy fields, vegetable

gardens, deserted soils and

pond areas

HNO3 +HClO4

ICP-AESc, (PE

b

Optima 3300DV)

Cd ICP-MSh,

(PEb Elan

6100DRCPlus)

Heavy metal content (mg/kg) in soils from

vegetable garden, paddy, incineration site,

deserted and pond soil :

Cd (0.9, 1.0, 17.1, 0.25, 5.45), Cr (12.3,

17.3, 68.9, 6.41, 38.9), Cu (324, 155,

11140, 72.4, 3550), Ni (8.83, 34.5,

60.1,10,3 2.2), Pb (95.6, 61.8, 4500, 52.2,

1880), Zn (122, 166, 3690, 62.6, 1160)

respectively.

Luo et al.

(2011)

59. Cr, Cu,

Ni, Pb,

Zn Co

and V

Hyderaba

d (India)

Contaminated soils around

Balanagar industrial area

with steel, petrochemicals,

automobiles, refineries and

battery manufacturing

industries.

Soil pellets XRFg (

Philips

PW2440 model

MagiX PRO)

Range of eavy metal content (mg/kg)

observed:

Cr (82.2-2,264), Cu (31.3-1,040), Ni (34.3-

289.4), Pb (57.5-1,274), Zn (67.5-5819.5),

Co (8.6-54.8) and V (66.6-297).

Machender

et al. (2011)

60. Cd, Co,

Cr, Cu,

Mn,Ni,

Pb, and

Zn

Guwahati,

Assam

(India)

Soils from different types

of land use, viz.,

residential, commercial,

industrial, public utilities

(parks, place of worship,

etc.), and roadside affected

due to variety of natural

and anthropogenic

processes (weathering

of rocks, atmospheric

deposition of particles from

industrial emissions, and

the application of fertilizers

HCl+HNO3+H2SO4 Flame AASa (PE

b

AAnalyst200


observed:

In Residential soils: Cd (9.2-18.0), Co

(22.5-50.7), Cr (70.3-122.9), Cu (124.4-

162.0), Mn (194.8-456.6), Ni (29.7-119.0),

Pb (71.0-175.0) and Zn (117.4-438.6).

In commercial soils: Cd (8.0-14.0), Co

(31.0-51.2), Cr (60.1-132.0), Cu (77.1-

151.6), Mn (223.2-466.3), Ni (78.8-112.1),

Pb (139.5-228.0) and Zn (165.8-1606.2).

In Industrial soils: Cd (6.5-12.3), Co

(29.3-55.0), Cr (101.8-730.7), Cu (130.3-

247.6), Mn (300.8-636.0), Ni (24.1-84.5),

Pb (129.0-272.5) and Zn (138.0-461.0).

Mahanta and

Bhattacharya

, (2011)


In Utilities soils: Cd (6.9-11.9), Co (36.6-

49.6), Cr (96.4-157.6), Cu (93.7-238.1),

Mn (246.8-503.8), Ni (23.9-144.2), Pb

(26.0-185.5) and Zn (132.6-1117.2).

In Roadside soils: Cd (3.1-15.9), Co (21.6-

61.9), Cr (99.9-128.6), Cu (103.9-239.8),

Mn (321.8-512.2), Ni (65.8-127.4), Pb

(110.0-285.5) and Zn (166.8-697.2).

61. Pb, Zn,

Cd, Cu,

Cr, Ni,

Co

Pazang &

Lahor

sites in

Besham

area,

Kohistan

region

(Northern

Pakistan)

Planted soil near Pb–Zn

sulfide bearing mineralized

zone of Indian plate(IP) in

the Pazang and Lahor sites

HF+HCl AASa (PE

b AAS-

PEA-700).

Range of heavy metal content (mg/kg) in

soils of:

Pazang: Cr (64-318), Ni (42-756), Co (92-

169), Cu (32-412), Pb (12-1337), Zn (95-

1072) and Cd (0.6-5.0).

Lahor: Cr (18-324), Ni (92-850), Co (45-

136), Cu (111-345), Pb (5-10300), Zn

(213-29755) and Cd (0.4-184)

Muhammad

et al. (2011)

62. As, Cu,

Cd, Pb,

V and

Zn

Niger

Delta,

Nigeria

Soils (Dump site,

industrial, commercial and

gas flaring) of Port

Harcourt and its environs

from the Niger Delta region

characterized by a lot of

industrial and commercial

activities

HCl+HNO3 AASa Range of heavy metal content (×10

-6)

observed:

Pb (2.49-8.86), Cd (0.00-0.05), As (0.00-

0.01), Zn (7.45-95.00), Cu (10.80-15.60)

and V (0.20-1.36)

Odigi et al.

(2011)

63. Zn, Pb,

and Cd

Ishiagu,

Nigeria.

Soil from clans that make

up Ivo local government of

Ebonyi State & consists of

17 villages at sampling

distance of 1, 100, 200, and

300m from the quarry site

(quary is land-use practice

for extracting non-fuel

minerals from rock)

HNO3+ HCIO4 Flame AASa

(UNICAM 919

model).

Heavy metal content (mg/kg) at sampling

distance of 1, 100, 200, 300 and 2000:

Zn (101.00, 34.01, 11.02, 2.10, 0.17); Pb

(36.05), 28.00, 15.03, 7.04, 0.08); Cd

(12.31, 8.07, 3.03, 1.48, 0.02) respectively.

Ogbonna

et al. (2011)


64. Pb, Cd

and Ni

Akure

City

(Nigeria)

Soil samples from different

parts of the city

(mechanic/battery charger

workshops, markets, petrol

stations, vulcanizer

stations, motor parks,

roadsides, and gutter

deposits)

HF+ HCI+ HNO3 Flame AASa Heavy metal content (mg/kg) observed:

Roadside soils: Pb (6.84), Cd (7.48) and

Ni (14.92)

Petrol station soils: Pb (6.84), Cd (8.2)

and Ni (13.51)

Vulcanizer workshops: Pb (6.92), Cd

(8.45) and Ni (16.7)

Markets: Pb (6.27), Cd (7.17) and Ni

(10.35)

Gutter deposits: Pb (6.72), Cd (7.73) and

Ni (9.22)

Motor parks: Pb (6.81), Cd (8.79) and Ni

(16.32)

Mechanic/battery charger workshops:

Pb (10.34), Cd (14.78) and Ni (46.19)

Olorundare

et al. (2011)

65. Cd, Hg,

As,

Pb, Cr,

Cu, Zn,

and Ni

Shandong

Province

(China)

Top soils (0-20 cm) from

greenhouses and farmlands

of four main vegetable

production areas

Shouguang, Laiyang,

Jinxiang, and Zhangqiu

affected by anthropogenic

activities such as vehicle

and industrial fumes, waste

water irrigation. And use of

agrochemicals.

HCl+HNO3+HF+

HClO4

Flame AASa for

Cu, Ni, Cr and

Zn.

Graphite furnance

AASa for Cd and

Pb

Cold vapor AFSe

for As and Hg


observed:

Cd (0.03-0.99), Hg (0.01-0.34), As (4.68-

18.66), Pb (8.97-26.19), Cr (7.27-74.98),

Cu (1.20-159.78), Zn (24.5-222.60) and Ni

(15.90-74.83)

Ping et al.

(2011)

66. As, Cu,

Pb, Zn,

Mo and

Cd

Kerman

(Iran)

Topsoils and subsoils

contaminated due to due to

copper smelting and toxic

emissions from

Sarcheshmeh Copper

Deposit, the largest

porphyry copper deposit

HCl+ HNO3 ICP-OESd Range of heavy metal content ppm)

observed:

In top soils: As (23.7-427), Cu (64-

12,100), Mo (0.8-56.8), Zn (77-894), Cd

(0.2-12.8), Pb (9-332).

In sub soils: As (2.5-293), Cu (45-2,170),

Mo (0.5-7.7), Zn (54-486), Cd (0.1-2.9)

and Pb (4-790

Rastmanesh

et al. (2011)


67. Cd, Cr,

Cu, Pb,

Zn, As,

Mn &

Hg

Gorakhpur,

UP (India) Agricultural soils irrigated

with lake water (Lake

Ramgarh)

HNO3+ HClO4+HF AASa

(Varian

Spectra AA-250

Plus)


Zn (70), Cr (62), Mn (37), Cu (20), Pb

(17), As (5) Cd ( 0.02)

Singh et al.

(2011)

68. Cd, Cr,

Cu, Ni,

Pb and

Zn

Beijing

(China)

Urban soils with landuse

like business area (BA),

classical garden (CG),

culture and education area

(CEA), public green space

(PGS), residential area

(RA) and roadside area

(RSA) affected by

anthropogenic sources

HNO3+HF+ HClO4 ICP-MSh (X

Series II, Thermo

Fisher Scientific)

for Cd, Cr, Cu,

Ni, & Pb

ICP-OESd (IRIS

Intrepid II,

Thermo Fisher

Scientific) for Zn


the urban soils of Beijing :

Cd (0.082-0.591), Cr 39.23-102.1), Cu

(15.12-212.3), Ni (16.43-40.32), Pb (20.53-

135.1), Zn (42.33-307.9)..

Xia et al.

(2011)

69. Cd, As,

Pb, Zn,

Mn,

Cu, Cr

and Hg.

Gebze

town of

Kocaeli

City

(Turkey)

Agricultural soils in the

vicinity of large industrial

zones; the Gebze

Organized Industrial Zone

(GOIZ) & Dilovası

Organized Industrial Zone

(DOIZ). Industrial facilities

including paint, plastic,

electric, metal, textile,

automotive cosmetics, etc.

operates in these industrial

zones.

HCl+ HNO3 ICP-MS h Average and range of heavy metal content

(mg/kg) observed:

As 953 (1.50-65.60), Cd 4.41 (0.05-176),

Cr 118 (10-1161), Co 95.88 (7.87-725),

Mn 1824 (196- 10,000), Pb 246 (17,07-

8469), Zn 632 (29.50-10,000), Hg 102

μg/kg (9-2721).

Yaylal-

Abanuz

et al. (2011)

70. Hg, As,

Cr, Cd,

Pb, Cu,

Zn and

Fe

Three

Gorges

Reservoir

(TGR)

(China)

Vegetated soils from TGR

with water-level-fluctuation

zone (WLFZ)

HF+HClO4+HNO3 Flame AASa

(Analytikjena

vario6, Germany)

for Cr, Cd, Pb,

Cu, Zn, Fe, and

Mn.

Cold vapor AASa

for Hg

Heavy metal content (mg/kg) before and

after submergence:

Hg (0.07, 0.12); As (22.24, 6.59); Cr

(41.99, 44.72); Cd (0.40, 0.49); Pb (35.41,

42.89); Cu (29.78, 35.70); Zn (76.57,

88.09) respectively.

Ye et al.

(2011)


Diethyl disulfide

and carbamate

silver (DDC-Ag)

colorimetric

method for As

71. Cd, Cr,

Cu, Ni,

Pb and

Zn

Gijang

county,

Busan

(South

Korea)

Vegetated soils

contaminated with mine

drainage (CS) from an

abandoned mine and of a

nearby non-contaminated

area (NC)

HCl ICP AESc Heavy metal content (mg/kg) for CS and

NC soils:

Cd (7.12, 0.13); Cr (0.32, 0.11); Cu (21.67,

19.33); Ni (1.77, 0.22); Pb (249.70, 13.08)

and Zn (205.70, 8.67) respectively.

Yong- Park

et al. (2011)

72. Pb, Cu,

Ni, and

Zn

Edirne

(Turkey)

Roadside soils (sampling

locations viz. Kapıkule,

Selimiye Park, Autobahn,

Terminal station, Sarayici,

Industry field, University,

and Train station) polluted

due to traffic pollution of

Edirne-Turkey

HNO3 Graphite-AASa

(Varian AA280Z)

ICP-OESd (PE

b

Optima 2000DV).


In Kaprkule soils: Pb (3.63), Cu (15.33),

Ni (182.9) and Zn (331.5).

In Selimiye park soils: Pb (6.079), Cu

(15.99), Ni (91.01) and Zn (219.0).

In Autobahn soils: Pb (1.547), Cu (15.38),

Ni (133.5) and Zn (186.4).

In Terminal station soils: Pb (0.885), Cu

(6.64), Ni (53.31) and Zn (161.5).

In Sarayici soils: Pb (12.38), Cu (47.29),

Ni (101.6) and Zn (485.6).

In Industry station soils: Pb (3.399), Cu

(25.81), Ni (272.1) and Zn (202.0).

In University soils: Pb (1.034), Cu

(14.90), Ni (66.66) and Zn (171.9).

In Train station soils: Pb (1.172), Cu

(18.99), Ni (129.5) and Zn (181.8).

Aktas and

Kocabas (2010)


73. As, Cd,

Cu, Pb,

Zn

Eastern

region of

Yilong

lake

(China)

Soils from uncultivated,

cultivated wetland &

cultivated wetland after

abandonment

HClO4+HNO3+HF ICP-AESc Range of heavy metal content (mg/kg)

observed:

Wetland soils: As (10.32-24.16), Cd (0.79-

2.92), Cu (19.70-41.77), Pb (20.58-59.00),

Zn (42.91-11.64).

Abandoned tillage soils: As (6.85-12.12),

Cd (0.27-3.93), Cu (29.82-45.08), Pb

(36.75-62.75), Zn (57.21-97.94)

Conventional tillage soils: As (3.65-

15.31), Cd (0.11-1.18), Cu (14.53-61.40),

Pb (16.26-60.71), Zn (40.52-168.17).

Bai et al.

(2010)

74. Fe, Pb,

Cu, K,

Mn, Cr

and Zn

Turkey Soils around seismically

active area from the East

and North Anatolian Faults

System (EAFS) affected by

natural or artificial

radioactive nuclides

HNO3 AASa (ATI

UNICAM

9290


observed:

Pb (1.05-11.64), Cu (0.55-5.73), Mn (100-

9800, Cr (1.48-247.26), Zn (6.34-187.85)

and Fe (1.12-12.75 g/kg)

Baykara and

Dogru,

(2010)

75. Ti, Mn,

Zn, Pb,

As, Fe,

Rb, Sr,

Nb and

Zr

Barapukuria coal basin

located in

Dinajpur

District (Northern

part of

Bangladesh)

Soils of mine drainage and

surrounding agricultural

fields

HNO3 EDXRFj

Spectrometer

(Canberra, Model

SL 80175), for

Ti, Mn, Zn, Pb,

As, Fe, Rb, Sr,

Nb and Zr

AASa

(PEb Model

3110) for As


Ti (14427), Mn (1886), Fe (59853), Zn

(296), As (17.55), Rb (200), Sr (296), Pb

(296), Zr (433), Nb (877).

Bhuyian et

al. (2010)

76. Pb, Cd,

Cr, As

and Hg

Chongmin

g Island,

Shanghai

(China)

Agricultural top soils

(vegetable plots, paddy and

watermelon fields) where

crop straw burning was the

important source of heavy

metals of atmospheric

deposition.

HNO3+HF+ HClO4 AASa (PE

b

AANALYST

800) for Cr, Cd

and Pb

AFSe

(Titan

AFS9230) for As

and Hg


In paddy fields soils: Pb (22.2), Cd (0.17),

Cr (68.1), As (8.61) and Hg (0.12)

In vegetable soils: Pb (22.2), Cd (0.17), Cr

(68.1), As (8.61) and Hg (0.12)

In watermelon fields soils: Pb (19.4), Cd

(0.18), Cr (70.3), As (8.13) and Hg (0.14)

Chao et al.

(2010)


77. Cr, Co,

Ni, Cu,

Zn, As,

Cd, Pb,

Sr and

Hg

Zambia

(Africa)

Soils from road side,

industrial areas of various

cities and towns (Lusaka,

Kabwe, and the Eastern,

western, southern and

Northern areas). Potential

sources of HM‟s were the

mining activities

H2SO4+HNO3+

HClO4

Cr, Co, Ni, Cu,

Zn, As, Sr, Cd,

and Pb by

Analyst™ 800

AASa (PE

b, USA)

with either an

acetylene flame

(Cu and Zn) or an

argon non-flame

(Cr, Co, Ni, As,

Sr, Cd and Pb).


In soils of Lusaka: Cr (39), Co (11), Ni

(20), Cu (343), Zn (147), As (4), Sr (9), Cd

(0.11), Hg (0.02), Pb (48).

In soils of Kabwe: Cr (39), Co (46), Ni

(47), Cu (572), Zn (16991), As (32), Sr

(13), Cd (7.12), Hg (01), Pb (7076).

In soils of Eastern area: Cr (31), Co (11),

Ni (18), Cu (37), Zn (32), As (2), Sr (3), Cd

(0.05), Hg (0.00), Pb (13).

In soils of Western area: Cr (31), Co (11),

Ni (18), Cu (37), Zn (32), As (2), Sr (3), Cd

(0.05), Hg (0.00), Pb (13).

In soils of Northern area: Cr (33), Co

(94), Ni (29), Cu (1646), Zn (99), As (3),

Sr (23), Cd (0.14), Hg (0.02), Pb (52).

In soils of Southern area: Cr (17), Co (7),

Ni (15), Cu (39), Zn (42), As (3), Sr (3), Cd

(0.06), Hg (0.01), Pb (27).

Ikenaka

et al. (2010)

78. Zn and

Cd

Hangzhou

city,

Zhejiang

province

(East

China).

Agricultural soils near

copper smelter with HM

pollution were collected

namely a less polluted (S1)

& a more polluted (S4)

soil. Soil were mixed for

transitional levels, S2 (2

parts S1 & 1 part S4) & S3

(1 part S1& 2 parts S4).

HNO3+HCl AASa (Varian

SpectrAA 220

FS, 220Z, Varian,

Palo Alto, CA)

Heavy metal content (mg/kg) for S1, S2,

S3 and S4:

Cd (1.11, 5.82, 10.6, 15.3) and Zn (321,

2367, 4343, 6499) respectively.

Jiang et al.

(2010)

79. Cr, Co

and Ni

Vietnam Agricultural soils around

the Co Dinh mine is the

largest chromite mine in

the country

HNO3+HClO4 AASa for Co, Cr,

Cu, Ni and Pb.

ICPAESi for As


Cr (5,750) Co (375) and Ni (5590).

Kien et al.

(2010)


80. Cu, Zn,

Ni, Pb,

Cd and

Hg

Dongguan

City, Guangdong

(China)

Agricultural soils affected

due to rapid industrialization and urbanization.

HNO3+HCl+HF+H

ClO4

AASa Range of heavy metal content (mg/kg)

observed:

Cu (5.08–105.60), Zn (8.43–169.50), Ni

(2.29–57.46), Zn (20.36–137.20), Ni (2.29-

57.46), Cr (2.28-86.59), Pb (20.36-137.20),

Cd (0.02–0.67), As (0.40–28.87) and Hg

(0.01-1.01)).

Limei et al.

(2010)

81. Cr, Ni,

Cu, As,

Cd, Pb,

and Zn

Siping

City

located in

the

middle-

north of

Jilin

Province

(China)

Soils under four land use

patterns (greenhouse field

(GF), uncovered vegetable

field (UF), maize field

(MF) & forest field (FF).

HNO3+H2O2 ICP-MSh

(PQExCell,

TJA Solutions,

USA)

Heavy metal content (mg/kg) for GF, UF,

MF and FF soils:

Cr (67.50, 56.79, 46.49, 35.44); Ni (25.17,

23.91, 16.76, 12.74); As (12.47, 10.69,

8.38, 6.56); Cd (0.467, 0.209, 0.114,

0.089); Pb (17.98, 16.19, 15.74, 16.28) and

Zn (87.68, 68.47, 49.11, 40.32),

respectively.

Ling-yu et

al. (2010)

82. Pb, Cd,

Cu and

Zn

Belgrade

(Serbia)

Agricultural soils contaminated

with pesticides and heavy metals.

HCl+ HNO3 Flame AASa (A

Varian SpectrAA

220)


observed:

Pb (13.4-27.4), Cd (0.11-0.27), Cu (20.5-

34.4) and Zn (75.3-142)

Markovic et

al. (2010)

83. Fe, Zn,

Mn,

Cu, Pb,

Cr and

Cd

Middle

Benue

Trough,

Nigeria

Surface soils from

farmlands close to the mine

dump and unmineralized

areas in the vicinity of

Arufu lead-zinc mine.

HNO3+HCl+HF AASa (model

ZGP 210,

Gallemcent

manufactured)


Farmlands soils nearby mine and in

unmineralized areas: Fe (154,178), Zn

(171, 150), Mn (137, 130), Pb (40, 52), Cu

(20, 17), Cr (2, 2) and Cd (4,3)

respectively.

Nganje et al.

(2010)

84. Pb, Cr,

Cu, and

Zn

Western

Kenya

Top soil samples from

Nzoia Nucleus estate

sugarcane farms polluted

due to extensive use of

inorganic fertilizers durin

dry and wet season.

HNO3+HCl AASa (AA-6200,

Shimadzu)


In Dry season soils: Cr (142.38), Pb

(59.12), Cu (73.35), Zn (116.27) and Pb

(409.84).

In wet season soils: Cr (144.22), Pb

(50.29), Cu (72.14), Zn (158.81) and Pb

(368.83).

Omwoma et

al. (2010)


85. Pb, Zn

and Cd

Zanjan

city (Iran)

Topsoil samples in the Zinc

Industrial Complex in

Zanjan which is a large

metalliferous site whose

emissions are most possibly

the major source of heavy

metal

HCl+ HNO3 AASa (Varian,

Germany).


Pb (63), Zn (3066) and Cd (18.5) in most

polluted area of Zanjan city.

Parizanganeh

et al. (2010)

86. As, Cd,

Cu, Pb,

and Zn

Kocani

(Macedonia) Agricultural soils (Paddy

fields of Kocani) irrigated

with Zletovska and

Bregalnica river water

which is contaminated due

to of the discharge of acid

mine and untreated

effluents from the ore-

processing facilities from

the Zletovo mine and Sasa

mine into the above

mentioned rivers.

HCl+HNO3+H2O ICP-MSh Heavy metal content (mg/kg) observed:

As (8.3-42.0), Cd (0.2-5.6), Cu (26-99), Pb

(24-892) and Zn (100-1,134)

Rogan et al.

(2010)

87. Cd, Co,

Cr, Cu,

Mn, Ni,

Pb and

Zn

Zagreb

area

(Northwest

Croatia)

Anthropogenic sources of

heavy metal contamination

are mainly

combustion processes in

industry and transportation.

HNO3+HCl ICP-OESd on a

Vista MPX AX

(Varian).


observed:

Cd (0.02-4.94), Co (2.74-47.2), Cr (11.5-

400), Cu (3.64-13335), Ni (0.70-488), Pb

(1.00-216) & Zn (27.1-479).

Sollitto et al.

(2010)

88. Cu, Ni,

Pb, and

Zn

Spain and

New

Zealand

Soils from spain were

agricultural soils exposed to

sewage sludges spiked with

heavy metal salts where as

soils from New Zealand

includes sludge spiked soils,

pasture soils impacted by

runoff from the timber

industry with preservative

salts containing Cu and Cr,

HNO3 ICP-OESd

(Varian 720-ES,

Melbourne,

Australia)


observed:

Spain agricultural soil: Cd (0.41-1.29), Cr

(16.3-57.0), Cu (16.3-59.0), Ni (6.3-18.0),

Pb (11.1-68.4) and Zn (34.4-96.0).

New Zealand sludge spiked soils: Cd

(0.09-0.16), Cr (12.6-19.5), Cu (3.7-119.4),

Ni (4.8-10.8), Pb (12.1-13.8) and Zn (46.9-

386.4)

Soriano-

Disla et al.

(2010)


orchard grown soils where

Cu salts were used as a

fungicide and soil from

galvanized steel power

plant.

Pasture soils: Cd (0.71-0.87), Cr (23.6-

526.3), Cu (16.3-489.1), Ni (7.7-10.0), Pb

(15.6-43.8) and Zn (84.1-162.5)

Orchard soils: Cd (2.08-5.63), Cr (22.0-

29.2), Cu (17.9-919.5), Ni (16.4-20.7), Pb

(46.0-379.9) and Zn (46.0-142.8)

Galvanized steel power plant: Cd (2.48),

Cr (23.1), Cu (6.2), Ni (10.9), Pb (13.3)

and Zn (489.9).

89. Fe, Ti,

As, Sb,

Zn As,

Au, Br,

Co, Cr,

Cu, Cd,

Hg, Dy,

Hf, In,

Mn,

Mo, Ni,

Cd, Cu,

Hg, In,

Pb, Rb,

Sb, Se,

Sm, Sr,

Tb, Th,

U, V,

W, Yb

and Zn

Veles region

(Republic

of

Macedonia).

Soils from urban area,

industrial zone, cultivable

land, uncultivable land

from polluted city of Veles

region which is known for

its lead and zinc industrial

activity

HCl+ HNO3 Flame AASa

(Varian- SpectrAA

55B) or by

Zeeman

electrothermal

AASa (Varian

SpectrAA 640 Z)

for Cd, Cu, Pb

and Zn

Cold vapor

system AASa

(VarianVGA-76)

for Hg

ENAAk for Fe,

Ti, As, Au, Br,

Co, Cr, Cu, Dy,

Hf, In, Mn, Mo,

Ni, Rb, Sb, Se,

Sm, Sr, Tb, Th,

U, V, W, Yb and

Zn

Heavy metal content (mg/kg) in cultivable,

uncultivable, urban and polluted area:

As (7.8, 11, 13, 15); Cd (6.1, 4.8, 12, 32);

Cu (52, 41, 52, 69); Hg (0.20 0.18 0.36

0.50); In (0.22, 0.31, 0.65); Pb (170, 140,

340, 800). Sb (1.6, 1.7, 4.5, 6.7); Zn (210,

180, 460, 1100); Br (3.2, 3.8, 4.5, 4.5); Co

(11, 13, 16, 14); Cr (150, 130, 180, 150);

Dy (3.9, 4.0, 4.6, 4.3); Hf (4.2, 4.3, 5.0,

4.8), Mn (760, 870, 950, 1000), Mo (0.44,

0.49, 0.84, 0.82); Ni (42, 53, 78, 68); Cd

(6.1, 4.8, 12, 32); Rb (92, 93, 100, 110); Se

(0.19, 0.19, 0.42,1.3); Sm (5.1, 5.6, 6.2,

6.1); Sr (190, 180, 270, 260); Tb (0.69,

0.81, 0.91, 0.83); Th (9.9, 9.9, 12, 12); U

(2.2, 2.3, 2.8, 2.7); V (71, 84, 89, 83); W

(11, 7.1. 10,11); Yb (1.9, 2.2, 2.4, 2.2); Au

(10, 10, 12, 14 ug/g); Fe (2.4, 2.9, 3.5, 3.0

%); Ti (0.33, 0.39, 0.39, 0.38 %)

respectively.

Stafilov

et al. (2010)


90. As, Co,

Cr, Cu,

Hg,

Mn, Ni,

Pb,

V, Zn

Beijing

(China)

Rural soils with agricultural

activities affected by

anthropogenic activities

HNO3+ HF+ HClO4

For As and Hg:

HNO3+ HCl+

KMNO4 + Oxalic

acid.

ICP-MSh (X

Series II, Thermo

Fisher Scientific)

for Co, Cr, Cu, Ni

& Pb.

ICP-OESd (IRIS

Intrepid II,

Thermo Fisher

Scientific) for

Mn, V & Zn.


As (7.7), Co (12.0), Cr (60.8), Cu (22.0),

Hg (.059), Mn (643), Ni (24.7), Pb (23.7),

V (79.2), and Zn (74.8).

Wu et al.

(2010)

91. Cd, Cr,

Cu, Ni,

Pb and

Zn

Panyu-

Nansha

Area

(PNA) of

Pearl

River

estuary in

south of

Guangdon

g province

(South

China).

Riparian wetland soil in

Panyu-Nansha area of Pearl

River estuary which is

affected due to rapid

economic growth & urban

development which has led

to excessive release of

wastewater into the Pearl

River and its tributaries

HNO3+ HClO4 +HF ICP-AESc Heavy metal content (mg/kg) observed:

Cd (2.38), Cr (109.7), Cu (65.36), Ni

(50.56), Pb (79.27), Zn (244.42).

Zhang et al.

(2010)

92. Cd, Cu,

Pb, Hg

and Zn.

Wenling,

in the

Zhejiang

province

(China)

Agricultural soils sampled

from the study area which

is one of the well-known

electronic and electric

waste (e-waste) recycling

centers

HF+HNO3 +HClO4 GFf AAS

a, (PE

b

AA800, USA) for

Cd

Flame AASa, PE

b

AA800, USA) for

Cu, Ni, Pb and Zn


Cd (0.31), Cu (41.13), Ni (33.89), Pb

(48.30), Zn (137.03) respectively.

Zhao et al.

(2010)

93. As, Ba,

Be, Cr,

Cu, Pb,

Mn, Ni

and Hg

US

Census

Metropolit

an

Statistical

Area

(U.S).

Soils with different land

covers like rural, urban

residential, commercial and

industrial from a rural area

with no increased

prevalence of mental

retardation and

developmental delay

HCl+ HNO3 ICP-AESc

Heavy metal content (mg/kg) in area A, B,

C :

As (1.3, 0.97, 4.1), Ba (20, 13, 73), Be

(0.13, 0.004, 0.15), Cr (7.0, 3.0, 17), Cr

(7.0, 4.4, 24), Cu (3.2, 3.0, 17), Pb (12, 17,

45), Mn (86, 22, 260), Ni (1.8, 0.86, 6.4),

Hg (0.03, 0.022, 0.023) respectively.

Aelion et al.

(2009)


(MR/DD) (Area A), and a

rural area (Area B) and an

urban area (Area C) with

significantly higher

prevalence of MR/DD in

children as compared to the

state-wide average.

94. Cd, Pb,

Zn, Cu,

Ni and

Cr

Mornag

(Tunisia)

Agricultural soils of farms

of Agronomic National

Institute of Tunis (INAT)

following history of 5 yr

application of municipal

solid waste compost (C) &

farmyard manure (M).

HCl+ HNO3+ HF

AASa (PE

b

Spectra AA 220

FS)

Range of heavy metal content (mg/kg) at

different depths:

For ‘C’ soils: Cu (45.8-98.5), Zn (68.6-

131.1), Pb (61.0-97.1), Cd (1.3-2.8), Ni

(31.5-37.5), Cr (57.6-65.0).

For ‘M’ soil: Cu (41.2-57.3), Zn (69.2-

84.1), Pb (44.6-55.8), Cd (1.1-1.8), Ni

(28.6-33.7) and Cr (54.8-60.7).

Achiba et al.

(2009)

95. Ni, Pb,

Cd and

Cr

Ota, Ogun

State

(Nigeria)

Soils within and around the

polluted premises of an

Metropolitan Battery

Company (MBC) by slag

from the company

HNO3 AASa (2003

model) with PEb

Analyst 200


observed:

Ni (7.10-79.00), Pb (243-129000), Cr (50-

309), Cd (0.2-34.90).

Adie and

Osibanjo

(2009)

96. As, Hg,

Pb, Co,

Se, Cd

Fe, Mn,

Cr, Cu,

Zn, and

Ni

Faridpur

(FD) &

Dhamrai

(DM)

(Banglade

sh)


floodplain agricultural

fields under high risk of

contamination due to

ground water irrigation rich

with As & trace metals.

HNO3+HCl ICP-MSh for As,

Hg, Pb, Co and

Se.

ICP-AESc for Fe,

Mn, Cr, Cu, Zn,

Ni by

GFf-AAS

a for Cd

Heavy metal content (mg/kg) in in FD and

DM soils:

As (33.15, 6.10), Cu (48.42, 31.84), Ni

(48.85, 46.09), Zn (97.24, 98.85), Cr

(85.95, 76.08), Cd (0.153, 0.155), Pb

(26.82, 24.85), Se (1.04, 1.01), Co (17.40,

16.41), Hg (0.081, 0.78), Mn (449.68,

553.75) Fe (48.37, 37.00) respectively.

Ahsan et al.

(2009)

97. Fe, Mn,

Zn, Cu,

Co, Ni,

Cr, Pb,

Cd

Turkey Agricultural soils during

sampling periods 1 and 2

(before and after irrigation

season) irrigated with water

from lake Iznik which has

been polluted with

industrial and municipal

wastes.

HNO3+HCl AASa Heavy metal content (mg/kg) during

period1 and period 2:

Fe (4420, 41201), Mn (1131, 1081), Zn

(69.2, 76.4), Cu (73.9), Cr (32.2, 33.3), Ni

(42.3, 42.8), Co (14.0, 15.3) and Pb (17.3,

15.2)

Basar et al.

(2009)


98. Hg, Cu,

Ni, Cr,

V, Pb,

Cd and

As

Almade´n

(Spain) Soil samples of periurban

waste mine tailing

Soil pellets for

Sequential XRFq

XRFj Range of heavy metal content (mg/kg)

observed:

Hg (3.44-6,987.00), V (69-1,209), Cr (86-

344), Ni (21.2-125.6), Pb (1.4-1,013), As

(13.3-284.5) and Cu (19.3-135.0)

Bueno et al.

(2009)

99. Pb, Cu,

Zn, Ni,

Cr, Cd,

As and

Hg.

Kavala

(Greece)

Street dust and road side

soils polluted with

vehicular local traffic,

nearby phosphoric fertilizer

and petrochemical

industries

HNO3 + H2O2 AASa

GFf-AAS

a PE

b

for Pb, Cu, Zn,

Ni, Cr and Cd

Flame AASe

PEb

for As and Hg

Heavy metal content (μg/g) observed:

In street dust soil:

Pb (300.9), Cu (123.9), Zn (271.6), Ni

(57.5), Cr (196.0), Cd (0.2), As (16.7) and

Hg (0.1)

In roadside soil (μg/g): Pb (359.4), Cu

(42.7), Zn (137.8), (Ni) 58.2, Cr (193.2),

Cd (0.2), As (62.3) and Hg (0.1).

Christoforidis

and Stamatis

(2009)

100. Cd, Cr,

Cu,

Mn, Ni,

Pb and

Zn.

Zlatibor

ecosystem

(Serbia)

Vegetated soils receiving

emissions from polluted

areas in the vicinity of

remote & industrial

facilities.

HNO3+HCl AASa. Range of heavy metal content (mg/kg)

observed:

Cd (0.01-4.20), Cr (0.01-74.9), Cu (2.00-

27.3), Ni (0.01-182), Pb (0.01-98.1) & Zn

(21.6-65.2).

Dragovic

and

Mihailovic

(2009)

101. Cd, Cu,

Cr, Pb,

Mn, Ni,

and Zn

Aboh-

Abalagada

in the

northern

parts of

the Niger

Delta

(Nigeria)

Soils at two depths (0-15

and 15–30 cm from

farmlands and adjoining

swamps and creeks affected

due to spillage of one of the

biggest oilfields in the

Nigeria Delta.

HNO3+HCl+HF AASa (Varian

spectro AA-10)


In Topsoil: Cd (1.12), Cu (11.21), Cr

(28.75), Pb (25.02), Mn (201.8), Ni (31.7)

and Zn (29.30).

In subsoil: Cd (0.77), Cu (9.12), Cr

(23.42), Pb (23.90), Mn (168.95), Ni

(26.50) and Zn (30.76)

Iwegbue et al.

(2009)

102. Ni, Pb,

Cr, Zn,

Cu, and

Cd

Zhengzho

u-Putian

section of

Longxi-

Haizhou

Railroad

(China)

Railroad side soils at

different distance along

railroad edge

For Ni, Pb, Zn. Cu

and Cd:

HNO3+ HF+ HClO4

For Cr:

H2SO4+HCL+HNO3

+ HF

AASa (AA-

6601F, Shimadzu

Co., Japan)

GFf - AAS

a for Pb

and Cd

Flame AASa

for

Cu, Zn, Cr, and

Ni


Zn (178-512.59), Pb (52.57-146.80), Cu

(24.1-50.73), Cd (0.16-0.46) and Ni (17.58-

23.71)

Jian-Hua

et al. (2009)


103. Cd, Co,

Cr, Cu,

Mn, Ni,

Pb, Zn

Rosia

Montana

(Romania)

Soils affected by the past

Rosia Montana gold and

silver ore mining. (area

where the existing and

proposed future gold-silver

ore extraction and

processing is to take place)

HNO3+ HClO4+HCl AASa in air

acetylene flames


Cd (0.93) Co(18), Cr (32), Cu (41), Mn

(800), Ni (34), Pb (48) and Zn (89)

Lacatusa

et al. (2009)

104. Zn, Cu,

Pb, Cd,

Hg and

As

Huangpu

District of

Guangzho

u City

(China)

Agricultural soils in the

vicinity of petrochemical

complex.

For Zn, Cu and Pb:

HNO3+HF+ HClO4

For Hg:

H2SO4+HNO3+KM

NO4

For As:

HCl+ HNO3

Flame AASa

(Hitachi Z-5000)

for Cu and Zn

GFf-AAS

a

(AA800, PE Inc.)

for Pb and Cd

Cold vapor AASa

(F732-

V,Shanghai) for

Hg

Hydride

generation AFSe

(HG-AFS 230,

Beijing) for As


observed:

Zn (19.6-386.1), Cu (3.6-294.3), Pb (25.4-

182.1), Cd (0.020-1.03), Hg (0.028-2.4)

and As (0.012-15.5).

Li et al.

(2009)

105. Fe, Cd,

Cr, Cu,

Pb and

Zn

Hangzhou

City,

(Eastern

China)

Soils from urban areas:

Zone I (old urban area) -

Soil with history of

residential, industrial &

commercial land use

situated in the city center.

Zone II (new urban area)-

urban rural transect soils

beginning from new urban

area through suburban and

ending in rural area in the

eastern part of city.

Zone III & IV (suburban

HNO3+HCl+ HClO4

AASa . Heavy metal content (mg/kg) observed:

In soils of Zone I: Cd (1.4), Cr (60), Cu

(57), Pb (90), Zn (200) and Fe (30.01g/kg)

In soils of Zone II: had Cd (1.0), Cr (59),

Cu (50), Pb (65), Zn (199) and Fe (28.80

g/kg)

In soils of Zone III: Cd (0.3), Cr (58), Cu

(39), Pb (57), Zn (44.5) and Fe (20.75

g/kg)

Lu et al.

(2009)


land)- soil under typical

suburban land use type

such as housing, industrial

and vegetable production.

Zones V, VI, VII & VIII (rural soils)- soils under

cultivation of rice and

economic crops minimally

influenced by

anthropogenic factors but

application of chemical

fertilizer to soils can be an

important source of heavy

metals

In soils of Zone IV: Cd (0.3), Cr (56), Cu

(39), Pb (57), Zn (45) and Fe (23.07 g/kg)

In soils of Zone V: Cd (0.3), Cr (38), Cu

(30), Pb (40), Zn (44.5) and Fe (25.65

g/kg)

In soils of Zone VI : Cd (0.3), Cr (38), Cu

(31), Pb (41), Zn (43.5) and Fe (24.17

g/kg)

In soils of Zone VII: Cd (0.4), Cr (38), Cu

(33), Pb 41), Zn (42.5) and Fe (27.98 g/kg);

In soils of Zone VIII: Cr (37), Cd (0.4),

Cu (33), Pb (40), Zn (42.5) and Fe

30.17g/kg)

106.

Cd, Co,

Cr, Cu,

Fe, Mn,

Ni, Pb

and Zn

Potasze

Forest

division

Salicarium

(Poland)

Forested soils HNO3+HCl EAASm as well as

flame AASa

(AA

Varian Spectra

200).


Cd (0.445), Co (1.522), Cr (5.852), Cu

(4.313), Ni (4.581), Pb (7.214), Zn

(21.536), Fe (0.324%) and Mn (0.063%)

Mleczek

et al. (2009)

107. Cr, Ni

and Cu

Melide

(Galicia,

NW

Spain)

Soils of farms (pastures

sown with mixture of

ryegrass (Lolium perenne

L.), hybrid rye grass

(Lolium × boucheanum

Kunth) and white clover

(Trifolium repens L.) on

highly serpentinized basic

and ultra basic rocks with

high levels of Cr/Ni.

HNO3 ICP- AESc, PE

b

Optima 4300 DV


observed in agricultural soils :

Cr (10.0-1162), Ni (5.91-940) and Cu

(2.37-79.6).

Miranda

et al. (2009)

108. Fe, Mn,

Cu, Zn

and Cd

Bustarviej

o (Madrid,

Spain)

Vegetated soils affected by

Mining activities (Monica

mine)

HNO3+H2O2 AASa (PE

b

Analyst 800).

Heavy metal content (mg/kg) in soils close

to mining dumps, soils affected by mine

drainage and unaffected soils:

Cd (16.35, 8.68, 2.91); Cu (308.7, 182.2,

16.6), Mn (353.6, 432.6, 427.8), Zn (845.7,

571.1, 96.7) respectively.

Moreno-

Jimenez

et al. (2009)


109. As, Cr,

Cu, Ni,

Pb, and

Zn

Ohio

(USA)

Soil samples from

agricultural, adjacent

forest and boundary soils

affected due different land-

use activities.

HNO3+H2O2 ICP-AESc Range of heavy metal content (mg/kg)

observed:

In agricultural soils: As (5.3-18), Cr (18-

28), Cu (11-35), Ni (18-32), Pb (7.6-20)

and Zn (48-81).

In forest soils: As (4.7-18), Cr (20-27), Cu

(12-32), Ni (21-56), Pb (9.2-18) and Zn

(75-80).

In boundary soils: As (5.5-6.7), Cr (17-

25), Cu (12-25), Ni (21-48), Pb (6-14) and

Zn (48-74).

Neupane and

Roberts,

(2009)

110. Pb, Zn,

Cu and

Cd

Ciudad

Real

(Spain)

Arable and Pasture soils

(polluted soils) in the

vicinity of Pb-Zn mine area

HNO3+ HCl

ICP-AESc in a

Thermo

iCAP6500

spectrometer

(Thermo

Electron,

Cambridge, UK).


Pb (28,453.50), Zn (7000.44), Cd (20.57).

Rodriguez et

al. (2009)

111. Cd, Cr,

Cu, Ni,

Pb, V,

Fe, Ti

and Zn

Murgia

Hills, in

the

province

of Bari

(Italy).

Natural and polluted soils

(Six soil samples) : a silty

loam soil collected in the

Murgia hills, in the

province of Bari, Italy, with

a typical natural content of

heavy metals (S0), two

polluted loam soils from

the same area (S1 and S2),

a clay loam soil from

Milano, Lombardia,

Northern Italy, and a sandy

clay loam soil from Fano,

Marche, Central Italy (S3

and S4), and a depurated

sewage sludge (SS) of

known Cr concentration

HNO3+ HCl+H2O2 .

Soil pellets for LIBS

ICP- OESd (iCAP

6000 Series,

Thermo Electron

Corporation) for

Cd, Cr, Cu, Ni,

Pb, V and Zn

LIBSl for Cd, Ni,

Fe and Ti

Heavy metal content (mg/kg) in S0, S1, S2,

S3, S4 and SS using ICP- OESg:

Cd (6.2, 5.6, 5.9,nd, nd, nd); Cr (127.2,

939.7, 1738.5, 550, nd, 79); Cu (30.9,

152.4, 334.1, 1000, 100, 145.5); Ni (36.1,

60.6, 72.5, 130, nd, 27.2); Pb (134.5, 179.6,

230.8, nd, 87, 155.1); V (117.1, 83.5, 46.2,

nd, nd, nd, nd); Zn (100.2, 682.8, 896.7,

200, 1500, 1412) respectively.

Heavy metal content in S0, S1, S2 and SS

using LIBSo :

Fe (3.50, 2.26, 180, 1.74 %); Ti (685.3,

103.5, 375.7, 224.1 mg/kg); Cd and Ni

were not quantified due to low limit of

detection

Senesi et al.

(2009)


112. Cu, Pb,

Zn, Cr,

Ni and

Cd

Huang Pu

(HP) Guangzhou

(China)

Forested sites soils from:

Huang Pu industrial district

(HP)- the most polluted site

due to electroplating,

refining & petrochemical

plant, South China

Botanical Garden (BG)-

affected due to high traffic

loadings, Mao Feng

Mountain (MF)- suburban

zone, having no direct

pollution source and Nan

Kun Mountain (NK)-

natural reserve, a remote

rural area

HF + HNO3

+ HClO4

ICP-AESc

(Optimal 2000,

USA) for Cu, Pb,

Zn, Cr, Ni

GFf-AAS

a (Zeenit

60, German) for

Cd


observed:

Cu (3.8- 7.5), Cd (.25- .02), Pb (39 -55), Zn

(110), Cr ( 10-13), S (100-800) and for BG,

Cu (2- 5), Cd (.01-.1), Pb ( 38-55), Zn (86-

100), Cd (10-15), Ni (2- 4), S (250-590).

Sun et al.

(2009)

113. Cu, Fe,

Mn, Zn,

Ni, Pb,

Cr and

Cd

State of

Sao

Paulo,

(Brazil).

The catchment soils with

vineyard, Pastures, natural

vegetation and other kinds

of orchards in the vicinity.

Cultivation may cause

contamination by HM, and

specifically, by copper in

vineyard areas.

Diethylenetriamine

penta acetic acid

(DTPA)

ICP-OESd Heavy metal content (mg/dm) in the

surface and subsurface layers:

Cu (3.9, 3.2), Fe (113.4, 87.2), Mn (17.4,

12.4), Zn (7.6, 7.4), Cd (0.1, 0.0) Cr

(0.1,0.1), Ni (0.6,0.5) and Pb (1.5, 1.3)

respectively.

Valladares et

al. (2009)

114. Cd, Cu,

Pb and

Zn

Ramallosa

in

Pontevedra

(Spain)

Marsh salt soils formed by

local river Minor receives

number of discharges with

the potential to contaminate

the marsh soils. Study area

under horticultural

production commonly

receives fertilizer, manure

& slurry inputs.

HNO3 +HCl+HF

ICP-OESd Range of heavy metal (mg/kg) observed:

Cd (53.60-134.60), Cu (132.58-207.03), Pb

(235.67-1076.50) and Zn (300.67-1689.0).

Vega et al.

(2009)


115. Pb, Cu,

Zn, and Cd.

Bucharest

and

Pantelimon

(Romania)

Urban soils near industrial

area (having companies

processing different,

nonferrous solid wastes for

recovery of heavy metals

and producing different

nonferrous alloys and lead

batteries).

HNO3+HCl AASa . Heavy metal content (mg/kg) around the

study site at 0-5cm depth:

Pb (750), Cu (350), Zn(1,300) and Cd (7).

Velea et al.

(2009)

116. Cu, Zn,

Pb, Cd and Ni

Fuyang

County,

situated at

the north

of

Zhejiang

Province,

near

Hangzhou

city, China

Agricultural Soils (Paddy

cultivated)

HNO3 + HClO4 ICP-MSh for Cu,

Pb, Cd and Ni

ICP-OESd for Zn


Cu (40.77), Zn (159.85), Pb (40.59), Cd

(0.37) and Ni (21.92)

Zhang et al.

(2009)

117. Cu, Zn,

Pb and Cd)

Villages near

Dabaoshan mine in

east Shaoguan

city,

(Zhongxin, Fandong,

Shangba

Liangqiao) (Southern

China )

Agricultural and Garden

soil in the vicinity of the

mine. Area is contaminated

due to irrigation by wastewater.

HNO3+ HClO4+HF AASa

(GBC932AA)


Cu (703), Zn (1100), Pb (386) and Cd

(5.5).

Zhuang et al.

(2009)


118. Fe, Mn,

Cu, Cr,

Ni, Zn,

Pb, and

Co

Dengkil,

Selango

Malaysia

Soil Beneath a Waste

Disposal Site from

downslope (ATD), soil-

waste, interface (ATI), and

upslope (ATU) points

HNO3+HCl+HF ICP-AESc (PE

b

model Optima

4300 Dual View

Heavy metal content (mg/kg) from

upslope, interface and down slope points:

Fe (149.96, 312.25, 168.71), Mn (7.52,

11.35, 8.87), Cu (0.28, 2.21, 0.54), Co

(0.13, 0.79, 0.14), Cr (1.06, 11.51, 2.59),

Ni (0.82, 4.35, 1.81), Zn (1.57, 12.97, 5.41)

and Pb (1.36, 12.78, 3.38)

Bahaa-Eldin

et al. (2008)

119. La, Th

and Pb

Alluvial

plain of

Scarpe–

Escaut

close to

Belgium

border

(Northern

France).

Soils from Conventional

Arable land (CA) and

permanent grassland

/Pasture (PP) in the vicinity

of zinc-smelter complex

HF+ HClO4+HNO3 ICP-MSh

with an

ELAN 5000

(Sciex/PEb).


observed:

In PP soils: La (8.7-12.6), Th (2.5-3.4), Pb

(7.3-51.2).

In CA soils: La (10.1-18.1), Th (11.5-

69.9), Pb (11.5-69.9).

Fernandez

et al. (2008)

120. As, U,

Th, Br,

Cr, Sc,

Zn, Co

and Fe

Feni

district

(Banglade

sh)

Vegetable cultivated soils

from upazillas of Feni

district (namely as

Sonagazi, Dagan-bhuiyan,

Parshuram, Feni Sadar, and

Phulgazi)

Powdered soil NAAn (

TRIGA

MARK II

research reactor)

Heavy metal content (ppm) observed:

In Sonagazi soils: As (0.88), U (0.42), Th

(23.77), Br (1.19), Cr (92.13), Sc (8.14), Zn

(142.110), Co (13.80,) and Fe (4.0)

In Dagan-bhuiyan soils: As (1.27), U

(0.39), Th (17.90), Br (1.10), Cr (78.38), Sc

(12.86), Zn (189.92), Co (14.0) and Fe

(3.70)

In Parshuram soils: As (2.55), U (1.58),

Th (15.29), Br (1.54), Cr (77.98), Sc (1.97),

Zn (94.58), Co (7.63) and Fe (1.97)

In Feni Sadar soils: As (3.04), U (1.01),

Th (16.31), Br (2.90), Cr (79.52), Sc (3.93),

Zn (101.62), Co (10.78) and Fe (3.93.

Karim et al.

(2008)


In Phulgazi soils: As (4.96), U (1.98), Th

(14.84), Br (2.10), Cr (73.53), Sc (9.21), Zn

(132.79), Co (12.15) and Fe (2.93)

121. Cd, Cr,

Cu, Ni,

Pb and

Zn

Northwest

of

Thessalon

iki (North

Greece)

Soils from closed unlined

landfill with largest

industrial areas where

metal-processing, chemical,

cement, food and wood

industries are located

HNO3 AASa ( PE

b 403) The soil contained HM content (mg/kg):

Cd (0.50-18.75), Cr (3.88-171.88), Cu

(8.13-356.25), Ni (5.63-63.75), Pb (2.50–

92.50) and Zn (6.38-343.75).

Kasassi et al.

(2008)

122. Cd, Cu,

Ni, Pb

and Zn

Marrakech

(South

Morocco)

Soils from mining area

(pollymetallic mine and

copper mine) and nearby

agricultural fields

contaminated with tailing

runoffs and polluted stream

water.

HNO3 +HClO4 Flame AASa

(ATI-

UNICAM929,

Unicam

Absorption

Atomic,

Cambridge

CB12SU, UK)


observed:

In pollymetallic tailings runoff

contaminated soils Cd (0.05-83.7), Cu (0.3-

83.7), Mn (nd-3,462), Zn (116-5,730).

In Agricultural soils in vicinity of

pollymetallic mine Cd (<dl), Cu (18.7-

300), Mn (,532-683), Zn (73.7-142)

In stream water contaminated soils near

copper mine Cd (0.6-3.3), Cu (241-2,175),

Mn (377-2,841), Zn (22.1-578).

Khalil et al.

(2008)

123. Al, Cd,

Cr, Fe,

Ni, Pb

and Zn

Talcahuano

urban area in Chile

Soils were sampled from

industrial (three sites: IS1,

IS2, IS3), residential and

commercial (one site: RC),

roadside fields (one site:

RS), school grounds (one

site: SG), and a city park

(one site: CP). Area is

effected by the local

pollution through industrial

dust.

HNO3+HCl ICP-AESc Heavy metal content (mg/kg) observed in

soil samples:

IS1, IS2 and IS3: Cr (43.2, 13.7 and 52.2);

Ni (21.0. 31.5 and 21.5), Pb (129, 12.5 and

30.3), Zn 1,016, 280 and 238) respectively.

RC: Cr (38.0), Ni (23.0), Pb (8.0) and Zn

(60.0).

SG: Cr (22.0), Ni (18.0), Pb (10.0) & Zn

(49.3).

CP: Cr (39.0), Ni (22.0), Pb (14) and Zn

(91.7).

Tume et al.

(2008)


124. Pb, Ni,

Cr, Cu,

Hg, As

and V

Nanjing

City

(China)

Soils around a ferroalloy

manufacturing plant

(random sampling sites

around the residuum yard

(soil A) & surrounding the

Cr plant & residuum yard

were collected by grid

sampling (100 m × 100 m

at soil D and 75 m × 75 m

at soils B and C) with

global positioning system

HNO3+H2O2 ICP-AESc

(Jarrell-Ash Mark

III 1100) for Pb,

Ni, Cr, Cu, and V

Cold vapor AASa

(PEb, Model

5100, PC) for Hg

Spectrophotometric

method with potassium

borohyride and

silver nitrate for As

Heavy metal content (mg/kg) in A, B, C

and D soil:

Pb (24.55, 17.31, 17.97, 16.41); Hg (0.068,

0.029, 0.031. 0.032); As (15.05, 16.80,

15.37, 16.94); Ni (42.46, 36.25, 31.04,

31.56); Cr (340.6, 243.0, 174.8, 124.1), Cu

(42.93, 38.64, 37.45, 37.69); V (109.4,

105.8, 129.5, 119.9) respectively.

Wei-Xin

et al. (2008)

125. Pb, Cd,

As, and

Hg

Jordan Agricultural soils affected

by anthropogenic sources

(pesticides, heavy metals,

acid deposition and a range

of industrial chemicals that

reach soils by various

routes

HNO3 +HCl+ HF Flameless AASa Range of heavy metal content (ppm)

observed:

Pb (21.5-194.5), Cd (2-7), As (0-850) and

Hg (0.14-1.7)

Banat et al.

(2007)

126. Cd, Cr,

Cu, Ni,

Pb and

Zn

Coruna

Province

(NW

Spain).

Soils developed on

serpentinitic rocks

containing endogenous

contaminants

HNO3+ HCIO4+HF ICP-OESd (PE

b

Optima 4300 DV)

Total metal (mg/kg) of soil samples ranged

as:

Cd (0.55-2.28), Cr (353.41-2966), Cu

(26.1-90.53), Ni (152.63-2718.38), Pb

(17.52-23.84), and Zn (19.4-42.12)

Covelo et al.

(2007)

127. Cr, Mn,

Ni, Cu,

Zn, As,

Pb, Cd

and Hg

Xijia

Village,

Pudong

District,

Shanghai

(China)

Sewage irrigated soils

(cultivated with crops like

Greengrocery, water

convolvulus, cabbage,

cucumber and cowpea)

receiving sewage water

irrigation from open canal

in which the sewage flowed

through Huamu, Beicai,

Zhangjiang, Tang and

Heqing Town.

H2SO4 +HNO3

+HClO4

AASa for Cr, Mn,

Ni, Cu, Zn, Pb

and Cd

ICPo with hydride

generation system

(HVG-1

Shimadzu) for As

Reduction vapor-

flameless AASa

(MVU-

1A,Shimadzu) for

Hg


At 0-15 cm depth Cd (2.15) Zn (455), Cu

(78.0), Pb (45.0), Cr (159), Mn (531), Ni

(41.6), As (13.4), Hg (0.51).

At 15-30 cm depth Cd (1.98) Zn (414), Cu

(69.5), Pb (41.0), Cr (140), Mn (504), Ni

(40.8), As (12.4), Hg (0.51).

Chen et al.

(2007)


128. Cd, Cr,

Cu, Ni,

Pb and

Zn

Coruna

Province

(NW

Spain).

Soils developed on

serpentinitic rocks

containing endogenous

contaminants

HNO3+ HCIO4+HF ICP-OESd (PE

b

Optima 4300 DV)

Total metal (mg/kg) of soil samples ranged

as:

Cd (0.55-2.28), Cr (353.41-2966), Cu

(26.1-90.53), Ni (152.63-2718.38), Pb

(17.52-23.84), and Zn (19.4-42.12)

Covelo et al.

(2007)

129. Cd, Co,

Cr, Cu,

Mn, Ni,

Pb and

Zn

City of

Santiago

de

Composte

la (North

western

Spain

Agricultural soils (F20 &

control). F20 soil was from

a field where dry digested

sewage sludge from the

Wastewater Treatment

Plant for the city has been

applied every 2 year for

more than 10 years & the

control soil from an

adjacent non-amended field

was taken.

HNO3+HCl AASa. Heavy metal content (mg/kg) in control

and F20 soils:

Mn (152.0, 162.8); Co (8.2, 8.8); Cr (30.0,

42.9); Cu (12.0, 131.6); Ni (20.0, 24.9); Pb

(36.0, 46.8); Zn (78.0, 146.5).

Kidd et al.

(2007)

130. Cd, Cu,

Pb and

Mn

Guangxi

(South

China)

Vegetated soils from

restored Lipu manganese

mineland area

HCl+HNO3+HF+H

CIO4

ICP-AESc Heavy metal content (mg/kg) observed:

Cd (3.55), Cu (105.5), Pb (59.5) and Mn

(10000). Average Cu (62.8), Cd (0.45), Pb

(24.2) and Mn (2772).

Li (2007)

131. As Zimapan

Valley

(Mexico)

Vegetated surface soils

known for elevated soil As

contamination due to

mining and smelting

activities (Pb- Zn- Ag ore).

HNO3+HCl ICP-OESd or GF

f

AASa

Heavy metal content (mg/kg) of As:

(4-14700).

Ongley et al.

(2007)

132. Cd, Cu,

Zn, Pb,

Cr, Mn,

and Ni

Varanasi

city, in the

Eastern

Gangetic

plain

(Northern

India).

Vegetable grown soil from

urban & suburban areas of

Varanasi (Dinapur,

Shivpur, and Lohta)

receiving waste water from

sewage treatment plant

(DSTP) at Dinapur and

effluents from major

industrial and residential

areas at Shivpur and lohta

for irrigation purposes.

HNO3 +

H2SO4+HClO4

AASa (PE

b model

2130,USA)

Range of heavy metal content (mg/kg) of

HM in soil of the study sites:

Cd (0.55-8.85), Zn (14.23-387.78, Cr

(13.40 - 679.89), Mn (0.36 -339.36), Cu

(2.55- 203.45), Pb (0.46 -44.50), and Ni

(2.00 -34.45) during different months.

Sharma et al.

(2007)


133. As, Hg,

Cu, Zn,

Pb, Cr,

and Cd

Wuxi

City,

Jiangsu

Province

(China)


industry-based peri-urban

area under anthropogenic

influence on heavy metal

distribution in the soil of

this area.

HNO3+HCl AFSe by As

Cold AASa for

As

Flame AASa for

Pb, Zn, Cu, Cd,

and Cr


As (14.3), Hg (0.161), Cu (40.4), Zn

(112.9), Pb (46.7), Cr (58.6), Cd (0.143).

Yan-Feng

et al. (2007)

134. Cu, Cu,

Pb &

Zn

Naleczow

Plateau of

the Lublin

Upland

(Eastern Poland)

Holocene slope deposits of

the loess agricultural area

(Five soil profiles were examined)

HCl+ HNO3 AASa

(AA 880

Varian)

Heavy metal content (mg/kg) observed :

Cd (<0.5), Cu (upto 15), Pb (upto 20) and Zn (upto 70).

Zglobicki

and Rodzik,

(2007)

135. Fe, Cu,

Zn, Ni and Pb

Karak city

(Jordan)

Indoor dust, street dust &

soils polluted with HM due

to atmospheric aerosols,

industrial activities

especially Karak Industrial

Estate (KIE) and storage of

discarded vehicles around

the area.

HNO3 AASa

(Model

AA-6200, Shimadzu)

Heavy metal content (mg/kg) from

industrial complexes viz. Car service,

Furniture and wood, Steel and non- steel

and oven and smelters :

In Indoor dust: Fe (94.4, 89.1, 90.4, 83.1);

Cu (20.9, 7.3, 13.8, 67.4); Zn (60.8, 15.5,

33.2, 50); Ni (4.9, 5.7, 1.9, 4.1) and Pb (51.4, 3.7, 26.15, 120.71) respectively.

Heavy metal content (mg/kg) in street dust

and soils from KIE:

Fe (42.6, 22.7), Ni (4.2, 3.15), Pb (11.2,

5.35), Zn (13.1, 4.95) and Cu (11.3, 0.85)

respectively

Al-

Khashman (2006)

136. Cd, Cr,

Cu, Pb

and Zn

Hezhang

(China)

Soils from Zn Smelting

sites (Zhazichang,

Xinguangzhai- Dapingzi and Heinizhai)

HF+HNO3+HClO4 AASa (PE

b-5100) Heavy metal content (mg/kg) observed in

soils of Zhazichang, Xinguangzhai -

Dapingzi and Heinizhai soils:

Cd (43, 24, 18), Cr (130, 140, 150), Cu

9120, 150, 52, 26), Pb (9000, 260, 520) and

Zn (11,000, 1400, 1300), respectively.

Bi et al.

(2006)


137. Cd, Cu,

Ni, Pb,

Zn and

Mn

South

Morocco

Soils from metalliferous

Sites. Mine A (Cu and Mo

mine); Mine B

(polymetallic mine); Mine

C (Cu mine); Mine D

(polymetallic mine); Mine

E (Mn mine).

HCl+ HNO3 + H2O2

ICP-AESc Heavy metal content (mg/kg) in soils of :

Mine A: Cd (0.57-2.38), Cu (84- 154), Ni

(22.7-22.8), Pb (51.0-170.0), Zn (173-651)

Mine B was Cd (nd-31.5), Cu (34-570), Ni (nt), Pb (27-5756), Zn (116-8361)

Mine C was Cd (0.14-0.97), Cu (42-1683),

Ni (23.2-53.2), Pb (31.7- 42.4), Zn (110-269)

Mine D Cd (41.5-228), Cu (2910-8635), Ni

(4.6-12.4), Pb (14600-30100), Zn (21400-108000)

Mine E was Cd (0.1-0.5), Mn (251-3780),

Ni (9.6-27.2), Pb (14-286), Zn (34-165).

Boularbah

et al. (2006)

138. Cd, Ni,

Pb, Cu

and Zn

Fort

Pierce

(Florida)

Wabasso soil, (agricultural

soil in the Indian River area

in Fort Pierce, Florida.

HNO3+ HCl+HF ICP-AESc

(Ultima, JY

Horiba Inc.,

Edison, NJ,

USA).


Cu (1.78), Zn (9.74), Pb (1.72), Cd (0.02) and Ni (0.46)

Chen et al.

(2006)

139. Cd, Hg,

Cu, Ni,

Zn, Pb and Cr

Reunion

Soils

(volcanic

island in

the Indian Ocean)

Agricultural and

uncultivated soil exposed to

local contamination by

human activities and

sewage sludge spreading in Reunion.

HF + HNO3 +

HClO4

ICP-OESd (Vista-

PRO, Varian, Inc.)

for Cu, Ni, Zn, Pb and Cr.

GFf

AASa 5100 S,

(PEb) for Cd

AMA 254 mercury

analyzer (Leco

Corp.) for Hg


Cd (0.15), Hg (0.16), Cu (52.9), Ni (92.1), Zn (146.1), Pb (0.75) and Cr (165.9).

Doelsch

et al. (2006)


140. Fe, Mn,

Zn, Cu,

Co, Ni

and Pb

El-Sadat

city,

Minufiya

Governora

te, (Egypt)

Agricultural soils dug to

the depth of 150 cm from

two sites . The first site is

irrigated from well water

and the second with treated

Industrial wastewater.

HClO4+HF AASa Range of heavy metal content (mg/kg) in

soils at depth of 0-150 cm irrigated with

treated water and well water:

Fe (35.4-52.9) (29.6-30.8); Mn (13.0-

26.28) (10.26 -10.57); Zn (4.20-11.72)

(4.10-4.23); Cu (1.89-6.96) (1.45-1.55); Co

(02.22-1.91) (0.25-0.45); Ni (0.62-1.88)

(0.43-0.99); Pb (2.43-6.82) (0.02-0.05)

respectively.

El-Arby

et al. (2006)

141. Cu, Zn,

Pb, Cr,

Cd, Ni,

As, Se,

Hg &

Co

Daxing,

Beijing

(North

China

Plain)

Agricultural Soils HNO3+ HClO4 +

HF

ICP-AFSi

-230,

Beijing, China for

As, Hg and Se

ICP-MSh

(POEMS 111,

TJA, USA) for

Cd

ICP-AESc, IRIS

Intrepid, TJA,

USA) for Cu, Zn,

Pb, Cr, Ni and

Co


Cr (67.8), Ni (24.0), Hg (0.08), Co (11.6),

Cu (26.1), Zn (61.2), Pb (18.8), As (7.7),

Se (0.10) and Cd (0.24)

Ke-Lin et al.

(2006)

142. Fe, Al,

Cu, Cd,

Cr, Ni,

Pb, and

Zn

Ho Mei

and Nan-

Tou

counties

(Central

Taiwan)

Agricultural soils (Rice

cultivated) contaminated

due to irrigation with water

laden with heavy metals

from illegal discharge of

industrial

Waste water.

HNO3+HCl AASa.and all

other metals by

ICPO for Al and

Fe


In Ho-Mei soil: Cd (3.92,), Cr (720), Cu

(800), Ni (850), Pb (1849), Zn (1222), Al

(14.8g/kg) and Fe (38.8g/kg)

In Nan-Tou soil: Pb (1849mg/kg), Al

(13.6g/kg) and Fe (28.0g/kg).

Kuo et al.

(2006)

143. Cu, Zn,

Pb, Cr

and Cd.

Hangzhou

e-

Jiaxinge-

Huzhou

(HJH)

Plain

(China)

Rice/Paddy field soils

affected due to rapid

urbanization and

industrialization.

HNO3 +HClO4 AASa The minimum and maximum content

(mg/kg) of heavy metal observed:

Cu (8.84-67.99), Zn (13.51-246.44), Pb

(1.11-85.9), Cr (1.58-182.64), Cd (0.01-

0.65)

Liu et al.

(2006)


144. As, Cd,

Cr, Cu,

Fe, Hg,

Mn, Ni,

Pb, W

and Zn

Urban

Uppsala,

(Sweden)

Playground soils where

urban children spend most

of their time outdoors and

most frequently come in

contact with soil.

HNO3+HCl ICP-AESc Heavy metal content (mg/kg) observed:

As (3.4), Cd (0.21), Cr (32), Cu (25), Fe

(2.5), Hg (0.14), Mn (494), Ni (19), Pb

(26), W (0.35) and Zn (84).

Ljung et al.

(2006)

145. Cd, Cr,

Cu, Hg,

Ni, Pb

and Zn

Ebro basin

(Spain).

Agricultural soils on a local

scale have been greatly

altered by intense

agricultural practices

HNO3+HCL +H2O2 ICP-OESd for Cr,

Ni, Pb, Cu, Zn

and Cd

Cold vapour

AFSe (PE

b model

2100) for Hg


Cd (0.415), Cr (20.27), Cu (17.33), Ni

(20.50), Pb (17.54), Zn (17.53) and Hg

(35.6).

Martin et al.

(2006)

146. Cd, Co,

Cr, Cu,

Fe, Mn,

Ni, Pb

and Zn

Alicante

province

in

southeast

Spain

(European

Mediterra

nean

region).

Agricultural soils affected

due to intense agriculture

activities.

HNO3+HCl AASa

for Co, Cr,

Cu, Fe, Mn, Ni,

Pb and Zn

GFf-AAS

a for Cd


Fe (13608), Mn (295), Zn (52.8), Cr ( 26.5

), Pb (22.8), Cu (22.5), Ni (20.9), Co

(22.5), Cd (.34 ).

Mico et al.

(2006)

147. Cd, Cu,

Pb, Zn,

Ni, and

Cr

Shenyang

Liaoning

(Northeast

China)

Agricultural soils along the

upper, middle and lower

reaches of the wastewater

disposal channel where

wastewater is used for

irrigation

HNO3+HCIO4 AASa

(Varian-

800)


observed:

Cd (1.2 - 30.0), Cu (22.2-277) Zn (87.0-

498), Ni (2.5 -11.9), Pb (45.0-565) and Cr

(14.6- 124).

Song et al.

(2006)

148. As, Pb,

Cu and

Zn

Villa de la

Paz in

state of

San Luis

Potosi

(Central &

Northern

Mexico)

Vegetated soils near mine

spills in San Luis Potosi

(Mexico) which were

active since about 1800

AD.

25 % HNO3 AASa- PE

b 2380

with hydride

generation system

for As

AASa-Varian

Spectra AA 220

for Pb, Cu and

Zn.


As (8-22,992), Pb (31- 1845), Cu (27-

1620) and Zn (81-4218)

Vasquez

Murrieta

et al. (2006)


149. Ag, Al,

Cd, Co,

Cu, Fe,

Pb and Zn

Egypt

Bay,

Maine

(USA)

Upland soil samples from

the mine tailings pile near

Hagan Mine which is one

of the mines extracting

metals from the sulfide

deposits.

HNO3 ICP-AESc

(Thermo

Jarrell Ash Iris

Advantage Dual

view)


observed:

Al (1781-26105), Fe (2314- 23986), Zn

(6.1-84.4); Cu (3.21- 18.4) and Co (1.96-

21.3)

Osher et al.

(2006)

150. Cr, Co,

Cd, Ni,

Pb, Fe,

Mn and

Zn

Peshawar,

North

West Frontier

Province

(Pakistan)

Irrigated soils affected due

to anthropogenic

contamination mainly by leather and textile

Industries.

HCl+ HNO3 Flame AASa

(AA-670,

Shimadzu)

Range of metal content (mg/kg) observed:

Cr (0.810-100.2), Co (1.260-8.110), Cd

(0.140-1.00), Ni (1.020-9.720), Pb (2.270-

8.430), Fe (0.110-87.10), Mn (0.430- 9.680) and Zn (0.230-8.420).

Tariq et al.

(2006)

151. Fe, Mn,

Zn, Cu,

Co, Cr,

Ni and Pb

Bursa

Province (Turkey)

Agricultural soils

(Fluvisols and Vertisols)

polluted due to industry

and city sewage in the

surface waters, which is

used directly by local irrigation

HNO3+HCl Flame AASa Range of heavy metal content (g/kg)

observed:

In Fluvisols soils: Fe (24.0-34.2), Mn

(1.05-1.17), Zn (0.070-0.083), Cu (0.047-

0.059 Cd (0.0010-0.0028), Co (0.016-

0.027), Cr (0.027-0.037), Ni (0.128-0.140)

and Pb (0.0036- 0.0048).

In Vertisols soils: Fe (29.7 62.5), Mn

(1.15-2.34), Zn (0.060-0.407), Cu (0.056-

0.165), Cd (0.0015-0.0063), Co (0.027-

0.104) Cr (0.095 -0.351), Ni (0.340-0.967)

and Pb (0.017-0.052)

Aydinalp et al.

(2005)

152. Pb, Cd,

Zn, Cr,

and Hg.

Fuheis,

Central

Jordan

Soil samples collected from

parks, play yards, and

residential areas around the cement factory

HF+ HCl+ HNO3 AASa Heavy metal content (mg/kg) observed:

Pb (62.2), Cr (84.4), Cd (4.9), Zn (146) and Hg (1.8).

Banat et al.

(2005)

153. Cu, Ni,

Pb and Zn

Beijing

city

(Northern

China)

Urban parks soil of

commercial, industrial and traditional residential

areas of Beijing.

HNO3+H2O2 Flame AASa

(Vario 6, Jena

Co. Ltd.,

Germany).


Cu (24.1-457.5), Ni (6.10-37.2), Pb (25.7-

207.5), Zn (25.7-196.9).

Chen et al.

(2005)


154. As, Cd,

Co, Cr,

Cu, Ni,

Pb and Zn

Vilnius-

Klaipėda

highway (Lithuania).

Roadside forest soil located

nearby most intensive

traffic road with heavy

traffic loadings of <12,500 to 15,000 vehicles per day

HNO3+HCl Flame AASa

(PEb,AANALYS

T-100) for Cu,

AS, Co, Cr, Ni and Zn

GFf AAS

a (PE

b,

ANALYST -100) for Pb and Cd


observed:

Pb (7.88-54.27), Cu (1.32 - 11.25), Cd (0.096 - 1.19).

Grigalaviciene et al. (2005)

155. Cd, Co,

Cr, Cu,

Ni, Pb

& Zn

Kowloon

Peninsula

(Hong

Kong )

Soils from Urban areas

(residential, commercial

and industrial estates) and

Hotspots (Volcanic regions).

HNO3+ HClO4 ICP-AESc (PE

b

3300DV).


observed in soils of:

Urban areas: Cd (0.10-5.59), Co (0.24-

6.09), Cr (8.03-92.0), Cu (0.76-117), Ni

(5.29-40.1), Pb (12.1-360) & Zn (20.7-577).

Hotspot areas: Cu (71.0), Ni (23.8), Pb

(231.1) & Zn (343.0).

Li et al.

(2004)

156. Cd, Cu,

and Zn.

Dashanch

ong (Soil

A) and

Chenzhou

(Soil B)

both forest

areas in

Hunan

Province

(South

China)

Vegetated soils from soil A

with no reports of deposit,

contamination of heavy

metals and soil B near

Shizhuyuan mining area

with mining activities of

heavy metals and ore waste tailings deposits.

HNO3+HF+HCIO4 GFf

AASa (AA-

6800 Shimadzu)


Soil A: Cd (1.2), Cu (33.0) and Zn (243.5).

Soil B: Cd (1.9), Cu (21.2) and Zn (289.4).

Liao et al.

(2005)

157. Cu, Zn,

Cd, Ni,

Cr and Pb

Harare (Zimbabwe)

Vegetable gardens soils

from three sites in Harare

where wastewater was used

to irrigate vegetable

gardens for at least 10 years.

HCl + HNO3 AASa (Model:

Philips AA-10).


observed:

Cu (7.0 -145), Zn ( 14 -228), Cd (0.5 - 3.4),

Ni (<0.01-21 ), Cr (33 – 225) and Pb (4 -59) for 0–20 cm soil depths.

Mapanda

et al. (2005)


158. Co, Cr,

Cu, Ni,

Pb and

Zn

Damascus

Ghouta,

Damascus

city (Syria)

Agricultural soils irrigated

by Barada and Ahwash the

main rivers crossing

Damascus Ghouta,

receving treated as well as

untreated sewage effluents

from a tannery industrial

estate

HNO3+HCl Flame AASa

(Varian, Germany).


top soils:

Co (7-23), Cr (12-116), Cu (16-97), Ni (24-58), Pb (<-108) and Zn (46-293).

Moller et al.

(2005)

159. Zn, Cu,

Cd and

Pb

Biesbosch

floodplains

(Netherlands)

Vegetated soils from metal

polluted floodplain soils of

Biesbosch National Park

were taken where the

polluted rivers Meuse and

Rhine join. Area is a fresh

water tidal ecosystem &

has been assigned the status

of „wetland of international

importance‟ by Ramsar

Convention

HNO3+HCl Flame AASa

(Varian 1100

PEb)

Range of heavy metal content (μg/g) in the

polluted soils:

Zn (850-1650), Cu (90-350), Cd (5-17), Pb

(210-410).

Notten et al.

(2005)

160. Zn, Cu,

Fe, Mn,

Ni, Pb, Cd

Western

Delhi,

(India)

Peri urban Agricultural

soils of six villages, viz.

Nilothi, Mundka, Ranhola,

Bakarwala, Hirankudna &

Dichaonkalan under

irrigation with sewage

effluents originating from

Keshopur Sewage

Treatment Plant

Extracted with

DTPA

AASa (GBC 932). Heavy metal content (mg/kg) for Nilothi,

Ranhola, Mundka, Bakarwala, Hirankudna

and Dichaonkalan villages:

Zn (7.31, 9.28, 3.68, 6.38, 2.72, 5.02); Cu

(4.91, 7.04,4.39, 6.53, 1.71, 2.65); Fe (20.1,

4.31, 50.2, 62.2, 11.3, 15.6); Mn (3.29,

4.19, 7.46, 8.43, 5.67, 6.92); Cd (0.20,

0.15, 0.11, 0.22, 0.14, 0.14); Pb (1.91, 2.37,

2.60, 2.64, 1.21, 1.11); Ni (1.19, 1.04, 0.58,

1.29,0.29, 0.36) respectively.

Rattan et al.

(2005)

161. Fe, Mn,

Cr, Co,

Cd, Ni,

Pb and

Zn

Kasur

(Pakistan)

Soil samples receiving

heavily polluted effluents

from close proximity of different tanneries.

Aqua extraction Flame AASa

(AA670

Shimadzu)

Heavy metal content (mg/L) observed:

Cr (16.7), Fe (14.8), Co (2.33), Pb (0.958),

Mn (0.647), Ni (0.535), Zn (0.396) and Cd

(0.191).

Tariq et al.

(2005)


162. S, Cd,

Cr, Cu,

Fe, Ni,

Pb and Zn

Tavsanli

District in

the

Kutahya

Province

(Turkey)

Tree Planted soils in the

vicinity of Tuncbilek

Thermal Power Plant (TPP)

contaminated due to emissions of the plant

HClO4+ HNO3+HCl

Flame AASa

(Varian Spectra A 250 Plus model)


S (0.28), Cd (0.2), Cr (15), Cu (13), Fe

(450), Ni (25), Pb (11), Zn (20).

Cicek and

Koparal (2004)

163. Cd,Cu,

Pb and

Zn

Biesbosch,

(Netherlan

ds)

Two Vegetated field sites

from the National Park “De

Brabantsche Biesbosch” in

the Netherlands Lage Hof

(LH) & Petrus Plaat Oost

(PO) in Floodplains of the

heavily polluted European rivers Rhine and Meuse

HClO4+ HNO3 AASa (PE

b-AAS,

type 1100 B).

Flame AASa- in

case of higher metal content

GF AASa – in

case of low metal content.

Heavy metal content (mg/kg) for PO and

LH soils:

Zn (1140, 2333), Cu (142, 387), Pb (278,

600) and Cd (11.7, 19.3) respectively.

Hobbelen

et al. (2004)

164. Cd, Cu,

Pb and Zn

City

center of

Moscow

(Russia)

Grassland and forest top

soils along a transect from

Moscow to a distance of 50

km from the city centre were collected

HNO3+HCl AASa

with

graphite tube or

flame techniques

(Varian

SpectrAA 800

and SpectrAA 200, PE

b 3100)


Cd (0.09–0.33), Cu (6.8–24), Pb (6.7–31),

Zn (25–54).

Ilg et al.

(2004).

165. Pb, Sb,

Ni, Zn,

Mn, Sb and Cu

Clay

pigeon

shooting

range

located

near

Siena,

Tuscany

(Central

Italy).

Vegetated soils in clay

pigeon shooting ranges

which were contaminated

by HM present in

ammunition (pellets

contained in ammunition

are composed of Pb, Sb, Ni, Zn, Mn and Cu)

HNO3 ICP-AESc (ICP-

Plasma PEb

Plasma 400) &

GFf AAS

a (PE

b

Analyst 700).


observed :

Ni (47.4-76.9), Cu (40.0-64.9), Zn (115-

171), Mn (757-1034), Pb (212-1898), Sb (13.9-16.3).

Migliorini

et al. (2004)


166. As, Cu,

Pb and

Zn

Semi-arid

climate

mining

area of

Villa de la

Paz-

Matehuala

and San

Luis

Potosí

(Mexico)

Soils from urban areas,

school playgrounds and

agricultural land where a

Pb-Zn-Ag (Cu-Au) skarn

ore system has been mined

for more than 200 yr

25 % HNO3 AASa- PE

b 2380

with hydride

generation system

for As

AASa Varian

Spectra AA 220

for Pb, Cu and Zn


observed:

As (19-17384), Cu (15-7200), Pb (31-

3450) and Zn (26–6270).

Razo et al.

(2004)

167. Cd, Cr,

Cu, Ni,

Pb, and

Zn

Coruna

province

in Galicia

(Spain)

Mine soils of Touro: Cu &

Meirama: lignite mine

(Mine soils of Touro spoils

(TE); decantation-bank

(TB), where sludge from

Co extraction in the

flotation plant; wetland soil

at Meirama mine spoil

(MH); Thin soil having

limitations for plant growth

(MI); vegetated Soils (ME)

HNO3+HF+ HCIO4 ICP AESc Heavy metal content (mg/kg) for TE, TB,

MH, MI and ME soil:

Cd (1.6, 1.7, 0.5, 1.3, 2.1); Cr (90, 151,

30,60, 30); Cu (200, 1217, <50, <50,<50);

Ni (72, 62, 10, 16, 8); Pb (14, 18, 30, 22,

33); Zn (111, 83, 60, 63, 56) respectively.

Vega et al.

(2004)

168. Pb, Zn

and Cd

City of

Nancy and

Luxembo

urg

(France)

Surface soils affected due

vehicular pollution.

Site 1: agricultural soil,

which is constituted of

„„silt of plateau‟‟.

Site 2: soils from the flood

plain of the Moselle and

was of the alluvial type

HNO3+HCL AASa

with air-

acetylene flame

for Zn (PEb 2380)

GFf AAS

a (Varian

SpectrAA

300/400) for Pb

and Cd


soils of :

Site 1: Zn (70-330); Pb (20-90) and Cd

(0.3-1.2).

Site 2: Zn (90-500); Pb (10-120) and Cd

(0.2-0.4).

Viard et al.

(2004)

169. Si, Al,

Fe, Mn,

Cr, Cu,

Ni, Pb

and Zn

Solofrana

river

valley

(SRV)

(Southern

Italy)

Volcanic Agricultural soils

irrigated for a long time

with contaminated river

water or subjected to

overflowing, were collected

HF+ HNO3

AASa (PE

b 3030

B)

Heavy metal content (mg/kg) in soils

affected by flooding of SRV :

At 0-5, 5-8 and 8-30 cm depth:

Fe (36.4, 36.9, 53.3); Cr (219, 335, 187),

Cu (77, 104, 239); Mn (550, 530, 6110, Ni

Adamo et al.

(2003)


(56, 60,80) Pb (98, 58, 33); Zn (109, 109,

92) respectively.


shallow soils (40 cm depth) irrigated with

river water: Fe (51.7-60.1), Cr (142-246),

Cu (170-565), Mn (609-752), Ni (60-84),

Pb (21-68), Zn (99-135).

170. Cd, Zn,

Pb, Ni,

Cu, and

Cr

Greater

Delhi area

(India)

Street dust soils from 3

localities viz. Wazirpur

Industrial Area (A1),

Karnal Bypass (A2) &

Nangli Puna (A3) affected

due to anthropogenic

activities (vehicular, refuse

burning & emissions from

industrial processes

HCIO4+ HNO3 +

HF

Fame AASa

(GBC model 902

AAS).

Heavy metal content (μg/g) in A1, A2 and

A3:

Pb (150, 200,120); Cd (17, 15,13); Cu

(1300, 200); Cr (9000, 800); Ni (1000, 110,

90); Zn (370, 320, 120) respectively.

Banerjee,

(2003)

171. Cr, Zn,

Pb, Ni,

Cu, Co

and Cd

French

network

RENECO

FOR

(France)

Forested soils (ferric

podzol, cambic podzol,

haplice podzol, dystric

cambisol, humic cambisol,

dystric planosol, stagnic

luvisol, calcaric cambisol

and mollic andosol) located

in areas sensitive to acid

atmospheric inputs

HF+HNO3+HClO4+

H2O2

ICP-MS h (PE

ELAN 6000)

Range of heavy metal (μg/g) observed:

Cr (3.01-144), Co (0.16-22.8), Ni (0.03-

72.64), Cu (0.52-37.85), Zn (1.56-134.57),

Cd (0.09-0.63), Pb (3.2-134.45), Sc (0.5-

39.72).

Hernandez et

al. (2003)

172. Cu, Cr,

Pb and

Zn

Naples

city (Italy)

Soils from urban core area,

including western &

eastern districts of the city,

(in gardens, parks, roadside

fields & industrial sites)

where the motorway and

the old oil refineries along

with combustible deposits

are located

HNO3+HCl AASa (PE

b 3030

B)

Heavy metal content (mg/kg) observed :

Cu (6.2- 286), Cr (1.7-7.3), Pb (4-3420),

Zn (30-2550).

Imperato et

al. (2003)


173. V, Cr,

Mn, Fe,

Ni, Cu,

Zn, As

and Pb

Vrbnik

town, on

island of

Krk

(Northern

Adriatic)

Soils from Vrbnicko polje

vineyard area affected due

to extensive use of blue

vitriol a fungicide

C2H3O2+

C5H9NS2.NH3

+HNO3

EDXRF j Range of heavy metal content (ug /g)

observed:

V (64-133), Cr (58-255), Mn (722-2023),

Fe (23-64 mg/kg), Ni (5-29), Cu (24-108),

Zn (67-157), As (10-25) and Pb (9-171)

Orescanin

et al. (2003)

174. V, Cr,

Cu, Zn

and Pb

Teddingto

n (United

Kingdom)

.

Coal carbonization site soil

(LGC6138) certified by

The Laboratory of The

Government Chemist

(Teddington, UK).

HNO3+ HF ICP-AESc with

PEb (Optima

2000 DV optical

emission

spectrometer)


V (54.6), Cr (99.3), Cu (132), Zn (456), Pb

(490).

Sandroni et

al. (2003)

175. Pb, Cd,

Co, Cr,

Mn and

Ni

Tokat

(Turkey)

Uncontaminated

agricultural soil (Control)

and soils near roadside and

textile plants

HCl+ HNO3

+HClO4

Flame AASa (PE

b

AAnalyst 700

Heavy metal content (μg/kg) in

uncontaminated soils:

Pb (15.3), Cd (0.25), Co (4.20), Cr (10.9),

Mn (139), Ni (32), Cu (7.50), Zn (38) and

Fe (7368).

Heavy metal content (μg/kg) in soils near

roadside and textile plants: Pb (45.1, 25.4),

Cd (1.63, 4.88), Co (12.4, 5.60), Cr (31.7,

19.7), Mn (320, 258), Ni (54, 41), Cu

(38.3, 24.2), Zn (60, 74), Fe (10344, 9574)

respectively

Tuzen,

(2003)

176. Co, Cu,

Mn Pb,

and Zn.

Izmit Gulf

region

(Turkey)

Soil surface from rural,

urban and mostly industrial

sites in the vicinity of the

Gulf of Izmit

HCl|+HNO3+HF+H

3BO3

AASa (Varian

SpectrAA 200)


Industrial soils: Cd (nd), Co (24), Cr (72),

Cu (37), Ni (43), Pb (44), Zn (74), Fe

(30g/kg), Mn (0.8g/kg)

Rural soils: Cd (nd), Co (13), Cr (31), Cu

(21), Ni (14), Pb (16), Zn (56), Fe (20g/kg),

Mn (0.6g/kg)

Urban soils: Cd (nd), Co (25), Cr (62), Cu

(35), Ni (41), Pb (41), Zn (78), Fe (38

g/kg), Mn (1.0 g/kg)

Yilmaz et

al. (2003)


177. Al, Cd,

Cr, Cu,

Fe, Mn,

Ni, Pb,

Ti and

Zn.

Piedmont

Region

(North-

western

Italy).

Agricultural

soils

HF+ HNO3+HCl ICP-AESc

(Varian Liberty

100) for metal

with high

concentrations

GFf - AAS

a (PE

b

5100) for metals

lower or closer to

detection limits


observed:

Cd (0.01-2.0), Cr (5-1500), Cu (2-250), Fe

(7000-42000), Mn (20-10000), Ni (2-300),

Pb (2-300) and Zn (1-900)

Abollino

et al. (2002)

178. Cr, Cu,

Fe, Mn,

Ni, Pb

and Zn.

European

region

(ES);

Soils from

Switzerlan

d (SW),

Soils from

Northamp

ton,UK

Sewage sludge amended

soils

HNO3+ HCl LIBSl spectra of

soil was recorded

by an Intensified

Coupled Charged

Detector (ICCD)

multichannel

analyzer and ICPo

Ratios of the average heavy metal content

(mg/kg) of each element measured by ICP

and LIBS:

Cr (0.65), Cu (1.16), Fe (1.02), Mn (0.57),

Ni (0.25), Pb (0.47), Zn (0.84).

Capitelli

et al. (2002)

179. Cd, Pb,

Cr Cu,

Ni, V

and Zn

Kpogame

and

Hahotoe

(Southern

Togo)

Soils from minning areas

(abandoned minning pits

and transport routes)

composed of a mixture of

overburden and ore waste

HF+HNO3+HClO4 ICP-AESc


observed:

Cd (0.2-43), Pb (15-115), Cr (18-356), Cu

(15-432), Ni (15-432), V(90-513) Zn (35-

563)

Gnandi and

Tovschall,

(2002)

180. As, Be,

Cd, Cr,

Ni, Pb,

Ti, V

and Zn

Alcala de

Henares

(Madrid,

Spain)

Municipal garden soils

from area under industrial

activities and intense traffic

and 5 samples near campus

zone in the vicinity of an

important industrial zone

and also near a motorway

with a high traffic density.

HNO3 ICP-MSh ( PE

b

Elan 6000).

Heavy metal content (μg/g) for campus and

urban zone:

As (9.20, 3.40), Be (0.79, 0.35), Cd (0.07,

0.07); Cr (12.96, 6.20); Cu (8.30, 6.5); Ni

(10.73, 4.29); Pb (14.4, 24.35); Ti (0.14,

0.06); V (15.10, 6.01); Zn (33.41, 31.17)

respectively.

Granero and

Domingo,

(2002)


181. Cd,

Cr,Cu,

Ni, Pb

and Zn

Greater

Cleveland

area

(Ohio,

USA)

Brownfield and Park area

soil from historic industrial

sites where venerable

industrial buildings were

abandoned or greatly

underutilized.

HCl AASa (Varian

Spectra AA 250)

Range of heavy metal content (mg/kg):

In Brownfield soils: Cd (0.1-216), Cr (1-

574), Cu (19-22,500), Pb (38-15,170) and

Zn (93-13400).

In Parks soils: Cd (0.4-1.8), Cr (3-70), Cu

(12-360) Pb (19-811) and Zn (37-527).

Jennings et al.

(2002)

182. As Cd,

Cu, Pb

and Zn

Duckum

(Korea)

Agricultural (Paddy

grown), Farmland soils and

dressing plant soil in the

vicinity of Duckum Au-Ag

mine

HNO3+ HCl

ICP-AESc for Cd,

Cu, Pb and Zn

Hydride

generation ICPo

for As


In Paddy soils As (6.7-35.0), Cd (0.9-2.7),

Cu (6-48), Pb (19-530), Zn (46-321).

In Farmland soils As (23.3-75.9), Cd (2.8-

6.1), Cu (40-99), Pb (289-948), Zn (275-

568).

In dressing plant soil As (176.0- 491.0), Cd

(3.6-99.6), Cu (63-404), Pb (1378-14454),

Zn (483-12342).

Kim et al.

(2002)

183. Cd, Zn,

Cu and

Pb

Shooting

Range

(South

Korea)

Bald (BS) and vegetated

(VS) spot soils from firing

range contaminated with

HM and TNT from

cannonballs and having 45

years history of being the

shooting land.

HNO3+ HCl Graphite and

flame AASa (PE

b,

Boston,

MA,USA).

Heavy metal content (mg/kg) during spring

and autumn :

Bald spot soils (BS): Cd (1.85, 1.01), Zn

(218.30, 252.72), Cu (161.07, 164.70), Pb

(165.85, 100.28), Pb (165.85, 100.28)

respectively.

Vegetated spot soils (VS): Cd (1.03, 0.91),

Zn (172.48, 199.53), Cu (127.25, 129.71),

Pb (78.00, 80.04) respectively

Lee et al.

(2002)

184. Cr, Cu

Pb Zn,

Mn, Ni

and Fe

Seville Urban parks soil from main

public parks and gardens

HNO3+HCl ICP-OESd Heavy metal content (mg/kg) at 0-10 cm

and 10-20 cm depth:

Cr (39.4, 40.7), Cu (68.2, 77.3), Pb (137,

163), Zn (145, 131), Mn (471, 510), Ni

(21.9, 23.2) and Fe (20100, 21400)

respectively.

Madrid et al.

(2002)


185. Co, Cu,

Pb and Zn

Pearl River Delta

(PRD) South

Guangdong

(China)

Agricultural (crop &

paddy) & Natural soils affected due to rapid

Urban & industrial

activities & increasing

reliance on agrochemicals

in the last several decades

HNO3+ HCIO4 ICP AES c (PE

b

3300DV).

Heavy metal content (mg/kg) in crop,

paddy and natural soils:

Cr (71.4, 57.1, 51.8); Cu (33.0, 20.7, 14.7),

Ni (21.2, 17.0, 16.5), Pb (40.0, 35.1, 29.9);

Zn (84.7, 61.1, 50.7); Cd (0.58, 0.34, 0.52); Co (9.11, 6.53, 7.12) respectively.

Wong et al.

(2002)

186. Cd, Cu,

Pb and

Zn

Korea Surface soils (Paddy soils,

forest soils, tailings) from

in and around the Au-Ag-Pb-Zn mine.

HNO3+HCl

HCl only

ICP-AESc

Heavy metal content (mg/kg) extracted

with aqua regia and HCl:

In Paddy soils: Cd (1.78, 0.78), Cu (42.2,

13.3), Pb (133, 33.2), Zn (328, 73.6) respectively.

In Forest soils: Cd (1.30, 0.38), Cu (42.7,

9.3), Pb (155, 36.3), Zn (198, 33.4)

respectively.

In Tailings soils: Cd (8.57, 5.67), Cu (481,

130), Pb (4,450, 84.4), Zn (753, 100)

respectively.

Lee et al.

(2001)

187. Cu, Cd,

Pb and

Zn

West

Bank

(Palestine)

Roadside surface soil along

Nablus-Ramallah main

road that connects the

northern West Bank with

the middle and southern parts

HNO3+HClO4 ICPo Range of heavy metal content (ug /g)

observed:

Cu (18.59-30.5), Cd (0.25-0.73), Pb (89.1-

245.0), Zn (95.8-170.6)

Swaileh et al.

(2001)

188. Cd, Cr,

Cu, Ni,

Pb, and

Zn

Vietnam. Agricultural soils (paddy

and upland fields at different locations)

HF+ HNO3+ HCIO4 AASa Range of heavy metal content (mg/kg)

observed:

Cd (nd-0.09), Cr (25-479), Cu (16-106), Ni

(8-251), Pb (11- 33) and Zn (32-195)

Tra and

Egashira (2001)


189. Cd, Co,

Cr,

Cu,

Mn, Ni,

Pb, and

Zn

Standard

reference

soils at

ENEL &

UCSC

Laboratori

es, both

located in

Piacenza

(Italy)

5 BCR standard reference

materials (CRM 141R

„Calcareous Loam Soil‟,

CRM 142 „Light Sandy

Soil‟, CRM 143 „Sewage

Sludge Amended Soil‟,

CRM 277 „Estuarine

Sediment‟ and CRM 320

„River Sediment‟)

HF+ HCl+ HNO3 ICP-OESd

(Optima 3000

(PEb LLC, USA)

.

Heavy metal content (mg/g) in CRM 141R,

CRM 142, CRM 143, CRM 277, CRM

320:

Cd (13.2, <4.7, 31.8, 12.3, <4.7); Co (11.3,

10.1, 11.2, 17.7, 20.5); Cr (164, 69.1, 215,

215, 122); Cu (42.4, 25.5, 236, 101.7,

42.0); Mn (630, 550, 883, 1471, 697); Ni

(88, 27.1, 99.2, 42.6, 72.4); Pb (55.8, 38.0,

1151, 143, 40.6); Zn (273, 91.2, 1240, 551,

138) respectively.

Bettinelli

et al. (2000)

190. Zn, Sb,

Cd, Pb,

Cu, Sn,

As, Cr,

Hg, Ni

East St.

Louis, IL

(Illinois,

Urbana)

Soils (Industrial and

residential) with history of

abundant industrial

activities like smelters of

ferrous and nonferrous

metals, a coal-fired power

plant, organic and

inorganic chemicals

companies, municipal

waste incinerators and

petroleum refineries.

Soil pellets for

Sequential XRFq

Powdered soil for

NAAr

Sequential XRFg

spectrometer

(Phillips PW-

1450)

NAAn


observed:

Zn (79-10,360), Sb (0.86-40.2), Cd (<1-

12.5), Pb (35-1860), Cu (11-14,400), Sn

(<1-12.5), As (5.9-74.7), Cr (48.4-299), Hg

(< 0.12-1.7) and Ni (15-414)

Kaminski

and

Landsberger

(2000)

191. Cd, Cu,

Mo, Pb,

Sb, Sn,

Zr, Cr,

Fe, Mn,

Ni, Si,

Ti and

Zn

Arctic

Ocean

Soils from Arctic region:

Spitsbergen Island from

the Atlantic Ocean Coast:

Bjorndalen; Grumantbreen;

Colesbukta; Longyearbyen,

Cape linee soils.

Greenland

Western Greenland:

Qaanaak; Thule Air Base;

Kangerlussuaq

North Siberia: Hatanga

Arctic region soils were

polluted with heavy metal

ions from different areas of

the North Hemisphere

HCl ICP-AESc

Spectroflame-P

(SPECTROAnaly

tical

Instruments,

Germany).

Heavy metal content (mg/kg) at depth of 0-

9 , 9-12, 12-20:

In Bjorndalen soils: Cd (22, 24, 5) Cu (59,

49, 27), Mo (20, 14, <1), Pb (95, 73, 73), Sb

(12, 8, 19), Sn (<10, <10, <10), Zr (83, 69,

110), Cr (430, 460, 180), Fe (52100, 53200,

36400), Mn (300, 290, 380), Ni (230, 220,

100), Si (nd), Ti (4500, 4500, 3200) and Zn

(180, 150, 110).

In Grumantbreen soils: Cd (28, 24, 7) Cu

(69, 66, 91), Mo (24, 22, 1), Pb (96, 86, 64),

Sb (10, 10, 64)), Sn (<10, <10, <10), Zr

(<10, 19, 150), Cr (430, 460, 250), Fe

(64200, 56300, 37900), Mn (600, 410, 250),

Ni (230, 230, 110), Si (nd), Ti (4800, 3400,

3900) and Zn (190, 160, 140).

Negoita and

Ropota

(2000)


In Colesbukta soils: Cd (18, 8, 6) Cu (42,

39, 37), Mo (22, 6, 1), Pb (97, 110, 78), Sb

(10, 54, 17), Sn (<10,<10<10), Zr (160, 67,

120) Cr (330, 360, 220), Fe (4300, 47000,

43100), Mn (300, 770, 400), Ni (180, 210,

680), Si (nd), Ti (4200, 4500, 3900) and Zn

(140,180,120).

In Cape linee soils: Cd (26, 6, 7) Cu (66,

10, 31), Mo (10, 2, 3), Pb (106,70, 70), Sb

(14, 12, 21), Sn (<10, <10,<10), Zr (69,

140, 130) Cr (420, 200, 330), Fe (53100,

42800, 47600), Mn (1300, 1100, 1200), Ni

(230, 90, 190), Si (nd), Ti (5500, 5500,

4400) and Zn (120, 90, 100).

Two sites from Longyearbyen at depth of

0-5 cm : Cd (7, 2) Cu (21, 24), Mo (1, <1),

Pb (78, 57), Sb (22, 13), Sn (<10, <10), Zr

(340, 75), Cr (240,180), Fe (32800, 8500),

Mn (250, 470), Ni (120, 90), Si (nd, 810),

Ti (4000,700) and Zn (nd,160).

In Greenland soils: Cd (16, 20, 6) Cu (73,

44, 41), Mo (30, 30, 8), Pb (67, 99,70), Sb

(8, 3, 16), Sn (<10, <10, <10), Zr (51, 86,

84) Cr (700, 510, 340), Fe (37900, 49200,

34200), Mn (600, 380, 330), Ni (240, 230,

270), Si (nd), Ti (3400, 4100, 2800) and Zn

(140, 180, 340).

In Western Greenland soils i.e Qaanaak,

Thule Air Base & Kangerlussuaq (at

depth of 0-5 cm): Cd (5, 5, 7) Cu (50, 58,

73), Mo (<1, <1, <1), Pb (70, 87, 60), Sb

(20, 39, 18), Sn (<10, <10, <10), Zr (160,

92, 260), Cr (180, 180,210), Fe (23100,

19800, 31900), Mn (560, 310, 680), Ni

(100, 110, 110), Si (17000, 19600, 6300), Ti

(3200, 2600, 2900) and Zn (130, 100, 100)

respectively.


Two sites from Hatanga soils (at depth of

0-5 cm): Cd (4, 5) Cu (22, 21), Mo (5, 3),

Pb (55, 60), Sb (19, 18), Sn (<10, <10), Zr

(86, 91) Cr (330, 300), Fe (30200, 30000),

Mn (980, 900), Ni (180, 160), Si (5000,

400), Ti (2800, 2500) and Zn (80, 960).

192. Cd, Cr,

Cu,

Mn, Ni,

Pb and

Zn

Sicily

(Italy)

Soils developed from

various parent materials

and influenced by a

Mediterranean climate.

HF+ HNO3+ HCIO4 GFf

AASa

(PEb -

5100)


observed:

Cd (0.12-2.4), Cr (15-137), Cu (12-172),

Mn (259-2967), Ni (15-104), Pb (5-45), Zn

(47-152).

Palambo

et al. (2000)

193. Cd, Cu,

Mn, Ni,

Pb and

Zn

Dhaka

city

(Banglade

sh)

Agricultural soils in

vicinity of tannery,

ceramic, textile dying and

sulphuric acid producing

industrial sites

HNO3 +HClO4 AASa- PE

b Range of heavy metal (mg/kg) observed:

Cd (0.1-1.8), Cu (28 - 217), Mn (106 -

577), Ni (25 - 112), Pb (17 - 99) and Zn (53

- 477).

Kashem and

Singh (1999)

194. Cd, Cr,

Cu, Pb

and Zn

Castellon

(Spain)

Soils samples from 0-5 and

10-25 cm depth on which

sewage sludges were

dumped:

Soils 1 and 2 (blanks and

indicate the natural heavy

metal content) - from areas

free of sewage sludges,

close to those where

sludges 1 and 2 (two

different sludge samples).

Soil 3- soil beneath the

sewage sludge 1, whereas

Soil 4 and 5 are two soils

with different texture

characteristics on which

sludge 2 is dumped.

HNO3+HC1O4+HF

for soil samples

HNO3 +H2O2 for

sewage sludge

samples

AASa (PE

b -

2380)

Total heavy metal content (mg/kg)

observed:

In Soils 1, 2, 3, 4 and 5 at sampling depth

of 0-5 cm depth: Cd (1.30, 0.81, 1.79,

1.26, 2.13), Cr (49.4, 5.25, 63.3, 6.02,

8.38), Cu (27.6, 7.92, 50. 0, 31.7, 24.5), Pb

(90.4, 22.2, 240, 84.8, 102) and Zn (540,

24.4, 1430, 257, 743).

In Soils 1, 2, 3, 4 and 5 at sampling depth

of 10-25 cm depth: Cd (0.65, 0.43, 1.50,

1.04, 1.17), Cr (56.9, 5.58, 62.0, 5.73,

7.40), Cu (14.4, 7.84, 17.0, 10.2, 13.2), Pb

(32.0,23.8, 35.4, 31.8, 36.7) and Zn (61.5,

22.4, 68.5, 30.0, 37.6).

In sludge 1 and 2: Cd (9.54, 23.6), Cr

(107, 90.9), Cu (645, 640), Pb (3180, 5850)

and Zn (27070, 31060)

Escrig and

Morell (1998)


195. Zn, Pb,

Cu, Ni,

Cr, Cd,

Hg, Sb

and As

Arnoldstei

n,

Brixlegg,

Slovakian

(eastern

Austria),

Saualpe

(Central

Austrian)

and Alps

Carinthia.

Heavy metal contaminated

soils with emissions of

smelters

HNO3+HCl ICP-AESc flame

P/M, Spectro A.I)

for Zn, Cu, Ni

and Cr

AASa (PE

b 4100

ZL) for Sb, As

and Se

Cold vapour

AASa (FIMS,

PEb) for Hg

GF-AASd-

SIMAA-600,

PEb) for Cd

Heavy metal content (mg/kg) observed in

soils from:

Arnoldstein: Zn (2257), Pb (10779), Cu

(93.1) Ni (21.73) Cr (25.14), Cd (25.0), Hg

(0.417), Sb (88.4) and As (110).

Brixlegg: Zn (3630), Pb (2057), Cu

(3203), Ni (71.2), Cr (25.34), Cd (15.5),

Hg (387), Sb (211), As (0.149).

Slovakian had elevated content of Sb

(6720) and As (2940) however Zn (16.9),

Pb (69.5), Cu (22.6), Ni (23.4), Cr (22.9),

Cd (0.26) and Hg (0.86).

Carinthian soils: Zn (92.7) Pb (55.4), Cu

(43.1), Ni (50.2), Cr (56.2), Cd (0.656), Sb

(0.518), Hg (42.1) except for As (1540).

Knasmuller

et al. (1998)

196. Al, Fe,

Mn,

Cd, Cr,

Cu, Ni,

Pb and

Zn

Thonburi

city

(Bangkok)

Surface soil samples

contaminated from urban

parts of the Bangkok

metropolitan region

HNO3+ HCIO4 AASa (Varian

AA 10, AA 400,

and AA 400Z) for

Cd, Cu, Fe, Ni,

Pb, and Zn using

both flame for

Cu, Fe, Zn. and

graphite tube

techniques for

Cd, Cu, Ni, and

Pb.

Heavy metal content (g/kg) observed:

Al (13.8), Fe (16.1), Mn (0.34) and content

(mg/kg) of Cd (0.29), Cr (26.4), Cu (41.7),

Ni (24.8), Pb (47.8), Zn (118).

Wilcke et al.

(1998)

197. Pb, Zn,

Cu and

Cd

Wuhan

city

(China)

Soils along heavily

trafficked roadside (Luomo

road- LM, Luoshi road- LS,

Daqia road- DQ) and

Botanical garden (BG).

HNO3+HCl Flame AASa

(Shimadzu AA-

680) for Pb, Zn

and Cu

GFf

AASa(Shimadzu

AA-680) for Cd

Heavy metal content (ug /g) observed:

In BG soils: Pb (23.38), Zn (98.23), Cu

(30.05) and Cd (0.061)

In LM soils: Pb (742.04), Zn (129.84), Cu

(42.04) and Cd (0.102).

In LS soils: Pb (97.45), Zn (182.98), Cu

(56.53) and Cd (0.103)

Xiong (1998)


In DQ soils: Pb (210.84), Zn (263.48), Cu

(91.69) and Cd (0.147)

198. As, Cd,

Cu, Pb,

Mn, Fe,

Al, Ba,

Cr, Ni,

Sr and

V

Andes of

Northern

Peru

(Latin

America)

Agricultural soils (crop

cultivated) in the vicinity of

Copper mine.

HNO3+HClO4+H2S

O4

ICP-AESc (Yvon

JY38-VHR).


As (143-7670), Cd (8.9-499), Cu (69-

5270), Pb (87.0-341) Mn (213-965), Fe

(2.81-5.80%), Al (0.91-9.14%).

Bech et al.

(1997)

199. Cd, Cu,

Fe, Mn,

Ni, Pb

and Zn

Southern

France

Vegetated soils from

different sites for soil

ecotoxicological

assessment.

HNO3+HCl Air-acetylene

flame AASa

(Varian AA

SPECTRA)


observed:

Cd (0.35-7.5), Cu (14.9-237), Pb (19.5-

8415), Zn (39.6-1682), Ni (3.3-49), Mn

(33.5-2325), Fe (6161-64930).

Motalib et

al. (1997)

200. Cd New

Zealand

Farms (pastoral) and non-

agricultural (native) soils.

Eight major soil groups

were categorized i.e.,

alluvial , brown granular

loams (BGL), gleys,

yellow-brown earths

(YBE), yellow-brown

loams (YBL), yellow-

brown pumice soils (YBP),

yellow grey earths (YGE),

and peats respectively

HNO3+ HCIO4 GFfAAS

a

Heavy metal content ( μg/g) for alluvial,

BGL, Gley, Peat, YBE, YBL, YBP and

YGE observed:

In non-agricultural soils: Zn (0.13, 0.19,

0.24, 0.22, 0.16, 0.23, 0.31, 0.13).

In Farm soils: Zn (0.16, 0.49, 0.42, 0.69,

0.22, 0.70, 0.75,0.12)

Roberts

et al. (1994)

201. Cd, Pb,

Zn, Cu,

and Ni

St. Clair

County

(USA)

Agricultural soils at least 8

km downwind from any

stack emitter (coal fired

electric generator, smelter,

foundry, etc.), 200 m from

U.S. or state highways, 100

m from rural road ways,

100 m from current,

abandoned, or known

obliterated building sites,

HNO3 GFf AAS

a (PE

b

Model 603).

Anodic Stripping

Voltameter (ESA

Model 3010A)

for Pb

DCP-MAESp

(Beckman

Instrument Co.,


observed:

Cd (0.16–0.32), Pb (10–20), Zn (40–80),

Cu (10–20), and Ni (10–20).

Holmgren

et al. (1993)


and 50 m from field

boundaries.

Fulletron, CA;

Spectraspan

Model III) for Zn,

Cu, and Ni

bdl- below detection limit, nd- not detectable; Ag-silver, As-arsenic, Au-gold, Ba-barium, Cd- cadmium, Ce-cesium, Co-cobalt, Cr-

chromium, Cu- copper, Dy-dysprosium, Fe-iron, Hf-hafnium, Hg-mercury, La-lanthanum, Mg-magnesium, Mn-manganese, Mo-

molybdenum, Na-sodium, Nb-niobium, Nd-neodymium, Ni-nickel, Pb-lead, Sb-antimony, Sc-scandium, Se- selenium, Sm-samarium, Sr-

srtontium, Tb-terbium, Th-thorium, Ti-tin, Tm-thulium, U-uranium, V-vanadium, W-tungsten, Yb- ytterbium, Zn-zinc, Zr-zirconium.

a. Atomic Absorption Spectrophotometer/Spectroscopy.

b. Perkin-Elmer.

c. Inductively Coupled Plasma Atomic Emission Spectrometer.

d. Inductively Coupled Plasma-Optical Emission Spectrometer.

e. Atomic Fluorescence Spectrometer

f. Graphite Furnace.

g. X-ray fluorescence spectrometer

h. Inductively Coupled Plasma Mass Spectrometer.

i. Inductively Coupled Plasma Atomic Fluorescent Spectrometer.

j. Energy Dispersive X-ray Fluorescence Spectrometer

k. Epithermal Neutron Activation Analysis

l. Laser Induced Breakdown Spectroscopy

m. Electrothermal Atomization Absorption Spectrometry

n. Neutron Activation Analysis

o. Inductively Coupled Plasma Spectrophotometer

p. Direct current plasma-multielement atomic emission spectrograph

Chapter-3

Materials

&

Methods

The joy of research must be found in doing since every other harvest is uncertain

-Theobald Smith

Chapter-3

MATERIALS AND METHODS

The present study was planned to investigate the agricultural soils of Amritsar

under rice and wheat cultivation for genotoxicity and antioxidative defense response in

A. cepa test system. The study also comprised the estimation of various physico-

chemical parameters viz., soil texture, pH, alkalinity, calcium, magnesium, nitrates,

phosphates, potassium, sodium and metals viz. chromium (Cr) and cadmium (Cd),

copper (Cu), iron (Fe), lead (Pb), manganese (Mn), nickel (Ni) and zinc (Zn).

3.1. Collection of soil samples

3.1.1. Study area

The study area of present investigation is the district Amritsar, Punjab (India). It

extends between 31028'30" to 32

003'15" north latitude and 74

029'30" to 75

024'15" east

longitude with population of 2,490,656 and has 778 villages (Census of India, 2011;

Kaur et al., 2013). Intensive cultivation of main crops like rice and wheat as well as less

popular maize, sugarcane and vegetable crops is being done in this region. An extensive

survey of different regions of Amritsar was made after dividing it into four equal zones

viz. North East (NE), North West (NW), South East (SE) and South West (SW). Two

agricultural fields from each zone were selected for further investigations (Fig.1).

INDIA PUNJAB AMRITSAR

Fig. 1. Location of different regions of study area

Source: http://cgwb.gov.in

NE

SE SW

NW

http://cgwb.gov.in/

Materials and Methods

46

3.1.2. Sites of sample collection

The soil samples were collected from 8 agricultural fields comprising of two

fields from each zone of Amritsar. All the fields were selected on the basis of random

sampling. Soil samples were collected from each site twice a year from fields under rice

cultivation during September, 2009 (r1 sampling) and September, 2010 (r2 sampling)

and wheat cultivation during March, 2010 (w1sampling) and March, 2011 (w2

sampling). The samples were coded and details of sample codes are given in Table 4.

Table 4. Description of Soil samples codes

S.No. Sample code Zone Description Address of agricultural field

1. NEFIr1 North East

Rice cultivation

S. Baldev Singh S/o

S. Udam Singh, Vill. Saran,

Ajnala, Amritsar

2. NEFIw1 Wheat cultivation

3. NEFIr2 Rice cultivation

4. NEFIw2 Wheat cultivation

5. NEFIIr1 North East Rice cultivation

S. Bira Singh S/o

S. Bawa Singh, Vill. Wishova,

Ajnala, Amritsar

6. NEFIIw1 Wheat cultivation

7. NEFIIr2 Rice cultivation

8. NEFIIw2 Wheat cultivation

9. NWFIr1 North west Rice cultivation

S. Gurmej Singh S/o

S. Satwant Singh, Vill. Bhala

Pind, Amritsar

10. NWFIw1 Wheat cultivation

11. NWFIr2 Rice cultivation

12. NWFIw2 Wheat cultivation


47

13. NWFIIr1 North west Rice cultivation

S. Bawa Singh, Sheller Valae,

Dalam Pind, Amritsar

14. NWFIIw1 Wheat cultivation

15. NWFIIr2 Rice cultivation

16. NWFIIw2 Wheat cultivation

17. SEFIr1 South East Rice cultivation

S. Dalbir Singh Bal S/o S.

Sukbir Singh, V.P.O Sathiala,

Tehsil Baba Bakala, Amritsar

18. SEFIw1 Wheat cultivation

19. SEFIr2 Rice cultivation

20. SEFIw2 Wheat cultivation

21. SEFIIr1 South East Rice cultivation

S.Mukhtiar Singh, Vill.

Dhardeo Tehsil, Baba Bakala,

Amritsar

22. SEFIIw1 Wheat cultivation

23. SEFIIr2 Rice cultivation

24. SEFIIw2 Wheat cultivation

25. SWFIr1 South West Rice cultivation

S. Kulwant Singh, S/o S.

Sohan Singh, Vill. Ranikae,

Near Attari, Amritsar.

26. SWFIw1 Wheat cultivation

27. SWFIr2 Rice cultivation

28. SWFIw2 Wheat cultivation

29. SWFIIr1 South West Rice cultivation

S. Pritam Singh, S/o S. Mohan

Singh V.P.O Khasa, Amritsar

30. SWFIIw1 Wheat cultivation

31. SWFIIr2 Rice cultivation

32. SWFIIw2 Wheat cultivation

NE- north east; NW- north west; SE- south east; SW- south west; r1 - Sampling during September,

2009 (rice cultivation); w1 - Sampling during March, 2010 (wheat cultivation); r2- Sampling during

September, 2010 (rice cultivation); w2 - Sampling during March, 2011 (wheat cultivation).


48

3.2. Physico-chemical analysis

Estimation of physico-chemical parameters like soil texture, pH, alkalinity,

contents of calcium, magnesium, sodium, potassium, phosphorous and heavy metals,

was carried out for the collected samples using different techniques (Trivedy et al.,

1987; Chand et al., 2011).

3.2.1. Soil texture

Soil texture is a soil property used to describe the relative proportion of different

grain sizes (sand, silt and clay) of mineral particles present in soils. These particles are

grouped according to their size into soil separates.

3.2.1.1. Procedure

Soil texture was determined by calculating percentage of each fraction.

3.2.2. Preparation of soil extracts

For physico-chemical analysis, the soil extract (1 : 5 w/v) was prepared. The soil

samples collected from different sites were air dried and extracted by using distilled

water (soil : water :: 1 : 2 w/v). 20 g of soil was suspended in 100 ml of distilled water,

shaken on mechanical shaker for 12 h at room temperature and filtered through

Whatman No. 1 filter paper. The filtrate was termed as soil extract and was used for

further analysis.

Air dried Sample on Shaker (12hrs) Filtration

Clay < 0 .002 mm Silt (0.002-0.05 mm)

20 g of soil

Each portion of soil weighed

Passed through series of sieves of decreasing pore size

Sand (0.5-2.0 mm)


49

3.2.3. pH

pH refers to the measure of hydrogen ion concentration and depends largely on

the relative amount of the absorbed hydrogen and hydroxyl ions. It measures the

intensity of acidity and alkalinity of soil-water suspension and provides a good

indication of chemical nature of soil.

3.2.3.1. Procedure

The pH of soil extract was determined by using pH meter (Systronics; model µ

pH system 361).

3.2.4. Alkalinity

Alkalinity is a measure of the acid neutralization capacity of a solution which

gives stoichiometric sum of bases in the solution. In the natural environment, the most

common sources of alkalinity include carbonates, bicarbonates, borates, phosphates,

silicates, nitrates and hydroxides etc. Carbonates make up most of total alkalinity due to

the presence of carbonate rocks and carbon dioxide in the environment. It act as a buffer

system in preventing drastic changes in pH and generally expressed in parts per million

(ppm) or milligrams per liter (mg/l) or milliequivalents (mEq).

3.2.4.1. Preparation of reagents

Phenolphthalein indicator

5 g of phenolphthalein indicator was dissolved in 500 ml of 95% ethyl alcohol

and distilled water was added to make final volume of 1000 ml.

pH determined using pH meter

Filtrate (Soil extract)

Filtered with Whatman No. 1

20 g of soil + 100 ml distilled water


50

Methyl orange indicator (0.05 %)

0.5 g of methyl orange was dissolved in distilled water to make the final volume

of 1000 ml.

0.1N HCl

8.33 ml of HCl was dissolved in distilled water to make final volume of 100 ml.

3.2.4.2. Procedure

Alkalinity was determined as

Alkalinity= ml of titrant used × Normality of HCl

× 500 (mEq/100 g) Volume of sample taken (ml)

5 ml of Filtrate (Soil extract) diluted with distilled water to make final volume of 50 ml

Solution turned pale yellow

2 drops of phenolphthalein indicator

Titrated against

0.01N HCl

Solution turned pink (end point)

Before titration

After titration

2 drops of methyl orange

If solution turned pink If no change in solution color

Solution titrated with 0.01 N HCl

Solution turned colorless

After titration

After titration


51

3.2.5. Calcium (Ca)

Calcium (Ca) is essential part of plant cell wall structure which regulates the

transport and retention of other elements in the plants. It controls the effect of alkali

salts and organic acids within the plants.


Murexide indicator

0.20 g of ammonium purpurate and 100 g of sodium chloride mixture was

ground to fine powder to obtain murexide indicator.

1 N NaOH

40 g of sodium hydroxide was dissolved in distilled water, to prepare final

volume of 1000 ml.

0.01 M Ethylene-diamine-tetracetic acid (EDTA)

3.723 g of disodium salt of EDTA was added in 100 ml of distilled water to

further make final volume to 1000 ml and the solution was stored in polyethylene

bottle.

3.2.5.2. Procedure

Calcium was calculated as

Content of calcium (mg/g) = Volume of EDTA used (ml) × 400.8

Volume of sample taken (ml)

5 ml of Filtrate (Soil extract) diluted with

distilled water to make final volume of 50 ml

2 ml of 1N NaoH

100 mg of Murexide Indicator

Solution turned Pink

Titrated against 0.01 N EDTA

Solution turned dark purple (end point)


52

3.2.6. Magnesium (Mg)

Magnesium (Mg) is one of the important components of chlorophyll pigment in

all green plants. The major sources of magnesium for plants include minerals, organic

matter and fertilizers which are present in soil. The study of magnesium in the soil is

important because the deficiency of magnesium in plants leads to yellowing of leaves

where as excess of Mg in the soil is toxic to the plants. Mg is determined as the

difference between the Ca + Mg titration and titration alone for Ca.


Erichrome Black T (EBT) indicator

0.40 g of Erichrome Black T (EBT) salt was added to 100 g of sodium chloride

and the mixture was ground to fine powder.

0.01 N Ethylene-diamine-tetracetic acid (EDTA)

3.723 g of disodium EDTA salt was dissolved in distilled water to prepare 1000

ml of EDTA and was stored in polyethylene bottle.

3.2.6.2. Procedure

5 ml of Filtrate (Soil extract) diluted with

distilled water to make final volume of 50 ml

Calcium (X) Hardness (Y) Ca +Mg

2 ml of 1N NaoH

100 mg of Murexide Indicator

Solution turned Pink

Solution turned dark

purple (end point)


1 ml of Buffer 100 mg of EBT Indicator

Solution turned wine red


Solution turned blue

(end point)


53

Magnesium was calculated as

Magnesium (mg/g) = (Y-X) × 400.8

Volume of sample taken (ml) × 1.645

Where X = volume of EDTA (ml) used for Ca determination

Y = volume of EDTA (ml) used for hardness (Ca +Mg) determination

3.2.7. Nitrates (NO3-)

Nitrogen (N) an essential nutrient acts as an important component of different

proteins, enzymes and metabolic processes of growing plants. It is available in the form

of ammonia and nitrate ions to the plants. N2 fixing bacteria in the soil convert nitrites

into nitrates. It is also formed during the breakdown of fertilizers, decaying of plants,

manure and other organic residues by microorganisms.


Nitrate stock solution (1000 mg/l)

1.62 g of potassium nitrate (KNO3) was dissolved in distilled water to prepare

1000 mg/l nitrate stock solution.

Standard solutions

Stock solution was diluted 10 times to prepare intermediate nitrate solution,

which was further serially diluted to obtain different concentrations ranging from 0.1,

0.2, 0.4, 0.6, 0.8, 1.0 mg/l.

3.2.7.2. Procedure

5 ml of soil extract was taken in crucible, heated till dryness and cooled

Final volume made to 50 ml with distilled water

To Residue, left 2 ml of phenol disulphonic acid added

Optical density taken at 410 nm using spectrophotometer (Systronics; model GS5701A)

6 ml ammonium hydroxide (30%) and kept

undisturbed for 15 min till yellow color develops

Concentration of nitrates calculated using standard graph curve (0.1- 1 mg/l).


54

3.2.8. Phosphate (PO43-

)

Phosphorus is the second most important macronutrient after N and is essential

for the plant growth. It plays an important role for the development of healthy roots and

fruits. It provides disease resistance and is available to the plants in the form of

phosphate ions.


Ammonium molybdate reagent

25 g of ammonium molybdate was dissolved in 175 ml of distilled water. In

separate beaker, 280 ml of concentrated H2SO4 was added to 400 ml of distilled water

and cooled. Ammonium molybedate solution was added to this mixture and final

volume was made to 1000 ml.

Standard stannous chloride solution

2.5 g of stannous chloride was dissolved in 100 ml of glycerol by heating on hot

water.

Phosphate stock solution (1000 mg/l)

1.447 g of potassium dihydrogen orthophosphate (KH2PO4) was dissolved in

distilled water so as to make final volume of 1000 ml.

Standard solution

The stock solution was diluted 10 times to prepare intermediate solution which

was further serially diluted to prepare different concentrations ranging from 0.1, 0.2,

0.4, 0.6, 0.8, 1.0 mg/l.

3.2.8.2. Procedure

5 ml of Filtrate (Soil extract) diluted with distilled water to make final volume of 50 ml

Mix was kept undisturbed for 15 min. for blue color development

2 ml ammonium molybdate

0.5 ml stannous chloride

Optical density taken at 625 nm using spectrophotometer (Systronics; model GS5701A)

Concentration of phosphates calculated using standard graph curve (0.1-1.0 mg/l).


55

3.2.9. Potassium (K)

Potassium is an important nutrient for the growth of plants. It plays an important

role in photosynthesis and also helps in building of protein, fruit quality and

improvement in root length, drought resistance and reduction of crop disease. The

content of potassium in the soil extract was estimated using flame photometer.

3.2.9.1. Principle of flame photometer

The sample in flame photometer is aspirated into a flame in the form of a fine

spray. After the solvent gets evaporated leaving the dehydrated salt behind in the flame,

the salt is dissociated into free gaseous atoms in their ground state. The atoms at ground

level absorb energy from flame and reach to their excited electronic state. The excited

atoms while returning back to ground level state emit the radiation of a characteristic

wavelength. The emitted monochromatic radiation is measured with the help of a

monochromator and detector set.

3.2.9.2. Preparation of standard solutions

Potassium stock solution (1000 mg/l)

1.9067 g of potassium chloride (KCl) was added to distilled water to prepare

1000 ml of stock solution.

Standard potassium solution

The stock solution was diluted 10 times to prepare the intermediate potassium

solution which was further serially diluted for preparation of standard solutions of

different concentrations ranging from 20, 40, 60, 80 and 100 mg/l .

3.2.9.3. Procedure

Filtrate (soil extract) was injected into flame photometer (ELICO; CL 26D)

Optical density was taken at 589 nm

Concentration of Potassium was calculated using standard graph curve (20 - 100 mg/l)


56

3.2.10. Sodium (Na)

Sodium is involved in regeneration of phosphoenolpyruvate (an anion with high

energy phosphate bond involved in biosynthesis of various aromatic compounds) in

Crassulacean Acid Metabolism (CAM). It helps in cation-anion exchange processes in

soils. Deterioration of soil is caused by high concentration of sodium (Trivedy et al.,

1987).

3.2.10.1. Preparation of standard solutions

Sodium stock solution (1000 mg/l)

2.544 g of sodium chloride (NaCl) was dissolved in distilled water to prepare

1000 ml stock solution.

Standard sodium solution

Intermediate sodium solution was prepared by diluting the stock 10 times.

Standard solutions corresponding to 20, 40, 60, 80 and 100 mg/l were prepared from the

intermediate solution.

3.2.10.2. Procedure

Filtrate (soil extract) was injected into flame photometer (ELICO; CL 26D)

Optical density was taken at 768 nm

Concentration of Sodium was calculated using standard graph curve (20- 100 mg/l)


57

3.2.11. Estimation of heavy metals

Estimation of heavy metal contents plays a major role to assess the quality of

soil and helps to set stringent regulatory limits by the government agencies. Therefore,

the level of essential (Iron (Fe), Copper (Cu) Manganese (Mn) and Zinc (Zn)) and toxic

(cadmium (Cd), chromium (Cr), Nickel (Ni) and lead (Pb)) from soil collected from

different regions of Amritsar, India under cultivation of rice and wheat crops using

Atomic absorption spectrophotometer (Shimadzu model, AA 630, Tokyo Japan) were

estimated in the present study (Chand et al., 2011). Heavy metal analysis was done at

Analytical Laboratory, Institute of Himalayan Bioresource Technology (IHBT), Council

of Scientific and Industrial Research, Palampur, India.

3.2.11.1. Principle of Atomic Absorption Spectrophotometer (AAS)

AAS uses the absorption of light to measure the concentration of gas phase

atoms. Since samples are usually liquids or solids, the analyte atoms or ions must be

vaporized in a flame or graphite furnace. The atoms absorb ultraviolet or visible light

and make transition to higher electronic energy levels. The analyte concentration is

determined from the amount of absorption. Applying the Beer- Lambert law directly in

AAS is difficult due to variations in the atomization efficiency from the sample matrix

and non uniformity of concentration and path length of analyte atoms. Concentration

measurements are usually determined from a working curve after calibrating the

instrument with standards of known concentrations.

3.2.11.2. Chemicals make

Various chemicals for physico-chemical estimation were obtained from Sisco

Research Laboratories (SRL), Mumbai, India, Loba Chemie, Mumbai, India and

Spectrochem, Mumbai, India. The chemicals for genotoxicity study were obtained from

Spectrochem, Mumbai, India, Thomas and Baker chemicals Mumbai, India and

Qualigens Fine Chemicals, Mumbai, India. For the enzyme analysis substrates and

reagents were obtained from Himedia, Mumbai, India. A.cepa bulbs were purchased

from the local market.


58

3.2.11.3. Procedure

Analysis of heavy metals in soil samples were carried out as follows

Soil extracts were prepared by digesting 1 g of soil sample in glass digestion tube of 250 ml along with 15

ml of nitric acid (HNO3) at 140°C.

The dried sample treated with 3 ml of perchloric acid (HClO4) for further oxidation of the sample for 30 min

at 245°C.

Solution was evaporated to dryness.

After digestion, the content was cooled, filtered and final volume was made up to 100 ml with distilled

water

Estimation of heavy metals was done using Atomic Absorption Spectrophotometer (Shi-madzu model

AA 6300 Tokyo Japan).

The Hallow cathode lamps (HAMA- MATSU PHOTONICS K.K. JAPAN) of different metals were

used as radiation source

The concentrations of the heavy metals were

calculated from the calibrated standard curve


59

3.3. Estimation of genotoxic potential

Genotoxicity of agricultural soil samples was estimated by using Allium cepa

root chromosomal aberration assay (AlRCAA).

3.3.1. Preparation of stain

Aceto-orcein (2% orcein in 45% acetic acid)

100 ml of 45% acetic acid was prepared by mixing 45 ml of glacial acetic acid

and 55 ml of distilled water. The mixture was heated to boiling in a conical flask. 2 g of

orcein dye was added slowly to the boiling mixture and stirred with glass rod till the dye

dissolved. It was brought down to room temperature, filtered and stored in a dark

coloured bottle with cover.

3.3.2. Allium cepa root chromosomal aberration assay

Onion bulbs were selected as experimental material because these are easily

available, economic and provide quick and good cytological results as well as have low

number (2n=16) and large size of the chromosomes (10 µm).

3.3.2.1. Treatment

Two modes of treatment, viz., in situ and root dip treatment were applied to

evaluate genotoxicity of agricultural soil samples by using Allium cepa root

chromosomal aberration assay.

3.3.2.1.1. In situ treatment

In situ conditions were simulated by conducting the pot culture experiments in

which fresh, healthy and uniform sized denuded onion bulbs were allowed to root

directly in the soil samples contained in pots for 24-36 h. Sand was used as a negative

control.

3.3.2.1.2. Root dip treatment

The soil extracts were prepared by suspending soil in distilled water in ratio of

1:2 (w/v) on shaker for 12 h and then filtered (Cabrera and Rodriguez, 1999a). The

filtered extract was considered as 100 % and further different concentrations (20, 40,

60, 80 and 100%) of soil extract were made. The denuded onion bulbs were placed on


60

3.3.2.1.3. Procedure (In situ treatment)

Denuded onion bulbs allowed to root directly

in soil samples contained in earthen pots

Root tips washed thoroughly, cut and fixed in

Farmer’s fluid (3:1::ethanol:acetic acid glacial)

Root tips treated with 1N HCl with intermittent

heating for ≤ 1 min.

60 secs

Staining of root tips in a mixture of aceto-orecin

and 1N HCl (9 : 1) with intermittent warming of

the mixture

Root tips squashed in 45 % acetic acid and slides

screened under microscope

Cells scored for different types of aberrations

24-48 h


61

3.3.2.1.4. Procedure (Root dip treatment)

Denuded onion bulbs placed on Couplin jars

for rooting

Onion bulbs with root tips of 0.5 - 1.0 cm size

treated with different concentrations (20, 40,

60, 80 and 100 %) of soil extracts

)

Staining of root tips in a mixture of aceto-orecin

and 1N HCl (9 : 1) with intermittent warming of

the mixture

Root tips treated with 1N HCl with intermittent

heating for ≤ 1 min.

60 secs

Root tips washed thoroughly, cut and fixed in

Farmer’s fluid (3:1::ethanol:acetic acid glacial)

36-48 h

3 h

Cells scored for different types of aberrations

using microscope

Root tips squashed in 45 % acetic acid and slides

were screened


62

Couplin jars for rooting. After 24-36 h, the emerged roots of about 0.5- 1.0 cm length

were treated for 3 h by placing them on treatment jars containing different

concentrations (20, 40, 60, 80 and 100%) of each soil extract. Distilled water was used

as negative control.

3.3.2.2. Cytological investigations

3.3.2.2.1. Fixation

After treatment, the bulbs were thoroughly washed, root tips were plucked and

fixed in Farmer’s fluid (glacial acetic acid: ethanol::1:3) for 24 h and transferred to vials

for further use.

3.3.2.2.2. Squash preparation

The fixed Allium root tips were hydrolyzed in 1N HCl with intermittent heating

for 1 min. and then transferred to a watch glass containing mixture of 1N HCl and

aceto-orecin (1 : 9). The root tips in watch glass were heated intermittently for 3 - 5

min, covered and kept aside undisturbed for 15 - 20 min. Then the root tips were

squashed in a drop of 45% glacial acetic acid by covering it with cover slips (Sharma

and Sharma, 1980).

3.3.2.2.3. Scoring

The slides were observed for different types of chromosomal aberrations. About

900-950 dividing cells from 9 root tips (~ 100/ root tip) were scored. Photomicrographs

were taken with the help of a digital camera fixed on microscope (Olympus) that was

connected to a computer in order to transport images of chromosome and related

aberrations.

3.4. Estimation of protein content in A. cepa bulbs

Total protein content in the onion bulbs was estimated following Lowry’s

method with slight modifications.


63

3.4.1. Principle

Lowry’s method is the commonly used to estimate protein content of plant

material due to its high sensitivity. This method is based on reactivity of peptide

nitrogen with copper (II) ions under alkaline conditions to give a blue colored complex.

The subsequent reduction of the Folin-Ciocalteau phosphomolybdic and

phosphotungstate acids to heteropolymolybdenum blue compound by the copper

catalyst, is the main principle behind Lowry’s method (Lowry et al., 1951).

3.4.2. Exposure of Allium cepa bulbs to different soil samples

Fresh, medium and uniform sized healthy bulbs of A. cepa were purchased from

the local market and their loose outer brown scales and old roots were removed by

using hard pointed forceps. The denuded bulbs were placed on the top of each pot filled

with soil from different agricultural sites. Washed sand was considered as control. The

treatment was continued for 72 h in seed germinator with dark conditions at about 25oC.

The basic protocol of Fiskesjo (1985) with slight medications as by Fatima and Ahmad

(2005) was followed. The experiment was conducted in triplicate.

3.4.3. Preparation of sample extract

A. cepa bulbs after 72 h of treatment were cut in small pieces with the help of

sharp knife and 1 g of sample was homogenized with the chilled sodium phosphate

buffer (50 mM, pH 7.0) in a pre-chilled mortar and pestle. The homogenate thus

obtained was centrifuged at 8,000 rpm for 20 min at 4oC. The supernatant was further

used for biochemical analysis of proteins and antioxidant enzymes as described by

Sharma et al. (2011a,b).

3.4.4. Preparation of reagents

Bovine serum albumin stock solution (1000 µg/ml)

1000 µg of BSA was mixed in 1 ml of distilled water. Different dilutions of BSA

stock were prepared by dissolving stock BSA solution and distilled water in the test

tubes. Differents concentrations of BSA ranging from 0 - 300 µg/ml were prepared.


64

Phosphate buffer (50m M; pH 7.0)

Stock solution Concentration Name of compound Quantity Final volume

Solution A 50 mM KH2PO4 0.684 g 100 ml

Solution B 50 mM K2HPO4 0.870 g 100 ml

K2HPO4= Dipotassium hydrogen orthophosphate

KH2PO4= Potassium dihydrogen orthophosphate

For preparation of 100 ml of 50 mM phosphate buffer stock solution, 39 ml of

stock solution A was properly mixed with 61 ml of stock solution B and pH was set at

7.0 using pH meter (Systronics; model µ pH system 361).

Reagent A- Alkaline Na2CO3 reagent

2 g of Na2CO3 was added to 0.1 N NaOH to make final volume of 100 ml.

Reagent B- Copper sulphate reagent (CuSO4)

0.5 g of CuSO4.5H2O was dissolved in 100 ml of 1 % sodium potassium

tartarate solution.

0.1N NaOH

4.0 g of NaOH was dissolved in distilled water and more distilled water was

added to make final volume of 1000 ml.

Reagent C- Alkaline copper sulphate reagent

For the alkaline copper sulphate reagent, 50 ml of reagent A was mixed in 1 ml

of reagent B. The solution was prepared fresh for use.

Reagent D- Folin-Ciocalteau reagent (FC) (1 : 2)

Folin-Ciocalteau reagent was prepared by mixing 1 ml of FC reagent in 2 ml of

distilled water in dark.

Trichloracetic acid (20%)

20 g of Trichloracetic acid (TCA) was dissolved in distilled water to give 100 ml

of solution.


65

3.4.5. Procedure

3.4.6. Calculations

The content of protein in samples was estimated by plotting graph between

absorbance and concentration of standard solutions of BSA in the range of 0 - 300

µg/ml. The total protein content was calculated as protein mg/g fresh weight of the

sample.

3.5. Estimation of different antioxidative enzymes

Plants develop different strategies to overcome various types of heavy metal

stress. First strategy is reduction of metal assimilation while other is internal defense

mechanisms. To cope up with the toxicity and to regulate the oxidant level, the plants

develop antioxidant defense system which includes antioxidative enzymes. It is clear

that the capacity and activity of the antioxidative defense system are important in

limiting oxidative damage and in destroying active oxygen species that are produced in

excess of those normally required for metabolism. The antioxidative/ detoxification

To 100 µl of sample extract added 990 µl of double distilled

water in a test tube

Added 5 ml of Reagent C

Mixed properly and allowed to stand for 10 min

Mixed well and allowed to incubate for 30 min. at room

temperature under dark conditions

Added 0.5 ml of Reagent D

Development of Blue color

Absorbance was recorded by using UV- visible spectrophotometer (Systronics; model GS5701A)

After 30 min.


66

enzymes like ascorbate peroxidase (APX), catalase (CAT), dehydroascorbate reductase

(DHAR), glutathione reductase (GR), glutathione-S-transferase (GST), guaicol

peroxidase (POD) and superoxide dismutase (SOD) were evaluated using the Allium

cepa (onion) test system. These antioxidant enzymes were assayed in onion bulbs which

were exposed to collected agricultural soil samples for 72 h.

3.5.1. Preparation of extract

Same as mentioned in section 3.4.3

3.5.2. Ascorbate peroxidase (APOX, EC 1.11.1.11)

APX is a member of a large family of enzymes that are ubiquitous in the cell

and have numerous roles in plant metabolism mainly to remove hydrogen peroxide

formed due to induced stress using different reductants. The activity of ascorbate

peroxidase was determined spectrophotometrically according to the protocol described

by Nakano and Asada (1981).

3.5.2.1. Principle

APX catalyses the reduction of H2O2 by ascorbate.

Ascorbate + H2O2 dehydroascorbate + 2 H2O

One mole of ascorbate oxidizes one mole of H2O2 to produce one mole of

dehydroascorbate. The rate of oxidation was estimated by recording decrease in

absorbance at 290 nm.


Phosphate buffer (50 mM, pH, 7.0)

Phosphate buffer was prepared as described in section 3.4.4 and pH was

maintained at 7.0 using pH meter (Systronics; model µ pH system 361).

Ascorbate (0.5 mM)

0.088 g of ascorbate was mixed in 100 ml of phosphate buffer to prepare the

stock solution of 5 mM. To make the final concentration of 0.5 mM, 300 µl of stock

solution was used in 3 ml of reaction mixture.

APX


67

Hydrogen peroxide (1.0 mM)

About 10 µl of 10 mM H2O2 was dissolved with 9.990 ml of phosphate buffer to

prepare final solution of 10 ml of 1.0 mM H2O2.

Ethylenediaminetetraacetic acid (EDTA, 0.1mM)

0.037 g of EDTA was mixed in 100 ml of phosphate buffer to prepare the stock

solution of 1 mM. To make the final concentration of 0.1 mM, 300 µl of stock solution

was used in the reaction mixture.

3.5.2.3. Procedure

3.5.2.4. Calculations

The enzyme activity was calculated using an extinction coefficient 2.8 mM-1

cm-1

.

One unit of APX activity is defined as the amount of enzyme that can oxidize 1 μmol of

ascorbate per minute.

APX activity =

Change in absorbance/minute×Total volume ×Fresh weight

(Units/min/g tissue) Extinction coefficient×volume of sample extract

Where extinction coefficient is 2.8 mM-1

cm-1

.

Decrease in absorbance was observed at 290 nm for 3 minutes at 25○C using spectrophotometer (Systronics; model GS5701A)

Reaction Mixture of 3 ml

2200 µl of 50 mM potassium

phosphate buffer

300 µl of 0.1 mM

EDTA

100 µl of sample

extract

Cuvette

300 µl of 0.15 mM

EDTA

100 µl 1.0 mM

H2O2


68

3.5.3. Catalase (CAT, EC 1.11.1.6)

Catalase exists mainly in the peroxisomes and during stress the number of these

organelles increase, CAT plays an important role in detoxification of H2O2 that can

diffuse into the peroxisome from other cell locations where it is produced (Mittler,

2004).

3.5.3.1. Principle

Catalase catalyzes the decomposition of H2O2 to give final products H2O and O2.

2 H2O2 2 H2O + O2

The activity of catalase was measured in terms of initial rate of H2O2 disappearance or

liberation of O2 according to the protocol of Aebi (1984) with slight modifications.

The catalase activity was calculated by decrease in absorbance at 240 nm for 1 min as a

result of H2O2 decomposition. The difference in extinction coefficient per unit time is a

measure of catalase activity.


Phosphate buffer (50 mM, pH 7.0)

Same as described in section 3.4.4

Hydrogen peroxide (15 mM in phosphate buffer)

1.546 µl of H2O2 was dissolved in 100 ml 50 mM phosphate buffer to make

stock solution of 150 mM. 300 µl of H2O2 (stock solution) was used in the reaction

mixture.

Catalase


69

3.5.3.3. Procedure


The enzyme activity was calculated using an extinction coefficient 39.4 mM-1

cm-1

for H2O2.

Catalase activity =



3.5.4. Dehydroascorbate reductase (DHAR)

The enzymatic activity of dehydroascorbate reductase was measured according

to the protocol of Dalton et al. (1986).

Decrease in H2O2 and CAT was measured as decrease in optical density at 240 nm for 30 s at 25 oC as a result of H2O2 decomposition

using Uv-vis spectrophotometer (Systronics; model GS5701A)


2600 µl of 50 mM potassium phosphate buffer

100 µl 15 mM H2O2

100 µl of sample

extract

Cuvette


70

3.5.4.1. Principle

It catalyzes the reduction of dehdroascorbate which involves the oxidation of

reduced glutathione (GSH) to form ascorbate and glutathione disulphide (GSSG) as

their final products.

Dehydroascorbate + 2 GSH Ascorbate + GSSG

The final activity of DHAR was estimated by recording increase in absorbance at 265

nm.



Same as in section 3.4.4

Ethylene-diamine-tetraacetic acid (EDTA, 0.1 mM)

0.372 g of disodium salt of EDTA was dissolved in 100 ml phosphate buffer to

make 10 mM (stock solution). To make the final concentration of 0.1 mM, 300 µl of

stock solution was used in 3 ml of reaction mixture.

Dehydroascorbate (0.2 mM)

0.696 g of dehydroascorbate was dissolved in phosphate buffer to make final

volume of 100 ml (stock solution). To make the final concentration of 0.2 mM, 300 µl

of stock solution was used in 3 ml of reaction mixture.

Reduced glutathione (2.5 mM)

0.7683 g of reduced glutathione was dissolved in phosphate buffer and final

volume was made upto 100 ml (stock solution). To make the final concentration of

2.5mM, 300 µl of stock solution was used in 3 ml of reaction mixture.

DHAR


71

3.5.4.3. Procedure


The enzyme activity was calculated using an extinction coefficient of 14 mM-1 cm

-1.

DHAR activity =



3.5.5. Glutathione reductase (GR, EC 1.6.4.2)

GR activity was measured according to the protocol by Carlberg and Mannervik

(1975).

3.5.5.1. Principle

The enzymatic activity of glutathione reductase was measured using oxidized

glutathione (GSSG) as a substrate and decrease in NADPH. GR catalyzes the reduction

of glutathione disulphide (GSSG) involving the oxidation of NADPH

NNAADDPPHH ++ HH++ ++ GGSSSSGG 22GGSSHH ++ NNAADDPP

++

DHAR activity was measured by following the increase in absorbance at 265 nm due to formation of ascorbate and glutathione disulphide as their final product at

265 nm using spectrophotometer (Systronics; model GS5701A)



phosphate buffer

300 µl of 1 mM EDTA

100 µl of sample extract

Cuvette

300 µl of 2.5 mM reduced

glutathione

300 µl of 0.2 mM

dehydroascorbate


72




maintained at 7.6 with pH meter (Systronics; model µ pH system 361).

Oxidized glutathione (1 mM)

0.612 g of oxidized glutathione was dissolved in 100 ml phosphate buffer to

make 10 mM (stock solution). 300 µl of stock solution was used in 3 ml of reaction

mixture.

Ethylenediaminetetraacetic acid (EDTA, 0.5 mM)

1.861 g of disodium salt of EDTA was dissolved in 100 ml phosphate buffer to

make 5 mM (stock solution). To make final concentration of 0.5 mM, 300 µl of stock

solution was used in 3 ml of reaction mixture.

Nicotinamide adenine dinucleotide phosphate reduced (NADPH, 0.1 mM)

0.083 g of NADPH was dissolved in phosphate buffer and final volume was

made upto 100 ml (stock solution). To make final concentration of 0.1 mM, 300 µl of

stock solution was used in 3 ml of reaction mixture.

3.5.5.3. Procedure

The absorbance of GR activity was measured spectrophotometerically as oxidation of NADPH at 340 nm using UV-vis spectrophotometer (Systronics; model GS5701A)



phosphate buffer with pH 7.6

300 µl 1 mM oxidized

glutathione

300 µl of 0.1 mM reduced nicotinamide adenine dinucleotide

phosphate

Cuvette

300 µl of 0.5 mM EDTA



73

3.5.5.4. Calculation

The enzyme activity was calculated using an extinction coefficient of 6.22 mM-1 cm

-1.

GR activity =



The enzyme activity was calculated using an extinction coefficient of 6.22 mM-1

cm-1

.

3.5.6. Gluthione-S-transferase (GST, EC 2.5.1.18)

The activity of GST was determined following the method of Habig and Jacoby

(1981) with slight modifications.

3.5.6.1. Principle

It is based on the reaction of 1-chloro, 2, 4- dinitro benzene (CDNB) with

reduced glutathione (GSH). Due to the formation of CDNB conjugate, increase in

optical density was recorded at 340 nm.


80% methanol

80 ml of methanol was dissolved in 20 ml of distilled water to prepare 80%

methanol.

1-chloro, 2, 4-dinitro benzene (2 mM in 80% methanol)

0.405 g of CDNB was dissolved in 80% methanol to make the final volume of

100 ml to prepare 20 mM stock solution. To make final concentration of 2mM, 300 µl

of stock solution was used in the reaction mixture.

Glutathione reduced (2 mM)

0.6146 g of glutathione reduced was dissolved in 100 ml of distilled water to

make final stock solution of 20 mM. To make final concentration of 2mM, 300 µl of

stock solution was used in the reaction mixture



maintained at 7.4.


74

3.5.6.3. Procedure


The enzyme activity was calculated using an extinction coefficient of

9.6 × 10 -3

mM-1

cm-1

.

GST activity =



3.5.7. Guaiacol peroxidase (POD, EC 1.11.1.7)

The enzymatic activity of guaiacol peroxidase was measured using the protocol

of Sanchez et al. (1995) with slight modifications.

3.5.7.1. Principle

Glutathione peroxidase is used to reduce lipid hydroperoxidase to their

corresponding alcohols and reduce free hydrogen peroxide to water.

2GSH + H2O2 → GS–SG + 2H2O

The increase in absorbance/min was recorded at 340 nm using UV-vis spectrophotometer (Systronics; model GS5701A)


2300 µl of 50 mM potassium phosphate buffer with pH 7.4

300 µl 2 mM reduced

glutathione

Cuvette


300 µl of 2 mM CDNB


75




maintained at 7.6 using pH meter (Systronics; model µ pH system 361).

Guaiacol (20 mM)

2.262 ml of guaiacol was dissolved in 100 ml phosphate buffer to make 200 mM

(stock solution). 300 µl of stock solution was used in 3 ml of reaction mixture.

Hydrogen peroxide (12.3 mM in phosphate buffer)

1.267 µl of H2O2 was dissolved in 100 ml 50 mM phosphate buffer to make

stock solution of 123 mM. To make final concentration of 12.3 mM, 300 µl of stock

solution was used in the reaction mixture.

3.5.7.3. Procedure

The absorbance of POD activity was measured at 436 nm using UV-vis spectrophotometer (Systronics; model GS5701A)


2300 µl of 50 mM potassium phosphate buffer with pH 7.0

300 µl of 20 mM Guaiacol

Cuvette

300 µl of 12.3 mM of H2O2

100 µl of enzyme extract


76

3.5.7.4. Calculation

The enzyme activity was calculated using an extinction coefficient of 26.6 mM-1

cm-1

.

One unit of POD activity represents the amount of enzyme catalyzing the oxidation of 1

μmol of guaiacol in 1 min.

POD activity =



3.5.8. Superoxide dismutase (SOD, EC 1.15.1.1)

3.5.8.1. Principle

The enzymatic activity of superoxide dismutase was measured as the ability of

the sample enzyme extract to inhibit the photochemical reduction of nitroblue

tetrazolium (NBT) dye which is reduced to superoxide radicals produced from auto

oxidation of hydroxylamine (Kono, 1978).


Sodium carbonate buffer (50 mM, pH 10.2)

Stock solution Concentration Name of compound Quantity Final volume

Solution A 50 mM Sodium carbonate 0.529 g 100 ml

Solution B 50 mM Sodium bicarbonate 0.420 g 100 ml

For 50 mM sodium carbonate buffer, 55 ml of stock solution A and 45 ml of stock

solution B were properly mixed and pH was set at 10.0 using pH meter (Systronics;

model µ pH system 361).

Nitroblue tetrazolium (24 µM)

0.0196 g of NBT was dissolved in 100 ml of phosphate buffer to prepare stock

solution of 240 µM. To make the final concentration of 24 µM, 300 µl of stock solution

was used in the reaction mixture


77

Hydroxylamine hydrochloride (0.1 mM)

0.0069 g of hydroxyalamine hydrochloride was dissolved in 100 ml phosphate

buffer to make stock solution of 1mM. To make final concentration of 0.1mM, 300 µl

of stock solution was used in 3 ml of reaction mixture.

Triton X-100 (0.03% (v/v)

30 µl of Triton X-100 was dissolved in 99.970 ml of phosphate buffer to prepare

100 ml of Triton X-100.

3.5.8.3. Procedure


One unit of SOD activity was defined as the amount of enzyme required that

caused 50% of NBT reduction.

SOD activity =

Change in abs./minute (Blank) - change in abs./minute (sample) ×100=X

(Units/min/g tissue) Change in absorbance/min (Blank)

300 µl of 0.1 µM

EDTA

300 µl of 0.1 µM

hydroxylamine

300 µl of 0.03 %

triton X-100

The activity was calculated by recording increase in absorbance at 560 nm for 2 minutes using UV-vis Spectrophotometer (Systronics; model GS5701A)


1730 µl of 50 mM Na2CO3

buffer

(pH 10.2)

300 µl of 24 µM

NBT

70 µl of sample

extract

Cuvette


78

Where X is % inhibition produced by 70 µl of sample.

Thus, 50% inhibition is produced by

sampleoflyX

7050

3.6. Statistical analysis

All the experiments were conducted in triplicate and the results were expressed

as Mean + S.E. The data was analyzed for statistical significance using analysis of

variance (one-way ANOVA). The difference among average values was compared by

high-range statistical domain (HSD) using Tukey’s test. The significance was checked

at p ≤ 0.05.

Results

Chapter-4

What we observe is not nature itself but nature exposed to our mode of questioning

-Werner Heisenberg

Chapter-4

RESULTS

4.1. Physico-chemical analysis

Physico-chemical analysis constituted estimation of parameters like soil texture,

pH, alkalinity, contents of calcium, magnesium, nitrates, phosphates, sodium and

potassium. The content of eight heavy metals (cadmium, chromium, copper, iron, lead,

manganese, nickel and Zinc) in all the soil samples was also studied. Physico-chemical

analysis of different agricultural soil samples collected from fields under rice cultivation

during September, 2009 (r1) and September, 2010 (r2) and wheat cultivation during

March, 2010 (w1) and March, 2011(w2) are summarized in Tables 5 - 12 and Figs. 2 -

23.

4.1.1. Soil texture

Soil texture determines the relative proportion of different sized particles (sand,

silt and clay) present in soils. In the present study, the analysis of textural composition

of different soil samples revealed that content of sand in all soil samples was maximum

followed by clay and content of silt was very low (< 3%). All the agricultural soils were

found to be of sandy clay type.

4.1.1.1. Soil texture of rice cultivated soils

Variation in particle size of different soil samples collected from rice cultivated

agricultural fields during September, 2009 (r1) and September, 2010 (r2) sampling of

agricultural soils under rice cultivation are shown in Table 5 and Fig. 2. Among soil

samples of r1 and r2 sampling the content of sand, silt and clay particles ranged from

56.80 - 65.71 %, 0.43-1.96 %, 32.81 - 41.49 % and 53.72 - 61.44 %, 0.96 - 1.35 %,

37.42 - 44.94 %, respectively.

4.1.1.2. Soil texture of wheat cultivated soils

Soil texture analysis of all the agricultural soils studied under wheat cultivation

is depicted in Table 6 and Fig. 3. Textural analysis of soil samples collected during

March, 2010 (w1) and March, 2011(w2) sampling revealed that the content of sand, silt

Results

80

and clay ranged from 56.21 - 65.71%, 1.00 - 2.84% and 33.29 - 42.34% and 54.72 -

64.78%, 1.05 - 2.82% and 33.96 - 44.23 %, respectively.

4.1.2. pH

pH is an important parameter which indicates the chemical composition and

acidic or alkaline nature of soil. Moreover, availability of nutrients is also directly

influenced by pH of the soil. pH of most of the soil samples varied from 7 - 8 except

few where pH was more than 8.

4.1.2.1. pH of rice cultivated soils

pH of agricultural soil samples under rice cultivation are shown in Table 7 and

Fig. 4. Among soil samples collected from rice fields during September, 2009 (r1)

sampling, the soil sample NWFIIr1 showed maximum pH (8.07) and sample collected

from SEFIIr1 showed minimum pH (7.39). Among soil samples collected during

March, 2010 (r2 sampling), the soil sample SWFIIr2 showed maximum pH (8.51) and

sample SEFIIr2 showed minimum pH (7.32). All the rice cultivated agricultural soil

samples (collected during r1 and r2 sampling) have shown the pH in the range of 7 - 8

except for four samples (NEFIIr1, NWFIIr1, SWFIr2 and SWFIIr2) which had pH

greater than 8.

4.1.2.2. pH of wheat cultivated soils

The pH values of wheat cultivated soil samples collected during w1 and w2

sampling are depicted in Table 8 and Fig. 5. In soil samples collected during March,

2010 (w1) sampling, the sample SWFIIw1 showed maximum pH (8.50) and sample

SEFIw1 showed minimum pH (7.40). Among soil samples collected during March,

2011 (w2) sampling the maximum pH value of 8.51 was shown by sample SWFIIw2

and minimum (7.42) pH value by sample NWFIw2. As can be seen from Table 8, the

pH values of most of the soil samples were found to be alkaline. Among all the wheat

cultivated agricultural soil samples of Amritsar (collected during w1 and w2 sampling),

samples NEFIw1, NEFIIw1, SWFIw1, SWFIIw1, NWFIw1 NWFIIw1, SWFIw2 and

SWFIIw2) have shown the pH in the range of 7 - 8 while samples NEFIw1, NEFIIw1,

SWFIw1, SWFIIw1, NWFIw1, NWFIIw1, SWFIw2 and SWFIIw2 have shown pH

greater than 8.

Results

81

4.1.3. Alkalinity

Alkalinity of soil measures the amount of carbonates, bicarbonates and

hydroxides in soil and is important in determining the fertility of soil and its impact on

plant growth. In present study, the alkalinity of all the agricultural soil samples

collected ranged from 0.23 mEq/100g to 3.30 mEq/100g.

4.1.3.1. Alkalinity of rice cultivated soils

Alkalinity of different soil samples under rice cultivation is shown in Table 7

and Fig. 6. The soil samples collected during r1 sampling (September, 2009) showed

the range of alkalinity from 0.53 mEq/100g (SEFIIr1) to 3.30 mEq/100g (NEFIIr1).

Among soils collected during r2 sampling (September, 2010) under rice cultivation, the

content of alkalinity varied from 0.23 mEq/100g (NEFIr2, SEFIIr2 and NWFIr2) to

0.98 mEq/100g (SWFIIr2).

4.1.3.2. Alkalinity of wheat cultivated soils

Alkalinity of different soil samples under wheat cultivation is shown in Table 8

and Fig. 7. Among agricultural soil samples of w1 sampling (March, 2010) the

maximum alkalinity of 1.46 mEq/100 g was found in sample SEFIw1 where as

minimum value (0.53 mEq/100 g) was observed in sample NWFIIw1. The alkalinity

among soil samples collected from wheat cultivated soils during w2 sampling (March,

2011) varied from 0.64 mEq/100g (NWFIIw2) to 1 mEq/100g (NEFIIw2).

4.1.4. Calcium

Calcium is an essential component of plant cell wall structure. Content of

calcium of all the agricultural soil samples collected from rice and wheat cultivated

fields was found to range from 21.37 mg/g – 101.50 mg/g and 30.44 mg/g – 74.82

mg/g, respectively. The range of content of calcium in the soil samples collected from

rice fields was comparatively higher than wheat fields.

4.1.4.1. Calcium content of rice cultivated soils

The calcium content of different agricultural soil samples under rice cultivation

is shown in Table 7 and Fig. 8. Among soil samples of rice fields of r1 sampling

Results

82

(September, 2009), the sample NEFIr1 showed minimum content (48.10 mg/g) of

calcium while maximum content (101.50 mg/g) of calcium was observed in sample

SWFIr1. In soil samples collected from rice fields of r2 sampling (September, 2010) the

sample NEFIIr2 showed minimum content of 21.37 mg/g whereas maximum calcium

content of 43.31 mg/g was found in SWFIIr2.

4.1.4.2. Calcium content of wheat cultivated soils

In the present study, the content of calcium in different soils under wheat

cultivation is depicted in Table 8 and Fig. 9. Among agricultural soil samples of w1

sampling (March, 2010), the maximum calcium content of 74.82 mg/g was found in

sample SEFIIw1, where as minimum content of 32 mg/g was observed in sample

SWFIw1. The soil samples of w2 sampling (March, 2011) showed maximum calcium

content of 47.31 mg/g in sample SEFIw2 and minimum calcium content of 30.44 mg/g

in NEFIIw2.

4.1.5. Magnesium

Magnesium is one of the main components of chlorophyll required by all green

plants. The major sources of magnesium for plants include minerals, organic matter and

fertilizers which are present in soil. Magnesium content in all the agricultural soil

samples collected from rice and wheat cultivated fields was found to range from 86.61

mg/g - 318.80 mg/g and 101.50 mg/g - 279 mg/g, respectively.

4.1.5.1. Magnesium content of rice cultivated soils

The content of magnesium in all agricultural soil samples collected during

September, 2009 (r1) and September, 2010 (r2) sampling is given in Table 7 and Fig.

10. Present study showed that among agricultural soil samples collected from rice fields

during r1 sampling, maximum content of magnesium (318 mg/g) was found in sample

SWFIr1 and minimum content (130.5 mg/g) from sample SWFIIr1. In soil samples

collected from fields during r2 sampling the soil sample NWFIIr2 showed maximum

content (175.9 mg/g) whereas sample from NWFIr2 showed minimum content (86.61

mg/g) of magnesium.

Results

83

4.1.5.2. Magnesium content of wheat cultivated soils

The magnesium content of agricultural soil samples collected during March, 2010

(w1) and March, 2011 (w2) sampling is given in Table 8 and Fig. 11. Among different

agricultural soil samples collected from wheat fields during w1 sampling, the content of

magnesium was found to be maximum in SWFIIw1 (279 mg/g) and minimum in

SEFIIw1 (105.10 mg/g). Among the soil samples collected from wheat fields during

sampling w2, the sample SWFIw2 sample showed maximum content of magnesium

(174.61 mg/g) and sample NEFIIw2 reported minimum content (101.50 mg/g).

4.1.6. Nitrates

Nitrogen acts as an important component of different proteins, enzymes and

metabolic processes of growing plants. Nitrogen is available to plants in the form of

nitrates. Content of nitrates of all the agricultural soil samples collected from rice and

wheat cultivated fields was found to range from 0.31 mg/g - 2.2 mg/g and 0.10 mg/g -

1.94 mg/g, respectively.

4.1.6.1. Nitrate content of rice cultivated soils

Content of nitrates in agricultural soil samples collected during r1 and r2

sampling are given in Table 9 and Fig. 12. Among different agricultural soil samples

collected from rice fields during September, 2009 (r1) sampling, the sample coded

NEFIr1 showed minimum content (0.30 mg/g) and sample SEFIr1 (0.62 mg/g) showed

maximum content of nitrates. In soil samples collected from fields during r2 sampling

the soil sample SEFIIr2 showed maximum content (2.20 mg/g) whereas sample

NWFIIr2 showed minimum content (1.63 mg/g) of nitrates.

4.1.6.2. Nitrate content of wheat cultivated soils

The nitrate content of agricultural soil samples collected during March, 2010

(w1) and March, 2011 (w2) sampling is given in Table 10 and Fig. 13. In soil samples

from wheat fields collected during w1 sampling, the sample SWFIw1 showed

maximum (0.55 mg/g) content of nitrates while sample NEFIw1 showed minimum

(0.10 mg/g) content of nitrates. Among soil samples collected during w2 sampling, the

Results

84

sample NEFIIw2 showed minimum content (0.97 mg/g) of nitrates while sample

SWFIw2 showed maximum (1.94 mg/g) content of nitrates.

4.1.7. Phosphates

Phosphates play an important role for the development of healthy roots and

fruits and provide disease resistance to the plants. Among all the agricultural soil

samples collected from rice and wheat cultivated fields, the content of phosphates was

found to range from 0.54 mg/g – 2.57 mg/g and 1.12 mg/g – 2.06 mg/g, respectively.

4.1.7.1. Phosphate content of rice cultivated soils

The content of phosphates in different agricultural soil samples is given in Table

9 and Fig. 14. In the present study, the phosphate content of different soil samples

collected during r1 and r2 sampling ranged from 0.54 mg/g (NEFIIr1) to 2.13 mg/g

(NWFIIr1) and 1.22 mg/g (SWFIIr2) to 2.57 mg/g (SWFIr2).

4.1.7.2. Phosphate content of wheat cultivated soils

The content of phosphates in different agricultural soil samples is given in Table

10 and Fig. 15. Among different agricultural soil samples collected from wheat fields of

sampling w1, the content of phosphates ranged from 1.32 mg/g (NEFIw1) - 2.06 mg/g

(SEFIIw1). In soil samples collected from fields under wheat cultivation during w2

sampling, the range of phosphate content varied from 1.12 mg/g (SWFIIw2) - 1.84

mg/g (SEFIIw2).

4.1.8. Potassium

Potassium helps in the process of photosynthesis, improves plant growth and

provides resistance against drought and crop diseases. Among all the agricultural soil

samples collected from rice and wheat cultivated fields the content of potassium varied

from 0.008 mg/g - 0.135mg/g and 0.044 mg/g - 0.194 mg/g, respectively.

4.1.8.1. Potassium content of rice cultivated soils

The content of potassium in different agricultural soil samples is given in Table

9 and Fig. 16. Among all soil samples of r1 sampling (September, 2009), sample from

SEFIr1 showed maximum content (0.103 mg/g) of potassium while minimum content

Results

85

(0.008 mg/g) was shown by sample SWFIr1. In agricultural soil samples collected

during r2 sampling (September, 2010), sample SEFIr2 showed maximum content

(0.135 mg/g) whereas sample NWFIr2 showed minimum content (0.109 mg/g)

potassium.

4.1.8.2. Potassium content of wheat cultivated soils

Content of Potassium in soil samples collected from wheat cultivated soils is

depicted in Table 10 and Fig. 17. Among all the soil samples collected from wheat

fields during sampling w1 (March, 2010), the maximum content of potassium is

reported in sample SEFIIw1 (0.083 mg/g) and minimum in NEFIw1 (0.044 mg/g). Soil

samples of w2 sampling (March, 2011) showed the minimum content of potassium

(0.087 mg/g) in two samples viz. NEFIw2 and NWFIw2 while sample SEFIIw2 showed

maximum potassium content of 0.194 mg/g.

4.1.9. Sodium

Sodium plays an important role in various physiological processes of plants.

Sodium content for all the agricultural soil samples collected from rice and wheat

cultivated fields was found to range from 0.038 mg/g – 0.349 mg/g and 0.038 mg/g –

0.252 mg/g, respectively.

4.1.9.1. Sodium content of rice cultivated soils

Sodium content of different soil samples under rice cultivation is shown in Table

9 and Fig. 18. The soil samples collected during September, 2009 (r1) sampling showed

the range of sodium from 0.038 mg/g (NWFIIr1) to 0.135 mg/g (SEFIr1). Among soils

collected during September, 2010 (r2) sampling under rice cultivation, the content of

sodium varied from 0.230 mg/g (SWFIIr2) to 0.349 mg/g (SEFIr2).

4.1.9.2. Sodium content of wheat cultivated soils

Content of sodium of soil samples collected from wheat cultivated fields during

sampling w1 and w2 are given in Table 10 and Fig. 19. Among different agricultural

soil samples collected from wheat cultivated lands during w1 sampling, the maximum

content of sodium was observed in sample SWFIw1 (0.147 mg/g) while minimum in

SWFIIw1 (0.038 mg/g). Among soil samples of w2 sampling the sample NWFIw2

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86

showed the highest content (0.252 mg/g) of sodium whereas lowest content (0.048

mg/g) was found in sample NEFIw2.

4.1.10. Heavy metals

Heavy metals are ubiquitous in the environment and are of considerable

concern. Although some are essential for normal plant growth but elevation in their

concentration can lead to interactions at cellular and molecular levels resulting in

various toxicity symptoms. In the present study, metals viz. cadmium (Cd), chromium

(Cr), copper (Cu), iron (Fe), lead (Pb), manganese (Mn), nickel (Ni) and zinc (Zn)

were studied. The metal content of different soil samples collected from rice and wheat

cultivated fields during r1, r2, w1 and w2 samplings is shown in Tables 11 - 12 and

Figs. 20 - 23.

4.1.10.1 Heavy metal content of rice cultivated soils

Content of different heavy metals in soil samples of rice cultivated fields of both

r1 and r2 sampling is depicted in Table 11 and Figs. 20 - 21. Metal concentrations

showed significant variations between different agricultural soil samples of Amritsar.

During r1 sampling, cadmium was not detectable for most of the soil samples except

NWFIIr1 (4.63 mg/kg) and SEFIr1 (0.65 mg/ kg). The mean concentration (mg/ kg) of

other heavy metals ranged from 2.35 (NEFIr1) to 13.15 (SWFIr1) for chromium, 14.03

(SEFIr1) to 28.41 (NWFIIr1) for copper, 14804.73 (SEFIr1) to 20536 mg/kg (NWFIr1)

for iron, 7.01 (SWFIIr1) to 17.31 (NEFIIr1) for lead, 207.91 (SWFIIr1) to 457.66

(NWFIr1) for manganese, 15.81 (NEFIr1) to 34.51 (NWFIr1) for nickel and 53.41

(NEFIr1) to 82.20 (NEFIIr1) for zinc. In the present study, agricultural soil samples

collected from different rice cultivated fields of Amritsar during r2 sampling also

showed varied levels of metals. The range of cadmium during r2 sampling was found to

be 0.54 mg/kg (SWFIIr2) – 16.90 mg/kg (NEFIr2) except for samples SEFIr2, SEFIIr2

and NWFIIr2 where cadmium was not detectable. The content of other heavy metals

(mg/ kg) ranged between 10.80 (SEFIr2) to 22.50 (SEFIIr2) for chromium, 11.61

(SEFIr2) to 31.08 (SWFIIr2) for copper, 15227.30 (SEFIr2) to 19140.65 mg/kg

(NEFIr2) for iron, 3.07 (SEFIr2) to 14.46 (SWFIIr2) for lead, 214.19 (SEFIr2) to

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87

539.70 (NWFIIr2) for manganese, 15.67 (SEFIr2) to 29.08 (NWFIIr2) for nickel and

48.07 (SEFIr2) to 87.55 (NEFIIr2) for zinc. The content of heavy metals observed

during both r1 and r2 sampling was found in the following order Fe > Mn > Zn > Ni >

Cu > Pb > Cr > Cd.

4.1.10.2. Heavy metal content of wheat cultivated soils

Heavy metal content of different agricultural soil samples of wheat cultivated

fields of both w1 and w2 sampling is shown in Table 12 and Figs. 22 - 23. Significant

variations between different agricultural soil samples of Amritsar were observed.

Among soil samples of wheat cultivated fields collected during sampling w1, the

maximum cadmium content (11.21 mg/g) was observed in NEFIIw1 while minimum

content (0.97 mg/g) was observed in NEFIw1. Soil samples studied for content of other

heavy metals varied from 8.92 mg/kg in NEF1w1 to 18.78 mg/kg in SEFIIw1for

chromium; 13.26 mg/kg in SEFIw1 to 28.91 in NWFIIw1 for copper; 15024.33 mg/kg

in SEFIw1 to 19639.33 mg/kg in NEFIIw1 for Iron; 8.10 mg/kg in SEFIw1 to 19.72

mg/kg in NWFIIw1 for lead; 208.44 mg/kg in SWFIIw1 to 418.89 mg/kg in NWFIIw1

for manganese; 16.16 mg/kg in SEFIw1to 31.31 mg/kg in NWFIIw1 for nickel and

56.96 mg/kg in SEFIw1 to108.99mg/kg in NEFIIw1for zinc. In agricultural soil

samples collected from different wheat cultivated fields of Amritsar during w2

sampling also showed varied levels of metals. Among different agricultural soils studied

cadmium could not be detected in four samples (NEFIIw2, SEFIw2, SEFIIw2 and

NWF1w2) while for the remaining samples, its content ranged from 0.078 mg/kg

(NEFIw2) to 21.26 mg/kg (NWFIIw2). The content of other metals was found to vary

from: 8.22 mg/kg in NEFIw2 to 32.90 mg/kg in SWFIIw2 for chromium; 9.62 mg/kg in

SEFIw2 to 31.18 mg/kg in SWFIIw2 for copper, 13270.18 mg/kg in SEFIw2 to

18996.28 mg/kg in NEFIIw2 for iron; 11.17 mg/kg in NWFIw2 to 23.18 mg/kg in

SWFIIw2 for lead; 192.88 mg/kg in SEFIw2 to 383.94 mg/kg in NWFIIw2 for

manganese; 10.29 mg/kg in SEFIw2 to 28.59 mg/kg in SWFIw2 for nickel and 44.55

mg/kg in SEFIw2 to 105 mg/kg in NEFIIw2 for zinc. The content of heavy metals

observed during both w1 and w2 sampling was found in the order as: Fe > Mn > Zn >

Ni > Cu > Pb > Cr > Cd.

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88

4.2. Genotoxicity of agricultural soils using Allium cepa root chromosomal

aberration assay (AlRCAA)

Allium cepa root chromosomal aberration assay (AlRCAA) was used to estimate

genotoxicity of all the agricultural soil samples collected from two fields of north east,

south east, south west and northwest part of Amritsar under rice cultivation during

September, 2009 (r1), September, 2010 (r2) and wheat cultivation during March, 2010

(w1) and March, 2011 (w2), respectively. Two modes of treatment viz., in situ and root

dip were used to evaluate genotoxicity of agricultural soil samples in terms of

chromosomal aberrations in root tip cells of A. cepa. The squash preparations from root

tip cells of control A. cepa bulbs revealed a large number of dividing cells at different

stages of mitosis (Plate I). Different kinds of chromosomal aberrations were observed

and were apportioned into physiological aberrations attributable to spindle inhibition

and clastogenic aberrations attributable to direct action on chromosomes. Various types

of physiological (c-mitosis, delayed anaphases, stickiness laggards, vagrants) as well as

clastogenic (chromosomal breaks, chromatin bridges and ring chromosomes)

aberrations were observed during both modes of treatments (Plate II - VIII). Some

abnormalities which could not be included in any of above category were counted as

abnormal metaphases (such as asteroid structures and deviation of alignment of

chromosomes at equatorial plate) and abnormal anaphases (asteroid structures and

deviation of chromosomes from the poles at anaphase). The squash preparations from

root tip cells of control A. cepa bulbs revealed a large number of dividing cells at

different stages of mitosis.

4.2.1. In situ treatment

4.2.1.1. Agricultural soils under rice cultivation

Frequency of chromosomal aberrations induced by different soil samples were

higher than negative control (sand) which was 4.14 %. A few cells with c-mitosis,

delayed anaphase, stickiness and bridges were observed in control bulbs. No instance of

laggards, vagrants, abnormal anaphase, abnormal metaphase, breaks and ring

chromosomes was found in control bulbs. The squash preparations from root tip cells of

treated A. cepa bulbs revealed different types of aberrations. Among physiological

Results

Plate I. Photomicrographs representing normal dividing cells: prophase (a-c),

metaphase (d-f), anaphase (g-i) and telophase (j-l) in root tip cells of

Allium cepa.

Results

Plate II. Photomicrographs representing spindle inhibition at metaphase (a-d)

and anaphase (e-f) in root tip cells of Allium cepa following treatment

with different agricultural soil samples.

Results

Plate III. Photomicrographs representing delayed anaphases (a-e) and lagging

fragments/chromosomes (f-h) in root tip cells of Allium cepa following

treatment with different agricultural soil samples.

Results

Plate IV. Photomicrographs representing stickiness in root tip cells of Allium cepa

following treatment with different agricultural soil samples.

Results

Plate V. Photomicrographs representing vagrant chromosomes in root tip cells of

Allium cepa following treatment with different agricultural soil samples.

Results

Plate VI. Photomicrographs representing single chromatin bridge in root tip cells

of Allium cepa following treatment with different agricultural soil

samples.

Results

Plate VII. Photomicrographs representing double chromatin bridges in root tip

cells of Allium cepa following treatment with different agricultural soil

samples.

Results

Plate VIII. Photomicrographs representing chromosomal breaks and ring

chromosomes in root tip cells of Allium cepa following treatment with

different agricultural soil samples.

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89

aberrations, percentage of delayed anaphases was highest where as chromatin bridges

dominated clastogenic aberrations. Among all soil samples from different rice

cultivated fields of sampling 1, the sample SWFIIr1 showed maximum (23.79 %)

percentage of chromosomal aberrations while sample NEFIIr1 showed minimum (7.71

%). Among the soil samples collected from rice cultivated fields of sampling 2,

SWFIIr2 again showed maximum (19.84 %) chromosomal aberrations while sample

NWFIIr2 showed minimum (9.04 %) chromosomal aberrations. The frequency of root

tip cells with physiological and clastogenic aberrations following treatment with

different soil samples ranged from 7.18 % - 21.50 %; 0.43 % - 2.29 % for r1 sampling

and 6.971 % - 15.48 %; 0.65 % - 4.36 % for r2 sampling, respectively. Different types

of chromosomal aberrations observed in Allium root tip cells treated with rice cultivated

soils of both samplings are given in Table 13 and Fig. 24. One way ANOVA revealed

that the frequencies of chromosomal aberrations of all soil samples under rice

cultivation collected from different sites (r1and r2) differed significantly (Table 13.1

and Table 13.2).

4.2.1.2. Agricultural soils under wheat cultivation

Frequency of chromosomal aberrations induced by different soil samples

collected from wheat cultivated sites were higher than negative control (sand) which

was 4.14%. Root tip cells treated with control showed 3.60 % of physiological

aberrations (c-mitosis, delayed anaphase and stickiness) and 0.54 % of clastogenic

aberrations (Chromatin bridges). The squash preparations from root tip cells of treated

A. cepa bulbs revealed different types of aberrations. Among physiological aberrations,

percentage of delayed anaphases was highest where as chromatin bridges dominated

clastogenic aberrations. Among all soil samples from different wheat cultivated fields of

sampling w1, the sample SWFIIw1 showed maximum (20.21 %) percentage of

chromosomal aberrations while sample SWFIw1 showed minimum (7.40 %). Among

the soil samples collected from wheat cultivated fields of sampling w2, SWFIIw2

showed maximum (15.54 %) percentage of chromosomal aberrations while sample

NEFIIw2 showed minimum (7.75 %) percentage of chromosomal aberrations. The

frequency of root tip cells with physiological and clastogenic aberrations following

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90

treatment with different soil samples ranged from 6.86 % - 13.18 % ; 0.54 % - 3.16 %

for w1 sampling and 6.12 % - 13.36 %; 0.26 % - 3.25 % for w2 sampling, respectively.

Different types of chromosomal aberrations observed in Allium root tip cells treated

with wheat cultivated soils of both samplings are given in Table 14 and Fig. 25. One

way ANOVA analysis revealed that the frequencies of chromosomal aberrations of all

soil samples under rice cultivation collected from different sites (w1 and w2) differed

significantly from the control (Table 14.1 and Table 14.2).

4.2.2. Root dip treatment

The squash preparations of A. cepa root tips treated with distilled water (control)

showed 3.46 % of cells with physiological aberrations and 0.21 % of clastogenic

aberrations. During the root dip mode of treatment, the squash preparations of root tips

of A. cepa bulbs treated with different concentrations (20%, 40%, 60%, 80% and 100%)

of collected soil samples revealed spectrum of different types of chromosomal

aberrations. The aberrations were categorized as physiological aberrations which

includes c-mitosis, delayed anaphases, stickiness, laggards, vagrants, abnormal

anaphasess, abnormal metaphases and clastogenic aberrations constituted chromatin

bridges, chromosomal breaks and ring chromosomes. Different types of aberrations

observed in Allium root tip cells during root tip treatment with different soil extracts are

given in Table 15 - 30 and Fig. 26-33.

4.2.2.1. Agricultural soils under rice cultivation

The order of different samples of r1 and r2 sampling with respect to percent

aberrant cells observed at maximum concentration of soil extract tested from the

minimum to maximum percent were: NWFIr1 (12.51%) < NEFIIr1 (16.17%) <

SWFIr1(16.88) < NEFIr1 (19.26) < SEFIr1 (20.26%) < NWFIIr1(22.70%) < SEFIIr1

(24.07%) < SWFIIr1 (25.95%); NWFIr2 (13.49%) < NEFIIr2 (17.60%) < SEFIr2

(18.32%) < NEFIr2 (18.56%) < SWFIr2 (18.96%) < SEFIr2(22.19%) < NWFIIr2

(22.86%) < SWFIIr1 (26.47%), respectively. All the soil samples collected from rice

cultivated fields showed increase in chromosomal aberrations with increase in

concentration of soil extract.

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91

4.2.2.1.1. Samples collected from North East zone during r1 sampling (NEFIr1

and NEFIIr1)

Different types of aberrations observed in Allium root tip cells during root dip

mode of treatment with agricultural soil sample extracts of NEFIr1 and NEFIIr1are

given in Table 15 and Fig. 26. For the sample NEFIr1 the percent of total aberrations

ranged from 12.84 % – 19.26 % for the lowest (20 %) and the highest concentration

(100%) of the extract, respectively. For the sample NEFIIr1, dose dependent increase in

chromosomal aberrations was observed. Percent of total aberrations ranged from 5.93 %

– 16.17 % for the lowest (20 %) and the highest concentration (100%) of the extract,

respectively. Abnormal anaphases and abnormal metaphases among physiological and

chromosomal breaks and ring chromosomes among clastogenic aberrations were

completely absent.

4.2.2.1.2. Samples collected from North East zone during r2 sampling (NEFIr2 and

NEFIIr2)


mode of treatment with agricultural soil sample extracts of NEFIr2 and NEFIIr2 are

given in Table 16 and Fig. 26. For the sample NEFIr2, the percent of total aberrations


(100%) of the extract, respectively. Abnormal anaphases and abnormal metaphases

among physiological and chromosomal breaks and ring chromosomes among

clastogenic aberrations were completely absent. For the sample NEFIIr2, the percent of

total aberrations ranged from 5.42 % – 17.60 % for the lowest (20 %) and the highest

concentration (100%) of the extract, respectively. Dose dependent increase in

chromosomal aberrations was observed with no instance of abnormal anaphase and

metaphase. Occurrence of chromosomal breaks and ring chromosomes was found only

at highest dose of the extract tested (100%).

4.2.2.1.3. Samples collected from South East zone during r1 sampling (SEFIr1 and

SEFIIr1)


mode of treatment with agricultural soil sample extracts of SEFIr1 and SEFIIr1 are

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92

given in Table 17 and Fig. 27. Dose dependent increase in chromosomal aberrations

was observed in both the samples. For the sample SEFIr1, the percent of total

aberrations ranged from 10.23 % – 20.26 % for the lowest (20 %) and the highest

concentration (100%) of the extract, respectively. Abnormal anaphases and abnormal

metaphases were present at all the doses except 20 % of the soil extract tested whereas

chromosomal bridges and ring chromosomes were completely absent in all the

concentrations except 80% of the soil extract. For the sample SEFIIr1, the percent of

total aberrations ranged from 11.43% – 24.07 % for the lowest (20 %) and the highest

concentration (100%) of the extract, respectively.

4.2.2.1.4. Samples collected from South East zone during r2 sampling (SEFIr2 and

SEFIIr2)


mode of treatment with agricultural soil sample extracts of SEFIr2 and SEFIIr2 are

given in Table 18 and Fig. 27. For the sample SEFIr2 the percent of total aberrations


(100%) of the extract, respectively. Abnormal anaphases among physiological and

chromosomal bridges and ring chromosomes among clastogenic aberrations were

completely absent in all the doses tested. For the sample SEFIIr2 the percent total

aberrations ranged from 11.11 % - 22.19 % for the lowest (20 %) and the highest


chromosomal aberrations was observed with no instance of abnormal anaphases (Aa),

abnormal metaphases (Am) and ring chromosomes (Rc).

4.2.2.1.5. Samples collected from South West zone during r1 sampling (SWFIr1

and SWFIIr1)

Different types of aberrations observed in A. cepa root tip cells during root dip

mode of treatment with agricultural soil sample extracts of SWFIr1 and SWFIIr1 are

given in Table 19 and Fig. 28. For the sample SWFIr1 the percent of total aberrations


(100%) of the extract, respectively. No instance of abnormal anaphases and abnormal

metaphases were observed in both the samples at all the dose tested. For the sample

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93

SWFIIr2, the percent of total aberrations ranged from 13.78 % - 25.95 % for the lowest

(20 %) and the highest concentration (100%) of the extract, respectively. Dose

dependent increase in chromosomal aberrations was observed with maximum number

of delayed anaphases followed by stickiness, c-mitosis, chromosomal breaks, bridges,

laggards and ring chromosomes.

4.2.2.1.6. Samples collected from South West zone during r2 sampling (SWFIr2

and SWFIIr2)


mode of treatment with agricultural soil sample extracts of SWFIr2 and SWFIIr2 are

given in Table 20 and Fig. 28. C-mitosis, delayed anaphases, stickiness, laggards and

vagrants among physiological and chromosomal bridges, breaks and ring chromosomes

among clastogenic aberrations showed an increase in their frequency in both the

samples. For the sample SWFIr2, the percent of total aberrations ranged from 12.26 % –

18.96 % for the lowest (20 %) and the highest concentration (100%) of the extract,

respectively. For the sample SWFIIr2 the percent of total aberrations ranged from

14.23% – 26.40 % for the lowest (20 %) and the highest concentration (100%) of the

extract, respectively.

4.2.2.1.7. Samples collected from North West zone during r1 sampling (NWFIr1

and NWFIIr1)


mode of treatment with agricultural soil sample extracts of NWFIr1 and NWFIIr1 are

given in Table 21 and Fig. 29. For the sample NWFIr1, the percent of total aberrations


(100%) of the extract, respectively. Increase in physiological aberrations and decrease

in clastogenic aberrations was observed with no instance of vagrants, abnormal

anaphases, abnormal metaphases and ring chromosomes. For the sample NWFIr1, the

percent of total aberrations ranged from 8.54 % – 22.70 % for the lowest (20 %) and the

highest concentration (100%) of the extract, respectively. Dose dependent increase in

total chromosomal aberrations was observed

Results

94

4.2.2.1.8. Samples collected from North West zone during r2 sampling (NWFIr2

and NWFIIr2)

Different types of aberrations observed in A.cepa root tip cells during root dip

mode of treatment with agricultural soil sample extracts of NWFIr2 and NWFIIr2 are

given in Table 22 and Fig. 29. Increase in both physiological aberrations and

clastogenic aberrations was observed with no instance of abnormal anaphase, abnormal

metaphase in both samples For the sample NWFIr2, the percent of total aberrations


(100%) of the extract, respectively. Chromosomal breaks were found to be completely

absent in all the concentration of the samples tested. For the sample NWFIr2 the percent

of total aberrations ranged from 9.38% – 22.86 % for the lowest (20 %) and the highest

concentration (100%) of the extract, respectively. Dose dependent increase in total

chromosomal aberrations was observed.

4.2.2.2. Agricultural soils under wheat cultivation

The soil samples were collected from two fields each of north east, south east,

south west and north west part of Amritsar under wheat cultivation in two different

years March 2010 (w1) and March 2011 (w2). The order of different soil samples of w1

and w2 sampling with respect to percent aberrant cells observed at maximum

concentration of soil extract tested from the minimum to maximum percent were:

SWFIw1 (12.75%) < NWFIw1 (13.08%) < SEFIw1 (19.52 %) < NEFIIw1 (21.46 %) <

NEFIw1 (21.49 %) < NWFIIw1 (21.99 %) < SEFIIw1 (23.43 %) < SWFIIw1 (27.09

%); NWFIw2 (13.46 %) < NEFIIw2 (16.32%) < SWFIw2 (17.55%); SEFIw2 (18.77%)

< NEFIw2 (20.08%) < NWFIIw2 (22.93%) < SWFIIw2 (24.94%) < SEFIIw2 <

(25.59%), respectively. All the soil samples collected from wheat cultivated fields

showed increase in chromosomal aberrations with increase in concentration of soil

extract.

4.2.2.2.1. Samples collected from North east zone during w1 sampling (NEFIw1

and NEFIIw1)


mode of treatment with agricultural soil sample extracts of NEFIw1 and NEFIIw1 are

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95

given in Table 23 and Fig. 30. Dose dependent increase in chromosomal aberrations

was observed in both the samples. For the sample NEFIw1, the percent of total


concentration (100%) of the extract, respectively. For the sample NEFIIw1 the percent

of total aberrations ranged from 6.63 % – 21.46 % for the lowest (20 %) and the highest

concentration (100%) of the extract, respectively. Increase in both physiological as well

as clastogenic aberrations was observed.

4.2.2.2.2. Samples collected from North East zone during w2 sampling (NEFIw2

and NEFIIw2)

Different types of aberrations observed in A.cepa root tip cells during root dip

mode of treatment with agricultural soil sample extracts of NEFIw2 and NEFIIw2 are

given in Table 24 and Fig. 30. For the sample NEFIw2, the percent of total aberrations


(100%) of the extract, respectively. For the sample NEFIIw2, the percent of total



chromosomal aberrations was observed with maximum number of delayed anaphases

followed by stickiness, c-mitosis, chromosomal bridges, vagrants, laggards, ring

chromosomes and chromosomal breaks.

4.2.2.2.3. Samples collected from South East zone during w1 sampling (SEFIw1,

and SEFIIw1)


mode of treatment with agricultural soil sample extracts of SEFIw1 and SEFIIw1 are

given in Table 25 and Fig. 31. For the sample SEFIw1, the percent of total aberrations


(100%) of the extract, respectively. For the sample SEFIIw1, the percent of total


concentration (100%) of the extract, respectively. Increase in both physiological as well

as clastogenic aberrations was observed.

Results

96

4.2.2.2.4. Samples collected from South East zone during w2 sampling (SEFIw2

and SEFIIw2)


mode of treatment with agricultural soil sample extracts of SEFIw2 and SEFIIw2 are

given in Table 26 and Fig. 31. For the sample SEFIw2, the percent of total aberrations


(100%) of the extract, respectively. For the sample SEFIIw2, the percent of total


concentration (100%) of the extract, respectively. With increasing concentration, rise in

both physiological as well as clastogenic aberrations was observed. Dose dependent

increase in chromosomal aberrations was observed with maximum number of delayed

anaphase. No instance of abnormal anaphase, abnormal metaphase, chromosomal

bridges and ring chromosomes was found.

4.2.2.2.5. Samples collected from South West zone during w1 sampling (SWFIw1

and SWFIIw1)


mode of treatment with agricultural soil sample extracts of SWFIw1 and SWFIIw1 are

given in Table 27 and Fig. 32. No instance of ring chromosomes was observed in both

the samples SWFIw1 and SWFIIw1 at all the doses tested. For the sample SWFIw1 the

percent of total aberrations ranged from 10.61 % – 12.75 % for the lowest (20 %) and

the highest concentration (100%) of the extract, respectively. For the sample SWFIIw1

the percent of total aberrations ranged from 13.12 % - 27.09 % for the lowest (20 %)

and the highest concentration (100%) of the extract, respectively. Physiological

aberrations were dominated by delayed anaphases where as chromosomal breaks

dominated clastogenic aberrations.

4.2.2.2.6. Samples collected from South West zone during w2 sampling (SWFIw2

and SWFIIw2)

Chromosomal aberrations observed in root tip cells of Allium cepa during root

dip mode of treatment with agricultural soil sample extracts of SWFIw2 and SWFIIw2

are given in Table 28 and Fig. 32. For the sample SWFIw2, the percent of total

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97


concentration (100%) of the extract, respectively. Percent of total aberrations for the

sample SWFIIw2 ranged from 13.80 % at 20% to 24.94 % at 100% of the soil extract.

Spectrum of chromosomal aberrations was dominated by delayed anaphases followed

by stickiness, c-mitosis, laggards, vagrants, chromosomal breaks, bridges and ring

chromosomes.

4.2.2.2.7. Samples collected from North West zone during w1 sampling (NWFIw1

and NWFIIw1)

Genotoxic effects of different concentrations of soil samples NWFIw1 and

NWFIIw1 are given in Table 29 and depicted in Figure 33. C-mitosis, delayed

anaphases, laggards, stickiness and vagrants constituted the spectrum of physiological

aberrations with no occurrence of abnormal anaphases and abnormal metaphases. In

both the samples the frequency of cells with delayed anaphases was found to be

maximum followed by c-mitosis and stickiness. Few cells with laggards and vagrants

were also seen. Among clastogenic aberrations, chromosomal bridges were maximum

followed by chromosomal breaks. The frequency of root tip cells with chromosomal

aberrations following treatment with different concentrations of NWFIw1 ranged from

7.19 % at 20 % to 13.08 % at 100% of the soil extract. The total aberration frequency

for the sample NWFIIw1 ranged from 8.16 % at 20 % to 21.99 % at 100 % of the soil

extract. Dose dependent increase in percent physiological and clastogenic aberrations

was observed.

4.2.2.2.8. Samples collected from North West zone during w2 sampling (NWFIw2

and NWFIIw2)

Different types of chromosomal aberrations observed in root tip cells of Allium

cepa treated with soil extracts of samples NWFIw2 and NWFIIw2 are shown in Table

30 and depicted in Fig. 33. With increasing concentration, rise in both physiological as

well as clastogenic aberrations was observed. Dose dependent increase in chromosomal

aberrations was observed with maximum of delayed anaphases. Total chromosomal

aberrations for the sample NWFIw2 ranged from 6.52 % - 13.46 % for the lowest (20

%) and the highest concentration (100%) of the soil extract, respectively. For the

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98

sample NWFIIw2, the percent of total aberrations ranged from 9.13 % - 22.93 % for the

lowest (20 %) and the highest concentration (100%) of the extract, respectively.

4.3. Biochemical parameters

Biochemical analysis plays a crucial role in the establishment of normoxia in

biological systems and in resistance to oxidative stress. Biochemical parameters

constituted the estimation of protein content and enzymatic antioxidative system

which involves activities of enzymes such as ascorbate peroxidase (APX), catalase

(CAT), dehydroascorbate reductase (DHAR), glutathione reductase (GR), glutathione -

S - transferases (GST), guaicol peroxidae (POD) and superoxide dismutase (SOD). The

protein content and activities of antioxidative enzymes in Allium cepa bulbs exposed

to different soil samples collected from agricultural fields under rice cultivation during

September, 2009 (r1) and September 2010 (r2) and wheat cultivation during March

2010 (w1) and March, 2011(w2) are summarized in Tables 31 - 32 and Figs.34 - 37.

4.3.1 A. cepa bulbs exposed to rice cultivated soils

Total protein content and activities of different antioxidative enzymes shown by

extracts of A. cepa bulbs treated with different rice field soil samples of r1 and r2

sampling are shown in Table 31 and Fig. 34 - 35.

4.3.1.1. Protein content

Total protein content of A. cepa bulbs treated with rice cultivated agricultural soil

samples collected during r1 and r2 sampling is given in Table 31 and Fig. 34. In the

present study, washed sand was used as negative control. Total protein content of the

onion bulbs treated with soil samples collected from rice cultivated fields of r1

sampling was found to vary from 1.08 mg/g for sample NWFIr1 to 2.49 mg/g FW for

sample SWFIIr1 which was observed to be higher as compared to negative control (0.99

mg/g FW). Among the rice cultivated soil samples of r2 sampling, the bulbs treated

with soil sample NWFIr2 showed minimum protein content (1.04 mg/g FW) while

maximum protein content (2.65 mg/g FW) was shown by bulbs treated with soil sample

SWFIIr2 which was again found to be higher than negative control bulbs (0.80 mg/g).

One way ANOVA also revealed that protein content of A.cepa bulbs treated with soil

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99

samples under rice cultivation collected during both sampling (r1 and r2) differed

significantly from the control bulbs (Table 31.1 and Table 31.2).

4.3.1.2. Activities of antioxidative enzymes

To overcome stress, plants develop detoxification mechanisms to regulate the

level of free radicals, which include activation of efficient antioxidative system

consisting of enzymatic antioxidants like APX, CAT, DHAR, GR, GST, POD and

SOD. In the present study, the activities of APX, CAT, DHAR, GST were found to be

higher in A. cepa bulbs treated with all the soil samples as compared to control whereas

activities of GR and POD were observed to be low.

4.3.1.2.1. Ascorbate peroxidase (APX)

APX activity was found to be higher in A. cepa bulbs treated with soil samples

collected during both r1 and r2 sampling as compared to control sample. During r1

sampling, the negative control showed APX activity of 0.361 mol UA mg/fresh protein.

Among all the soil samples collected from different fields under rice cultivation of r1

sampling, the maximum APX activity (1.57 mol UA/mg fresh protein) was shown by

onion bulbs treated with sample SWFIIr1 and the minimum APX activity (0.42 mol

UA/mg fresh protein) was shown by onion bulbs treated with sample SWFIr1. During

r2 sampling, the negative control showed APX activity of 0.44 mol UA mg/fresh

protein. Among all the soil samples collected from rice fields under r2 sampling, the

maximum APX activity (1.46 mol UA/ mg fresh protein) was shown by onion bulbs

treated with sample of NEFIIr2 and minimum APX activity (0.51 mol UA/mg fresh

protein) was shown by onion bulbs treated with sample SEFIr2 (Fig. 34). One way

ANOVA also revealed that activity of APX in A. cepa bulbs treated with soil samples

under rice cultivation collected during both samplings (r1 and r2) differed significantly.

(Table 31.3 and Table 31.4).

4.3.1.2.2. Catalase (CAT)

Control bulbs showed CAT activity of about 0.005 mol UA/mg fresh protein

during both r1 and r2 sampling. Among all the soil samples of r1 sampling, collected

from different fields under rice cultivation, maximum CAT activity (0.012 mol UA

Results

100

mg/fresh protein) was shown by the onion bulbs treated with sample NWFIr1 and

minimum (0.005 mol UA/ mg fresh protein) was shown by the onion bulbs treated with

sample NEFIIr1 which was similar to that of control onion bulb. Among the soil

samples collected from rice fields under r2 sampling, onion bulbs treated with the soil

sample collected from NEFIr2 showed maximum CAT activity (0.008 mol UA

mg/fresh protein) while minimum APX activity (0.005 mol UA/mg fresh protein) was

shown by onion bulbs treated with the soil sample collected SEFIr2 and SWFIr2 which

was similar to that of control bulbs (Fig. 34). One way ANOVA also revealed that

activity of CAT in A. cepa bulbs treated with soil samples under rice cultivation

collected during both sampling (r1 and r2) differed significantly (Table 31.5 and Table

31.6).

4.3.1.2.3. Dehydroascorbate reductase (DHAR)

Increase in activity of DHAR was observed in all A. cepa bulbs exposed to

agricultural soil samples. The control onion bulbs (negative control) showed DHAR

activity of 0.12 mol UA/ mg fresh protein. Among all the soil samples studied during r1

sampling, the onion bulbs treated with soil sample collected from SWFIIr1 showed

maximum DHAR activity (0.32 mol UA/mg fresh protein) while minimum activity

(0.18 mol UA/ mg fresh protein) was shown by the onion bulbs treated with soil sample

NEFIIr1. During r2 sampling, onion bulbs grown in washed sand which was considered

as negative control showed total DHAR activity of 0.14 mol UA/mg fresh protein.

Among all the soil samples collected from fields under rice cultivation, during r2

sampling, the maximum DHAR activity (0.33 UA/ mg fresh protein) was shown by the

onion bulbs treated with sample SWFIr2 and minimum DHAR activity (0.14 UA/ mg

fresh protein) was shown by the onion bulbs treated with sample SEFIIr2 (Fig. 34). One

way ANOVA also revealed that activity of DHAR in A. cepa bulbs treated with soil

samples under rice cultivation collected during both sampling (r1 and r2) differed

significantly (Table 31.7 and Table 31.8).

4.3.1.2.4. Glutathione reductase (GR)

The GR activity declined in A. cepa bulbs treated with rice cultivated

agricultural soil samples collected from different sites of Amritsar. During r1 sampling

Results

101

it was found that control onion bulbs (negative control) showed GR activity of 0.51 mol

UA/mg fresh protein as shown in Fig. 35. During r1 sampling, in all the soil samples

studied, the onion bulbs treated with soil sample SWFIIr1 showed the minimum GR

activity (0.034 mol UA/mg fresh protein) while maximum GR activity was shown by

NEFIIr1 (0.42 mol UA/ mg fresh protein). Among the soil samples collected from fields

under r2 sampling, A. cepa bulbs treated with the soil sample collected from NEFIr2

showed maximum GR activity (0.45 mol UA/mg fresh protein) while minimum GR

activity (0.07 mol UA/ mg fresh protein) was shown by onion bulbs treated with the soil

sample collected from SWFIIr2 which was observed to be less as compared to negative

control (0.66 mol UA/ mg fresh protein) (Fig. 35). One way ANOVA analysis also

revealed that activity of GR in A. cepa bulbs treated with soil samples under rice

cultivation collected during both sampling (r1 and r2) differed significantly (Table 31.9

and Table 31.10).

4.1.1.2.5. Gluthione-S-Transferase (GST)

Increase in GST activity was observed in onion bulbs treated with agricultural

soil samples from different samples. The control onion bulb (negative control) during r1

sampling showed GST activity of 0.06 mol UA/ mg fresh protein as shown in Figure 35.

Among all the soil samples studied, the onion bulbs treated with soil sample SWFIIr1

showed maximum GST activity 0.19 mol UA/ mg fresh protein while minimum GST

activity (0.10 mol UA/mg fresh protein) was shown by onion bulbs treated with soil

sample SEFIr1.The control onion bulbs (negative control) during r2 sampling showed

GST activity of 0.09 UA/mg fresh protein as shown Fig.35. Among all the soil samples

of r2 sampling, the onion bulbs treated with soil sample SWFIIr2 showed maximum

GST activity (0.22 mol UA/ mg fresh protein) while minimum GST activity (0.10 mol

UA/ mg fresh protein) was shown by the onion bulbs treated with soil sample SWFIr2

(Fig. 35). One way ANOVA also revealed that activity of GST in A. cepa bulbs treated

with soil samples under rice cultivation collected during both sampling (r1 and r2)

differed significantly (Table 31.11 and Table 31.12).

Results

102

4.1.1.2.6. Guaicol peroxidase (POD)

Onion bulbs grown in washed sand which was used as negative control showed

total POD activity of 0.019 mol UA/mg fresh protein as shown in Figure 35. Among all

the soil samples of r1 sampling, collected from different fields under rice cultivation,

maximum POD activity (0.031 mol UA/mg fresh protein) was shown by onion bulbs

treated with sample SWFIr1 and minimum (0.007 mol UA/ mg fresh protein) was

shown by onion bulbs treated with sample NWFIIr1. The control onion bulbs (negative

control) during r2 sampling, showed POD activity of 0.026 UA/mg fresh protein.

Among the A. cepa bulbs treated with agricultural soils samples collected during r2

sampling the activity of POD in sample SWFIr2 was observed to be maximum (0.050

mol UA/mg fresh protein) and lowest in case of NWFIIr2 (0.014mol UA/mg fresh

protein). The activities of POD in A. cepa bulbs did not revealed any particular trend

with treatment with different agricultural soil samples (Fig. 35). One way ANOVA

analysis also revealed that activity of POD in A. cepa bulbs treated with soil samples

under rice cultivation collected during both sampling (r1 and r2) differed significantly


4.3.1.2.7. Superoxide dismutase (SOD)

A significant increase in SOD activity was found in onion bulbs treated with

different soil samples of both sampling as compared to control onion bulbs (negative

control). During r1 sampling, control bulb showed SOD activity of 37.82 mol UA/ mg

fresh protein. Among all the soil samples collected from rice fields, the onion bulbs

treated with SEFIIr1 showed maximum SOD activity (97.91 mol UA/ mg fresh protein)

while minimum activity (43.42 mol UA/mg fresh protein) was shown by onion bulbs

treated with soil sample NEFIr1. During the r2 sampling, the control bulbs of A. cepa

showed SOD activity of 42.92 UA/mg fresh protein. The bulbs treated with soil sample

SEFIIr2 showed maximum SOD activity (106.03 UA/mg fresh protein) while lowest

SOD activity (43.48 UA/mg fresh protein) was shown by onion bulbs treated with soil

sample SEFIr2 (Fig. 35). One way ANOVA analysis also revealed that activity of SOD

in A. cepa bulbs treated with soil samples under rice cultivation collected during both

sampling (r1 and r2) differed significantly (Table 31.15 and Table 31.16).

Results

103

4.3.2. A. cepa bulbs exposed to wheat cultivated soils

The content of protein and activity of different antioxidative enzymes shown by

extracts of A. cepa bulbs treated with soil samples of wheat cultivated fields of w1 and

w2 sampling is shown in Table 32 and Fig. 36 - 37.

4.3.2.1. Protein content

Change in total protein content of A.cepa bulbs exposed to different agricultural

soil samples of w1 and w2 sampling are shown in Table 32 and Fig. 36. A. cepa bulbs

exposed to sand which was used as negative control showed total protein content of

0.79 mg/g FW. The onion bulbs treated with different agricultural soil samples collected

during w1 sampling was found to be enhanced than control bulbs. The sample SEFIIw1

showed minimum protein content of 1.06 mg/g FW while sample SWFIIw1 showed

maximum protein content of 2.10 mg/g FW. The total protein content of the onion bulbs

treated with different agricultural soil samples of w2 sampling, also showed increase in

its content as compared to the control which showed content of 0.90 mg/g FW. Among

all the soil samples collected from wheat fields of w2 sampling , the onion bulbs treated

with soil sample collected SWFIIw2 showed maximum protein content (3.30 mg/g FW)

while minimum protein content (1.12 mg/g FW) was shown by the A. cepa bulbs treated

with the soil sample collected from NEFIw2. One way ANOVA revealed that protein

content of A. cepa bulbs treated with soil samples of both w1 and w2 sampling differed

significantly (Table 32.1 and Table 32.2).

4.3.2.2. Activities of antioxidative enzymes

Activities of APX, CAT, DHAR, GST were found to be higher in A. cepa bulbs

treated with all the wheat soil samples as compared to control whereas activities of GR

and POD were low.

4.3.2.2.1. Ascorbate peroxidase (APX)

As compared to control, the APX activity was found to be higher in A. cepa

bulbs treated with agricultural soil samples of both w1 and w2 sampling. During w1

sampling, the negative control showed APX activity of 0.38 mol UA/mg fresh protein.

Among all the soil samples collected from different fields under rice cultivation of r1

sampling, the maximum APX activity (0.93 mol UA/mg fresh protein) was shown by

Results

104

onion bulbs treated with sample SWFIIw1 and the minimum APX activity (0.36 mol

UA/mg fresh protein) was shown by onion bulbs treated with sample NWFIIw1. During

w2 sampling, the APX activity of 0.29 mol UA mg/fresh protein was shown by control

bulbs. Among soil samples of r2 sampling, the sample NEFIw2 showed maximum APX

activity (1.15 mol UA/ mg fresh protein) while sample SWFIw2 showed minimum

APX activity (0.30 mol UA/mg fresh protein) (Fig.36). One way ANOVA showed

significant difference of APX activity of A. cepa bulbs treated with soil samples as

compared to control bulbs (Table 32.3 and Table 32 .4).

4.3.2.2.2. Catalase (CAT)

Control bulbs showed the CAT activity of about 0.005 mol UA/ mg fresh

protein and 0.004 mol UA/mg fresh protein during w1 and w2 sampling. Among all the

soil samples of w1 sampling, collected from different fields under wheat cultivation,

maximum CAT activity (0.013 mol UA/ mg fresh protein) was shown by the onion

bulbs treated with sample SEFIIw1 and minimum (0.005 mol UA/ mg fresh protein)

was shown by the onion bulbs treated with sample SWFIw1 and NWFIW1 which was

similar to that of control onion bulb. Among the soil samples collected from wheat

fields under w2 sampling, onion bulbs treated with the soil sample collected from

NWFIIw2 which showed maximum CAT activity (0.012 mol UA/mg fresh protein)

while minimum CAT activity (0.005 mol UA mg/fresh protein) was shown by onion

bulbs treated with the soil sample SWFIw2 (Fig. 36). One way ANOVA analysis

summary showed significant difference of activity of CAT in A. cepa bulbs treated with

soil samples (Table 32.5 and Table 32.6).

4.3.2.2.3. Dehydroascorbate reductase (DHAR)

The control onion bulbs (negative control) showed DHAR activity of 0.16 mol

UA/ mg fresh protein as shown in Table 32 and Fig. 36. Among all the soil samples

collected from wheat fields of w1 sampling, the onion bulbs treated with soil sample

SWFIIw1 showed maximum activity (0.27 mol UA/mg fresh protein) while minimum

DHAR activity (0.18 mol UA/mg fresh protein) was shown by the onion bulbs treated

with soil samples NEFIIw1, NWFIIW1 and SWFIw1. During w2 sampling negative

control showed DHAR activity of 0.17 mol UA mg/fresh protein. Among the soil

samples collected from fields during w2 sampling, onion bulbs treated with the soil

Results

105

sample NWFIw2 showed maximum DHAR activity (0.38 mol UA/mg fresh protein)

while minimum DHAR activity (0.17 mol UA/mg fresh protein) was shown by onion

bulbs treated with the soil sample SWFIw2. One way ANOVA analysis revealed

significant difference of DHAR activity in A .cepa bulbs treated with soil samples


4.3.2.2.4. Glutathione reductase (GR)

The GR activity was found to decline in A. cepa bulbs treated with wheat

cultivated agricultural soil samples collected from different sites of Amritsar. During

w1and w2 sampling it was found that control onion bulbs (negative control) showed GR

activity of 0.66 mol UA/ mg fresh protein and 0.57 mol UA/ mg fresh protein as shown

in Fig. 37 and was found to be higher as compared to soil treated bulbs. In all the soil

samples of w1 sampling , the onion bulbs treated with soil sample NEFIIw1 showed the

maximum GR activity (0.41 mol UA/mg fresh protein) while minimum GR activity was

shown by NWFIIw1 (0.17 mol UA/ mg fresh protein). Among the soil samples

collected from fields under w2 sampling, A. cepa bulbs treated with the soil sample

collected from NEFIIw2 showed maximum GR activity (0.36 mol UA/mg fresh protein)

while minimum DHAR activity (0.03 mol UA/ mg fresh protein) was shown by onion

bulbs treated with the soil sample collected from SWFIIw2 (Fig. 37). One way ANOVA

summary showed significant difference of activity of GR in A.cepa bulbs (Table 32.9

and Table 32.10).

4.3.2.2.5. Gluthione-S-Transferase (GST)

The control onion bulbs (negative control) showed GST activity of 0.09 mol

UA/ mg fresh protein during both w1 and w2 sampling. Among all the soil samples

collected from wheat fields during w1 sampling, the onion bulbs treated with soil

sample SWFIIw1 showed maximum GST activity (0.33 mol UA/ mg fresh protein)

while minimum GST activity (0.11 mol UA/ mg fresh protein) was shown by the onion

bulbs treated with soil sample NWFIIw1. Among soil samples collected during w2

sampling, onion bulbs treated with the soil sample SWFIIw2 showed maximum GST

activity (0.22 mol UA/ mg fresh protein) while minimum GST activity (0.10 mol UA/

mg fresh protein) was shown by onion bulbs treated with the soil sample SWFIw2 Fig.

Results

106

37). One way ANOVA summary showed significant difference of activity of GST in A.

cepa bulbs treated with soil samples (Table 32.11 and Table 32.12).

4.3.2.2.6. Guaicol peroxidase (POD)

Onion bulbs grown in washed sand which was used as negative control showed

total POD activity of 0.15 mol UA/mg fresh protein as shown in Figure 37. Among all

the soil samples of w1 sampling, collected from different fields under wheat cultivation,

maximum POD activity (0.09 mol UA/mg fresh protein) was shown by onion bulbs

treated with sample SEFIIw1 and minimum (0.02 mol UA/ mg fresh protein) was

shown by onion bulbs treated with sample SWFIIw1. The control onion bulbs (negative

control) during w2 sampling showed POD activity of 0.15 UA/mg fresh protein. Among

the A. cepa bulbs treated with agricultural soils samples collected during w2 sampling

the activity of POD in sample NEFIw2 was observed to be highest (0.11 mol UA/mg

fresh protein) and lowest in case of onion bulbs treated with SWFIw2 and SWFIIw2

(0.01mol UA/mg fresh protein).The activities of POD in A. cepa bulbs was observed to

be less as compared to control bulbs (Fig. 37). One way ANOVA analysis showed

significant difference of activity of POD in A.cepa bulbs treated with soil samples


4.3.2.2.7. Superoxide dismutase (SOD)

SOD activity showed a significant increase in onion bulbs treated with soil

samples of both sampling w1 and w2 sampling. During w1 sampling, control bulb

showed SOD activity of 42.92 mol UA/ mg fresh protein. Among all the soil samples

collected from wheat fields, the onion bulbs treated with SEFIIw1 showed maximum

SOD activity (106.03 mol UA/ mg fresh protein) while minimum activity (43.48 mol

UA/mg fresh protein) was shown by onion bulbs treated with soil sample SEFIw1.

During the w2 sampling, the control bulbs of A. cepa showed SOD activity of 39.92

UA/mg fresh protein. The bulbs treated with soil sample SEFIIw2 again showed

maximum SOD activity (138.01 UA/mg fresh protein) while minimum SOD activity

(45.22 UA/mg fresh protein) was shown by onion bulbs treated with soil sample

NEFIw2 (Fig. 37) One way ANOVA analysis also revealed that activity of SOD in A.

cepa bulbs treated with soil samples of all samplings differed significantly (Table 32.15

and Table 32.16).

Results

Table 5. Soil texture (%) of different agricultural soils of Amritsar under rice

cultivation.

Table 6. Soil texture (%) of different agricultural soils of Amritsar under wheat

cultivation.

Sample code

@ Soil Texture (%)

Sand Silt Clay

NEFIw1 56.21±1.04 1.45±0.01 42.34±0.06

NEFIIw1 58.36±0.04 1.15±0.00 40.49±1.02

SEFIw1 61.83±0.01 1.09±0.00 37.08±0.00

SEFIIw1 63.72± 0.03 1.14±0.02 35.14±0.02

SWFIw1 63.32±0.05 1.28±0.02 35.40±0.03

SWFIIw1 60.06±0.08 2.84±0.00 37.10±1.11

NWFIw1 62.48±0.01 1.10±0.00 36.42±0.01

NWFIIw1 65.71±0.01 1.00±0.03 33.29±0.02

NEFIw2 61.27±0.00 2.82±0.00 35.91±0.00

NEFIIw2 60.07±0.97 1.45±0.04 38.48±0.73

SEFIw2 54.72±0.00 1.05±0.03 44.23±0.01

SEFIIw2 64.78±0.01 1.26±0.04 33.96±0.03

SWFIw2 61.83±0.01 1.09±0.00 37.08±0.00

SWFIIw2 58.63±0.01 1.55±0.02 39.82±0.05

NWFIw2 61.37±0.02 1.22±0.02 37.41±0.01

NWFIIw2 64.66±0.02 1.40±0.00 33.94±0.02

Data shown are Mean ± S.E. of three replicates @

Described in chapter “Materials and Methods” (Table 4)

Sample code@

Soil Texture (%)

Sand Silt Clay

NEFIr1 59.80±0.07 1.08±0.01 39.12±0.03

NEFIIr1 61.11±0.01 1.96±0.00 36.93±0.04

SEFIr1 56.80±0.00 1.71±0.03 41.49±0.01

SEFIIr1 65.71±0.01 1.48±0.02 32.81±0.01

SWFIr1 60.82±1.09 0.43±0.02 38.75±0.05

SWFIIr1 59.98±0.63 1.65±0.01 38.37±0.05

NWFIr1 61.15±0.01 0.49±0.01 38.36±0.00

NWFIIr1 63.67±0.00 1.39±0.00 34.94±0.00

NEFIr2 60.20±0.02 1.23±0.01 38.57±0.02

NEFIIr2 57.65±0.08 1.15±0.00 41.20±0.00

SEFIr2 59.78±0.00 0.96±0.00 39.26±0.00

SEFIIr2 61.44±0.01 1.14±0.01 37.42±0.01

SWFIr2 58.04±1.03 0.99±0.00 40.97±1.02

SWFIIr2 57.12±0.96 1.35±0.00 41.53±0.39

NWFIr2 60.83±0.02 1.09±0.04 38.08±0.02

NWFIIr2 53.72±0.00 1.34±0.00 44.94±0.01

Results

Table 7. pH, alkalinity and contents of calcium and magnesium of different

agricultural soil samples of Amritsar, Punjab (India) under rice

cultivation.

Sample code @

Physico-chemical parameters (Mean ± S.E.)

pH Alkalinity

(mEq/100g)

Calcium (mg/g) Magnesium

(mg/g)

NEFIr1 7.57±0.00 3.16±0.03 48.10±0.00 224.80±6.43

NEFIIr1 8.06±0.00 3.30±0.05 80.16±0.00 226.50±6.60

SEFIr1 7.80±0.00 1.53±0.00 80.16±0.00 299.80±0.00

SEFIIr1 7.39±0.00 0.53±0.03 88.17±0.00 211.83±0.00

SWFIr1 7.73±0.00 0.70±0.05 101.50±2.6 318.80±2.66

SWFIIr1 7.63±0.00 1.26±0.08 69.47±2.67 130.50±2.67

NWFIr1 7.68±0.00 1.46±0.05 50.70±2.67 189.20±2.67

NWFIIr1 8.07±0.00 1.23±0.03 80.16±0.00 139.84±0.00

NEFIr2 7.40±0.00 0.23±0.03 29.34±2.67 137.30±5.81

NEFIIr2 7.55±0.00 0.36±0.03 21.37±2.66 105.30±5.81

SEFIr2 7.32±0.01 0.33±0.00 32.06±0.00 139.90±8.33

SEFIIr2 7.42±0.00 0.23±0.00 34.73±2.67 138.60±5.81

SWFIr2 8.14±0.00 0.70±0.03 40.22±1.23 174.61±2.08

SWFIIr2 8.51±0.01 0.98±0.06 43.31±0.94 163.22±2.19

NWFIr2 7.40±0.00 0.23±0.03 26.71±2.67 86.61±5.811

NWFIIr2 7.58±0.00 0.26±0.03 24.04±0.00 175.9±0.017

Table 8. pH, alkalinity and contents of calcium and magnesium of different

agricultural soil samples of Amritsar, Punjab (India) under wheat

cultivation.

Sample code@


pH Alkalinity

(mEq/100g)

Calcium (mg/g) Magnesium

(mg/g)

NEFIw1 8.11±0.00 1.13±0.06 50.77±2.67 189.30±2.66

NEFIIw1 8.24±0.00 0.83±0.03 37.41±2.67 202.60±2.67

SEFIw1 7.40±0.02 1.46±0.08 61.46±2.67 238.20±2.40

SEFIIw1 7.91±0.00 1.26±0.03 74.82±2.67 105.10±2.66

SWFIw1 8.48±0.00 0.72±0.05 32.00±0.00 207.00±0.00

SWFIIw1 8.50±0.00 1.26±0.08 40.08±0.00 279.00±0.00

NWFIw1 8.33±0.00 1.40±0.07 66.80±2.67 166.50±0.66

NWFIIw1 8.41±0.00 0.53±0.03 40.08±0.00 139.90±0.00

NEFIw2 7.83±0.00 0.98±0.05 34.31±3.10 138.60±4.10

NEFIIw2 7.94±0.00 1.00±0.06 30.44±2.18 101.50±1.12

SEFIw2 7.81±0.00 0.84±0.02 47.31±1.34 143.34±2.40

SEFIIw2 7.94±0.00 0.79±0.03 41.24±0.82 151.44±2.32

SWFIw2 8.14±0.00 0.70±0.03 40.22±1.23 174.61±2.08

SWFIIw2 8.51±0.01 0.98±0.06 43.31±0.94 163.22±2.19

NWFIw2 7.42±0.00 0.78±0.03 45.28±2.43 150.41±0.24

NWFIIw2 7.68±0.00 0.64±0.03 34.12±1.02 143.62±2.41 @


Results

Table 9. Contents of nitrates, phosphates, sodium and potassium of different

agricultural soil samples of Amritsar, Punjab (India) under rice

cultivation.

Sample

code@


Nitrates (mg/g) Phosphates (mg/g) Potassium (mg/g) Sodium(mg/g)

NEFIr1 0.30±0.02 1.26±0.03 0.076±0.00 0.065±0.00

NEFIIr1 0.48±0.06 0.54±0.03 0.073±0.00 0.081±0.00

SEFIr1 0.62±0.11 1.00±0.02 0.103±0.00 0.135±0.00

SEFIIr1 0.51±0.11 1.07±0.02 0.030±0.00 0.091±0.00

SWFIr1 0.51±0.04 1.29±0.02 0.008±0.00 0.068±0.00

SWFIIr1 0.51±0.03 1.37±0.05 0.046±0.00 0.085±0.00

NWFIr1 0.44±0.02 1.44±0.02 0.053±0.00 0.055±0.00

NWFIIr1 0.50±0.18 2.13±0.00 0.052±0.00 0.038±0.00

NEFIr2 1.8± 0.01 2.10±0.07 0.130±0.00 0.238±0.00

NEFIIr2 2.03±0.02 1.74±0.10 0.133±0.00 0.296±0.05

SEFIr2 1.80±0.11 2.11±0.03 0.135±0.00 0.349±0.00

SEFIIr2 2.20±0.03 1.82±0.02 0.114±0.00 0.294±0.00

SWFIr2 2.16±0.03 2.57±0.02 0.132±0.00 0.299±0.00

SWFIIr2 2.03±0.04 1.22±0.06 0.113±0.00 0.230±0.00

NWFIr2 2.07±0.00 1.850.090 0.109±0.00 0.272±0.00

NWFIIr2 1.63±0.00 1.34±0.08 0.129±0.00 0.263±0.00

Table 10. Contents of nitrates, phosphates, sodium and potassium of different

agricultural soil sample of Amritsar, Punjab (India) under wheat

cultivation.

Sample

code@


Nitrates (mg/g) Phosphates (mg/g) Potassium (mg/g) Sodium(mg/g)

NEFIw1 0.10±0.01 1.32±0.12 0.044±0.00 0.087±0.00

NEFIIw1 0.27±0.01 1.53±0.02 0.061±0.00 0.111±0.00

SEFIw1 0.31±0.20 1.43±0.04 0.071±0.00 0.145±0.00

SEFIIw1 0.52±0.03 2.06±0.03 0.083±0.00 0.101±0.00

SWFIw1 0.55±0.06 1.92±0.02 0.071±0.00 0.147±0.00

SWFIIw1 0.28±0.04 1.44±0.01 0.050±0.00 0.038±0.00

NWFIw1 0.51±0.02 1.41±0.02 0.049±0.00 0.070±0.00

NWFIIw1 0.32±0.01 1.82±0.04 0.057±0.00 0.094±0.00

NEFIw2 1.45±0.00 1.53±0.18 0.087±0.00 0.048±0.00

NEFIIw2 0.97±0.00 1.60±0.03 0.098±0.00 0.063±0.00

SEFIw2 1.42±0.01 1.72±0.01 0.146±0.00 0.198±0.00

SEFIIw2 1.67±0.03 1.84±0.06 0.194±0.00 0.210±0.00

SWFIw2 1.94±0.03 1.82±0.12 0.147±0.00 0.147±0.00

SWFIIw2 1.66±0.02 1.12±0.16 0.091±0.00 0.172±0.00

NWFIw2 1.42±0.01 1.66±0.12 0.087±0.00 0.252±0.00

NWFIIw2 1.77±0.01 1.71±0.20 0.098±0.00 0.247±0.00 @


Results

Table 11. Content of heavy metals (mg/kg) of soil samples collected from rice cultivated agricultural fields of Amritsar, Punjab

(India).

Sample code @

Heavy metal content (mg/ kg) (Mean ± S.D.)

Cadmium Chromium Copper Iron Lead Manganese Nickel Zinc

NEFIr1 - 2.35 ± 1.07 16.33 ± 0.32 17238.67 ± 61.38 12.49 ± 1.31 329.69± 2.55 15.81± 0.20 53.41 ± 0.24

NEFIIr1 - 8.45 ± 0.70 23.07 ± 0.29 20012.30 ± 56.06 17.31± 1.00 359.13±1.69 24.76 ± 0.54 82.20 ±0.19

SEFIr1 0.65 ± 0.104 8.22 ± 1.07 14.03 ± 0.21 14804.73 ± 59.54 8.54 ± 0.65 251.23± 2.08 18.08 ± 0.55 56.56 ± 0.17

SEFIIr1 - 11.04 ± 1.07 24.90 ± 0.28 17823.95 ± 157.21 11.39 ± 0.38 356.32± 1.72 25.47 ± 0.98 74.65 ± 0.21

SWFIr1 - 13.15 ± 0.81 24.12 ± 0.09 18358.00± 207.70 14.17 ± 1.00 397.63± 2.97 29.13 ± 0.35 70.10 ± 0.32

SWFIIr1 - 4.23 ± 1.46 14.75 ± 0.14 15677.80 ± 143.44 7.01 ± 1.42 207.91± 1.44 18.92 ± 0.53 55.83 ± 0.07

NWFIr1 - 10.57 ±1.22 28.13 ± 0.09 20536.00 ± 44.11 12.7 1± 0.38 457.66 ± 2.02 34.51 ± 0.20 76.84 ± 0.22

NWFIIr1 4.63 ± 0.066 9.86 ± 0.70 28.41 ± 0.40 19999.50 ± 59.70 11.83 ± 0.65 412.53 ± 2.10 32.60 ± 0.40 68.66 ± 0.16

NEFIr2 16.90 ± 0.19 12.91 ± 0.40 13.63 ± 0.10 16375.15 ± 47.44 8.54 ± 0.65 298.79 ± 0.23 17.72 ± 0.55 83.86 ± 0.39

NEFIIr2 5.77 ± 0.22 14.10 ± 1.22 21.18 ± 0.26 19140.65 ± 99.46 14.24 ± 0.38 321.87 ± 0.86 23.52 ± 0.00 87.55 ± 0.23

SEFIr2 - 10.80 ± 0.41 11.61 ± 0.37 15227.30 ± 129.48 3.07± 0.381 214.19 ± 2.05 15.67 ± 0.20 48.07 ± 0.52

SEFIIr2 - 22.50 ± 0.70 21.21 ± 0.37 17807.33 ± 47.85 8.326± 0.37 341.23 ± 3.14 23.38 ± 0.69 66.98 ± 0.42

SWFIr2 6.58 ± 0.11 19.70 ± 1.22 25.37± 0.29 17977.00 ± 37.97 12.05± 0.37 328.50 ± 1.05 26.05 ± 0.74 82.22 ± 0.44

SWFIIr2 0.54 ± 0.06 20.00 ± 0.41 31.08 ± 0.29 17034.82 ± 57.03 14.46 ± 0.65 231.46 ± 2.26 21.64 ± 1.00 69.07 ± 0.42

NWFIr2 10.89 ± 0.31 16.90 ± 0.70 21.86 ± 0.21 19054.93 ± 95.06 10.51 ± 0.65 401.15± 0.60 26.05 ± 0.48 63.21 ± 0.44

NWFIIr2 - 14.60 ± 1.08 25.40 ± 0.19 18584.23 ± 64.41 14.02 ± 0.38 539.70 ± 3.19 29.08 ± 0.27 62.31 ± 0.68

@


Results

Table 12. Content of heavy metals (mg/kg) of soil samples collected from wheat cultivated agricultural fields of Amritsar, Punjab

(India).

Heavy metal content (mg/ kg) (Mean ± S.E)

Sample code @

Cadmium Chromium Copper Iron Lead Manganese Nickel Zinc

NEFIw1 0.97 ± 0.16 8.92 ± 1.46 15.77 ± 0.05 18005.07 ±101.55 14.02± 1.36 310.52± 2.10 21.46 ± 0.38 60.74 ± 0.47

NEFIIw1 11.21 ± 0.17 10.33 ± 1.07 23.196± 0.09 19639.33 ±141.57 13.80± 0.65 345.75± 1.92 29.21 ± 0.76 108.99 ± 0.43

SEFIw1 4.48 ± 0.27 14.79 ± 1.41 13.26 ± 0.05 15024.33 ±126.60 8.10± 0.75 245.72± 0.95 16.16 ± 0.70 56.96 ± 0.09

SEFIIw1 9.43 ± 0.00 18.78 ± 1.07 22.73 ± 0.15 18010.30 ± 86.74 10.51± 1.13 351.31± 3.17 24.80 ± 0.30 69.89 ± 0.19

SWFIw1 7.92 ± 0.03 17.145±1.07 26.67 ± 0.14 19457.35 ±102.99 19.71± 0.65 380.44± 0.37 29.13 ± 0.13 87.71 ± 0.55

SWFIIw1 2.29 ± 0.96 17.14 ± 1.07 26.33 ± 0.28 15682.25 ±103.68 17.96± 1.00 208.44± 1.24 19.55 ± 0.93 61.02± 0.12

NWFIw1 4.73 ± 0.15 11.50 ± 1.62 23.25± 0.23 19807.30 ± 33.75 16.87± 1.00 418.04± 2.23 26.63 ± 0.67 63.01 ± 0.11

NWFIIw1 6.90 ± 0.08 14.79 ± 1.41 28.91 ± 0.21 19513.35 ±181.47 19.72± 0.65 418.89± 1.54 31.31 ± 0.33 71.17 ± 0.17

NEFIw2 0.07 ± 0.04 8.22 ± 1.77 14.31 ±0.19 15845.85 ± 87.36 12.05 ± 0.75 299.54 ± 2.98 16.39 ± 0.33 66.43 ± 0.30

NEFIIw2 - 17.10 ± 1.47 22.01 ± 0.28 18996.28±187.90 17.96 ± 0.75 352.00 ± 1.55 24.49 ± 0.42 105.00 ± 0.26

SEFIw2 - 10.80 ±0.41 9.62 ± 0.32 13270.18 ± 128.02 11.83 ± 1.13 192.88 ± 2.30 10.29 ± 0.13 44.55± 0.17

SEFIIw2 - 14.60 ± 1.08 19.65 ± 0.56 13345.43 ± 13.85 12.71 ± 0.38 327.98 ± 1.61 15.27 ± 0.07 62.11 ± 0.25

SWFIw2 5.18 ± 0.10 24.80 ± 0.71 24.03 ± 0.15 17081.18 ± 75.29 18.80 ± 0.62 295.29 ± 1.95 28.59± 0.54 80.28 ± 0.16

SWFIIw2 12.9 ± 0.15 32.90 ± 2.28 31.18 ± 0.15 15486.28 ± 79.71 23.18 ± 0.62 223.31 ± 1.90 26.79 ± 0.57 71.00 ± 0.08

NWFIw2 - 16.90 ± 1.87 20.18 ± 0.05 16639.37 ± 322.32 11.17±0.65 365.08 ± 0.95 22.04 ± 0.70 67.91 ± 0.41

NWFIIw2 21.26 ± 0.27 18.30 ± 0.70 26.92 ± 0.18 18709.33 ± 71.04 13.36 ± 0.37 383.94 ± 1.93 27.08 ± 0.46 66.02 ± 0.35

@


Results

Table 13. Genotoxic potential of different agricultural soil samples of Amritsar under rice cultivation in Allium cepa root

chromosomal aberration assay (in situ studies).

No. of cells with aberrations

Physiological aberrations (PA) Clastogenic aberrations (CA) TABC

Sample

code@

No of dividing

cells

Cm

Da

Lg

St

Vg

Aa

Am

Total PA

Bg

Bk

Rc

Total CA PA+CA

No. % No. % No. %

Control 916 5 27 - 1 0 0 0 33 3.60 5 - - 5 0.54 38 4.14

NEFIr1 932 - 77 - 8 2 7 11 105 11.26 14 - - 14 1.50 119 12.76

NEFIIr1 933 1 37 - 13 - 2 2 67 7.181 4 - 1 5 0.53 72 7.71

SEFIr1 911 17 52 1 6 1 1 2 80 8.781 5 - - 5 0.54 85 9.33

SEFIIr1 937 3 71 - 15 8 14 11 130 13.87 4 1 - 5 0.53 135 14.40

SWFIr1 910 1 68 1 6 1 1 2 80 8.791 5 - - 5 0.54 85 9.34

SWFIIr1 916 14 68 3 15 8 32 57 197 21.50 13 4 4 21 2.29 218 23.79

NWFIr1 922 2 57 - 22 3 14 9 107 11.60 11 - 1 12 1.30 119 12.90

NWFIIr1 921 - 48 2 9 1 6 5 71 7.70 4 - - 4 0.43 75 8.14

NEFIr2 916 25 62 1 6 3 - - 97 10.58 6 - - 6 0.65 103 11.24

NEFIIr2 914 18 61 - 18 3 - - 100 10.94 22 - 1 23 2.51 123 13.45

SEFIr2 925 20 71 1 4 - - - 96 10.37 22 - - 22 2.37 118 12.75

SEFIIr2 920 20 82 3 11 5 - - 121 13.15 30 1 2 33 3.58 154 16.73

SWFIr2 923 13 88 1 13 1 - - 116 12.56 19 - 2 21 2.27 137 14.84

SWFIIr2 917 14 103 3 20 2 - - 142 15.48 34 5 1 40 4.36 182 19.84

NWFIr2 918 23 63 - 4 3 1 - 94 10.23 18 - - 18 1.96 112 12.20

NWFIIr2 918 13 41 1 8 1 - - 64 6.97 19 - - 19 2.06 83 9.04

@ Described in chapter “Materials and Methods” (Table 4 )

Control (Sand); TABC-Total aberrant cells; Cm-C-mitosis; Da-Delayed anaphases Lg-Laggards; St-Stickiness; Vg-Vagrants; Aa-Abnormal

anaphases; Am-Abnormal metaphases; Bg-Chromatin bridges; Bk-Chromosomal breaks; Rc-Ring chromosomes

All the data were significantly different at p≤0.05 with respect to control

Results

Table 13.1. One way ANOVA summary for effects of different rice cultivated soil

samples of r1 sampling on chromosomal aberrations in root tip cells of

A. cepa.

Source of Variation SS df MS F-ratio

Between groups 2351.111 8 293.8889 164.7405*

Within groups 128.4444 72 1.783951

Total 2479.556 80

*p≤0.05

HSD= 1.903


samples of r2 sampling on chromosomal aberrations in root tip cells of

A. cepa.


Between groups 1516.444 8 189.5556 374.4878*

Within groups 36.44444 72 0.506173

Total 1552.889 80

*p≤0.05

HSD=1.013

Results

Table 14. Genotoxic potential of different agricultural soil samples of Amritsar under wheat cultivation in Allium cepa root

chromosomal aberration assay (in situ studies).



Sample

code@

No of dividing

cells

Cm Da Lg St Vg Aa Am Total PA Bg Bk Rc Total CA PA+CA

No. % No. % No. %

Control 916 5 27 - 1 0 0 0 33 3.60 5 - - 5 0.54 38 4.14

NEFIw1 922 6 49 1 20 2 - 3 81 8.78 20 - 1 21 2.27 102 11.06

NEFIIw1 921 4 48 - 30 3 3 - 88 9.55 11 - - 11 1.19 99 10.74

SEFIw1 918 43 45 1 24 2 6 - 121 13.18 18 - - 18 1.96 139 15.14

SEFIIw1 923 18 45 - 23 2 4 2 94 10.18 20 - - 20 2.16 114 12.35

SWFIw1 918 24 30 - 4 - - - 63 6.86 5 - - 5 0.54 68 7.40

SWFIIw1 915 11 123 - 16 6 - - 156 17.04 24 5 - 29 3.16 185 20.21

NWFIw1 922 2 68 - 8 4 1 - 83 9.00 13 - - 13 1.40 96 10.41

NWFIIw1 919 - 59 - 14 2 - - 75 8.16 11 1 - 12 1.30 87 9.46

NEFIw2 923 9 54 - 8 - - - 71 7.69 18 - - 18 1.95 89 9.64

NEFIIw2 915 14 39 3 - - - - 56 6.12 15 - - 15 1.63 71 7.75

SEFIw2 913 8 51 - 3 1 - - 63 6.90 10 - 1 11 1.20 74 8.10

SEFIIw2 923 5 82 - 7 3 - - 97 10.50 17 - - 17 1.84 114 12.35

SWFIw2 923 10 73 1 7 - - - 91 9.85 23 1 - 24 0.26 115 12.45

SWFIIw2 920 23 78 3 15 4 - - 123 13.36 20 - - 20 2.17 143 15.54

NWFIw2 922 3 78 - 14 2 - - 97 10.52 30 - - 30 3.25 127 13.77

NWFIIw2 921 7 71 1 6 - - - 85 9.22 24 - 1 25 2.714 110 11.94

@ Described in chapter “Materials and Methods” (Table 4 )

Control (Sand); TABC-Total aberrant cells; Cm-C-mitosis; Da-Delayed anaphases Lg-Laggards; St-Stickiness; Vg-Vagrants; Aa-Abnormal

anaphases; Am-Abnormal metaphases; Bg-Chromatin bridges; Bk-Chromosomal breaks; Rc-Ring chromosomes

All the data were significantly different at p≤0.05 with respect to control

Results

Table 14.1. One way ANOVA summary for effects of different wheat cultivated

soil samples of w1 sampling on chromosomal aberrations in root tip

cells of A. cepa.


Between groups 1432.247 8 179.0309 117.8984

Within groups 109.3333 72 1.518519

Total 1541.58 80

*p≤0.05

HSD= 1.717


soil samples of w2 sampling on chromosomal aberrations in root tip

cells of A. cepa.


Between groups 939.0617 8 117.3827 260.4932

Within groups 32.44444 72 0.450617

Total 971.5062 80

*p≤0.05

HSD=0.956

Results

Table 15. Genotoxic potential of different concentrations of two agricultural soil sample (NEFIr1 and NEFIIr1) extracts in

Allium cepa root chromosomal aberration assay (Root dip treatment).

Sample

Code@

Conc.

(%)



TNDC Cm Da Lg St Vg Aa Am Total PA Bg Bk Rc Total CA PA+CA

No. % No. % No. %

NC 924 4 25 - 1 - - - 30 3.24 2 - - 2 0.21 32 3.46

NEFIr1

20 919 20 59 1 24 - 7 3 114 12.40 4 - - 4 0.43 118 12.84

40 919 2 68 - 45 5 14 9 143 15.55 11 3 - 14 1.52 157 17.08

60 917 14 79 6 40 5 14 9 147 16.03 16 - - 16 1.74 163 17.77

80 924 10 87 12 40 1 1 - 151 16.34 13 2 - 15 1.62 166 17.96

100 919 16 89 - 37 1 4 4 151 16.43 24 1 1 26 2.82 177 19.26

NEFIIr1 20 910 5 36 2 5 1 - - 49 5.38 5 - - 5 0.54 54 5.93

40 920 10 59 2 7 1 - - 79 8.58 1 - - 1 0.10 80 8.69

60 919 10 68 4 8 - - - 90 9.57 7 2 1 10 1.08 100 10.88

80 917 12 85 3 15 1 - - 116 12.64 16 3 - 19 2.07 135 14.72

100 921 12 96 2 18 - - - 128 13.89 15 4 2 21 2.28 149 16.17

NC-Negative control (Distilled water); TNDC-Total no. of dividing cells; Cm-C-mitosis; Da-Delayed anaphases; Lg-Laggards; St-Stickiness; Vg- Vagrants; Aa-

Abnormal anaphases; Am-Abnormal metaphases; Bg-Chromatin Bridges; Bk-Chromosomal Breaks; Rc-Ring chromosomes; TABC-Total Aberrant Cells (PA+CA). @

Described in “Materials and Methods” (Table 4)

Results

Table 16. Genotoxic potential of different concentrations of two agricultural soil sample (NEFIr2 and NEFIIr2) extracts in


Sample

Code@

Conc.

(%)




No. % No. % No. %

NC 924 4 25 - 1 - - - 30 3.24 2 - - 2 0.216 32 3.46

NEFIr2

20 918 31 46 3 5 3 - - 88 9.58 5 - - 5 0.544 93 10.13

40 911 10 73 2 14 3 - - 102 11.19 22 - - 22 2.410 124 13.61

60 919 14 88 1 16 2 - - 121 13.16 19 - - 19 2.067 140 15.23

80 921 20 94 1 22 5 - - 142 15.41 15 - - 15 1.628 157 17.04

100 925 45 75 - 27 11 - - 158 17.08 13 - - 13 1.405 171 18.56

NEFIIr2 20 921 2 40 1 4 - - - 47 5.10 3 - - 3 0.325 50 5.42

40 918 10 51 5 10 3 - - 79 8.60 7 - - 7 0.762 86 9.36

60 919 24 63 5 8 4 - - 104 11.31 7 - - 7 0.761 111 12.07

80 921 22 78 5 17 6 - - 128 13.89 15 - - 15 1.628 143 15.52

100 920 23 92 7 18 5 - - 145 15.76 15 1 1 17 1.847 162 17.60

NC-Negative control (Distilled water); TNDC-Total no. of dividing cells; Cm-C-mitosis; Da-Delayed anaphases; Lg-Laggards; St- Stickiness; Vg-Vagrants;

Aa-Abnormal anaphases; Am-Abnormal metaphases; Bg-Chromatin Bridges; Bk-Chromosomal Breaks; Rc-Ring chromosomes; TABC-Total Aberrant Cells

(PA+CA). @


Results

Table 17. Genotoxic potential of different concentrations of two agricultural soil sample (SEFIr1 and SEFIIr1) extracts in


Sample

Code@

Conc.

(%)




No. % No. % No. %

NC 924 4 25 - 1 - - - 30 3.24 2 - - 2 0.21 32 3.46

SEFIr1

20 920 29 48 1 13 - - 1 92 10.00 - 3 - 3 0.03 95 10.32

40 916 21 52 4 39 1 3 4 124 13.53 - 6 - 6 0.65 130 14.19

60 915 25 41 2 70 3 6 1 148 16.17 - 10 - 10 1.09 158 17.11

80 923 27 56 8 54 4 8 7 164 17.76 4 10 - 14 1.51 178 19.28

100 923 10 62 10 70 8 10 16 175 18.95 - 12 - 12 1.30 187 20.26

SEFIIr1 20 918 32 46 - 14 - - - 92 10.02 - 13 - 13 1.41 105 11.43

40 920 37 53 1 19 1 - - 111 12.06 - 15 - 15 1.63 126 13.69

60 919 42 71 1 21 - - - 135 14.68 1 23 - 24 2.61 159 17.30

80 912 39 86 - 32 2 - - 159 17.43 3 24 1 28 3.07 183 20.06

100 918 46 110 1 34 1 - - 192 20.91 2 26 1 29 3.15 221 24.07

NC- Negative control (Distilled water); TNDC-Total no. of dividing cells; Cm-C-mitosis; Da-s; Lg-Laggards; St-Stickiness; Vg-Vagrants; Aa-Abnormal

anaphases; Am-Abnormal metaphases; Bg-Chromatin Bridges; Bk-Chromosomal Breaks; Rc-Ring chromosomes; TABC-Total Aberrant Cells (PA+CA). @


Results

Table 18. Genotoxic potential of different concentrations of two agricultural soil sample (SEFIr2 and SEFIIr2) extracts in


Sample

Code@

Conc.

(%)




No. % No. % No. %

NC 924 4 25 - 1 - - - 30 3.24 2 - - 2 0.21 32 3.46

SEFIr2

20 921 20 40 - 9 - - 1 70 7.60 - 4 - 4 0.43 74 8.03

40 918 24 48 1 12 - - - 85 9.25 - 7 - 7 0.76 92 10.02

60 914 32 49 - 27 - - - 108 17.81 - 10 - 10 1.09 118 12.91

80 919 34 56 1 36 2 - - 129 14.63 - 12 - 12 1.30 141 15.34

100 917 38 61 - 51 1 - - 151 16.46 - 17 - 17 1.85 168 18.32

SEFIIr2 20 918 33 45 - 13 - - - 91 9.91 - 11 - 11 1.19 102 11.11

40 919 38 52 - 17 - - - 107 11.64 - 13 - 13 1.41 120 13.07

60 920 41 69 - 11 - - - 121 13.15 2 19 - 21 2.28 142 15.34

80 921 41 76 1 14 1 - - 133 14.44 1 23 - 24 2.60 157 17.04

100 919 43 109 1 19 2 - - 174 18.93 - 30 - 30 3.26 204 22.19

NC-Negative control (Distilled water); TNDC-Total no. of dividing cells; Cm-C-mitosis; Da-Delayed anaphases; Lg-Laggards; St-Stickiness; Vg-Vagrants;


(PA+CA). @


Results

Table 19. Genotoxic potential of different concentrations of two agricultural soil sample (SWFIr1 and SWFIIr1) extracts in


Sample

Code@

Conc.

(%)




No. % No. % No. %

NC 924 4 25 - 1 - - - 30 3.24 2 - - 2 0.21 32 3.46

SWFIr1

20 926 43 46 4 5 1 - - 99 10.69 - 8 - 8 0.86 107 11.55

40 923 43 59 - 8 - - - 114 12.35 - 10 - 10 1.08 124 13.43

60 919 42 48 4 18 2 - - 114 12.40 - 16 2 18 1.74 130 14.68

80 920 42 56 1 14 4 - - 117 12.71 3 18 - 13 2.28 138 15.00

100 918 31 75 4 21 3 - - 134 14.59 2 19 - 21 2.87 155 16.88

SWFIIr1 20 914 26 64 3 18 2 - - 113 12.36 - 13 - 13 1.42 126 13.78

40 919 31 88 2 21 1 - - 143 15.56 2 16 1 19 2.06 162 17.62

60 913 44 96 3 34 2 - - 179 19.60 2 19 - 19 2.30 200 21.90

80 911 46 113 2 41 3 - - 205 22.50 - 25 - 25 2.74 230 25.24

100 924 52 120 3 40 2 - - 217 23.48 4 38 1 43 4.65 255 25.95

NC- Negative control (Distilled water); TNDC-Total no. of dividing cells; Cm-C-mitosis; Da- Delayed anaphases; Lg-Laggards; St-Stickiness; Vg-Vagrants;


(PA+CA). @


Results

Table 20. Genotoxic potential of different concentrations of two agricultural soil sample (SWFIr2 and SWFIIr2) extracts in


Sample

Code@

Conc.

(%)




No. % No. % No. %

NC 924 4 25 - 1 - - - 30 3.24 2 - - 2 0.216 32 3.46

SWFIr2

20 913 57 47 1 3 1 - - 109 11.06 - 3 - 3 0.32 112 12.26

40 914 59 60 - 3 - - - 122 13.34 - 5 - 5 0.54 127 13.89

60 919 58 72 - 2 - - - 132 14.36 2 7 - 9 0.97 141 15.34

80 929 61 76 1 4 - - - 143 15.39 2 12 1 15 1.61 158 17.00

100 928 64 89 - 7 1 - - 160 17.24 1 15 - 16 1.72 176 18.96

SWFIIr2 20 913 28 62 2 19 - - - 114 12.48 2 14 - 16 1.75 130 14.23

40 913 33 84 3 24 6 - - 150 16.42 4 25 1 30 3.28 180 19.35

60 919 45 99 1 24 6 - - 175 19.04 2 30 1 32 3.48 207 22.52

80 911 43 98 3 34 3 - - 184 20.19 4 32 2 38 4.17 222 24.26

100 914 46 129 2 21 6 - - 202 22.10 3 36 1 40 4.37 242 26.47

NC- Negative control (Distilled water); TNDC-Total no. of dividing cells; Cm-C-mitosis; Da-Delayed anaphases; Lg- Laggards; St- Stickiness; Vg-Vagrants;


(PA+CA). @


Results

Table 21. Genotoxic potential of different concentrations of two agricultural soil sample (NWFIr1 and NWFIIr1) extracts in


Sample

Code@

Conc.

(%)




No. % No. % No. %

NC 924 4 25 - 1 - - - 30 3.24 2 - - 2 0.21 32 3.46

NWFIr1

20 919 10 40 - 5 - - - 45 4.89 5 - - 5 0.54 50 5.44

40 910 9 49 1 4 - - - 63 6.92 3 - - 3 0.32 66 7.25

60 918 10 56 2 4 - - - 72 7.84 6 1 - 7 0.76 79 8.60

80 921 13 65 4 8 - - - 90 9.77 4 - - 4 0.43 94 10.20

100 919 21 79 5 6 - - - 111 12.07 4 - - 4 0.43 115 12.51

NWFIIr1 20 925 18 44 1 5 1 - - 68 6.48 11 - - 11 1.18 79 8.54

40 921 16 79 1 12 1 - - 109 11.77 26 - - 26 2.82 135 14.65

60 926 30 98 3 14 2 - - 147 15.87 29 2 - 31 3.34 178 19.22

80 922 35 95 2 21 2 1 - 156 16.91 31 2 - 34 3.68 190 20.60

100 925 36 108 2 24 2 2 - 174 18.81 34 2 - 36 3.88 210 22.70

NC- Negative control (Distilled water); TNDC-Total no. of dividing cells; Cm-C-mitosis; Da-Delayed anaphases; Lg-Laggards; St-Stickiness; Vg-Vagrants;


(PA+CA). @


Results

Table 22. Genotoxic potential of different concentrations of two agricultural soil sample (NWFIr1 and NWFIIr1) extracts in


Sample

Code@

Conc.

(%)




No. % No. % No. %

NC 924 4 25 - 1 - - - 30 3.24 2 - - 2 0.21 32 3.46

NWFIr1

20 921 10 40 - 8 - - - 58 6.29 - - - - - 58 6.29

40 918 7 51 2 3 1 - - 64 6.97 4 - 1 5 0.54 69 7.51

60 917 13 64 - 9 - - - 86 9.37 - - - - - 86 9.37

80 924 23 69 1 5 - - - 98 10.60 3 - - 3 0.32 101 10.93

100 919 31 74 2 12 1 - - 120 13.05 4 - - 4 0.43 124 13.49

NWFIIr1 20 916 18 49 - 10 3 - - 80 8.73 5 1 - 6 0.65 86 9.38

40 915 19 58 3 29 5 - - 124 13.55 14 3 - 17 1.85 141 15.40

60 924 32 85 - 31 4 - - 152 16.45 14 - 14 1.51 166 17.96

80 918 34 99 2 38 1 - - 174 18.95 9 - 9 0.98 183 19.93

100 927 34 115 3 39 2 - - 193 20.81 17 1 1 19 2.04 212 22.86



(PA+CA). @


Results

Table 23. Genotoxic potential of different concentrations of two agricultural soil sample (NEFIw1 and NEFIIw1) extracts in


Sample

Code@

Conc.

(%)




No. % No. % No. %

NC 924 4 25 - 1 - - - 30 3.24 2 - - 2 0.21 32

NEFIw1

20 925 19 70 - 13 7 - - 109 11.78 12 - - 12 1.29 121 13.08

40 919 34 76 6 15 4 - - 135 14.68 13 2 - 15 1.63 150 16.32

60 919 48 66 12 25 5 - 1 157 17.08 22 2 1 25 2.72 182 19.80

80 929 26 91 - 40 9 - - 166 17.86 24 2 1 27 2.90 193 20.77

100 921 30 74 3 52 13 - - 172 18.67 27 - - 27 2.93 199 21.49

NEFIIw1 20 920 9 42 - 9 1 - - 61 6.63 6 - - 6 0.65 61 6.63

40 921 10 51 1 6 2 - - 70 7.60 6 5 12 1.30 82 8.90

60 923 16 73 - 3 1 - - 112 12.31 33 - - 33 3.57 145 15.70

80 919 22 73 4 34 1 - - 135 14.68 38 2 - 40 4.13 173 18.82

100 927 24 97 - 38 2 - - 159 17.15 38 - - 38 4.31 199 21.46


Aa-Abnormal anaphases; Am-Abnormal metaphases; Bg-Chromatin Bridges; Bk- Chromosomal Breaks; Rc-Ring chromosomes; TABC-Total Aberrant Cells

(PA+CA). @


Results

Table 24. Genotoxic potential of different concentrations of two agricultural soil sample (NEFIw2 and NEFIIw2) extracts in


Sample

Code@

Conc.

(%)




No. % No. % No. %

NC 924 4 25 - 1 - - - 30 3.24 2 - - 2 0.21 32 3.46

NEFIw2

20 920 16 68 - 12 6 - - 102 11.08 10 10 1.08 112 12.17

40 918 19 72 1 14 6 - - 112 12.20 15 1 - 16 1.74 128 13.94

60 921 19 88 - 18 5 - - 135 14.65 14 2 - 16 1.73 151 16.39

80 924 24 96 1 20 4 - - 145 15.69 16 1 - 17 1.83 162 17.53

100 921 36 94 3 25 6 - - 164 17.80 18 3 - 21 2.28 185 20.08

NEFIIw2 20 921 7 32 - 7 2 - - 48 5.21 5 - - 5 0.54 53 5.75

40 923 13 46 1 9 2 - - 71 7.69 5 - - 5 0.54 76 8.24

60 919 14 52 - 8 2 - - 76 8.62 8 1 - 9 0.97 85 9.29

80 923 17 76 2 7 1 - - 103 11.15 12 - 1 13 1.40 116 12.57

100 925 24 88 2 23 3 - - 137 14.81 13 - 1 14 1.51 151 16.32

NC-Negative control (Distilled water); TNDC-Total no. of dividing cells; Cm-C-mitosis; Da-Delayed anaphases; Lg-Laggards; St-Stickiness; Vg- Vagrants;


(PA+CA). @


Results

Table 25. Genotoxic potential of different concentrations of two agricultural soil sample (SEFIw1 and SEFIIw1) extracts in


Sample

Code@

Conc.

(%)




No. % No. % No. %

NC 924 4 25 - 1 - - - 30 3.24 2 - - 2 0.21 32 3.46

SEFIw1

20 921 31 42 - 10 5 - - 88 9.55 - - - - - 88 9.55

40 922 43 56 - 18 3 - - 120 13.01 1 - - 1 0.10 121 13.12

60 919 38 56 2 31 6 - - 133 14.47 - 17 - 17 1.84 150 16.32

80 929 27 81 - 26 3 - - 137 14.74 - 15 - 15 1.61 152 16.36

100 917 43 73 3 39 2 - - 160 17.44 2 17 - 19 2.07 179 19.52

SEFIIw1 20 918 34 41 - 10 - - - 85 9.52 - 10 - 10 1.08 95 10.34

40 920 36 56 - 14 - - - 106 11.52 1 12 - 12 1.41 119 12.93

60 919 44 69 1 23 - - - 137 14.90 - 15 - 15 1.63 152 16.53

80 914 40 82 - 30 - - - 152 16.63 - 18 - 18 1.96 170 18.59

100 913 41 116 1 32 2 - - 192 21.02 1 21 - 22 2.40 214 23.43


Aa-Abnormal anaphases; Am-Abnormal metaphases; Bg-Chromatin Bridges; Bk-Chromosomal Breaks; Rc- Ring chromosomes; TABC- Total Aberrant Cells

(PA+CA). @


Results

Table 26. Genotoxic potential of different concentrations of two agricultural soil sample (SEFIw2 and SEFIIw2) extracts in Allium

cepa root chromosomal aberration assay (Root dip treatment).

Sample

Code@

Conc.

(%)




No. % No. % No. %

NC 924 4 25 - 1 - - - 30 3.24 2 - - 2 0.21 32 3.46

SEFIw2

20 919 30 40 2 6 - - - 78 8.48 - - - - - 78 8.48

40 920 38 49 1 4 3 - - 95 10.32 - - - - - 95 10.32

60 918 32 56 - 17 3 - - 108 11.76 - 14 - 14 1.52 122 13.28

80 914 30 72 - 21 2 - - 125 13.67 1 16 14 17 1.85 142 15.53

100 916 36 85 1 27 2 - - 151 16.48 - 21 16 21 2.29 172 18.77

SEFIIw2 20 917 36 40 - 13 - - - 89 9.70 - 9 - 9 0.98 98 10.68

40 915 43 58 - 19 - - - 120 13.11 - 13 - 13 1.42 133 14.53

60 923 44 81 - 28 2 - - 155 16.79 - 19 - 19 2.05 174 18.85

80 919 42 87 1 29 1 - - 160 17.41 - 24 - 24 2.61 184 20.02

100 918 46 121 1 34 2 - - 204 22.22 - 31 - 31 3.33 235 25.59



(PA+CA). @


Results

Table 27. Genotoxic potential of different concentrations of two agricultural soil sample (SWFIw1 and SWFIIw1) extracts in


Sample

Code@

Conc.

(%)




No. % No. % No. %

NC 924 4 25 - 1 - - - 30 3.24 2 - - 2 0.21 32 3.46

SWFIw1

20 914 32 50 3 4 2 - - 91 9.95 - 6 - 6 0.65 97 10.61

40 921 33 40 2 10 5 - - 90 9.77 1 11 - 12 1.30 102 11.074

60 917 31 58 2 4 1 - - 96 10.46 - 8 - 8 0.87 104 11.34

80 921 23 64 5 6 1 - - 99 10.74 1 8 - 9 0.97 108 11.72

100 917 20 73 2 10 2 - - 107 11.66 1 9 - 10 1.90 117 12.75

SWFIIw1 20 922 9 58 - 27 1 - - 95 10.30 - 26 - 26 2.81 121 13.12

40 920 16 60 - 29 2 - - 115 12.50 4 32 - 36 3.91 151 16.41

60 921 12 86 3 60 6 - - 163 17.69 4 29 - 33 3.58 196 21.28

80 913 13 89 7 44 4 - - 159 17.41 12 40 - 52 5.69 211 23.11

100 919 15 86 8 53 10 - - 172 18.71 7 70 - 77 8.37 249 27.09



(PA+CA). @


Results

Table 28. Genotoxic potential of different concentrations of two agricultural soil sample (SWFIw2 and SWFIIw2) extracts in


Sample

Code@

Conc.

(%)




No. % No. % No. %

NC 924 4 25 - 1 - - - 30 3.24 2 - - 2 0.21 32 3.46

SWFIw2

20 921 40 47 - 6 4 - - 93 10.09 - 9 - 9 0.97 102 11.01

40 923 42 53 - 5 - - - 100 10.83 1 11 - 12 1.30 112 12.13

60 919 45 68 - 2 - - - 119 12.94 - 12 - 12 0.30 127 13.81

80 918 50 75 - 2 - - - 127 13.83 1 14 - 15 1.63 139 15.14

100 923 56 87 - - - - - 143 15.49 1 18 - 19 2.05 162 17.55

SWFIIw2 20 916 14 59 2 24 1 - - 100 10.91 - 27 - 27 2.94 127 13.80

40 919 16 67 2 23 1 - - 109 11.86 - 29 - 29 3.15 138 15.01

60 914 19 73 - 30 - - - 122 13.34 - 36 1 37 4.04 159 17.39

80 921 24 88 3 36 1 - - 152 16.50 - 40 - 40 4.34 192 20.84

100 918 32 110 2 41 - - - 185 20.15 3 42 1 44 4.79 229 24.94

NC-Negative control (Distilled water); TNDC-Total no. of dividing cells; Cm-C-mitosis; Da-Delayed anaphase; Lg-Laggards; St-Stickiness; Vg-Vagrants;


(PA+CA). @


Results

Table 29. Genotoxic potential of different concentrations of two agricultural soil sample (NWFIw1 and NWFIIw1) extracts in


Sample

Code@

Conc.

(%)




No. % No. % No. %

NC 924 4 25 - 1 - - - 30 3.24 2 - - 2 0.21 32 3.46

NWFIw1

20 917 7 46 1 8 1 - - 63 6.87 3 - - 3 0.32 66 7.19

40 919 4 53 - 4 4 - - 65 7.07 7 1 - 8 0.87 73 7.94

60 918 7 64 4 12 2 - - 89 9.69 - - - - 0.43 89 9.64

80 921 14 76 3 13 - - - 106 11.50 4 - - 4 0.32 110 11.94

100 925 17 87 2 12 - - - 118 12.75 3 - - 3 0.32 121 13.08

NWFIIw1 20 919 16 51 - 6 - - - 73 7.94 2 - - 2 0.21 75 8.16

40 921 17 60 - 8 - - - 85 9.22 4 - - 4 0.43 89 9.66

60 918 33 88 1 12 - - - 134 14.59 5 3 8 0.87 142 15.46

80 925 38 95 5 20 - - - 158 17.08 12 4 2 18 1.94 176 19.02

100 923 41 119 5 26 - - - 191 20.69 12 - - 12 1.30 203 21.99

NC-Negative control (Distilled water); TNDC-Total no. of dividing cells; Cm-C-mitosis; Da-Delayed anaphase; Lg-Laggards; St-Stickiness; Vg-Vagrants;


(PA+CA). @


Results

Table 30. Genotoxic potential of different concentrations of two agricultural soil sample (NWFIw2 and NWFIIw2) extracts in


Sample

Code@

Conc.

(%)




No. % No. % No. %

NC 924 4 25 - 1 - - - 30 3.24 2 - - 2 0.21 32 3.46

NWFIw2

20 919 5 42 - 5 4 - 1 57 6.20 3 - 3 0.32 60 6.52

40 917 10 46 4 8 3 3 - 74 8.06 3 1 - 4 0.43 78 8.50

60 921 17 53 3 12 1 2 - 88 9.55 5 - 5 0.54 93 10.09

80 924 21 67 1 10 - - - 99 10.71 8 2 - 10 1.08 109 11.79

100 921 21 85 - 4 - - - 110 11.94 14 - - 14 1.52 124 13.46

NWFIIw2 20 920 14 60 - 7 - - - 81 8.80 3 - - 3 0.32 84 9.13

40 919 18 76 - 8 - - - 102 11.09 3 - - 3 0.32 106 11.53

60 924 20 89 2 12 3 - - 126 13.63 18 - - 18 1.94 144 15.58

80 921 29 93 3 25 3 - - 153 16.61 26 3 - 29 3.14 182 19.76

100 920 31 120 4 20 3 - - 178 19.31 32 1 - 33 3.58 211 22.93



(PA+CA). @


Results

Table 31. Total Protein content and antioxidative enzymes activity in Allium cepa bulbs exposed to different agricultural soil

samples of Amritsar, Punjab (India) under rice cultivation.

Sample code@

Protein content

(mg/g FW)

Specific activity of antioxidative enzymes (mol UA mg/g fresh protein) (Mean ± S.E.)

APX CAT DHAR GR GST POD SOD

Control 0.99±0.01 0.36±0.01 0.005±0.000 0.12±0.01 0.51±0.00 0.06±0.00 0.019±0.001 37.82±0.03

NEFIr1 1.21±0.06 1.22±0.07* 0.008±0.000

* 0.19±0.01 0.23±0.01

* 0.13±0.00

* 0.010±0.001 43.42±1.22

NEFIIr1 1.53±0.08* 1.40±0.11

* 0.005±0.000 0.18±0.00 0.42±0.02

* 0.11±0.01

* 0.012±0.001 60.94±2.78

*

SEFIr1 1.89±0.11* 0.46±0.03

* 0.006±0.000 0.19±0.00 0.13±0.02

* 0.10±0.00

* 0.011±0.002 44.38±0.70

SEFIIr1 2.09±0.04* 0.60±0.02

* 0.007±0.000 0.20±0.01 0.13±0.00

* 0.11±0.00

* 0.012±0.004 97.91±4.09

*

SWFIr1 1.47±0.01* 0.42±0.03

* 0.007±0.000 0.31±0.01 0.10±0.01

* 0.17±0.00

* 0.031±0.002

* 54.27±3.98

*

SWFIIr1 2.49±0.12* 1.57±0.20

* 0.007±0.000 0.32±0.01 0.03±0.00

* 0.19±0.00

* 0.022±0.003 93.57±2.95

*

NWFIr1 1.08±0.02 0.94±0.06* 0.012±0.001

* 0.28±0.01 0.38±0.02

* 0.16±0.00

* 0.012±0.002 86.33±3.48

*

NWFIIr1 1.54±0.05* 0.54±0.02

* 0.009±0.000

* 0.26±0.00 0.09±0.00

* 0.18±0.01

* 0.007±0.001 46.04±5.66

Control 0.80±0.09 0.44±0.08 0.005±0.000 0.14±0.03 0.66±0.08 0.09±0.01 0.026±0.004 42.92±3.86

NEFIr2 1.62±0.10* 1.01±0.06

* 0.008±0.001 0.16±0.03 0.19±0.00

* 0.18±0.02

* 0.017±0.004 46.26±3.15

NEFIIr2 1.27±0.08* 1.46±0.06

* 0.007±0.000 0.30±0.03

* 0.44±0.01

* 0.15±0.00 0.024±0.002 56.60±2.14

SEFIr2 1.87±0.12* 0.51±0.00 0.005±0.001 0.22±0.02 0.21±0.03

* 0.14±0.01 0.016±0.003 43.48±3.93

SEFIIr2 1.96±0.08* 0.60±0.02 0.007±0.001 0.14±0.01 0.14±0.01

* 0.11±0.00 0.026±0.001 106.03±4.61

*

SWFIr2 1.28±0.11* 0.63±0.05 0.005±0.001 0.33±0.03

* 0.26±0.01

* 0.10±0.00 0.050±0.004

* 57.70±4.44

SWFIIr2 2.65±0.12* 0.91±0.06

* 0.006±0.000 0.20±0.01 0.07±0.01

* 0.22±0.01

* 0.026±0.002 93.69±4.57

*

NWFIr2 1.04±0.03 0.94±0.05* 0.007±0.001 0.30±0.01

* 0.45±0.01

* 0.21±0.03

* 0.021±0.002 72.44±3.23

*

NWFIIr2 1.48±0.08* 0.63±0.09 0.006±0.000 0.27±0.02

* 0.15±0.01

* 0.15±0.00 0.014±0.001 54.83±4.19

@

Described in chapter “Materials and Methods” (Table 4) * represents significance at p≤0.05 with respect to control

Results


samples of r1 sampling on protein content of Allium cepa.


Between groups 5.789318 8 0.723665 49.44916*

Within groups 0.263421 18 0.014635

Total 6.052739 26

*p≤0.05

HSD = 0.317


samples of r2 sampling on protein content of Allium cepa.


Between groups 7.407429 8 0.925929 31.96646*

Within groups 0.521381 18 0.028966

Total 7.928811 26

*p≤0.05

HSD = 0.446


samples of r1 sampling on APX activity of Allium cepa.


Between groups 5.1125 8 0.639062 28.7115*

Within groups 0.400645 18 0.022258

Total 5.513145 26

*p≤0.05

HSD = 0.391


samples of r2 sampling on APX activity of Allium cepa.


Between groups 2.453238 8 0.306655 27.38525*

Within groups 0.201561 18 0.011198

Total 2.654798 26

*p≤0.05

HSD = 0.277

Results


samples of r1 sampling on CAT activity of Allium cepa.


Between groups 0.000142 8 1.77E-05 8.917767*

Within groups 3.58E-05 18 1.99E-06

Total 0.000178 26

*p≤0.05

HSD = 0.003


samples of r2 sampling on CAT activity of Allium cepa.


Between groups 2.72E-05 8 3.4E-06 1.288196*


Total 7.46E-05 26

*p≤0.05

HSD = 0.004


samples of r1 sampling on DHAR activity of Allium cepa.


Between groups 5.788387 8 0.723548 49.48429*

Within groups 0.263192 18 0.014622

Total 6.051579 26

*p≤0.05

HSD = 0.317


samples of r2 sampling on DHAR activity of Allium cepa.


Between groups 0.129422 8 0.016178 9.160868

Within groups 0.031787 18 0.001766

Total 0.16121 26

*p≤0.05

HSD = 0.112

Results


samples of r1 sampling on GR activity of Allium cepa.


Between groups 0.737178 8 0.092147 124.5296*

Within groups 0.013319 18 0.00074

Total 0.750497 26

*p≤0.05

HSD =0.071


samples of r2 sampling on GR activity of Allium cepa.


Between groups 0.71235 8 0.089044 54.72992*

Within groups 0.029285 18 0.001627

Total 0.741635 26

*p≤0.05

HSD = 0.105


samples of r1 sampling on GST activity of Allium cepa.


Between groups 0.08997 8 0.011246 77.30985*

Within groups 0.002618 18 0.000145

Total 0.092588 26

*p≤0.05

HSD = 0.031


samples of r1 sampling on GST activity of Allium cepa.


Between groups 0.16812 8 0.021015 30.11158*

Within groups 0.012562 18 0.000698

Total 0.180682 26

*p≤0.05

HSD = 0.069

Results


samples of r1 sampling on POD activity of Allium cepa.


Between groups 0.001284 8 0.000161 10.38986*

Within groups 0.000278 18 1.55E-05

Total 0.001563 26

*p≤0.05

HSD = 0.010


samples of r2 sampling on POD activity of Allium cepa.


Between groups 0.002757 8 0.000345 13.6387*

Within groups 0.000455 18 2.53E-05

Total 0.003212 26

*p≤0.05

HSD = 0.013


samples of r1 sampling on SOD activity of Allium cepa.


Between groups 13287.57 8 1660.946 52.06036*

Within groups 574.2762 18 31.90423

Total 13861.84 26

*p≤0.05

HSD = 14.819


samples of r2 sampling on SOD activity of Allium cepa.


Between groups 12232.04 8 1529.004 33.19083*

Within groups 829.2075 18 46.06708

Total 13061.24 26

*p≤0.05

HSD = 17.807

Results

Table 32. Total Protein content and antioxidative enzymes activity in Allium cepa bulbs exposed to different agricultural soil

samples of Amritsar, Punjab (India) under wheat cultivation.

Sample code@

Protein content

(mg/g FW)

Specific activity of antioxidative enzymes (mol UA mg/g fresh protein) (Mean ± S.E.)

APX CAT DHAR GR GST POD SOD

Control 0.79±0.02 0.38±0.04 0.005±0.001 0.16±0.00 0.66±0.00 0.09±0.00 0.15±0.002 42.92±3.86

NEFIw1 1.53±0.01* 0.76±0.03 0.007±0.001 0.21±0.02 0.19±0.00

* 0.12±0.01 0.06±0.001

* 46.26±3.15

NEFIIw1 1.33±0.01 0.70±0.10 0.007±0.002 0.18±0.01 0.41±0.00* 0.19±0.02 0.08±0.009

* 56.60±2.14

*

SEFIw1 1.73±0.02* 0.80±0.07 0.006±0.001 0.23±0.01 0.20±0.00

* 0.20±0.03 0.05±0.007

* 43.48±3.93

SEFIIw1 1.06±0.07 0.57±0.02 0.013±0.002* 0.21±0.02 0.30±0.00

* 0.31±0.03

* 0.09±0.008

* 106.03±4.61

*

SWFIw1 1.36±0.15 0.84±0.02* 0.005±0.001 0.18±0.03 0.20±0.00

* 0.32±0.04

* 0.03±0.007

* 57.71±4.44

*

SWFIIw1 2.10±0.05* 0.93±0.05

* 0.007±0.002 0.27±0.02

* 0.30±0.00

* 0.33±0.02

* 0.02±0.003

* 93.70±4.57

*

NWFIw1 1.44±0.18* 0.57±0.02 0.005±0.000 0.20±0.01 0.26±0.00

* 0.18±0.05 0.04±0.006

* 72.44±3.23

*

NWFIIw1 1.61±0.10* 0.36±0.05 0.006±0.000 0.18±0.02 0.17±0.00

* 0.11±0.01 0.04±0.005

* 54.83±4.19

Control 0.90±0.04 0.29±0.02 0.004±0.000 0.17±0.01 0.57±0.03 0.09±0.01 0.15±0.009 39.92±1.99

NEFIw2 1.12±0.09 1.15±0.09* 0.008±0.000 0.36±0.03

* 0.27±0.03

* 0.18±0.02

* 0.11±0.001 45.22±4.98

NEFIIw2 1.59±0.02* 0.58±0.03 0.007±0.002 0.27±0.01

* 0.36±0.02

* 0.15±0.00 0.06±0.002

* 52.16±3.34

SEFIw2 2.15±0.10* 0.54±0.02 0.006±0.000 0.19±0.02 0.14±0.02

* 0.14±0.01 0.05±0.002

* 50.41±1.77

SEFIIw2 2.32±0.04* 0.37±0.05 0.009±0.000

* 0.20±0.00 0.13±0.01

* 0.11±0.00 0.04±0.004

* 138.01±6.68

*

SWFIw2 1.57±0.03* 0.30±0.02 0.005±0.000 0.17±0.02 0.05±0.00

* 0.10±0.00 0.01±0.001

* 56.95±4.82

SWFIIw2 3.30±0.10* 0.37±0.04 0.007±0.001 0.20±0.01 0.03±0.00

* 0.22±0.01

* 0.01±0.001

* 105.55±6.93

*

NWFIw2 1.15±0.09 0.72±0.03* 0.010±0.002

* 0.38±0.03

* 0.37±0.03

* 0.21±0.03

* 0.06±0.003

* 86.58±6.58

*

NWFIIw2 1.28±0.07* 0.88±0.18

* 0.012±0.001

* 0.30±0.02

* 0.13±0.01

* 0.15±0.00 0.04±0.003

* 74.80±0.88

*

@

Described in chapter “Materials and Methods” (Table 4) * represents significance at p≤0.05 with respect to control

Results


soil samples of w1 sampling on protein content of Allium cepa.


Between groups 3.487789 8 0.435974 7.73987*

Within groups 1.013909 18 0.056328

Total 4.501699 26

*p≤0.05

HSD = 0.622


soil samples of w2 sampling on protein content of Allium cepa.


Between groups 13.89413 8 1.736767 100.3568*

Within groups 0.311507 18 0.017306

Total 14.20564 26

*p≤0.05

HSD = 0.345


soil samples of w1 sampling on APX activity of Allium cepa.


Between groups 0.970776 8 0.121347 4.503811*

Within groups 0.484977 18 0.026943

Total 1.455753 26

*p≤0.05

HSD = 0.430


soil samples of w2 sampling on APX activity of Allium cepa.


Between groups 2.062443 8 0.257805 15.43807*

Within groups 0.300588 18 0.016699

Total 2.363031 26

*p≤0.05

HSD = 0.339

Results


soil samples of w1 sampling on CAT activity of Allium cepa.


Between groups 0.000158 8 1.98E-05 4.086361*


Total 0.000245 26

*p≤0.05

HSD = 0.005


soil samples of w2 sampling on CAT activity of Allium cepa.


Between groups 0.000154 8 1.92E-05 4.978225*


Total 0.000223 26

*p≤0.05

HSD =0.005


soil samples of w1 sampling on DHAR activity of Allium cepa.


Between groups 5.655388 8 0.706923 11.8832*

Within groups 1.070808 18 0.059489

Total 6.726196 26

*p≤0.05

HSD = 0.639


soil samples of w2 sampling on DHAR activity of Allium cepa.


Between groups 0.164616 8 0.020577 13.23133*

Within groups 0.027993 18 0.001555

Total 0.192609 26

*p≤0.05

HSD = 0.103

Results


soil samples of w1 sampling on GR activity of Allium cepa.


Between groups 0.681984 8 0.085248 311.8777*

Within groups 0.00492 18 0.000273

Total 0.686904 26

*p≤0.05

HSD = 0.043


soil samples of w2 sampling on GR activity of Allium cepa.


Between groups 0.71235 8 0.089044 54.72992*

Within groups 0.029285 18 0.001627

Total 0.741635 26

*p≤0.05

HSD = 0.105


soil samples of w1 sampling on GST activity of Allium cepa.


Between groups 0.204858 8 0.025607 8.654375*

Within groups 0.05326 18 0.002959

Total 0.258118 26

*p≤0.05

HSD = 0.142


soil samples of w2 sampling on GST activity of Allium cepa.


Between groups 0.051454 8 0.006432 8.009797*

Within groups 0.014454 18 0.000803

Total 0.065907 26

*p≤0.05

HSD = 0.074

Results


soil samples of w1 sampling on POD activity of Allium cepa.


Between groups 0.002767 8 0.000346 14.23295*

Within groups 0.000437 18 2.43E-05

Total 0.003204 26

*p≤0.05

HSD = 0.012


soil samples of w2 sampling on POD activity of Allium cepa.


Between groups 0.853919 8 0.10674 164.3354*

Within groups 0.011691 18 0.00065

Total 0.86561 26

*p≤0.05

HSD = 0.066


soil samples of w1 sampling on SOD activity of Allium cepa.


Between groups 23401.56 8 2925.194 140.3764*

Within groups 375.0881 18 20.83823

Total 23776.64 26

*p≤0.05

HSD = 11.976


soil samples of w2 sampling on SOD activity of Allium cepa.


Between groups 25603.26 8 3200.408 26.15042*

Within groups 2202.922 18 122.3846

Total 27806.18 26

*p≤0.05

HSD = 29.025

Results

Fig. 2. Soil texture (%) of different agricultural soil samples@

under rice

cultivation of Amritsar, Punjab (India).

Fig. 3. Soil texture (%) of different agricultural soil samples@

under wheat


@ Described in chapter “Materials and Methods” (Table 4)

Results

Fig. 4. pH of different agricultural soil samples@

under rice cultivation of

Amritsar, Punjab (India).

Fig. 5. pH of different agricultural soil samples@

under wheat cultivation of

Amritsar, Punjab (India).


Results

Fig. 6. Alkalinity (mEq/100g) of different agricultural soil samples@

under rice


Fig. 7. Alkalinity (mEq/100g) of different agricultural soil samples@

under wheat



Results

Fig. 8. Calcium (mg/g) content of different agricultural soil samples@

under rice


Fig. 9. Calcium (mg/g) content of different agricultural soil samples@

under wheat



Results

Fig. 10. Magnesium (mg/g) content of different agricultural soil samples@

under

rice cultivation of Amritsar, Punjab (India).

Fig. 11. Magnesium (mg/g) content of different agricultural soil samples@

under

wheat cultivation of Amritsar, Punjab (India).


Results

Fig. 12. Nitrate (mg/g) content of different agricultural soil samples@

under rice


Fig. 13. Nitrate (mg/g) content of different agricultural soil samples@

under wheat



Results

Fig. 14. Phosphate (mg/g) content of different agricultural soil samples@

under rice


Fig. 15. Phosphates (mg/g) content of different agricultural soil samples@

under

wheat cultivation of Amritsar, Punjab (India).


Results

Fig. 16. Potassium (mg/g) content of different agricultural soil samples@

under

rice cultivation of Amritsar, Punjab (India).

Fig. 17. Potassium (mg/g) content of different agricultural soil samples@

under

wheat cultivation of Amritsar, Punjab (India). @


Results

Fig. 18. Sodium (mg/g) content of different agricultural soil samples@

under rice


Fig. 19. Sodium (mg/g) content of different agricultural soil samples@

under wheat



Results

Fig. 20. Content of heavy metals (mg/kg) of soil samples from rice cultivated

agricultural fields of Amritsar, Punjab (India).


0

2

4

6

8

10

12

14

16

18Cadmium

0

5

10

15

20

25Chromium

0

5

10

15

20

25

30

35Copper

0

5000

10000

15000

20000

25000

Sample code@

Iron

Heavy m

eta

l con

ten

t (m

g/k

g)

Results

Fig. 21. Content of heavy metals (mg/kg) of soil samples from rice cultivated



0

4

8

12

16

20

Lead

0

100

200

300

400

500

600 Manganese

0

5

10

15

20

25

30

35 Nickel

0

20

40

60

80

100

Sample code@

Zinc

Heavy m

eta

l con

ten

t (m

g/k

g)

Results

Fig. 22. Content of heavy metals (mg/kg) of soil samples from wheat cultivated



0

5

10

15

20

25 Cadmium

0

10

20

30

40Chromium

0

10

20

30

40 Copper

0

4000

8000

12000

16000

20000

Sample code@

Iron

Heavy m

eta

l con

ten

t (m

g/k

g)

Results

Fig. 23. Content of heavy metals (mg/kg) of soil samples from wheat cultivated



0

5

10

15

20

25Lead

0

100

200

300

400

500

Manganese

0

10

20

30

40Nickel

0

20

40

60

80

100

120

Sample code@

Zinc

Heavy m

eta

l con

ten

t (m

g/k

g)

Results

Fig. 24. Spectrum of chromosomal aberrations induced in root tip cells of Allium

cepa following in situ treatment with different agricultural soil samples of

Amritsar, Punjab (India) under rice cultivation.

Fig. 25. Spectrum of chromosomal aberrations induced in root tip cells of Allium

cepa following in situ treatment with different agricultural soil samples of

Amritsar, Punjab (India) under wheat cultivation.

Control (Sand); Cm-C-mitosis; Da-Delayed anaphases; Lg-Laggards; St-Stickiness;

Vg-Vagrants; Aa-Abnormal anaphases; Am-Abnormal metaphases; Bg-Chromatin

bridges; Bk-Chromosomal breaks; Rc-Ring chromosomes.


Sample Code@

Sample Code@

Results

Concentration of the extract

Fig. 26. Spectrum of different types of chromosomal aberrations induced in root

tip cells of Allium cepa following root dip treatment with different

concentrations of extracts of soil samples from rice fields of North East

zone (NEFIr1, NEFIIr1, NEFIr2, NEFIIr2) of Amritsar, Punjab (India).

Control (distilled water); Cm-C-mitosis; Da-Delayed anaphase; Lg-Laggards;

St-Stickiness; Vg-Vagrants; Aa-Abnormal anaphases; Am-Abnormal metaphases;

Bg-Chromatin bridges; Bk-Chromosomal breaks; Rc-Ring chromosomes.


%C

hrom

oso

mal

ab

errati

on

s

@

@

@

@

Results




concentrations of extracts of soil samples from rice fields of South East

zone (SEFIr1, SEFIIr1, SEFIr2, SEFIIr2) of Amritsar, Punjab (India).



Bg- Chromatin bridges; Bk-Chromosomal breaks; Rc-Ring chromosomes.


%C

hrom

oso

mal

ab

errati

on

s

@

@

@

@

Results




concentrations of extracts of soil samples from rice fields of South West

zone (SWFIr1, SWFIIr1, SEFIr2, SWFIIr2) of Amritsar, Punjab (India).





%C

hrom

oso

mal

ab

errati

on

s

@

@

@

@

Results


Fig. 29. Spectrum of different types of chromosomal aberrations induced in root tip

cells of Allium cepa following root dip treatment with different

concentrations of extracts of soil samples from rice fields of North West

zone (NWFIr1, NWFIIr1, NWFIr2, NWFIIr2) of Amritsar, Punjab (India).



Bg-Chromatin bridges; Bk-Chromosomal breaks; Rc-Ring chromosomes..


%C

hrom

oso

mal

ab

errati

on

s

@

@

@

@

Results


Fig. 30. Spectrum of different types of chromosomal aberrations induced in root tip

cells of Allium cepa following root dip treatment with different

concentrations of extracts of soil samples from wheat fields of North East

zone (NEFIw1, NEFIIw1, NEFIw2, NEFIIw2) of Amritsar, Punjab (India).



Bg-Chromatin bridges; Bk-Chromosomal breaks; Rc-Ring chromosomes..


%C

hrom

oso

mal

ab

errati

on

s

@

@

@

@

Results




concentrations of extracts of soil samples from wheat fields of South East

zone (SEFIw1, SEFIIw1, SEFIw2, SEFIIw2) of Amritsar, Punjab (India).





%C

hrom

oso

mal

ab

errati

on

s

@

@

@

@

Results




concentrations of extracts of soil samples from wheat fields of South West

zone (SWFIw1, SWFIIw1, SWFIw2, SWFIIw2) of Amritsar, Punjab

(India).





%C

hrom

oso

mal

ab

errati

on

s

@

@

@ @

Results




concentrations of extracts of soil samples from wheat fields of North West

zone (NWFIw2, NWFIIw2, NWFIw2, NWFIIw2) of Amritsar, Punjab

(India).





%C

hrom

oso

mal

ab

errati

on

s

@

@

@

@

Results

Fig. 34. Total Protein content and activities of antioxidative enzymes in Allium

cepa bulbs exposed to different agricultural soil samples of Amritsar,

Punjab (India) under rice cultivation.


0

0.5

1

1.5

2

2.5

3Protein

0

0.4

0.8

1.2

1.6

2APX

0

0.003

0.006

0.009

0.012

0.015CAT

0

0.1

0.2

0.3

0.4

Sample code@

DHAR

Prote

in c

on

ten

t

(mg/g

FW

) S

pec

ific

act

ivit

y (

mol

UA

/mg f

resh

pro

tein

)

Results

Fig. 35. Activities of antioxidative enzymes in Allium cepa bulbs exposed to

different agricultural soil samples of Amritsar, Punjab (India) under rice

cultivation.


0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

GR

0

0.05

0.1

0.15

0.2

0.25

0.3

GST

0

0.01

0.02

0.03

0.04

0.05

0.06POD

0

20

40

60

80

100

120

Samples codes@

SOD

Sp

ecif

ic a

ctiv

ity

(m

ol

UA

/mg f

resh

pro

tein

)

Results

Fig. 36. Total Protein content and activities of antioxidative enzymes in Allium

cepa bulbs exposed to different agricultural soil samples of Amritsar,

Punjab (India) under wheat cultivation.


0

0.5

1

1.5

2

2.5

3

3.5 Protein

0

0.2

0.4

0.6

0.8

1

1.2

1.4 APX

0

0.003

0.006

0.009

0.012

0.015CAT

0

0.1

0.2

0.3

0.4

0.5

Sample codes@

DHAR

Prote

in c

on

ten

t

(mg/g

FW

) S

pec

ific

act

ivit

y (

mol

UA

/mg f

resh

pro

tein

)

Results

Fig. 37. Activities of antioxidative enzymes in Allium cepa bulbs exposed to

different agricultural soil samples of Amritsar, Punjab (India) under

wheat cultivation.


0

0.2

0.4

0.6

0.8GR

0

0.1

0.2

0.3

0.4GST

0

0.04

0.08

0.12

0.16

0.2POD

0

40

80

120

160

Sample codes@

SOD

Sp

ecif

ic a

ctiv

ity

(m

ol

UA

/mg f

resh

pro

tein

)

Chapter-5

Discussion

Here are the opinions on which my facts are based

-Anon

Chapter-5

DISCUSSION

Soil is one of the most dynamic ecological factor that supports life on earth and

have always been vital to human beings and their health. Soils are increasingly

becoming sinks for a wide range of hazardous pollutants like pesticides, chemical

fertilizers, industrial wastes etc. from various anthropogenic activities like industrial,

domestic and intensive agricultural practices (White and Claxton, 2004; Ping et al.,

2011; Pohren et al. 2013). These omnipresent compounds get accumulated in the soil

matrix due to their persistent and hydrophobic nature. Among different pollutants,

contamination of soil by heavy metals have attained focus from scientific community

worldwide to assess their potential risks. Accumulation of heavy metals in agricultural

soils can lead their entry into human beings via various routes of exposure such as

consumption of contaminated drinking ground water and food crops; dermal contact

and through food chain. Therefore, it is necessary to acquire better understanding of soil

contamination in a comprehensive manner in order to evade potential risks linked with

contaminated agricultural soils and associated food chains. Hence, there is an urgent

need to have information regarding this important component of environment (Majer et

al., 2002; Motelay-Massei et al., 2004; Feng et al., 2013).

Apart from being toxic, the danger of heavy metals being mutagenic, genotoxic

as well as carcinogenic poses a problem of great concern. It is mandatory to evaluate

agricultural soils for their potential risks in biological systems as physico-chemical

analysis alone could not evaluate the quantitative risk of soil pollution (Bierkens et al.,

1998; Maxam et al., 2000; Monarco et al., 2002; White and Claxton, 2004; Visioli et

al., 2013). The genotoxicity assessment of the contaminated soils using well

established bioassays have been widely documented (Kwasniewska et al., 2012; Feng

et al., 2013; Rodriguez-Ruiz et al., 2014).

Among various bioassays, plant bioassays have gained a great attention due to

the fact that plants are readily available and it is easier to handle the plant material as

compared to animals. Chromosomal aberration assays using various plant systems

Discussion

108

including Allium cepa, Tradescantia and Vicia faba has been validated by International

Programme on Chemical safety (IPCS) under the auspices of World Health

Organization (WHO) and United Nations Environment Programme (UNEP) to

estimate genotoxicity of various compounds, wastewater samples, soil solutions and

agricultural soils contaminated with pesticides and heavy metals (Cabrera and

Rodriguez, 1999a,b; Chandra et al., 2005; Fernandes et al., 2007; Ferreira et al., 2011;

Masood and Malik, 2013; Souza et al., 2013). Among different higher plant bioassays,

Allium cepa root chromosomal aberration assay has been recommended as it is simple,

less expensive, requires least facilities, has large and less number of chromosomes,

chromosome morphology similar to that of mammals and has unique adaptation for in

situ studies. Apart from the genotoxic responses, biochemical changes/enzyme

alterations also play a significant role to indicate the magnitude of toxicity.

Enzymes of the detoxification machinery serve as important markers of

environmental pollution (Filho et al., 2001). The efficacy of antioxidant enzymes as

biomarkers of contaminants, especially that of heavy metals has been established by

several researchers (Ahmad et al. 2000; Geret et al. 2002, 2003; Nadgorska-Socha et

al., 2013). Several studies indicated that heavy metals caused oxidative stress by

intervening the activities of antioxidative enzymes (Fatima and Ahmed, 2005; Hu et

al., 2007; Tabrez and Ahmed, 2009a,b,c,d; Tang, 2012; Yu et al., 2013; Irfan et al.,

2014).


essential nutrients particularly in wheat and rice crop fields. The district Amritsar of

Punjab (India), an agricultural land, is under intensive cultivation of wheat, rice and

some vegetable crops. In order to have high yield, vast varieties of pesticides and

fertilizers, both organic and inorganic, are being used by the farmers which ultimately

result in soil pollution. Apart from this, the direct use of sewage sludge, industrial

wastes and waste water to agricultural land as source of plant nutrients aroused serious

concern as they are known to contain many toxic metals along with useful nutrient

elements. Keeping in view the alarming consequences of contamination of agricultural

soils of Amritsar, Punjab (India), the present study was carried out to evaluate the

genotoxic potential of different soil samples collected from rice and wheat fields

Discussion

109

employing Allium cepa root chromosomal aberration assay (AlRCAA) using in situ and

root dip modes of treatment.

Estimation of various physiochemical parameters viz, pH, alkanity, soil texture,

calcium, magnesium, nitrates, phosphates, sodium, potassium and some of the heavy

metals were also carried out. The study also involves estimation of total protein content

and analysis of responses of certain antioxidative enzymes viz. ascorbate peroxidase

(APX), catalase (CAT), dehydroascorbate reductase (DHAR), glutathione reductase

(GR), glutathione-S-transferase (GST), guaicol peroxidase (POD) and superoxide

dismutase (SOD), in A. cepa bulbs exposed to these soils. For convenience of study, the

results have been discussed under different headings: physico-chemical analysis of soil,

genotoxicity studies in Allium cepa root chromosomal aberration assay and biochemical

parameters

5.1. Physico-chemical analysis of soil

Analysis of physico-chemical characteristics included parameters like soil

texture, pH, alkalinity, contents of calcium, magnesium, nitrates, phosphates, sodium,

potassium and content of heavy metals (cadmium, chromium, copper, iron, lead,

manganese, nickel and zinc). Soil samples were collected from different agricultural

fields under rice cultivation during September, 2009 (r1) and September 2010 (r2); and

under wheat cultivation during March 2010 (w1) and March, 2011(w2).

Soil texture refers to relative proportion of different sized particles (sand, silt

and clay) that make up the soil. These particles are grouped according to their size into

soil separates. The size of particles varies for sand from 0.075 mm to 1.5 mm, silt from

0.075 mm to 0.02 mm and clay from 0.02 mm to 0.0015 mm. Soil texture classification

is based on fractions of soil separates present in a soil. In the present study, analysis of

textural composition of soil samples collected from different agricultural fields under

rice and wheat cultivation revealed that content of sand in all soil samples was highest

followed by clay. The content of silt was very low and was observed to be less than <

3%. The content of sand, silt and clay particles in all the samples collected during both

samplings r1 and r2 from rice cultivated agricultural soils ranged from 56.80 - 65.71 %;

0.43 - 1.96 % ; 32.81 - 41.49 % and 53.12 - 61.44 %; 0.96 - 1.35 % ; 37.42 - 44.94 %

Discussion

110

respectively. Among soil samples (w1 and w2 sampling) of wheat cultivated fields the

content of sand, silt and clay particles ranged from 56.21 - 65.71 %, 1.00 - 2.84 % and

33.29 - 42.34% and 54.72 - 64.78 %, 1.05 - 2.82% and 33.94 - 44.23 % respectively.

All the soil samples studied were observed to be in the category of sandy clay loam

based on Soil Textural Triangle as shown below which gives names to the soil

depending upon various combinations of sand, silt and clay sized particles.

SOIL TEXTURAL TRIANGLE

Source: http://www.nrcs.usda.gov/

Several reports have mentioned the soil textural analysis throughout the world

and supports our present study. Vidhya et al. (2001) analyzed textural composition of

soils irrigated with effluents from a small scale chemical industry and found that

content of sand, silt and clay particles ranged from 52 - 79 %, 4.5 - 26 % and 3.0 -

9.0%. Gokalp et al. (2010) analyzed textural composition of saline and alkaline

grassland soils of Kayseri, Turkey and found that the content of sand, silt and clay

http://www.nrcs.usda.gov/

Discussion

111

content to vary from17.2 - 93.4, 3.15 - 67.0 and 0.62 - 73.4%, respectively. Ashraf et al.

(2012) estimated physico-chemical characteristics of the grassland soils of Yusmarg

Hill Resort (Kashmir, India) and found that major proportion being comprised by the

sand fraction and soils were found to have a sandy silt character. Chen et al. (2014)

studied soil texture of heavy metals polluted soil of Jiangsu province of China,

traditionally cultivated under rice and winter wheat rotation crops. Soil particle fractions

of different sizes were obtained and the content of coarse sand, fine sand, silt and clay

in the studied soil was found to be 20.5 %, 33.6 %, 31.9 % and 14.0 %, respectively.

Jansa et al. (2014) estimated various physico-chemical soil properties of crop fields of

Switzerland and reported the content of clay and sand to range 8.2 - 55.5 % and 4.3-

71.4%, respectively. The soil texture for different sampling sites was recorded to vary

from heavily clayey to very sandy. Nakase et al. (2014) also reported the textural

composition of agricultural soils collected from central Arizona, USA, affected due to

prehistoric human activity and eolian deposition. The content of sand, silt and clay were

found to range from 7.0 - 17.7%, 40.8 - 66.0% and 21.5 - 52.2%, respectively.

pH is an important parameter which measures hydrogen ion concentration and

depends largely on the relative amount of the absorbed hydrogen and hydroxyl ions. It

indicates the chemical composition and acidic or alkaline nature of soil. Availability of

macronutrients to plants tend to decrease in soils with low pH whereas micronutrients

tend to be less available in soils with high pH. Moreover, availability of nutrients is also

directly influenced by pH of the soil (Adhikary, 2014). Various anthropogenic activities

result in change in pH of soil and also enhance toxicity of soil. pH of all the soil

samples collected from the fields under rice and wheat cultivation ranged from 7.32 to

8.51. In this study, pH of all the agricultural soil samples was found to be alkaline in

nature and the results were similar to previous report by Zaiad (2010) who reported the

alkaline range of pH of soil samples collected from sides of Al- Khums city between 8.1

- 8.6. Joshi and Kumar (2011) also evaluated agricultural soil of Sanganer region of

Jaipur, Rajasthan for various physico-chemical parameters and reported pH to range

from 7.6 - 9.2. Pujar et al. (2012) analyzed the physico-chemical characteristics of soil

in Bijapur taluka, Karnataka and found that pH of soil ranged from 7.9 to 8.4. Several

other studies have also reported the alkaline nature of different soil samples collected

Discussion

112

from different parts of the world and indicated the pH value of more than 7 (Masakorala

et al., 2013; Moghimi et al., 2013; Wagh et al., 2013; Ma et al., 2013; Masakorala et

al., 2014).

Alkalinity is a measure of the acid neutralization capacity of a solution which

gives stoichiometric sum of bases in the solution (Katnoria et al., 2011). In the natural

environment, the most common sources of alkalinity include carbonates, bicarbonates,

borates, phosphates, silicates, nitrates and hydroxides etc. Carbonates make up most of

total alkalinity due to the presence of carbonate rocks and carbon dioxide in the

environment. In the present study, the alkalinity of all the agricultural soil samples

collected ranged from 0.23 mEq/ 100g to 3.30 mEq/100g. Katnoria et al. (2008)

estimated physico-chemical characteristics of four soil samples collected from different

agricultural fields of Amritsar (Punjab). The alkalinity of soil samples ranged from 0.13

to 0.96 mEq/100g. Prrveen et al. (2012) estimated alkalinity of soil collected from

different sites of Nanded city, Maharashtra. The alkalinity of soil samples was observed

to range between 1.0 to 3.5 meq/100g . Devdatta and Shashikant (2014) also analyzed

soils along the estuarine area of Bhayander and Naigaon, Thane, Maharashtra for

various physico-chemical parameters and reported the content of alkalinity in the range

of 1.21-4.37 meq/100. Differences in content of alkalinity of different soil samples have

been reported in enormous number of studies (Kelly- Quinn, 2003; Bhat et al., 2011;

Pujar et al., 2012).

Calcium and magnesium elements have been identified as essential plant

nutrients which serve as raw material for growth and development of plants. Calcium in

the form of calcium pectate is an essential component required for the cell wall

formation. Apart from its nutrient value, calcium in soil also helps to regulate the

transport and retention of other nutrients like phosphorus, nitrogen and molybdenum.

However, excess of calcium decrease the uptake of potassium and magnesium (Helper,

2005; Caffall and Mohnen, 2009). Magnesium is an essential element in biological

systems and forms the vital component of chlorophyll and photosynthesis. It helps in

the uptake of minerals such as nitrogen and phosphorus and plays a significant role

(Huber and Jones, 2013; Verbruggen and Hermans, 2013). In the present study, all the

agricultural soil samples collected from rice and wheat cultivated fields showed calcium

Discussion

113

content to range from 21.37 mg/g - 101.50 mg/g and 30.44 mg/g - 74.82 mg/g

respectively while the content of magnesium was found to range from 86.61 mg/g -

318.80 mg/g and 101.50 mg/g - 279 mg/g, respectively. Enormous studies from other

regions have also reported the content of calcium and magnesium in different soil

samples (Jodral-segadoj et al., 2006; Udotong et al., 2008; Doi and

Ranamukhaaracchchi , 2009; Acosta et al., 2011; Kebir and Bouhadjera, 2011; Johnson

et al., 2012; Tamminen et al., 2012; Upadhyay et al., 2013). Doi and

Ranamukhaaracchchi (2009) estimated content of calcium of soil under paddy

cultivation from different villages (Udom Sup village, Wang Nam Kiao district, Nakhon

Ratchasima Province) of Thailand. The study revealed that content of calcium ranged

from 1.47 to 5.44 mg/g in all the soil samples studied. Moraetis et al. (2011) also

analyzed physico-chemical characteristics of soil samples from cultivated and

uncultivated areas located in the region of Peloponnese in Greece affected due to olive

mill wastewater irrigation. The content of calcium and magnesium was observed to be

917 mg/kg, 899 mg/kg and 482 mg/kg, 669 mg/kg for cultivated and uncultivated soil

samples, respectively. Panwar et al. (2011) examined agricultural soils of Jalpaiguri

district of humid subtropical India (West Bengal) for their physico-chemical

characteristics. Range of calcium and magnesium content observed was 0.621 - 0.729

cmol/ kg and 0.236 - 0.351 c mol/ kg, respectively. Ahmed et al. (2012) studied the

effect of industrial effluents on physico-chemical properties of agricultural soils

collected from Bhairavgarh, Ujjain, MP, India. The content of calcium in contaminated

and uncontaminated soil was found to range between 189 to 273 mg/kg and 63 to 94.5

mg/kg while content of magnesium was found to be 8.50 to 45.9 mg/kg and 3.08 to

6.99mg/kg, respectively. Ganorkar and Chinchmalatpure (2013) studied soils from

Rajura bazar in Amravati district of Maharastra (india) for their physicochemical

characteristics. The content of calcium and magnesium were found to range from 0.07 -

0.16 % and 0.842 – 0.895 %.

Soils are the main terrestrial reservoir of nutrients and their evaluation forms an

important aspect for sustainable agricultural production (Quinton et al., 2010).

Nitrogen, phosphorus and potassium are important soil elements that control soil

fertility and yield of crops (Singh and Mishra, 2012). Nitrogen is one of the essential

Discussion

114

nutrients for healthy plant growth. It acts as fundamental component of different

proteins, enzymes and metabolic processes of growing plants. The vast majority of the

total nitrogen in soil (> 98%) is unavailable to plants and is in the form of organic

matter. It is available to the plants in the inorganic form of ammonium, nitrite and

nitrate by the action of symbiotic nitrogen fixing microorganisms like Nitrosomonas,

Nitrosospiras and Nitrobacter species present in the soil (Gordon et al., 2001; Rubio-

Asensio et al., 2014 ). Depending upon the soil type, microbial activity, use of

agrochemicals etc. the content of nitrates in soil varies both spatially and temporally.

Enormous number of surveys and reviews have reported variation in the levels of nitrate

content of soil from few hundred micromolar to milimolar concentrations (Wolt, 1994;

Reisenauer, 1996; Miller and Smith, 1992, 2008; Dechorgnat et al., 2011).

In the present study, the content of nitrates in rice cultivated soil samples was

found to range from 0.30 mg/g - 2.20 mg/g while soil samples from wheat cultivated

fields showed the nitrate content of 0.10 mg/g - 1.94 mg/g. Our results were in

conformity with study by Chaudhuri et al. (2009) who estimated the content of nitrates

in soil of mangroves of the Andaman affected by tsunami and found the content to be in

the range of 2.0 - 2.85 mg/g. Bahuguna et al. (2011) also evaluated physico-chemical

parameters of polycyclic aromatic hydrocarbons contaminated soils of Uttarakhand,

India and the content of nitrates was reported to vary from 0.221 - 7.112 μg/g.

Masakorala et al. (2013) reported nitrogen content of 6.60 - 20.81 mg/kg in the soil

contaminated with total petroleum hydrocarbon from DaGang oil field at southeast of

Tianjin, China. Ma et al. (2014) reported nitrogen content in the range of 0.59 - 1.84

g/kg in soil samples collected from the different sites of Daqing, China. On the

contrary, few studies have shown high content of nitrates for example Bhat et al. (2011)

analyzed the physico-chemical parameters of soils collected from two different sites of

Chandur Bazar tehsil of Amravati district and reported the content of nitrates to be

179.2 mg /kg and 153.0 mg/kg. Yao et al., (2013) studied the influence of sewage

irrigation on agricultural soils of China and found the mean content of 653.18 mg/kg

and 514. 17 mg/ kg for waste water and regular water irrigated fields, respectively.

Phosphorus is one of the most limiting macronutrients for plant growth. It plays

an important role for the development of healthy roots and fruits and also provides

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disease resistance. It is present at levels of 400 – 1,200 mg/kg of soil. Phosphorus is

present in soil as, organic and inorganic phosphates (Igual et al., 2001; Rodriguez et al.,

2006; Thuynsma et al., 2014). It is available to the plants exclusively in the form of

inorganic phosphates. Low availability of phosphorus due to its slow diffusion and high

fixation in soils make it as major limiting factor for plant growth. Although in the last

few decades, application of chemical phosphorus fertilizers and animal manure have

improved soil production but have also caused damage to environment (Richardson et

al., 2009; Shen et al., 2011). Phosphorus fertilizers are often over-applied to obtain

maximum crop yield, which lead to degradation of soil and water eutrophication (Vance

et al., 2003; Conley et al., 2009; Wu et al., 2013). The phenomenon of phosphorus

fixation and precipitation in soil is generally dependent on pH and soil type (Hayat et

al., 2010). In the present study, phosphate content of different soil samples from rice

and wheat cultivated field ranged from 0.54 mg/g to 2.57 mg/g and 1.12 mg/g to 2.06

mg/g, respectively. The content of phosphorus was very low as compared to several

other reports from different regions of the world. Katnoria et al. (2008) estimated

different physico-chemical parameters of four soil samples collected from different

agricultural fields of Amritsar, Punjab and reported the concentration of phosphates as

0.75 mg/g to 6.90 mg/g. Rabah et al. (2010) also determined physico-chemical

parameters of soil contaminated with effluents of Sokoto meteropolis, Nigeria and

found the content of phosphorus to be 5.60 mg/g. Bahuguna et al. (2011) reported the

content of phosphate in the range of 0.030 - 0.499 mg/g in polyaromatic hydrocarbon

contaminated soils of Uttarakhand, India. Content of phosphates was reported by Khan

et al. (2011) who conducted a study on six agriculturally important, water eroded soil

series of Sharkul area of district Mansehra, Hazara Division, Khyber Pakhtunkhwa in

Pakistan. The phosphate content in surface and subsurface layers of slightly eroded soil

samples (Dosera and Girari), moderately eroded soil series (Nakholi and Sharkul) and

severely eroded soil series (Ahl and Banser) was found to be 4.40, 4.6 mg/kg and 2.3,

2.7 mg/kg; 3.77, 3.45 mg /kg and 1.25, 1.34 mg/kg; 2.77, 2.84 mg /kg and 0.82, 0.96

mg/kg respectively.

High content of phosphate was reported by Rai et al. (2011) in soils irrigated

with sewage water and canal water of Dehradun city, India. The content was reported to

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be 108.44 mg/kg and 23.43 mg/kg, respectively. Ashraf et al. (2012) estimated the

content of phosphorus in soils collected from Yusmarg hill resort, Kashmir and found it

to range from 12 µg/g to 36 µg/g in different soil samples. Pandeeswari and Kalaiarasu

(2012) studied physico-chemical parameters of the soil from different locations of

tsunami affected sites of Cuddalore district of Tamil Naidu and reported the content of

phosphates from 6.00 kg/ha to 14.53 kg/ha. Velmurugan et al. (2012) also estimated

soil samples collected from different (red soil and sandy loam) sunflower fields

contaminated with chemical fertilizers and organic manure treatment and showed

phosphorus content of 92.23 kg /ha to 184.731 kg/ha and 95.41 kg /ha to 186.61 kg/ha

for red and sandy sunflower growing soil, respectively. Peng et al. (2013) studied

eroded rhizosphere soils under revegetation from the towns of of Zuolin (gully bed) and

Yuanma (slope area) in the Yuanmou dry-hot valley, China. The content of total

phosphorus and available phosphorus was observed to be 0.24 - 0.31; 0.15 - 0.24 g/kg

and 21.8 - 23.2; 19.0 - 21.5 mg/kg, respectively. Waterlot et al. (2013) estimated

phosphorus content in kitchen and lawn soil samples contaminated with effluents of

lead and zinc smelters in France. The content was reported to be 1.3 g/kg and 0.5 g/kg,

respectively. Masakorala et al. (2014) reported the content of extractable phosphorus in

the range of 14.38 mg/kg to 19.67 mg /kg in the contaminated soil samples from

DaGang oil field in China

Potassium is the third essential and most abundant element that assists in water

absorption and retention, improves plant growth (strong roots and sturdy stems) and

provides resistance against drought and crop diseases by providing longer shelf life to

plants. Potassium is supplied to plants by soil minerals, organic material and fertilizers

(Romheld and Kirby, 2010; Dreyer, 2014). Potassium also accounts for the micro

structural stability of soil and sustains the network between soil particles by forming

new linkages between them. Consequently, potassium preserves and improves the

structure of soil and provides resistance against shearing and loading forces thereby

enhancing the storage capacity of soil for plant available water (Holthusen, et al., 2010,

Ingo, 2014). The content of potassium in the upper 0.2 m of most agricultural soils

ranges between 10 and 20 g/kg. Mineral soils have potassium of 0.04 - 3% yet, most of

the potassium content of soil (90 - 98%) is incorporated in the crystal lattice structure of

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117

minerals and thus not directly available for plant uptake (Jackson, 1964; Sparks, 1987;

Zorb et al., 2014). Availability of potassium differs with different soil types and is

affected by physico-chemical properties of the soil. Soils with acid sandy, waterlogged

and saline characteristics showed deficiency of potassium content (Rengel and Damon

2008; Mengel and Kirkby 2001). In the Indian subcontinent progressive decline of crop

yields was attributed to interruption of recycling of organic matter and mineral nutrients

especially potassium in soil. As a result, decline in content of potassium and organic

matter of soil was supposed to be a major cause of lower crop yields (Benbi et al., 2006;

Brar, 2009; Romheld and Kirby, 2010). The present study showed content of potassium

to range from 0.008 mg/g 0.135mg/g in rice cultivated soil samples while the content

was found to be 0.44 mg/g to 0.194 mg/g in wheat cultivated soil samples. Our results

are in conformity with several other reports. Katnoria et al. (2008) reported the

potassium content of 0.16 - 0.25 mg/g in agricultural soil samples of Amritsar, Punjab.

Joshi et al. (2009) conducted some physico-chemical analysis of four farm site soils in

an area surrounding Rajkot, Gujarat, India and reported the potassium content of 0.21 -

0.29 meq/l. Attah (2010) estimated the potassium content of cereal cultivated

rhizospheric soils of Ambo, Woreda, Westshoa and Euthopia. The content of potassium

was found to be high in all the soil samples i.e. from 240 - 496 mg/kg. Rai et al. (2011)

reported potassium content of 121.66 mg/kg and 81.66 mg/kg in soil- irrigated with

sewage water and canal water of Dehradun city, India respectively. Ashraf et al. (2012)

also reported content of potassium (5.0 mg/100g - 9.35 mg/100g) in grassland soils of

Yusmarg Hill Resort, Kashmir, India. Pujar et al. (2012) conducted soil characterization

of samples collected from various localities of Bijapur taluka, Karnataka. The content of

available phosphorus was found to range from 8.0 - 10.1 kg/ha. Peng et al. (2013)

studied eroded rhizosphere soils under revegetation from the towns of Zuolin (gully

bed) and Yuanma (slope area) in the Yuanmou dry-hot valley, China and the content of

available potassium was found to range as 115.3 - 122.7; 52.8 -115.3 mg/kg,

respectively. Ma et al. (2014) also reported the available potassium content of 235.97,

137.24, 162.80 and 112.40 mg/kg in soil samples from four different sites of an oil

producing region of China.

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Sodium is a functional nutrient beneficial to plants with potassium deficiency. It

is involved in regeneration of phosphoenolpyruvate (an anion with high energy

phosphate bond involved in biosynthesis of various aromatic compounds) in

Crassulacean Acid Metabolism (CAM) in C4 plants (Maathuis, 2014). It helps in

cation-anion exchange processes in soils. Sodium can have beneficial effects on growth

of plants due to its osmotic function and thus regarded as functional nutrient (Wakeel et

al., 2011). Soil salinity due to high concentration of sodium cause plant growth

inhibition. Sodium ions compete with the potassium ions in the soil to find their entry

into the plant cells because of their similarity in ionic radius and hydration energies

resulting into sodium toxicity and potassium deficiency (Rubio et al. 1995; Gorham et

al., 1997; Maathuis and Amtmann 1999; Schachtman and Liu, 1999; Schachtman 2000;

Maser et al. 2001; Elumalai et al., 2002; Wang et al., 2004; Zhang et al., 2010 a,b,c).

Our results showed range of sodium content to vary from 0.038 mg/g to 0.349 mg/g in

rice cultivated soil samples and 0.038 mg/g to 0.252 mg/g in soil samples collected

from wheat cultivated fields. Several studies have reported varied range of sodium

content in soils from different regions. Udotong et al. (2008) estimated potassium and

sodium content of wetland soils of Eket, Nigeria for two seasons and found the content

to range from 0.04 - 0.07 and 0.0 - 0.07 mg/g in wet season and 0.06 - 0.12 mg/g and

0.06 - 0.32 mg/g in dry season respectively. Ashraf et al. (2012) found the range of

content of sodium to be in the range of 4.5 mg/100g to 11.7 mg/100g in soils of

Kashmir, India. Stauffer et al. (2014) assessed physico-chemical properties of soil

affected by short rotation coppice compared to soil samples collected from forest,

grassland and arable land use located in the Aisne river valley of Rethel at north of

France. The content of sodium in soil was reported to be 0.11cmol/kg, 0.09 cmol/kg,

0.13 cmol/kg, and 0.04 cmol/kg in top soil samples collected from short rotation crop

fields, grasslands, foresst and agrosystems, respectively.

Heavy metals are ubiquitous in the environment and are categorized as the most

hazardous class of anthropogenic environmental pollutants due to their persistent

nature and higher toxicity. Although some are essential for normal plant growth and

are constituent of proteins including enzymes but elevation in their concentration

causes interactions at cellular and molecular stages. These interactions induce various

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119

toxicity symptoms at different levels of morphological, physiological, biochemical and

molecular processes. Some heavy metals also stimulate the inhibition of plant growth,

formation of free radicals and reactive oxygen species causing oxidative stress (Hall,

2002). Soil is one of the most important sink of heavy metals and is exposed to a

number of toxic heavy metals by various natural and anthropogenic activities which

include rapid expansion of industrial areas, industrial emissions, excessive use of

agrochemicals, fertilizers, pesticides, sewage sludge, waste water irrigation and

atmospheric deposition. Heavy metal contamination of soil pose risk hazards to

humans via direct ingestion or dermal contact, contaminated water and through food

chains (soil-plant-human or soil-plant-animal-human) (Mclaughltn et al., 2000; Ling-

yu et al., 2010; Zhang et al., 2010, Wuana et al., 2012). Due to accumulation of heavy

metals in soil and their adverse effects on productivity of agricultural crops by

affecting food safety and marketability, crop growth due to phytotoxicity and

environmental health of soil fauna, the identification and quantification of these

elements is of great concern. Consequently, the influence of plant metabolic activities

cause the geological and biological redistribution of heavy metals through pollution of

the air, water and soil (Nagajyoti, 2010). In the present study, we have estimated the

content of chromium (Cr), cadmium (Cd), copper (Cu), iron (Fe), lead (Pb), manganese

(Mn), nickel (Ni) and zinc (Zn) in agricultural soil samples under rice and wheat

cultivation.

Cadmium is one of the most abundant and highly toxic trace pollutants of great

concern to environment and ecosystem health. It has been recognized as a potent

cytotoxic, mutagenic, genotoxic and carcinogenic agent that affects plants and animals

(Santos et al. 2010; Aimola et al., 2012; Monteiro et al., 2012). Cadmium occurs

naturally in soils in complex forms but anthropogenic emissions mostly due to non

judicial use of phosphate fertilizers, industrial and mining activities, municipal and

sewage effluents as soil amendment, burning of fossil fuels, atmospheric depositions

and vehicular emissions are the significant sources of soil contamination (Wuana and

Okieimen, 2011; Irfan et al., 2014; Anjum et al., 2014a,b). Cadmium exists in soils in

several chemical forms with varying degrees of solubility and bioavailability and its

uptake by plants is influenced by its concentration in the soil and various biotic and

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abiotic factors. Range of cadmium in soil varies as 0-1 mg/kg in normal soils; 1-

3mg/kg in slight contaminated soils and 3- 10mg/kg for cadmium polluted soils (Irfan et

al., 2014; Spence et al., 2014). Cadmium exposure to humans results in etiology of

health effects which includes nephrotoxicity, hepatotoxicity, reproductive toxicity;

osteoporosis, fibrosis cardiovascular diseases (Chedrese et al., 2006; Rennolds et al.,

2010; Nair et al., 2013; Rani et al., 2014) Cadmium in plants affects crop productivity,

efficiency of photosynthesis and content of pigments and interferes with various

physiological processes of plants. Stress of Cadmium in plants leads to a battery of

toxicity symptoms reflected in terms of necrosis, chlorosis, wilting, interference in

mineral accumulation, oxidative stress, carbohydrate metabolism, cell progression and

may contribute to reduced biomass production (Papoyan et al., 2007; Monteiro et al.,

2009; Santos et al., 2010; Gill et al., 2012, Dias et al., 2013). Our study has shown the

content of cadmium to range from 0.541 mg/kg (SWFIIr2) to 16.90 mg/kg (NEFIr2) in

soil samples from rice cultivated fields. Among all the samples from wheat cultivated

soils, Cd was not detectable in four of the soil samples viz. NEFIIw2, SEFIIw2,

SEFIIw2 and NWFIw2 whereas as for other samples, the content was found to range

from 0.078 mg/kg (NEFIw2) – 21.26 mg/kg (NWFIIw2). Mahanta and Bhattacharyya

(2011) also evaluated Cd content in soils of Guwahati, Assam (India) under different

types of land use, viz., residential, commercial, industrial, public utilities (parks, place

of worship, etc.) and roadsides affected due to variety of natural and anthropogenic

processes like atmospheric deposition of particles from industrial emissions and the

application of fertilizers. The range of content of Cd was found to be 9.2 - 18.0 mg/kg,

8.0 - 14.0 mg/kg, 6.5 – 12.3 mg/kg, 6.9 - 11.9 mg/kg, 3.1 -15.9 mg/kg, respectively. Lee

et al. (2012) reported the cadmium content of 11.27 mg/kg in agricultural soil (paddy)

from an area adjacent to the Seosung mine in Korea. Al-Farraj et al. (2012) estimated

Cd content in contaminated soil samples collected from an area bounded by Mahad AD’

Dahab gold mine from south-east part of Medina area, Saudi Arabia and revealed the

content of Cd as 17.2 mg/kg and 18.1 mg/kg using Hossner and USEPA 3051 method,

respectively. Afkhami et al. (2013) studied sediment samples collected from the local

soil, drilling mud and the waste pit nearby Ahwaz oil field in Iran and content of

cadmium was shown to be 5.40 mg/kg, 7.20 mg/kg and 7.90 mg/kg, respectively. Zhen-

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Xing et al. (2014) estimated soil cadmium in agricultural soils from six villages viz.

Yanghe, Xiaozhen, Shangba, Luqiao, Tangxin and Xinyi in vicinity of Dabaoshan Mine

in Shaoguan, China and the content was reported to range from 1.13 - 1.94 mg/kg, 0.62

- 2.75 mg/kg, 0.97 - 3.29 mg/kg, 1.54 - 3.51 mg/kg, 1.47 -3.16 mg/kg and 0.48 - 4.42

mg/kg, respectively.

Chromium is a highly toxic non-essential metal which can enter the soil

ecosystem mainly through anthropogenic depositions. It exists in several oxidation

states but the most stable and common forms are Cr(0), Cr(III) and Cr(VI) species.

Chromium exists in soil as combination of Cr(III) and Cr(VI) and persist there for

years, especially if the soils are sandy or have low levels of organic matter. The

concentration of chromium showed wide variation in soils and their content was found

to range from 0.2 to 1000 mg/kg. Cr is toxic for agronomic plants at concentration of

about 0.5 to 5.0 mg/l when present in nutrient solution and 5 to 100 mg/g in soil

(McGrath, 1995; Oliver, 1997; Wuana and Okieimen, 2011; Oliveira et al., 2012; Dhal

et al., 2013). Cr toxicity in plants depends on its valence state. Cr(III) is less mobile and

not so toxic but can be harmful to human body and have teratogenic effects. Cr(VI) is

highly mobile and shows stronger toxicity and has also have been considered as strong

carcinogenic substance (Song et al., 2014). Chromium compounds are highly toxic and

detrimental for growth and development of plants (Nagajyoti et al., 2010; Singh et al.,

2013). Cr is taken up by plants through carriers of essential ions such as sulphates

Symptoms of Cr toxicity in plants are diverse and include decrease of physiological

processes like seed germination (Zeid et al., 2001; Zhou and Li 2004; Lopez-Luna et

al., 2009; Datta et al., 2011) decrease of growth (Ozdener et al., 2011; Tang et al.,

2012), decrease of yield (Zou et al. 2006; Lakshmi and Sundaramoorthy 2010)

impairment of photosynthesis and enzymatic activities (Speranza et al., 2009;

Eleftheriou et al., 2012), oxidative stress (Redondo-Gomez et al., 2011; Fargasova,

2012), and mutagenesis (Shanker et al. 2009; Mangabeira et al., 2011) Present study

showed varied content of chromium in the range of 2.35 mg/kg (NEFIr1) - 22.50 mg/kg

(SEFIIr2) in soil samples collected from rice cultivated fields and 8.22 mg/kg

(NEFIw2) - 32.90 mg/kg (SWFIIw2) in soils of wheat cultivated fields. Enormous

studies have reported the content of chromium in soils from other regions of the world

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(Chanda et al., 2011; Koz et al., 2012; Amuno, 2013; Krishna et al., 2013; Zhang et al.,

2013).

Results from our study are in consistence with study of Aelion et al., 2009 who

revealed the chromium content of 7.0 mg/kg, 4.4 mg/kg and 24 mg/kg in soils with

different land covers collected from commercial, rural and urban sites of US census

metropolitan statistical area, respectively. Sun et al. (2009) also estimated the chromium

content in forested soils from site Huang Pu (HP) and Botanical garden (BG) of

Guangzhou, China polluted due to various anthropogenic activities. The range of

chromium content observed for HP and BG was 10 - 13 mg/kg and 2 - 5 mg/kg,

respectively. Nganje et al. (2010) studied farmlands soils nearby mine and

unmineralized areas of Middle Benue Trough, Nigeria and found the content of

chromium to be 2 mg/kg. Soriano-Disla et al. (2010) also estimated chromium content

in agricultural soils of Spain exposed to sewage sludges spiked with heavy metal salts.

The content of Cr was found to range from 16.30 mg/kg to 57.0 mg/kg. Liu et al. (2011)

also found chromium content of 41.78 mg/kg in soils from greenhouses and farmlands

from four main vegetable production areas of Shandong province, China. Chopra and

Pathak, (2013) estimated metals in soils from Spinacea oleracea cultivated microplots

contaminated due to irrigation with sugar mill effluent collected from R.B.N.S. Sugar

mill Ltd., Laksar (Uttarakhand). The content of chromium was reported to be 22.31

mg/kg.

Copper is an essential micronutrient required for growth and development of

plants particularly for various electron transport reactions in photosynthesis and

respiration. In plants, Cu is especially important in various physiological processes like

seed production, nitrogen fixation and reduction, disease resistance, protein metabolism

and regulation of water (Yruela et al., 2005; Fernandez-Calvino et al., 2009; Peng et al.,

2012; Ruyters et al., 2013; Lesniewska et al., 2014). Increase in industrialization, waste

deposition and various anthropogenic activities have contributed to the increasing level

of copper in ecosystems which has emerged as one of the major environmental

pollutants. Different human activities like excessive use of copper containing fertilizers,

fungicides, herbicides and pesticides lead to further increase in its content in

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agricultural soil (He et al., 2004; Ke- Lin et al., 2006; Bouazizi et al., 2010; Lukatkin et

al., 2013)

Copper has a low mobility in soil and due to its non- degradable and persistent

nature it may tend to accumulate in the upper soil layers and cause contamination of

water resources through leaching. Range of copper in soil is very wide and it varies

from 1 - 140 mg/kg, with average values in the range of 13 - 24 mg/kg (Mackie et al.,

2012; Lesniewska et al., 2014). Copper toxicity in humans can cause anaemia, liver and

kidney damage, and stomach and intestinal irritation (Wuana and Okieimen, 2011;

Wang et al., 2013; Pal, 2014) Copper contamination poses a cytotoxic role, induces

oxidative stress and causes injury to plants (Nagajyoti et al., 2010). Various toxicity

symptoms include retardation of seed and plant growth (Muccifora and Bellani, 2013)

leaf chlorosis (Toselli et al., 2009; Yang et al., 2011; Miotto et al., 2014) disturbance of

metabolic pathways (Bouazizi et al., 2010; Sanchez-Pardo et al., 2012; Elleuch et al.,

2013) and damage to macromolecules (Nagarani et al., 2012; Babu et al., 2014).

In the present study, the content of copper range from 11.61 mg/kg (SEFIr2) to

31.08 mg/kg (SWFIIr2) in soil samples under rice cultivation and 9.62 (SEFIw2) mg/kg

to 31.18 mg/kg (SWFIIw2) in soil samples under wheat cultivation. Our results are in

conformity with several other reports from different regions of the world. Bai et al.

(2010) reported the copper content of 19.70 - 41.77 mg/kg, 29.82 - 45.08 mg/kg and

14.53 - 61.40 mg/kg in soils from uncultivated, cultivated wetland and cultivated

wetland after abandonment collected from eastern region of Yilong lake in China. Cui

and Du (2011) studied paddy field soils from the vicinity of an abandoned mine area

polluted by heavy metals from the tailings of the Pb–Zn mine in Shangyu city of eastern

China and the content was found to be 25.6 - 26.0 mg/kg. Al-Khasman et al. (2012) also

reported copper content in soils of Jordan at 0 - 10 cm and 10 - 20 cm depth in the range

of 8.60 mg/kg - 48.65 mg/kg and 10.51 - 24.30 mg/kg, respectively. Amuno (2013)

found the copper content of 19.57 mg/kg in cemetery soils of Gisozi memorial centre at

Kigali, Rwanda. Cai et al. (2013) also reported the copper content of 12.9 mg/kg, 77.4

mg/kg, 15.2 mg/kg, 60.9 mg/kg, 41.6 mg/kg and 116.3 mg/kg in soil samples of parks,

roadside, residential, sport grounds, urban and dust soils, respectively collected from

region of Guangzhou in China. Similar range of copper content (6.5 - 32.9 mg/kg) as

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found in the present study was also observed by Novaes dos Santos and Alleoni (2013)

who studied farm soils from Brazilian agricultural frontier of southwestern Amazon

with naturally occurring heavy metal content in soil. Mackie et al. (2013) studied the

effect of long term copper application in soil samples taken from an organic vineyard in

Brackenheim, Baden Wurttemberg, Germany and revealed the content of copper to

range from 43 mg/kg - 142 mg/kg.

Iron is an essential micronutrient which plays many physiological and

biochemical functions in plants and animals. It is an integral part of several enzymes

and also participates in various redox reactions (Nagajyoti et al., 2010; Nunez et al.,

2012; Balk and Schaedler, 2014). In soils, iron exists as ferric hydroxides under aerobic

conditions while in anaerobic soils it is reduced to ferrous ions (Fe2+)

which are taken up

excessively by plants (Becker and Asch 2005; Shahid et al., 2014; Wu et al., 2014). It is

required in several vital processes of photosynthesis, oxidative phosphoryaltion,

nitrogen fixation, DNA replication and hormone synthesis. However, in excess it can

induce the production and accumulation of reactive oxygen species causing oxidative

stress and altering the physiological, morphological and biochemical characteristics of

the plants. Various toxicity effects include protein oxidation, lipid peroxidation, damage

of chlorophyll pigment and nucleic acids leading to apoptosis, reduction in

photosynthesis and yield (Arora et al., 2002; de Dorlodot et al. 2005; Moller et al.,

2007; Nagajyoti et al., 2010; Xing et al., 2010; Jucoski et al., 2013). In our study, the

content of iron was found to be 14804.73 mg/kg (SEFIr1) to 20536.00 mg/kg and

13270.18 mg/kg (SEFIw2) to 19807.30 mg/kg (NWFIw1) in rice and wheat cultivated

soil samples, respectively. Ahsan et al. (2009) also studied agricultural soils from

floodplain agricultural fields of Faridpur (FD) and Dhamrai (DM) region of Bangladesh

contaminated due to ground water irrigation rich with As and trace metals and reported

iron content to be 48.37 mg/kg and 37.00 mg/kg, respectively. High content of iron in

present work is in conformity with study by Basar et al. (2009) who estimated content

of iron in agricultural soils of Turkey before and after irrigation with water from lake

Iznik which has been polluted with industrial and municipal wastes. The iron content

was found to be 4420 mg/kg and 41201 mg/kg, respectively. Bhuiyian et al. (2010)

conducted metal analysis of soils of mine drainage and surrounding agricultural fields

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of Barapukuria coal basin located in Dinajpur district of northern part of Bangladesh

and reported iron content of 59853 mg/kg. Sehgal et al. (2012) reported iron in the

range of 4431.5 – 4915.3 mg/kg in agricultural soil from fields along the course of river

Yamuna near Wazirabad-Okhla barrage at New Delhi, India which is affected due to

anthropogenic pollution of mining, industrial processing, agricultural run-off and

sewage disposal. Nazzal et al. (2013) also reported high content of iron in roadside

samples from 401, 400, 404 highway and Don valley parkway region of greater

Toronto, Canada polluted due to vehicular emissions. The content of iron was reported

to 51,784.2 mg/kg, 52,558.5 mg/kg, 44,799.6 mg/kg and 44,988.5 mg/kg in samples

from 401, 400, 404 and Don valley parkway regions, respectively.

Lead is one of the most abundant toxic heavy metals of great concern. It is a

widely distributed environmental and occupational contaminant known for its

mutagenic, clastogenic and carciniogenic properties (Poreba et al., 2011; Green and

Pain, 2012; Thompson et al., 2014). Lead contamination of soil and plants can be

through exhausts of vehicular emissions, mineral wastes, industrial and atmospheric

emissions, land application of animal manures, pesticides, fertilizers, use of wastewater

and sewage sludge, coal combustion residues, spillage of petrochemicals (Khan et al.,

2008; Zhang et al., 2010; Ali, 2014). Pb in the environment exists as an insoluble form.

Ionic lead, Pb(II), lead oxides and hydroxides and lead metal oxy anion complexes are

the general forms of Pb that are released into the soil, groundwater, and surface waters.

Lead accumulates in the upper 8 inches of the soil and is highly immobile. Typical

mean Pb concentration for surface soils worldwide averages 32 mg/kg and ranges from

10 mg/kg to 67 mg/kg (Kabata-Pendias and Pendias 2001; Nagajyoti et al., 2010;

Wuana and Okieimen 2011). In the environment, lead is known to be toxic to plants,

animals, and microorganisms. Within living systems, lead reacts or complexes with

many biomolecules and adversely affects the reproductive, nervous, gastrointestinal,

immune, renal, cardiovascular, skeletal, muscular and hematopoietic systems as well as

developmental processes (Brent, 2006; Navas-Acien et al., 2007; Cleveland et al., 2008;

Rastogi, 2008; Vij, 2009; Sanders et al., 2009; Flora et al., 2011; Flora and Gupta,

2012; Agrawal et al., 2014). Lead is not an essential element required by plants but they

absorb it from the ambient environment mainly in soils contaminated due to automotive

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emissions and excessive use of agrochemicals which contain heavy metal as impurities

(Adriano, 2001; Lamhamdi et al., 2013). Lead affects the metabolism of plants and can

cause a broad range of physiological and biochemical effects like inhibition of seed

germination, impaired plant growth and root elongation. Lead also affects the processes

of transpiration, chlorophyll production and cell division (Sharma and Dubey, 2005;

Seregin and Kosevnikova, 2008; Lamhamdi et al., 2011; Akinci et al., 2010; Ali et al.,

2013a,b; Lamhamdi et al., 2013; Antunes and Kreager, 2014; Shahid et al., 2014; Tian

et al., 2014).

In our study, we observed that the content of lead ranged from 3.07 mg/kg

(SEFIr2) to 17.31 mg/kg (NEFIIr1) in soil sample from rice cultivated fields and 8.10

mg/kg (SEFIw1) to 23.18 mg/kg (SWFIIw2) in soils samples from wheat cultivated

fields. Results from present study are in conformity with several other studies where the

content of lead in soils from other parts of the world. Content of lead was also observed

by Tra and Egashira (2001) who studied agricultural soils (paddy and upland fields at

different locations) of south east part of Medina area, Saudi Arabia and reported the

content of lead in the range of 11 g/kg to 33 mg/kg. Neupane and Roberts, (2009)

reported lead content of 7.6 - 20 mg/kg, 9.2 - 18 mg/kg and 6 - 16 mg/kg in soil samples

from agricultural, adjacent forest and boundary soils affected due to different land-use

activities, respectively. Similar content of lead has been reported in study by Iwegbue et

al. (2009) who analyzed soils at two depths (0 - 15 and 15 - 30 cm from farmlands and

adjoining swamps and creeks affected due to spillage of one of the biggest oilfields in

the Nigeria Delta. The content of lead was reported to be 25.02 mg/kg and 23.90 mg/kg

in top soil and sub soil samples, respectively. Baykara and Dogru (2010) also reported

the content of lead to range from 1.05 - 11.64 mg/kg in soil samples affected due to

natural or artificial radioactive nuclides of seismically active areas of Turkey. Ling-yu

et al. (2010) estimated content of lead in soils of China under four land use patterns like

greenhouse field (GF), uncovered vegetable field (UF), maize field (MF) and forest

field (FF). The content was reported to be 17.98 mg/kg, 16.19mg/kg, 15.74mg/kg and

16.28 mg/kg in GF, UF, MF and FF soils, respectively. Kizilkaya et al. (2011) also

analyzed agricultural soils of Bafra Plain, Turkey from fields with application of high

rates of phosphorus fertilizers and intense soil cultivation and revealed the lead content

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to range from 4.45mg/kg to 33.33 mg/kg. Amuno (2013) also estimated the content of

lead in cemetery soils from Gisozi memorial centre of Kigali, Rwanda and reported the

content of lead to be 28 mg/kg. Novaes dos Santos and Alleoni (2013) studied

agricultural soils of southwestern Amazon in Brazil, polluted with heavy metals for

content of lead and the content was found to range from 5.2 mg/kg to 25.8 mg/kg.

Manganese is an essential micronutrient that plays an important role in various

redox reactions of plants. It help plants in water splitting and O2 evolution system in

photosynthesis, activation of several enzymes involved in various physiological

reactions, protects the photosynthetic apparatus from deleterious action of reactive

oxygen species, helps in formation and organisation of the lamellar system of

chloroplasts (Zanao Junior et al., 2010; Broadley et al. 2012; Millaleo et al., 2013;

Tewari et al., 2013). Commercial fertilizers containing synthetic manganese (Mn)

chelates and complexes are the major source of manganese alleviation in agricultural

soils. Reduced form of Mn (Mn II) in soil is readily available for plant acquisition

(Akter et al., 2014; Lopez-Rayo et al., 2013). Toxicity of Mn adversely affects the

physiological and biochemical functions of plants (Millaleo et al., 2013). Toxicity

symptoms of manganese include necrosis and chlorosis of older leaves necrotic lesions,

leaf browning and even death, loss of apical dominance and proliferation of axillary

shoots (Fuhrs et al., 2009,2011 Millaleo et al., 2010; Nagajyoti et al., 2010; Fuhrs et al.,

2011; Kovacik et al., 2014).

In the present study, the content of manganese ranged from 207.91 mg/kg

(SWFIIr1) to 539.70 mg/kg (NWFIIr2) in rice cultivated soil samples while in wheat

cultivated soil samples the content ranged from 192.88 mg/kg (SEFIw2) to 383.94

mg/kg (NWFIIw2). Our results are in accordance with several other studies which

reported the content of manganese in different soil samples from different regions. Chen

et al. (2007) studied cultivated soils of Xijia village, contaminated due to sewage water

irrigation from open canal receiving sewage from Huamu, Beicai, Zhangjiang, Tang and

Heqing Town of China. The content of manganese was reported to range from 504

mg/kg to 531 mg/kg. Khalil et al. (2008) also studied agricultural soils in vicinity of

pollymetallic mine and revealed the content of manganese to range from 532 mg/kg -

683 mg/kg. Ahsan et al. (2009) reported manganese content of 449.68 mg/kg and

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553.75 mg/kg in agricultural soils of two different locations of Bangladesh

contaminated with polluted ground water irrigation. Moreno- Jimenez et al. (2009) also

found manganese content of 353.6 mg/kg, 432.6 mg/kg and 427.8 mg/kg in cultivated

soils in vicinity of mine dumps, mine drainage and unaffected areas of Spain,

respectively. Singh et al. (2011) observed manganese content of 37 mg/kg in lake water

irrigated agricultural soils of Gorakhpur, Uttar Pradesh, India. Kanman and

Gandhimathi (2012) also reported the content of manganese in soil samples collected

from and around the open dumpsite Ariyamangalam municipal solid waste at Tamil

Naidu, India. In their study, they found content of manganese in the range of 42.07

mg/kg to 171.6 mg/kg. Mahanta and Bhattacharaya (2012) studied soils of Guwati,

Assam, India under different types of land uses viz., residential, commercial, industrial,

public utilities and road side and revealed the manganese content in the range of 194.8 -

456.6 mg/kg, 223.2 - 466.3 mg/kg, 300.8 - 636.0 mg/kg, 246.8 - 503.8 mg/kg and 321.8

- 512.2 mg/kg, respectively. Massas et al. (2013) studied the soils of Thriassio plains of

Greece under mixed land uses (i.e., residential, agricultural, and industrial) between

Elefsina and Aspropyrgos two major towns affected due to increase in industrialization

(steel industries cement factories, petroleum recycling units, large warehouses, oil and

many chemical industries). They found the content of manganese in range of 160.3

mg/kg to 588.5 mg/kg.

Nickel is recognized as an essential trace element which is distributed uniformly

throughout the soil profile but gets accumulated at the surface soils by activities of

industrial and agricultural depositions. Anthropogenic activities further release Ni into

the soil by various sources such as vehicular emissions, metallurgical and electroplating

industries, emissions of smelters, municipal and industrial wastes, application of

agricultural lands with phosphate fertilizers, pesticides, organic manure and sewage

sludges (Cempel and Nikel, 2006; Nagajyoti et al., 2010, Wuana and Okieimen, 2011;

Hamner et al., 2013) Nickel in soil exists in various forms as: inorganic crystalline

minerals or precipitates, free ion complexed or adsorbed on organic and inorganic

cation exchange surfaces or soil solution. Its content in soil varies in a wide range from

10 to 1000 mg/kg (Izosimova, 2005). Exposure of humans to nickel polluted

environments can cause number of pathological effects like dermatitis, immunotoxicity,

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nephrotoxicity, hematological disorders, cancer of the respiratory tract and nickel

poisoning (Sivulka, 2005, Pietruska et al., 2011, Clancy and Costa, 2012, Magaye and

Zhao, 2012, Wang et al. 2013, Ahmad et al., 2013, Holmes et al., 2013, Miguel et al.,

2013). Several physiological alterations and diverse toxicity symptoms were found in

plants exposed to excess of nickel in soil (Boisvert et al., 2007, Chen et al., 2009, Yusuf

et al., 2011, Sreekanth et al., 2013). Various toxicity symptoms included inhibition of

seed germination (Bhardwaj et al., 2007), necrosis and chlorosis in different plant

species (Rahman et al., 2005), decrease in water uptake (Gajewska et al., 2006;

Gajewska et al., 2009), disorder of cell membrane functions due to impairment of

nutrient balance (Sreekanth et al., 2013). Besides this excess of nickel also effects

growth pattern and development of plants (Gajewska et al., 2009; Yusuf et al., 2011;

Stanisavljevic et al., 2012; Hu et al., 2013; Pietrini et al., 2014).

In the present study, the content of nickel for all the soil samples from

agricultural fields under rice and wheat cultivation ranged from 15.67 mg/kg (SEFIr2)

to 34.51 mg/kg (NWFIr1) and 10.29 mg/kg (SEFIw2) to 31.31 mg/kg (NWFIIw1),

respectively. Enormous studies throughout the world have shown varied content of

nickel in different soil samples. Achiba et al. (2009) studied agricultural soils of farms

of agronomic national institute of Tunisia (INAT) following history of 5 yr application

of municipal solid waste compost and farmyard manure. The range of nickel content

observed was found to be 31.5 mg/kg to 37.5 mg/kg for compost soils and 28.6mg/kg to

33.7 mg/kg for manure soils. Jian-Hua et al. (2009) also reported the range of nickel

as17.58 mg/kg to 23.71 mg/kg in railroad side soils of Longxi-Haizhou railroad, China.

Neupane and Roberts (2009) evaluated soil samples from agricultural, forested and

boundary soils affected due to different land-use activities in Ohio, USA and reported

the content of nickel in the range of 18 - 32 mg/kg, 21 - 56 mg/kg and 21 - 48 mg/kg,

respectively. Stafilov et al. (2010) estimated nickel content of 42 mg/kg, 53 mg/kg, 78

mg/kg and 68 mg/kg in soils from cultivable, uncultivable, urban and polluted areas of

Veles region from Republic of Macedonia. Koz et al. (2012) also reported nickel

content of 28.74 mg/kg in surface soil collected from vegetated sites in vicinity of

copper mining in east of Turkey’s black sea region. Jalali and Hemati (2013) conducted

metal analysis of soils from three agricultural areas (paddy fields) affected due to

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natural and anthropogenic inputs of metals and reported the content of nickel to range

from 10.8 mg/kg to 18.4 mg/kg.

Zinc is an essential trace element that is required to maintain several metabolic

processes of plants and animals. In animals, it is important in homeostasis, immune

function, oxidative stress, apoptosis, aging, etc. (Chasapis et al., 2011). In plants, it is

one of the important micronutrients essential for carrying out many physiological and

biochemical processes like photosynthesis, respiration, chlorophyll biosynthesis,

metabolism of carbohydrates, lipids, proteins and also plays a structural role in

regulating gene expression and regulation (Chen et al. 2009; Disante, 2014). Zinc

occurs naturally in soil but its concentration increases rapidly due to various

anthropogenic activities like industrial processes, such as mining, steel processing, coal

and waste combustion, sewage sludge, industrial waste products and application of zinc

containing fertilizers. In soil, it exists in several forms with varying solubility but plants

absorb zinc in its divalent form (Rehman et al., 2012; Disante, 2014). The common

range of zinc is 10 - 300 µg/kg while in uncontaminated soils concentrations ranged

from 17 - 160 µg/kg (Kiekens, 1995; Oliver, 1997; Rehman et al., 2012). Zn in the

range of 150 - 300 mg/kg was reported in polluted soils (Devries et al. 2002; Warne et

al. 2008; Nagajyoti et al., 2010). High concentration can produce a wide range of

phototoxic effects like decrease in growth and development of plant, induction of

oxidative damage, inhibition of many plant metabolic functions (Dazy et al. 2009;

Disante et al., 2010; Disante et al., 2011; Sytar et al., 2013; Barrameda-Medina et al.,

2014).

In the present study, the content of zinc ranged from 48.07 mg/kg (SEFIr2) to

87.55 mg/kg (NEFIIr2) for soil samples collected from rice cultivated fields whereas in

soil samples from wheat cultivated fields the content varied from 44.55 mg/kg

(SEFIw2) to 108.99 mg/kg (NEFIIw1). The results showed the contamination of all the

soil samples under rice and wheat cultivation with Zn. El-Arby et al. (2006) studied

agricultural soils from two sites of El-Sadat city at Minufiya Governorate, Egypt

irrigated from well water and treated industrial wastewater and reported the zinc content

in the range of 4.20 mg/kg to 11.72 mg/kg. Zinc content of 3.51 mg/kg to 246.44 mg/kg

was also observed in soils under rice/paddy cultivation from Hangzhoue-Jiaxinge-

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Huzhou plains of China affected due to rapid urbanization and industrialization by Liu

et al. (2006). Martin et al. (2006) estimated the content of zinc in agricultural soil

samples contaminated due to intense agricultural practices in region of Ebro basin in

Spain and reported the content to be 17.53 mg/kg. Mico et al. (2006) also evaluated

agricultural soils under intense agriculture activities from Alicante province in southeast

Spain and found the zinc content of 52.8 mg/kg. Yan-Feng et al. (2007) reported the

zinc content of 112.9 mg/kg in agricultural soils from industry-based peri-urban area

under anthropogenic influence on heavy metal distribution in the soil of Wuxi city of

Jiangsu province in China. Lu et al. (2009) estimated zinc in vegetated soils from four

zones (V, VI, VII and VII) of Hangzhou city of China under cultivation of rice and

economic crops affected by application of chemical fertilizers and other anthropogenic

factors. The content of zinc was found to be 44.5 mg/kg, 43.5 mg/kg, 42.5 mg/kg and

42.5 mg/kg for zones V, VI, VII and VII, respectively. In an another study by Soriano-

Disla et al. (2010), content of zinc was estimated in agricultural soils of Spain exposed

to sewage sludge and was found to range from 34.4 mg/kg to 96.0 mg/kg. Markovic et

al. (2010) also reported content of zinc (75.3 mg/kg to 142 mg/kg) in agricultural soils

contaminated with pesticides and heavy metals. Kizilkaya et al. (2011) studied

agricultural soils from fields with application of high rates of phosphorus fertilizers and

intense soil cultivation and reported the range of zinc content to vary from 20.51 –

90.13 mg/kg. Novaes dos Santos and Alleoni (2013) reported zinc content in the range

of 1.2 mg/kg to 100.9 mg/kg in soils from agricultural frontier of southwestern Amazon

in Brazil.

Metal concentrations showed significant variations amongst different

agricultural soil samples. Comparing results from our study to those from other studies

in the literature, it appears that in the present study the soil samples were slightly

contaminated with one or the other heavy metals which could be because of excessive

use of agrochemicals, existence of industrial activities as well as urban and vehicular

emissions. Agricultural soil samples showed varied levels of metals and content of

metals was found in the order as: Fe > Mn > Zn > Ni > Cu > Cr > Pb > Cd. Our results

were in consistence with the study by Jalali and Hemati (2013) who studied soils of

agricultural fields of Isfahan province in central Iran and reported that Fe (1240.4

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132

mg/kg) was the most abundant metal followed by Mn (95.7 mg/kg), Pb (51.6 mg/kg),

Zn (23.8 mg/kg), Ni (13.4 mg/kg), Cu (7.0 mg/kg) and Cd (2.8 mg/kg). Present study

revealed the moderate contamination of soils when compared to soils of other places of

India like Varanasi (Sharma et al., 2007), Karnatka (Sehgal et al. 2012) and Delhi

(Krishna et al. 2013). Chopra and Pathak (2013) also estimated metals in soils from

Spinacea oleracea cultivated microplots contaminated due to irrigation with sugar mill

effluent collected from R.B.N.S. Sugar Mill Ltd., Laksar (Uttarakhand). The content of

heavy metals was reported to be Cr (22.31), Ni (213.27), Cd (8.72), Zn (49.64) and Fe

(183.11). Concentrations of Cd and Zn however were found to be higher when

compared with mean metal limits recommended by the UK Interdepartmental

Committee for Restoration of Contaminated Land (ICRCL 1987) for Cd (1 mg/kg) and

Zn (25 mg/kg) in soil used for agriculture and recreation.

5.2. Genotoxicity studies in A. cepa root chromosomal aberration assay

Soils are exposed to large number of contaminants from different industrial

activities, discharge of untreated sewage, incomplete combustion of the fuel in the

heating plants and automobiles and excessive use of chemical pesticides and fertilizers

(Alam et al., 2009; Kaur et al., 2011). The study concerns estimation of genotoxic

potential of soil samples of agricultural fields from different regions of district

Amritsar, Punjab (India) in A. cepa root chromosomal aberration assay employing in

situ and root dip mode of treatments. Soil samples were collected from different

agricultural fields under rice cultivation during September, 2009 (r1) and September

2010 (r2); and under wheat cultivation during March 2010 (w1) and March, 2011 (w2).

In order to delineate the potential of agricultural soils to induce one or another

kind of chromosomal aberrations, different kinds of induced aberrations were

apportioned into physiological aberrations attributable to spindle abnormalities

manifested by c-mitosis (Cm), delayed anaphases (Da), laggards (Lg), stickiness (St)

and vagrant chromosomes (Vg) and clastogenic aberrations, attributable to the direct

action on chromosomes resulting in the manifestation of chromosomal breaks (Bk),

chromatin bridges (Bg) and ring chromosomes (Rc). Some aberrations such as asteroid

structures, deviation of chromosomes from the poles at anaphase, deviation of

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133

alignment of chromosomes at metaphase were counted as abnormal metaphase (Am)

and abnormal anaphase (Aa). In our study, different types of chromosomal aberrations

were observed in A. cepa root tips treated with different soil samples.

During in situ treatment, different types of chromosomal aberration were

observed in root tip cells of A.cepa on treatment with different agricultural soil samples.

The squash preparations from treated root tip cells of A. cepa bulbs revealed high

percentage of chromosomal aberrations as compared to control (4.14%). A few cells

with c-mitosis, delayed anaphases, stickiness and bridges were observed in control

bulbs. No instance of Lg, Vg, Aa, Am, Bk and Rc was found in control bulbs. Among

all the soil samples collected from rice cultivated agricultural fields of sampling r1 and

r2, sample SWFIIr1 (23.79%) and SWFIIr2 (19.847%) showed maximum while soil

sample NEFIIr1 (7.71%) and NWFIIr2 (9.041%) showed minimum percentage of total

chromosomal aberrations, respectively. Soil samples from wheat cultivated fields of

both w1 and w2 sampling also resulted in appearance of both physiological and

clastogenic aberrations. Among all soil samples of w1 and w2, sampling maximum

percentage of aberrations was observed in soil sample SWFIIw1 (20.21%) and

SWFIIw2 (15.54%) while minimum in sample SWF1w1 (7.40%) and NEFIIw2

(7.75%). The frequency of root tip cells with physiological and clastogenic aberrations

following treatment with different soil samples ranged from 7.18 % - 21.50 %; 0.43 % -

2.29 % for r1 sampling; 6.971 % - 15.48 %; 0.65 % - 4.36 % for r2 sampling; 6.86 % -

17.04 % ; 0.54% - 3.16 % for w1 sampling and 6.12 % - 13.36 %; 0.26 % - 3.25 % for

w2 sampling, respectively.

During Root dip mode of treatment, the squash preparations of root tips of

control A. cepa bulbs showed 3.46% of cells with chromosomal aberrations. Squash

preparations from root tip cells of treated A. cepa bulbs showed increase in

chromosomal aberrations with increase in concentration of soil extract for all soil

samples. Among different rice cultivated soil samples, of r1 and r2 sampling again the

samples SWFIIr1 (25.95 %) and SWFIIr2 (26.47%) showed maximum while samples

NWFIr1 (12.51%) and NWFIr2 (13.49%) showed minimum percentage of aberrations

at the highest concentration tested (100%). It was observed that soil sample SWFIIw1

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134

(27.09 %); SWFIw1 (12.75 %) and SEFIIw2 (25.59 %); NWFIw2 (13.46 %) of w1 and

w2 sampling showed maximum and minimum percentage of chromosomal aberrations

at the maximum dose tested, respectively.

To evaluate genotoxicity of contaminated soils, plants are suitable systems

(Cotelle et al. 1999; Knasmüller et al., 1998; Souza et al., 2009, 2013). Allium cepa is

among the most widely and sensitive plant species to evaluate the genotoxicity of

complex environmental matrices due to large size and quantity of its metacentric

chromosomes (Ma et al., 1995). Appearance of anomalies during mitosis in the

chromosomes of A. cepa meristematic cells is an easy method to study effect of several

genotoxic and mutagenic compounds (Konuk et al. 2007; Leme and Marin-Morales

2008; Yildiz et al., 2009; Ozkara et al. 2011; Liman et al. 2012 ; Herrero et al., 2012;

Liman et al. 2013), different environmental mixtures such as aquatic (Caritá and Marin

Morales 2008; Radic et al., 2010; Bianchi et al. 2011; Dusman et al., 2014; Radic et al.,

2014) as well as terrestrial habitats (Souza et al. 2009; Christofoletti et al., 2013; Souza

et al., 2013)

Chromosomal aberrations are considered as end result of genotoxic effects of

various physical and chemical agents and are also estimates of exposure of various

organisms to these agents that impair human health (Pohren et al., 2013). The term c-

mitosis was coined by Levan (1938) to describe the effects of some chemicals which act

in a fashion similar to that of colchicine and prevents the assembly of spindle

microtubules by dissociating disulphide bonds. In delayed anaphases the two anaphasic

groups of chromosomes lie close to each other near equatorial plate. Chromosomal

breaks observed in the present study are considered to involve the DNA molecule

responsible for linear continuity of the chromosomes and may be due to unfinished or

misrepair of DNA (Evans 1977). The formation of anaphasic chromatin bridges may be

attributed to unequal exchanges resulting in formation of dicentric chromosomes which

are pulled equally to both poles at anaphase (Sax and Sax 1968). According to Al-

Najjar and Soliman (1980) in addition to unequal translocation or inversion of

chromosome segments, the formation of chromatin bridges may be attributed to

chromosome stickiness and subsequent failure of free anaphasic separation.

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135

Genotoxicity of the soil samples in the present study could be attributed to

excessive use of vast varieties of pesticides and fertilizers; both organic and inorganic

by the farmers, which ultimately result in soil pollution. High genotoxicity in terms of

chromosomal abnormalities observed could also be attributable to presence of various

industries like paper industry, sugar mill industry and distillery in the vicinity of

agricultural fields from where some soil samples were collected. Apart from this, the

direct use of sewage sludge, industrial wastes and waste water to agricultural land as

source of plant nutrients aroused serious concern as they are also known to contain

many toxic metals along with useful nutrient elements.

In the present study, the chromosome aberrations induced could also be due to

aneugenic agents, such as heavy metals, present in soil matrix which are found to be

potentially mutagenic and are closely associated with environmental pollution and

support findings of our study. Several studies have shown genotoxic potential of metals

to induce alterations in chromosome structure, and number and disturbances in the

mitotic apparatus (Fiskejo 1983; Minissi and Lombi 1997). Christofoletti et al. (2013)

also reported the genotoxic and mutagenic potential of heavy metals in soil as

demonstrated by increase in chromosomal aberrations like micronuclei and

chromosomal break, metaphase with adherence, anaphase with chromosomal bridge,

anaphase with chromosome loss and polyploidy. Heavy metals have been reported to

inhibit mitotic index and results in appearance of chromosomal aberrations, micronuclei

and binucleate cells in Allium cepa (Marcano et al., 2006; Konuck et al., 2007; Yi et al.,

2007; Achary et al., 2008,2010; Kumari et al., 2011; Patnaik et al., 2013; Pakrashi et

al., 2014). Our result on genotoxicity confirmed the use of A. cepa as a bioindicator of

soil pollution and forms an important base for future application of such bioassays to

evaluate the content of chemicals present in environmental matrix. Therefore, further

comprehensive studies and regular surveys are recommended to carefully monitor

heavy metals and trace elements of the region.

Enormous studies have shown different types of mitotic and chromosomal

abnormalities indicating genotoxic potential of contaminated soil from different parts of

the world (Kovalchuk et al.,1998; Kong and Ma 1999; Andrade et al, 2008; Katnoria et

al., 2009; Masood and Malik, 2013; Souza et al., 2013). Our results are in conformity

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136

with an earlier study by Dragoeva et al. (2009) who evaluated genotoxic potential of

agricultural soil and reported various chromosomal abnormalities like vagrant

chromosomes, chromosomal fragments at anaphase and telophase and multipolar

anaphases. Leme et al., (2012) also assessed the genotoxicity of contaminated soil

matrix by using A. cepa root chromosomal aberration assay and reported various

chromosomal abnormalities in merismatic cells of A. cepa.

Masood and Malik (2013) reported the cytotoxic and genotoxic potential of soil

from various toxic metal contaminated agricultural fields in the vicinity of industrial

area of Jajmau, Kanpur (India) in terms of chromosomal aberrations which included c-

mitosis, anaphase bridges, laggards, stickiness, broken and unequal distribution of

chromosomes. In another study by Pohren et al. (2013) chromosomal aberrations like

multipolar anaphases, metaphases with adherence, anaphases with bridges,

chromosomal losses, chromosomal breaks and micronucleated cells were observed in A.

cepa root tip cells treated with soils contaminated from municipality effuluents of

Triunfo, state of Rio Grande do Sul in Brazil.

Souza et al. (2013) also reported the clastogenic potential of polluted cultivated

soils using the A. cepa root chromosomal aberration assay. Different types of mitotic

and chromosomal abnormalities were observed in A. cepa root cells which included

anaphase with chromosome loss and chromosomal adherence, multipolar anaphases,

chromosomal breaks and bridges. Arora et al. (2014) also evaluated genotoxic risks in

soil with temporal imbalance of ammonium–nitrate ratio employing Allium cepa root

tip chromosomal aberration assay. The cytological analysis of root tips showed dividing

cells with different types of division anomalies which included mitotic abnormalities as

well as interphase nuclear abnormalities.

5.3. Biochemical parameters

Agricultural soils throughout the world are found to be polluted with different

heavy metals, such as arsenic (As), cadmium (Cd), cobolt (Co), chromium (Cr), copper

(Cu), lead (Pb), molybdenum (Mo), nickel (Ni) and zinc (Zn). Toxicity by heavy metals

cause deleterious biological effects due to their persistent and non-degradable nature.

These metals find their way into the agricultural soils by application of fertilizers,

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137

pesticides, sewage sludge, emissions of various industrial activities, automobile

exhausts and bad watering practices for long-term utilization. These heavy metals can

cause stress to the plants by increased production of reactive oxygen species (ROS) and

cause interference with their physiological, biochemical and molecular mechanisms

vital for metal tolerance and acclimatization (Liu et al., 2008a; Kaur et al. 2011;

Kumchai et al., 2013; Martins et al., 2014; Srivastava et al., 2014).

Keeping this in view, the present study involved evaluation of the biochemical

parameters such as content of total proteins and antioxidative enzymes viz., ascorbate

peroxidase (APX), catalase (CAT), dehydroascorbate reductase (DHAR), glutathione

reductase (GR), glutathione-S-transferases (GST), guaicol peroxidae (POD) and

superoxide dismutase (SOD) in A. cepa bulbs exposed to agricultural soil samples from

different fields under rice cultivation during September, 2009 (r1) and September 2010

(r2) and under wheat cultivation during March 2010 (w1) and March, 2011(w2).

Proteins are considered as the most important group of biomolecules and their

types as well as quantity vary not only among different organisms but also in different

parts of the same organism. In our study, total protein content of onion bulbs treated

with different soil samples of r1and r2 sampling varied from 1.08 mg/kg (NWFIr1) to

2.49 mg/g (SWFIIr1) and 1.04 mg/g (NWFIr2) to 2.65 mg/g (SWFIIr2), respectively.

Agricultural soil samples from wheat cultivated fields of w1 and w2 sampling also

revealed increase in content of protein varying from 1.06 mg/kg (SWFIw1) to 2.10

mg/kg (SWFIIw1) and 1.12 mg/kg (NEFIw2) to 3.30 mg/kg (SWFIIw2), respectively.

The total protein content was found to be higher in A. cepa bulbs treated with all the soil

samples as compared to untreated bulbs (control). Elevation in total protein content of

Allium bulbs treated with polluted soils (due to excessive use of agrochemicals, heavy

metals and industrial emissions) observed in present study is in consistence with report

by Mahajan and Tuteja (2005) who also assumed that increase in protein content could

be attributed to the fact that contaminants present in the soil caused stress, resulting in

activation of some genes responsible for overcoming stress. Although both the

expression and functions of such proteins are unclear, yet it indicates that there is a

relationship between some forms of plant adaptation and tolerance to stresses and

expression of stress-induced proteins (Gupta and Ahmad 2011). Our results are in

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conformity with those of Olorunfemi and Lolodi (2011) who estimated total protein

content in A. cepa treated with different concentrations (0 %, 0.2 %, 0.4 %, 0.8 % 1 %,

2 %, 3 %, 4 % and 5 %) of fresh effluents from the cassava processing mills in Uselu

Quarters, Benin City and found dose dependent increase in protein content up to four-

fold at 1% as compared to control. After 1 % concentration, protein content was found

to decrease. Sharma et al. (2011a) also reported higher content of total soluble proteins

in Raphanus sativus seedlings under nickel stress as compared to control.

Different types of environmental stresses not only enhance the protein content

but also trigger the active defense mechanisms in plants resulting in expression of

various detoxifying enzymes (Mahajan and Tuteja, 2005). The balance between ROS

production and detoxification is maintained by enzymatic antioxidative system which

involves different enzymes such as APX, CAT, DHAR, GST, POD and SOD. It is well

established that reactive oxygen intermediates (ROI) and antioxidative enzymes play a

crucial role in the establishment of normoxia in biological systems and in resistance to

oxidative stress. The dual role of ROI as toxic and signaling molecules are ensured by

complex and elaborate system controlling intracellular ROI levels. The ROI are

detoxified by single or a series of antioxidative enzyme reactions. It is clear that the

capacity and activity of the antioxidative defense system are important in limiting

oxidative damage and in destroying active oxygen species that are produced in excess

of those normally required for metabolism. Consequently, the role of these antioxidative

enzymes, such as SOD, CAT, POD, GR, APOX, MDHAR and DHAR becomes very

important as all these enzymes act in a co-ordinated manner and constitute “Asada-Foyer-

Halliwell pathway” (Noctor and Foyer, 1998; Arora et al., 2002; Asada, 2006; Sharma et

al., 2011a,b).

In the present study, the activities of APX, CAT, DHAR, GST and SOD were

found to be higher in A. cepa bulbs treated with all the soil samples as compared to

control whereas activities of GR and POD were low. Our findings showed that increase

in specific activity of APX (mol UA mg/g fresh protein) of onion bulbs treated with

different soil samples of r1 sampling and r2 sampling varied from 0.42 (SWFIr1) to

1.57 (SWFIIr1) and 0.51 SEFIr2) to 1.46 (NEFIIr2), respectively as compared to

untreated bulbs. Agricultural soil samples from wheat cultivated fields of w1 and w2

Discussion

139

sampling also revealed increase in APX activity (mol UA mg/g fresh protein) varying

from 0.36 (NWFIIw1) to 0.93 (SWFIIw1) and 0.30 (SWFIw2) to 1.15 (NEFIw2),

respectively. Present study was observed to be in conformity with previous studies

which have shown alterations in antioxidative enzymes under stress in plant systems.

Bhardwaj et al. (2009) estimated effect of enhanced lead and cadmium in soil on

physiological and biochemical attributes of Phaseolus vulgaris L. and found that

activity of APX increased with increasing concentration of metals. Tepe and Aydemir

(2011) reported increase in activity of APX in barley plants under boron toxicity.

Mishra et al. (2013) studied antioxidative defense responses of Glycine max L. CV.

Merrill under lead induced oxidative stress and revealed increase in activity of APX.

Catalase and other peroxidases are the major enzymes, required for the

detoxification of H2O2 produced during photorespiration and are present in peroxisomes

and different cell compartments such as chloroplasts, cytosol, peroxisomes and

mitochondria (Tabrez and Ahmad 2011a,b). Present study showed slight increase in

activity of CAT in Allium bulbs on treatment with different samples as compared to

control. CAT activity (mol UA mg/g fresh protein) of onion bulbs treated with different

soil samples of r1 sampling and r2 sampling varied from 0.005 (NEFIIr1) to 0.012

(NWFIr1) and 0.005 (SEFIr2 and SWFIr2) to 0.008 (NEFIr2), respectively. Among

bulbs treated with soil samples of w1 sampling, the sample NWFIw1 and SWFIw1

showed minimum CAT activity of 0.005 mol UA mg/g fresh protein whereas sample

SEFIIw1 showed maximum activity of 0.013 mol UA mg/g fresh protein. A.cepa bulbs

treated with soil samples of w2 sampling also revealed increase in content of CAT

varying from 0.005 - 0.012 mol UA mg/g fresh protein for samples SWFIw2 and

NWFIIw2, respectively. Our results are in conformity with earlier reports where

increase in activity of CAT was found under metal stress. Bhardwaj et al. (2009)

estimated effects of enhanced lead and cadmium in soil on physiological and

biochemical attributes of Phaseolus vulgaris L. and found that activity of APX

increased with increasing concentration of metal. Tepe and Aydemir (2011) found

increase in activity of APX in barley plants under boron toxicity. Zou et al. (2012) also

reported increase in activity of CAT in Allium cepa Var. Agrogarium under metal

stress. Nadgorska-Socha et al. (2013) explored effects of soil contamination by selected

Discussion

140

metals (cadmium, copper, nickel, lead or zinc) on the antioxidant response of Vicia faba

plants and found increase in activity of CAT.

Dehydroascorbate reductase (DHAR) plays an important role in signaling and

maintains the cellular level of ascorbic acid by regulating its redox state thereby,

affecting cell response and tolerance to oxidative stress (Chen and Gallie, 2006; Gallie

et al., 2012; Anjum et al., 2014a,b). Present study showed increase in activity of DHAR

in all A.cepa bulbs exposed to agricultural soil samples as compare to untreated bulbs

(0.12 mol UA mg/g fresh protein). Soil samples of r1and r2 sampling showed DHAR

activity (mol UA mg/g fresh protein) of 0.18 (NEFIIr1) to 0.32 (SWFIIr1) and 0.14

(SEFIIr2) to 0.33 (SWFIr2), respectively. Agricultural soil samples from wheat

cultivated fields of w1 and w2 sampling also revealed increase in specific activity of

DHAR (mol UA mg/g fresh protein) varying from 0.18 (NEFIIw1, SWFIw1 and

NWFIIw1) to 0.27 (SWFIIw1) and 0.17 (SWFIw2) to 0.38 (NWFIw2), respectively.

Our study is in conformity with several other reports where increase in activity of

DHAR was reported. Arora et al. (2012) studied antioxidative defense system of

Brassica juncea L. subjected to cobalt ion toxicity and reported increase in activity of

DHAR. Karuppanapandian and Kim, (2013) also investigated the effects cobalt induced

oxidative stress, as well as the role of antioxidant systems on the leaves of

hyperaccumulating plant Brassica juncea L and reported the increase in activity of

DHAR. Oryza sativa L. seedlings also showed increase in activity of DHAR on

exposure to slight salinity by NaCl.

Glutathione reductase is a member of flavoenzyme family which catalyzes the

NADPH dependent reduction of glutathione disulphide (GSSG) to glutathione (GSH)

and it maintains glutathione in the reduced state which in turn reduces dehydroascorbate

to ascorbate. The GR activity in agricultural soil exposed A. cepa bulbs declined with

respect to control. Decrease in activity of GR was observed in A.cepa bulbs treated with

different soil samples as compared to control. Bulbs treated with soil samples of r1

sampling and r2 sampling showed GR activity (mol UA mg/g fresh protein) of 0.03

(SWFIIr1) to 0.42 (NEFIIr1) and 0.07 (SWFIIr2) to 0.44 (NEFIIr2), respectively.

Agricultural soil samples from wheat cultivated fields of w1 and w2 sampling also

revealed decreasee in specific activity GR (mol UA mg/g fresh protein) varying from

Discussion

141

0.17 (NWFIIw1) to 0.41 (NEFIIw1) and 0.03 (SWFIIw2) to 0.37 (NWFIw2),

respectively. GR participates not only in H2O2 scavenging, but also favors a high

GSH/GSSG ratio to maintain a proper cellular redox mechanism (Sharma et al.,

2011a,b). Several other reports have mentioned decrease in activity of GR under abiotic

stress (Gao et al., 2008; Karruppanapandian et al., 2009; Nouairi et al., 2009; Anna et

al. 2011; Gangwar et al., 2011). In a study by Kumar et al. (2009), suppressed activity

of GR was observed in maize plants colonized with Piriformospora indica. Similary,

Tabrez and Ahmad (2011d) reported decrease in GR activity under the effect of

trichlroethylene in A. cepa.

Glutathione-S-transferases (GSTs) are a super family of enzymes, principally

known for their important role in detoxification reactions (Kumar et al., 2013; Dubey et

al., 2014). The activity of GST in Allium bulbs treated with all the soil samples was

found to be higher than control bulbs. GST activity (mol UA mg/g fresh protein) of

onion bulbs treated with different soil samples of r1 and r2 sampling varied from 0.10

(SEFIr1) to 0.19 (SWFIIr1) and 0.10 (SWFIr2) to 0.22 (SWFIIr2), respectively. Among

soil samples collected during wheat cultivation, the bulb treated with sample NWFIIw1

showed minimum GST activity of 0.11 mol UA mg/g fresh protein whereas sample

SWFIIw1 showed maximum activity of 0.33 mol UA mg/g fresh protein. A. cepa bulbs

treated with soil samples of w2 sampling also revealed increase in content of GST

varying from 0.10-0.22 mol UA mg/g fresh protein for samples SWFIw2 and SWFIIw2,

respectively. The findings of our study is in consistence with reports by Sun et al.

(2007), Shukla et al. (2012) and Dubey et al. (2014) that glutathione levels in plant

tissues are known to increase under metal stress. Similar increase in activity of GST has

been reported by Gupta and Ahmad (2011) when they studied the effects of Mathura

Refinery waste water (MRWW) in A. cepa bulbs. Significant increase in activity of

GST was reported by Dubey et al. (2014) while studying the heavy metals induce

oxidative stress and genome-wide modulation in transcriptome of rice. Increase in

activity of GST and their role in detoxification is well documented by researchers

throughout the world (Tabrez and Ahmad 2009; Tabrez and Ahmad 2011b; Sbartai et

al., 2011; Perez-chaca et al., 2014)

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142

POD also plays an important role in carrying out different physiological

functions which include biosynthesis of cell wall components like lignin and suberin,

oxidation of toxic compounds and various developmental processes (Sharma et al.,

2011a). In the present study, POD activity in Allium bulbs treated with soil samples of

r1, r2, w1 and w2 sampling showed a decrease as compared to control. Only A. cepa

bulbs treated with two samples SWFIr1 and SWFIIr1 in r1 sampling and one sample

SWFIr2 of r2 sampling showed increase in activity of POD. Variation in activity of

POD was also reported by Tang (2012) where enzyme activity showed an increase and

then decrease in Mimusops elengi seeds experiencing intense oxidative stress following

desiccation induced changes. The results were in consistence with other report where

Kou et al. (2013) found a decrease in POD activity in Huang guan pears (Pyrus

purifolia Nakai) that were treated with calcium chloride, chitosan and pullulan for

prolonging the post harvest life of pears.

SOD is a family of metaloenzymes which is considered as the first line of

defense against ROS generation because superoxide radical is considered as a precursor

to several other ROS. Superoxide is considered as the central component of the signal

transduction which activates the genes responsible for enzymes of defense system

including SOD and could serve a very useful marker for metal stress. Superoxide

radical generated by any means, is dismutated to H2O2 and oxygen by SOD, and this

H2O2 generated is then removed by peroxidases. H2O2 is converted to water by APX in

concert with oxidation of ascorbate and also by GPX with oxidation of GSH in the

cytosol and chloroplasts (Fatima and Ahmad 2005; Tabrez and Ahmad 2011a, d). Bulbs

treated with soil samples of r1 sampling and r2 sampling showed SOD activity (mol UA

mg/g fresh protein) of 43.42 (NEFIr1) to 97.91 (SEFIIr1) and 43.48 (SEFIr2) to 106.03

(SEFIIr2), respectively. Agricultural soil samples from wheat cultivated fields of w1

and w2 sampling also revealed significant increase in specific activity SOD (mol UA

mg/g fresh protein) varying from 43.48 (SEFIw1) to 106.03 (SEFIIw1) and 45.22

(NEFIw2) to 138.01 (SEFIIw2), respectively. Increase in SOD activity in the present

study was consistent with various other studies. Fatima and Ahmad (2005) also reported

Discussion

143

the utility of SOD in A. cepa as biomarker for detection of toxic metals in waste water

collected from the industrial estate of Aligarh city, India. Shi et al. (2009) showed

alleviated level of SOD in Cannabis sativa L. under Cd-induced oxidative stress.

Significant enhancement of about 8 times in SOD activity was found with respect to

control. Tepe and Aydemir (2011) also reported an increase in SOD activity of lentil

and barley plants when exposed to boron stress. Saidi et al. (2014) reported increase in

activity of SOD in seedlings of Helianthus annuus under Cd-induced oxidative stress.

Thounaojam et al. (2014) studied activities of antioxidant enzymes in Oryza sativa L.

under copper stress and reported dose dependent increase in activity of SOD. Several

other studies have reported increase in activity of SOD in different plants under

different abiotic stresses (Zou et al., 2012; Lu et al., 2013; Perez-Chaca et al., 2014;

Zhang et al., 2014)

Results of the present study revealed that activities of APX, CAT, DHAR, GST

and SOD were higher in A. cepa bulbs treated with soil samples as compared to control

whereas activities of GR and POD were low. Metal analysis have shown the presence of

one or the other heavy metal in all the soil samples tested and thus the increase in

enzyme activities may be attributed to heavy metal induced oxidative stress indicating

response of cells to cope up with increased level of ROS (Gupta and Ahmad, 2011).

Similar increases in activities of antioxidative enzymes have been reported by several

researchers (Zhang et al., 2008; Nadgórska-Socha et al., 2010; Dubey et al., 2014). In

our study, the activity of APX, CAT, DHAR, GST and SOD in Allium bulbs was

enhanced indicating the active involvement of these enzymes in detoxification of heavy

metal induced production of ROS. These results are in consistent with several studies

reporting the ability of plants to tolerate metal stress by enhancement of antioxidant

defense system (Shi et al., 2009; Duarte et al., 2014; Lamhamdi et al., 2013; Yu et al.,

2013; Sanchez-Pardo et al., 2014).

Our findings suggest that A. cepa test model is a simple and sensitive system for

monitoring the toxicity of soil samples polluted with contaminants from various

anthropogenic activities. The study prelude the use of combination of physico-chemical

Discussion

144

and analytical analysis along with biological systems to evaluate the potential toxicity

of contaminated soils of Amritsar. The data from genotoxicity and oxidative stress

studies using chromosomal aberration assay and antioxidative enzyme activities,

respectively serve as useful biomarkers and provide significant information for soil

monitoring.

Summary

Chapter-6

…and to make an end is to make a beginning, the end is where we start from

-T.S.Eliot

Chapter-6

SUMMARY

Soil is one of the most important ecological factors that support life on earth and

have always been vital to human beings. It acts as a connecting link between inorganic,

organic and living systems of the ecosystem. Apart from these, soil serves as a reservoir

of nutrients, provides mechanical anchorage and favorable tilth. In recent decades, soil

has been exposed to a number of pollutants including toxic heavy metals by various

natural and anthropogenic activities. Heavy metals find their way into the agricultural

soils through application of fertilizers, pesticides, sewage sludge, emissions of various

industrial activities, automobile exhausts and bad watering practices over long period.

Heavy metal contaminated soil has potential to pose severe health risk to humans

through various routes of exposure such as consumption of contaminated drinking

ground water and food crops; dermal contact and through food chain. Therefore, it is

necessary to acquire the better understanding of soil contamination in a comprehensive

manner in order to evade potential risks linked with contaminated agricultural soils and

associated food chains. Apart from being toxic, the subtle danger of availability of

heavy metals in the soil lies in their being mutagenic, genotoxic as well as carcinogenic.

Thus, it becomes important to evaluate agricultural soils for their potential risks in

biological systems along with physico-chemical/analytical parameters. In this respect,

bioassays provide an alternative because they constitute a measure for environmentally

relevant toxicity i.e. the effects of bioavailable fractions of interacting pollutants present

in a complex environmental matrix. Among wide range of bioassays used, Allium cepa

root chromosomal aberration assay has obtained very good acceptability in genotoxicity

studies. Biochemical changes/enzyme alterations have also been well documented to

play a significant role to indicate the magnitude of toxicity.


essential nutrients particularly from fields under rice and wheat cultivation. The district

Amritsar of Punjab (India), an agricultural land, is under intensive cultivation of wheat,

rice and vegetable crops. In order to have high yield, vast varieties of organic and

inorganic pesticides as well as fertilizers containing different heavy metals, are being

Summary

146

used by farmers which ultimately result in soil and water pollution. Apart from this, the

direct application of sewage sludge, industrial effluents and waste to agricultural lands

as source of plant nutrients also aroused serious concern as they contain many toxic

metals along with useful nutrient elements.

Keeping this in mind, the present study was planned to analyze agricultural soil

samples of Amritsar under rice and wheat cultivation for various physico-chemical

parameters including heavy metals viz. copper (Cu), cadmium (Cd), chromium (Cr),

iron (Fe), manganese (Mn), nickel (Ni), lead (Pb) and zinc (Zn). The study also

involved evaluation of genotoxic potential in terms of chromosomal abnormalities

induced by collected soil samples using Allium cepa root chromosomal aberration assay

following in situ and root dip modes of treatment. The study also comprised the

estimation of total protein content and responses of certain antioxidative/ detoxification

enzymes viz. ascorbate peroxidase (APX), catalase (CAT), dehydroascorbate reductase

(DHAR), glutathione reductase (GR), glutathione-S-transferase (GST), guaicol

peroxidase (POD) and superoxide dismutase (SOD), in Allium cepa bulbs upon

exposure to such soils

An extensive survey of different regions of Amritsar was made after dividing it

into four equal zones viz. North East (NE), North West (NW), South East (SE) and

South West (SW). Agricultural soil samples were collected from fields under rice

cultivation during September, 2009 (r1) and September, 2010 (r2) and wheat cultivation

during March, 2010 (w1) and March, 2011(w2). Two agricultural fields (FI and FII)

from each zone were selected for further investigations.

The physico-chemical properties of soil samples viz., pH, calcium, magnesium,

alkalinity, nitrates, phosphates, sodium and potassium were determined following

standard protocols. Soil texture classification, based on fractions of soil separates (sand,

silt and clay) present in a soil, was done using sieving method. pH was measured by pH

meter (Systronics; model µ pH system 361). The parameters like alkalinity, calcium and

magnesium were determined using titration method. Nitrates and phosphates were

estimated using a UV-Visible spectrophotometer (Systronics; model GS5701A). The

contents of sodium and potassium were estimated using flame photometer (ELICO;

model CL 26D). Atomic absorption spectrophotometric method was used for

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147

estimation of eight heavy metals viz. copper (Cu), cadmium (Cd), chromium (Cr), iron

(Fe), manganese (Mn), nickel (Ni), lead (Pb) and zinc (Zn). The genotoxic potential of

collected soil samples was estimated using Allium cepa root chromosomal aberration

assay by applying two modes of treatment, viz., in situ and root dip. In situ conditions

were simulated by allowing the denuded onion bulbs to root directly in soil samples

while for root dip treatment the soil extracts were prepared by suspending soil in

distilled water in ratio of 1 : 2. Protein content and responses of antioxidative enzymes

viz. APX, CAT, DHAR, GR, GST, POD and SOD were assayed in Allium cepa bulbs

exposed to agricultural soils.

The important findings of this work are summarized as below:

The content of sand, silt and clay particles in all the samples collected during

both samplings r1 and r2 from rice cultivated agricultural soils ranged from 56.80 -

65.71 %, 0.43 - 1.96 %, 32.81 - 41.49 % and 53.12 - 61.44 %, 0.96 - 1.35 %, 37.42 -

44.94 %, respectively. Among soil samples (w1 and w2 sampling) of wheat cultivated

fields the content of sand, silt and clay particles ranged from 56.21 – 65.71 %, 1.00 -

2.84 %, 33.29 - 42.34% and 54.72 - 64.78 %, 1.05 - 2.82 %, 33.94 - 44.23 %,

respectively. Based on soil textural triangle, all the soil samples studied were observed

to be in the category of sandy clay loam. pH of all the soil samples collected from the

fields under rice and wheat cultivation ranged from 7.32 to 8.51. In the present study,

the alkalinity of all the agricultural soil samples collected ranged from 0.23 to 3.30

mEq/100g. All the agricultural soil samples collected from rice and wheat cultivated

fields showed calcium content to range from 21.37 - 101.50 mg/g and 30.44 - 74.82

mg/g, respectively while the content of magnesium was found to range from 86.61 -

318.80 mg/g and 101.50 - 279 mg/g, respectively. The content of nitrates in rice

cultivated soil samples was found to range from 0.30 - 2.20 mg/g while soil samples

from wheat cultivated fields showed the nitrate content of 0.10 - 94 mg/g. Phosphate

content of different soil samples from rice and wheat cultivated field ranged from 0.54

to 2.57 mg/g and 1.12 to 2.06 mg/g, respectively. The present study showed content of

potassium to range from 0.008 to 0.135 mg/g in rice cultivated soil samples while 0.44

to 0.194 mg/g in wheat cultivated soil samples. Wide range of sodium content i.e. 0.038

to 0.349 mg/g in rice cultivated soil samples and 0.038 to 0.252 mg/g in soil samples

collected from wheat cultivated fields was observed during the present study.

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148

Metal concentrations showed significant variations amongst different

agricultural soil samples and the content of metals was found in the following order: Fe

> Mn > Zn > Ni > Cu > Cr > Pb > Cd. The content of cadmium ranged from 0.54

(SWFIIr2) to 16.90 mg/kg (NEFIr2) in soil samples from rice cultivated fields. Among

all the samples from wheat cultivated soils, Cd was not detectable in four of the soil

samples viz. NEFIIw2, SEFIIw2, SEFIIw2 and NWFIw2 whereas as for other samples,

the content of cadmium ranged from 0.078 (NEFIw2) - 21.26 mg/kg (NWFIIw2).

Present study showed varied contents of chromium i.e. in the range of 2.35 (NEFIr1) -

22.50 mg/kg (SEFIIr2) in soil samples collected from rice cultivated fields and 8.22

(NEFIw2) - 32.90 mg/kg (SWFIIw2) in soils of wheat cultivated fields. In the present

study, the content of copper ranged from 11.61 (SEFIr2) to 31.08 mg/kg (SWFIIr2) in

soil samples under rice cultivation and 9.62 (SEFIw2) to 31.18 mg/kg (SWFIIw2) in

soil samples under wheat cultivation whereas the content of iron ranged as 14804.73

(SEFIr1) to 20536.00 mg/kg and 13270.18 (SEFIw2) to 19807.30 mg/kg (NWFIw1) in

rice and wheat cultivated soil samples, respectively. The content of lead ranged from

3.07 (SEFIr2) to 17.31 mg/kg (NEFIIr1) in soil sample from rice cultivated fields and

8.10 (SEFIw1) to 23.18 mg/kg (SWFIIw2) in soils samples from wheat cultivated

fields. The content of manganese ranged from 207.91 (SWFIIr1) to 539.70 mg/kg

(NWFIIr2) in rice cultivated soil samples while 192.88 (SEFIw2) to 383.94 mg/kg

(NWFIIw2) in wheat cultivated soil samples. The content of nickel for all the soil

samples from agricultural fields under rice and wheat cultivation ranged from 15.67

(SEFIr2) to 34.51 mg/kg (NWFIr1) and 10.29 (SEFIw2) to 31.31 mg/kg (NWFIIw1),

respectively while the content of zinc ranged from 48.07 (SEFIr2) to 87.55 mg/kg

(NEFIIr2) for soil samples collected from rice cultivated fields whereas 44.55 (SEFIw2)

to 108.99 mg/kg (NEFIIw1) in soil samples from wheat cultivated fields. Comparing

results from our study to those with other studies from the literature, it appears that in

the present study, the soil samples were slightly contaminated with one or the other

heavy metals which could be because of excessive use of agrochemicals, existence of

industrial activities as well as urban and vehicular emissions.

In our study, different types of chromosomal aberrations were observed in

Allium cepa root tip cells treated with different soil samples. During in situ treatment,

different types of chromosomal aberrations were observed in root tip cells of A. cepa.

Summary

149

Among all the soil samples collected from rice cultivated agricultural fields of sampling

r1 and r2, maximum number of aberrant cells were observed in sample SWFIIr1

(23.79%) and SWFIIr2 (19.84%) and minimum number of aberrant cells were observed

in NEFIIr1 (7.71%) and NWFIIr2 (9.04%), respectively. Among all soil samples of w1

and w2 (wheat cultivated) sampling, maximum percentage of aberrations was observed

in root tip cells following treatment with soil sample SWFIIw1 (20.21%) and SWFIIw2

(15.54%) while minimum for sample SWF1w1 (7.40%) and NEFIIw2 (7.75%),

respectively. The frequency of root tip cells with physiological and clastogenic

aberrations following treatment with different soil samples ranged from 7.18 % - 21.50

% and 0.43 % - 2.29 % for r1 sampling; 6.97 % - 15.48 % and 0.65 % - 4.36 % for r2

sampling; 6.86 % - 17.04 % and 0.54% - 3.16 % for w1 sampling and 6.12 % - 13.36 %

and 0.26 % - 3.25 % for w2 sampling, respectively. During root dip mode of treatment

(100% soil extract) among different rice cultivated soil samples of r1 and r2 sampling,

again the samples SWFIIr1 (25.95 %) and SWFIIr2 (26.47%) showed maximum while

samples NWFIr1 (12.51%) and NWFIr2 (13.49%) showed minimum percent aberrant

cells. Further, soil samples SWFIIw1 (27.09 %) and SWFIw1 (12.75 %) of w1;

SEFIIw2 (25.59 %) and NWFIw2 (13.46 %) of w2 sampling induced maximum and

minimum percentage of chromosomal aberrations at the maximum dose tested,

respectively. The order of different samples of r1 and r2 sampling, with respect to

percent aberrant cells observed at maximum concentration of soil extract tested, from

the minimum to maximum percent was NWFIr1 (12.51%) < NEFIIr1 (16.17%) <

SWFIr1(16.88) < NEFIr1 (19.26) < SEFIr1 (20.26%) < NWFIIr1(22.70%) < SEFIIr1

(24.07%) < SWFIIr1 (25.95%) and NWFIr2 (13.49%) < NEFIIr2 (17.60%) < SEFIr2

(18.32%) < NEFIr2 (18.56%) < SWFIr2 (18.96%) < SEFIr2(22.19%) < NWFIIr2

(22.86%) < SWFIIr1 (26.47%), respectively. All the soil samples collected from rice

cultivated fields showed increase in chromosomal aberrations with increase in

concentration of soil extract. The order of different soil samples of w1 and w2 sampling

with respect to percent aberrant cells observed at maximum concentration of soil extract

tested from the minimum to maximum was SWFIw1 (12.75%) < NWFIw1 (13.08%) <

SEFIw1 (19.52 %) < NEFIIw1 (21.46 %) < NEFIw1 (21.49 %) < NWFIIw1 (21.99 %)

< SEFIIw1 (23.43 %) < SWFIIw1 (27.09 %) and NWFIw2 (13.46 %) < NEFIIw2

(16.32%) < SWFIw2 (17.55%); SEFIw2 (18.77%) < NEFIw2 (20.08%) < NWFIIw2

Summary

150

(22.93%) < SWFIIw2 (24.94%) < SEFIIw2 < (25.59%), respectively. All the soil

samples collected from fields under wheat cultivation showed increase in chromosomal

aberrations with increase in concentration of soil extract.

Genotoxicity of the soil samples in the present study could be attributed to

aneugenic agents, such as heavy metal present in soil matrix which are found to be

potentially mutagenic and are closely associated with environmental pollution. High

genotoxicity in terms of chromosomal abnormalities observed could also be attributable

to presence of various industries like paper industry, sugar mill industry and distillery in

the vicinity of agricultural fields from where some soil samples were collected. Apart

from this, the direct use of sewage sludge, industrial wastes and waste water to

agricultural land as source of plant nutrients aroused serious concern as they are also

known to contain many toxic metals along with useful nutrient elements.

The present study also involved evaluation of various biochemical parameters

such as content of total proteins and activities of antioxidative enzymes viz., ascorbate

peroxidase (APX), catalase (CAT), dehydroascorbate reductase (DHAR), glutathione

reductase (GR), glutathione - S- transferases (GST), guaicol peroxidae (POD) and

superoxide dismutase (SOD) in A. cepa bulbs exposed to agricultural soil samples

under rice and wheat cultivation. In our study, total protein content of onion bulbs

treated with different soil samples of r1 and r2 sampling varied from 1.08 mg/kg

(NWFIr1) to 2.49 mg/g (SWFIIr1) and 1.04 mg/g (NWFIr2) to 2.65 mg/g (SWFIIr2),

respectively. Agricultural soil samples from wheat cultivated fields of w1 and w2

sampling also revealed increase in content of protein varying from 1.06 mg/kg

(SWFIw1) to 2.10 mg/kg (SWFIIw1) and 1.12 mg/kg (NEFIw2) to 3.30 mg/kg

(SWFIIw2), respectively. The total protein content was found to be higher in A. cepa

bulbs treated with all the soil samples as compared to untreated bulbs.

In the present study, the activities of APX, CAT, DHAR, GST and SOD were

found to be higher in A. cepa bulbs treated with all the soil samples as compared to

control whereas activities of GR and POD were low. The specific activity of APX (mol

UA mg/g fresh protein) of onion bulbs treated with different soil samples of r1 sampling

and r2 sampling varied from 0.42 (SWFIr1) to 1.57 (SWFIIr1) and 0.51 SEFIr2) to

1.46 (NEFIIr2), respectively. Agricultural soil samples from wheat cultivated fields of

Summary

151

w1 and w2 sampling also revealed increase in APX activity (mol UA mg/g fresh

protein) varying from 0.36 (NWFIIw1) to 0.93 (SWFIIw1) and 0.30 (SWFIw2) to 1.15

(NEFIw2), respectively. Slight increase in activity of CAT was observed in Allium cepa

bulbs on treatment with different samples as compared to control. CAT activity (mol

UA mg/g fresh protein) of A. cepa bulbs treated with different soil samples varied from

0.005 (NEFIIr1) to 0.012 (NWFIr1) for r1 sampling and 0.005 (SEFIr2 and SWFIr2) to

0.008 (NEFIr2) for r2 sampling. Among bulbs treated with soil samples of w1

sampling, the samples NWFIw1 and SWFIw1 showed minimum CAT activity of 0.005

mol UA mg/g fresh protein whereas sample SEFIIw1 showed maximum activity of

0.013 mol UA mg/g fresh protein. A.cepa bulbs treated with soil samples of w2

sampling also revealed increase in content of CAT which varied from 0.005 - 0.012

mol UA mg/g fresh protein for samples SWFIw2 and NWFIIw2, respectively.

Increase in activity of DHAR in all A. cepa bulbs exposed to agricultural soil

samples was observed as compared to untreated bulbs (0.12 mol UA mg/g fresh

protein). Soil samples of r1and r2 sampling showed DHAR activity (mol UA mg/g fresh

protein) as 0.18 (NEFIIr1) to 0.32 (SWFIIr1) and 0.14 (SEFIIr2) to 0.33 (SWFIr2),

respectively. Agricultural soil samples from wheat cultivated fields of w1 and w2

sampling also revealed increase in specific activity of DHAR (mol UA mg/g fresh

protein) varying from 0.18 (NEFIIw1, SWFIw1 and NWFIIw1) to 0.27 (SWFIIw1) and

0.17 (SWFIw2) to 0.38 (NWFIw2), respectively. Decrease in activity of GR was

observed in A. cepa bulbs treated with different soil samples as compared to control.

Bulbs treated with soil samples of r1 sampling and r2 sampling showed GR activity

(mol UA mg/g fresh protein) as 0.03 (SWFIIr1) to 0.42 (NEFIIr1) and 0.07 (SWFIIr2)

to 0.44 (NEFIIr2), respectively. Agricultural soil samples from wheat cultivated fields

of w1 and w2 sampling also revealed decrease in specific activity of GR (mol UA mg/g

fresh protein) varying from 0.17 (NWFIIw1) to 0.41 (NEFIIw1) and 0.03 (SWFIIw2) to

0.37 (NWFIw2), respectively.

GST activity (mol UA mg/g fresh protein) of onion bulbs treated with different

soil samples of r1 and r2 sampling varied from 0.10 (SEFIr1) to 0.19 (SWFIIr1) and

0.10 (SWFIr2) to 0.22 (SWFIIr2), respectively. Among bulbs treated with soil samples

of w1 sampling, the bulbs treated with sample NWFIIw1 showed minimum GST

activity of 0.11 mol UA mg/g fresh protein whereas with sample SWFIIw1 showed

Summary

152

maximum activity of 0.33 mol UA mg/g fresh protein. A. cepa bulbs treated with soil

samples of w2 sampling also revealed increase in content of GST varying from 0.10 -

0.22 mol UA mg/g fresh protein for samples SWFIw2 and SWFIIw2, respectively. In

the present study, POD activity in Allium bulbs treated with all the soil samples showed

a decrease as compared to control. Only A. cepa bulbs treated with two samples SWFIr1

and SWFIIr1 during r1 sampling and one sample (SWFIr2) during r2 sampling showed

increase in activity of POD. Bulbs treated with soil samples of r1 sampling and r2

sampling showed SOD activity (mol UA mg/g fresh protein) of 43.42 (NEFIr1) to 97.91

(SEFIIr1) and 43.48 (SEFIr2) to 106.03 (SEFIIr2), respectively. Agricultural soil

samples from wheat cultivated fields of w1 and w2 sampling also revealed significant

increase in specific activity of SOD (mol UA mg/g fresh protein) varying from 43.48

(SEFIw1) to 106.03 (SEFIIw1) and 45.22 (NEFIw2) to 138.01 (SEFIIw2), respectively.

Results of the present study revealed that activities of APX, CAT, DHAR, GST

and SOD were higher in A. cepa bulbs treated with soil samples as compared to control

whereas activities of GR and POD were low. Metal analysis have shown the presence of

one or the other heavy metal in all the soil samples tested and thus the increase in

enzyme activities may be attributed to heavy metal induced oxidative stress indicating

response of cells to cope up with increased level of ROS. In our study the activity of

APX, CAT, DHAR, GST and SOD in Allium bulbs was enhanced indicating the active

involvement of these enzymes in detoxification of heavy metal induced production of

ROS. The results are in line with several studies reporting the ability of plants to

tolerate metal stress by enhancement of antioxidant defense system.

Our findings suggest that A. cepa test model is a simple and sensitive system for

monitoring the toxicity of soil samples polluted with contaminants from various

anthropogenic activities. The study prelude the use of combination of physico-chemical

and analytical analysis along with biological systems to evaluate the potential toxicity

of contaminated soils of Amritsar. The data from genotoxicity and oxidative stress

studies using chromosomal aberration assay and antioxidative enzyme activities,

respectively serve as useful biomarkers and provide significant information for soil

monitoring.

If I have seen further, it is by standing on the shoulders of giants

-I.Newton

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