Ph.D Thesisprr.hec.gov.pk/jspui/bitstream/123456789/1049/2/1129S.pdfIqbal Bhanger, Director National...

Ph.D Thesis

Chemical Analysis of Arsenic in Environmental and Biological Samples of Selected Areas of Sindh, Pakistan and its Removal from

Water

THESIS SUBMITTED TOWARDS THE PARTIAL FULFILLMENT OF THE REQUIREMENT FOR THE AWARD OF DOCTOR OF PHILOSOPHY

DEGREE IN ANALYTICAL CHEMISTRY

Jameel Ahmed Baig

National Center of Excellence in Analytical Chemistry, University of Sindh, Jamshoro - PAKISTAN

2011

i

DEDICATED

This Endeavors Is Dedicated To my Beloved parents, my affectionate supervisor Prof. Dr. Tasneem Gul Kazi, brothers and sister especially Dr. Akhtar Mehmood Baig for their love, support and continuous prayer have Enabled me to achieve the

Honour of the Highest Seat of Learning

ii

Acknowledgements I bow my head before Almighty Allah, The omnipotent, the omnipresent, the merciful,

the most gracious, the compassionate, the beneficent, who is the entire and only source of every knowledge and wisdom endowed to mankind and who blessed me with the ability to do this work and his prophet Hazrat Muhammad (Salallah-o-Allaehe Wasallim) who gave us the spirit to learn. It is the blessing of Almighty Allah and his Prophet (Salallah-o-Allaehe Wasallim), who’s spiritual guides enabled me to make my efforts a success.

I wish to acknowledge the NCEAC University of Sindh Jamshoro and Higher Education Commission for providing the bursary, which made this study possible. I meant what I wrote about my indebtedness to my teachers, on all levels and to all the research collaborators that I worked with over the years and directly or indirectly contributed to this thesis. Specially, I would like to take this opportunity to convey my cordial gratitude and appreciation to my admirable, respectfully and zealot supervisors Prof. Dr. Tasneem Gul Kazi, without whose constant help, deep interest and vigilant guidance, the completion of this thesis was not possible. I am really indebted to him for her accommodative attitude, thought provoking guidance, immense intellectual input, patience and sympathetic behavior.

I would like to pay my deepest gratitude and appreciation to Prof. Dr. Muhammad Iqbal Bhanger, Director National Centre of Excellence in Analytical Chemistry, University of Sindh, Jamshoro, Pakistan, for his generous cooperation, providing good research facilities, excellent research environment and nice caring and guidance to complete all of my research work and compilation successfully.

With due respect, I am deeply and strongly obliged to Prof. Dr. S. Tufail Hussain Sherazi, Prof. Dr. Sirajdin, Prof. Dr. Shahabuddin Memon, Dr. Amber Rehena Solangi, Dr. Najma Memon, Dr. Farah Naz Talpur, Engg. Mehraj Ahmad Noorani, Mr. Sarfaraz Mehasar, Dr. Aamna Baloch and Miss Huma Ishaq for their research consultancy. I would extend my sincere and heartily thanks and appreciation to my friends Dr. Hassan Imran Afridi, Dr. Muhammad Khan Jamali, Dr. Muhammad Bilal Arain, Dr. Naveed Gul Kazi, Dr. Atif Gul Kazi, Dr. Ghulam Abbas Kandhro, Dr. Raja Adil Sarfraz, Mr. Abdul Qadir Shah, Mr. Imam Bakhsh Solangi and Muhammad Afzal Kambho. I have no words to acknowledge the unconditioned support. They always encouraged and cooperated with me and made every possible effort to provide the invaluable input for the improvement of this study.

I would like to place on record sincere thanks to research fellows and colleagues, especially of Miss. Sumaira Khan, Miss. Nida Fatima Kolachi, Mr. Sham Kumar Wadhwa, Mr. Faheem Shah, Mr. Naeenullah, Mr. Abdul Rauf Khaskheli, Mr. Abdul Sattar Soomro, Mr. Mansoor Ahmed Qazi, Mr. Imdadullah, Mr. Munawer Saeed and rest of my fellows for their assistance, good company, marvelous behavior, friendly attitude and keeping excellent healthy and competitive environment for learning purpose in the research Labs. I am highly thankful Young Welfare Society, Al-Mustafa Welfare Association and IRC Khairpur for their precious and constructive attitude during the sampling of environmental and biological samples. Mr. Pir Ziauddin, Mr. Imran-ul-Haq, Mr. Jawad Ahmad, Mr. Munawar Ali Soomro, Mr. Mudasir Ahmed Arain and Mr. Shafiq Ahmed Bhutto are gratefully acknowledged. I am also highly thankful to Mr. Akhtar Ali Vighio, Mr. Nasrullah, Pir Sirajuddin, Junaid Talpur and the rest staff members of center.

At last but not the least, I really acknowledge and offer my heartiest gratitude to all members of my family especially, parents, brothers and sister for their great sacrifice, moral support, cooperation, encouragement, even and odd disturbance, patience, tolerance and prayers for my health and success during this work.

Jameel Ahmed Baig

iii

Abstract The river, canal, tube well, hand pump and municipal water samples were evaluated as

possible sources of arsenic (As) contamination in different districts of Sindh, Pakistan. The total

arsenic (As) contents in surface and ground water samples were evaluated. The arsenic

concentrations in surface and ground water samples from the two areas of Sindh under study

(Jamshoro, Khairpur, Sukkur and Hyderabad) were found in the range of 4.2-18 and 9.20-361 μg

L-1, respectively. The underground and in some surface water total arsenic exceeded the WHO

provisional guideline values 10 μg L-1 and reached upto 362 μg L-1. It was observed that hand

pumps and tube well water samples have high level of arsenic than canal, river and municipal

water samples. This is due to widespread water logging from Indus river irrigation system, which

causes high concentration of salts in this semi-arid region and results in enrichment of As in

shallow groundwater. Besides total As other physicochemical parameters, nitrite, nitrate,

chloride, sulphate, sodium, potassium, calcium, magnesium and iron were evaluated for the

quality and safety assurance of drinking water. Among them iron, calcium, magnesium and

sulphate were observed to be higher than WHO recommended level.

In addition of total arsenic, its inorganic speciation in water samples from the different

districts was evaluated. The inorganic As species (As3+ and As5+) were separated from organic

forms by adsorbing on alumina (Al2O3) where as the organic As was elute out. The retained

inorganic As species was eluted by 0.2 M HCl. Then trivalent and pentavalent arsenic in the

eluent were complex with molybdate and ammonium pyrrolidinedithiocarbamate (APDC),

respectively. Then the trivalent arsenic - APDC and the pentavalent arsenic molybdate

complexes were quantitatively extracted into Triton X-114. The main factors affecting the

separation and cloud point extraction (CPE) were investigated in detail. Total inorganic As in

collected water samples was determined by using titanium dioxide (TiO2) as adsorbent. The

standard spiking method was used for validation and the %recoveries of As species were found

in the range of 98 - 99%. The mean concentrations of inorganic trivalent and pentavalent arsenic

in the surface water and ground water samples were ranged from 3.00-53.0 and 6.00-352 µg L-1,

respectively. Principal component analysis scores allowed the samples to be classified by cluster

analysis. Principal component analysis of the data from the hand pump samples and from the

well tube samples showed two significant components were responsible for sixty percent of the

variance.

iv

A single-step extraction procedure (S-BCR) was developed and validated as a

replacement for the BCR sequential extraction procedure (BCR-SES). The same reagents and

operation conditions are used procedures. The single-step extraction procedure was applied to

investigating arsenic partitioning sediments samples, collected from lake, canals and river of

district Jamshoro, Pakistan. The results obtained for As with single step extraction were

compared to those obtained from BCR-SES and validated using CRM-BCR 701. There was no

significant difference in extraction efficiency between S-BCR and BCR-SEC for arsenic content

at 95% confidence limit. The precision of the proposed S-BCR (expressed as % RSD) was lower

than 10 %. The sediment samples collected from different ecosystem have different physico-

chemical characteristic and As content. The arsenic mobility of the samples collected from the

various locations was found to decrease in the order: acid soluble fraction > oxidizable fraction >

reducible fraction. The fraction of As dissolving in 0.11 mol L-1 acetic acid was higher in the

lake sediment samples as compared to those sediment samples obtained from river and canal,

showing the contamination of lake.

To evaluate the uptake of arsenic by crops (vegetables and grain), they were grown in

agricultural soil irrigated for long period with tube well water containing high concentrations of

arsenic and their arsenic contents were compared to crops of same species grown in soil irrigated

with canal water Having a much lower arsenic concentration. In addition, the total and EDTA

(pH 7) extractable As soils irrigated with tube well and canal water were determined and

correlated with total concentrations of As in edible parts of vegetables. Statistically significant

correlations were obtained between the total and EDTA extractable fractions of As in soil. The

high level of total and EDTA extractable As were found in tested samples as compared to

controlled samples. This investigation highlights the increased danger of growing food crops in

the agricultural land continuously irrigated by As contaminated water.

The effects of As exposure via drinking water was evaluated by analysis of As levels in

scalp hair of children (age < 10 year) and adults (16-45) years of both gender collected from sub

districts of Khairpur, Pakistan having different As contents in surface and underground water.

For comparative purposes scalp hair samples of age-matched children and age matched adults

were also collected from an area having low level of As (<10 μg L-1) in drinking water. The As

concentrations in scalp hair samples of subjects belonging to non exposed, less exposed and

v

high As exposed areas were found in the range of from 0.01 – 0.27, 0.11-1.31 and 0.36-6.80 µg

g-1, respectively. 20% of total children belong to high As exposed area have skin lesion on their

hands and feet. A positive correlation coefficient (r = 0.91 - 0.99) was obtained between As

contents in drinking water and scalp hairs of children and adults of areas investigated The arsenic

hazard quotient was estimated on the basis of the arsenic concentrations in drinking water and

scalp hair of the male subjects in both age groups consuming drinking water in the study areas. A

toxicity risk assessment provides a hazard quotient corresponding to <10, indicates non-

carcinogenic exposure risk from the consumption of drinking water in the study areas.

For remediation of As from water samples indigenous materials (stem and leave) of

Acacia nilotica have been studied. The effects of various parameters vis, pH, biosorbent dosage,

temperature and exposure time for bio-sorption of As were investigated in detail. The resulting

data were evaluated using Dubinin-Radushkevich (D-R), Freundlich and Langmuir isotherms. It

was observed that As biosorption best fit the Langmuir and Freundlich isotherms. The free

energy of transfer (E) calculated on the basis of D–R model, indicated physico-chemical

biosorption. The thermodynamic study indicated that the bio-sorption mechanism was

endothermic, spontaneous and feasible for As removal. The kinetics of As biosorption was better

interpreted by pseudo-second-order rate equation with good correlation coefficients. The

removal of As by biomass of A. nilotica was > 95% at the concentration level of As < 200 µg L-1

of As solution. The uptake capacity of the biomass studies was 50.8 mg As g-1.

vi

Summery of Contents

Dedicated …….……………………………………………………………………………..i

Acknowledgement………………………………………………………………………… ii

Abstract…………………………………………………………………………………… iii

Contents ………………….……………………………………………………………….. vi

Abbreviation……………………………………………………………………………….. xxiii

List of Publications…………………………………………………..……………………. xxvii

Contents Chapter - 1

INTRODUCTION 1-15

1.1. Arsenic in surface and ground water 1

1.1.1. Arsenic speciation in water 2

Table 1 Inorganic As speciation in water 3

1.2. Arsenic in soil and sediment 3

1.3. Translocation of Arsenic in grain crops and vegetables 3

1.4. Biological specimens of human as biomarkers 4

1.5. Effects of Arsenic on human health 5

1.6. Methodology 6

1.6.1. Optimization of Methods for Speciation of As in water (Multivariate

strategy)

6

1.6.2. A multivariate study for arsenic speciation and physico-chemical parameter

in water

7

1.6.3. Fractionation of As in soil and sediments 8

1.6.3.1. Single extraction 8

1.6.3.2. Sequential Extraction Method 8

1.6.3.3. Single step extractions based on sequential extraction schemes 10

1.7. Description of study area 10

vii

1.8. Remediation of Arsenic from water 11

1.9. Aims and objectives 13

Chapter - 2

Literature Review 16-28

2. Over view of Arsenic 16

2.1. Arsenic in water 16

2.1.1. Arsenic species in water 17

2.1.2. Physico-chemical parameter and As species in natural water 17

2.1.3. Advance extraction method of arsenic species in natural water. A

multivariate study

18

2.2. Arsenic in soil and Sediments 20

2.2.1. Fractionation of As in soil and sediments 21

2.2.2. Single extraction 21

2.2.3. Sequential extraction 22

2.2.4. Single step extraction based on Sequential extraction Schemes 23

2.3. Uptake of Arsenic by grain crops and vegetables 24

2.4. Effects of Arsenic on human health 25

2.5. Removal of Arsenic from water 27

Chapter – 3

EXPERIMENTAL 29-67

3. Plan of Work 29

3.1. Study Materials 30

3.1.1. Sample collection and pre-treatment 30

viii

3.1.2. Scalp hair sampling 31

Fig. 1a. Sampling map of water sampling from Jamshoro district 32

Fig. 1b. Sampling map of sediment sampling from Jamshoro district 33

Fig. 1c. Sampling map of water, sediment and soil sampling from Khairpur

Mir’s district

34

Fig 2a. Environmental sampling from different areas of Sindh Pakistan 35

Fig 2b. Biological and agriculture sampling from different areas of Sindh

Pakistan

36

Fig 2c. Biological and agriculture sampling 37

3.1.3. Scalp Hair Sample treatment 39

3.1.4. Certified samples 39

3.1.5. Sampling of biosorbent and pretreatment 40

3.2. Apparatus 40

Table 2: Measurement conditions for atomic absorption spectrometer AAS 700

a) Flame atomic absorption (FAAS)

42

b. Instrumental settings for Electrothermal and Hydride Generation Atomic

absorption spectrometry

43

Table 3: Measurement conditions for Ion chromatograph Metrohm 86 44

3.3. Chemical, Reagents and Glass Wares 44

3.4. Preparation of Internal Standards Solutions for metals and metalloids 45

3.4.1. Arsenic 1000 ppm 45

3.4.2. Iron 1000 ppm 45

3.4.3. Calcium 1000 ppm 45

3.4.4. Potassium 1000 ppm 45

3.4.5. Magnesium 1000 ppm 45

3.4.6. Sodium 1000 ppm 46

3.4.7. Working standards 46

3.5. Preparation of Chemical Modifiers 46

3.6. Procedure for determination of total contents of elements 46

3.7. Reagents and standards preparation for anions 46

3.7.1. Reagent water 46

ix

3.7. 2. Eluent solution 46

3.7. 3. 1000ppm Fluoride 47

3.7.4. 1000ppm Chloride 47

3.7. 5. 1000ppm Nitrite 47

3.7. 6. 1000ppm Nitrate 47

3.7. 7. 1000ppm Phosphate 47

3.7. 8. 1000ppm Sulphate 47

3.7. 9. Working standards 47

3.8. pH Measurements 47

3.8.1. Reagents 47

3.8.1.1. Borax 0.01 mol L-1 solution, pH=9.2 47

3.8.1.2. Saturated solution of Potassium Hydrogen Tartrate 0.03 mol L-1,

pH=3.05

47

3.8.1.3. Potassium Hydrogen Phthalate 0.05 mol L-1, pH=4.005 48

3.8.2. Procedure pH of the water, soil and sediment 48

3.9. Total and Calcium Hardness 48

3.9.1. Reagents 48

3.9.1.1. Na2 H2 EDTA solution 48

3.9.1.2. Buffer solutions for Total Hardness 48

3.9.1.3. Indicator for total Hardness 48

3.9.1.4. Buffer Sodium hydroxide for Calcium Hardness 48

3.9.1.5. Indicator for Calcium Hardness 48

3.9.2. Procedure 49

3.9.2.1. Calculation 49

3.10. Alkalinity 49

3.10.1. Reagents 49

3.10.1.1. Hydrochloric acid (HCl) solution (0.1N) 49

3.10.1.2. Phenolphthalein Indicator solution 50

3.10.1.3. Sodium carbonate solution (0.1N) 50

3.10.1.4. Methyl orange Indicator solution 50

3.10.2. Procedure 50

x

3.10.2.1. Phenolphthalein Alkalinity 50

3.10.2.2. Methyl orange Alkalinity 50

3.10.3. Calculation 50

3.11. Cloud point Extraction and Solid Phase Extraction of As speciation 51

3.11.1. Preparation of Reagents 51

3.11.1.1. 1% Triton X-114 51

3.11.1.2. 0.1% ammonium-pyrrolidinedithiocarbamate (APDC) 51

3.11.1.3. 1% of Ammonium molybdate tetrahydrate 51

3.11.1.4. 0.1 mol/L buffer solution 51

3.11.2. Procedure for the determination of inorganic As by solid Phase

Extraction (SPE)

51

3.11.3. Procedure for the determination of As3+ by cloud point extraction (CPE) 52

3.11.4. Procedure for the determination of As5+ 52

3.12. Experimental Design 53

3.12.1. The fractional factorial design for CPE and SPE 53

3.12.2. Central 23+ star orthogonal composite designs 53

Table 4. Variables and levels used in the factorial design for As3+ and total iAs 54

3.13. Determination of cation exchange capacity using sodium as index ion 55

3.13.1. Reagents 55

3.13.1.1. Sodium Acetate solution 1 mol L-1 55

3.13.1.2. Ammonium Acetate Solution 1 mol L-1 55

3.13.1.3. Ethanol 95% 55

3.13.2. Procedure 55

3.14. Single Extraction 55

3.14.1. Reagents 55

3.14.1.2. EDTA 0.05 mol L-1 55

3.14.2. Procedures for extraction of EDTA 0.05 mol L-1 56

3.15. BCR Sequential Extractions 56

3.15.1. Reagents 56

3.15.1.1. Acetic acid (0.11 mol L-1) 56

3.15.1.2. Hydroxylammonium chloride (hydroxylamine hydrochloride 0.5 56

xi

mol L-1)

3.15.1.3. Hydrogen peroxide, 300 mg/g (8.8 mol L-1) 56

3.15.1.4. Ammonium acetate (1 mol L-1) 56

3.15.1.5. Aqua regia 57

3.15.2. Procedure modified BCR sequential extraction scheme 57

3.15.2. Procedure for Single step extraction based on BCR sequential extraction

scheme (S-BCR)

57

3.16. Total arsenic determination in soil, sediment, grain crops, vegetables and scalp

hair

58

3.16.1. Microwave – assisted digestion procedure 58

3.16.2. Cloud point extraction (CPE) procedure 59

3.17. Risk assessment 59

3.17.1. Arsenic risk assessment 59

3.17.2. Carcinogenic Risk assessment 60

3.18. Statistical analysis 60

3.19. Analytical Figures of Merit 62

Table 5. Slope & Intercepts with linear regression lines of Concentration versus

Absorption data of Standard solutions of different element/ions

63

3.20. pH and surface area of biosorbent material 64

3.21. Sorption procedure 64

3.22. Desorption 65

3.23. Interference studies 66

3.24. Theoretical background of adsorptions 66

xii

Chapter – 4

Results and Discussion 68-232

4.1. Arsenic in surface and ground water 68

4.1. Physico-chemical parameters and Arsenic in surface and ground water of

Jamshoro, Pakistan

68

4.1.1. Results 68

4.1.1.1. Physicochemical parameters 68

4.1.1.2. Major ions in water samples 69

4.1.1.3. Iron and Arsenic 70

4.1.1.4. Cluster analysis (CA) 70

Table 6a. Major element chemistry and arsenic contaminations in ground water

from district Jamshoro Sindh, Pakistan

71

Table 6b. Major element chemistry and arsenic contaminations in surface water

from district Jamshoro Sindh, Pakistan

73

Table 7. Ranges of analytical data of the ground and surface water samples in

district Jamshoro, Sindh, Pakistan

75

Fig. 3 Dendrogram showing sites cluster on the Jamshoro (Surface water) 77

Fig. 4. Dendrogram showing sites cluster on the Jamshoro (Ground water) 77

4.1.2. Discussion 78

Fig. 5. Relation ships between various chemical components of analyzed in groundwater samples. (a) Dolomite saturation index (SId) and calcite saturation index (SIc); (b) dolomite saturation index (SId) and gypsum saturation index (SIg); (c) calcite saturation index (SIc) and Ca2+; (d) dolomite saturation index (SId) with Mg2+; (e) gypsum saturation index (SIg) and Ca2+; (f) gypsum saturation index (SIg) and SO4

2− (Square icon for TS and triangle for HS).

80

4.1.3. Conclusion 81

4.2. Assessment of physico-chemical parameter and Arsenic speciation in surface and

ground water samples of Jamshoro Pakistan

83

4.2.1. Physico-chemical parameter 83


district Khairpur Mir’s, Sindh, Pakistan

85

Table 9. Linear correlation coefficient matrix for different physico chemical 89

xiii

parameters, Fe and As species Significant at 5% level

Fig. 6. Dendrogram showing clustering of different origins of surface and ground

water according to distribution of As species

91

Table 10. Loadings of experimental variables (19) on significant principal

components for ground water of district Jamshoro

92

Fig. 7. Plots of PCA scores for combined data set of groundwater samples for

distribution of Fe, As species and water quality parameters in district of

Jamshoro

94

4.2.2. Conclusions 94

4.3. Physico-chemical parameters and speciation of Arsenic in water samples of

different origin

96

4.3.1. Results and Discussion 96

4.3.1.1. Physico-chemical parameters 96


district Khairpur Mir’s, Sindh, Pakistan

97

Fig. 8. Dendrogram showing clustering of different origins of surface and ground

water according to distribution of As species

98

4.3.1.2. Total Arsenic and Iron 99

4.3.1.3. Inorganic arsenic (iAs) 100

Table 12(a). Linear correlation coefficient matrix for different physico chemical

parameters, Fe and As species in ground and surface water

101

Table 13. Analytical results for surface and ground water samples and comparison

with literature values

103

4.3.1.4. Inorganic arsenic species 104

4.3.1.5. Principal component analysis 106

Table 14. Loadings of experimental variables (19) on significant principal

components for ground water of district Khairpur Mir’s

107

Fig. 9. Plots of PCA (a) scores for combined data set groundwater samples (b)

scores for distribution of Fe, As species and water quality parameters in

sub-district of Khairpur Mir’s

108


xiv

4.4. Method development 111

4.4.1. Advance extraction methods for speciation of arsenic in water samples 111

4.4.1.1 Optimization of the experimental conditions for factorial design 111

4.4.1.2. Estimated effects of variables for As3+ and iAs 111

Table 14a. Design matrix and the results of As+3 %extraction (n = 6) 112

Table 14b. Design matrix and the results of iAs %extraction (n = 6) 113

Fig. 10. Pareto chart (As3+) of the fractional factorial experimental design for the

analysis of the variables: (S) Surfactant (Triton X-114); (C) Complex

(APDC); (p) pH; (I) Incubation time; (T) Temperature; (V) Volume

114

Fig. 11. Pareto chart (As total) of the fractional factorial experimental design for

the analysis of the variables: (M) Mass of TiO2; (U) Ultrasonic Exposure

Time; (p) pH.; (T) Temperature; (V) Volume

115

4.4.1.3. Optimization by central composite design for As3+ and iAs 116

Table 15a. Central 23 + star central composite design (n = 16) for the set of (S),

(C) and (P) in As3+

117

Table 15b. Central 23 + star central composite design (n = 16) for the set of (M),

(U) and (P) in total iAs

118

Fig. 12. Three dimension (3-D) surface response for % recovery of As3+ by CPE (a)

Interaction b/w (pH-Triton X-114) and (b) Interaction b/w (pH-APDC)

119

Fig. 13.Three dimension (3-D) surface response for % recovery of total As by TiO2-

slurry method (a) Interaction b/w (pH-Mass of adsorbent) and (b)

Interaction b/w (Temperature-Mass of adsorbent)

120

4.4.1.4. Interference study 122

Table 16. Foreign ions effect on the % recoveries of 5.0 µg L-1 of As3+ and total

iAs

123

Table 17. The results for tests of addition/recovery for As3+ and total iAs

determination in water samples

124

Table 18. Analytical results of Total As, Total iAs, As3+ and As5+ in natural waters 125

xv

4.4.1.2. Applications 125

4.4.1.3. Conclusions 127

4.4.2. Separation and preconcentration of As in surface and ground water 128

4.4.2.1. The Optimization of separation and extraction methods for organic and

inorganic As species

128

4.4.2.1.1. Effects of sample volume, eluents and its flow rate 129

4.4.2.2. Cloud point extraction method 129

4.4.2.2.1. Effect of pH 129

Fig 14. Effect of pH on the CPE of 10 µg L-1 As3+ /As5+. Other CPE

conditions: 0.007% APDC/0.0006% molybdate, 0.14%/0.12%

concentration of Triton X-114, equilibration temperature 35/55 ○C,

equilibration time 5 min.

130

4.4.2.2.2. Effects of concentration of APDC and molybdate 130

Fig. 15. Effect of concentration of APDC/molybdate on the CPE of 10 µg L-

1 As3+/As5+. Other CPE conditions: 0.14/0.12% (v/v) concentration

of Triton X-114, pH 4.3/2.2, equilibration temperature 35/55 ○C,

equilibration time 5 min.

131

4.4.2.2.3. Effect of Triton X-114 concentration 131

Fig. 16. Effect of concentration of concentration of APDC/molybdate on the

CPE of 10 µg L-1 As3+/As5+. Other CPE conditions: 0.14/0.12%

(v/v) concentration of Triton X-114, pH 4.3/2.2, equilibration

temperature 35/55 ○C, equilibration time 5 min.

131

4.4.2.2.4. Effects of equilibration temperature and time 132

4.4.2.2.5. Interference of co-existing ions 132

4.4.2.3. Application 133

Table 19 The results for tests of addition/recovery for As3+ and As5+ determination

in ground water samples (n= 6)

135

Table 20 Analytical data of the ground water samples of district Sukkur, Sindh,

Pakistan

136

Table 21 Analytical results for ground water samples and comparison with

literature values

137

xvi


4.5. Evaluation the arsenic fractions in sediments 139

4.5.1. Physico-chemical parameter of sediments 139

Table 22. Total Basic characteristics of the sediment samples of Jamshoro district 139

Fig. 17. Correlation coefficient of total arsenic (AsT) in sediments with pH, %

Silica and CEC

140

4.5.2. Total arsenic in sediment 140

4.5.3. Comparison of BCR sequential and single step BCR extraction methods 141

Table 23. Results obtained for As in sediment certified reference material BCR 701

(mg kg-1) using conventional BCR sequential extraction scheme (BCR-

SES) and single step BCR extraction (S-BCR).

141

4.5.4. Application 143

Table 24. Results obtained for As in sediment samples (expressed in mg kg-1) using

conventional BCR sequential extraction scheme (BCR-SES) and single

step BCR extraction (S-BCR) n = 240

144

Fig. 18. Ratio of individual As bonded fraction in sediments: lake (a), canal (b) and

river (c) sediments

145


4.6. Evaluation of arsenic in soils and its translocation to grain crops and vegetable 147

4.6.1 Evaluation of arsenic in grain crops and soil by cloud point extraction 147

4.6.1.1. Optimization of Cloud point extraction 147

4.6.1.1.2. Effect of pH 147

4.6.1.1.3. Effect of APDC concentration 148

Fig 19. Effect of pH on the CPE of 10µg L-1 As. Other CPE conditions: 4.3x 10-4

mol L-1 APDC, 0.12% concentration of Triton X-114, equilibration

temperature 35 ○C, equilibration time 10 min.

148

Fig 20. Effect of concentration of APDC on the CPE of 10µg L-1 As. Other CPE

conditions: 0.12% (v/v) concentration of Triton X-114, pH 4.5,

equilibration temperature 35 ○C, equilibration time 10 min.

148

Fig 21. Effect of concentration of Triton X-114 on the CPE of 10µg L-1 As. Other

CPE conditions: 4.3x 10-4 mol L-1 APDC, pH 4.5, equilibration temperature

149

xvii

35 ○C, equilibration time 10 min.

4.6.1.1.4. Effect of Triton X-114 149


4.6.1.1.6. Interferences 150

4.6.1.1.7. Analytical performance 150

Table 25. The results for tests of addition/recovery for Asaqueous and TAs

determination in soil samples by CPE (n= 6)

151

Table 26. Comparative data of Analytical characteristics of the CPE method for

As (µg L-1)

152


Table 27. Total As (TAs) and water extractable As (Asaqueous) concentrations in soil

(µg g-1) by CPE

154

Table 28. Concentration of total As in different part of maize with CPE (µg g-1) and

contamination factor (CF)

154


4.6.2. Evaluation arsenic in irrigation water and its translocation from soil to grain

crops

157

4.6.2.1. Optimization of methodology for As3+ in water 157

4.6.2.2. Physico-chemical parameters of soil 157

Table 29. Physico-chemical characteristics of the sampled soils irrigated with tube

well water (SIT) and soils irrigated with canal water (SIC)

158

4.6.2.3. Total and inorganic species of arsenic in water 158

Table 30. Arsenic concentration in soil irrigated with tube well water (SIT) and soil

irrigated with canal water (SIC) in µg/g and Arsenic in water (µg L-1)

160

4.6.2.4. Bioavailable fraction of As in soil 161

4.6.2.5. Total As in soil and grain crops 162

Table 31. Uptake of arsenic (µg g-1) by grain crops grown in soil irrigated with

canal water as control grain crops samples (CGCs) and soil irrigated with

tube well water (SIT) of three sub districts as tested grain crops samples

(TGCs)

164

xviii

Table 32. Coefficients of determination (R2) of arsenic in soils (SIC and SIT of Faiz

Ganj, Thari Mirwah, and Gambat) with (CGCs and TGCs)

165


4.6.3. Translocation of As from soil to vegetables 166

4.6.3.1 Bio-accumulation and levels of total arsenic in vegetables 166

Table 33. Uptake of arsenic (µg/g) by vegetables grown in soil irrigated with canal

water as control vegetable samples (CVS) and soil irrigated with tube

well water (SIT) of three sub district as tested vegetable samples (TVS)

167

Table 34. Coefficients of determination (R2) of arsenic in soils (SIC and SIT of Faiz

Ganj, Thari Mirwah, and Gambat) with (CVS and TVS)

168


4.7. Exposure study of Arsenic 170

4.7.1. Determination of arsenic in biological samples with and without enrichment 170

4.7.1.1. Optimization of microwave assisted digestion-cloud point Extraction

(MAD-CPE) method

170

4.7.1.1.1. Effect of pH 170

4.7.1.1.2. Effect of APDC concentration 171

4.7.1.1.3. Effect of Triton X-114 171

Fig 22. Effect of pH on the CPE of 10µg L-1 As. Other MAD-CPE conditions:

0.008% (w/v) APDC, 0.12% concentration of Triton X-114, equilibration


171


MAD-CPE conditions: 0.12% (v/v) concentration of Triton X-114, pH 4.5,

equilibration temperature 35 ○C, equilibration time 10 min.

172


MAD-CPE conditions: 0.008% (w/v) APDC, pH 4.5, equilibration


172

Fig 25. Effect of foreign ions on the pre-concentration and determination of As (10

µg L-1)

173


xix

4.7.1.1.5. Interferences 173

Table 35. Determination of As in certified human hair samples with and without

MAD-CPE (n = 6)

174

4.7.1.1.6. Validation of MAD-CPE 174


Table 36: Concentrations of As in Scalp hair Samples (µg g-1) 175

Table 37. Comparison of the mean /ranges of arsenic concentrations in water

samples and hair samples with the literature

175


4.7.2. Arsenic toxicity in children 178

4.7.2.1. Environmental Risk Assessment of Arsenic in Children through drinking

water

178

4.7.2.1.1. Results 178

Table 38. Parametric presentation of As concentration in groundwater from study

areas and As in scalp hair samples of children of different age and

gender.

179

Table 39. Linear regression and Pearson coefficient for As concentrations in scalp

hair samples of children (boys and girls) vs. As in groundwater

180

4.7.2.1.2. Discussion 181

4.7.2.1.3. Conclusion 184

4.7.2.2. Arsenic in Scalp Hair samples of Children belong to exposed and non-exposed

areas

185

4.7.2.2.1. Arsenic in drinking Water 185

4.7.2.2.2. Arsenic in Scalp hair samples of Children 186

Table 40. Parametric presentation of arsenic concentration in surface and

groundwater from study areas and arsenic in scalp hair samples of

children

187

4.7.2.2.3. Correlation between Arsenic level in drinking water with As contents in

Scalp Hair sample of Children of both gender

187

Table 41. Linear Regression and Pearson coefficient for arsenic concentrations in

scalp hair samples of adolescent (boys and girls) vs. As in ground water

188

xx

4.7.2.3. Conclusion 189

4.7.3. Arsenic in Scalp Hair samples of adult males and evaluation of toxic risk factor 190

4.7.3.1. Arsenic in drinking water 190

4.7.3.2. Arsenic in scalp hair of male subjects 191

4.7.3.3. Correlation of Arsenic levels in scalp hair with drinking water 191

Table 42. Analytical results of total As and inorganic iAs in natural waters and SH

of male subject of two age group of three regions

192

Table 43. Linear Regression and Pearson coefficient for arsenic concentrations in

scalp hair samples of male subject of two age groups (16 - 30 Years and

31 - 60 Years) vs. As in water

193

4.7.3.4. Arsenic toxicity and cancer risk factor 194

Table 44. Risk assessment of high, less and unexposed area of Sindh Pakistan 196

4.7.3.5. Conclusion and recommendations 198

4.8. Remediation of arsenic from drinking water 199

4.8.1. Biosorption studies on powder of stem of Acacia nilotica 199

4.8.1.1. Characterization of biosorbent surface by FTIR 199

Fig. 26. FTIR spectra of unloaded (red line ‘a’) and loaded with As ions (blue line

‘b’) on biomass of A. nilotica

200

Fig. 27. Scanning electron micrograph of (a) unloaded (b) loaded biomass of A.

nilotica (1800× magnification) Bar is 10µm.

201

4.8.1.2. Characterization of biosorbent surfaces by SEM 202

4.8.1.3. Effect of biosorbent dosage 202

Fig. 28. Effect of dosage on the adsorption of As to biomass of A. nilotica at As

concentration 200 µg L-1, contact time 15 minutes and pH 7.5

203

Fig. 29. Effect of As adsorbate concentration on biomass of A. nilotica at

biosorbent dose 4 g L-1, contact time 15 minutes and pH 7.5

203

Fig. 30. Effect of pH on the adsorption of As to biomass of A. nilotica at As

concentration 200 µg L-1, biosorbent dose 4 g L-1 and contact time 15

minutes

204

Fig. 31. Effect of contact time and temperature on the biosorption of As to biomass

of A. nilotica at As concentration 200 µg L-1, biosorbent dose 4 g L-1,

204

xxi

contact time 15 minutes and pH 7.5

4.8.1.4. Effect of sorbate concentration 205

4.8.1.5. Effect of pH 205

4.8.1.6. The effect of contact time and kinetics of biosorption 206

4.8.1.7. Biosorption isotherm 206

Table 45. Langmiur, Freundlich and D-R characteristic constants for As

biosorption onto BM

207

4.8.1.8. Biosorption kinetics 208

Table 46. Kinetic parameters obtained from pseudo-first-order and pseudo-second-

order for As biosorption onto BM

209

Table 47. Thermodynamic parameters of As biosorption onto BM 209

4.8.1.9. Biosorption thermodynamics 210

4.8.1.10. Effect of concomitant ions 211

Table 48. Interferences of cations and anions on the sorption of As onto BM 212

Table 49. Influence of various eluents on the desorption of As ions from BM. 213

4.8.1.11. Desorption and regeneration studies 213

4.8.1.12. Application on natural water 213

Table 50. The physico chemical parameters of water samples before and after

biosorption on biomass

214


4.8.2. Biosorption studies on leaves of Acacia nilotica 217

4.8.2.1. Results 217

4.8.2.1.1. Characterization of biosorbent 217

Fig. 32. FTIR spectra of unloaded (red line) and loaded (blue line) IB 218

Fig. 33. Scanning electron micrograph of (a) unloaded (b) loaded IB

(3000× magnification) Bar is 5 µm.

219

Fig. 34. Energy dispersive spectroscopy (EDS) analysis of without As

loaded and with As loaded IB.

220

4.8.2.1.2. Influence of different factors on biosorption efficiency 220

4.8.2.1.3. Effect of concomitant ions 222

xxii

Table 51. Isotherm characteristic constants for Langmiur, Freundlich and

D-R and Thermodynamic

223

Table 52. Interferences of cations and anions on the sorption of As ions

onto A. nilotica

224

4.8.2.2. Discussion 225

4.8.2.2.1. Characterization of biosorption 225

4.8.2.2.2. Optimization of adsorption parameters 225

4.8.2.2.3. Evaluation of biosorption theoretical feasibility 226

Fig. 35. (a) Pseudo-first-order and (b) pseudo-second-order kinetic plots

for the biosorption of As onto IB at biosorbent dose 8 g L-1 and pH

7.5

229

Table 53. The physico-chemical parameters of water and removal of As by

the leaves of Acacia nilotica

230

4.8.2.2.4. Application on groundwater samples 231


Chapter – 5

Conclusion 233-260

Conclusion 233

Socioeconomic Impacts 238

Recommendations 239

References 240

Reprints from the publications

xxiii

Abbreviations and Acronyms (%) Percentage Ø Particle size AAS Atomic absorption spectrometry ANOVA Analysis of variance Ar Argon As Arsenic AsT Total Arsenic As3+ Arsenite As5+ Arsenate BCR Community Bureau of Reference °C Degree Celsius Ca Calcium CCD Central composite design CA Cluster analysis CPE Cloud Point Extraction CRM Certified Reference Material CDM Conventional wet acid digestion method CEC Cation exchange capacity Cf Contamination factors D-R Dubinin–Radushkevich EC Electrical conductivity EDTA Ethylenediaminetetraaceticacid Eh Redox Potential ETAAS Electrothermal Atomic Absorption Spectroscopy EPA Environmental Protection Agency FTIR iAs Inorganic Arsenic ICP–AES Inductively Coupled Plasma Atomic Emission Spectrometry ICP–MS Inductively Coupled Plasma Mass Spectrometry HPLC High Performance Liquid Chromatography Pb-PDC Lead PCA Principal Component Analysis PCRWR Pakistan Council of Research in Water Resources SEM-EDX SH Scalp hair SPE Solid Phase Extraction SRM Standard certified Reference Material SRP UNICEF United Nation International Children and Education Fund USGS US EPA United State Environmental Protection Agency WHO World Health Organization SIT Soil Irrigated with Tube Well TGCs Test Grains crop Samples

xxiv

TVS Test Vegetable Samples Gps Global Positioning System Sic Soil, Irrigated With Fresh Canal Water Cgcs Control Grains Crop Samples Cvs Control Vegetable Samples Le Less Exposed Area He High Exposed Area Ne Non Exposed Area Iaea International Atomic Energy Agency Ib Indigenous Biosorbent (Leave And Stem Of Acacia Nilotica) Ft-Ir Fourier Transforms Infrared Spectrometer SEM–EDS Scanning Electron Microscope–Energy Dispersive X-Ray Spectrometry WWF-Pak World Wild Fund Pakistan APDC Ammonium-Pyrrolidinedithiocarbamate S Surfactant (%) C Complexing agent (%) P pH I Incubation time (min) T Temperature (ºC) V Volume of sample (mL) A Mass of adsorbent (mg) T Temperature (ºC) U Ultrasonic exposure time (min) S-BCR Single Step Extraction Based On BCR Sequential Extraction Scheme HQ Hazard Quotient RfD toxicity Reference Oral Dose ADD Average Daily Dose Cwater As concentration in water (mg L-1) IRwater water ingestion rate (L day-1) EF Exposure Frequency (days year-1) ED Exposure Duration (years) AT Average Age Time (days) BW body weight QA/QC Quality assurance and Quality control CPE-ETAAS Cloud Point Extraction Electro Thermal Atomic Absorption

Spectroscopy SPE-AAS Solid Phase Extraction Atomic Absorption Spectroscopy Ci Initial Concentrations Ce Final Concentrations qe amounts of As biosorbed at equilibrium (mg/g) qt amounts of As biosorbed at (mg/g) t (min) k1 Rate constant Q monolayer biosorption saturation capacity (mol/g) b Enthalpy of biosorption (L/mol), Xm maximum biosorption capacity (mol/g)

xxv

β Activity coefficient (mol2/J2) related to biosorption mean free energy (kJ/mol) an

ΔH○ Enthalpy change ΔG○ Free energy change ΔS Entropy change SI saturation index FA Factor analysis FAAS Flame Atomic Absorption Spectrometry FAO Food and Agriculture Organization of the United Nations Fe Iron g Gram GFAAS Graphite furnace atomic absorption spectrometry HCl Hydrochloric acid HGAAS Hydride Generation Atomic Absorption Spectrometry HMs Heavy Metals IARC International Agency for Research on Cancer IC Ion Chromatography K Potassium Kg Kilogram L Litter LOD Limit of Deduction LOQ Limit of Quantitation M Molar Mg Magnesium mg Milligram µg Micro gram mL Milliliter µL Micro-liter mm Millimeter mS Micro Siemens MW Microwave N Nitrogen Na Sodium NIST National Institute of Standards and Technology (USA) NRC National Research Council (Canada) OC Organic Carbon OM Organic Matter P Phosphorus PCA Principal Component analysis pH Negative logarithm of hydrogen ion concentration ppb Part Per Billion ppm Parts Per Million QC Quality Control r Correlation coefficients rpm Rounds Per Minute RSD Relative Standard Deviation

xxvi

SD Standard Deviation SE Sequential Extraction Schemes Tf Transfer factor WHO World Heath Organization

xxvii

List of Publications

This thesis is based on the following publications 1. J.A. Baig, T.G. Kazi, A. Q. Shah, H.I. Afridi, G. A. Kandhro, S. Khan, N.F. Kolachi, S.K.

Kumar Wadhwa, F. Shah, M.B. Arain, M.K. Jamali Evaluation of arsenic levels in grain crops samples, irrigated by tube well and canal. Food and Chemical Toxicology 49, (2011) 265–270. doi:10.1016/j.fct.2010.11.002 (I.F. 2.114)

2. J.A. Baig, T. G. Kazi, A. Q. Shah, G.A. Kandhro, Hassan I. Afridi, Sumaira Khan, Bio-sorption studies on powder of stem of Acacia nilotica: Removal of arsenic from surface water. Journal of Hazardous Materials. Journal of Hazardous Materials 178 (2010) 941–948. doi:10.1016/j.jhazmat.2010.02.028 (I.F = 4.144)

3. J.A., Baig, T.G. Kazi, M.B., Arain A.Q. Shah, H.I., Afridi, G.A., Kandhro, S., Khan, Speciation and evaluation of Arsenic in surface and ground water: A multivariate case study. Ecotoxicology and Environmental Safety 73, (2010), 914–923. doi:10.1016/j.ecoenv.2010.01.002 (I.F = 2.133)

4. J.A. Baig, T.G. Kazi, A. Q. Shah, M.B. Arain, H.I. Afridi, S. Khan, G. A. Kandhro, Naeemullah, A. S. Soomro Evaluating the accumulation of arsenic in maize (Zea mays L.) plants from its growing media by Cloud Point Extraction. Food and Chemical Toxicology 48, (2010) 3051–3057. doi: 10.1016/j.fct.2010.07.043 (I.F. 2.114)

5. J.A. Baig, T.G. Kazi, A. Q. Shah, M. B. Arain, S. Khan, H. I. Afridi, G. A. Kandhro, N. F. Kolachi, Optimization of cloud point extraction and solid phase extraction methods for speciation of arsenic in natural water using multivariate technique, Analytica Chimica Acta (2009), 651 57–63.doi:10.1016/j.aca.2009.07.065. (I.F. 3.75)

6. Baig, J.A., T.G Kazi, Arain, M.B., Afridi, H.I., Kandhro, G.A., Sarfraz, R.A., Jamal, M.K., Shah, A.Q. Evaluation of arsenic and other physico-chemical parameters of surface and ground water of Jamshoro, Pakistan Journal of Hazardous Materials (2009) 66, 662–669. doi:10.1016/j.jhazmat.2008.11.069 (I.F. 4.14).

7. J.A. Baig, T.G. Kazi, Arain, M.B., Shah, A.Q., Sarfraz, R.A., Afridi, H.I., Kandhro, G.A., Khan, S. Arsenic fractionation in sediments of different origins using BCR sequential and single extraction methods Journal of Hazardous Materials (2009), 167, 745–751.doi:10.1016/j.jhazmat.2009.01.040 (I.F 4.144)

8. J.A., Baig, T.G. Kazi, H.I. Afridi, A.Q. Shah, S. Khan, N.F. Kolachi, Arsenic speciation and other water quality parameters of surface and ground water samples of Jamshoro Pakistan. International Journal of Environmental Analytical Chemistry. (2010), (I.F = 1.146) (Accepted).

9. J.A. Baig, T.G. Kazi A. Q. Shah, S. Khan, Nida F. Kolachi, H.I. Afridi1, G. A. Kandhro, S. K. Wadhwa, A. M. Baig, F. Shah, F. H. Kanhar, Determination of arsenic scalp hair of

xxviii

children and drinking water for risk assessment. Journal of Human and Ecological Risk (2011), 17, 266-280 (I.F 1.528).

10. T.G Kazi, J.A., Baig, A.Q. Shah, G.A. Kandhro, Afridi, H.I., S. Khan, N.F. Kolachi, S.K. Wadhwa, F. Shah, Determination of arsenic in scalp hair Samples of exposed subjects using advance Extraction with and without enrichment. AOAC International, (2011), 94(1), 293-299 (I.F. 1.549).

11. T.G. Kazi , J A. Baig, A. Q. Shah, H.I. Afridi, G. A. Kandhro , S. Khan, Nida F. Kolachi, S. K. Wadhwa, F. Shah, Determination of arsenic in scalp hair of children and its correlation with drinking water in exposed areas of Sindh Pakistan. Biological Trace Element Research, (2010) Accepted. (I.F. 1.13).

12. J A. Baig, T.G. Kazi , A. Q. Shah, H.I. Afridi, G. A. Kandhro , S. Khan, Nida F. Kolachi, S. K. Wadhwa, F. Shah, Evaluation of toxic risk assessment of arsenic in male subject through drinking water in Southern Sindh Pakistan. Biological Trace Element Research, (2010) Accepted. (I.F. 1.13).

13. J A. Baig, T.G. Kazi, A. Q. Shah, H.I. Afridi, G. A. Kandhro , S. Khan, Nida F. Kolachi, S. K. Wadhwa, F. Shah, A green analytical procedure for selective determination of arsenic in scalp hair samples of arsenic exposed adults of both genders. Pakistan Journal of Analytical and Environmental Chemistry (2010)11(2), 23-29.

14. J A. Baig, T.G. Kazi, A. Q. Shah, H.I. Afridi, G. A. Kandhro , S. Khan, Nida F. Kolachi, S. K. Wadhwa, F. Shah, Inorganic arsenic speciation in ground water samples using electrothermal atomic spectrometry following selective separation and cloud point extraction. Analytical Sciences. (2011), 27(4), 439-445.

15. J.A. Baig, T.G. Kazi, A. Q. Shah, H.I. Afridi, G. A. Kandhro, S. Khan, N.F. Kolachi, S.K. Kumar Wadhwa, F. Shah, M.B. Arain, M.K. Jamali. Determination and evaluation of arsenic contents in vegetables grown in soils, irrigated with tube well and canal water in Pakistan. Agriculture water Management (Revised Submission) (2011).

1

Chapter - 1

INTRODUCTION

The populations throughout the world have sound knowledge about the complexity

of nature and its weak balance in the global ecology. Human activities were directly or

indirectly involving in variation of the natural ecological network. The ecosystems were

extensively contaminating with metals and metalloids throughout the world and numerous

studies have been published (Garrett, 2000; Jordao et al.,, 2002). The contaminants were

interring in aquatic environment from natural processes as well as from harmful waste of

human activities (Karadede et al., 2004; Iwegbue et al., 2007; Zhou et al., 2008).

Among metal and metalloids, arsenic (As) is of increasing concern due to its high

toxicity and widespread natural abundance in the environment. It is widely distributed in

the earth’s crust with an average level of 2 mg As kg-1. It is commonly found in waters,

atmosphere, rocks, sediments, soils, as well as in flora and fauna. It is primarily produced

as a by-product from smelting of metallic ores (Hossain, 2006). It can exist in four

valency states (–3, 0, +3 and +5) and considered as a global environmental calamity

(Smedley and Kinniburgh 2002; Soylak and Yilmaz 2006). The mobilization of As in any

ecosystem might be due to the natural processes such as weathering reactions, biological

activities and volcanic emissions as well as through a range of anthropogenic sources

(Mandal and Suzuki 2002; Kundu and Gupta 2006). Arsenic contaminated drinking water

is a primary source of human exposure in Indo-Pak sub-continent (Smedley and

Kinniburgh 2002; Arain et al., 2008). Smedley et al., 2002 was addressing the transport

and transformation of As in stream-aquifer systems.

1.1. Arsenic in surface and ground water

The reservoirs of surface and ground water are important sources of water, because

they are providing several beneficial assistances of life (domestic usage and irrigation of

crops). Surface and ground waters are contact with ores or tailings and were

contaminating with As and other contaminants. Thus, surface waters near former

smelting or mining sites have elevated levels of As.

2

Surface and ground water were contaminating with As throughout the world

(Mandal and Suzuki, 2002; Chowdhury et al., 2000). It has been reported that about 60–

100 million people in India and Bangladesh were at risk due to As-contaminated drinking

waters (WHO, 1993, 2001; Cidu et al, 2003; Chakraborti et al, 2002, 2004). The World

Health Organization (WHO) and United State Environmental Protection Agency (US

EPA) were revising the maximum limit of contamination of As in drinking water as 10µg

L-1 (WHO, 1996; EPA, 2001). The highly As contaminated (>50 mg L-1) groundwater

has been reported in various parts of world (Chowdhury et al., 2000; Focazio et al., 2000;

Mukherjee and Bhattacharya, 2001; Smedley and Kinniburgh, 2002; Bhattacharya et al.,

2002; Farooqi et al., 2007). In Pakistan, researchers and agencies (Pakistan Council of

Research in Water Resources ‘PCRWR’ and UNICEF) were reporting the level of As >

100 µg L-1 in groundwater (Tahir, 2000; Nickson et al., 2007; Farooqi et al., 2007;

Kahlown, et al., 2002). The mortality of more than 40 people was reporting in Hyderabad

city, Pakistan during 2004, due to the contaminated municipal water. The source of

municipal water is river Indus, which was contaminating with lake water containing high

level of As and other toxic metals during that period (Farooqi et al., 2007; Arain et al.,

2008).

1.1.1. Arsenic speciation in water

However, total As contents in contaminated environmental samples are the poor

indicator of As toxicity because toxicity and bio-availability of As compounds were

depending on their chemical forms. In drinking water, it is predominantly occurred in

inorganic (As3+ and As5+) and organic forms (methyl and dimethyl arsenic compounds)

(Smedley et al., 2002). The As5+ species are stable and predominant under aerobic

environment, while As3+ species are found under reducing anaerobic condition like

groundwater (Vijayaraghavan and Yun 2008). Redox potential (Eh) and pH were said to

be the most important factors, which may controlling the As speciation (Vaclavikova et

al., 2008). The H2AsO4- is dominant at low pH (> pH 6.9), whilst at higher pH, HAsO4

2-

becomes dominant (Vaclavikova et al., 2008). The H3AsO4 and AsO43- may be present in

extremely acidic and alkaline conditions, respectively as shown in Table 1.1

(Vaclavikova et al., 2008). The H3AsO30 is a predominate specie of arsenite under

3

reducing environment at pH < 9.2l (Brookins, 1988; Yan et al., 2000). The

underdeveloped countries were suffering from the contamination due to high rate

industrial growth. The toxicity of As species has been reported in decreasing order as

inorganic As3+> organic As3+> organic As5+> inorganic As5+.

Table 1 Inorganic As speciation in water

pH As3+ pH As5+

0-9 H3AsO3 0-2 H3AsO4

10-12 H2AsO3- 3-6 H2AsO4

-

13 HAsO32- 7-11 HAsO4

2-

14 AsO33- 12-14 AsO4

3-

1.2. Arsenic in soil and sediment

The mobilization of As in any eco-system may be happen by natural processes

and a range of anthropogenic sources (Mandal and Suzuki, 2002; Kundu and Gupta

2006). In general, low level of As was reporting in soils and sediments, while elevated

levels of As were recorded in those soils and sediments which have been affected by

anthropogenic activities (Arain et al., 2008). Soil and sediment are considered as most

important environment contributor to sink of elements including As in ecosystem.

Mobilization and chemical partition of As is directly effect on soil and sediment due to

their physico-chemical characteristics such as oxides of iron and manganese (Bose and

Sharma, 2002; Manning et al., 2002; Jiang et al., 2005).

1.3. Translocation of Arsenic in grain crops and vegetables

Food commodities (grain crops and vegetables) are considered as major path for

entrance of metals and metalloids into food chain (Das et al., 2004; Arain et al.,, 2009). It

is because of cultivated (soil of irrigated land and irrigation water) and fertilizing media

4

(fertilizers, pesticides and herbicides). The translocation of As and other toxic elements

by plants are largely dependent on the bioavailable As rather than their total contents in

soil (Wang et al., 2003; Norvell et al., 2000; Zhang et al., 2002; Liu et al., 2003). The As

contaminated water used for irrigation may decreased plant height, crop production and

root growth (Zhang et al., 2002; Abedin et al., 2002; Liu, et al., 2003; Wang-da, et al.,

2006; Hossain, 2006). Agriculture soil and edible plants were used as indicator of long-

term and short-term As exposure (Arain et al.,, 2009).

After entering the plant, As can disturb plant metabolism as phosphorylation is

decouple in mitochondria by arsenate and the enzymes activities may cut off by arsenite,

when it reacts with sulphydryl groups of proteins (Yun-Sheng et al., 2007). The uptake of

As by plants may compete with other nutrient in soil such as phosphorus via phosphate

transport systems (Cao et al., 2003). On the other hand, phosphate may directly effect on

As contents of soil, to enhance the phytoavailability of As (Yun-Sheng et al., 2007).

The economy of Pakistan is mostly dependent on agricultural product for their

domestic usage and > 85% of the population (males and females) concerned with this

field. National development depends on the yield of farming production in south Asian

(World Development Indicators, 1998). The growth rate of population is gradually

increased in Pakistan. This fact indicated the high requirement of food production

(especially grains and vegetables) has been a demanding issue.

Large amount of As deposits on the irrigated lands throughout the year is depending

on the irrigation water obtained from surface and underground resources. Arsenic

transport from irrigation media (soil) to groundwater and vice versa is dependent on

water–soil interaction in environment of subsoil (Signes-Pastor et al., 2007). This fact

indicated that the sources of As in groundwater might be due to the geological activities.

The real mechanism of As mobility is still unclear.

1.4. Biological specimens of human as biomarkers

Determinations of As and other elements in human fluids and tissues lead us to

acquired the information for environmental exposure (Kazi et al., 2008, 2009; Arain et

5

al., 2009). The scientists have used blood, urine, hair and nail samples as biomarkers for

detection of As in human (Mercedes et al., 2004; Uchino et al., 2006; Kazi et al.,, 2008,

2009). In the majority of cases, whole blood, serum, plasma and hair were analyzed (Kazi

et al.,, 2008). The concentration of essential trace and toxic elements in whole blood

provides useful information about elements, including intracellular and extra-cellular

compartments of blood cells (Brettell et al., 2005).

The metabolism of elements in human body is controlled by homeostatic process

which helps in excretion of extra amount of any essential or toxic metals from the body,

and this metabolic system explains basically the short term usefulness of blood analysis

(Tuzen, 2002). Scalp hair analysis is an easy method for the exposure study of As and

other trace elements (Brettell et al., 2005). Hair analysis is also used to identify

environmental pollutants, because the concentration of As in hair are usually ten time

higher as compare to other tissues (Wright et al., 2006).

In forensic science, human hair has been demonstrated to be one of the most

useful clinical samples to assess drug consumption, so drugs abuse and/or metabolites

analysis in human hair is now well established and the methods are recommended

(Pereira et al., 2004). The metal of endogenous origin is looked for the surface

contamination, if significant, has to be removed from the hair before analysis (Sera et al.,

2002). An ideal cleaning procedure that removes the element from external sources

without removing any metal of endogenous origin is not a matter of course. The problem

of hair cleaning is discussed and the effect of washing, using different procedures, is

described in the literature (Arain et al., 2009).

1.5. Effects of Arsenic on human health

Adverse health effects arising from the consumption of As contaminated drinking

water, is a serious problem in Taiwan, Argentina, Chile, Mexico, India, Bangladesh,

China, Vietnam and Cambodia (Chowdhury et al., 2000; Jiang, 2001; Mandal and

Suzuki, 2002; Ng et al., 2003; Jack et al., 2003; Ahmad et al., 2004; Uddin et al., 2006;

Arain et al., 2008, 2009).

6

Human health effects were also observed in local of Pakistan, due to the

consumption of As contaminated surface and ground waters having As > 50 µg L-1

(Tahir, 2000; Nickson et al., 2007; Farooqi et al., 2007; Kazi et al., 2009; Fatmi et al.,

2009; Arain et al., 2009). The most common sign of As exposure is hyper pigmentation.

These skin lesions generally develop five to ten years after exposure commences,

although shorter latencies are possible (Nielsen, 2001; Arain et al., 2009). Many other

signs and symptoms have also been reported in Bangladesh, i.e. chronic cough,

crepitating in the lungs, diabetes mellitus, hypertension, and weakness (Arain et al.,

2009). Inorganic As in drinking water is generally found > 95% of total As, which can be

absorbed easily in the gastrointestinal tract (Milton et al., 2004). Approximately 80–

100% of inhaled and ingested As is absorbed through the gastrointestinal tract and lungs

but up to 50–70% of the absorbed As is gradually eliminated by methylation in the

kidneys through urine. When ingestion is greater than excretion, it tends to accumulate in

the hair and nails (Kazi et al., 2009; Arain et al., 2009; Fatmi et al., 2009; Kazi et al.,

2010).

1.6. Methodology

1.6.1. Optimization of Methods for As Speciation in drinking water (Multivariate strategy)

The atomic absorption spectrometry is a powerful analytical technique for the

determination of total contents of trace elements including As, but the direct

determination of different species of As is difficult (Wang and Mulligan 2006). This

trend was reversed, when scientists were developed new sample preparation (pre-

concentration) methodologies for the determination of total metals and metalloids

contents and their speciation (Hirata et al.,, 2005; Murata et al.,, 2005). The cloud point

extraction (CPE) method was applied for the separation of As species (As3+ and As5+),

using non-ionic surfactants from aqueous solution (Pereira and Arruda, 2003; Zhang and

Minami 2004; Bezerra et al.,, 2005; Tang et al.,, 2005; Murata et al.,, 2005). Inorganic

As (iAs) by solid phase extraction was frequently used (Zhang et al.,, 2004; Zhang et al.,,

2005; Zhang et al.,, 2007). These sample pre-concentration methodologies are simple,

low cast, environmental friendly and provides high pre-concentration factor.

7

Procedures for optimization of factors by multivariate techniques have been

encouraged, as they are faster, more economical and effective, and allow more than one

variable to be optimized simultaneously (Ferreira et. al., 2003; Jalbani et al.,, 2008).

Among the different groups of designs, Plackett–Burman design, introduced in 1946 by

Plackett and Burman (Arain et al.,, 2008; Jalbani et al.,, 2008). Plackett-Burman designs

constitute a variation on saturated fractional designs, allowing the evaluation of either

system with few experiments; k factors can be studied in k+1 runs (only the main effects

are estimated). These designs can be used only when k +1 is a multiple of 4 (i.e., k=3, 7,

11…..) (Karadede and Unlu 2000; Jalbani et al.,, 2008; Cespon-Romero and Yebra-

Biurruna 2008 Arain et al.,, 2009). Ferreira, et al.,, 2002; Soylak et. al., 2005 were

applying factorial design as a screening method in order to select the variables that have

influence on a system.

1.6.2. A multivariate study for arsenic speciation and physico-chemical parameter in

water

A lot of research has been conducted throughout the world to find out natural and

anthropogenic contamination of entire eco-system by micronutrients, trace and toxic

metals (Bengraine and Marhaba 2003; Mendiguchia et al.,, 2007). The investigation

water quality parameter and As speciation has been done to develop analytical techniques

and processes as quick and cheap. So, the screening and monitoring of surface and

ground water quality and As speciation is most important for consistence and reliable

information (Wagner et al.,, 2005; Arain et al.,, 2009). However, the shortest and the

most economical screening studies demand the decrease in the number of analyses

(Blomqvist 2001; Jalbani et al.,, 2007).

Large and complex data sets contain physico-chemical parameters and As

speciation of surface and ground water are difficult to communicate and to draw

meaningful conclusions. Therefore, it is compulsory to apply chemometric techniques

based on statistical methods (Malinowski, 2002; Jolliffe, 2002; Arain et al.,, 2009).

Shrestha and Kazama (2007) reported that results of statistical multivariate data analysis

in a complex data matrix comprise of a large number of physico-chemical parameters,

which are often difficult to understand and illustrate meaningful results (Arain et al.,,

8

2009). The application of different statistical multivariate techniques [factor analysis

(FA), principal component analysis (PCA), cluster analysis (CA) and discriminant

analysis], helps for illustration of complex data set of water quality and ecological

condition of understudy area (Bengraine and Marhaba 2003; Wagner et al.,, 2005; Choi

et al.,, 2009).

1.6.3. Fractionation of As in soil and sediments

1.6.3.1. Single extraction

The total As content in soils and sediments is a poor indicator of its bioavailability,

mobility or toxicity (Hullebusch et al., 2005). These properties are basically depending

on the chemical association between different components of the sample (Hullebusch et

al., 2005). The uses of single and sequential extraction methods were providing important

approaches to assess the interaction of As with different fractions of soils and sediments

as reported in literature (Markert and Friese 2000; Hullebusch et al., 2005).

The European Commission has adopted a standardized extraction method with

ethylenediaminetetraaceticacid (EDTA) to represent the ‘available fraction’ of toxic

elements in soil and sediment (McLaughlin et al., 2000; Jamali et al., 2007). It is used as

an extractant for bio-available fraction of any analyte (Jamali et al.,, 2006). In some trials,

EDTA was found to give a very good indication of the pollution hazard of toxic elements

in soils as well as being a reliable test for predicting plant-available metals (Berti and

Jacob, 1996; van Erp et al., 1998; Takeda et al., 2006; Houba et al., 2000; McBride et al.,

2003; Xiao-ping et al., 2004; Jamali et al.,, 2006,2007, 2008; Kuo et al., 2006; Menzies et

al., 2007; Arain et al.,, 2009).

1.6.3.2. Sequential Extraction Method

Arsenic ions in sediments and soils are present along different phases, i.e.

oxyhydroxides of aluminum, iron, organic matter, phyllosilicate minerals, manganese,

sulfides and carbonates (Quevauviller, 2003). The ions of As are retained on solid phases

by different mechanisms (ion exchange, outer- and inner-sphere surface complexation

(adsorption), precipitation or co-precipitation). Taking into account the diversity of

9

existing procedures and lack of consistency in different protocols used by various

researchers in 1987, the Standards Measurement and Testing Programme (formerly BCR

was launching a project to harmonies measurements of the extractable elemental fractions

in soils and sediments (Quevauviller, 2003; Arain et al.,, 2008b). This programme was

starting with the comparison of existing procedures tested in two interlaboratory

exercises (Quevauviller, 2003). Therefore, a three-step extraction procedure was designed

based on acetic acid extraction (step 1), hydroxylammonium chloride extraction (step 2)

and hydrogen peroxide/ammonium acetate extraction (step 3) (Quevauviller, 2003).

The acid-soluble fraction generally contains a relatively small percentage of the

total metal content and is precipitated or co-precipitated with carbonate (Sahuquillo et al.,

2003; Kazi et al., 2005). Carbonate is a significant sorbent for metals especially on those

areas, where the abundance of other fractions are less (Canepari et al., 2006; Jamali et al.,

2007). This fraction is loosely metal bound phase and changed with respect to

environmental condition. Therefore, this fraction is vulnerable for leaching at acidic

condition at pH in between 4-5 (Jamali et al., 2007). The 0.11 mol L-1 acetic acid can be

dissolve carbonates and dolomite without significant attack on organic matter (Arain et

al., 2009).

In reducible fraction Fe and manganese hydrous oxides were extracted. The

hydroxylamine hydrochloride in nitric acid medium is the reagent most widely used to

leach easily reducible fractions (Jamali et al., 2007). In modified BCR procedure a high

amount of hydroxylamine hydrochloride to extract maximum level of metals in reducible

fraction (Arain et al., 2009; Jamali et al., 2007; Jamali et al., 2009).

The organic bonded fraction may release in oxidizable step. Therefore, it was not

considering as a bioavailable or mobile fraction (Jamali et al.,, 2007). This fraction is

associated with humic substances, which are stable substances due to their high

molecular weight. Thus, in this fraction metals or metalloid may release in small quantity

(Lombi et al., 2000; Jamali et al.,, 2007). These substances have a high degree of

selectivity for divalent ions then the monovalent ions (Wenzel et al., 2001; Keon et al.,

2001; Nystrom et al., 2003; Canepari et al., 2006; Arain et al., 2009).

10

Residual fraction contains primary and secondary mineral, which may deposit in

the crystalline lattice. For this fraction strong acids (HF, HClO4, HCl and HNO3) were

used, to digest the residual portion of As in soil or sediment. The amount of coupled

metals is also evaluated by some authors as the difference between total concentration

and sum of all fractions of metals extracted by different steps of sequential scheme (Kazi

et al., 2005; Martinez-Sanchez et al., 2008)

1.7.3.3. Single step extractions based on sequential extraction schemes

An attractive approach was designed to replace the sequential extractions by

single step extractions using same reagents and operating parameters, but using a separate

aliquot of same sample for each reagent (Arain et al., 2008b). This approach has been

investigated on Tessier’s and three-step BCR sequential extraction procedures (Tack et

al., 1996; Perez-Cid et al., 2001; Greenway and Song 2002; Filgueiras et al., 2002; Arain

et al., 2008b). The major advantage of single step extraction is that all fractions were

simultaneously extracted, except oxidizable fractions, at the expense of wasting larger

amounts of sample (Arain et al.,, 2008b).

1.8. Description of study area

Sindh is 3rd largest province of Pakistan, situated in South Asia, neighboring the

Iranian plateau at the west. This province is located at coordinate of 24° 52′ N and 67°

03′ E. The annual average rainfall is about 200-300 mm (SRP, 2004). The annual

maximum and minimum average temperature is 46 °C and 4 °C, respectively. The delta

of Sindh is composed of quaternary alluvial deltaic sediments derived from Himalayan

rocks (Farooqi et al., 2007). Whereas, most of its area like Dadu and Jamshoro are

situated at offshoots of Kirthar range with quaternary and tertiary volcanic rocks having

thermal springs. This province is divided into 29 districts. The current study was focused

on four districts named Jamshoro, Hyderabad, Khairpur Mir’s and Sukkur.

Jamshoro district is situated at right bank of Indus river and positioned between

25o19′-26o42′ N and 67o12′- 68o02′ E. It has four sub-districts (Sehwan, Manjhand,

Jamshoro and Thana Bula Khan). Dadu district covers an area of 19,070 square

11

kilometers. It has four sub-districts (Dadu, Khairpur Nathan Shah, Mehar and Johi).

Geographically it is spanned from 27°05' to 28°02' north latitudes and from 68°47' to

69°43' east longitudes at an altitude of 220 feet (67 m) from sea level. Hyderabad district

is administratively subdivided into four sub-districts (Hyderabad, Tando Jam, Latifabad

and Qasimabad). Geographically it is spanned from 24° 20' to 25° 30' north latitudes and

from 68° 40' to 68° 30' east longitudes at an altitude of 180 feet (67 m) from sea level.

The district Khairpur is situated on the east bank of the Indus river composed of

quaternary alluvial-deltaic sediments coming from Himalayan rocks. It has eight sub-

districts (Khairpur, Kingri, Gambat, Kot Diji, Subho Diro, Thari Mirwah, Faiz Ganj and

Nara). The understudied district lies in between Latitude 26° 0′ - 27° 45′ and Longitude

68° 0′ - 70° 15′. It is a semiarid and subtropical continental climate and temperatures

ranged from 12 to 50 °C. The district has an area of 15,910 square kilometers. The

district of Sukkur covers an area of 5,165 square kilometers. It has four sub-districts

(Sukkur, Rohri, Saleh Pat and Pano Akil). Geographically it is spanned from 27° 05' to

28° 02' north latitudes and from 68° 47' to 69° 43' east longitudes at an altitude of 220

feet (67 m) from sea level.

1.8. Remediation of Arsenic from water

Many scientists were trying to remove As from the drinking water as well as

industrial effluents using conventional techniques, such as coagulants, solvent extraction,

ion exchange, iron co-precipitation and reverse osmosis (Pena et al.,, 2005; Balaji et al.,,

2005; Singh and Pant, 2006; Kundu and Gupta 2006). The applicability of these

procedures is limited to several drawbacks such as, incomplete remedy of As, high

operational and capital expenditures, costly reagents, low selectivity, high energy

requirements and presence of interfering species from toxic sludge/ waste products that

are hard to be removed (Zhang et al.,, 2008).

Adsorption is an efficient method for treatment of As contaminated water. The

biosorption by variety of bio-materials is an excellence technique for remedial solution of

metal ions from aqueous media (Ferraz et al.,, 2004). The biosorption is accomplished

due to presence of carbohydrates, proteins and phenolic moieties, having different

12

functional groups such as hydroxyl, carboxyl, sulfate, phosphate and amino (Cao et al.,,

2004; Mungasavalli et al.,, 2007). The biosorption procedure has some advantages such

as, reusability of bio-material, minimum operating cost and time, enhanced the selectivity

for analyte of interest and efficient As removal from waste water without producing

secondary complex (Cao et al.,, 2004). However, several investigations were reported for

the use of biosorbents materials, i.e., alginate, chitosan, orange waste, methylated

biomass obtained from yeast, fungal biomass, and chicken feathers to eliminate As from

water solution (Teixeira and Ciminelli, 2005). Selective adsorption was achieved by

using inorganic mineral i.e., oxides, biological materials, polymer resins or activated

carbons (Sari and Tuzen 2009). However, it is still a strong challenge in developing

economical and frequently available bio-sorbents for As removal.

13

1.9. Aims and objectives

The present study is a part of a comprehensive program conducted to evaluate the

toxicological effects of arsenic in surface and ground water of selected areas of Sindh

(Khairpur, Sukkur, Hyderabad and Jamshoro), which are located in the region of lower

Indus basin and considered as aquifer with some what high-As groundwater sources

(British Geological Survey, 2004). It mobility from soil and sediments, impact on plants

and human and removal from water is also a part of this study. The aims and objective of

present study are

Collection of the surface and groundwater and sediment samples from selected

areas of Sindh, Pakistan.

For chemical speciation, the saturation indices of Ca2+, Mg2+, CO32-

and SO42- was

calculated by using speciation-modeling geochemical computer program

PHREEQC (USGS, 2007) at equilibrium conditions of the minerals possibly

controlling the soluble chemical species.

The multivariate technique, cluster analysis (CA) was used to evaluate

information about the similarities and dissimilarities present among the different

sampling sites where as an other multivariate technique, principle component

analysis (PCA) was also applied to identify possible sources to influence of As

species within investigated ground water samples of Jamshoro and Khairpur

districts, Sindh, Pakistan.

Interpreting the large data set of water samples in comprehensively and concisely

ways using multivariate techniques, cluster analysis (CA) and principle

component analysis (PCA). The CA was used to evaluate information about the

similarities and dissimilarities present among the different sampling sites whereas

PCA was applied to identify possible sources of As contamination in ground

water samples of Jamshoro and Khairpur districts Sindh, Pakistan.

14

For As speciation, the total arsenic (AsT), inorganic arsenic (iAs), and arsenic

species (As3+ and As5+) were determined in surface and ground water samples of

Khairpur Mir’s and Jamshoro Pakistan, collected during 2007 to 2010, using

conventional preconcentration, solid phase extraction, co-precipitation and cloud

point extraction methods.

Optimization of the cloud point extraction (CPE) and co-precipitated with Pb-

PDC using multivariate technique for the determination of As3+ whereas, solid

phase extraction (SPE) methods was used to determined iAs in natural water

(surface and ground water) and validated by a certified reference material of water

(SRM 1643e) and standard addition methods.

To find out the possible mechanism of As mobility in water, the correlation study

of As species with physico-chemical quality parameters of water and Fe contents

was carried out.

Sampling of agricultural soil (irrigated with canal and tube well water) and

different crops cultivated on these soils such as grains and vegetables.

To check the mobility of As different fractions of As (exchangeable, reducible

and oxidizable) by BCR-SES were determined and compared them with the

results obtained from single step extractions, using the same operating conditions

(BCR-SES). The accuracy of the methodologies has been assessed with a certified

reference material of sediment (BCR 701).

To estimate the cumulative exposure of arsenic in water and its relation with As

levels in scalp hair of males and evaluate the potential risk factors. For this

purpose, scalp hair samples of male subjects of two age groups (16 – 30 and 31 –

60 years) were collected simultaneously from same households where water

sampling was conducted.

15

For the remediation study a biomass taken from leave and stem powder of a

thorny Acacia species Acacia nilotica, were sampled, pretreated and

characterized. and bio-sorption

The bio-sorption efficiency of Acacia nilotica for As removal from water,

different parameters, i.e., biomass dosage, pH, temperature and contact time were

optimized.

For the theoretical validation, the Langmuir, Freundlich and Dubinin–

Radushkevich isotherm models were applied to explain equilibrium biosorption

condition. Thermodynamic and kinetic parameters were computed to illustrate the

biosorption method of As onto the treated indigenous biomass.

16

Chapter - 2

Literature Review

2. Over view of Arsenic

Arsenic (As) is an element has both non metallic and metallic characteristics with

atomic mass unit 74.92 and atomic number (33). It is 20th, 14th and 12th most abundant

mineral of earth's crust, seawater and human body, respectively (Sullivan, 1969). It was

reported that the As is used in different fields i.e. agriculture, medicine, electronics,

metallurgy and livestock industry (WHO, 2001). It is contaminating the environment via

various sources like industrial effluents, agricultural wastage, mining, poultry farming

and arsenical pesticides production and their application.

2.1. Arsenic in water

Arsenic in the water is a serious natural calamity and a public health hazard,

which originated from natural systems including, both anthropogenic and geological

sources (Wang et al., 1998; UNEP, 2000). In 994, the first report of waterborne As

toxicity in northern China (Datong Basin of Shanxi province) was recognized by Guo et

al., 2003 and Li et al., 2005. Later on Guo et al., 2003 and Xie et al., 2008 were examined

high concentration of As along River of Huangshui within shallow aquifers.

Arsenic is found in the shallow ground water of many countries like Pakistan,

Bangladesh, India, Argentina, Mexico, Mongolia, Germany, Thailand, China, Chile,

USA, Canada, Hungary, Romania and Vietnam as reported by Kamal et al., 2002;

Chakraborti et al., 2003; Dang et al., 2004 and Berg et al., 2007.

Prasenjit et al., 2007 was described the concentration of As > 1000 μg L-1 in

Bangladesh. Like Bangladesh and other neighboring countries, Pakistan is facing the

serious public health disaster due to arsenic contaminated water and has acknowledged

the need of apprizing drinking water quality and As problem. Shrestha et al., 2002 was

explained high As concentration in drinking water (ground and surface water) in

Pakistan. The Pakistan Council of Research in Water Resources (PCRWR) and UNICEF

17

reported that As contaminated groundwater (10-200 µg l-1) was observed in some areas of

Punjab province. In Sindh, 16-36 % people are exposed to As over 10-50 μg l-1 in

groundwater as demonstrated by Ahmad et al., (2004). He has also revealed some hot

spots of As enrichment in the basin of Indus plane. Manchar Lake, the largest freshwater

lake in Sindh Pakistan is a main source of water for domestic and agricultural purposes.

Lake and ground water in the vicinity of Manchar Lake is saline with high As

contamination and unfit for domestic and irrigation usage as described by Arain et al.,

2007.

2.1.1. Arsenic species in water

The bioavailability and toxicity of As depends on its binding form. Arsenic is

present in different organic and inorganic forms. Inorganic forms of As are more toxic

than organic species, with As3+ being more toxic than As5+ as reported by Elci et al.,

(2008) and Shah et al., (2009). Wang et al., (2006) has been reviewed that the calculated

half-lives of As3+ in surface water is 4–9 days and in the ratio of As3+/As5+ was increased

with depth. Hossain (2006) accounted that As3+ is more toxic than As5+. He has explained

that inorganic forms of As dissolved in drinking water are the most significant forms of

natural exposure, whereas, the organic forms of As present in food are much less toxic to

humans (Hossain, 2006). In biochemical reaction, the phosphate molecule can replaced

by As5+ and block the transformation of adenosine triphosphate to adenosine

triphosphate. Whereas, the activities of thiol groups of proteins may deactivate by As3+.

2.1.2. Physico-chemical parameter and As species in natural water

The regular monitoring programs are required for surface and underground water

because the spatial and temporal variations deteriorated the water quality as pointed out

by Singh et al., 2005. Large and multifactor water quality data matrix of surface and

ground water is difficult to understand and describe the significant fates and conclusions

(Peirce et al., 1998; Gray, 2005). Therefore, different multivariate statistical techniques

[factor analysis (FA)/principal component analysis (PCA), discriminant analysis and

cluster analysis (CA)] have been used for the evaluation of the complex environmental

data as conducted by Singh et al., 2004. The multivariate statistical methods were

18

frequently used for identification of the possible contamination sources in a water system

(Simeonova et al., 2003; Bengraine and Marhaba, 2003; Liu et al., 2003; Simeonov et la.,

2003). Moreover, these techniques were also applicable to manage appropriate strategies

for water resources as reported by Singh et al., 2004.

The multivariate techniques, PCA and CA were used to categorize and control the

different pollutants in river water. Kowalkowski et al., 2006 was monitored the quality of

river water with the help of PCA and CA. Da Silva and Sacomani, in 2001 and De

Andrade et al., in 2007, were also investigated the water quality of surface water in

Brazil using multivariable statistical techniques. De Andrade et al., 2007 was analyzed in

details several water quality parameter of water. Mendiguchia et al., 2007, studied the

waters quality of river water polluted by anthropogenic and natural sources by

chemometric techniques. Later on Venugopal et al., 2008 was applied multivariate

statistical techniques to assess possible factors, which may be responsible for the

variations in chemical composition of groundwater. His group was drawn Box-whisker

graphs to assess chemical and seasonal effect on physico-chemical characteristics of

water quality (Venugopal et al., 2008). Hussain et al., 2008, has been used cluster

analysis, to evaluate the quality of surface water. Singh et al., 2004 and Arain el al., 2008

was observed that for rapid evaluation of water quality, only one site in each cluster may

serve as good in spatial estimation of the water quality as the whole network. The

multivariate techniques (PCA and CA) were successfully applied by Baig et al., 2010, to

evaluate the distribution of arsenic species with respect to other water quality parameters

in surface and ground water of district Khairpur Sindh, Pakistan.

2.1.3. Advance extraction method of arsenic species in natural water. A multivariate study

The speciation of As is most important for the assessment of toxicological and

environmental impact of arsenic. Therefore, Hirata and Toshimitsu 2005; Wang, and S.,

Mulligan 2006; Zhang et al.,,2007; Hu et al., 2008; have been pointed out that high

sensitive and simple methods are necessary for determining the concentration of the

different oxidation state of the As in the environment, because of its bioavailability,

physiological and toxicological effects.

19

Jitmanee et al., (2005), Coelho et al., (2005), Gregori et al., (2005), and Kile et

al., 2007 were investigated and reported the As species by inductively coupled plasma

atomic emission spectrometry (ICP–AES), inductively coupled plasma mass

spectrometry (ICP–MS), high performance liquid chromatography (HPLC), electro

analytical techniques and different hyphenated coupled techniques. For ultra trace

quantity of As in natural waters, the coupled detectors except ICP-MS are poor in

sensitivity (Zhang et al., 2007). The ICP-MS with high sensitivity is too expensive for the

most researchers to be equipped (Zhang et al., 2007). Furthermore, the combination of

instruments makes the determined procedure more complex and the continuous

determining mode is also not suitable for atomic absorption spectroscopy as also

explained by Zhang et al., 2007.

It is possible by applying sample separation and pre-treatment procedures prior to

determine As species by atomic absorption spectroscopy. The separation and pre-

concentration methods i.e., solvent extraction, solid phase extraction, co-precipitation and

cloud point extraction have been conducted by Kile et al., (2007), Jitmanee et al., (2005)

and Ferguson et al., (2005). These are fast, low cost and simple techniques as compared

to chromatographic techniques. Inorganic metal oxides, such as aluminum oxide (Al2O3),

cobalt oxide and titanium dioxide (TiO2), have been used to concentrate trace and ultra

trace metallic elements as sorbents. Whereas, the Al2O3 showed high adsorption ability

for target metal ions due to its ordered mesoporous structure with a pore size of about 10

nm as studied by Hu et al., (2008). Ferreira et al., (2007) has been reviewed the

separations and pre-concentrations of different elements by cloud point extraction (CPE).

The CPE has been applied for As species (As3+ and As5+) using different complexing

reagents i.e., ammonium pyrrolidine dithiocarbamate, ammonium O, O-diethyl-

dithiophosphate, molybdate as chelating agents and Sodium diethyldithiocarbamate (da

Silva et al., 2000; Shemirani et al., 2005; Piech and Kubiak 2007; Zhang et al., 2007).

The co-precipitation method using APDC was frequently applied for the determination of

As3+ in natural water, a selective macromolecule for co-precipitation of inorganic As3+

(Zhang et al., 2007). According to Zhang et al., (2004) and Shah et al., (2009) the atomic

absorption spectrometry equipped with graphite furnace (GFAAS) or hydride generation

(HGAAS) were frequently used for quantitative determination of As. We have found that

20

HGAAS response is strongly high for As, but GFAAS is preferable as compare to

HGAAS for the measurements of As species using CPE and solid phase extraction due to

less matrix interference and low cost of sample preparation.

Ferreira et al., (2003) was pointed out that the application multivariate techniques

for optimization strategies of analytical method as rapid, effective and economical as

compare to traditional methods. Arain et al., 2008 and Jalbani et al., (2006), were

reporting that among different experimental designs the Plackett–Burman design was

most reliable to screen out the most significant variables in a system with only few

experiments. It is full two-level factorial design by center point replication and inclusion

of an axial portion (Jalbani et al., 2006). It is widely applied for the optimization of

sample pre-treatments and some instrumental conditions (Ferreira et al., 2002; Ferreira et

al., 2003; Jalbani et al., 2006). Soylak et al., (2005) has been applied two-level factorial

design for the optimization of a separation and pre-concentration system based on solid-

phase extraction phenomenon for lead from several sample matrixes like tea, soil and

water. We have been optimized and improve the CPE and SPE methods using

multivariate technique for the determination of As3+ and iAs in natural water.

2.2. Arsenic in soil and Sediments

In aquatic systems elements including As are present in the form of dissolved

ions, complexes and suspended colloids. The high concentrations of As in sediments are

of potential concern, as it might be added to pore or surface waters through desorption or

dissolution and thus deserves immense importance in the planning, management and

design of aquatic pollution research studies (Lumsdon et al., 2001; Filgueiras et al., 2002;

Taggart et al., 2004) .

21

2.2.1. Fractionation of As in soil and sediments

Total As concentrations in soils and sediments do not provide any information

regarding its chemical form, potential mobility, and bioavailability (Nadal et al., 2004;

Jamali et al., 2007; Reyes et al., 2008). Many studies have demonstrated good

correlations between total As content in soil and uptakes by plants (Jamali et al., 2007).

The success of risk assessment of As contaminated soils depends on how accurately one

can assess the bio-availability of As in soil and transfer to the human food chain (Enright

et al., 2005; Jamali et al., 2007). The toxic effects of elements including As also been

related to some operationally defined extractable fractions (Morselli et al., 2005; Jamali

et al., 2007).

2.2.2. Single extraction

Many chemical methods were used to study the bio-availability/mobility of As in

soils and sediments (Arain et al., 2009). The extraction with single solvent was performed

to determine different metals fractions of soil (bio-available, mobile or associated with

molecules) as reported by Signes-Pastor et al., 2007. Perez, et al., 2008 were investigated

that for soil and sediment samples the most commonly used leaching/extraction tests

were selected in order to identify the degree of similarity, exchangeability and/or

complementary nature of data. These tests consisted of single extractions using water,

mild salts (CaCl2, NaNO3), acid (CH3COOH) and complexing extractants (EDTA,

DTPA) (McBride et al., 2003; Cappuyns et al., 2004; Fuentes et al., 2006; Meers et al.,

2007; Arain et al., 2008; Cappuyns and Swennen, 2008).

The extraction with ethylene di-aminetetraacetic acid (EDTA) was found to give a

very good indication of the pollution hazard of metals in sediment and soils as well as

being a reliable test for predicting plant-available metals (Cajuste and Laird 2000; Jamali

et al., 2008). Neutral salt extractants are generally weaker extractants than EDTA and

give an indication of the immediately exchangeable (therefore immediately plant-

available) metals (McLaughlinet al., 2000; Jamali et al., 2008). Acid (CH3COOH) and

complexing agents (EDTA) were more effective in remobilizing metals from

environmental samples (Alvarez, et al., 2006).

22

2.2.3. Sequential extraction

Scientific interest in the application of sequential extraction has been growing,

ever by Arain et al., 2009, proposed a concept of chemical pools in soil and sediment to

account for the leaching behavior of elements studied. Since elements were extracted to

different extents under different reagent and procedural conditions. Thus a water soluble

pool, ion exchangeable pool, a strongly bound pool extractable by chelating agents, a

secondary mineral pool and a primary mineral pool were proposed. This classification

was to be broadened by the work of Tessier et al., 1979, to describe metal fractions in

sediment. The chemicals were chosen based on their ability to remove analytes from

specific, major, sediment phases – either by exchange processes or by dissolution of the

target phase. Sequential extraction was thus originally developed to provide information

on potential impacts of sediment bound potentially toxic elements on water quality.

The use of different procedures, with different numbers of steps, reagents and

extraction conditions, meant that it quickly became difficult to draw meaningful

comparisons between results obtained in different laboratories. In 1987, the Community

Bureau of Reference (BCR) was arranged a strategy to harmonies the schemes of

sequential extraction, applied for measurement of elemental fraction in different

environmental specimen and certified reference materials (Jamali et al., 2007). The

principal difference in this new scheme, with respect to that of Tessier, was that the first

two steps of the Tessier scheme were replaced by a single step.

Sahuquillo et al., 1999 and Jamali et al., 2009 have been revised the original BCR

procedure due to irreproducibility, particular reducing extraction (NH2OH.HCl) fraction

of Step 2. They demonstrated pH adjustment could be a major source of uncertainties.

Therefore, the concentration of NH2OH.HCl was increased to 0.5 mol L-1, whereas, pH of

reagent was maintained to 1.5 with appropriate volume of HNO3 (Jamali et al., 2009).

This procedure is very popular during recent years and their application has increased

lately, during the certification of Reference Materials reported by (Perez Cid et al., 2001,

Mossop and Davidson, 2003, Kazi et al., 2006a; Jamali et al., 2009).

23

The advantages, limitations and future of sequential chemical extraction for

assessment of environmental samples were described by Bacon and Davidson 2008. They

are focused on major issues of sequential extractions i.e., methodologies, nomenclature,

explanation of data set and reported their recent applications. A major disadvantage of

sequential extraction is that it is time-consuming. For example, the BCR procedure

involves three periods (16 h) of overnight shaking. Together with aqua regia digestion of

the residue, and analysis of extracts and digests, this means that approximately one week

may be required to obtain results from a batch of samples. This problem has also been

noted by other researchers, who have been published papers focused on reducing the

lengthy treatment time, and replacing the conventional procedures by other alternatives,

such as microwave heating (Perez Cid et al., 2002; Arain et al., 2008) and ultrasonic

shaking (Filgueiras et al., 2002; Greenway et al., 2002).

Davidson and Delevoye (2001) were developed two alternative extraction

methods—a routine ultrasonic bath and a microwave oven in the three-stage sequential

extraction procedure proposed by the European Standards Measurements and Testing

(SM&T) Programme, formerly Bureau Communitaire the Reference (BCR), for the

operationally defined speciation of heavy metals in homogenized estuarine sediment

(Arain et al., 2008). They optimized their developed methodology conventional and by

the analysis of certified sediment sample BCR 601, which is certified for the three-step

BCR sequential extraction procedure. Filgueiras et al., 2002 was developed a small-scale

extraction method with ETAAS determinations (i.e. 25 mg mass in 1 mL extractant), with

considerable time saving by using ultrasonic probes. Extraction yields were comparable

to those of the conventional BCR protocol.

2.2.4. Single step extraction based on Sequential extraction Schemes

Fernandez, 2000; Filgueiras et al., 2002 have used single extractions to obtained

information about extractable metal content more simply than by sequential extraction.

The single extraction procedure on the bases of same reagents used in sequential

extraction scheme, harmonized by standards, measurement and testing programme for

elemental fractionation in sediments and soils (Cid et al., 2001). Smith 1996 and Jin 1999

24

were proposed that the single extraction procedure could be improved by using

microwave irradiation, which are applied for acceleration of different chemical processes,

including multi-step sequential extraction methods (Cid et al., 2001). Campos et al., 1998

was reported that the microwave energy could be introduced to replace the conventional

and magnetic shaking in the single step extractions, in order to shorten the treatment time

(Cid et al., 2001).

2.3. Uptake of Arsenic by grain crops and vegetables

In addition to water, food is another source of As for humans is the consumption

of grains in the form of cereal as a daily diet as reported by Samøe-Petersen et al., 2002;

Hossain, 2006 and Nickson et al., 2007. The transportation of toxic element from soils to

plant, human and animals may cause several healths hazardous (Pendergrass et al., 2006).

For example, concentration of As in soil greater than 40 mg kg-1 may cause toxicological

risks, especially in children (Pendergrass et al., 2006).

Hossain, 2006; Nickson et al., 2007 were reviewed that in uncontaminated

environments, ordinary crops do not accumulate enough As to be toxic to man. Whereas,

in As contaminated soil, the uptake of As by the plant tissue is significantly increased.

The assessment of As contents in soils and grains irrigated by As contaminated

groundwater is a matter of health concern, because these were widely used as cumulative

matrices or bio-indicators for long-term and short-term exposure to establish the degree

of pollution related to chemicals in the environment and to diagnose abnormal plant

development (Das et al., 2004; Meneses et al., 1999). Both the farmland and urban

environment often suffer from the As contamination due to the irrigation with As

contaminated surface, ground and wastewater (Hossain, 2006).

Nutrient addition to a soil may cause competition between elements for fixation

sites in the soil and for root uptake Signes-Pastor (2007). Fertilizer additions can

significantly affect available soil As in cases of high contamination (100–500 mg As kg−1

soil) Signes-Pastor (2007). Manning and Goldberg, (1996) and Pendergrass et al., (2006)

were explained that bio-available contents of As increased by the addition of fertilizer

materials (nitrogen, phosphorus and potassium). Among them phosphorus (P) is one of

25

the most significant fertilizer, as its chemistry is same as like arsenate (As5+) and thus

competes to similar binding sites in plant and/or soils during transport systems. Signes-

Pastor (2007) was reported the low levels of P added to As contaminated soil will

dislodge As from soil to enhance toxicity to plants, but larger applications of P will

compete at the root surface and decrease toxicity.

Meneses et al., 1999 and Nadal et al., 2004 had been considered the grain crops

and vegetables as cumulative matrices or bio-indicators for long-term and short-term

exposure of As in the environment as well as for the diagnoses of abnormal plant growth.

Zhang et al., 2001 was reported that total elemental content in soil were not accounting as

immediately available fractions of elements including As to plants and micro-organisms.

A good correlations between total As levels in soil and it translocation to plants (Enright

et al., 2005). Morselli et al., 2002 was notifying that a successful risk assessment of As

contaminated soils depends bio-availability of As in soil and transfer to food chain.

Hossain, 2006; Lyubun et al., 2006 were reviewed that As contamination problem

especially wheat producing areas. Due to high population and consumption of wheat, it

becomes a challenging task.

Das et al., 2004 and Hossain, 2006 were described translocation of elevated levels

of As from topsoil to grain crops and vegetables and transfer to the human food chain via

the consumption of these food stuffs. Reyes et al., in 2008 has been studied that farmland

and urban environment often suffer from the As contamination due to the irrigation of As

contaminated surface, ground and wastewater. Therefore, assessment of contaminated

soils and vegetables irrigated by As contaminated surface and groundwater is a matter of

health concern.

2.4. Effects of Arsenic on human health

The human are at high risk due to the consumption of As contaminated ground waters

in Pakistan as examined by Tahir 2000; Kahlown et al., 2002; Farooqi et al., 2007; Arain et

al., 2008. The As contaminated surface and ground water in Sindh, Pakistan were also

observed by Arain et al., 2008. Howard Hu, 2002 reported severe As toxicity and its adverse

effects on cellular system of different tissues especially the blood vessels, gastrointestinal

26

tissues and normal functioning of the heart and brain is not very common. The long term

exposure to lower concentrations of arsenic outcome in some skin disorders such as hyper

and hypo pigmentation, rough skin, peripheral nerve damage results in lack of sensation, and

weakness in the feet and hands, while other physiological disorders, diabetes and blood

vessel damage also occur (Hu, 2002). The prevalence of different cancers especially skin and

liver cancer is also a factor of continual arsenic exposure (Morales et al., 2000). Chen and

Ahsan 2004; Fatmi et al., 2009, Kazi et al., 2009 and Kazi et al., 2011 were studied that the

long term consumption of As contaminated drinking water may cause skin lesions

characterized by symmetrical bilateral hyperkeratosis (hardening) on palms and soles. In

many exposed populations, some individuals are extra sensitive whereas some extra tolerant

(Kazi et al., 2011).

Mukherjee et al., 2005 was demonstrated that symptoms of As toxicity may take 8–14

years to be evidenced in a person's body by continuous drinking As contaminated water

(Kazi et al., 2011).. This period differs from person to person, depending on the

quantity/volume of As ingested, nutritional status of the person, immunity level of the

individual and the total time-period of As ingestion as also reported by Mazumder et al.,

2000; Kazi et al., 2011. It is feared that skin behavioral and skin developmental impairment

may become the next childhood epidemic. The World Health Organization (1996) suggested

that these symptoms could take 5–10 years of constant exposure to As to develop (Kazi et al.,

2009).

Shemirani et al., 2005 was described that approximately 80–100% of the inhaled and

ingested As was absorbed through the gastrointestinal tract and lungs but up to 50–70% of

the absorbed As is gradually eliminated by methylation in the kidneys through urine. Nielsen

2001 have been investigated that if the ingestion is greater than excretion, it tends to

accumulate in the hair and nails. Pangborn 2003 and Kazi et al., 2009, 2010 have been stated

that hair has a long history in human studies of revealing chronic exposure to As and can

provide useful information in chronic As poisoning (Monroy-Torres et al., 2009). Because

hair is biologically stable, accurate assays can be performed by hair. Thus, profound

accumulation of As in hair during exposure is of value in the diagnosis of As poisoning, as

also reported by Brima et al., 2006; Gault et al., 2008; Sampson et al., 2008. Moreover,

27

studies of Kurttio et al., 1998; Agusa et al., 2006 have shown that hair As concentrations are

well correlated with drinking water As contents and can be used as biomarkers for arsenic

exposure in humans.

The exposure impact on children are severe than adults, because children have greater

body surface (Calderon et al., 2001; Chakraborti et al., 2003; Wasserman et al., 2004; Mitra

2004; Minamoto et al., 2005; Watanabe et al., 2005; Monroy-Torres et al., 2009). Mosaferi et

al., 2005 has been studied the exposure via drinking water implies lifelong exposure

beginning in early childhood; therefore, it is need of hour to study the children As exposure.

Moreover, UNICEF with government of Pakistan conducted a screening survey on As

contents in ground water in 2004 and found that the ground water sources were contaminated

with As in the range of 1.0–500 µg L-1 as reported by Arain et al., 2009; Kazi et al., 2009;

Baig et al., 2009, 2010.

2.5. Removal of Arsenic from water

Several methodologies were used for arsenic removal from surface and ground

water i.e., flocculation, coagulation, precipitation, ion exchange, adsorption, membrane

filtration, ozone oxidation, electrochemical treatments and bioremediation. Jackson and

Miller 2000, Wickramasinghe et al., 2004, Singh and Pant 2004, Leupin, and Hug, 2005

and Hansen, et al., 2006 were contributed for the removal of As species from drinking

water using different types of ion exchangers and adsorbents based on organic, inorganic

and bio materials.

Choong et al., 2007 was reviewed that McNeill and Edwards, 1995 had been first

time study coagulation of As(V) using alum treatment material with removal efficiency in

the range of 6–74%. Zouboulis and Katsoyiannis, 2002, Karcher et al., Guo et al.,,2000,

Han et al., 2002, Yuan et al., 2003 and Wickramasinghe et al., 2005 were studied

different types of coagulants and flocculants for the remediation of As species from

water. Choong et al., 2007 has been reported several low cost adsorbent such as coconut

husk, rice husk, coconut coir modified by amine, residues of orange juice, modified wood

powder, sawdust and waste tea fungal biomass were used for the removal of different As

species from surface, ground and waste waters.

28

Currently an increasing awareness is being developed on the use of renewable

natural materials as adsorbents for various water purification purposes (Kumari et al.,

2005). Sorption using plant biomass has emerged as the potential alternative to chemical

techniques for the removal and recovery of arsenic from aqueous solutions ((Kumari et

al., 2005). Bio-remediation as a variant and green technology becomes promising to

remediate the environmental pollutions as pointed out by Cao et al., 2004. The

bioremediation technologies have several advantages i.e., environmental friendly,

excellent performance, possible recycling and low cost for remediation of As from

contaminated water as reported Teixeira and Ciminelli, 2005. A range of biosorbents

have been reported for efficient remediation of As from water such as, chitosan, alginate,

orange waste, fungal biomass, methylated yeast biomass and chicken feathers (DeMarco

et al., 2003; Ghimire et al., 2003; Bargali et al., 2009).

The cell wall of biosorbent have different functional groups such as amino,

hydroxyl, carboxyl and sulphate, which can act as binding sites for removal of metals via

electrostatic attraction, ion exchange and complexation (Pokhrel and Viraraghavan 2008).

However, there is still a strong challenge in developing economical and commonly

available biosorbents for As removal. Therefore, we have characterized a cheapest and

easily available indigenous biomass taken from stem of a thorny Acacia species Acacia

nilotica (L.) Willd. ex Del, commonly known as babool or kikar (in Urdu) or babur (in

Sindhi) for the removal of As from aqueous media.

29

Chapter – 3

EXPERIMENTAL

3. Plan of Work

The experimental section of the present study was achieved in different steps as:

i. Collection of surface and ground water, sediment and soil samples on monthly

basis from eight districts of Sindh Pakistan (2007-2010).

ii. Sampling of soil from agricultural land (irrigated with tube and canal water) as

well as different crops cultivated in it (grains and vegetables).

iii. Biological samples of humans (children, adult male and female) were collected

from the subjects, residing in villages of district Khairpur Mir’s.

iv. Evaluated physico-chemical parameters of surface and ground water samples and

the results were compared with the recommended permissible limits (WHO,

2004).

v. The chemometric multivariate techniques, principle component analysis (PCA),

and cluster analysis (CA) was applied for the interpretation of physico chemical

data of collected surface and ground water samples.

vi. Evaluation of arsenic speciation in surface and ground water samples.

vii. Method development for extraction and fractionation of As in sediment and soil

using single and sequential extraction schemes.

viii. Development of sample preparation methods for measurement of arsenic in

vegetable and grain crops using advance extraction method.

ix. Arsenic in vegetable and grain crops collected from soil irrigated with arsenic

contaminated ground water was developed and compared with the vegetable and

30

grain crops samples of same species irrigated with fresh canal water, have lower

level of arsenic, during 2009–2010.

x. Determination of As in biological samples (scalp hair) as a bio indicator.

xi. Development of new As biosorption method based on indigenous material.

3.1 Study Materials

3.1.1 Sample collection and pre-treatment

The ground and surface water samples including water of main canals of Indus river,

river Indus, tube wells and hand pump have been collected from four districts of Sindh Pakistan

(Sukkur, Khairpur, Hyderabad and Jamshoro) with the help of global positioning system (GPS)

in 2007 - 2010. The ground water, tube well and hand pump samples were collected from the

depth of <25 m. Whereas, the surface water samples lake, canal and river Indus were collected

from different sampling points of eight districts. The samples from local government water

supply were also collected. All water samples collected from 5-6 spots of each station of surface

and ground water were kept in well stoppered polyethylene plastic bottles, previously soaked in

10% nitric acid for 24 h and rinsed with ultrapure water obtained from ELGA Labwater system

(Arain et al.,, 2008a; Korai et al., 2010). All water samples were stored in insulated cooler

containing ice and delivered on the same day to laboratory and all samples were kept at 4 °C

until processing and analysis (Arain et al., 2008a). The sediment samples were also collected

from same stations of lake, canal and river by using Ekman dredge from 5 to 7 spots of same

station as reported for water sampling (Arain et al., 2008a).

Vegetables i.e., Okra (Abelmoschus esculentus L.), Spinach (Spinacia oleracea L.),

Brinjal (Solanum melongena L.), Bitter Gourd (Momordica charantia L.), Sponge gourd (Luffa

cylindrica L.), Cluster Beans (Cyamopiss tetragonoloba L.), Bottle gourd (Lagenaria siceraria

L.), Peas (Pisum sativum L.), Pepper mint (Mentha piperita L.), Indian Squash (Praecitrullus

fistulosus L.), as well as seeds (grain) samples of Sorghum (Sorghum bicolor L.), wheat

(Triticum aestivum, L.) and maize (Zea mays L.) were collected from three sub districts of

Khairpur Mir’s (Faiz Ganj, Thari Mirwah and Gambat), where agricultural soil irrigated with

tube well (SIT), as test grains samples (TGS). The same grain samples were collected from

31

agricultural soil, irrigated with fresh canal water (SIC) as control grains samples (CGS). Surface

soil samples (0-25 cm) with a stainless steel auger were collected from the same locations

simultaneously with the grains. On returning to the laboratory, the soil samples and grains were

spread on the plastic trays in fume cupboards, air dried for eight days at room temperature. All

the collected samples from agricultural field, were kept in separate plastic bags, stored in a cold

box at 4 ○C and transported to laboratory on the same day. After keeping at room temperature, all

vegetables and grain samples were put through a three step washing sequence, which involved

agitating and rinsing first with distilled water followed by three separate washings with

deionized water as reported in our previous work (Arain et al., 2009). The quartz knife was used

for cutting vegetable samples into pieces. All collected vegetables and grains, samples were air

dried for 72h, after that oven-drying at 70 ˚C for complete dryness (Arain et al., 2009). The dried

soil, sediment, vegetables and grain crop samples were homogenized by grinding in an agate

mortar separately, sieved through a nylon sieve (<65 µm), and stored at room temperature in

labeled polypropylene containers (Arain et al., 2009).

3.1.2. Scalp hair sampling

Scalp hair (SH) samples of children (n = 510) were collected during 2008 - 2009 from

Faiz Ganj, Thari Mirwah and Gambat sub-districts of Khairpur district, Sindh, southern part of

Pakistan (Table 1). The children are divided into two age groups of 1- 5 years (171 girls, 111

boys) and 6 – 10 years (121 girls, 107 boys). The SH samples were collected from nape of the

head using stainless steel scissors. The SH samples were sealed separately in labelled

polyethylene zip lock bags and were not opened until return to laboratory for cleaning.

Scalp hair of children [boys (n = 184) and girls (n = 226)] and elder [males (n = 360) and

females (n = 280)] subjects were simultaneously collected with water samples from three sub

districts of Khairpur, Pakistan. The study subjects were divided into two age groups of 16-30

years (m = 190) and 31-60 years (n = 170). The study areas of Khairpur Mir’s district were

divided into less and high exposed areas, based on the levels of arsenic concentration in water. In

less exposed area (LE), the As in drinking water was found to be < 50 µg L-1 (Thari Mirwah sub-

district where understudy subjects have no any apparent arsenicosis symptoms) and high exposed

area (HE), where the As in drinking water was > 50 µg L-1 (The under study male subjects

belong to Gambat sub-district have hyperkeratosis on the palms of hands and soles of the feet).

32

Fig. 1a Sampling map of water sampling from Jamshoro district

33

Fig. 1b Sampling map of sediment sampling from Jamshoro district

34

Fig. 1c Sampling map of water, sediment and soil sampling from Khairpur Mir’s district

35

Fig 2a. Environmental sampling from different areas of Sindh Pakistan

36

Fig 2b. Biological and agriculture sampling from different areas of Sindh Pakistan

37

Fig 2c. Biological and agriculture sampling

38

For comparative purposes, scalp hair samples were also collected from referent subjects

(n = 180) residing in non contaminated area (NE) (Hyderabad, Pakistan), who consumed

municipal treated drinking water (<10 μg L-1 As). The body mass indexes of male subjects

belong to understudy areas were also estimated.

The persons who gave their consent were recruited for biological sample (scalp hair)

collection. Before start of this study, each participant was informed about the aim of study in

local language (Sindhi) with the help of a local non Government Organizations (Young Welfare

society, Khairpur and Khamtio Welfare organization, Gambat) through a consent form about the

aim of study using a formatted questionnaire to obtain verbal information, because most of the

study subjects belongs to sub districts of Khairpur were illiterate. The information including

demographic and lifestyle characteristics such as smoking, tea consumption, duration of living in

understudy areas, sources of drinking water and have or have not any physiological disorders

were collected. The > 50 % participant belongs to HE were reported cough, shortness of breath,

weakness, and arsenic induced skin lesion (confirmed by dermatologist). The all out comes were

examined according to arsenic levels in the drinking water and SH of each participant.

Questionnaire employed in sampling campaign

Subject No.: ……………………………………………….…………………………………

Full Name: .………………………………… S/O, D/O, W/O: ………….………………….

Full address: ……………………………………………..…………………………………...

Sex (male):…………………………….…… (Female):………………….………………….

Age:………………(years)…………………………(Month)………..………….…….(days)

Residency period:……………(years)…………………(Month)………..…………….(days)

Weight: …………………………………………………..…………………….……….. (kg)

Height:………………………………………………………………..….……………… (m)

39

Health conditions (brief description):

………………………………..….…….………….……………………………………….....

……………………………………………………………………………………………….

Comments on food habits and lifestyle in general (brief description)

…………………………………………………..………………….………………….……...

Specific remarks (e.g., type of soap or shampoo normally used, frequency of application,

and the like)

………………………………………..…………………………………………………….…

3.1.3. Scalp Hair Sample treatment

The SH samples were collected from nape of the head using stainless steel scissors. The

hair samples were sealed separately in labelled polyethylene zip lock bags and were not opened

until return to laboratory (Kazi et al., 2009; Arain et al., 2009). Prior to analysis, all hair samples

were cut into small pieces (2 cm). The washing procedure carried out, was that proposed by

International Atomic Energy Agency (IAEA). Thus, hair samples were first washed with

ultrapure water and then three times with acetone, finally washed with ultrapure water (three

times). The samples were then oven-dried at 60 ○C.

3.1.4. Certified samples

For the validity and accuracy of different methodologies, standard reference materials

SRM 1643e (Water) [National Institute of Standards and Technology (NIST), Giathersburg, MD,

USA.], BCR 701 (sediment sample) and BCR 189 (Whole meal flour) [Bureau of References of

European Communities] were used.

40

3.1.5. Sampling of biosorbent and pretreatment

An indigenous biosorbent (Leave and stem of Acacia nilotica) (IB) was collected from

the rural areas of Jamshoro Sindh, Pakistan. The leave and stem samples of indigenous plant

(Acacia nilotica) were washed carefully with deionized water and placed in an oven at 70 oC for

48 h. The dried biosorbent was crushed and sievied by Ro-Tap type electrical sieve shaker.

Sieving gave particles size of 50 µm for biosorption process. The sieved biosorbent was washed

twicely with deionized water to eliminate the fine particles and dried in an electrical oven at 70 oC. The dried biosorbent was stored in a vacuum desiccator for further analysis.

3.2. Apparatus

Global positioning system (GPS) (iFINDER, LowranceTM, Mexico) for identity of

sampling site locations.

A WTW 740 Germany inoLab pH-meter was employed for pH measurement and

adjustment of the reagents, water, soil and sediment samples (Arain et al., 2008).

Electrical conductivity was measured in water and extracts of sediment (1g

sediment: 10 ml deionized water) using an EC meter (WTW inoLab Cond: 720

Germany).

Sonicor, Model No. SC-121TH, Sonicor Instrument Corporation Copiague, N.Y,

USA was used to induce the ultrasonic assisted, leaching, extraction and pseudo-

digestion procedure (UASD).

WIROWKA Laboratoryjna type WE-1, nr- 6933 centrifuge; speeds range 0-6000

rpm, timer 0-60 minutes, 220/50HZ, Mechanika Phecyzyjna, Poland was used to

separate the supernatant from the sample extracts.

A horizontal flask shaker, Electric shaker (Gallenkamp) 220/60 HZ CAT No

SGL-700-010V App No 7b1063E made in England was used for shaking the

samples.

41

A PEL domestic microwave oven (Osaka, Japan), programmable for time and

microwave power from 100 to 900 W, was used for total digestion of samples.

Metrohm 781 pH/ion meter (Ion selective electrode 6.0502.15 (F-/0…80 oC) AG

CH-9101 Herisau Switzerland) was used for fluoride determination.

Agate ball mixer mill (MM-2000 Haan, Germany), was used for grinding the

dried collected samples, to reduce the particle size.

Metals and metalloids were determined in digests and extracts using atomic

absorption spectrometers, Model AAnalyst 700 Perkin Elmer (Norwalk, CT,

USA), assembled with graphite furnace (HGA-400), auto-sampler (AS-800),

integrated platform pyro-coated graphite tube and Hydride system (MHS 15):

were used for the analysis (Table 2 a, b)

Chromatographic analysis of chloride, fluoride, bromide, sulphate, phosphate,

nitrate and nitrite was performed with Metrohm 861 Advanced Compact IC with

853 CO2 Suppressor and column METROSEP A SUPP 4 – 250 ion

chromatography (IC) CH-9101 Herisau /Switzerland. (Table 3)

The surface area of indigenous biosorbent was determined analysed by a three

point N2 gas adsorption method using quanta sorb surface area analyzer model

Q5-7 (Quanta Chromo Corporation, USA), which is 450 m2 g-1.

The infrared spectra (IR) of dried unloaded biosorbent and As-loaded biosorbent

were recorded on a Thermo Nicollet 5700, Fourier transform infrared

spectrometer (FT-IR) (WI, USA), as KBr pellets at 400-4000 cm-1.

The scanning electron microscope (SEM) analyses were carried out for the treated

biosorbent and As loaded biosorbent by using scanning electron microscope–

energy dispersive X-ray spectrometer (SEM–EDS) (JEOL Electronics Company,

Japan).

42

Table 2: Measurement conditions for atomic absorption spectrometer AAS 700 a) Flame atomic absorption (FAAS)

Elements

Wave length

(nm)

Slit width

(nm)

Lamp current

(mA)

Oxidant

(Air L min-1)

Fuel

(acetylene L min-1)

Ca 422.7 0.7 30 17 2.2

Fe 248.3 0.2 10 17 2.0

K 766.5 0.7 7.5 17 2.0

Mg 285.2 0.7 7.5 17 2.0

Na 589.0 0.2 7.5 17 2.0

D2 lamp used for background correction

43

b. Instrumental settings for Electrothermal and Hydride Generation Atomic absorption spectrometry

Parameters As

Lamp Current (mA) 18

Wave length (nm) 193.7

Slit-width (nm) 0.7L

Electrothermal atomic absorption (ETAAS) Hydride Generation Atomic Absorption

(HGAS)

Ashing temperature (°C)/ time

ramp/hold) (s) 1300/10/20 Oxidant (Air) L min-1 17

drying temperature (°C)/time

(ramp/hold) (s)

Fuel (acetylene L min-

1) 2.2

Atomization 2300/0/5 Atomization Site

Pre heated Quartz

tube Atomizer

(QTA)

Modifier (Mg(NO3)2 + Pd(NO3)2)

Pre-reaction purge

time approx. 50 s

Post-reaction purge

time approx. 40 s

44

Table 3: Measurement conditions for Ion chromatograph Metrohm 86

Column Metrosep A Supp 4 - 250 (6.1006.430)

Size 4.0 × 250 mm

Part. Size 9.0 μm

Eluent 1.8 mmol L-1 Na2CO3 / 1.7 mmol L-1 NaHCO3

Flow 1.50 ml min.-1

Temperature 20°C

Pressure 7.2 M Pa

3.3. Chemical, Reagents and Glass Wares

ELGA Labwater System (Bucks, UK) prepared ultrapure water. All chemicals and reagents,

Ammonium acetate was obtained from Sigma (Aldrich Co. Ltd.), Sulphuric acid (98%), Acetic acid

(glacial 100%), Hydrochloric acid (37%), nitric acid (65%), and hydrogen peroxide (30%) were

analytical reagent grade E. Merck (Darmstadt, Germany). Hydrogen per oxide was purchased from

Sigma–Aldrich (St. Louis, USA). Hydroxyl ammonium Chloride, Mg(NO3)2 analytical grade (Merck

Ltd., Poole, Dorset, UK) and Pd stock standard solution, used as a chemical modifiers, was prepared

from Pd 99.99 % (Aldrich, Milwaukee, WI, USA). A 0.1% (w/v) of Ammonium pyrrolidine

dithiocarbamate (APDC), Fluka Kamica (Bushs, Switzerland) solution was prepared by dissolving in

ultrapure water. Titanium (IV) dioxide (99%, 0.5µm) Merck (Darmstadt, Germany) was used as a

sorbent. Certified standards of each metal understudy (1000g m L-1) were purchased from (Fluka

Chemika Switzerland). An intermediate multi element stock standard solution containing 100 mg

L−1 of each of the following analytes: Fe, Na, K, Ca and Mg acidified to 1.0 M HNO3. Working

standard solutions were prepared freshly prior to analysis, through stepwise dilution of the stock

standard solutions. Different buffer solutions (pH 1–10) were made by mixing appropriate volumes

45

of 0.1 mol L-1 solutions of KCl and HCl (pH 1–3), sodium acetate and acetic acid (pH 4–6) as well

as boric acid and sodium hydroxide (pH 7–10) solutions. The standard acid and base solutions (0.1

mol L-1 HCl/0.1 mol L-1 NaOH) used for pH adjustments.

The certified reference materials, sediment BCR 701, whole meal flour BCR 483 and human

hair BCR 397 were purchased from the Bureau of References of European Communities (Brussels,

Belgium) whereas, a certified reference material of water SRM 1643e was purchased from National

Institute of standards and Technology (NIST), Giathersburg, MD, USA.

3.4. Preparation of Internal Standards Solutions for metals and metalloids

The internal standards for elements were prepared from corresponding salts or pure

metals of analytical grade.

3.4.1. Arsenic 1000 ppm: 1.320 g of Arsenite (As2O3) was dissolved in 25.0 ml of HNO3 and

then the volume was made up to 1000 ml with deionised water in volumetric flask to obtain 1000

ppm stock solution of Arsenic.

3.4.2. Iron 1000 ppm: Iron wire was washed with dilute Hydrochloric acid, deionised water and

finally with Acetone. 1.000 g of washed and dried Iron wires was accurately weighed and

dissolved in 25.0 ml of concentrated HNO3 and some deionised water by heating, and finally the

volume was made up to 1000 ml by deionised water to obtain 1000 ppm solution of Iron.

3.4.3. Calcium 1000 ppm: 2.497 g of Calcium Carbonate (CaCO3) primary standard grade was

dissolved in 1 Litre flask with 300 ml deionised water and add 10.0 ml HCl. After CO2 was

completely released, 25 ml of 1 M HNO3 was added and then the volume was made up to 1000

ml with deionised water in volumetric flask to obtain 1000 ppm solution of Calcium.

3.4.4. Potassium 1000 ppm: 1.907 g of Potassium Chloride (KCl) was dissolved in 25.0 ml of 1

M HNO3 and the final volume was made up to 1000 ml with deionised water in volumetric flask

to obtain 1000 ppm solution of Potassium.

3.4.5. Magnesium 1000 ppm: 1.658 g of Magnesium Oxide (MgO) was dissolved in 25.0 ml of

HNO3 and the solution was made up to 1000 ml with deionised water in volumetric flask to

obtain 1000 ppm solution of Magnesium.

46

3.4.6. Sodium 1000 ppm: 2.542 g of Sodium Chloride (NaCl) was dissolved in 25.0 ml of 1 M

nitric acid (HNO3) and the final volume was made up to 1000 ml with deionised water in

volumetric flask to obtain 1000 ppm solution of Sodium.

3.4.7. Working standards

Working standards of fourteen elements were prepared freshly from internal standards

and certified standards prepared in our laboratory by appropriate diluting with 1M HNO3

deionised water

3.5. Preparation of Chemical Modifiers

Mg(NO3)2 stock standard solution, 5.0 g L-1, used as a chemical modifier, was prepared

from Mg(NO3)2 (Merck Ltd., Poole, Dorset, UK). Pd stock standard solution, 3.0 g L-1 used as a

chemical modifier, was prepared from Pd 99.999% (Aldrich, Milwaukee, WI, USA). Magnesium

nitrate and palladium: 5 μg Pd + 3 μg Mg(NO3)2 (10 ml+10 ml from strock solution in 100 ml)

used for As, iAs, As3+ and As5+.

3.6. Procedure for determination of total contents of elements

Total contents of elements were determined in understudy water samples, surface (lake, river

and canal) and ground (hand pump and tube well), via five times pre-concentration at 70 ˚C on

an electric hot plate. The concentrated water samples were filtered and kept at 4˚C till further

analysis. For accuracy, certified reference sample of water (SRM 1643e), with certified value of

total As was treated as described in previous work (Arain et al., 2008).

3.7. Reagents and standards preparation for anions

3.7.1. Reagent water: Distilled or deionizer water, free of the anions of interest. Water should

filter by 0.45 μm membrane filters (Millipore).

3.7. 2. Eluent solution: Sodium carbonates 1.7 mmol L-1and Sodium bicarbonate 1.8 mmol L-1

in reagent water. Sodium carbonates 0.191g and Sodium bicarbonate 0.143g dissolved in 1000ml

deionized water. The eluent (buffer) was filtered through 0.45μm membrane filters by using

suction vacuum pump.

47

3.7. 3. 1000ppm Fluoride: 2.211 g of Sodium Fluoride (NaF) was dissolved in 1000 ml

ultrapure water in volumetric flask to obtain 1000 ppm solution of Fluoride.

3.7.4. 1000ppm Chloride: 1.648 g of Sodium Chloride (NaCl) was dissolved in 1000 ml

ultrapure water in volumetric flask to obtain 1000 ppm solution of Chloride.

3.7. 5. 1000ppm Nitrite: 1.499 g of Sodium nitrite (NaNO2) was dissolved in 1000 ml ultrapure

water in volumetric flask to obtain 1000 ppm solution of Nitrite.

3.7. 6. 1000ppm Nitrate: 1.288 g of Sodium nitrate (NaNO3) was dissolved in 1000 ml ultrapure

water in volumetric flask to obtain 1000 ppm solution of Nitrate.

3.7. 7. 1000ppm Phosphate: 1.433 g of Potassium phosphate, monobasic (KH2PO4) was

dissolved in 1000 ml ultrapure water in volumetric flask to obtain 1000 ppm solution of

phosphate.

3.7. 8. 1000ppm Sulphate: 1.522 g of Sodium Sulphate (Na2SO4) was dissolved in 1000 ml

ultrapure water in volumetric flask to obtain 1000 ppm solution of Sulphate

3.7. 9 . Working standards

Working standards of anions were prepared freshly from stock standard solution in our

laboratory by appropriate diluting with deionised water.

3.8. pH Measurements

3.8.1. Reagents

3.8.1.1. Borax 0.01 mol L-1 solution, pH=9.2: Dissolved 3.814 g of sodium tetraborate

decahydrate (B4Na2O7.10H2O) in carbon dioxide free water and diluted to 1000 ml with

deionised water. The solution was protected from exposure to atmospheric carbon dioxide and

replaced with fresh solution after 30 days.*

3.8.1.2. Saturated solution of Potassium Hydrogen Tartrate 0.03 mol L-1, pH=3.05: PHT was

dried at 110 ºC for 1-2 hours before use. Dissolved dried 5.645 g of PHT in deionised water and

diluted to 1000 ml to be preserved with a few crystals of thymol.

48

3.8.1.3. Potassium Hydrogen Phthalate 0.05 mol L-1, pH=4.005: 10.21 g of solid PHP (dried at

110 ºC) was dissolved in deionised water and diluted to 1000 mL. The buffer capacity was rather

low and the solution was replaced after 2 weeks.

3.8.2. Procedure for measurement of pH of the water, soil and sediment

pH values of water were measured at the sampling points, while for each batch of soil

and sediment, by using a ratio of soil and sediment separately to ultrapure water of 1:2.5 (w/v).

The mixture was shaken in a mechanical, end-over-end shaker at a speed of 30 rpm for 1 h at

room temperature and centrifuged for 20 min at 3,000 rpm. The supernatant was used for pH

measurement with WTW inoLab pH 740 meter. The pH meter was standardized using the

aqueous solutions of exactly known pH as described above (Arain et al., 2008) (See results in

Results and Discussion).

3.9. Total and Calcium Hardness

3.9.1. Reagents

3.9.1.1. Na2 H2 EDTA solution: Disodium ethylenediamine tetraacetate (0.01M) 3.723g was

dissolved in distilled water and volume was made up to 1000 mL.

3.9.1.2. Buffer solutions for Total Hardness: Ammonium chloride (16.9g) was dissolved in

143ml concentrated ammonium hydroxide and was added 1.25g of Mg-EDTA, The volume was

made up with distilled water to 250 mL.

3.9.1.3. Indicator for total Hardness: Eriochrome Black-T 0.5g was mixed well with 100.0g of

NaCl and the mixture was valid up to one year.

3.9.1.4. Buffer Sodium hydroxide for Calcium Hardness: 8.0 g of NaOH was dissolved in

distilled water and volume was made up to 1000 mL.

3.9.1.5. Indicator for Calcium Hardness: Murexide 1g was mixed well with 99g of NaCl and

the mixture was valid up to one year.

49

3.9.2. Procedure

Total hardness and Ca hardness were measured by EDTA complexometry titration, the

indicators are Eriochrome Black T and Murexide at pH 10 and 12, respectively (Kazi et al.,

2009b). For total hardness: The sample (10 mL) was added 1ml of buffer (NH4Cl-NH4OH)

solution and about 5 mg of indicator and titrated with 0.01 mol L-1 EDTA solution, the color of

indicator turned from reddish to blue at the end point. For total Ca hardness: The sample (10 mL)

was added 1ml of buffer (NaOH) solution and about 5mg of indicator Murexide and titrated with

0.01 mol L-1 EDTA solution, the color of indicator turned from reddish to blue at the end point.

Standardization of the EDTA solution was carried out with standard calcium carbonate and

following the above procedure. The blank determination with distilled was also carried out

(Results are given Results and Discussion).

3.9.2.1 . Calculation

Total Hardness in mg L-1 as CaCO3 = (A-B) ×E ×1000 / ml of sample (S)

Ca Hardness in mg L-1 as CaCO3 = (A-B) ×E ×1000 / ml of sample (S)

Where

A=Volume of titrant (mL) consumed for test sample.

B=Volume of titrant (mL) consumed for blank.

E=mg CaCO3 equivalent to 1 ml EDTA

S=Volume of sample

1000= Constant value

3.10. Alkalinity

3.10.1 Reagents

3.10.1.1.Hydrochloric acid (HCl) solution (0.1N): Hydrochloric acid 37% (8.3ml) was diluted

and volume made up to 1L with distilled water.

50

3.10.1.2.Phenolphthalein Indicator solution: Phenolphthalein (0.5g) was dissolved in 50 ml of

ethyl alcohol (95%) and diluted with distilled water to 100ml. A few drops of 0.227N NaOH

were added to produce faint pink color of indicator.

3.10.1.3. Sodium carbonate solution (0.1N): Pre dried standard salt 1.06g of sodium carbonate

was dissolved in distilled water and volume made up to 100ml.

3.10.1.4. Methyl orange Indicator solution: Methyl orange (0.5g) was dissolved in distilled

water and volume made up to 1000ml.

3.10.2. Procedure

3.10.2.1. Phenolphthalein Alkalinity

The sample (10ml) was added 3-4 drops of phenolphthalein indicator and color turned to pink,

than it was titrated against HCl (It may standardized 0.1 mol L-1 against standard sodium

carbonate using methyl orange indicator) till the color changed from pink to colorless.

3.10.2.2. Methyl orange Alkalinity

The same sample was added 3-4drops of methyl orange and titrated with HCl till end point

appeared with a change in color from yellow to reddish.

3.10.3. Calculation

(S)sampleofml

50000NACaCOasmg/Lin Alkalinity 3

Where

N= Normality of titrant

A= Volume of titrant consumed in ml for test sample

S= Volume of test sample in ml

50000 = Constant to convert alkalinity in equivalent weight to mg L-1 as CaCO3.

51

3.11. Cloud point Extraction and Solid Phase Extraction of As speciation

3.11.1. Preparation of Reagents

3.11.1.1. Triton X-114 (0.1%): 10 g of non ionic surfactant Triton X-114 (octylphenoxy

polyethoxyethanol) was dissolved in 25.0 ml deionised water, then finally the volume was made

up to 1000 ml to obtain 0.1% solution of Triton X-114.

3.11.1.2. Ammonium-pyrrolidinedithiocarbamate (APDC) of 0.1%: 1g of APDC was dissolved

25.0 ml deionised water, then finally the volume was made up to 1000 ml to obtain 0.1% APDC

solution.

3.11.1.3. Ammonium molybdate tetrahydrate of 1%: 10g of Mo7O24.6(NH4).4(H2O) was

dissolved 25.0 ml deionised water, then finally the volume was made up to 1000 ml to obtain 1%

molybdate solution.

3.11.1.4. Buffer solution (0.1 mol L-1) : was prepared by dissolving appropriate amounts of

acetic acid and its sodium salt in ultrapure water, and a range of 4-8 were prepared with (0.1 mol

L-1) of HNO3/NaOH.

3.11.2. Procedure for the determination of inorganic As by solid Phase Extraction (SPE)

The factorial design was carried out to determine the optimal experimental conditions for

total inorganic As (iAs) by slurry sampling method using TiO2 as adsorbent. To optimized the

different analytical variables, six replicate of a sub samples of canal water in volume range of

10-50 ml were taken in flasks (100 ml in capacity), with and without spiking known amount of

analytes, and added complexing agent TiO2 (10-30 mg) separately. Then pH 1-4 was adjusted

using 0.5M HCl. The flasks were placed inside the ultrasonic water bath and were subjected to

ultrasonic energy at 35 kHz for different ultrasonic exposure time interval (5–15 min). The

temperature range of ultrasonic water bath was 20 to 60oC. Then the sample solutions were

centrifuged, separate the precipitate and added 5 ml of ultrapure water and subjected to

ultrasonic bath for 2 min. Then slurry with modifier was injected into a graphite tube by an auto-

sampler. The same procedure was applied for blank.

52

3.11.3. Procedure for the determination of As3+ by cloud point extraction (CPE)

The As3+ was determined by cloud point extraction, using a complexing agent

ammonium-pyrrolidinedithiocarbamate (APDC) and a nonionic surfactant

octylphenoxypolyethoxyethanol (triton X-114). To optimize CPE, six replicate of sub samples

(1-2 mL) of canal water, spiked with and without known standards taken in PTFE tubes (25 ml in

capacity).The pH was set in range of (2-6), added 0.001-0.01 % (w/v) APDC and 0.05-0.2%

(v/v) Triton X-114 to the content of the tubes and heated in a thermostatic water bath at 20-60oC

for 5-15 min. The mixture was centrifuged at 4000 rpm for phase separation (5 min), and then

cooled in an ice-bath for 10 min to increase the viscosity of the surfactant-rich phase. The

supernatant aqueous phase was carefully removed with a pipette. For the formation of surfactant-

rich phase, 0.5 ml of 0.1 M HNO3 in methanol was added, to reduce its viscosity before ETAAS

determination.

3.11.4. Procedure for the determination of As5+

25 ml of desorbed solution and triplicate groundwater samples, spiked with and without

known standards of As5+, were introduced in centrifuge tubes (50 ml in capacity). Added 0.02–

0.1% of molybdate and 0.01–0.25% (w/v) of Triton X-114 solution, then the pH was adjusted in

the range of 1 - 4 using 0.1 mol L-1 of NaOH/H2SO4 with the help of a pH-meter. The solution

was heated in an ultrasonic water bath for 10 min at 30- 80 ºC. Then the mixture after different

time intervals, centrifuged at 3500 rpm (1852.2 × g) for 2 -10 min for phase separation. After

cooling in an ice a mixture of NaCl (5 min), the surfactant-rich phase became viscous. Then, the

supernatant aqueous phase was discarded, and the remaining micellar phase was diluted with 0.2

ml of HNO3 in methanol (1:10 v/v) (Khan et al., 2010). The volume of the surfactant-rich phase

after the phase separation was measured by using a graduated cylinder. The resulting solution

was injected into the electro thermal atomizer with modifier.

53

3.12. Experimental Design

3.12.1. The fractional factorial design for CPE and SPE

The original Plackett-Burman approach is based on balanced incomplete blocks and

suggests designs for eight, twelve, sixteen, etc., variables or factors. For the evaluation of

different factors at two levels a Plackett-Burman design with minimum number of experiments

was described instead of the 25 = 32 required for a full factorial design. For the evaluation of six

and five factors for As3+ and iAs, at two levels a Plackett–Burman design with only sixteen and

eight experiments were described instead of the 26 = 64 and 25 = 32 respectively, required for

full factorial designs. The Plackett–Burman matrix shown in Table 4, where the low (−) and

high (+) levels are specified. The resulting values for both experiment (1–16) and (1-8) being of

six replicates. The experimental data were processed using the Minitab 13.2 (Minitab Inc., State

College, PA) and STATISTICA computer program 2007.

3.12.2. Central 23+ star orthogonal composite designs

For optimization of proposed methods (CPE and SPE procedures), a central 23 +star

orthogonal composite design with 6 degrees of freedom and involving 16 experiments was

performed (AOAC, 1998; Massart et al., 2003). For As3+, the variables (S, C and P) were

optimized, while for iAs (M, U and P) were studied (See detail in results and discussion).

54

Table 4.

Variables and levels used in the factorial design for As3+ and total iAs

Variables Symbol Low (-) High (+)

As3+

Surfactant (%) S 0.05 0.2

Complexing agent (%) C 0.001 0.01

pH P 2 6

Incubation time (min) I 5 15

Temperature (ºC) T 20 60

Volume of sample (mL) V 1 2

Total inorganic arsenic (iAs)

Mass of adsorbent (mg) A 5 30

Temperature (ºC) T 20 60

pH P 1 4

Ultrasonic exposure time (min) U 5 15

Volume of sample (mL) V 10 50

55

3.13. Determination of cation exchange capacity using sodium as index ion

3.13.1. Reagents

3.13.1.1. Sodium Acetate solution 1 mol L-1: Dissolved 136.1 g sodium acetate trihydrate in

1000 ml deionized water and adjusted pH 8.2 by adding drop wise 1 mol L-1 Acetic Acid.

3.13.1.2. Ammonium Acetate Solution 1 mol L-1: Added 57.0 ml glacial acetic acid and 68.0 ml

of strong Ammonium Hydroxide to 800 ml deionised water in 1000 ml volumetric flask and

adjusted pH 7.

3.13.1.3. Ethanol 95%: Dissolved 95.0 ml ethanol in 100 ml volumetric flask and volume made

up to mark with deionised water.

3.13.2. Procedure

Weighed 2.0 g dried (105 ºC) soil into a 50.0 ml centrifuge tube, added 30.0 ml of

sodium acetate solution and shacked for 5 minutes. The tubes had to be stoppered with polythene

stoppers and not corks, which caused errors. The tubes were centrifuged at 6000 rpm for about

10 minutes until the supernatant liquid clear. Decanted and discarded the liquid and repeated the

shaking and centrifuging four times more with fresh portions of acetate solution. The soil was

shaked with 30.0 ml of 95% ethanol for 5 minutes, centrifuge and discard the liquid. The ethanol

washing was repeated three times. Finally the soil extracted with three 30.0 ml portion of

ammonium acetate solution and collected the extracts in 100 ml graduated flask. Infrequently it

was necessary to filter the extracts after centrifuging. Diluted the combined extracts to 100 ml

and determined the Sodium content by FAAS.

3.14. Single Extraction

3.14.1. Reagents

3.14.1.1. EDTA 0.05 mol L-1: The extractant solution 0.05 mol L-1 EDTA pH 7 was prepared by

dissolving di-sodium dihydrogen ethylene diamine tetra acetate salt dihydrate (Na2

H2EDTA×2H20 Merck). The pH solution was adjusted to 7.0 adding hydrochloric acid or

NH4OH solution (trace element quality, Fisher) (Kazi, Jamali et al., 2006).

56

3.14.2. Procedures for EDTA extraction

Weighed air dried 0.5 g of soil and DWS sample of each batch in extraction bottle (250

ml polypropylene bottles) directly, added 50.0 ml of 0.05 mol L-1 EDTA (Arain et al., 2008). The

mixture was shaking in a mechanical, end-over-end shaker at a speed of 30 rpm for 1 h at room

temperature (Arain et al., 2008). The extract was separated by centrifuging at 3000 rpm, and the

supernatant liquid was filtered and stored in polyethylene bottles at 4 ºC until analysis (Arain et

al., 2008).

3.15. BCR Sequential Extractions

3.15.1. Reagents

3.15.1.1. Acetic acid (0.11mol L-1): Added 25.0 ml of glacial acetic acid to about 500 ml of

deionised water in a 1000 ml volumetric polyethylene flask and made up to 1000 ml with

deionised water. Took 250 ml of this solution (acetic acid, 0.43 mol L-1) and diluted to 1000 ml

with deionised water to obtain an acetic acid solution of 0.11 mol L-1.

3.15.1.2. Hydroxylammonium chloride (hydroxylamine hydrochloride 0.5 mol L-1): Dissolved

34.75 g of hydroxylammonium chloride in 400 ml deionised water. Transfered the solution to a

1000 ml volumetric flask, and added 25.0 ml of 1 mol L-1 HNO3 (prepared by weighing from a

suitable concentrated solution), solution diluted to 1000 ml with deionised water. Prepared this

solution on the same day the extraction was carried out and adjusted pH 1.5 with 1 mol L-1

HNO3.

3.15.1.3. Hydrogen peroxide, 300 mg g-1 (8.8 mol L-1): Used the hydrogen peroxide as supplied

by the manufacturer, i.e., acid-stabilized to pH 2-3.

3.15.1.4. Ammonium acetate (1 mol L-1): Dissolved 77.08 g of ammonium acetate in 800 ml

deionised water. The pH was adjusted to 2.0±0.1 with concentrated HNO3 and solution diluted to

1000 ml with deionised water.

57

3.15.1.5. Aqua regia: 65% HNO3 analytical grade and 37% HCl were added by 1:3 ratios

3.15.2. Procedure modified BCR sequential extraction scheme

Using BCR-SES as shown in fig 1, the acid soluble fraction (first step) was treated with

0.11M acetic acid, while for reducible fraction (second step), 0.5 mol L-1 of NH2OH·HCl at pH

1.5 was used (Kazi et al., 2005). The BCR-SES was applied, to replicate six samples of 0.5 g of

BCR 701 and duplicate (0.5 g) of each composite sample of sediments were collected from three

different origins. The extraction was carried out in 50 ml polyethylene acid washed centrifuged

tubes, which were also used for centrifugation to minimize the possible loss of solid. For

corrections to dry mass, a separate 1.0 g air dried sample of each batch of different sediment

samples and triplicate sample of BCR 701 were dried in an oven at 100 ± 5°C until a constant

mass was achieved. From this a “dry mass correction” was obtained, which was applied to all

analytical values reported (i.e., results shall be quoted as quantity of As mg kg−1 dry weight).

This treatment caused 2.5 % loss of weight in BCR 701 where as, different ranges were observed

for lake, canal and river sediment samples.

Blank extractions (without sample) were carried out through both extraction methods. For

original BCR method, the details of the weight of the samples, volume of extractants and the

experiment protocol are available elsewhere (Sahuquillo et al., 1999; Kazi et al., 2005).

3.15.2. Procedure for Single step extraction based on BCR sequential extraction scheme (S-

BCR)

The single step extractions, based on BCR sequential extraction scheme (S-BCR) were

carried out by employing a separate duplicate aliquot (0.5g), of each composite sediment

samples of lake, canals and river separately, and six replicate samples of BCR 701, for each

individual reagent and using the same operating conditions listed in Fig 2.. However in S-BCR

extraction method, the solid residue was rejected at each step. Centrifugation and storage of

extracts was performed as described in the BCR-SES. The major benefit of this proposed method

is that all fractions can be extracted at the same time, hence, making S–BCR less time consuming

as compared to BCR-SES method but needs a high amount of samples, as the solid residue was

58

rejected at each step which is not a problem in case of abundantly available environmental

samples.

The pseudo-total As contents of sediment samples was determined via digestion with

aqua regia using a microwave-assisted digestion procedure. The residues from step 3, and 200

mg of duplicate air-dried samples of all twenty batches of sediment and six samples of BCR 701,

were weighed and then added 65% suprapur HNO3 (2 mL) and 37% of HCl (6mL) in

polytetrafluoroethylene (PTFE) flasks ( 25 ml in capacity). The flasks were then placed at room

temperature for about 2 h. Then, the flasks were kept in a programmable domestic microwave

oven, with microwave power from 100 to 900W, and heated at 80% of total power for 15min

(Jamali et al., 2008). Cool and evaporated the extra acids, then diluted with 10 ml of 0.2 mol L−1

nitric acid and filtered through a Whatman 42 filter paper, transferred into a 25 ml flask, and

volume was made up with ultra-pure water. Analytical blanks were prepared in the same way,

without addition of any sample (Kazi et al., 2005).

3.16. Total arsenic determination in soil, sediment, grain crops, vegetables and scalp hair

3.16.1. Microwave – assisted digestion procedure

A microwave – assisted digestion procedure has been applied in order to achieve a

shorter digestion time (Arain et al., 2009). Replicates six samples of certified reference materials

[sediment (BCR 701), hair (BCR-397) and Whole meal flour (BCR 189)] (0.2 g) and triplicate

samples of soil, sediment, vegetables, grains (wheat, maize and sorghum) and scalp hair samples

were directly weighed into Teflon PTFE flasks (25 ml in capacity). About 2 ml of a freshly

prepared mixture of concentrated HNO3–H2O2 (2:1, v/v) was added to each flask and kept for 10

min at room temperature, and flasks were placed in a covered PTFE container. All flasks were

kept at room temperature for 5 h, then placed in a PTFE container and heated following a 1-stage

digestion programmed at 80% of total power (900 W). Complete digestion of scalp hair samples

required 3 - 4 min. After the digestion, the flasks were left to cool and the resulting solution was

evaporated to semidried mass to remove excess acid. After cooling, sample digests were filtered

through a Whatman 42 filter paper, transferred into a 25.0 ml flask and brought to volume with

Milli Q water (Afridi et al., 2006). Blank extractions (without sample) were carried through the

complete procedure (Afridi et al., 2006).

59

3.16.2. Cloud point extraction (CPE) procedure

The six replicates samples of CRM and triplicate of SH samples (0.2 g) were directly

weighed into PTFE flasks (25 ml in capacity). Two ml of a freshly prepared mixture of

concentrated HNO3 and H2O2 (2:1, v/v) was added to each flask and was kept for 10 min at room

temperature. The flasks were sealed and submitted to the microwave heating program. Following

digestion, samples were transferred to 20 ml volumetric flask and the volume was made-up with

ultrapure water (Shah et al., 2010). The digested samples were further divided into two set, one

set of digested solution was subjected to ETAAS for total As determination, while other set was

subjected to cloud point extraction of As ,prior to subjecting ETAAS (Shah et al., 2010).

Aliquots of 10 ml of standard solutions containing As in the range of 10 – 50 µg L-1,

replicate six samples of 10 ml of digested CRM and triplicate of each SH samples taken in

graduated centrifuge tubes (25 ml in capacity). Then, the CPE method applied as mention in

section 3.11.3. A blank submitted to the same procedure was measured parallel to the calibration

solutions of standards, human hair CRM (BCR 397) and real samples.

3.17. Risk assessment

3.17.1. Arsenic risk assessment

The arsenic risk has been calculated for non-carcinogenic exposure, as Hazard Quotient

(HQ), can be calculated as,

HQ = ADD/RfD

where RfD is the oral toxicity reference value for As equaling to 3.04 × 10-4 mg kg-1day-1 and

ADD is the average daily dose from ingestion (mg kg-1day-1).

ADD = [(Cwater × IRwater) × EF × ED] / (AT × BW)

Where, Cwater indicate the As concentration in water (mg L-1), IRwater the water ingestion rate (L

day-1), EF the exposure frequency (days year-1), ED the exposure duration (years), AT the

average age time (days), and BW is the body weight (kg). If the calculated HQ is <1, then no

60

adverse health effects are expected as a result of exposure. If the HQ was > 1, then adverse

health effects are possible (EPA 1995; USEPA 1998).

Body weights were obtained by weighing each individual with a body weight scale. The

water ingestion rate was determined by asking the question ‘‘How many glasses of water do you

have drink per day?” Domestic furnishings varying little in such localities, most households use

the same size (250 mL) and style of glasses. The interview outcomes indicate that > 80%

consumed 2-3 L (average 2.5L) of water daily.

3.17.2. Carcinogenic Risk assessment

Carcinogenic risk is the probability of an incidence of cancer from chemical exposure and

can be computed as:

R = 1 – exp [-(SF×ADD)]

Where, SF is the oral slope factor. Toxicity data for threshold and non-threshold effects from As

exposure are available from the USEPA database, Integrated Risk Information System (IRIS)

(EPA 1995; USEPA 1998). The oral slope factor (SF) for As is 1.5 mg kg-1 day-1.

In order to estimate carcinogenic risk, it was supposed that people are dependent on

groundwater in Pakistan for their drinking and other domestic purposes. In understudied areas of

Pakistan, the rural population mostly relies on under groundwater resources. This corresponds well

with the report of WWF-Pakistan, which demonstrated that the principal source of drinking water

for the majority of people in Pakistan is groundwater and > 60% of the population gets their

drinking water from hand or motor pumps, with the figure in rural areas being over 70% (WWF-

2007). It is reported by UNICEF that 20-40% patients in Pakistan, suffering from water-related

bacterial diseases, such as typhoid, cholera, dysentery and hepatitis, which are responsible for one

third of all deaths (PSCEAR 2006; WWF-Pakistan 2007).

3.18. Statistical analysis

Data processing and statistical analysis were conducted by using computer program Excel

2003 (Microsoft Office ®), XLState (Addinsoft, NY, USA), Minitab 13.2 (Minitab Inc., State

College, PA) STATISTICA 6 (StatSoft, Inc.® OK, USA). Normally distributed data were

61

expressed as means ± std, Student's t-test and Mann-Whitney test were used to assess the

significance of the differences between the As content in SH of children exposed to different levels

of As via drinking water. All tests were two-sided and a p-value of ≤0.05 was considered

significant. Pearson product-moment correlation coefficients were calculated to test linear

correlations between arsenic on hair, age, water intake, As concentration in water, weight, and

body mass index The average daily intake of As was calculated according to the volume of water

consumed by children day-1.

The Cluster analysis (CA) technique is an unsupervised classification procedure that

involves measuring either the distance or the similarity between objects to be clustered. In

hierarchical clustering, clusters are formed sequentially by starting with the most similar pair of

objects and forming higher clusters step by step. Hierarchical agglomerative CA was performed

on the normalized data set (mean of observations over the whole period) by means of the Ward’s

method using squared Euclidean distances as a measure of similarity (Jalbani et al., 2007).

The analyzed data of under ground water samples was also performed through principal

component analysis (PCA). The PCA is designed to transform the original variables into new,

uncorrelated variables (axes), called the principal components, which are linear combinations of

the original variables. The new axes lie along the directions of maximum variance. PCA provides

an objective way of finding indices of this type so that the variation in the data can be accounted

for as concisely as possible (Sarbu and Pop, 2005). PC provides information on the most

meaningful parameters, which describes a whole data set affording data reduction with minimum

loss of original information (Helena et al., 2000; Arain et al., 2009). The principal component

(PC) can be expressed as:

zij = ai1x1j + ai2x2j + ai3x3j + … + aimxmj

Where z is the component score, a is the component loading, x the measured value of

variable, i is the component number, j the sample number and m the total number of variables.

62

3.19. Analytical Figures of Merit

Quality assurance and control (QA/QC), data was performed according to the specified

method (AOAC, 1998). The equations for the linear range of As species, elements and ions

standards calibration curves are given in table 5.

The relationship between the blank limit of detection (LOD) and limit of quantitation

(LOQ) by showing the probability density function for normally distributed measurements of

blank, at the LOD defined as 3 x standard deviation of the blank, and at the LOQ defined as 10 x

standard deviation of the blank. The LOD and LOQ were calculated using following formula:

ms 3LOD

and ms 10LOQ

Where “s” is the standard deviation of ten measurements of the blank and “m” is the

slope of the calibration graph were also obtained for each case. For a signal at the LOD, the

alpha error (probability of false positive) is small (1%). However, the beta error (probability of a

false negative) is 50% for a sample that has a concentration at the LOD. This means a sample

could contain an impurity at the LOD, but there is a 50% chance that a measurement would give

a result less than the LOD. At the LOQ, there is minimal chance of a false negative.

Ionic balances

Calculated as,

Ionic balance = (cations – anions) / (cations – anions) × 100

The average ion balance 1.17 % with two outliers of 1.8 % and -3.2 %, for which no explanation

is impending; the mean balance is 0.5%.

63

Table 5. Slope & Intercepts with linear regression lines of Concentration versus Absorption data of Standard solutions of different element/ions

Element or ions

/Method

Conc. range Y=m(x) +c R2 LOD /LOQ

(ng mL-1)

As/ETAAS 0.000 – 20.00# Y=(0.236)(As)+(0.0002) 0.9950 0.22/0.73

As/HGAAS 0.000 – 20.00# Y=(0.016)(As)+(0.003) 0.9980 0.02/0.066

As3+/CPE-ETAAS 0.000 – 20.00# Y=(0.580)(As)+(0.002) 0.9970 0.04/0.13

As5+/CPE-ETAAS 0.000 – 20.00# Y=(0.273)(As)+(0.005) 0.9890 0.20/0.66

As3+/SPE-AAS 0.000 – 20.00# Y=(0.712)(As)+(0.0022) 0.9880 0.031/0.105

iAs/SPE-AAS 0.000 – 20.00# Y=(0.009)(As)+(0.0024) 0.9960 0.12/0.391

Ca/FAAS 0.000 – 20.00# Y = 1.44×10-2 (Ca)+

4.0×10-4

0.9990 164/547

K/FAAS 0.000 -1.000* Y = 0.143 (K) - 5.0×10-4 0.9960 14.0/46.8

Mg/FAAS 0.000 - 125.0* Y = 9.0×10-4 (Mg) +

1.0×10-3

0.9980 2.46/8.21

Na/FAAS 0.000 - 0.500* Y = 0.363(Na) - 2.9×10-3 0.9910 5.52/18.4

Fe/FAAS 0.000 - 2.000*Y = 3.2×10-2 (Fe) -

4.0×10-4 0.9990 69.2/231

F-/IC 0.100 -10.00* y = 0.906(F) - 0.101 0.9930 2.26/7.51

Cl-/IC 0.200 - 20.00* y = 0.744(Cl) - 0.216 0.9970 3.59/11.9

NO2-/IC 0.100 - 10.00* y = 0.378(NO2)- 0.046 0.9990 2.82/9.36

Br-/IC 0.100 - 10.00* y = 0.210(Br) - 0.029 0.9990 2.59/8.69

NO3-/IC 0.100 - 10.00* y = 0.238(NO3) - 0.0047 0.9990 1.54/5.12

PO43-/IC 1.000 - 20.00* y = 0.048(PO4) - 0.081 0.9600 7.06/23.5

SO42-/IC 0.100 - 20.00* y = 0.164(SO4) + 0.019 0.9990 0.944/3.13

#µg L-1, *mg L-1

64

3.20. pH and surface area of biosorbent material

The pH value of the indigenous biosorbent material was measured as follows: 0.1 g of

samples were mixed with 10 ml of deionized water and shaken for 24 h at 298±0.5 K. After

filtration, the pH of solutions was determined by a pH meter (781-pH meter, Metrohm) glass-

electrode.

The surface area of the indigenous biosorbent material was calculated according to Sears’

method as follows: 0.5 g of biosorbent material sample was mixed with 50 ml of 0.1 mol L-1 HCl

solution and 10.0 g of NaCl salt. The mixture of pH 3.0 was titrated with standard solution 0.1

mol L-1 NaOH in a thermostatic bath at 298±0.5 K from pH 3.0 to 9.0. The surface area was

calculated from the following equation;

25-V32)gm( S 2 (1)

Where, S is the surface area, and V is the volume (mL) of NaOH solution required to raise the

pH from 3.0 to 9.0. The surface area of indigenous biosorbent material was also conformed by a

three point N2 gas adsorption method using quanta sorb surface area analyzer model Q5-7

(Quanta Chromo Corporation, USA), which is 350 m2 g-1.

3.21. Sorption procedure

To evaluate the performance of biosorbent material, batch experiments were carried out.

The biosorbent material (0.02-1.0 g) was placed in 100 ml glass-stoppered Erlenmeyer flasks

containing 50 ml of standard solutions of As (20 -1000 µg L-1). Then, pH 2-12 was adjusted by

adding 0.5 mol L-1 HCl or 0.1 mol L-1 NaOH solutions. The flasks were shaken at different

temperatures (298-318 K) on an electrical shaker (with water bath) at 120 rpm for a designated

time intervals (5–60 min). The time required for reaching the equilibrium condition was

estimated by analyzing the samples at regular intervals of time. The biomass were separated

from the solution by filtration and washed with deionized water twicely to remove all un-sorbed

As ions and resulting solutions were analyzed. The As concentrations in initial and final

solutions of As were determined by hydride generation atomic absorption spectroscopy

(HGAAS).

65

The experiments were conducted in triplicate of each origin of surface water samples and

discussed in results and discussion section. The percent biosorption of metal ion was calculated

as follows:

%Sorption = (Ci – Ce) / Ci × 100 (2)

Where, Ci and Ce are the initial and final concentrations expressed in mol L-1. The

amount of As biosorbed at equilibrium per unit mass of the biosorbent material (µmol g-1) and

distribution coefficient Rd was calculated using the mass balance equation;

q = (Ci – Ce) × V/W (3)

Amount of metal ion in adsorbent V Rd = × (4)

Amount of metal ion in solution W

Where Ci and Ce are the initial and equilibrium As concentrations in µmol L-1,

respectively; V is the volume of the solution in L; W is the weight of the biosorbent material in

gram. Biosorption experiments for investigating the effect of pH were conducted by using a

solution having 200 µg L-1 of As concentration with a biomass dosage of 4 g L-1. Throughout the

study, the contact time was varied from 5 to 60 min. The pH 2 to 11 was taken at initial metal

concentration from 20 to 1000 μg L-1 and the biosorbent material dosage from 0.4 to 20 g L-1.

3.22. Desorption

After the biosorption tests, the biomass was washed with ultrapure water , then added 15

ml of 0.5-1.0 mol L-1 of HCl and HNO3 separately and kept at 308 K for 30 min, in beakers. The

biomass was separated from the solution by filtration with whatman filter paper, and the biomass

was washed with 10 ml of deionized water, then washed biomass was dried in an electric oven at

333 K to use for further experiment. Analyte contents of the final solution were determined by

HGAAS. The same procedure was applied to the blank solution.

66

3.23. Interference studies

The As binding capacity experiments were repeated with solution containing the mixture

of different common ions usually present in water with As solution. The effects of ions (Ca2+,

Mg2+, Cl-, HCO3-, SO4

2- and PO4

3-) and (Al3+, Fe3+, Cd2+, Co2+, Cu2+, Mn2+, Ni2+, Pb2+, Zn2+, K+,

F-, Br-, CH3COO-, NO3-, CO3

2-, C2O42-, SO3

2-, and C6H5O73-) concentrations varying from 100 to

1000 and 10-50 mg L-1, respectively was investigated.

3.24. Theoretical background of adsorptions

The kinetics of adsorption method is important for the possible adsorption mechanism

with respect to time and temperature. The Lagergren first order rate model can be expressed as:

log (qe - qt) = log qe - (K1/2.303).t (5)

where qe and qt (mg g-1) are the amounts of As biosorbed at equilibrium (mg g-1) and t (min),

respectively, while k1 is the rate constant of the equation (min-1). The Lagergren second-order

rate model is given by the following expression:,

(t/qt) = (1/K2qe2) + (1/ qe) t (6)

Where K2 (g mol-1 min-1) is the rate constant of the second-order equation, qt (mol g-1) is the

amount of biosorption at time t (min) and qe is the amount of biosorption equilibrium (mol g-1).

In order to be able to estimate maximum capacities of adsorbents, it is necessary to know the

quantity of adsorbed metal as a function of metal concentration in solution. The biosorption data

have been subjected to Freundlich, Langmuir and Dubinin–Radushkevich (D–R) isotherm

models. A basic assumption of the Langmuir theory is that sorption takes place at specific

homogeneous sites within the sorbent. This model can be written in linear form [27].

Ce / qe = 1 / Qb + Ce / Q (7)

Where, Q is the monolayer biosorption saturation capacity (mol g-1) and b represents the

enthalpy of biosorption (L mol-1), independent of temperature. On the other hand, the Freundlich

equation [28] is represented by the following:

67

lnqe = ln Cm + 1/n lnCe (8)

Where, Ce is the equilibrium concentration (mol L-1), qe is the amount of As adsorbed (mol g-1),

Cm and n are Freundlich constants.

The equilibrium data were also analyzed using the D-R isotherm model to determine the

nature of biosorption processes as physical or chemical. The linear presentation of the D-R

isotherm equation is expressed by

lnqe = lnXm – βε2 (9)

where

ε = RTn (1 + 1/Ce) (10)

Where qe is the amount of As ions adsorbed on per unit weight of biosorbent material (mol L-1),

Xm is the maximum biosorption capacity (mol g-1), β is the activity coefficient (mol2 J2-1) related

to biosorption mean free energy (kJ mol-1) and ε is the Polanyi potential, where R (J mol-1K-1) is

the gas constant and T (K) is the absolute temperature. The constant β and Xm were obtained

from slope and intercept of the plot of ln qe against ε2.

In order to describe thermodynamic properties of the biosorption of As ions onto IB,

enthalpy change (ΔH◦), free energy change (ΔG◦) and entropy change (ΔS◦) was calculated from

the following set of equations:

ΔG◦ = -RTlnKa (11)

and,

lnKa = ΔS◦/R - ΔH◦/RT (12)

The equilibrium constant Ka of the adsorption process which is equal to the product Qb, is

calculated first.

68

Chapter – 4

RESULT AND DISCUSSION

4.1. Arsenic in surface and ground water

4.1. Physico-chemical parameters and Arsenic in surface and ground water of Jamshoro,

Pakistan

General Remark

The work presented in this section has been published as:

Jameel Ahmed Baig, Tasneem Gul Kazi et al., (2009). Evaluation of arsenic and

other physico-chemical parameters of surface and ground water of Jamshoro,

Pakistan. Journal of Hazardous Materials 166, 662–669.

doi:10.1016/j.jhazmat.2008.11.069

4.1.1. Results

The results of the chemical analyses were summarized in table 6 (a, b) and 7. For

convenience in description, groundwater samples were grouped into two categories according to

depth: ground waters sampled from 15-30 m depth of ground water hand pumps (HS) and 90-

150 m in depth of ground water tube wells (TS). The surface water samples were also divided

into two groups, surface water canals (CS) and surface water municipal treated water (MS) for

domestic areas (Table 6a). The charge balance of total cations and anions (meq L-1) was assured

to be <1 % (Table 6a, b).

4.1.1.1. Physicochemical parameters

The physicochemical characteristics of ground and surface water samples have been

explained in table 6 (a, b). The dissolved component characteristics of groundwater and surface

water were summarizing in table 6b. The pH values for HS and TS samples were observed in the

range of 7.1-8.4, while the pH of canals and municipal supply surface water samples were found

69

to be in the range of 6.9-8.5. The EC values in ground and surface water samples were in the

ranges of 0.401- 4.51 mS cm-1 and 0.321-5.84 mS cm-1, respectively.

The temperature of TS was higher than that of HS (Table 6a, b and 7). Since water

temperature is one of the conservative properties in the water cycle, the difference in temperature

ranges between the shallow and middle depth groundwater is indicative of the presence of two

separate confined aquifers. The TDS were determined in the ranges of 188-2210 and 150-2770

mg L-1 respectively in ground and surface water samples. Alkalinity was found in the range of

181-1350 mg L-1.

4.1.1.2. Major ions in water samples

The concentration of major cations and anions in surface and ground water were shown

in table 6 (a, b) and 7. Sulphate was one of the principal anions, with a concentration range of

113-1520 mg L-1, Cl ranges from 164- 721 mg L-1, while Na is a most governing cation was

found in the range of 191-945 mg L-1. Calcium concentrations ranged between 33.6 and 297 mg

L-1. The highest NO3 concentration was observed in hand pump samples at sampling spots 5-6

(Table 6a), probably due to the use of fertilizers for different crops in this area. The pH for TS

was observed in the range of 7.9-8.1. The alkalinity was ranging 210-310 mg L-1 and SO42- was

up to 766 mg L-1. The concentration of Na reached up to 396 mg L-1 while Ca and Cl

concentrations ranged as 47.0-69.0 and 131-291mg L-1, respectively. The both groundwater

samples (HS and TS) contain NO2 and NO3 within the WHO standard for drinking water (Table

7).

The surface water was less contaminated or polluted than the ground water samples,

except at two sampling points, i.e., CS at CS3 (Aral wah) and MS at MS5 of Bubak, near

Manchar lake. The CS has a pH range of 7.1-7.8. The SO42- was found in the range of 108-1240

mg L-1 where as Na ranges from 216-710 mg L-1. The concentration of Ca was observed in the

range of 8.2 to 85.5 mg l-1 and Cl- up to 265 mg L-1. The major ion composition of municipal

treated water group was similar to that of the canal surface water except sampling site CS16

(Table 6 b).

70

4.1.1.3. Iron and Arsenic

In groundwater the Fe was found in the range of 0.09-4.28 mg L-1 while in surface water

it was within the range of WHO recommended level except one sampling point of canal (CS3)

(Table 6a,b, 7). The distribution of As in ground water samples of studied area, Jamshoro varied

from 13.0 μg L-1 to 106 μg L-1. In the same way, the concentration of As in surface water varied

from 3.0 μg L-1 to 50.0 μg L-1 at measured pH (Table 7).

4.1.1.4. Cluster analysis (CA)

Cluster analysis (CA) was applied to identify spatial resemblance for grouping of

sampling sites. It provided a dendrogram (Fig. 3 and 4), grouping 25 sites for ground water and

23 locations for surface water of understudy area, into three statistically significant clusters for

each. The dendrogram (Fig 3) showed the abnormality of ground water sampling sites. The

sampling sits HS1 and HS3 made one group as cluster 1, which contain > 60 ppb As, the other

cluster due to mutual dissimilarity as cluster 2 (involved 10 site) and cluster 3, contains 13 sites,

corresponding to relatively higher, lower and moderate As and Fe contamination, respectively.

Similarly, dendogram (Fig 4) clarifies the dissimilarity of the sampling sites, MS5 and CS3

composed one group (cluster 1), have > 30 ppb As concentration as compared to other sampling

sites of surface water, and may be due to non-point sources, i.e., agricultural, industrial and

domestic activities. Besides cluster 1, the mutual dissimilarity among other sites made as cluster

2, which is further divided into two classes, class 1 (involved 16 site) has As <10 ppb and class 2

(sites MS10, MS12, MS16 and MS17) contains > 10 ppb As.

71

Table 6 a. Major element chemistry and arsenic contaminations in ground water from district Jamshoro Sindh, Pakistan

Sample

I.D T (oC) pH EC a TDS b Ca b Mg b Na b K b HCO3- b Cl- b NO2

- b NO3- b SO4

2- b As c Fe b Balance

HS1 29 8.1 2.41 1138 216.9 49.1 754 42.9 1352 233 2.44 24.8 1013 83.2 3.89 -1.8

HS2 27.9 7.8 1.69 796 165.1 26.9 652 8.9 972 266 1.36 12.4 708 15.1 0.25 0.6

HS3 28.3 8.4 4.14 1948 297.3 99.7 799 18.7 358 647 4.21 48.3 1516 106.3 4.28 1.1

HS4 26.6 7.8 3.93 1836 79.1 35.9 525 54.9 211 415 2.35 41.6 722 13.1 0.21 -0.1

HS5 27.9 8.2 2.98 1386 105.5 63.5 945 52.8 538 721 3.46 27.1 1050 58.3 1.21 1.1

HS6 29 7.7 1.90 896 87.3 42.7 520 17.2 218 347 1.05 12.6 829 29.0 0.52 -0.2

HS7 26.6 7.2 0.83 387 33.6 39.4 368 14.2 271 237 0.91 5.3 462 13.3 0.09 0.8

HS8 28.6 7.4 1.56 734 36.9 11.1 238 6.0 181 180 0.92 12.5 246 20.0 0.11 -0.9

HS9 26.4 7.2 0.57 270 38.2 26.8 191 4.4 269 199 1.39 1.4 113 57.0 0.69 0.2

HS10 27.5 8.2 2.14 869 110.0 67.0 736 9.9 520 521 7.50 25.1 903 20.3 0.25 0.6

HS11 31 8 4.51 2214 50.0 21.0 548 7.6 359 217 1.76 12.6 787 54.1 0.59 -0.9

HS12 30 7.8 1.11 524 89.1 25.9 597 17.1 330 295 1.21 12.6 877 27.0 0.19 0.9

HS13 30.4 7.9 1.06 499 54.5 21.5 293 13.0 280 173 0.92 9.7 418 55.0 0.49 -2.6

HS14 29.4 7.9 0.90 421 34.5 17.5 316 17.7 290 205 2.55 13.1 334 46.1 0.39 -1.6

72

HS15 28.4 8.2 2.52 1185 227.3 81.7 627 13.3 510 516 2.21 25.7 1107 42.0 0.48 -1.1

HS16 28.3 7.6 0.40 188 244.1 65.9 548 2.2 310 458 2.78 5.9 1121 58.3 0.51 -0.1

HS17 27.6 7.7 2.72 1280 124.5 46.5 486 10.9 285 236 0.43 24.9 979 29.0 0.38 -1.5

HS18 27.9 7.2 0.41 193 43.6 15.4 330 8.1 420 164 0.60 6.2 322 13.3 0.12 -1.1

HS19 31.8 7.1 0.57 266 77.7 24.3 419 11.9 420 230 0.50 6.3 562 20.0 0.32 -1.8

TS1 32.6 7.9 1.10 513 69.1 25.9 395 7.8 310 291 0.98 6.2 473 37.0 0.21 -0.8

TS2 29.6 8.1 0.52 321 48.9 21.1 241 7.1 240 130 0.20 0.9 366 65.0 2.45 -1.6

TS3 35.4 8 1.06 499 51.2 26.8 396 4.3 210 145 1.65 12.8 729 46.0 0.21 -2.2

TS4 28.2 8 1.08 524 79.0 26.0 395 7.80 310 291 0.98 7.2 473 39.0 0.31 0.2

TS5 25 7.9 1.14 612 59.0 21.0 241 7.10 240 130 0.20 0.9 366 45.0 0.48 0.1

TS6 32.5 7.8 1.07 674 41.2 27.0 396 4.30 210 195 1.65 12.8 629 36.0 0.34 -1.9

HS=Hand pump sampling point, TS=Tube well sampling point

a mS cm-1, b mg L-1, c µg L-1

73

Table 6 b. Major element chemistry and arsenic contaminations in surface water from district Jamshoro Sindh, Pakistan

Sample

I.D T (oC) pH EC a TDS b Ca b Mg b Na b K b HCO3

- b Cl- b NO2

- b NO3- b SO4

2- b As c Fe b Balance

CS1 22.5 7.1 0.41 190 25.9 13.1 221 4.3 179 136 0.44 6.4 248 3.0 0.08 -0.1

CS2 23.8 7.2 0.40 188 8.2 6.8 216 3.0 289 119 0.50 6.4 108 4.0 0.11 -0.4

CS3 24.2 7.8 2.66 1250 85.5 39.5 710 18.8 346 265 1.01 18.5 1240 37.0 0.38 -0.8

MS1 22.5 7.2 0.42 188 39.1 14.9 241 5.7 249 180 0.52 6.4 195 5.3 0.12 1.6

MS2 21.8 6.9 0.40 255 11.4 7.6 182 6.5 269 86 0.55 5.8 116 5.1 0.09 -0.7

MS3 23.5 8.4 1.85 865 51.6 20.4 461 17.2 229 271 1.58 15.7 613 16.0 0.10 0.3

MS4 22.6 7.1 0.45 210 15.5 5.5 246 7.3 208 172 0.55 6.2 187 6.3 0.14 -1.0

MS5 23.6 7.8 1.68 793 224.5 37.5 481 15.7 268 359 1.11 13.5 984 50.0 0.11 0.3

MS6 25.4 7.1 0.47 221 12.7 9.3 209 5.1 209 174 0.51 6.3 103 4.0 0.09 -0.4

MS7 22.5 7.9 3.61 1696 21.8 12.2 229 11.9 190 173 0.98 14.3 208 6.0 0.12 -1.4

MS8 23.8 7.1 0.42 198 6.4 23.6 270 34.9 202 322 0.45 4.9 126 5.0 0.09 -0.9

MS9 24.2 7.1 0.50 212 24.5 17.5 242 4.7 194 224 0.55 5.2 181 4.2 0.09 -0.5

MS10 22.8 7.1 0.49 208 47.7 18.3 212 14.9 187 206 0.62 6.1 214 10.2 0.02 -0.2

MS11 24.5 7.3 0.52 228 23.6 11.4 359 42.8 479 243 0.72 4.9 236 7.0 0.10 -2.4

74

MS12 23.4 7.2 0.41 194 86.4 33.6 404 7.2 172 282 2.38 73.8 597 17.0 0.14 -0.2

MS13 22.5 7.1 0.49 233 21.8 3.2 332 14.2 164 245 1.50 12.0 325 6.0 0.12 -1.5

MS14 21.8 7.8 3.72 1756 20.0 12.0 240 26.0 157 235 1.12 0.4 206 6.0 0.30 -1.7

MS15 23.5 7.1 0.54 214 17.3 2.7 251 14.4 149 196 0.60 0.5 215 5.2 0.10 -0.5

MS16 22.6 7.8 3.50 1646 71.8 45.2 540 9.9 141 316 3.19 48.6 891 11.1 0.02 0.0

MS17 23.6 7.8 1.42 669 81.8 25.2 438 7.1 134 325 1.28 12.5 622 11.2 0.27 1.5

MS18 25.4 8.1 3.25 1524 15.3 10.7 328 24.3 126 386 1.60 14.3 205 8.1 0.14 -3.2

MS19 22.5 8.5 0.93 945 24.5 10.5 136 9.3 119 53 0.88 2.3 234 4.2 0.12 -1.1

MS20 23.8 7.4 0.32 150 15.8 8.2 286 6.0 111 230 0.49 32.2 226 6.0 0.14 1.0

CS=Canal water sampling point, MS= Municipal water supply sampling point

a mS cm-1, b mg L-1, c µg L-1

75

Table 7. Ranges of analytical data of the ground and surface water samples in district Jamshoro, Sindh, Pakistan

Water Type

Ground water Surface water

Hand pump water Tube Well water Canal water Water supply

No. of samples n = 117 n = 36 n = 36 n = 120

Parameter

Recommended

Values♠ Min. Max. Mean Min. Max. Mean Min. Max. Mean Min. Max. Mean

T (oC) -- 26.4 31.8 28.56 29.6 35.4 32.53 22.5 24.2 23.5 21.8 25.4 23.3

pH 6.5– 8.5 7.1 8.4 7.758 7.9 8.1 8 7.1 7.8 7.4 6.9 8.5 7.5

EC a 0.40 0.401 4.51 1.91 0.522 1.095 0.89 0.4 2.66 1.16 0.32 3.72 1.3

TDS b 500 188 2214 896 321 513 444 188 1250 543 150 1756 620

Ca b 100 33.6 297.3 111.3 48.9 69.1 56.4 8.2 85.5 39.87 6.4 224.5 42

Mg b 50 11.1 99.7 41.14 21.1 26.8 24.6 6.8 39.5 19.8 2.7 45.2 16

Na b 200 190.9 944.9 520.5 240.5 396.1 344 216 710 382.2 135.9 539.9 304

K b 12 2.204 54.86 17.45 4.25 7.84 6.39 2.96 18.8 8.672 4.69 42.79 14

76

HCO3- b -- 180.5 1352 425.9 210 310 253 179 346 271.2 111.4 479.1 198

Cl- b 250 164 720.6 329.4 130.5 290.6 189 119 265 173.4 52.95 386 234

NO2- b 3 0.428 7.497 2.028 0.199 1.65 0.94 0.44 1.01 0.647 0.453 3.187 1.1

NO3- b 50 1.448 48.28 17.26 0.901 12.82 6.64 6.37 18.5 10.41 0.44 73.75 14

SO42- b 250 113.2 1516 740.5 365.7 729.1 523 108 1240 532 103.1 984.2 334

As c 10 13.0 106 40.02 37 65 49.3 3 37 14.7 4 50 9.7

Fe b 0.3 0.09 4.28 0.788 0.21 2.45 0.96 0.08 0.38 0.19 0.02 0.3 0.12

a mS cm-1, b mg L-1, c µg L-1 and ♠WHO (2004)

77

Fig. 3 Dendrogram

showing sites cluster on the Jamshoro (Surface water)

Fig. 4. Dendrogram showing sites cluster on the Jamshoro (Ground water)

78

4.1.2. Discussion

The surface (CS and MS) and groundwater samples (HS and TS) have been used as the

sole source of drinking water, cooking and personal hygiene in understudy area of Pakistan. In

fact, As is known as the most serious inorganic contaminant in drinking water. Our study

revealed elevated levels of As in ground and surface water samples (Table 6a, b).

The physico-chemical parameters of surface and groundwater samples are presented

(Tables 6a, b and 7). The pH is the most important parameter for test of water quality and useful

tool for interpretation of water chemistry. The pH of both types of water samples were found

from neutral to slightly alkaline, but it was within the WHO recommended values (Tables 6a, b

and 7). Mostly, the EC values of surface and groundwater samples were found to be higher than

WHO permissible level (0.4 mS cm−1), whereas, the TDS of all samples were within the limit

(1000 mg L−1), except in HS (Tables 6a, b and 7). The annual rainfall in this basin is < 200 mm,

which have no effect on values of EC in the rainy season. High EC in dry season represents

water with high electrolyte concentration, may be due to high rate of evaporation. It might be

contributed to the high salinity, mineral contents and lower water table. A significant positive

correlation was found between Ca and total hardness (r = 0.64–0.99), while low correlation was

observed between TDS and hardness (r = 0.37–0.40) which may be due to high level of sodium

and chlorides in understudy water samples. These dominant ions might be the result of ion

exchange and solubilization in the aquifer (Torres and Ishiga 2003). The studied ground waters

are usually basic in nature, have high EC due to elevated levels of TDS, reflecting moderate

mineral dissolution. The intensity of soluble minerals is expressed as saturation index. In

understudy groundwater samples, the saturation index (SI) of calcite has shown significant

correlation with that of SI of dolomite and gypsum (Fig. 5a and b). The positive correlation of SI

of calcite with

Ca2+, SI of dolomite with Mg2+, while Ca2+ and SO42− corresponds with SI of gypsum (Fig. 5c–

f), indicated that, these minerals are in a state of under saturation in ground water. The SI results

may be attributed to extensive water logging of study area and is promoting contamination of As

in the studied groundwater (Ito et al., 2001). Expected high As contamination in ground waters

79

might be caused by oxidizing environments due to elevated concentrations of Ca2+ (>100 mg

L−1), SO42− (>250 mg L−1) and pH > 7.5 (Smedley et al., 2002).

Arsenic elution from organic matter (in soil) may be due to elevated alkalinity of soil

(Webster and Nordstrom 2003; Welch et al., 2000). Therefore, desorption of arsenic can either

be promoted by an increase in pH or by the concentration of competing ions (Ca2+, Mg2+, Cl−,

HCO3− and SO4

2−). The pH was significantly correlated with As (r = 0.55, n = 153). The weak

correlation was observed between As and Cl− concentrations (r = 0.30, n = 153), however,

chloride showed a significant correlation with Ca2+, Mg2+, Na+ and SO42− (r = 0.64, 0.85, 0.81

and 0.74, respectively, n = 153), whereas HCO3− was not significantly correlated with Cl−

(r=0.15, n = 153).

The mean As concentration in surface water samples is 15.0 µg L−1, with a range of 3.00–

50.0µg L−1, which is lower than the reported values of other areas (Brandvold, 2001). In the

present study, most of the collected samples have As contents within the recommended values of

WHO, except in surface water samples of Manchar lake and its canal (Aral wah), i.e., sampling

point CS3 and the municipal water supply samples (MS5). This might be due to natural

processes, i.e., extensive evaporation of water due to high temperature and low rate of rain falls,

which enhance the amount of salts, trace and toxic elements and other pollutants. The possible

anthropogenic sources in study area include wastewater of agricultural lands, industrial effluent

and domestic wastes of urban areas, as described in previous study (Arain et al., 2008). The

average concentration of As in groundwater samples was found to be 41.0µg L−1, which was less

as compared to other countries like Bangladesh, India, Taiwan, China, Hungary, USA, Finland,

Thailand, Argentina, Taiwan, Chile, Japan and Vietnam (Mandal and Suzuki 2002; Wang and

Shpeyzer, 1997). Concentrations of naturally occurring arsenic in ground water are varied due to

the geological and climatic changes (Mandal and Suzuki 2002). The study area exhibited

elevated As concentrations in ground water, as it is situated in a zone of normal and hot spring

with great thickness of sediments, and depth of burial which has produced very high geothermal

temperatures. The literature counts various examples, which showed that trace elements

including arsenic are more readily mobilized and transported by warm or hot water in the

geothermal areas, like Jamshoro (Webster and Nordstrom 2003; Brandvold, 2001; Zaigham et

al., 2009).

80

Fig. 5. Relation ships between various chemical components of analyzed in groundwater

samples. (a) Dolomite saturation index (SId) and calcite saturation index (SIc); (b) dolomite

saturation index (SId) and gypsum saturation index (SIg); (c) calcite saturation index (SIc) and

Ca2+; (d) dolomite saturation index (SId) with Mg2+; (e) gypsum saturation index (SIg) and

Ca2+; (f) gypsum saturation index (SIg) and SO42− (Square icon for TS and triangle for HS).

81

The significant correlation of As with Fe (r = 0.83) in ground water indicated that the elevated

concentration of As in study area might be due to the presence of Fe containing ores (Ghaedi et

al., 2006). In this connection, three mechanisms may explain the As discharge from sediment

deposits to groundwater, the reduction of iron hydroxides, release of sorbed As from the

sediments following the oxidation of As-rich pyrite in the sediments and the anion exchange of

sorbed As with phosphate from fertilizers (Singh and Ma 2006). It was hypothesized that

desorption of As from Fe oxides could occur at reducing condition in alluvial sediments, which

could lead to high-As in ground waters (Smedley et al., 2002). According to our findings, the

iron concentrations in groundwater samples of different sampling sites were found to be higher

than those of the WHO recommended level (Tables 6a, b and 7).

Fertilizers such as di-ammonium phosphate and urea are extensively used, which may

seep down to underground water table, hence, altering its composition. A thermal power station,

many brick and chemical factories are located here. In thermal power station coal burning for

energy production is the main cause of air and terrestrial pollution, as, burning mineral coal is

known to emit toxic elements such as As (Ravenscroft et al., 2001). The high usage of arsenical

pesticides for protecting crops and industrial effluents from chemical and sugar industries are

also polluting aquatic system in the region. Keeping in view of the above said facts, these

sources of pollution are main source of As contaminations in water bodies of understudy areas.

4.1.3. Conclusion

The evaluation of total arsenic contents of groundwater (153 samples) as well as

of surface water (138 samples) in Jamshoro district, Sindh, Pakistan, was carried out in order to

have an insight about the extent of arsenic toxicity in study area. It was concluded that arsenic

concentration in almost all the studied samples was alarmingly higher than the permissible limits

proposed by WHO. The multivariate technique, cluster analysis of understudy sites clearly shows

the more polluted, medium and less polluted sites for surface and underground water. In

generally, the ground water arsenic level was considerably higher than that of surface water,

possibly due to some geothermal and anthropogenic factors, which were enhanced high

measured level of pH, Ca2+, SO42- and Fe. The HS3 station (106 μg L-1) of ground water and

station MS3 (50 μg L-1) of surface water exhibited highest arsenic concentration. These findings

82

demand serious concerns about realization of high toxicity of arsenic in water especially in

ground water, to take immediate measures and to control this threat to local residence, its flora

and fauna.

83

4.2. Assessment of physico-chemical parameter and Arsenic speciation in surface and

ground water samples of Jamshoro Pakistan

General Remark

The work presented in this section has been accepted as:

Jameel Ahmad Baig, Tasneem Gul Kazi et al., (2010). Assessment of water quality and Arsenic speciation in surface and ground water samples of Jamshoro Pakistan. International Journal of Environmental Analytical Chemistry (Accepted)

4.2.1. Physico-chemical parameter

The temperature in surface water was recorded in the range of 28–45 ˚C in summer and

18–25 ˚C in winter season. The physico-chemical parameters of HS, TS, CS, RS and MS water

samples listed in Table 4.3. In surface water pH was found within the WHO regulated levels in

the range of 6.90 to 8.5 in the samples surface and ground water (Table 8). EC and TDS in MS,

RS and CS were observed in between 0.32 to3.72 mS cm-1 and 150 to1756 mg L-1, respectively.

The EC exceeding the WHO guidelines (Table 8) for drinking water, due to high mineral

contents and salinity in RS and CS, our results, are closely correlate with other studies (Arain et

al., 2009; Baig et al., 2010). The EC and TDS in HS and TS were varied from 0.40 to 4.50 mS

cm-1 and 180 to 2214 mg L-1, respectively. Alkalinity was observed in the range of 180 to1352

and 170 to 479 mg L-1 in ground and surface water samples, respectively.

In HS and TS, the levels of SO42-, Cl-, Ca2+, and Na+ were observed higher than

permissible limit of WHO, whereas other anions and cations were within permissible limits

(Table 8). In MS, RS and CS, Ca2+ and Na+ were ranged from 6.40 to 85.1and 191 to 540 mg L-1,

respectively and Cl- contents reached up to 386 mg L-1. The concentration of PO43- and NO2

-

were found <10 mg L-1, whereas, the levels of SO42- and NO3

- were observed in between 107 to

984 and 5.20 to 73.75 mg L-1, respectively. In surface water samples the F- was observed within

WHO permissible limits (1.5 mg L-1), while in TS and HS, it was > 5.0 mg L-1 (Table 8). The

TDS and EC were significantly correlated with PO43-, NO3

-, NO2- , K+ and Ca2+ in TS and HS at

95% confidence level (Baig et al., 2009a; Lopez et al., 1999). While, in surface water TDS and

EC have high correlation with anions and cations except SO42-, Cl- and F- at confidence level of

95%. In groundwater the Fe concentration found in the range of 0.09 to 4.30 mg L-1, while it was

within the WHO recommended level in surface water (Table 8).

84

The total As in and HS of understudy areas was found in the range of 13 to 106 μg L-1,

whereas, it was ranged from 3.0 to 50 μg L-1 in surface water. The mean concentration of total As

in surface water samples was found to be 15 μg L-1, which is lower than the reported values for

surface water (Smedley and Kinniburgh, 2002; Baig et al., 2009a). The total As was observed >

40.0 μg L-1 in TS and HS samples, less than other countries as reported elsewhere (Jiang, 2001;

Smedley and Kinniburgh, 2002; Baig et al., 2009a,b).

85

Table 8. Ranges of analytical data of the ground and surface water samples in district Jamshoro, Sindh, Pakistan

Parameter WHO

Recommended Values

Canal Water

River Water

Municipal Water

Tube Well water

Hand Pump

n = 120 n = 36 n = 120 (60-120 m)

n = 36

(15-60 m)

n = 124

pH

6.5– 8.5

Min 7.1 7.1 6.9 7.9 7.1

Max 7.8 7.5 8.5 8.1 8.4

Mean 7.4 7.2 7.5 8 7.75

EC

mS/cm

0.4

Min 0.40 0.34 0.32 0.52 0.40

Max 2.66 0.49 3.72 1.09 4.50

Mean 1.16 0.40 1.30 0.89 1.91

TDS

mg L-1

1000

Min 188 150 150 321 180

Max 1250 450 1756 513 2214

Mean 543 188 620 444 896

Ca2+

mg L-1

100

Min 8.20 8.20 6.40 48.9 33.6

Max 85.5 39.1 85.1 69.1 297

Mean 39.8 25.9 42.0 56.4 111

Mg2+

mg L-1

50

Min 6.80 6.80 2.70 21.1 11.1

Max 39.5 13.1 45.2 26.8 99.7

Mean 19.8 10.9 16 24.6 41.1

Na+

mg L-1

200

Min 216 191 135 240 190

Max 710 225 540 396 945

Mean 382 211 304 344 520

K+ 12 Min 2.96 3 4.69 4.25 2.20

86

mg L-1

Max 18.8 5.7 42.8 7.84 54.8

Mean 8.67 4.3 14 6.39 17.4

HCO3-

mg L-1

--

Min 179 170 111.4 210 180

Max 346 288 479 310 1352

Mean 271 248 198 253 426

F-

mg L-1

1.5

Min 0.42 0.40 0.10 0.50 0.40

Max 1.40 1.30 3.00 1.10 5.00

Mean 0.73 0.73 0.80 0.97 1.52

Cl-

mg L-1

250

Min 119 0.47 53.0 130 164

Max 265 0.60 386 290 720

Mean 173 0.50 234 189 329

NO2-

mg L-1

3

Min 0.44 1.35 0.45 0.20 0.43

Max 1.01 1.79 3.19 1.65 7.50

Mean 0.64 1.19 1.10 0.94 2.03

NO3-

mg L-1

50

Min 6.37 5.20 0.44 6.37 1.45

Max 18.5 8.40 73.7 18.5 48.3

Mean 10.4 6.40 14.0 10.4 17.2

PO43-

mg L-1

--

Min 0.40 0.52 0.47 0.50 0.40

Max 0.60 0.70 0.85 0.70 5.10

Mean 0.48 0.59 0.57 0.60 0.82

87

SO42-

mg L-1

250

Min 108 107 103 108 113

Max 1240 201 984 1240 1516

Mean 532 144 334 532 740

Fe

mg L-1

0.3

Min 0.08 0.14 0.02 0.03 0.08

Max 0.38 0.21 0.30 0.32 0.38

Mean 0.19 0.17 0.12 0.12 0.19

AsT

µg L-1

10

Min 3.00 5.20 4.00 37.0 13.0

Max 37.0 10.0 50.0 65.0 106

Mean 14.7 6.50 9.70 49.3 40.0

iAs

µg L-1

--

Min 2.90 5.00 3.80 35.2 12.6

Max 35.8 9.50 48.0 62.9 104

Mean 14.2 6.20 9.10 47.7 38.0

As3+

µg L-1

--

Min 1.70 2.90 2.30 18.9 6.20

Max 20.7 5.40 30.5 36.4 51.0

Mean 8.20 3.60 15.8 27.6 18.0

As5+

µg L-1

--

Min 1.20 2.10 1.50 16.3 6.40

Max 15.1 4.10 17.5 26.5 53.0

Mean 6.00 2.60 4.20 20.1 20.0

aCanal water sample, bRiver water sample, cmunicipal treated water sample, dTube well sample, eHand

pump samples

88

The iAs was determined by solid phase extraction and found > 98% of total As

(Thirunavukkarasu et al., 2002). The As species in understudy ecosystems were obtained in

increasing order as: RS<CS<MS< TS< HS (Table 8). Arsenic speciation in groundwater is an

important factor in determining mobilization, toxicity and general water chemistry. The redox As

species are unstable in natural waters because of the transformation between As3+ and As5+, due

to the organic matrices, redox potential (Eh) and pH (Gong et al., 2002; McCleskey et al., 2004).

The pH and Eh are the most important factor controlling As speciation. Under oxidizing

conditions As5+, (H2AsO4-) is dominant at low pH (< pH 6.9), whilst at higher pH, HAsO42-

becomes dominant (H3AsO4 and AsO43- may be present in extremely acidic and alkaline

conditions, respectively) (Maeda, 1994). Under reducing conditions at pH less than about pH 9.2,

the uncharged arsenite species H3AsO3 will predominate (Smedley and Kinniburgh, 2002). So,

all water samples were delivered on the same sampling day to laboratory and analysis of As3+

was accomplished on same day, to avoid risk of transformation of species (Arain et al., 2008). It

was incorporated with these evidences and resulted data was presented in Table 8.

The average As3+ concentrations was found to be 8.20, 3.60 and 15.8 μg L-1 in water

samples of CS, RS and MS, respectively (Table 8). The high levels of As3+, as the most toxic

arsenic species in aquatic environment, found in canal and municipal treated water samples may

causes tracheae bronchitis, rhinitis, pharyngitis, shortness of breath, nasal congestions and black

foot disease (Maeda, 1994). A strong linear correlation coefficient was observed between the

concentrations of inorganic As species with different physico chemical parameters (TDS, EC,

Ca2+, Mg2+, Na+, Cl-, NO3- and SO4

2-) in surface water (Table 9.), indicating possible

contamination caused by both natural and anthropogenic sources (Arain et al., 2008).

89

Table 9. Linear correlation coefficient matrix for different physico chemical parameters, Fe and As species Significant at 5% level

Ground water Surface water

AsT Asi As3+ As5+ AsT Asi As3+ As5+

pH 0.551 0.551 0.548 0.546 0.459 0.458 0.461 0.454

EC 0.346 0.346 0.328 0.356 0.598 0.596 0.596 0.595

TDS 0.356 0.356 0.335 0.368 0.685 0.683 0.678 0.688

Ca2+ 0.585 0.585 0.594 0.570 0.903 0.904 0.908 0.896

Mg2+ 0.524 0.524 0.546 0.499 0.851 0.848 0.850 0.844

Na+ 0.377 0.377 0.364 0.381 0.867 0.865 0.863 0.866

K+ 0.171 0.171 0.185 0.157 0.510 0.510 0.506 0.513

HCO3- 0.253 0.253 0.222 0.273 0.606 0.604 0.601 0.608

F- 0.222 0.222 0.263 0.186 0.548 0.546 0.541 0.552

Cl- 0.363 0.363 0.388 0.339 0.743 0.742 0.741 0.742

NO2- 0.299 0.299 0.301 0.293 0.637 0.633 0.639 0.624

NO3- 0.370 0.370 0.406 0.336 0.571 0.567 0.576 0.553

PO43- -0.108 -0.108 -0.171 -0.058 -0.021 -0.020 -0.017 -0.024

SO42- 0.499 0.499 0.496 0.494 0.902 0.900 0.901 0.898

Fe 0.847 0.847 0.854 0.830 0.194 0.193 0.196 0.189

90

Mean concentration of As3+ in the TS and HS water samples were found to be 26.5 and

53.0 μg L-1, respectively. It was observed that most of the ground water (TS and HS) samples

were contaminated with high proportion of As5+ than surface waters (Table 8). It is reported in

literature that the elevated level of As5+ in ground waters under oxidizing condition are

characterized by elevated contents of SO42- and pH, that is responsible for the release of As in

oxidizing quaternary sedimentary aquifers (Smedley and Kinniburgh, 2002). The concentrations

of As3+ and As5+ in ground water were strongly correlated to Fe concentrations (Table 9). It is

reported in literature that As5+ is relatively immobile in the subsurface because it tends to sorb

onto positively charged particles, such as iron hydroxides. Changes in redox conditions, such as

reduction of metal oxides, may enhance the mobility of arsenic (Smedley and Kinniburgh, 2002).

The concentrations of As3+ and As5+ in ground water were strongly correlating to Ca2+

and Fe concentrations (Table 9), which proved above facts. It is reviewed by Smedley and

Kinniburgh (2002) and Sano et al. (2008) that his can provide an explanation for both the

oxidizing and reducing high-As environments. An abundant source of Fe oxides with its surface-

bound and co-precipitated As provides a ready source of As that may be released given an

appropriate change in geo-chemical conditions (Arain et al., 2008). Thus, the elevated

concentrations of As3+ and As5+ were more likely to be found in domestic HS with short screens

set in proximity to the upper confine aquifer as compare to deep ground water (Table 8). Our

results for AsT, iAs, As3+ and As5+ were comparable to those reported in the literature for ground

water while high value of all As species observed in surface water samples, but difference was

not significant (p>0.05) (Farooqi et al., 2007; Sano et al., 2008; Tuzen et all., 2009).

All this provides evidence that anthropogenic and geological environment play a key role

in the distribution of studied inorganic As species in water bodies of understudy areas (Pandey et

al., 2006) and makes a significant contribution to the total intake of inorganic arsenic. In district

Jamshoro most of the population of rural area, depends on ground water. The consumption of

drinking-water is approximately 4 L containing >50 µg L-1. Therefore, total consumption of iAs

is over 200 µg compared to an estimated daily intake of 12–14 µg iAs from diets of North

American population (Thornton and Farago 1997). Therefore, chronic exposure to iAs may give

rise to several health effects including gastrointestinal and respiratory tract disorders, skin, liver,

cardiovascular system, hematopoietic system, nervous system etc in understudied areas. The

91

earliest reports date back to the latter part of the 19th century when the onset of skin effects

(including pigmentation changes, hyperkerotosis and skin cancers) were linked to the

consumption of As through medicines and drinking water (Yost et al., 1998; Arain et al., 2009.

Fig. 6 Dendrogram showing clustering of different origins of surface and ground water

according to distribution of As species

92

Table 10. Loadings of experimental variables (19) on significant principal components for

ground water of district Jamshoro

Variables PC1 PC2 PC3

pH 0.759 -0.082 -0.254

EC 0.763 -0.275 -0.443

TDS 0.743 -0.251 -0.460

Ca2+ 0.774 -0.138 0.432

Mg2+ 0.628 -0.216 0.007

Na+ 0.776 -0.477 0.320

K+ 0.556 -0.430 0.241

HCO3- 0.246 -0.344 0.278

F- 0.640 0.205 -0.432

Cl- 0.925 -0.151 0.243

NO2- 0.647 -0.059 0.118

NO3- 0.839 -0.242 -0.313

PO43- 0.191 0.427 0.652

SO42- 0.574 -0.457 0.230

Fe 0.548 0.365 0.125

AsT 0.508 0.598 0.067

Asi 0.574 0.770 -0.020

As3+ 0.617 0.740 -0.038

As5+ 0.538 0.787 -0.006

Eigenvalue 7.99 3.51 1.75

%Total variance 42.03 18.47 9.23

Cumulative % 42.03 60.49 69.72

93

Cluster analysis was applied on surface and ground water quality data, to detect spatial

similarity and dissimilarity for grouping of different understudy ecosystems (spatial variability).

The resulted dendrogram (Fig. 6), grouped all the five sampling eco-systems into three

statistically significant clusters, as surface water eco-systems (MS) and (RS and CS) have low

mutual dissimilarities as compared to ground water ecosystems (HS and TS), which have 18% of

total dissimilarity. Due to high concentration of arsenic species in ground water samples of

understudy area, principal component analysis was performed on the analytical data set (19

variables) separately for ground water samples (HS and TS), in order to identify a reduced set of

factors that could capture the variance of data set. Following the criteria of PCA reported in

literature that PCs with eigenvalue >1 were retained (Helena et al., 2000; Sarbu and Pop 2005;

Baig et al., 2010). The first component (PC1) accounted for over 42.03% of the total variance in

the data set of the groundwater, in other words, the physical parameters, major cations, anions,

Fe and As species in the solution demonstrates similar behavior in the groundwater samples

(Table 10). In a macroscopic point of view all the physico-chemical parameters behave similarly,

i.e. high concentration of major elements as well as As species in main body of whole

groundwater, except in few cases where the variation in pollution loading has some temporal

effects. The strong positive loading on pH, EC, TDS, Ca2+, Na+, Cl- and NO3- were observed,

whereas, a low loading on PO43-. The anthropogenic pollution is mainly due to the discharge of

fertilizer and pesticides as a regular source, throughout the year. However, there is no available

data on the use of arsenical pesticides or industrial chemicals in the understudy area. But, it is

reported that about 5.6 million tonnes of fertilizer and 70 thousand tonnes of pesticides are

consumed in the country every year (Baig et al., 2010). Pesticides, mostly insecticides, sprayed

on the crops or mix with the irrigation water, which leaches through the soil and enters

groundwater aquifers (Baig et al., 2010). The trend obtained was also supported by the analysis

of the results on the raw data set. The second component (PC2), explaining 18.5% of the total

variance has strong positive loadings for Fe and As species, thus basically represents the

elements of pollution group. The third component (PC3) of PCA shows only 9.23% of the total

variation has positive loading of PO43-. The high values of Fe, As and major cations and anions

in underground water samples are above the permissible limit of WHO values for drinking water

(WHO 2004).

94

Fig. 7. Plots of PCA scores for combined data set of groundwater samples for distribution

of Fe, As species and water quality parameters in district of Jamshoro

The above observation is clearer to follow the Fig. 7, which shows the characteristics of

samples and help to understand their spatial distribution. It is evident that samples distributed in

upper right quadrant are more enriched with pH, EC,TDS, K+, F-, NO3-, Fe and As species, while

lower right quadrant with TDS, Na+, Ca2+, Mg2+, HCO3-, Cl-, NO2

- and SO42- as shown in Fig. 7.

The sample distributed in lower left quadrants is PO43- to a lesser extent. All these facts revealed

that the high level of As species in water is due to dissolution of As compounds coming from

Himalaya through Indus river and settled down through year to year and then introduced into

ground water by geothermal, geo-hydrological and bio-geo chemical factors as reported

elsewhere (Baig et al., 2009, 2010).

4.2.2. Conclusions

The speciation analysis has provided more information about toxicity, bioavailability, and

mobility of different As species in surface and ground water samples. Therefore, evaluation of

95

arsenic species of groundwater (160 samples) as well as of surface water (276 samples) in

Jamshoro district, Sindh, Pakistan, was carried out in order to have an insight about the extent of

arsenic toxicity in study area. It was concluded that the strong linear correlation coefficient was

observed between the concentrations of inorganic As species with different physico chemical

parameters (TDS, EC, Ca2+, Mg2+, Na+, Cl-, NO3- and SO4

2-) in surface water but in ground water

they were strongly correlated with Ca2+, SO42- and Fe. The concentrations of As species in five

studied origins were obtained in increasing order as: RS < CS < MS < TS < HS. Cluster analysis

grouped five sampling ecosystems into three clusters of similar surface and groundwater quality

characteristics and As species. Based on obtained information, it is possible to design a future,

optimal sampling strategy, which could reduce the number of sampling sites and associated cost.

PCA performed on combined (TS and HS) data set extracted two significant factors explaining

more than 60% of total variance. Thus, this study illustrates the usefulness of multivariate

statistical techniques for analysis and elucidation of complex data sets of groundwater quality

evaluation and identification of possible pollution sources.

96

4.3. Physico-chemical parameters and speciation of Arsenic in water samples of different

origin

General Remark


Jameel Ahmed Baig, Tasneem Gul Kazi et al., (2010). Speciation and evaluation of

Arsenic in surface water and groundwater samples: A multivariate case study.

Ecotoxicology and Environmental Safety 73, 914–923.

doi:10.1016/j.ecoenv.2008.02.024

4.3.1. Results and Discussion

4.3.1.1. Physico-chemical parameters

In surface water, the temperature showed a very characteristic annual cycle, with higher

values during the summer (30–49 ○C), and lower values in the winter season (12–28 ○C). The

results of physicochemical parameters of surface (CS, RS, LS and MS) and ground (HS and TS)

water samples are presented in Table 11. The analysis of the collected samples reveals some

level of compliance with regulated standards (WHO) for drinking water and the significant

deviations were equally noticed. The pH values fluctuated in between 7.1 to 8.2 in surface water

whereas, in ground water samples it found in the range of 7.0-8.50 (Table 11), which falls within

the WHO regulated values for drinking water. The range of TDS and EC in surface water (MS,

LS, RS and CS) were found in the range of 153–940 mg L-1 and 0.12–9.22 mS cm-1,

respectively. The EC values exceeding the WHO guidelines for drinking water (Table 11), which

attributed to the high salinity (1.2-1.8 mg L-1) and soluble electrolytes in LS water samples (Kazi

et al., 2009). The levels of TDS and EC in ground water samples were varied from 153-3350 mg

L-1 and 0.35-9.82 mS cm-1, respectively. Alkalinity was found in the range of 179–613 and 282-

786 mg L-1 in surface and ground water samples, respectively.

In ground water, the concentration of Na+, K+, Ca2+ and Mg2+ were found in the range of

190-1888, 6.80-37.6, 6.80-628 and 4.45-49.1 mg L-1, respectively. The range of

97

Table 11 Ranges of analytical data of the ground and surface water samples in district Khairpur Mir’s, Sindh, Pakistan

Parameter

WHO

Recommended

Values

Unit

CS RS LS MS TS

(20-100 m)

HS

(5-20 m)

n = 120 n = 120 n = 120 n = 120 n = 120 n = 240

Min Max Mea

n

Min Max Mea

n

Min Max Mea

n

Mi

n

Ma

x

Mea

n

Mi

n

Max Mea

n

Mi

n

Max Mean

Salinity -- % 0.0 0.1 0.1 0.0 0.0 0.0 0.5 1.8 1.1 0.0 0.0 0.0 0.0 1.5 0.8 0.0 1.2 0.5

pH 6.5– 8.5 7.1 7.6 7.27 7.1 7.5 7.2 7.10 8.20 7.4 7.1 7.5 7.3 7.2 8.5 7.9 7.0 8.4 7.48 aEC 0.40 mS cm-1 0.30 0.45 0.38 0.34 0.49 0.40 0.29 9.22 1.25 0.12 0.54 0.34 0.8 5.2 2.8 0.35 9.82 1.99 bTDS 500 mg L-1 369 678 486 190 188 188 153 940 390 285 456 347 370 1943 1111 153 3350 763 bCa++ 100 17.8 26.4 21.9 8.20 39.1 25.9 6.0 151 36 22.7 46.5 31.1 8.6 137 62.0 6.8 61.8 21.3 bMg++ 50 8.6 13.2 10.8 6.8 13.1 10.9 1.3 73.1 16.5 11.2 20.6 14.1 12.7 49.1 29.8 4.45 45.4 17.6 bNa+ 200 280 370 326 191 225 211 165 800 282 291 499 395 490 976 745 190 1888 769 bK+ 12 4.6 11.4 7.3 3.0 5.7 4.3 4.6 61.0 20.2 0.54 23.2 6.90 6.8 37.6 25.6 6.90 31.1 15.8 bHCO3

- -- 316 388 348 179 288 248 200 420 331 282 613 364 282 782 508 80.0 760 372 bF- 1.5 0.4 1.1 0.9 0.47 0.6 0.5 0.40 2.60 1.30 0.5 1.8 1.10 1.0 5.0 2.2 0.40 1.60 1.01 bCl- 250 170 275 206 135 179 119 102 851 319 120 204 149 152 900 431 93.0 325 124 bNO2

- 3 0.4 1.9 1.4 0.4 0.8 0.5 0.43 8.03 1.24 0.86 1.73 1.30 1.1 5.3 3.4 0.43 9.22 2.01 bNO3

- 50 12.6 24.9 16.8 5.2 8.4 6.4 8.8 97.6 19.3 9.93 32.1 15.5 12.6 74.1 40.9 4.91 97.3 21.8 bPO4

3- -- 0.4 0.6 0.48 0.52 0.7 0.59 0.47 0.80 0.58 0.47 0.85 0.57 0.5 0.7 0.6 0.47 5.00 0.79 bSO4

2- 250 179 338 240 107 201 144 92 951 411 230 733 478 695 1120 877 43.0 594 205 bFe 0.3 0.11 0.32 0.22 0.14 0.21 0.17 0.11 0.70 0.32 0.14 0.30 0.22 0.30 3.25 1.80 0.5 3.8 2.36 cAsT 10 µg L-1 4.2 8.0 6.1 3.0 5.3 4.0 10.0 18.3 12.0 5.00 8.30 6.40 9.2 163 53.8 9.20 361 68.3 cAsi -- 4.1 7.6 5.8 2.9 5.2 3.9 4.60 17.75 11.30 4.70 8.05 6.06 8.7 148 51.6 8.74 352 65.2 cAs3+ -- 2.1 3.2 2.6 1.3 3.0 2.3 1.93 5.68 5.09 1.90 3.38 2.54 3.1 71.2 22.7 2.80 114 25.4 cAs5+ -- 2.0 4.4 3.3 1.1 2.2 1.6 2.35 12.07 6.22 2.70 4.66 3.51 5.6 77.19 28.63 5.90 238 39.8

98

Fig 8. Dendrogram showing clustering of different origins of surface and ground water according to distribution of As species

SO42- was observed in ground water samples as 43 to 1120 mg L-1, while Cl- ranging from 93.0

to 900 mg L-1. The average values of NO2-, NO3

- and PO43- in ground water were observed 3.40,

37.0 and 70.0 mg L-1, respectively. Whereas in surface water, Na+ and Ca2+ were ranged from

191 to 800 and 6.02-46.5 mg L-1, respectively and Cl- concentration reached up to 851 mg L-1.

The levels of NO2-, and PO4

3- were observed <10 mg L-1, while the concentration of NO3- and

SO42- were found in the range of 5.20 to 97.3 and 92.0-733 mg L-1, respectively (Table 11). In all

surface and ground water samples the F- levels were within WHO permissible level (1.5 mg L-1),

whereas in LS and TS, it was observed >2.0 mg L-1 (Table 11). The physical parameters of water

(EC and TDS) are significantly correlated with cations and anions (Ca2+, K+, NO2- , NO3

- and

PO43-) in ground water samples (Table 12), which might be the result of ion exchange and

solubilization in the aquifer (Lopez et al., 1999; Baig et al., 2009b). Whereas, in surface water

EC and TDS have strong correlation with cations and anions except F-, Cl- and SO42- (Table 12).

In groundwater the Fe concentration was found in the range of 0.3-3.8 mg L-1 while it was within

the WHO recommended level in surface water except lake water samples (Table 11). It was

found that the contents As were significantly correlated As species in sues, whereas, in ground

water.

(Dlin

k/D

max

)*10

0

0

20

40

60

80

100

120

Lake River Canal Municipal Tube well Hand pump

99

4.3.1.2. Total Arsenic and Iron

Cluster analysis (CA) was applied on data set of total As and Fe content in six sampling

origins of surface and groundwater, to identify spatial similarity and dissimilarity for grouping of

sampling origins. The resulted dendogram (Fig. 8) grouped all the five sampling origins into

three statistically significant clusters, as sampling origin (LS) and (RS, CS and MS) have low

mutual dissimilarities as compared to sampling origins (TS and HS) has 14% of total

dissimilarity. The dendogram elucidated, the abnormality of the sampling origin LS, which was

grouped as cluster 1, receiving As from contaminant effluents from non-point sources, i.e.,

agricultural, industrial and domestic activities (Arain et al., 2008). Besides cluster 1, the mutual

dissimilarity among other sampling origins of groundwater made as cluster 2 (RS, CS and MS)

and cluster 3 (TS and HS) correspond to relatively moderate contaminated, low contaminated

and high contaminated regions, respectively. It was concluded that for rapid measurement of As

contamination in water, only one site in each cluster may serve as good in spatial assessment of

the whole data set. It is evident that the CA technique is helpful in offering reliable classification

of different origins of surface and groundwaters with adequate manner. Thus, the number of

sampling origins and cost in the monitoring network will be reduced without loosing any

significance of the outcome. This approach has consistency with literature reported research

(Kim et al., 2005; Arain et al., 2009).

The concentration of total As distributed in ground water samples of district Khairpur

(Pakistan) varied from 5.0 to 361 μg L-1, while it was ranged from 3.0 to 18.3 μg L-1 in surface

water (Table 4.6). On other hand in groundwater, the total Fe concentration was found in the

range of 0.3-3.8 mg L-1, while it was within the WHO recommended level in all surface water

origins except lake water samples (Table 11). The average concentration of total As in surface

water samples were found to be 8.0 μg L-1, which is lower than the reported values (Smedley and

Kinniburgh, 2002; Baig et al., 2009b). The concentration of total As was found to be higher in

LS than WHO permissible level (10 μg L-1), might be due to the natural processes i.e., extensive

evaporation of water due to high temperature and low rate of rain falls, enhanced the amount of

salts, trace and toxic elements in understudy Lake (Arain et al., 2008; Baig et al., 2009b). The

other possible factors are frequently uses of pesticides and insecticides in agricultural lands as

100

well as use of untreated waste water sewage sludge as agricultural fertilizer (Arain et al., 2008;

Baig et al., 2009b; Arain et al., 2009; Torres and Ishiga, 2003).

The average content of total As was found to be 54.2 μg L-1 in ground water samples of

understudied areas, higher than permissible limit of WHO but less than other countries as

reported elsewhere (Smedley and Kinniburgh, 2002; Smedley et al., 2002). It was also reported

that the high total As concentrations were observed in shallow groundwater while low total As

concentrations prevail in deep groundwater, our results are consistent with other studies (Focazio

et al., 2000; Ravenscroft et al., 2005). It may be due to the non-point sources i.e., agricultural,

industrial and domestic activities (Arain et al., 2008, 2009; Baig et al., 2009b). There are other

reports (Kazi et al., 2009; Mukherjee and Bhattacharya, 2001; Bhattacharya et al., 2002;

Smedley and Kinniburgh, 2002b; Focazio et al., 2000), where similar approach has successfully

been applied in water quality programs.

4.3.1.3. Inorganic arsenic (iAs)

Inorganic metal oxides have been applied as solid sorbent, such as aluminum oxide,

cobalt oxide and titanium dioxide (TiO2). With its high surface area TiO2 is chosen in pre-

treatment procedures for present study (Zhang et al., 2007). Therefore, it is used for the

determination of iAs. The concentration of iAs was found to be 2-7% lower than total As (Table

11), indicated the less availability of organic As in surface and ground water (Thirunavukkarasu

et al., 2002). The concentrations of iAs in six studied origins were obtained in increasing order:

RS<CS<MS< LS< TS< HS (Table 4.6).

101

Table 12. Linear correlation coefficient matrix for different physico chemi cal parameters, Fe and As species (Significant at 5% level, r > 0.649)

Ground water (n = 480)

pH EC TDS Ca++ Mg++ Na+ K+ HCO3- F- Cl- NO2

- NO3- PO4

3- SO42- AsT Asi As3+ As5+

EC 0.40

TDS 0.40 0.98

Ca++ 0.37 0.89 0.86

Mg++ 0.70 0.36 0.31 0.36

Na+ 0.42 0.03 0.01 0.06 0.59

K+ 0.40 0.93 0.87 0.84 0.38 0.10

HCO3- 0.26 0.02 0.01 -0.02 0.32 0.37 0.12

F- 0.58 0.08 0.01 0.03 0.50 0.34 0.11 0.08

Cl- 0.56 0.45 0.39 0.41 0.84 0.55 0.46 0.28 0.48

NO2- 0.42 0.86 0.81 0.74 0.41 0.21 0.87 0.18 0.08 0.44

NO3- 0.49 0.94 0.93 0.74 0.40 0.04 0.89 0.10 0.14 0.49 0.79

PO43- 0.57 0.94 0.93 0.78 0.52 0.15 0.86 0.18 0.21 0.57 0.81 0.97

SO42- 0.35 -0.11 -0.08 0.18 0.20 -0.07 -0.07 -0.09 -0.04 -0.07 -0.15 -0.15 -0.11

AsT 0.29 0.78 0.75 0.77 0.39 0.12 0.89 0.15 -0.05 0.46 0.61 0.77 0.73 0.07

Asi -0.26 -0.35 -0.38 -0.36 -0.14 -0.25 -0.23 -0.18 0.11 -0.22 -0.50 -0.29 -0.37 -0.03 -0.04

As3+ -0.27 -0.36 -0.39 -0.37 -0.14 -0.26 -0.25 -0.17 0.12 -0.22 -0.51 -0.30 -0.37 -0.04 -0.06 1.00

As5+ -0.19 -0.35 -0.39 -0.28 -0.05 -0.12 -0.23 -0.18 0.15 -0.21 -0.55 -0.29 -0.35 0.14 0.03 0.87 0.86

Fe -0.29 -0.31 -0.33 -0.38 -0.18 -0.32 -0.22 -0.14 0.09 -0.19 -0.40 -0.27 -0.34 -0.16 0.62 0.93 0.94 0.64

102

Surface water (n = 300)

EC 0.76

TDS 0.69 0.99

Ca++ 0.69 0.67 0.61

Mg++ 0.69 0.89 0.90 0.64

Na+ 0.75 0.95 0.94 0.66 0.92

K+ 0.57 0.75 0.74 0.57 0.75 0.76

HCO3- 0.47 0.77 0.80 0.51 0.68 0.73 0.51

F- 0.16 0.10 0.09 0.39 0.22 0.16 0.40 0.29

Cl- 0.22 0.07 0.06 0.02 0.11 0.15 0.07 0.03 -0.09

NO2- 0.63 0.92 0.93 0.56 0.87 0.89 0.75 0.68 -0.02 0.21

NO3- 0.67 0.94 0.94 0.60 0.86 0.92 0.81 0.70 0.17 0.01 0.89

PO43- 0.60 0.91 0.91 0.59 0.89 0.89 0.78 0.65 0.03 0.19 0.97 0.91

SO42- 0.32 0.20 0.13 0.40 0.28 0.27 0.04 0.03 0.02 0.16 0.09

0.18 0.21

AsT 0.55 0.68 0.65 0.51 0.69 0.70 0.95 0.41 0.34 0.00 0.61 0.74 0.67 0.10

Asi 0.54 0.77 0.80 0.47 0.80 0.81 0.60 0.70 0.17 0.17 0.82 0.73 0.79 0.06 0.47

As3+ 0.56 0.79 0.82 0.49 0.81 0.83 0.61 0.71 0.17 0.17 0.84 0.76 0.81 0.06 0.48 1.00

As5+ 0.36 0.51 0.55 0.21 0.52 0.53 0.37 0.45 0.00 0.27 0.64 0.47 0.57 -0.05 0.22 0.87 0.86

Fe 0.61 0.85 0.87 0.59 0.88 0.89 0.67 0.77 0.24 0.10 0.85 0.82 0.84 0.11 0.61 0.95 0.96 0.69

103

Table 13. Analytical results for surface and ground water samples and comparison with literature values

Samples Concentration (µg L-1)

As3+ As5+ Asi AsT Our results

River water 2.30±1.25 1.60 3.90±1.20 4.0±1.60 Canal water 2.60±2.50 3.30 5.80±1.90 6.10±2.15 Municipal water 2.54±2.60 3.51 6.06±1.60 6.40±1.75

Lake water 5.09±2.36 6.22 11.30±2.80 12.0±2.20 Hand pump 25.4±81.5 39.8 65.2±70.3 68.3±82.5

Tube well 22.7±93.7 28.63 51.6±62.6 53.8±93.4

Literature values River (Gregori et al. 2005) 0.54±0.03 1.02 1.56±0.05 Nd

Shallow Ground water ( Farooqi et al. 2007) nd Nd nd 235

middle depth Ground water (Farooqi et al. 2007) nd Nd nd 45

Deep Ground water (Farooqi et al. 2007) nd Nd nd 72

Rain water (Farooqi et al. 2007) nd Nd nd 30.0

Manza-Karabuki River (Sano and Kikawada 2008) 0.026 0.13 nd 0.16

Yu River (Sano and Kikawada 2008) 0.82 0.55 nd 1.37

Tap water (Tuzen et al. 2009) 0.11 ± 0.01 0.54 ± 0.03 nd 0.65 ± 0.03

River water (Tuzen et al. 2009) 0.32 ± 0.01 0.65 ± 0.02 nd 0.97 ± 0.04

Ground water (Pandey et al. 2006) nd Nd nd 143.8±176.9

Surface water (Pandey et al. 2006) nd Nd nd 74.4±63.7

Lake water (Hu et al. 2008) 1.22±0.07 2.84±0.09 nd Nd

Tap water (Hu et al. 2008) 0.89±0.06 1.20±0.04 nd Nd nd = not determined

104

4.3.1.4. Inorganic arsenic species

Arsenic speciation in groundwater is an important factor in determining mobilization,

toxicity, and general water chemistry. The redox As species are unstable in natural waters

because of the transformation between As3+ and As5+, due to the organic matrices, redox

potential (Eh) and pH (McCleskey et al., 2004). Arsenic is most problematic in the environment

because of its relative mobility over a wide range of redox conditions. The pH is the most

important factor controlling As speciation. Under oxidising conditions As5+, (H2AsO4-) is

dominant at low pH (< pH 6.9), whilst at higher pH, HAsO42- becomes dominant (H3AsO4 and

AsO43- may be present in extremely acidic and alkaline conditions respectively). Under reducing

conditions at pH less than about pH 9.2, the uncharged arsenite species H3AsO3 will predominate

(Smedley and Kinniburgh, 2002). To avoid such speculation, the surface and groundwater

samples were delivered on the same sampling day to laboratory for precised and accurate

determination of As3+ and As5+ (Gong et al., 2002). It was incorporated with these evidences and

resulted data was presented in Table 11.

The As3+ concentrations ranged from 2.1-3.2, 1.3-3.0, 1.93-5.68 and 1.90-3.38 μg L-1 in

water samples of CS, RS, LS and MS, respectively (Table 11). The LS water has high level of

As3+ (Maeda, 1994), which is more toxic and mobile than As5+ (Viraraghavan et al., 1999). It is

because of its ability to form complex with certain co-enzymes associated with biological

activity and dissolved organic water in natural water (Jiang, 2001). Thus, it might cause tracheae

bronchitis, rhinitis, pharyngitis, shortness of breath, nasal congestions and black foot disease (Liu

2004; Liu et al., 2006). A strong linear correlation coefficient was observed between the

concentrations of inorganic As species and different physico-chemical parameters (TDS, EC,

Mg2+, Na+, NO2-, NO3

-, PO43-) and Fe contents in surface water (Table 12a), indicating possible

contamination caused by both natural and anthropogenic sources (Arain et al., 2008; Jamali et

al., 2007).

The As3+ was observed as 3.1-71.2 and 2.80-114 μg L-1 in the TS and HS samples,

respectively. It was observed that most of the ground water (TS and HS) samples, the

contamination of As5+ was prominent as compare to As3+ (Table 11). It is reported in literature

that the elevated level of As5+ in groundwaters under oxidizing condition are characterized by

105

high contents of SO42- (>250 mg L-1) and pH > 7.5 (Smedley et al., 2002; Singh, 2006). Such

processes are considered to have been responsible for the release of As in oxidizing quaternary

sedimentary aquifers in study area (Smedley et al., 2002). The concentrations of As3+ and As5+ in

ground water were strongly correlated to Fe concentrations (Table 12b). It is reported in

literature that reductive desorption of As5+, reductive dissolution of iron oxides thus releasing

adsorbed As, and/or changes in mineral structure producing conditions where biosorption is no

longer possible (Smedley and Kinniburgh 2002). Thus, the source of the inorganic As species

might be due to pyretic material or black shale occurring in underlying geological strata

(Thornton and Farago, 1997).

The elevated concentrations of As3+ and As5+ were more likely to be found in domestic

HS with short screens set in proximity to the upper confine aquifer as compare to deep ground

water (Table 11 and 13). The obtained results and literature reported values (Gregori et al., 2005;

Farooqi et al., 2007; Sano and Kikawada, 2008; Tuzen et al., 2009; Pandey et al., 2006) of As

species in surface and ground water samples are shown in Table 13. Our results for AsT, iAs,

As3+ and As5+ were comparable to those reported in the literature for ground water while high

value of all As species observed in surface water samples, but difference is not significant (p >

0.05). All this provide evidence that anthropogenic and geological environment play a key role in

the distribution of studied inorganic As species in water bodies of understudy areas and makes a

significant contribution to the total intake of inorganic As.

The determination of iAs intake was based on the sum of iAs ingested from drinking

water, consumed by a normal adult during the 24-h period. In district Khairpur most of the

population of rural area, depends on ground water, the consumption of drinking-water is

approximately 4L containing > 50 µg iAs L-1. Thus, total consumption of iAs over 200 µg

compared to an estimated daily intake of 12–14 µg iAs from diets of North American population

(Yost et al., 1998). Therefore, chronic exposure to iAs may give rise to several health effects

including gastrointestinal and respiratory tract disorders, skin, liver, cardiovascular system,

hematopoietic system, nervous system etc in understudied areas. The earliest reports date back to

the latter part of the 19th century when the onset of skin effects (including pigmentation changes,

hyperkerotosis and skin cancers) were linked to the consumption of As in medicines and

drinking water (Crecelius, 1974).

106

4.3.1.5. Principal component analysis

Due to high concentration of As species in ground water samples of understudied area,

principal component analysis was also applied to the normalized data sets of ground water (19

variables) separately for 24 different sampling sites (n = 240). The first component (PC1)

accounted for over 50.17% of the total variance in the data set of the groundwater, in other

words, the physical parameters, major cations, anions, Fe and As species in the solution

demonstrates similar behavior in the groundwater samples (Table 14). In a macroscopic point of

view all the physico-chemical parameters behave similarly, i.e. high concentration of major

elements as well as As species in main body of whole groundwater, except in few cases where

the variation in pollution loading has some temporal effects. The strong positive loading on EC,

TDS, NO2- and NO3

- were observed, whereas, a negative loading on PO43-, indicates the role of

anthropogenic contamination. The anthropogenic pollution is mainly due to the discharge of

fertilizer and pesticides as a regular source, throughout the year. However, there is no available

data on the use of arsenical pesticides or industrial chemicals in the understudy area. But, it is

reported by WWF-Pakistan (2007) that about 5.6 million tonnes of fertilizer and 70 thousand

tonnes of pesticides are consumed in the country every year. Their use is increasing annually at a

rate of about 6%. Pesticides, mostly insecticides, sprayed on the crops (cotton, wheat, maize,

sugarcane and rice) mix with the irrigation water, which leaches through the soil and enters

groundwater aquifers (Nickson et al., 2007).

107

Table 14. Loadings of experimental variables (19) on significant principal components for

ground water of district Khairpur Mir’s

Variables PC1 PC2 PC3

pH 0.798 0.180 0.233

EC 0.907 0.091 -0.018

TDS 0.952 -0.128 0.261

Ca2+ 0.272 0.813 -0.128

Mg2+ 0.724 0.508 -0.100

Na+ 0.898 -0.117 0.165

K+ 0.564 0.490 -0.005

HCO3- 0.898 0.353 0.103

F- 0.799 0.196 -0.254

Cl- 0.827 -0.073 -0.289

NO2- 0.944 0.234 -0.091

NO3- 0.933 0.086 -0.147

PO42- -0.195 -0.158 0.919

SO4- 0.787 -0.006 0.370

AsT -0.483 0.861 0.008

Asi -0.483 0.864 0.009

As3+ -0.424 0.865 0.151

As5+ -0.499 0.750 -0.182

Fe -0.034 0.883 0.379

Eigenvalue 9.53 5.06 1.54

%Total variance 50.0 26.6 8.20

Cumulative % 50.2 76.8 85.0

108

Fig. 9 (b)

Nara

Khairpur

Kotdigi

Sobhodaro

Gambat

Kingri Thari Mir Wah

Faiz Ganj

-3

-2

-1

0

1

2

3

4

5

-3 -2 -1 0 1 2 3 4 5 6 7 8

F1 (50.17 %)

F2 (26.6

4 %

)

Fig. 9 (a)

Fe

As5+

As3+AsiAsT

SO4-

PO44-

NO3-

NO2-

Cl-

F-HCO3-

K+

Na+

Mg2+

Ca2+

TDS

EC

pH

-1

-0.75

-0.5

-0.25

0

0.25

0.5

0.75

1

-1 -0.75 -0.5 -0.25 0 0.25 0.5 0.75 1

F1 (50.17 %)

F2

(26.

64 %

)

Fig. 9. Plots of PCA (a) scores for combined data set groundwater samples (b) scores for

distribution of Fe, As species and water quality parameters in sub-district of Khairpur

Mir’s

109

The trend obtained was also supported by the analysis of the results on the raw data set.

The second component (PC2), explaining 26.6% of the total variance has strong positive

loadings for Fe and As species, thus basically represents the elements of pollution group. The

third component (PC3) of PCA shows only 8.20% of the total variation has positive loading of

PO43- and SO4

2-. The high values of Fe, TAs, iAs, As3+, As5+, major cations and anions in

underground water samples are above the permissible limit of WHO values for drinking water

(WHO, 2004).

The above observation is clearer to follow the Fig 9a and b, which shows the

characteristics of samples and help to understand their spatial distribution. It is evident that

samples distributed in upper right quadrant are more enriched with pH, EC, Ca2+, Mg2+, K+,

HCO3-, F-, NO2

- and NO3- while, those in lower right quadrant are less enriched with TDS, Na+,

Cl- and SO42 as shown in Fig. 9a. The samples distributed in other two quadrants (upper and

lower left) are enriched with Fe, As species and PO43- to a lesser extent. The scores plot (PC1

and PC2) for the groundwater samples (Fig. 9b) shows high distribution of Fe, As species and

other water quality parameters in groundwater samples of Gambat sub-district as appeared in the

upper right quadrant. Whereas, Thari Mirwah sub-district falls in lower right quadrant indicted

the 2nd most polluted sub-district with respect to Fe, As species and other water quality

parameters. The upper and lower left quadrants shows the mix distribution in groundwater

samples of Khairpur, Faiz Ganj, Kotdigi, Kingri, Nara and Sobhodiro.

The high level of As species in water is due to dissolution of arsenic compounds coming

from Himalaya through Indus river and settled down through year to year and than introduced

into ground water by geothermal, geo hydrological and bio geo chemical factors (Yost et al.,

1998; Smedley et al., 2002; Singh, 2006). It may be due the As containing insecticides and

herbicides used for agriculture purposes and from seepages from hazardous waste site (Smedley

and Kinniburgh 2002a).

110

4.3.2. Conclusions

The speciation analysis provided more information about toxicity, bioavailability and

mobility of different As species in surface and ground water samples. In this study, hierarchical

CA grouped the sampling sources into three clusters of similar characteristics reflecting the

water quality characteristics. The multivariate techniques (PCA and CA) were successfully

applied to proposed procedures based on solid phase extract for As3+ while iAs by Pb-PDC co-

precipitation and TiO2 based slurry methods. These methodologies offer a simple, rapid,

sensitive, inexpensive and non-polluting alternative to other separation/pre-concentration

techniques. The PCA yielded two significant (eigenvalue >1) PCs accounting for more than

99.75% of the total variance of the combined data set of six origins of surface and ground water.

These results may convincingly be presumed that, the contamination in surface water samples

might be due to anthropogenic contamination resulted from soil weathering, agricultural run-off,

leaching from solid waste disposal sites, domestic and industrial wastewater disposal. In

underground water samples, the domestic HS (shallow aquifer) were more contaminated with

inorganic As species as compare to TS (deep aquifer). This suggested that further studies should

be focused on the bioaccumulation of As speciation in aquatic biota and hazards associated with

their consumption.

111

4.4. Method development

4.4.1. Advance extraction methods for speciation of arsenic in water samples

General Remark


Jameel A. Baig, Tasneem G. Kazi, (2009). Optimization of cloud point extraction and solid phase extraction methods for speciation of arsenic in natural water using multivariate technique. Analytica Chimica Acta 651, 57–63.

doi:10.1016/j.aca.2009.07.065

4.4.1.1 Optimization of the experimental conditions for factorial design

Considering the CPE procedure, six factors were selected to be examined, volume of

surfactant (S), mass of complexing agent (C), pH (P), incubation time (I), temperature (T) and

volume of samples(V) to optimize the %recovery of As3 (Table 14a). In same way, the variables

chosen for iAs were mass of adsorbent (M), temperature (T) and pH (P) with its %recovery as

analytical responses by factorial designs (Table 14a and b). The data of both experiments were

evaluated by analysis of variance (ANOVA) and visualized by using a standardized (p~95.0%)

main effect Pareto chart, Fig. 10 and 11. The inference tests showed that the results produced at a

minimum t-value (95.0% confidence interval) were 2.2 and 2.8 for As3+ and iAs, respectively. A

factor is significant, when the t-value for a certain factor is higher than the minimum observed t-

values.

4.4.1.2. Estimated effects of variables for As3+ and iAs

As results shown in Pareto chart (Fig. 10) and Table 14a, the S, C and P are significant factor for

CPE of As3+. The %recovery of As3+ was found 47.2% in experiment 6, at (−) level of C, with

112

Table 14a Design matrix and the results of As+3 %extraction (n = 6)

Experiments A (S) B (C) C (P) D (I) E (T) F (V) (%) recovery

1 + - - - + - 50.2±1.20

2 + + - - - + 71.1±2.40

3 + + + - - - 54.9±3.30

4 + + + + - - 67.1±1.50

5 - + + + + - 98.9±0.95

6 + - + + + + 47.2±1.40

7 - + - + + + 70.4±1.60

8 + - + - + + 25.7±2.70

9 + + - + - + 45.6±1.80

10 - + + - + - 42.4±1.30

11 - - + + - + 28.5±1.45

12 + - - + + - 73.7±2.20

13 - + - - + + 46.6±1.30

14 - - + - - + 23.5±2.10

15 - - - + - - 33.5±1.70

16 - - - - - - 26.5±1.15

113

Table 14b Design matrix and the results of iAs %extraction (n = 6) Experiments A (A) B (U) C (P) D (T) E (V) (%) recovery

1 + - - + - 98.8±1.80

2 + + - - + 82.0±2.14

3 + + + - - 60.0±1.78

4 - + + + - 54.0±2.84

5 + - + + + 72.0±3.50

6 - + - + + 62.0±1.55

7 - - + - + 40.0±1.90

8 - - - - - 55.0±2.20

114

0 1 2 3

E

BD

ABC

BCD

F

ABD

AD

AC

D

C

A

B

Alpha = 0.05

A: Triton X-114B: APDCC: pHD: Incubation timeE: TemperatureF: Volume

3.52

3.80

-2.91

2.55

0.03

0.57

-1.41

0.92

2.37

-0.60

2.37

0.18

4Effect estimate ( Absolute Values)

Figure 10. Pareto chart (As3+) of the fractional factorial experimental design for the

analysis of the variables: (S) Surfactant (Triton X-114); (C) Complex (APDC); (p) pH; (I)

Incubation time; (T) Temperature; (V) Volume

115

Figure 11. Pareto chart (As total) of the fractional factorial experimental design for the

analysis of the variables: (M) Mass of TiO2; (U) Ultrasonic Exposure Time; (p) pH.; (T)

Temperature; (V) Volume

210

A

C

D

AC

E

AE

B

Alpha = 0.05

3 4

A: Mass of TiO2B: Exposure timeC: pHD: TemperatureE:

volumeSample

4.35

-3.05

2.21

-1.34

-0.44

0.44

-0.20

Effect Estimate (Absolute Value)

116

optimum values of other variables. The pH of the sample solution was the next critical variable

evaluated for its effect on the CPE of As3+. It was observed that the %recovery of As3+ was about

45.6% at low (−) level of pH (experiment 9), with maximum level of other two significant

variables, C and S. Whereas, the optimum recovery of As3+ (98%) was observed in experiment 5,

at (+) levels of C, P, I and T, while at (−) levels of S and V (Table 14a). It can be seen in

experiment 4, T at (−) level produced 67.1% recovery of As3+, while at (+) level in experiment 5,

optimum recovery of As3+ was obtained. The most significant interaction between two variables

was seen for A and C, while least relation was observed between variable B and D as shown in

Pareto chart (Fig. 10).

For iAs the mass of adsorbent (M) and temperature (T) were observed as the significant

factors for optimum %recovery using SPE method. The pH (P) is considered as one of the

appropriate variable for slurry method. The maximum recovery of iAs was obtained 98.8% in

experiment 1, where M, T were at (+) level, while P at (-) levels. The two variables M and T at

low levels (-) in experiment 7, shows that the recovery was 40%, while C, E were at maximum

level. The pH of the sample solution was next most significant variable to evaluate its effect on

SPE method for iAs and it was observed that at (-) level of pH, the maximum %recovery of iAs

was observed as shown in experiment 1 (Table 14b).The two order interaction between A and D

was found to be the most significant, whereas, least was obtained between A and C (Fig. 11).

4.4.1.3. Optimization by central composite design for As3+ and iAs

Having screened out the variables that did not have significant effect on the response of

As3+ using six variables, the remaining three factors C, S and P were optimized to provide the

maximum recovery. A central 23 +star orthogonal composite design with six degrees of freedom

and involving 16 experiments was performed, to optimize these three variables. The variables

that were shown to be insignificant by Plackett–Burman design were taken at fix values, volume

of sample (2 mL), incubation time (10 min) and temperature (room temperature ∼30 ○C). The

experimental field definition for this design is given in Table 14(a, b), while Table 15a shows the

central composite design together with the %response obtained for As3+ for six replicate.

117

It was observed that at low level (−) of C, the recovery of As3+ is 28.5%, which was 76.8% lower

as compared to the value obtain at high level of S (run 7 and 16), while maximum %recovery

was observed at average value of complexing agent APDC (run 16). These findings shown that

Table 15a Central 23 + star central composite design (n = 16) for the set of (S), (C) and (P) in As3+

Experiments A (S) B (C) C (P) (%) recovery

1 as0 bc

0 cp0 98.6±1.40

2 - - - 35.2±1.20

3 + - - 48.0±2.40

4 - + - 39.7±1.60

5 + + - 62.5±3.10

6 - - + 25.0±2.50

7 + - + 28.5±1.80

8 - + + 32.0±2.20

9 + + + 40.4±1.90

10 -s1 bc0 cp

0 32.0±2.80

11 +s2 bc0 cp

0 66.7±2.30

12 as0 -c1 cp

0 42.0±1.75

13 as0 +c2 cp

0 68.4±1.45

14 as0 bc

0 -p1 18.0±5.80]

15 as0 bc

0 +p2 38.5±4.50

16 as0 bc

0 cp0 99.8±2.50

Factors: 3, replicates: 6, design: 8, runs: 16, center points (total): 23

s1= -0.001%, s2=0.25%, as0 = 0.125% bc

0 = 0.006 %, c1= -0.002%, c2 = 0.005% cp0 =4.00, p1 =

0.63, p2= 7.3

118

Table 15b. Central 23 + star central composite design (n = 16) for the set of (M), (U) and (P) in total iAs

Experiments A (M) C (P) D (T) (%) recovery

1 am0 bu

0 cp0 99.2±2.60

2 - - - 32.0±2.50

3 + - - 41.0±2.30

4 - + - 35.2±1.90

5 + + - 62.4±1.40

6 - - + 38.3±2.20

7 + - + 46.6±1.60

8 - + + 34.2±1.40

9 + + + 86.7±2.70

10 m1 bp0 ct

0 12.5±3.40

11 +m2 bp0 ct

0 48.6±4.20

12 am0 P1 ct

0 22.2±2.80

13 am0 +p2 ct

0 68.8±5.22

14 am0 bp

0 -t1 22.7±1.50

15 am0 bp

0 +t2 57.4±1.20

16 am0 bp

0 ct0 99.5±1.44

Factors: 3, replicates: 6, design: 8, runs: 16, center points (total): 23

m1= 3.18 mg, m2=36.8 mg, am0 = 20 mg, bp

0 =2.5, p1 = -0.02, p2 = 5.02, ct0 =40 ºC, t1 =6.36ºC,

t2 = 73.6ºC

119

Fig 12. Three dimension (3-D) surface response for % recovery of As3+ by CPE (a) Interaction b/w (pH-Triton X-114) and (b) Interaction b/w (pH-APDC)

Fig 12 b

Fig 12 a

120

Fig 13. Three dimension (3-D) surface response for % recovery of total As by TiO2- slurry method (a) Interaction b/w (pH-Mass of adsorbent) and (b) Interaction b/w (Temperature-Mass of adsorbent)

Fig 13 b

Fig 13 a

121

large amount of APDC, reduce the extraction efficiency, because it operating with a high

quantity of ligand, which have require large volume of organic solvent (Tang et al., 2005).

Where as, high (+) level of surfactant (at experiment 9, table 15a), showed 40.4% of As3+

recovery, indicated that high amount of the surfactant increased the volume of the surfactant-rich

phase that is acquired after centrifugation of the analyte. Therefore, the high amount of surfactant

needed more solvent to reduce the viscosity, resulting in a loss of sensitivity and the surfactant

volume >0.14% (w/v), deteriorating the ETAAS signal. The pH is considered as third important

factor for metal-chelate formation and subsequent extraction of As3+ by CPE (AOAC, 1995).

The results indicated that high recovery of As3+ was obtained at pH >2 (experiments 2 and 7),

while, at average value (cp0), pH 4, the maximum recovery is seen (Exp 16, table 15a). The study

of estimated three dimension (3-D) surfaces response for variables [S-P] and [C-P] showed the

values of these variables for optimum recovery of As3+ (Fig. 12a and b). It was estimated by

quadratic equation on the bases of 3-D surface graph that the maximum %recovery of As3+ was

observed at optimum values of complexing agent (0.007%), Triton X114 (0.14%) and pH (4.2).

The over all experiments were performed at pH 4.2. As reported by Sun and Yang, that inorganic

species of As5+ not frequently react with APDC, therefore, the interference of As5+ was

negligible (Zhang et al., 2004; Sun and Yang et al., 1999). The estimated pH 4.2 for CPE

procedure by central composite design is consistent with previous study (Tang et al., 2005).

The central composite design matrices together with the response were also employed for

iAs (Table 14b and 15b). The mass of adsorbent (M) was a significant factor for the %recovery

of iAs by SPE method, which has a strong interaction with temperature (T) and pH (P). So, these

three factors were optimized to provide the maximum recovery of iAs. A central 23+star

orthogonal composite design with 6 degrees of freedom, involving 16 experiments were

performed to optimize these variables. The factors that were shown to be insignificant by SPE

method were taken at fix values, volume of sample 10-50 mL (based on total As concentrations

in understudy water samples) and ultrasonic exposure time (10 min). The central composite

studies showed that the maximum %recovery of iAs was achieved at (+) level of M (mass of

TiO2), while M at (-) level, %recovery of iAs is lower (experiments 2-4). The 99.5% recoveries

of iAs was obtained at optimum concentration of M (am0), as shown in experiment 1 and 16

(Table 15b). It was also reported in literature that, the high amount of titanium dioxide may

damages the graphite tube (Tang et al., 2005). For further work, the optimum concentration (20.0

122

mg) of TiO2 was chosen as a sorbent in the subsequent experiments. The other two significant

variables temperature (D) and pH (C) showed that optimum recovery of iAs was obtained at

average level of both factors, 40 ºC and 2.5, respectively.

Our experimental data is consistent with literature reported work that the biosorptions of

ions on amphoteric oxides, such as titanium dioxide, proceeds when the pH of the solution is

lower than the isoelectric point (IEP) of the oxide (Paleologos et al., 2002). The estimation of

three dimension (3-D) response surfaces for each pair of variables, [T-M] and [P-M] were

calculated by quadratic equation indicated that, M, T and pH were 17.6 mg, 40.7ºC and 2.1,

respectively are required for maximum %recovery of iAs [fig 13 a and b].

4.4.1.4. Interference study

The reliability of the proposed method was examined by recovery measurements in the

presence of possible interfering ions. The metallic ions, Na+ and Cl- (1000 mg), Ca2+ , Mg2+, K+,

SO42-

and PO43- (100.0 mg of each) Cu2+, Co2+, Se4+, Ni2+, Fe3+, Al3+ and Zn2+(1.0 mg of each)

were added to 1000 mL of sub sample of surface water and ground water separately and

subjected to corresponding methods. An ion was considered as interferent, when it caused a

variation in the absorbance of the sample greater than ±5%. The tolerance limits of various

foreign ions are given in Table 16. These results demonstrated that excess amounts of common

cations and anions do not interfere on the determination of trace quantities of As3+ and iAs while

nickel and copper have positive effect (2-3%).

123

Table 16. Foreign ions effect on the % recoveries of 5.0 µg L-1 of As3+ and total iAs

Ion Concentration added

mg L-1

Recovery (%) of total iAs

Recovery (%) of As3+

Na+ 1000 98 95

K+ 100 99.2 98

Ca2+ 100 96 95

Mg2+ 100 97 94

Cu2+ 1.0 99.3 100

Co2+ 1.0 108 112

Ni2+ 1.0 110 117

Fe3+ 1.0 96 94

Al3+ 1.0 97 95

SO42- 100 101 102

PO43- 100 103 105

Cl- 1000 95 96

124

Table 17. The results for tests of addition/recovery for As3+ and total iAs determination in water samples

Sample Species Added Conc.

(µg L-1) Mean±Std (µg L-1)

% Recovery

Canal Water

n = 6

As3+

0.00 4.50±0.50 --

2.5 6.94± 0.35 99.1

5.0 9.37±0.24 98.6

10.0 14.35±0.25 98.9

Total iAs

0.00 8.30±0.48 --

2.5 10.6±0.35 98.4

5.0 13.1±0.22 98.8

10.0 18.1±0.14 98.5

Validation for total arsenic (µg L-1)

Certified value of SRM 1643e

Found values

ntsx /

% recovery

(% RSD)

60.45 ± 0.72 58.9± 1.65 97.4

(2.80)

Paired t-test : tExperiment = 0.12, tcertical = 2.26 at 95% confidence limit (n=6)

125

Table 18. Analytical results of Total As, Total iAs, As3+ and As5+ in natural waters

4.4.1.2. Applications

To check the accuracy of methodologies, spiking was performed in six replicate at three

concentration levels 5, 10 and 20µg L-1, for both methods (Table 17). The accuracy of total As

was checked by using standard reference material SRM 1643e (Table 17). The detection limit of

the present CPE and SPE sampling methods for the determination of As3+ and iAs using ETAAS

are better than previously published work (Tang et al., 2005; Zhang et al., 2007). The

concentration factor, which is defined as the ratio of analytes in the final diluted surfactant-rich

extract and slurry, subjected to ETAAS determination and concentration in the initial solution,

was 40 for As+3 and iAs, better than previously reported work (Tang et al., 2005; Zhang et al.,

2007).

Sample Species Mean ± Std (µg L-1)

Canal Water Sample

n = 180

Total As 8.90±2.80

Total iAs 8.40±4.10

As3+ 4.80±2.60

As5+ 3.60±1.70

Hand pump Water Sample

n = 180

Total As 62.0±41.0


As3+ 24.5±14.7

As5+ 33.7±20.2

Tube Well Water Sample

n = 180

Total As 38.8±24.0


As3+ 22.2±12.5

As5+ 14.5±8.26

126

It is very important and necessary to determine trace amounts of iAs species in water

samples from the environmental point of view. The optimized methods were employed to the

determination of trace amounts of iAs, and As3+ in 180 water samples of each involving canal,

hand pump and tube well collected from south-west part of Pakistan. For comparative purposes

total arsenic was also determined in all understudied water samples. The mean concentrations of

different species of As expressed as, ntsx / (n=180 for each sampling origin) are shown in

Table 18. The water bodies (especially underground) of studied area are seriously contaminated

with As due to frequently use of pesticides and insecticides in agricultural lands as well as use of

untreated waste water sewage sludge as agricultural fertilizer (Jamali et al., 2007). Due to

unavailability of certified reference material of water for inorganic As species, therefore,

standard addition method was used for validation and optimization of both methods.

The two set of six replicate sub samples of a canal water, spiking with three concentration

levels (2.5–10 μg L-1) of As3+ and iAs and applied both methods i.e., CPE for As3+ and SPE

methods, respectively. The %recovery calculated as:

100CC

C Recovery %

Spiked analyte of Initial

spikingafter

The recoveries for As3+ and iAs spiked in the canal water samples studied were

calculated > 98%, indicating no interference encountered from these sample matrices.

The obtained results showed significant differences among the concentration of different

species of As in three sampling origins. All this provides evidence that anthropogenic and

geological environment play a key role in the distribution of inorganic As species in understudy

water bodies (Crecelius, 1997). The concentration of iAs in three studied origins was obtained in

increasing order: canal < tube well < hand pump (Table 18).The concentration of total As in

canal, hand pump and tube well water samples was observed in the ranges of 6.10-11.7, 21.0-

62.8 and14.8–103 µg L-1, respectively. Whereas, iAs was analyzed by SPE method was

measured about 5-10% lower than total As, indicated the less availability of organic As in

surface and ground water, our results are consistent with other study (Thirunavukkarasu et al.,

127

2002). The elevated level of all species in ground water samples (hand pump and tube well) are

may due to the geological conditions (Smedley and Kinniburgh 2002). But, in canal water

samples the ratio of As3+ contents were higher than ground water samples (hand pump and tube

well, Table 18), most probably due to anthropogenic contaminations (Smedley and Kinniburgh

2002).

4.1.3. Conclusions

The multivariate techniques were successfully applied for the optimization of cloud point

extract and solid-phase extraction (TiO2 based slurry) for As3+ and iAs, respectively. The

detection limits and enrichment factors of As3+ and iAs were better than reported procedures [25,

27]. The study indicated that optimized values of significant factors for CPE of As3+ were [pH

(4.2), C (0.007%) and S (0.138%)], while the values of different variables SPE method for iAs

were estimated as [pH (2.1), M (17.6 mg) and T (40.7ºC)]. The synchronized foreign ions

interferences and influence of organic compounds in environmental water sample using modifier

(Pd + Mg (NO3)2) show that the method is suitable for complicated matrix solutions. Speciation

of arsenic in surface and ground water plays an important role in understanding arsenic exposure

to human and animal health effects.

128

4.4.2. Separation and pre-concentration of As in surface and ground water

General Remark


Jameel A. Baig, Tasneem G. Kazi et al., (2009). Inorganic arsenic speciation in ground water samples using electrothermal atomic spectrometry following selective separation and cloud point extraction. Journal of Analytical Sciences. 27, 439-445.

4.4.2.1. The Optimization of separation and extraction methods for organic and inorganic As species

The speciation of As in groundwater samples used for domestic and agriculture

purposes was carried out by separation/pre-concentration methods. The activated alumina in

acidic form has a high affinity for a range of oxoanions like As (Zhang et al., 2004; Zhang et al.,

2005; Jitmanee et al., 2005; Baig et al., 2009a,b,c, 2010. Hence both As species as AsO32- and

AsO42-

could be retained and pre-concentrated on alumina column through selecting the suitable

pH. However, the organic As is neutral water cannot be retained on the small sized Al2O3 packed

column. The adsorption experiments were carried out at pH range of 1–6. The optimum

adsorption of only inorganic As species were obtained adequately at pH range of 2–3.5, on Al2O3

packed column, while di-methyl arsenite could not be retained on Al2O3 , at understudy pH

range.

Thus, for subsequent work a pH 3 was selected for bi-fragmentation of organic and

inorganic As species. The adsorption was found to be constant for inorganic As species upto pH

3. While the %sorption of As forms were slow down rapidly when it was > 3.5. Therefore, in this

work, pH 3 was adequate for maximum separation of organic and inorganic As species by Al2O3

packed column.

Adsorption capacity is a most important parameter for the characterization of

adsorbent. It is because of that adsorption capacity helps for the estimation of an adequate

amount of adsorbent, which may required for quantitative analysis of analytes of interest.

Therefore, 0.5g of Al2O3 was added to 50 mL volume of different concentrations of inorganic As

solutions at pH 3. The mixtures were placed in ultrasonic bath for 10-20 min at room

temperature. Then, it was separated by centrifugation method. The analyte in supernatant portion

129

was determined by ETAAS. The maximum adsorption capacities of Al2O3 (Ø 90 µm) for As3+

and As5+ was found in the range of 98 - 99 %.

4.4.2.1.1. Effects of sample volume, eluents and its flow rate

For higher pre-concentration/enrichment factor, a large volume of understudy sample

solution is needed. It was observed that high recoveries for organic and inorganic As species

were obtained for groundwater upto 100 mL. The sample flow rate was studied in the range of

0.2-2 mL min-1, the experimental results indicates the optimum recovery was obtained at 0.8 mL

min-1 and for further work 1.0 mL min-1 was selected.

For eluting the adsorbed inorganic species of As3+, different concentrations of, NaOH,

HCl and HNO3 were used as eluents to elute the inorganic species of As from the Al2O3 packed

column. The resulted data showed that 10 mL of 0.2M of HCl at the flow rate of 0.5 ml min-1

was sufficient for elution of upto 98% inorganic As species from the adsorbent, whereas, NaOH

and HNO3 could not elute the sorbed inorganic As species efficiently.

4.4.2.2. Cloud point extraction method

For speciation of inorganic As species, the As3+ and As5+ complexed with APDC and

molybdate, respectively than extracted in Triton X-114.

4.4.2.2.1. Effect of pH

The pH is an important parameter, which plays an important role in complex formation

and extraction. Therefore, the effect of pH on the % recovery of As3+ and As5+ were examined in

the range of pH 1 – 8 and pH 1-4, respectively at optimal levels of other variables. The Fig. 14

indicated that the maximum signal intensity was achieved in the pH range 3.5-5.0 for As3+ and

1.5-3.0 for As5+. Thus pH 4.3 and 2.2 was used as optimum pH levels for the maximum

extraction of As3+ and As5+ from ground water, respectively.

130

Fig 14. Effect of pH on the CPE of 10 µg L-1 As3+ /As5+. Other CPE conditions: 0.007% APDC/0.0006% molybdate, 0.14%/0.12% concentration of Triton X-114, equilibration temperature 35/55 ○C, equilibration time 5 min.

4.4.2.2.2. Effects of concentration of APDC and molybdate

Effect of APDC and molybdate concentration on %recovery of As3+ and As5+ was

studied. Pre-concentration step: 10 µg L-1 As3+; Triton X-114, 0.12% (w/v); pH 4.3, temperature

and incubation time at 35 °C and 5 min, respectively (Fig. 15). The influence of the amount of

APDC on the % recovery of As3+ was studied in the range of 0.001 –0.01% (m/v). It can be seen

that the extraction efficiency of As3+ was optimum at 0.007 % of APDC. Whereas, pre-

concentration step (10 µgL-1 As5+ Triton X-114, 0.14% (w/v); pH 2.2, temperature 55 °C and

incubation time 5 min) showed the influence of the amount of molybdate on the %recovery of

As5+ in the range of 0.0001–0.001% (m/v). It can be seen that the extraction efficiency of As5+

was optimum at 0.0006 % of molybdate (Fig. 15).

The excessive amount of chelating reagent was required to enhance the quantitative

chelate reaction due to presence of large number of elements in complex matrixes of ground

water samples, 0.007% and 0.0006% of APDC and molybdate complexing reagents for As3+ and

As5+, respectively were used for further experiments

As3+As5+

131

Fig 15. Effect of concentration of APDC/molybdate on the CPE of 10 µg L-1 As3+/As5+. Other CPE conditions: 0.14/0.12% (v/v) concentration of Triton X-114, pH 4.3/2.2, equilibration temperature 35/55 ○C, equilibration time 5 min.

4.4.2.2.3. Effect of Triton X-114 concentration

The influence of concentration of Triton X-114 on the CPE of As3+–APDC and As5+-

molybdate complexes were investigated within the surfactant concentration range of 0.05–0.2%

(%, w/v) (Fig. 16). Pre-concentration step: 10 µg L-1 As3+; APDC 0.007% (w/v); pH 4.3,

temperature and incubation time at 35 °C and 5 min, respectively showed the effect of Triton X-

114 concentration on %recovery in the range between 0.01–0.25% (w/v) (Fig. 16). The optimum

quantity of analyte was extracted at the concentration range of (0.1 – 0.16%), so for further

experiment 0.14% Triton X-114 was used.

Fig 16. Effect of concentration of concentration of APDC/molybdate on the CPE of 10 µg L-

1 As3+/As5+. Other CPE conditions: 0.14/0.12% (v/v) concentration of Triton X-114, pH 4.3/2.2, equilibration temperature 35/55 ○C, equilibration time 5 min.

As3+As5+

132

For pre-concentration of As5+ at 10 µgL-1 As5+, molybdate 0.0006% (w/v); pH 2.2,

55°C temperature and incubation time 5 min, showed the influence of Triton X-114 in the range

of 0.05-0.2%, w/v (Fig. 16). The maximum %recovery was found at the concentration of 0.1 -

0.16%. Thus, 0.12% of Triton X-114 was selected for further experiment. However, in both

cases > 0.14% a black smoke was appeared and signal intensity was disturbed. Moreover, the

high concentration of surfactant required a dilution of concentration of the extracted analytes

with less enrichment factor.

4.4.2.2.4. Effects of equilibration temperature and time

The effect of equilibration temperature was investigated with the temperature varying

from 30 - 60 and 40 – 80 °C for As3+ and As5+, respectively. The experimental results showed

that the maximum signal intensity for As3+ was attained in the range of 30 – 40 °C while the As5+

was detected with maximum signal intensity at 50-60 °C. As the CPE efficiency of the analyte

was decreased by increasing temperature. Therefore, equilibration temperature of 35 and 55 °C

for %extraction of As3+ and As5+, respectively was chosen for further experiments. The effect of

the incubation time was studied in the range of 2-10 min in an ultrasonic bath. The optimum

recovery was obtained at 5 min, and further increase in the incubation time resulted in a decrease

in the extraction efficiency. For the rest of the experiments, an incubation time of 5 min was

used.

4.4.2.2.5. Interference of co-existing ions

To check the interference of coexisting elements in matrixes of groundwater, a

composite mixture of 1000 mg L-1 of Na+, K+ and 500 mg L-1 of Mg2+, Ca2+, SO42-, Cl-, 50 mg L-

1 of Zn2+, Cu2+ and Al3+, and Fe3+ , while 10 mg L-1 of Pb2+ ions were added to solution of 10 µg

L-1 of As3+ and As5+. Than analyzed by ETAAS at the optimum instrumental conditions. The

groundwater may contain soluble organic and inorganic As compounds. The interference of

these matrices for the determination of different species of As was checked. The interference of

Pb2+ and Fe3+ were negative because these cations may react with APDC and molybdate to enter

a competitive reaction with complexing reagent, but the recoveries of the target As species were

more than 98%.

133

To overcome these interference excessive amounts of chelating reagent was used. It

was concluded that permissible quantity of co-existing ions were adequately high. Thus, the

proposed procedure is free from interference of co-existing ions in groundwater samples.

4.4.2.3. Application

It is important to know toxicological behavior and biochemical activity of As depends

on its chemical form. So, the speciation of As in ground water samples, used for domestic and

agricultural purposes is necessary. The organic As compounds are less available than inorganic

As in ground water because their less solubility and natural abundant in aquifer water (Baig et

al., 2010b). Due to the lack of reference material for As speciation, the validity of analytical

method was performed by replicate three sub samples of a canal water, spiking with As3+ and

As5+ at three concentration levels, then applied both methods. The %recoveries for the spiked

samples were calculated as:

100C

]C[CRecovery %

spiked

spiking before spikingafter

The recoveries for As3+ and As5+ were found in the range of 98 - 99% (Table 19). A

good agreement was obtained between the added and measured analyte concentration. These

results confirm the validity of proposed methods.

The optimized proposed methodologies were applied to the duplicate ground water

samples (n =160). The mean values expressed as Mean ± SD, range and medians of understudy

As species in ground water samples (Table 20). The concentration of TAs distributed in hand

pump samples of district Sukkur (Pakistan) was varied from 26.0 to 98.2 µg L-1, while the level

of TAs in tube well water samples was ranged from 19.7 to 136 μg L-1 (Table 20). The average

content of TAs was found to be 43.5 µg L-1 in ground water samples of understudy area, higher

than permissible limit of WHO but less than other countries as reported elsewhere (Smedley et

al., 2002; Smedley and Kinniburgh 2002; Baig et al., 2009a,b,c). This is due to the natural

processes and anthropogenic activities i.e., pesticides and insecticides used for agricultural lands,

untreated waste water sewage sludge as agricultural fertilizer and synthetic fertilizers (Baig et al.,

2009a; Baig et al., 2010a; Arain et al., 2008, 2009; Torres and Ishiga 2003). The obtained results

134

showed the significant differences among the concentration of organic and inorganic species of

As in two sampling origins of ground water (Table 20). The oAs was analyzed after separation

by Al2O3 and found about 2-5% of TAs in ground water samples (Table 20), indicated its less

availability (Thirunavukkarasu et al., 2002).

The As3+ is more toxic and mobile than As5+ (Viraraghaven et al., 1999). Because of

the variation in toxicity and removal efficiency of As3+ and As5+, knowledge on the speciation

distribution in drinking water is important (Jiang, 2001). The redox As species are unstable in

natural waters, because of the transformation between As3+ and As5+, due to the organic matrices,

redox potential (Eh) and pH (Thirunavukkarasu et al., 2002; McCleskey et al., 2004). Therefore,

for accurate determination of As species all water samples were delivered on the same sampling

135

Table 19. The results for tests of addition/recovery for As3+ and As5+ determination in ground water samples (n= 6)

Species Added Conc. (µg L-1) Mean±Std (µg L-1) % Recovery

As3+

0.00 10.30±0.50 --

2.5 12.6± 0.55 98.5

5.0 15.2±0.64 98.9

10.0 20.1±0.62 98.3

As5+

0.00 15.3±0.85 --

2.5 17.5±0.92 98.1

5.0 20.1±0.89 98.4

10.0 25.1±1.05 98.9

Validation for total As

Element

Certified value of SRM

1643e

Found values

ntsx /

% recovery (% RSD)

tExperiment

As 60.45 ± 0.72 58.9± 1.65 97.4 (2.56) 0.12

136

Table 20 Analytical data of the ground water samples of district Sukkur, Sindh, Pakistan

day to laboratory and analysis of As3+ and As5+ were accomplished on same day, to avoid risk of

transformation of species as reported elsewhere (Gong et al., 2002).

The As3+ concentrations ranged from 8.90-43.2 and 6.30-60.0 μg L-1 in water samples

of hand pump and tube well samples, respectively (Table 20). In the aquifers the redox reactions

may changed the As species either in oxidizing or reducing. Thus, high inorganic As

concentrations were found in both oxidising and reducing conditions in both origins of ground

water (hand pump and tube well, Table 20). The soluble inorganic arsenicals are more toxic than

the organic ones, and the iAs3+ are more toxic than iAs5+ (Baig et al., 2010a). It was observed

that in both origin of ground water samples contained high As5+ as compare to As3+, which might

be due to high level of pH and Fe contents (Baig et al., 2010a).

Mean±SD

Range

Median

Hand pump

(n = 120)

TAs 40.1±18.5 26.0-98.2 33.3

oAs 1.40±0.63 1.00-3.30 1.13

As3+ 14.1±6.10 8.90-43.2 12.2

As5+ 24.6±11.0 14.6-55.0 20.8

Tube Well

(n = 140)

TAs 47.8±28.9 19.7-136 40.4

oAs 2.00±1.00 0.80-4.90 1.7

As3+ 16.7±12.3 6.30-60.0 15.8

As5+ 29.1±17.2 13.4-76.2 23.8

137

Table 21 Analytical results for ground water samples and comparison with literature values

Samples Concentration (µg L-1)

As3+ As5+ oAs TAs

Current study

Hand pump 14.1±6.10 24.6±11.0 1.40±0.63 40.1±18.5

Tube well 16.7±12.3 29.1±17.2 2.00±1.00 47.8±28.9

Literature values

Hand pump (Baig et al., 2010a) 25.4±81.5 39.8 nd 68.3±82.5

Tube well (Baig et al., 2010a) 22.7±93.7 28.63 nd 53.8±93.4

Shallow Ground water

(Farooqi et al., 2007)

nd Nd nd 235

Middle depth Ground water (Farooqi et al., 2007)

nd Nd nd 45

Deep Ground water

(Farooqi et al., 2007)

nd Nd nd 72

Ground water (Pandey et al., 2006) nd nd nd 143.8±176.9

tube well water (Patel et al., 2005) 2.9-928.6 10.0-136.4 nd 7.3-894.8

nd = not determined

The literature were also provided relevant information regarding desorption of AsV

from oxide surfaces at pH >8.0 (Baig et al., 2010a). Such processes are considered to have been

responsible for the release of As in oxidizing quaternary sedimentary aquifers (Smedley et al.,

2002). The obtained results and literature reported values of As species in ground water samples

are shown in Table 21. The results obtained by current study for TAs, As3+ and As5+ were

comparatively lower than those reported in the literature for ground water (Baig et al., 2010a;

Farooqi et al., 2007; Pandey et al., 2006; Patel et al., 2005). However, the mean concentration of

inorganic As species were higher than WHO recommended level for total As. Thus, it was

suggested that the high level of As species might be due to anthropogenic and geological

138

activities, which may play a key role in the distribution of studied inorganic As species in water

bodies (Thornton and Farago 1997). According to the survey report, conducted by our sampling

team, the local population of rural area in district Sukkur was mainly dependent on ground water

and consumed approximately 3 L of drinking-water, containing >40 µg As L-1. Therefore, total

consumption of inorganic As over 120 µg compared to an estimated daily intake of 12–14 µg

inorganic As from diets of North American population (Yost et al., 1998). Thus, exposure to

inorganic As may give rise to several chronic health effects in these studied endemic areas.

4.4.2.3. Conclusions

A new non-chromatographic method was developed for the speciation of dissolved

organic and inorganic arsenic species in ground water samples. The separation of inorganic

arsenic and organic As species in ground water samples using separation/extraction methods was

studied first time in Pakistan. The inorganic and organic As species was separated by a small

sized alumina as an adsorbent. While CPE was used for the determination of trace quantity of

As3+ and As5+ in ground water samples, using APDC and molybdate as the complexing reagents

and Triton X-114 as the extractant. The proposed methods are simple, low cost and

environmental friendly, because they do not require carcinogenic organic solvents. These results

convincingly presume that, the contamination of As speciation is more prevalent in tube well

samples as compare to hand pump samples. This suggested that further studies should be focused

on the bioaccumulation of As speciation in aquatic biota and hazards associated with their

consumption.

139

4.5. Evaluation the arsenic fractions in sediments

General Remark


Jameel Ahmed Baig, Tasneem Gul Kazi, et al., (2009). Arsenic fractionation in sediments of different origins using BCR sequential and single extraction methods. Journal of Hazardous Materials 167, 745–751. doi:10.1016/j.jhazmat.2009.01.040

4.5.1. Physico-chemical parameter of sediments

The physico-chemical parameters of the sediment are shown in Table 22. The pH values

of collected sediment samples from different origins (lake, canal and river) were found in the

range of 6.8-7.9. The pH values can influence adsorption capacity of As, hence, its mobility and

availability are inversely proportional to pH. The correlation coefficients between total arsenic

with pH, % silica and CEC were not significant (p<0.05, Fig.17).

Table 22. Total Basic characteristics of the sediment samples of Jamshoro district

Origin of sediment AsT (mg kg-1)

pH

% Silica

CEC (meq 100gm-1)

Lake 42.5±1.45 7.0±0.18 82.7±4.61 9.30±1.83

Canal 15.7±2.86 7.5±0.32 79.6±5.03 8.20±0.73

River 16.9±2.45 7.2±0.43 79.9±5.05 8.50±1.13

nsx tnsx t nsx t nsx t

140

Fig. 17 Correlation coefficient of total arsenic (AsT) in sediments with pH, % Silica and CEC

4.5.2. Total arsenic in sediment

The total arsenic (AsT) values in 240 batches of sediment samples collected from lake,

canals and river were found in the ranges of 35.4-46.4, 12.8-19.5 and 12.3-18.9 mg kg−1

respectively. The lake sediment of study area has highest mean value of AsT (42.5 mg kg−1),

which may play a role in contamination of lake water with arsenic (Crecelius, 1974). Arsenic can

easily be accumulated in sediments by chemical and physical binding or by adsorption onto

organic and inorganic particles. Therefore, the As concentration in understudy lake sediment was

approximately three times higher than canals and river sediments samples, while it was also

higher than those reported for unpolluted ecosystem (Smedley and Kinniburgh 2002).

pH ( r = 0.40)

CEC ( r = 0.40)

% Silica ( r = 0.210)

0

20

40

60

80

100

10.0 20.0 30.0 40.0 50.0

AsT (mg kg-1)

---S

tan

da

rd U

nit

---

pH % Silica CEC mEq./100 g

141

4.5.3. Comparison of BCR sequential and single step BCR extraction methods

The extraction of each As fraction in replicate six specimens of BCR 701 and duplicate

samples of each batch of sediment samples of different origin by these two methods are

summarized in Table 23. A further three certified specimen and duplicate sediment samples were

digested in aqua regia to determine the pseudo-total As contents (AsT). A comparison between

pseudo-total results of the BCR 701 sample and the values from the three steps plus residue (∑ 3

steps + aqua regia extractable from residue) was shown in table 2. No significant difference was

observed between the pseudo-total As content and the sum of extracted As following the BCR-

SES. The relative errors < 1 %, indicated the validity of the method. The values of As obtained

from BCR-SES were used as reference values for calculations of the percentages recovered by

single step extraction method (S-BCR).

Table 23. Results obtained for As in sediment certified reference material BCR 701 (mg kg-

1) using conventional BCR sequential extraction scheme (BCR-SES) and single step BCR extraction (S-BCR).

BCR 701 Indicative values

BCR-SESa

BCR-SESb S-BCRb

tExperimenta (% Recovery)

at df =5 tcertical = 2.228

As acid soluble 2.1 2.24±0.22 2.24±0.21 100

As reducible 60.4 60.5±2.65 61.3±2.72 0.031 (101)

As Oxidisable 6.36 6.54±0.72 6.24±0.48 0.293 (98)

As Residual 36.0 36.1±1.62 --

∑ 3 steps + Residue NDc 105.4±2.83 --

Pseudo-total As 106±0.35 106.3±2.60

% RSD NDc 5.20 4.80

a Stand for references of the indicative values for BCR-SES.

b This work.

c Not determined.

142

The main advantage of proposed S-BCR procedure was simultaneous extraction of all

fractions, which gives faster results than three steps BCR-SES. The acid-soluble fraction of As is

commonly as precipitates or co-precipitates with carbonates, which is loosely bound and

transferable to water column by change in environmental conditions. So, this phase is subject to

changes in pH, being generally targeted by the use of a mild acid such as acetic acid

(Quevauviller, 2002; Tyagi et al., 1997). The exchangeable fraction constitutes the step 1 of the

BCR-SES method and consequently it is always directly extracted, so for this step, results

obtained by BCR-SES and S-BCR were same (Table 23). The acid soluble fraction of BCR 701

had good agreement with indicative values of As (Sahuquillo et al., 2003).

The reducible fraction of As which is bound to iron and manganese oxides is released

when the matrix is subjected to reducing conditions. The hydroxylamine hydrochloride in nitric

acid medium is the reagent most widely used to leach the reducible fraction (Filgueiras et al.,

2002).

The reducible fraction estimated from S-BCR, displayed variability in values of As, as

compared to those obtained by BCR-SES. The recovery of As obtained by S-BCR is higher than

those obtained from BCR-SES (101 %). The high amount of reducible content obtained by S-

BCR indicated that As was greatly extracted when sediment samples were directly treated with

hydroxylamine hydrochloride in acidic medium (Shaw, 2003; Kubova et al., 2004).

The organic fraction of As released under oxidizing conditions is not considered to be

mobile and bioavailable, but may be made mobilized by decomposition processes in acidic

conditions. It was observed that the As bound to oxidisable phase was extracted in lower range

by S-BCR as compared to those obtained from BCR-SES. The extraction efficiency of organic

and sulphate bound As was observed (98 %).

The sum of total extractable metal contents (∑three steps) obtained from the BCR-SES

method together with those evaluated by S-BCR is shown in table 23. The extractable total

contents of As obtained by BCR-SES was 65.0%, while values obtained by S-BCR was found to

be 65.7 % of total arsenic contents in BCR 701 and real sediment samples. The comparison

between BCR-SES and S-BCR methods was calculated by paired t-test, and compared the

tExperimental (tExp.) to that of theoretical value at 95% confidence limit (Table 23). In all cases the

143

tExp is less than that of the theoretical value, i.e. no difference was observed between the

extractable As by BCR-SES and S-BCR methods.

4.5.4. Application

Application of both methods based on BCR-sequential extraction schemes were made to the

same sub samples of sediment of different origins (lake, river and canals), the comparative

results are shown in Table 24. The relative mobility of As in lake, canals and river was obtained

in increasing order: acid soluble fraction < oxidisable fraction < reducible fraction (Fig 18).

According to this relationship, the “potential mobility” of As (relative to total concentration) in

sediment decreased in the order: lake > river > canals sediment. From the results, the percentage

of acid soluble fraction in lake sediments was observed higher than canal and river. The lake

ecosystem is highly contaminated due to a feeding source of lake; drainage of industrial and

agricultural wastes as well as from saline effluents (Arain et al., 2008). This fraction clearly

indicated that the lake water was highly contaminated with As. The reducible fraction is mostly

bound to the structure of primary and secondary minerals. Therefore, both BCR-SES and S-BCR

were applied for analyses of the samples to reveal the mineral compositions. The samples in

which a higher content of pyrite is present under oxidizing condition contains high As. This

might be a possible reason of As mobilization in sediment under reducible condition.

144

Table 24. Results obtained for As in sediment samples (expressed in mg kg-1) using

conventional BCR sequential extraction scheme (BCR-SES) and single step BCR extraction

(S-BCR) n = 240

I.D

Acid soluble fractiona Reducible fraction Oxidisable fraction

BCR-SES S-BCR BCR-SES S-BCR BCR-SES S-BCR

JLS1 3.50±0.12 19.3±0.94 19.5±1.05 2.74±0.08 2.86±0.11

JLS2 2.50±0.10 21.8±1.51 21.9±1.07 4.65±0.13 4.76±0.10

JLS3 3.02±0.08 18.5±1.12 18.57±1.13 6.75±0.10 7.02±0.08

JLS4 5.02±0.15 19.35±1.07 19.5±1.15 5.75±0.05 5.98±0.09

JCS1 1.05±0.07 9.10±0.40 9.21±0.57 1.36±0.07 1.42±0.05

JCS2 0.95±0.13 6.25±0.51 6.31±0.45 1.85±0.11 1.92±0.08

JCS3 1.12±0.06 8.15±0.56 8.22±0.39 1.95±0.09 2.03±0.06

JCS4 0.96±0.08 6.40±0.55 6.48±0.44 1.75±0.14 1.82±0.09

JCS5 1.81±0.11 7.10±0.44 7.17±0.35 2.50±0.10 2.56±0.12

JCS6 2.17±0.15 6.85±0.62 6.92±0.47 3.67±0.06 3.74±0.10

JCS7 1.02±0.12 10.3±0.50 10.35±0.48 4.25±0.04 4.30±0.14

JCS8 1.73±0.09 9.41±0.48 9.48±0.43 3.25±0.07 3.38±0.10

JCS9 1.13±0.08 7.94±0.44 7.99±0.50 3.90±0.13 3.98±0.08

JCS10 1.17±0.11 11.60±0.61 11.70±0.60 1.02±0.10 1.06±0.07

JRS1 0.55±0.07 11.5±0.70 11.56±0.41 1.80±0.04 1.87±0.05

JRS2 0.45±0.16 7.00±0.67 7.10±0.47 3.79±0.07 3.82±0.06

JRS3 0.54±0.13 6.55±0.44 6.64±0.55 2.65±0.09 2.72±0.09

JRS4 0.73±0.08 8.12±0.47 8.24±0.44 2.50±0.05 2.60±0.07

JRS5 1.05±0.06 8.06±0.66 8.17±0.36 3.25±0.10 3.28±0.04

JRS6 0.48±0.13 8.24±0.42 8.36±0.52 2.85±0.05 2.96±0.12 a= BCR-SES = S-BCR

145

Fig. 18. Ratio of individual As bonded fraction in sediments: lake (a), canal (b) and river (c) sediments

aAcid soluble

fraction13%

Oxidable fraction

18%

Reducible fraction

69%b

Oxidable fraction

21%

Acid soluble fraction

11%

Reducible fraction

68%c

Reducible fraction

71%

Acid soluble fraction

5%Oxidable fraction24%

146

A lesser abundance of the acid soluble arsenic (10 %) and concomitant increase in the

reducible fractions (70 %) showed the possible adsorption of As5+ onto appropriate adsorbent in

bottom sediments. The observations are consistent with the arsenic chemistry, where it is well

established that As5+ sorbs onto sediments and co-precipitation with iron and manganese

oxyhydroxides is also known to happen (Pandey et al., 2004; Arain et al., 2008). In present work

it was observed that 5-13 % As was present in easily extractable form, which may contaminate

the environment of ecosystem with variation in pH. The extractable level of As obtained in our

work is lower than prescribed by EPA (Pandey et al., 2004).

4.5.5. Conclusions

In present work, a comparative study for BCR sequential extraction method with newly

developed S-BCR method, for partitioning of As in sediment samples was carried out. The

application of BCR sequential extraction method for arsenic to sediment samples of different

origin provided related information about potential toxicity when it is discharged into the

environment. The lengthy treatment time required in this procedure was shortened by changing

the sequential treatment using developed single step extraction (S-BCR). Both BCR-SES and S-

BCR methods provided comparable information concerning the mobility and bioavailability of

arsenic under diverse environmental conditions.

Moreover, when the single extraction method was employed, the washing steps after each

sequential extraction stage were eliminated, which allowed us to accelerate the experimental

task. However, S-BCR method needs a larger amount of sample, which does not pose a

significant problem in case of bulky environmental samples. Hence, the use of single extractions

should allow one to evaluate the extractable metals / metalloids in sediment samples and might

be useful for a fast screening of the possible mobility and bioavailability of toxic metals /

metalloids in the environmental samples. The concentration of As in Indus river and linked

canals indicated that, the adsorbed arsenic carried out from upstream like the colloidal particles

could be the major source of arsenic along the Indus deltaic region, while in lake sediment the

high concentration of total as well as acid soluble As may exceed the sediment-quality guidelines

and probably cause the adverse effects on the aquatic environment.

147

4.6. Evaluation of arsenic in soils and its translocation to grain crops and vegetable

4.6.1 Evaluation of arsenic in grain crops and soil by cloud point extraction

General Remark


Jameel Ahmed Baig, Tasneem Gul Kazi, et al., (2010). Evaluating the accumulation of arsenic in maize (Zea mays L.) plants from its growing media by cloud point extraction. Food and Chemical Toxicology 48, 3051-3057.

doi:10.1016/j.fct.2010.07.043

4.6.1.1. Optimization of Cloud point extraction

For the optimization of CPE, five factors were selected to be examined, pH, mass of

complexing agent, amount of surfactant, equilibrium time and temperature.


It is known that the pH of the sample solution on the aqueous-organic extraction process

is very important because the complexation of metals with organic ligands mostly depends on its

form at a particular pH (Stalikas, 2002; Scaccia and Frangini, 2004). In order to evaluate the

effects of this important parameter, pH values of sample solutions were adjusted in the range of

1.0 to 10.0 with HCl or NaOH (Fig. 19). The complex formation was began at pH 2.5 and started

to decrease at pH 6, showed a plateau at pH values 3.5 to 5.5. The possible reasons are that the

stable complex formed between As3+ and APDC at pH > 3 and < 6 in organic aqueous phase

(Tang et al., 2005). Hence, APDC is selective for the formation of complex with reducing form

of As (As3+) at pH ranged 3 - 6. As the signal for As increases after pH 3.5 and becomes

decreased after pH 6. Thus, pH 4.5 was chosen as an adequate pH value for further studies.

148

0

0.2

0.4

0.6

0.8

1

1.2

0 2 4 6 8

APDC (x 10-4 mol L-1)

Ab

sorb

ance

4.6.1.1.3. Effect of APDC concentration

For current study, APDC was used as the chelating agent due to its highly hydrophobic nature

for metal/metalloid complex formation. The optimization of APDC concentration is important

parameter for maximum extraction efficiency of As by CPE in environmental and biological

Fig 19. Effect of pH on the CPE of 10µg L-1 As. Other CPE conditions: 4.3x 10-4 mol L-1 APDC, 0.12% concentration of Triton X-114, equilibration temperature 35 ○C, equilibration time 10 min.

Fig 20. Effect of concentration of APDC on the CPE of 10µg L-1 As. Other CPE conditions: 0.12% (v/v) concentration of Triton X-114, pH 4.5, equilibration temperature 35 ○C, equilibration time 10 min.

0

0.2

0.4

0.6

0.8

1

1.2

0 2 4 6 8 10 12

pH

Ab

so

rba

nc

e

149

Fig 21. Effect of concentration of Triton X-114 on the CPE of 10µg L-1 As. Other CPE conditions: 4.3x 10-4 mol L-1 APDC, pH 4.5, equilibration temperature 35 ○C, equilibration time 10 min.

samples. To check the fluctuation during complex formation at different concentration levels of

APDC is given in Fig. 20, ranged from 0.61×10-4 to 7.3×10-4 mol L-1. It was observed that the

extraction efficiency of CPE for As was enhanced rapidly as the concentration of APDC

concentration upto7.3×10-4 mol L-1. Therefore, for further experiments, 4.3×10-4 mol L-1 of

APDC concentration of was employed. The stoichiometry of the As-PDC complex was observed

1:3 ratios.

4.6.1.1.4. Effect of Triton X-114

There are several non-ionic surfactants (Triton X-114, and X-100 etc.) were used for

CPE. But, Triton X-114 was chosen for this study because of its higher extraction efficiency as

well as its lower cloud point temperature, which facilitates the phase separation by centrifugation

as compared with other tested surfactants (Silva et al., 2006; Shah et al., 2010). The Fig 21

shows the variation in extraction efficiency of As with APDC complex range of 0.01 - 0.25%

was observed. The 60-70 % recovery was observed at 0.05% of Triton X-114, while the

extraction efficiency reached a maximum in the concentration of 0.12%. So, a concentration of

0.12% was chosen as the optimum surfactant concentration in order to achieve the highest

possible extraction recovery of As for standards, CRM samples, while < 0.12% the extraction

0

0.2

0.4

0.6

0.8

1

1.2

0 0.05 0.1 0.15 0.2 0.25 0.3

Concentration of Triton (%, V/V)

Ab

sorb

ance

150

efficiency of complexes is low probably because of the inadequacy of the assemblies to entrap

the hydrophobic complex quantitatively. At volume higher than 0.12% (v/v), the signals decrease

because of the increment in the volumes and the viscosity of the surfactant phase. To decrease

the viscosity of extracts acidic ethyl alcohol 0.1 mol L-1 was added.


It was desirable to employ the shortest equilibration time and the lowest possible

equilibration temperature, as a compromise between completion of extraction and efficient

separation of phases. It was found that 35 ○C is adequate for these analyses. The dependence of

extraction efficiency upon equilibration time was studied for a time span of 5–20 min. An

equilibration time of 10 min was chosen for the quantitative extraction.

4.6.1.1.6. Interferences

The effects of the matrix ions were investigated for efficient extraction of As by CPE.

About 1 µg of As model solutions (100 mL) containing matrix ions were used in this study. The

results showed that Se4+, Pb2+, Ni2+, Co2+, Mn2+ and Fe2+ (up to the concentration level of 100

mg L-1), Na+ (up to 1000 mg L-1), Mg2+ and K+ (up to 500 mg L-1) did not cause any significant

interference on the CPE of As. Therefore, the proposed method has been shown good selectivity.

4.6.1.1.7. Analytical performance

The enhancement factor of about 50 was obtained by pre-concentration a 10 ml of sample

solutions. The results indicated that the method has good precision. The method was assured by

the analysis of triplicate samples, reagent blank, procedural blanks and standard reference

material. In order to validate the method for accuracy and precision, a certified reference material

of whole meal flour BCR 483 was analyzed with As content of 0.018± 0.0005 µg g−1 (indicative

value) (Jamali et al., 2008). The %recovery of As with CPE was observed 98.2 ± 0.3% (Table

25). The precision of the methods, expressed as the relative standard deviation (RSD) of 6

independent analyses of the same CRM sample with CPE was 1.70 %. The paired t-test was

calculated for (n -1 = 5) degrees of freedom, the value of tExperiment (0.12) was less than the tcertical

(2.57) at 95% confidence interval (Table 25), indicating no difference between found values and

indicative value. Due to the lack of reference material for As speciation, the validity of analytical

151

method was performed on replicate three sub samples of SIC, spiking with three concentration

levels of Asaqueous and TAs then applied both methods. The recoveries for Asaqueous and TAs were

generally greater than 98% (Table 25). A good agreement was obtained between the added and

measured analytes concentration.

The CPE of different forms of As using different organic ligands are shown in Table 26.

The comparative data of analytical characteristics shows that the characteristic parameters of

CPE viz. correlation coefficient (r), LOD/LOQ, precision and enrichment factor of present study

was comparatively better than previously reported works (Silva et al., 2000; Tang et al., 2005;

Baig et al., 2009). While the CPE characteristic parameters of our study were consisted with

those reported by Shemirani et al., (2005) and Jiang et al., 2008.

Table 25. The results for tests of addition/recovery for TAs determination in soil samples by CPE (n= 6)

a

Species Added Conc. ( µg L-1) Mean±Std

( µg g-1 )

a % Recovery

Total As

0.00 2.90±0.03 --

2.5 17.3±0.18 97.7

5.0 19.8±0.21 98.0

10.0 24.9±0.32 98.8

Validation for total As( mg Kg-1)

Element

Indicative value (Whole meal

flour BCR 189)

Found values

ntsx /

% recovery (% RSD)

tExperiment

total As 0.018± 0.0005 0.0177± 0.0003 98.2 (1.70) 0.27

tcertical = 2.57 at 95% confidence limit, (n = 6)

100CC

C Recovery%

spikedinitial

spikedafter

152

Table 26. Comparative data of Analytical characteristics of the CPE method for As (µg L-1)

Complexing

reagent/surfactant

Technique 1r LOD/

LOQ

Precision 2EF Reference

APDC/Triton X-

114

CPE/ETAAS 0.998 0.04/0.13 3.0

(n = 11)

36 Tang et al., 2005

APDC/Triton X-

114

CPE/ETAAS 0.999 0.03/0.11 <2.3%

(n = 6)

40 Baig et al., 2009

O,O-

Diethyldithiophosp

hate/Triton X-114

ultrasonic

nebulization

inductively

coupled

plasma mass

spectrometry

0.993 0.006/-- 3.8%

(n = 8)

42 Silva et al., 2000

APDC-Activated

Carbon

GFAAS -- 0.05/-- 4.1%

(n = 9)

50 Jiang et al., 2008

Molybdate/sulfuric

acid

CPE/ETAAS 0.999 0.011/0.037 5.00%

(n = 5)

52 Shemirani et al.,

2005

APDC/Triton X-

114

CPE/ETAAS 0.999 0.025/0.083 1.70%

( n= 6)

50 This work

1Correlation coefficient, 2enrichment factor

153


The accumulation of toxic metals in food crops has been recognized as an issue of high

priority by many governmental agencies around the world (OECD, 2003; USEPA, 2006). The

methods related with phytotoxicity should be enhanced in assessing the impacts of chemicals on

terrestrial ecosystem. The food crop serves as an important pathway for human exposure to toxic

elements including As (Cheng 2006). Thus current study has documented the concentration of

TAs and Asaqueous in soil samples and TAs concentration in different parts of maize plants (grain,

shoot and root). The developed CPE method was successfully applied for the analysis of As

contents in understudied agricultural soils and different parts of maize. The results shown in

Table 4 indicated that a high TAs concentration in SIT of two sub districts Khairpur and Kot Diji

was found in the range of 25.0-36.3 and 18.7-54.0 µg g-1, respectively. The TAs level in SIC of

both sub districts were obtained in the range of 20.5-31.0 and 15.2-48.2 µg g-1, respectively

(Table 27). The Asaqueous in SIT samples of both sub districts were ranged 0.85-1.95 and 0.71-

2.88 µg g-1, respectively and the level of Asaqueous in SIC samples of both district was 0.90-

1.40and 0.71-2.15 µg g-1, respectively (Table 27). The concentration of TAs and Asaqueous in SIT

were significantly higher than those observed in SIC (P<0.001). It is because of high As

concentration in ground water (>50 µg L-1), as compares to surface water (< 10 µg L-1) (Baig et

al., 2010). The high level of As in ground water (tube well water used for irrigation purposes) is

due to dissolution of As compounds coming from Himalaya through the Indus river and settled

down over the years and then introduced into groundwater by geothermal, geo-hydrological and

bio-geo-chemical factors (Baig et al., 2010; Smedley et al., 2002; Singh, 2006). Moreover, it

might be due to the As containing insecticides and herbicides used for agriculture purposes and

seepages from hazardous waste sites (Smedley and Kinniburgh, 2002).

The TAs contents in different parts of maize plant i.e., grain, shoot and root grown in soils

(SIT and SIC) understudied sub districts shown in Table 28. The mean TAs concentrations in

root, shoot and grain of maize (n =76), grown in SIT of Khairpur Mir’s were 2.05±0.66,

154

Table 27. Total As (TAs) and water extractable As (Asaqueous) concentrations in soil (µg g-1) by

CPE

Khairpur Mir's Kot Diji

1TWIS 2CWIS 1TWIS 2CWIS

TAs

Range 25.0-36.3 20.5-31.0 15.2-48.2 18.7-54.0

Mean 29.6 25.0 30.5 32.7

Median 12.5 5.90 15.8 9.30

Asaqueous

(Din-test)

Range 0.85-1.95 0.90-1.40 0.71-2.88 0.71-2.15

Mean 1.52 1.13 1.50 1.24

Median 1.48 1.05 1.42 1.21

1Tube well water irrigated soil, 2Canal water irrigated soil

Table 28. Concentration of total As in different part of maize with CPE (µg g-1) and contamination factor (CF)

Area Parts Maize samples grown in TWIS Maize samples grown in CWIS

Mean±Std CF Mean±Std CF

Khairpur Mir’s

Grain 0.302±0.05

0.10

0.151±0.06

0.03 Shoot 0.406±0.09 0.202±0.08

Root 2.05±0.66 0.50±0.0.9

Kot Diji

Grain 0.280±0.04

0.07

0.171±0.07

0.034Shoot 0.365±0.1 0.27±0.10

Root 1.74±0.53 0.542±0.08

155

0.406±0.09 and 0.302±0.05 µg g-1, respectively and in same tissues of maize (n = 83) grown in

SIC were found to be 0.50±0.0.9 0.202±0.08 and 0.151±0.06 µg g-1, respectively. These results

are consistent with the study conducted in Bangladesh by Das et al. (2004) found 2.4 µg g-1 As in

rice roots, 0.73 µg g-1 in shoots and 0.14 µg g-1 in grain. Whereas, the lower mean TAs contents

in root, shoots and grains of maize crop were observed 1.74±0.53, 0.365±0.1 and 0.280±0.04 µg

g-1 and 0.542±0.08, 0.27±0.10 and 0.171±0.07 µg g-1, respectively grown in SIT and SIC of Kot

Diji. The TAs in different parts of maize crops grown in SIT and SIC is shown in Table 28. The

TAs uptake by maize plants from both studied soil of two sub districts were observed in the

increasing order: grain < shoot < root. This is according to reported study as the roots accumulate

more toxicants, and is more sensitive, than shoot or grain (USEPA, 1996; An, 2004). The

concentrations of As in stems of plants were considerably lower as compared to the root system,

which proved that As movement along the plants conductive system was strongly limited.

The ratio between plant and soil concentrations of elements (contamination factor “CF”) is

an index of soil–plant transfer that favors the understanding of plant uptake characteristics

(Chamberlain, 1983) and it is widely used in bio-monitoring studies (Mingorance et al., 2005).

Ratios >1 indicate that plants are enriched with elements (accumulator), ratios around 1 indicates

that plants are not influenced by elements (indicator) and ratios < 1 shows that plants exclude the

elements from uptake (Baker, 1981). Results of CF (Table 28) display that both species exhibited

the same behavior. The CF values of this study were <1 for both SIT and SIC of understudied

sub districts, indicating a low translocation from soil to plant shoots in all sampling sites. These

findings indicate that differences in soil accumulation rates in SIT can occur not only laterally

but also between years. They point to the need for more study sites covering a wider range of

environmental conditions and for relevant parameters to be monitored over a period of years.


This study provided a safe alternative method based on CPE method for the preconcentration of

As in maize crop and adjoining soil samples and determination by ETAAS. The proposed method has

the following advantages; is a simple, rapid, sensitive, inexpensive, non-polluting technique with high

enhancement factor. The complexing agent, APDC, has enough hydrophobicity to be used in the

proposed procedure, being quite selective and stable at high acid concentrations, which is very

156

convenient, since the water samples are usually preserved with acids. The maximum extraction

efficiency was achieved at optimum levels of 4.5 pH, 4.3 x 10-4 mol L-1 of APDC concentration, 0.12%

of Triton X-114 amount, 35 ○C of equilibration temperature and 10 min equilibration temperature, for

As in soil and grain crop samples. The experimental results showed that the CPE was a successful

method for determination of As in maize and adjoining soils irrigated by tube well and canal water in

two sub districts of Pakistan with satisfactory recoveries. These findings urged more work on As

controlled and exposed grain crops and vegetables in detail and should take into consideration variations

in uptake between different species, cropping history, the levels of metals present in the atmosphere

(quantified) and the difference in maize uptake between soils and foliar mechanisms.

157

4.6.2. Evaluation arsenic in irrigation water and its translocation from soil to grain crops

General Remark


Jameel Ahmed Baig, Tasneem Gul Kazi, et al., (2011). Evaluation of arsenic levels in grain crops samples, irrigated by tube well and canal water. Food and Chemical Toxicology 49, 265-270. doi:10.1016/j.fct.2010.11.002

4.6.2.1. Optimization of methodology for As3+ in water

The effect of pH on the co-precipitation of As3+ with APDC was studied in the range of

pH 2–6, using 100 mL of standard solutions containing As in the range of 10 µg L-1. The As3+

reacted with APDC to form stable complexes in the aqueous solution, and was quantitatively co-

precipitated with Pb-PDC in the pH > 2.Whereas, at low pH (pH < 2), some co-precipitate was

dissolved, so the recoveries were lower than those in the pH 2–4. The optimum recovery of As3+

was obtained at pH 3. Thus, for further analysis pH 3 was used.

The influence of the amount of APDC and Pb (NO3)2 on the co-precipitation of As3+ was

investigated in the range of 0.005–0.02% (w/v) and 0.002–0.006% (w/v), respectively. It was

observed that the co-precipitation of As3+ was optimum at 0.015% of APDC and 0.004%

Pb(NO3)2. Thus, 0.015% of APDC and 0.004% Pb(NO3)2, were used for further experiments. To

understand the effect of stirring time on the recovery of As3+ the standards and samples were

investigated under the above optimal co-precipitation conditions. Stirring of the content of the

flask in thermostatic water bath at 25–45 ºC for 5–25 min was performed. The maximum

recovery of As3+ was observed at 35 ºC after 10 min. Therefore, 10 min of stirring time was

chosen for the subsequent experiments.

4.6.2.2.. Physico-chemical parameters of soil

The physico-chemical parameters of the soil irrigated with tube well water (SIT) and the

soil irrigated with canal water (SIC) were studied (Table 29). The pH of SIT samples was

obtained as 7.70 ± 0.22, while the pH value of SIC samples was found to be 7.00 ± 0.44. At low

pH, the mobility and leaching of As increase and its availability decreases as the pH approaches

neutral or rises above pH 7. The OM is an important component because it tends to form either

158

soluble or insoluble complexes with As, to migrate, or to be retained in the soil. The SIT and SIC

contain 26.0 ± 2.10% and 25.0 ± 1.60% of OM, respectively. The OC in SIT was 14.0 ± 1.90%

and SIC contains 15.0 ± 1.20%. The average contents of CEC in SIT and SIC were found to be

14.4 ± 1.10 and 14.0 ± 2.40 meq 100 g m-1, respectively.

Table 29 Physico-chemical characteristics of the sampled soils irrigated with tube well water (SIT) and soils irrigated with canal water (SIC)

Parameters SIT SIC

pH 7.67 ±0.22 7.01±0.44

Silica % 47.8±3.7 45.9±4.34

Organic matter % 25.7±2.1 24.9±1.68

Organic Carbon % 13.7±1.89 15.0±1.2

CEC (meq 100gm-1) 14.4±1.1 13.9±2.4

4.6.2.3. Total and inorganic species of arsenic in water

The mean concentrations of TAs in canal and tube well water samples of the sub-districts

of Faiz Ganj, Thari Mirwah and Gambat were observed to be 5.4 ± 0.07 and 15.4 ± 2.31, 7.0 ±

0.09 and 31.0 ± 8.21 and 8.2 ± 0.12 and 98.3 ± 68.7 µg L-1, respectively (Table 30). The average

concentration of TAs in surface water samples was found to be 8.0 µg L-1, which is lower than

the reported values for surface water (Kahlown et al., 2002; Mukherjee et al., 2005). The

concentration of TAs in tube well water was observed to be higher than the WHO permissible

level (10 µg L-1), which might be due to agricultural, industrial and domestic activities (Arain et

al., 2008; Baig et al., 2010b). In the study area, the As concentrations in water were consistent

with those reported in Mancher Lake water (35.00–157.00 µg L-1) and in adjoining groundwater

(23.30–387 96.30 lg L_1), Muzaffargarh (1.00–905.00 µg L-1), Lahore (<10.00–390 1900.00 µg

159

L-1) and Jamshoro (3.00–106.00 µg L-1) (Farooqi et al., 2007; Nickson et al., 2007; Arain et al.,

2009; Baig et al., 2009a).

The average concentration of iAs and As3+ was observed to be 5.40 ± 0.06 and 3.08 ±

0.08, 6.85 ± 0.11 and 3.99 ± 0.15 and 8.15 ± 0.12 and 4.35 ± 0.14 µg L-1 in the canal water

samples of Faiz Ganj, Thari Mirwah and Gambat sub-districts, respectively. Whereas, the mean

concentrations of As5+ in the tube water samples of Faiz Ganj, Thari Mirwah and Gambat sub-

districts were found as 7.20 ± 2.17, 16.0 ± 6.92 and 46.2 ± 45.6 µg L-1, respectively. It was

observed that in most of the tube well water samples, the levels of As5+ were prominent as

compared to those of As3+. It is because of the high contents of SO42- (>250 mg L-1), pH > 7.5

and Fe (>0.3 mg L-1) as reported in our previous work (Baig et al., 2010b,c). All these provide

evidence that anthropogenic and geological activities play a key role in the distribution of studied

inorganic As species in water bodies of the understudied areas and make a significant

contribution to the total intake of inorganic As. Therefore, the tube well water in this region is

not suitable for drinking, cooking and agricultural purposes.

160

Table 30. Arsenic concentration in soil irrigated with tube well water (SIT) and soil irrigated with canal water (SIC) in µg g-1 and Arsenic in water (µg L-1)

Arsenic in water (µg L-1)

Sub-districts Canal Tube well

Faiz Ganj

TAs 5.4±0.07 15.4±2.31

As3+ 3.08±0.08 8.16±1.95

As5+ 2.32±0.11 7.24±2.17

Thari Mirwah

TAs 7.0±0.09 31.0±8.21

As3+ 3.99±0.15 14.6±7.82

As5+ 3.01±0.14 16.4±6.92

Gambat

TAs 8.2±0.12 98.3±68.7

As3+ 4.34±0.14 52.1±34.8

As5+ 3.85±0.11 46.2±45.6

Arsenic in soil (µg g-1)

Sub-districts SIT SIC

AsExt. TAs AsExt. TAs

Faiz Ganj 0.207±0.08 6.1±3.10 0.11±3.90 4.26 ± 8.21

Thari Mirwah 1.2±3.40 29.5±12.6 0.15±3.45 4.94 ± 6.25

Gambat 2.20±1.14 57.3 ± 18.8 0.17±3.70 5.28 ± 10.6

161

4.6.2.4. Bioavailable fraction of As in soil

The bio-availability of As from SIC and SIT to plants provided the knowledge about the

potential risk of As for plants, animals and human beings. Therefore, it is necessary to evaluate

the mobile and/or the available fractions of As in soil. Many researchers have tried to find a way

of measuring the plant available fraction of elements in soils using different extraction

procedures (Jamali et al., 2006, 2008a, b; Smedley and Kinniburgh, 2002). These have mostly

been validated in field experiments by correlating plant contents with extractable soil contents;

e.g. the analysis of EDTA soil extracts is widely used in agriculture, their role is the prediction

and assessment of trace element deficiency or toxicity to crops or animals (Mir et al., 2007). In

this work 0.05 mol L-1 EDTA pH 7 was chosen as extracting solution because this reagent has

been recommended by the ‘‘Measurement and Testing Program’’ of the European Community

BCR to determine the extractable or mobile fraction of heavy metals from soils and sediments

(Jamali et al., 2008a; Gleyzes et al., 2001). EDTA solution is assumed to extract principally

organically bound and carbonate bound fractions of metals by forming strong soluble complexes

(Gregori et al., 2004). The EDTA extractable concentrations of As in SIT and SIC are listed in

Table 3. The percentage of extracted As relative to the total content in SIT and SIC samples is

also included. The extractable As (AsExt.) in SIT of Faiz Ganj, Thari Mirwah and Gambat was

found in the range of 0.06–0.34, 0.20–1.40 and 0.46– 2.70 mg kg-1, respectively, i.e. (<4.30% of

the total As contents). Results show that the available fractions of As were high in SIT samples

as compared to those obtained from SIC as shown in Table 30. Statistically significant

correlations was found in between total concentrations of As and the EDTA extractable in SIT

and SIC samples.

162

4.6.2.5. Total As in soil and grain crops

The TAs concentrations in SIT ranged from 2.0 to 10.0, 5.0 to 34.3 and 12.0 to 70 µg g-1

in Faiz Ganj, Thari Mirwah and Gambat sub-districts of Khairpur Mir’s, respectively. Whereas,

in case of SIC, the TAs concentration was found in the range of 1.0–5.0, 2.3–10.5 and 3.02–13.4

µg g-1 in Faiz Ganj, Thari Mirwah and Gambat sub-districts, respectively. The normal range for

As in soils of various countries was 0.1–40 µg g-1 (Ure et al., 1993). It was predicted by a

conservative risk analysis that TAs level in the soil could reach 4.0 µg g-1 without becoming a

hazard to the exposed organisms (Das et al., 1995). Thus, US EPA set a criterion for As contents

in the soil as 2–5 µg g-1, which is toxic (Chatterjee et al., 1993). Our finding reports that SIT

samples of Thari Mirwah and Gambat exceeded the maximum permissible level for TAs in soil

but most of the SIT samples of Faiz Ganj were within the permissible level (Table 30).

Therefore, the studied grain crop grown on SIT of Gambat showed the high accumulation of As,

which might be due to its elevated concentration in soil. It is predicted that As contaminated

grain crops affect food quality and subsequently human and animal health through contamination

of the food chain. Whereas, the mean concentrations of TAs in all SIC samples of the three

studied sub-districts being within the maximum permissible limit showed the favorability of SIC

for the agricultural production of grain crops.

The level of As contamination in groundwater and the accumulation of As in common

grain crops were investigated. The levels of TAs in all TGS, irrigated on SIT of Faiz Ganj, Thari

Mirwah and Gambat, were found to be higher than in those grown on SIC. The TGS grown on

SIT of Faiz Ganj sub-district were less contaminated with TAs as compared to those grown in

Thari Mirwah and Gambat sub-districts. It is because the TAs concentration in tube well water

and SIT samples of Thari Mirwah and Gambat were significantly higher (p = 0.05) as compared

to Faiz Ganj sub-district. Moreover, positive correlation (r = 0.94, p = 0.001), (r = 0.84, p =

0.001) and (r = 0.79, p = 0.001) of TAs in contaminated water and TAs in SIT was found in

Gambat, Thari Mirwah and Faiz Ganj sub-districts, respectively. This predicted the high

translocation of As to grains from tube well water and SIT. Similar trend was also observed in

Bangladesh (Islam et al., 2007).

163

The result of the current study and previously reported work had shown that As deposits

in the tissues of plants grown in arsenic-rich soil irrigated with As contaminated water (Bae et

al., 2002; Duxbury et al., 2003; Rahman et al., 2004, 2008). The contents of As in the edible

parts of most plants are generally low as compared to root and shoots (Rahman et al., 2004,

2008). Wheat is the main cereal cultivated in Pakistan and covers about 80% of the total cereal

cropped area and is largely used as human diet. The grains of maize and sorghum are used as a

major contributor in dairy and poultry. Considering the grains, the TAs levels increased in the

approximate order as: wheat < maize < sorghum in the studied SIT and SIC of the three sub-

districts (Table 31). Plants seldom accumulate arsenic at concentrations hazardous to human and

animal health because phytotoxicity usually occurs before such concentrations are reached

(Rahman et al., 2004, 2008).

164

Table 31. Uptake of arsenic (µg g-1) by grain crops grown in soil irrigated with canal water as control grain crops samples (CGCs) and soil irrigated with tube well water (SIT) of three sub districts as tested grain crops samples (TGCs)

Transfer factor (Tf) = Total As in grain crops/EDTA extractable As in soil

Crops

CGCs

TGCs

Faiz Ganj Thari Mirwah Gambat

Wheat 0.045±0.042 0.22±0.06 0.35±0.08 0.618±0.10

Maize 0.038±0.037 0.190±0.04 0.246±0.07 0.394±0.09

Sorghum 0.034±0.029 0.18±0.05 0.23±0.09 0.547±0.105

Transfer factor (Tf)

Wheat 0.28 1.06 0.46 0.45

Maize 0.24 0.92 0.40 0.38

Sorghum 0.21 0.87 0.34 0.30

165

Table 32. Coefficients of determination (R2) of arsenic in soils (SIC and SIT of Faiz Ganj, Thari Mirwah, and Gambat) with (CGCs and TGCs)

On the basis of these results, individual transfer factor (Tf) of the AsExt. in SIT and SIC

samples with respect to grain crops was defined as the ratio between the concentrations of TAs in

grains and the respective concentration in the EDTA extracts of both soil samples as shown in

Table 4. Significantly high Tf of AsExt can be observed, in grains of wheat (CGS and TGS, p <

0.01) as compared to the other two studied grain crops grown on the same agricultural sites.

Moreover, the results (Table 32) show that the concentration of TAs in TGS grown on SIT was

positively correlated with TAs in SIT (R2 = 0.922–0.995), while the CGS grown in SIC showed

lower correlation (R2 = 0.701–0.926) with TAs in SIC.


This study highlights the potential accumulation of As in grains grown in the agricultural soil

irrigated with tube well and canal water (SIT and SIC). High accumulation of As was found in grain

samples obtained from the SIT as compared to SIC. The TGS especially wheat grain from SIT

contained the high contents of As as compared to CGS grown in SIC. So, it is suggest that the grain

crops were cultivated by canal water or mixed with tube well water as, the contamination of As may

be minimized. The studied sub districts were assigned in increasing order with respect to As levels in

water, soil and vegetables as: Gambat < Thari Mirwah < Faiz Ganj. In TGS, the TAs levels increased

in the approximate order as: Wheat < maize < sorghum in studied sub districts. The bioavailable

fraction of As in soils using extraction procedures including EDTA would help in the understanding

of soil plant relationships regarding TAs uptake.

Vegetables Normal

( SIC with CGCs)

Faiz Ganj

( SIT with TGCs)

Thari Mirwah

( SIT with TGCs)

Gambat

( SIT with TGCs)

Wheat 0.902 0.922 0.976 0.987

maize 0.921 0.924 0.958 0.962

sorghum 0.931 0.938 0.975 0.981

166

4.6.3. Translocation of As from soil to vegetables

General Remark

The work presented in this section has been submitted as:

Jameel Ahmed Baig, Tasneem Gul Kazi, et al., (2011). Determination and evaluation of arsenic contents in vegetables grown in soils, irrigated with tube well and canal water in Pakistan. Agriculture water Management (Revised Submission).

4.6.3.1 Bio-accumulation and levels of total arsenic in vegetables

The vegetables grown on SIT of Gambat showed the high accumulation might be due to

the As contaminated soil. It is predicted that As contaminated vegetables affects food quality

and, subsequently, human and animal health through contamination of the food chain. Whereas,

the mean concentrations of TAs in all SIC samples of three studied sub districts were within

maximum permissible limit, showed the favorability of SIC for agricultural productions of

vegetable and crops.

167

Table 33. Uptake of arsenic (µg g-1) by vegetables grown in soil irrigated with canal water as control vegetable samples (CVS) and soil irrigated with tube well water (SIT) of three sub district as tested vegetable samples (TVS)

Vegetables

CVS TVS

Faiz Ganj Thari Mirwah Gambat Okra 0.051±0.052 0.20±0.041 0.80±0.079 0.89±0.079 Sponge gourd 0.098±0.033 0.360±0.029 0.504±0.089 0.612±0.12 Brinjal 0.08±0.09 0.170±0.08 0.390±0.025 0.570±0.065 Bitter Gourd 0.125±0.023 0.275±0.016 0.811±0.126 1.11±0.193 Bottle gourd 0.140±0.036 0.390±0.032 1.05±0.125 1.25±0.120 Cluster Beans 0.125±0.022 0.603±0.045 0.734±0.058 1.30±0.226 Spinach 0.085±0.016 0.280±0.022 0.90±0.18 1.10±0.146 Peppermint 0.185±0.074 1.01±0.082 1.20±0.224 1.70±0.224 Indian Squash 0.158±0.065 0.804±0.079 1.30±0.285 1.63±0.32 Peas 0.325±0.037 0.630±0.049 0.910±0.092 1.03±0.12

Transfer factor (Tf) Okra 0.594 0.962 0.668 0.406 Sponge gourd 0.462 1.714 0.420 0.278 Brinjal 0.448 0.810 0.325 0.259 Bitter Gourd 0.509 1.310 0.676 0.505 Bottle gourd 0.764 1.857 0.875 0.568 Cluster Beans 0.089 2.871 0.612 0.591 Spinach 0.061 1.333 0.750 0.500 Peppermint 0.132 4.810 1.000 0.773 Indian Squash 0.113 3.829 1.083 0.741 Peas 0.232 3.00 0.758 0.605 Transfer factor (Tf) = Total As in vegetables/EDTA extractable As in soil

168

Table 34. Coefficients of determination (R2) of arsenic in soils (SIC and SIT of Faiz Ganj, Thari Mirwah, and Gambat) with (CVS and TVS)

The level of As contamination in groundwater and accumulation of As in common

vegetables were investigated and resulted data was given in Table 33. The levels of TAs in all

TVS, irrigated on SIT of Faiz Ganj, Thari Mirwah and Gambat were found to be higher than

those grown on SIC (Table 33). The TVS grown on SIT of Faiz Ganj sub district were less

contaminated with TAs as compared to those grown in Thari Mirwah and Gambat sub districts. It

is because the TAs concentration in tube well water and SIT samples of Thari Mirwah and

Gambat were significantly higher (p = 0.05) as compare to Faiz Fanj sub district. Moreover,

positive correlation (r = 0.94, p = 0.001), (r = 0.84, p = 0.0010 and (r = 79, p = 0.001) of TAs in

contaminated water and TAs in SIT was found in Gambat, Thari Mirwah and Faiz Ganj sub

districts, respectively (Table 34). This predicted the high translocation of As to vegetables from

tube well water and SIT. Similar trend was also observed in Bangladesh (Das et al. 2004).

Vegetables Normal

( SIC with CVS)

Faiz Ganj

( SIT with TVS)

Thari Mirwah

( SIT with TVS)

Gambat

( SIT with TVS)

Okra 0.902 0.922 0.976 0.987

Sponge gourd 0.921 0.924 0.958 0.962

Brinjal 0.930 0.931 0.974 0.982

Bitter Gourd 0.908 0.938 0.955 0.992

Bottle gourd 0.915 0.925 0.972 0.992

Cluster Beans 0.855 0.925 0.951 0.995

Spinach 0.867 0.937 0.978 0.986

Peppermint 0.919 0.929 0.967 0.992

Indian Squash 0.925 0.935 0.983 0.989

Peas 0.931 0.938 0.975 0.981

169

Considering the normally-edible parts of the vegetables, the TAs levels decreased in the

approximate order as: Peppermint < Indian Squash < Bottle gourd < Cluster Beans < Spinach <

Bitter Gourd < Peas < Sponge gourd < Okra < Brinjal in studied SIT and SIC of three sub

districts. Thus, the TAs is more efficiently translocated by mint in both growing media. It is

because the leafy vegetables have high capability to accumulate high levels of trace metals and

minerals from soil than other vegetables (Jamali et al. 2008a).

Individual transfer factors (Tf) of the AsExt. in SIT and SIC samples, with respective to

different types of vegetables, defined as the ratio between the concentrations of TAs in vegetables

and the respective concentration in the EDTA extracts of both soil samples, were evaluated

(Jamali et al. 2007). The high Tf of AsExt. can be observed, in mint as CVS and TVS were

significantly higher (P < 0.01) as compared to the other vegetables grown on same agricultural

sites. The results indicate that the mint vegetable is very sensitive to As; especially SIT are not

recommended for this most significant and frequently consumable vegetable. Moreover, the

results (Table 33) show that the concentration of TAs in TVS grown on SIT, was positively

correlated with SIT (R2 = 0.922–0.995), while the CVS grown in SIC showed lower correlation

(R2 = 0.701–0.926) with TAs in SIC. It is concluded that soil type, crop species, As level in

irrigated water and As phytoavailability should be considered in the assessment of soil As

thresholds for potential dietary toxicity.


This study demonstrated potential translocation of As in vegetables grown in the agricultural

soil irrigated with tube well and canal water (SIT and SIC). The high Tf of AsExt. can be observed, in

mint as CVS and TVS were significantly higher (P < 0.01) as compared to the other vegetables

grown on same agricultural sites. The TAs levels increased in the approximate order as: Peppermint

< Indian Squash < Bottle gourd < Cluster Beans < Spinach < Bitter Gourd < Peas < Sponge gourd <

Okra < Brinjal in studied SIT and SIC of three sub districts.

170

4.7. Exposure study of Arsenic

4.7.1. Determination of arsenic in biological samples with and without enrichment

General Remark


Tasneem Gul Kazi, Jameel Ahmed Baig, et al., (2011). Determination of arsenic in scalp hair samples from exposed Subjects using microwave assisted digestion, cloud point Extraction with and without enrichment, and electrothermal Atomic absorption spectrometry. AOAC International 94(1), 293-299.

Jameel Ahmed Baig, Tasneem Gul Kazi, et al., (2010). A green analytical procedure for selective determination of arsenic in scalp hair samples of arsenic exposed adults of both genders. Pakistan Journal of Analytical and Environmental Chemistry 11(2), 23-29.

4.7.1.1. Optimization of microwave assisted digestion-cloud point Extraction (MAD-CPE)

method

The MAD-CPE of total As in standard, CRM and SH samples, were carried out by

addition of complexing reagent (APDC) and resulting As-PDC complex was entrapped in

nonionic surfactant (Triton X-114), and subjecting to ETAAS for As determination. It was

investigated in our previous work, that the combination of understudy chelating agent and Triton

X-114 has many advantages, due to high stability of APDC in acidic media, good hydrophobicity

of the complex and the relatively low cloud point of Triton X-114 (Shah et al. 2009; Baig et al.

2009c, 2010a,c). For the optimization of CPE, five factors were selected to be examined i.e.,

amount of surfactant, mass of complexing agent, pH, equilibrium temperature and time.


The pH have important role in complex formation and successive extraction (Baig et al.,

2009c). In order to evaluate the effect of pH on complex formation of As with APDC, the

experiments were carried out over the pH range of 1-10 with 0.1 mol L-1 of HCl/ NaOH (Fig. 1).

As shown in Fig. 22, the intense signal for As was observed in the range of 3.5–5.5. In

subsequent experiments a pH of 4.5 was chosen.

4.7.1.1.2. Effect of APDC concentration

171

0

0.2

0.4

0.6

0.8

1

1.2

0 2 4 6 8 10 12

pH

Ab

sorb

ance

In this work, APDC was used as the chelating agent due to highly hydrophobic nature of

its metal/metalloid complexes. The extraction recovery of As, as a function of the APDC

concentration is shown in Fig. 23, ranged in between 0.001 to 0.025% (w/v). The MAD-CPE

extraction for As is enhanced as the level of APDC increased from 0.003 to 0.008% (w/v). No

further enhancement of signal was found with increase of APDC contents upto 0.025%.

Therefore, 0.008% APDC was adequate for further experiments.

4.7.1.1.3. Effect of Triton X-114

For maximum extraction efficiency and high pre-concentration factor using MAD-CPE

should be achieved by reducing phase volume ratio (Vorg/Vaqueous). In present work Triton X-114

was chosen because of its higher extraction efficiency as well as its lower cloud point

temperature, which facilitates phase separation by centrifugation (Silva et al., 2006; Shah et al.,

2010). The low cloud point temperature avoids back extraction during centrifugation. The Fig 24

shows the variation in extraction efficiency of As by Triton X-114 range of 0.01- 0.25% was

observed. The 60-70 % recovery was observed at 0.05% of Triton X-114, while the extraction

efficiency reaches a maximum at the

Fig 22. Effect of pH on the CPE of 10µg L-1 As. Other MAD-CPE conditions: 0.008% (w/v) APDC, 0.12% concentration of Triton X-114, equilibration temperature 35 ○C, equilibration time 10 min.

172

0

0.2

0.4

0.6

0.8

1

1.2

0 0.05 0.1 0.15 0.2 0.25 0.3

Concentration of Triton (%, V/V)

Ab

sorb

ance

0

0.2

0.4

0.6

0.8

1

1.2

0 0.005 0.01 0.015 0.02 0.025APDC concentration (%, W/V)

Ab

so

rba

nc

e

Fig 23. Effect of concentration of Triton X-114 on the CPE of 10µg L-1 As. Other MAD-CPE conditions: 0.12% (v/v) concentration of Triton X-114, pH 4.5, equilibration temperature 35 ○C, equilibration time 10 min.

Fig 24. Effect of concentration of Triton X-114 on the CPE of 10µg L-1 As. Other MAD-CPE conditions: 0.008% (w/v) APDC, pH 4.5, equilibration temperature 35 ○C, equilibration time 10 min.

173

Fig 25. Effect of foreign ions on the pre-concentration and determination of As (10µg L-1).

concentration of 0.12%. So, a concentration of 0.12% was chosen as the optimum surfactant

concentration in order to achieve the highest possible extraction recovery of As from standards,

CRM and scalp hair samples. While less than 0.12% of Triton X 114 was lower the extraction

efficiency of complexes, because of the inadequacy of the assemblies to entrap the hydrophobic

complex quantitatively. At volume higher than 0.12% (v/v), the signals decrease because of the

increment in the volumes and viscosity of the surfactant phase. To decrease the viscosity of

extracts acidic ethyl alcohol 0.1 mol L-1 was added.


It was desirable to employ the shortest equilibration time and lowest possible equilibrium

temperature, as a compromise between completion of extraction and efficient separation of

phases. It was found that 35 ○C is adequate for these analyses. The dependence of extraction

efficiency upon equilibration time was studied for a time in the range of 5–20 min. An

equilibration time of 10 min was chosen for the maximum quantitative extraction.

4.7.1.1.5. Interferences

To evaluate the selectivity of the proposed method for determination of trace levels of

As, the effect of potential interfering ions (10 µg L-1) was investigated (Fig. 25). The results

showed that Se4+, Pb2+, Ni2+, Co2+, Mn2+ and Fe2+ (up to the concentration level of 100 mg L-1),

0.8

0.85

0.9

0.95

1

1.05

Na +

K +M

g 2+

Se 4+

Pb 2+

Ni 2+

Mn

2+Co

2+Fe

2+

Cu 2

Abs

orba

nce

174

Na+ (up to 1000 mg L-1), Mg2+ and K+ (up to 500 mg L-1) did not cause any significant

interference on MAD-CPE of As. Therefore, the proposed method had good selectivity.

Table 35. Determination of As in certified human hair samples with and without MAD-CPE (n = 6)

Certified sample of human hair (BCR 397) (µg g-1)

Certified valuesObtained

Values %RSD % recovery

Without MAD-CPE

0.31±0.02

0.298±0.012 4.02 96.1

With

MAD-CPE 0.306±0.004 1.30 98.7

4.7.1.1.6. Validation of MAD-CPE

The method was massured by the analysis of triplicate samples, reagent blank, procedural

blanks and standard reference material. In order to validate the method for accuracy and

precision, a certified reference material BCR 397 (human hair) was analyzed with As content of

0.31 ± 0.02 µg g-1. The %recovery of As with CPE was higher than those obtained without

MAD-CPE (Table 35). The precision of the methods expressed as the %relative standard

deviation (%RSD) of 6 independent analyses of the same sample with and without MAD-CPE

were found to be 1.3% and 4.6 %, respectively.


In present study, the scalp hair samples of both adult genders were used as biomarkers for

monitoring of As exposure and applied to estimate individual exposure through As contaminated

drinking water as reported in our previous work (Kazi et al., 2009). Determination of As in hair

samples is useful as a confirmatory feature in arsenic poisoning provided external contamination

by arsenic can be excluded (Hindmarsh, 2002). This study has documented the As concentration

175

in scalp hair sample of male and female subjects of two villages of district Khairpur Mir’s using

optimized CPE method. Experimental results are listed in Table 36. Analysis of the SH samples

showed that the As content in male and female ranging from 0.25 to 6.90 μg g-1 (n= 42, mean

1.50 μg g-1) and 0.32 to 7.82 μg g-1 (n = 90, mean 1.72 μg g-1), respectively.

Table 36: Concentrations of As in Scalp hair Samples (µg g-1) Male Female

Number of samples (n) 142 190

x±s 1.50 ± 0.43 1.72±0.86

Minimum 0.25 0.32

Maximum 6.90 7.82

Table 37. Comparison of the mean /ranges of arsenic concentrations in water samples and hair samples with the literature

Countries As in water

(µg L-1)

As in scalp hair

(µg g-1)

References

Mexico 6.0-517 0.006-1.304 Monroy-Torres et al., 2009

Iran 180 0.305 Mosaferi et al., 2005

Argentina 189 0.024-0.149 Concha et al., 2006

India 241-1000 1.02-10.9 Mandal et al., 2003

West Bengal, India 248–3003 1.548–18.245 Samanta et al., 2004

Egypt 1.0 0.353 Saad et al., 2001

Lahore, Pakistan -- 0.31 Anwar and Hassanien 2005

Khairpur, Pakistan 13-106 0.25-7.82 This work

176

The understudy populations are residents of two villages situated in Khairpur Mir’s, where the

underground water contains As > 50 µg L-1 (Brima et al., 2006). The concentration of As in scalp

hair sample of male and females was significantly higher than permissible levels of As in human

hair (0.08–0.25µg g-1) (Shemirani et al., 2005). Mandal et al. (2003) and Samanta et al. (2004)

have been reported that high accumulation of As in hair samples in the concentration ranges of

0.70–16.2 and 0.17–14.4 μg g-1, respectively in individuals consuming As contaminated

groundwaters in West Bengal., which are higher than our study.

For comparative study, a data set of our find and previously published work on same

trend is given in Table 37. The wide range interval of As in the literature indicates an extensive

As variation in SH of different geographic societies, which could be associated with the

differences in the environmental and nutritional sources (Mohammad et al. 2008). The

mean/range of As concentration found in understudy areas is lower than those values reported for

India (Ahmad et al., 2006; Mukherjee et al., 2005), but consisted with results reported in Mexico,

Iran, Argentina, Egypt and other areas of Pakistan (Monroy-Torres et al., 2009; Mosaferi et al.,

2005; Concha et al., 2006; Saad et al., 2001; Anwar and Hassanien 2005).

It was observed that among study subjects 20 to 30% male and females had skin

problems, and they have also some other physiological disorders such as chest infection,

nephrological and asthmatic problems, which might be due to poverty and lake of health care.

While other has no clear clinical sign of arsenicosis, consistent to other literature reported studies

(Milton, 2003; Milton et al., 2005; Kazi et al., 2009). These symptoms are common in As

endemic areas as reported in literature (Islam et al., 2004). However, the elevated As

concentrations found in the scalp hair samples indicates the sub-clinically exposure of As. The

people of both villages were using groundwater for drinking and domestic purposes via hand-

pumps, installed within or premises of the houses.


The proposed CPE method for the preconcentration of As as a prior step to its determination by

ETAAS, is a simple, rapid, sensitive, inexpensive and non-polluting preconcentration technique. It is

because, we have chosen Triton X-114 for the formation of the surfactant-rich phase due to its excellent

physicochemical characteristics: low CP temperature; high density of the surfactant rich phase, which

177

facilitates phase separation easily by centrifugation; commercial availability and relatively low price;

and the lack of electro-active groups in its molecule and low toxicity. APDC is a very stable and fairly

selective complexing reagent especially metalloids like As.

Therefore, the optimized values of different variables for CPE of As in hair samples were

calculated from batch experiment to be found as pH = 4.5, APDC concentration = 0.007%, Triton X-114

amount = 0.12%, equilibration temperature = 35 ○C and equilibration time = 10 min. The proposed

method is simple, high sensitive, indicates good stability, high enrichment factor (25) and tolerance to

coexisting substances. The proposed method can be applied to the determination of trace metals in

various biological samples. In the essay, the experimental results showed that the CPE was a successful

method for determination of As in hair samples with satisfactory recoveries. The concentration of As in

scalp hair (males and females) among rural poor residents of two villages of Khairpur Mir’s was higher

than permissible levels of As for human hair, clearly revealed that the potential risk of arsenicosis.

178

4.7.2. Arsenic toxicity in children

4.7.2.1. Environmental Risk Assessment of Arsenic in Children through drinking water

General Remark


Jameel Ahmed Baig, Tasneem Gul Kazi, et al., (2011). Determination of Arsenic in Scalp Hair of Pakistani Children and Drinking Water for Environmental Risk Assessment. Human and ecological Risk Assessment 17, 966–980.

4.7.2.1.1. Results

The As concentrations in underground water and scalp hair are shown in Table 38. The

range of total As concentration in the underground water samples of sub-districts Faiz Ganj,

Thari Mirwah, and Gambat were observed to be in the range of 8.50–20.0, 18.5–40.3, and 38.8–

362 μg L-1, respectively (Table 38).

The concentration of As in scalp hair samples of boys and girls of age groups 1–5 and 6–

10 years of sub-district Gambat, was significantly higher at 95% confidence interval (CI) [2.01,

2.55 and CI: 2.44, 2.95 μg g-1] and [CI: 1.94, 2.37 and CI: 2.37, 2.72 μg g-1] than the other two

sub-districts Thari Mirwah and Faiz Ganj (p > 0.002 and 0.0001), respectively. The boys and

girls of sub-district Thari Mirwah have As levels in their scalp hair [CI: 1.28, 1.38 and CI: 1.26,

1.32 μg g-1] and [CI: 1.51, 1.55 and Cl: 1.37, 1.44 μg g-1] for age groups 1–5 and 6–10 years,

respectively. The lowest level of As was observed in scalp hair of children belong to sub-district

Faiz Ganj [(boys 1–5 years) CI: 0.28, 0.35 and (boys 6–10 years) CI: 0.46–0.55] and [(girls 1–5

years) CI: 0.28–0.32 and (girls > 5 years) CI: 0.38, 0.46] μg g-1, respectively. The children of

sub-district Faiz Ganj were at lower risk due to lower exposure of As via drinking water but

higher than normal level of As in hairs as reported in literature (Arnold 1990).

179

Table 38. Parametric presentation of As concentration in groundwater from study areas and As in scalp hair samples of children of different age and gender.

Arsenic concentration (µg L-1) in groundwater

Faiz Ganj (n = 70)

Thari Mirwah (n = 60)

Gambat (n = 50)

As in Groundwater

Mean ±Std 15.2±1.35 28.5±8.2 98.3±64.5 Range 8.50–20.0 18.5–40.3 68.2–362

Median 14.9 32.4 102.4

Arsenic (µg g-1) in scalp hair samples of children of different age and gender

1–5 years

Boys

Mean 0.321±0.1 1.32±0.11 2.25 ±0.52 Range 0.21-0.44 1.13-1.53 1.33-3.88 Median 0.321 1.329 2.284 Confidence interval 0.28-0.35 1.28-1.38 2.01-2.55

Girls

Mean 0.303±0.1 1.33±0.06 2.19±0.44 Range 0.24-0.39 1.19-1.39 1.42-3.035 Median 0.294 1.297 2.136 Confidence interval 0.28-0.32 1.26-1.32 2.44-2.95

6-10 Years

Boys

Mean 0.504±0.13 1.52±0.05 2.72±0.54 Range 0.32-0.67 1.44-1.61 1.85-3.63 Median 0.488 1.53 2.79 Confidence interval 0.46-0.55 1.51-1.55 1.94-2.37

Girls

Mean 0.422±0.20 1.41±0.07 2.39 ±0.38 Range 0.29-0.59 1.25-1.52 1.73-3.27 Median 0.419 1.405 2.549 Confidence interval 0.38-0.46 1.37-1.44 2.37-2.72

180

The correlation of As in scalp hair samples of children of the studied areas with As levels

in groundwater was statistically analyzed by multiple linear regression equation and Pearson

correlation (Table 39). The correlation of As concentrations in water versus As in scalp hair of

boys and girls of age 1–10 years belonging to Gambat (r = 0.96–0.98, p < 0. 001) was found to

be higher than As levels in water versus As in scalp hair of boys and girls of age 1–10 years,

residents of Thari Mirwah and Faiz Ganj (r = 0.90–0.93, p < 0. 004) and (r = 0.85–0.89, p < 0.

008), respectively. The positive correlation values between As concentrated in drinking water

and As levels in scalp hair, agreed with published results (Chowdhury et al. 2000). The data

show that there was no significant difference of As levels in scalp hair of both genders (P < 0.5)

in each district. While the scalp hair As content in boys of both age groups was found higher as

compared to the girls, the difference was not significant.

Table 39. Linear regression and Pearson coefficient for As concentrations in scalp hair samples of children (boys and girls) vs. As in groundwater.

Sub-Districts Gender 1-5 Years 6-10 Years

Faiz Ganj

Boys y = 0.0473x - 0.3828

r = 0.86

y = 0.0631x - 0.4357

r = 0.85

Girls y = 0.0289x - 0.1264

r = 0.89

y = 0.0524x - 0.3577

r = 0.87

Thari Mirwah

Boys y = 0.011x - 0.0981

r = 0.91

y = 0.0045x - 0.03871 r

= 90

Girls y = 0.0072x - 0.073

r = 0.93

y = 0.0097x- 0.0977

r = 0.92

Gambat

Boys y = 0.0079x - 1.0008

r = 0.97

y = 0.0066x - 1.5265

r = 0.96

Girls y = 0.0082x - 1.027

r = 0.98

y = 0.0065x - 1.5275

r = 0.97

181

4.7.2.1.2. Discussion

A problematic scenario arises in developing countries like Pakistan, where people are

chronically exposed to As from contaminated groundwater. This is because of unavailability of

As-free water and lack of knowledge about As toxicity (Brima et al. 2006; Wasserman et al.

2004). Our research group is working on As occurrences, mechanism of its mobility, and toxicity

in southern parts of Pakistan (Baig et al. 2009a,b,c; 2010; Shah et al. 2009a,b, 2010; Arain et al.

2009). It was found that groundwater samples have elevated As concentrations in three districts

(Khairpur, Jamshoro, and Naushehro Feroz) of Sindh Pakistan at different levels ranging from 40

to 362 µg L-1. Therefore, the current study was conducted to evaluate children’s (boys and girls)

exposure to As in drinking water. The mean concentrations of As in the drinking water samples

collected from Faiz Ganj, Thari Mirwah, and Gambat sub-districts of Khairpur Mir’s, Sindh

Pakistan, were found to be 15.2, 28.5, and 98.3 μg L-1, marked as less, medium, and highly

contaminated areas, respectively. The mean concentration of studied sub-districts were higher

than the maximum permissible limit of As in drinking water (10 µg L-1), recommended by WHO

(2004). Our findings are in agreement with those reported by Pazirandeh et al. (1998), who

measured a concentration of 30–1040 µg As L-1 drinking water in the west of Iran (Mosaferi et

al. 2005).

There are relatively few reports concerning dose–response relationships between As

exposure and As-related adverse health effects in children (Chowdhury et al. 2000; Jain and Ali

2000; Mandal and Suzuki 2002; Jack et al. 2003), because it is often difficult to evaluate

individual As exposure. However, the knowledge of As exposure and unfavorable health effects

will help in estimation of health hazard and prevention of As poisoning in the future among the

residents in areas of endemic As poisoning and also among the workers occupationally exposed

to As (Chowdhury et al. 2000; Yoshida et al. 2004).

For the current study the scalp hair samples of 510 children (boys and girls) of two age

groups 1–5 years and 6–10 years, living in studied sub-districts were analyzed for As. The As

concentrations < 0.67 µg g-1 were observed in scalp hair samples of children of both age groups

and genders residing in sub-district Faiz Ganj, whereas, in sub-districts Gambat and Thari

182

Mirwah, the mean concentrations of As in scalp hair samples of studied subjects were found to

be 1.38 and 2.42 µg g-1, respectively. The normal level of As in hair samples has been reported

to be in the ranges of 0.08 to 0.25 µg g-1 (Arnold et al. 1990). Therefore, the As in scalp hair

samples of sub-districts Thari Mirwah and Gambat were 5–12 times higher than the normal level

of As. This could be attributed to higher sensitivity of children to the toxicants, most probably

due to their much greater surface-area-to volume ratios increasing the efficiency of uptake of As

from drinking water. Young children (< 10 years) exhibit oral exploratory behavior and mostly

play on the ground. This might be enhancing the chance of potential ingestion of contaminants

present in soil/dust. The children are more susceptible to toxicants as compared to adults,

because of their rate of growth, they are also more exposed to dietary sources of pollution, as

they need more nutrients and consume more food per unit body weight than the adults and the

excretion also varies with maturation of the kidney and other systems (Saad and Hassanien

2001).

It was found in our previous work that the adults with arsenical skin disease have a mean

hair As level of 2.7 µg g-1 and adults without arsenical skin disease had a mean hair As level of

1.6 µg g-1 (Kazi et al. 2009). Thus, 1.6 µg g-1 hair As is a biomarker of non-dermal toxicity level.

On the basis of non-dermal toxicity level, Thari Mirwah was found to be at risk, whereas the

Gambat sub-district was considered as a dermal-affected sub-district of Sindh Pakistan.

The clinical investigations were most decisive for the identification of chronic As

toxicity, which may induce harmful effects to various organs in the human body (Yoshida et al.,

2004). The examined boys and girls of both age groups belonging to Faiz Ganj and Thari

Mirwah have no remarkable dermatological symptoms, whereas, children of both age groups

belonging to Gambat have different physiological disorders such as breathing, gastrointestinal

and skin disorders, especially in older children. Some skin-diseased patients have severe itching

on exposure to sunlight; this was first time reported in west Bengal India (Mukherjee et al.,

2005). These findings were consistent with those from a study conducted in Cambodia (Guha

Mazumder et al., 2009),.where few cases were diagnosed to be suffering from arsenicosis, but all

had evidence of pigmentation and/or keratosis characteristic of arsenicosis, and had histories of

exposure to As-enriched water and/or elevated levels of As in hair and nails.

183

The comparison of our data with previously published As concentrations in water and

accumulation of As in scalp hair of children was conducted. The concentration ranges obtained

in this study are consistent with the levels reported in literature. The wide range interval of As in

the literature indicates an extensive As variation in scalp hair of different geographic societies,

which could be associated with the differences in the environmental and nutritional sources. For

instance, the mean As concentration found in studied areas is lower than the value reported for

India, Mexico, Iran, and Argentina (Ahamed et al. 2006; Monroy-Torres et al. 2009; Mosaferi et

al. 2005; Concha et al. 2006) but consistent with those values reported in Egypt and Lahore,

Pakistan (Saad and Hassanien 2001; Anwar 2005).

Most individuals in populations are exposed to low levels of As in drinking water, but

other routes of exposure, i.e., dietary inorganic As intake may be important. Food and water were

estimated to account for the majority of inorganic As exposure in the United States, with

background exposures from inhalation of airborne particles or ingestion of soils being negligible

in the general population (Meacher et al. 2002). Beverages and foods like coffee, soups, and tea,

as well as fish and seafood were prepared with water, and projected to comprise the greatest

percentage of total As exposure outside of plain drinking-water consumption (Moschandreas et

al. 2002). Contribution of each of these items to inorganic As exposure are remaining less clear.

However, there are some other co-factors that increase or accelerate the toxic effects of As, e.g..,

malnutrition and smoking (Mead 2005; Mitra et al. 2004).

On the other hand, our survey demonstrated that the children of the studied sub-districts

had poor socio-economic status. The mean values of body mass index of children were 14.5,

12.7, and 12.2 in Faiz Ganj, Thari Mirwah, and Gambat, respectively, which also confirmed the

poor malnutrition and health status of the studied population. Thus, the nutritional deficiency is

prevalent in rural areas of Pakistan (Kazi et al. 2009; Arain et al. 2009). On the basis of these

finding, the main root of As exposure in our study area might be due to the As-contaminated

groundwater.

However, the symptoms of As toxicity may take 8–14 years to be manifested in a

person's body by continuously drinking As-contaminated water (Mukherjee et al. 2005) and our

study subjects (children) have maximum age < 10 years. WHO (1996) suggested, that chronic

184

symptoms could take 5–10 years of constant exposure to As to develop dark spots on the skin to

a hardening of the skin into nodules—often on the palms and soles, which is also confirmed in

the present study.

4.7.2.1.3. Conclusion

It has been concluded that the major non-occupational contributors to elevated scalp hair

As levels in children of three sub-districts of Khairpur Mir’s, Pakistan, are due to As-

contaminated groundwater. It appears to be creating deleterious effects on the health of children

> 10 years. The positive linear regressions showed As concentrations in water versus scalp hair

of boys and girls of age 6–10 years was higher than As levels in water versus scalp hair of boys

and girls of age 1–5 years. As contents in boys 6–10 years old were found to be higher as

compared to the girls of same age group. The As in scalp hair samples were 5–12 time higher

than background levels (0.08–0.25 µg g-1) of As in sub-districts Thari Mirwah, and Gambat. This

could be attributed to higher sensitivity of children to the As, which might be due to their large

surface-area-to volume ratios, which enhanced uptake of As from drinking water.

The people of the studied areas are still drinking As-contaminated groundwater as this

problem was largely unrecognized until now. Due to lack of municipal treated water systems the

children and elderly have no alternate but to buy costly bottled mineral water. Thus, any

mitigation strategy needs to be location specific, depending on the availability of As-safe

options. Other alternative safe water options such as surface water, deep dug-wells, and

rainwater harvesting may also be explored, with measures taken against bacterial and other

chemical contaminants. Additionally, generating awareness about the As problem and adequate

supply of As-safe water to the affected population is required. It is clear that urgent action is

needed now to prevent children’s further exposure to As in the study area.

185

4.7.2.2. Arsenic in Scalp Hair samples of Children belong to exposed and non-exposed areas

General Remark


Jameel Ahmed Baig, Tasneem Gul Kazi, et al., (2011). Determination of arsenic in scalp hair of children and its correlation with drinking water in exposed areas of Sindh Pakistan. Biological trace Element Research (Accepted).

DOI:10.1007/s12011-010-8866-z.

4.7.2.2.1. Arsenic in drinking Water

The As concentrations in underground water and scalp hair of children are shown in Table 40. The

range of total As concentration in the underground water samples of Thari Mirwah and Gambat

were observed in the range of 18.5-40.3 and 68.2-362 μg L-1, respectively (Table 40). A

problematical situation is noticed in Pakistan and other countries, where people are chronically

exposed to As from contaminated groundwater, due to lake of pure and clean drinking water

(Wasserman et al., 2004; Brima et al., 2006). However, there are some other co-factors that

increase or accelerate the toxic effects of As i.e. malnutrition and smoking (Mitra et al., 2004;

Arain et al., 2009; Kazi et al., 2009). Our research group was starting work in 2005 on As

occurrences, mechanism of its mobility and toxicity. In present study, we selected two As-affected

towns of Khairpur (Sindh), which have As contaminated underground water , ranging from 30 to

362 µg L-1 (Arain et al., 2009; Kazi et al., 2009; Kazi et al., 2009; Baig et al., 2009b,c, 2010a,b,c;

Arai et al., 2009; Shah et al., 2009a,b,c). This might be due to anthropogenic pollution such as

discharge of fertilizer and pesticides used in agricultural system throughout the year (Baig et al.,

2010a). However, there is no available data on the use of arsenical pesticides or effluent of

chemicals coming from industries. But, it was reported that about 5.6 million tonnes of fertilizer

and 70 thousand tonnes of pesticides are consumed in the Pakistan every year (Baig et al., 2010a).

Pesticides and insecticides, sprayed on the crops or mix with the irrigation water, which leaches

through the soil and enters groundwater aquifers (Baig et al., 2010a). Therefore, current study was

conducted to evaluate As exposure from drinking water to children belongs to highly As

contaminated district Khairpur (Baig et al., 2010a). Immense investigations on As poisoning due to

consumption of As-contaminated water have been reported worldwide (Chowdhury et at., 2000;

Jain and Ali 2000; Mandal et al., 2003; Jack et al., 2003; Mosaferi et al., 2005). The high levels of

186

As in aquatic environment may cause tracheae bronchitis, rhinitis, pharyngitis, shortness of breath,

nasal congestions and black foot disease (Jack et al., 2003). There are relatively few reports

concerning on dose–response relationships between As exposure and As-related adverse health

effects on children. The knowledge of As exposure and unfavorable health effects will help to

estimate the hazard impacts and prevention of As poisoning in the future (Yoshida et al., 2004).

4.7.2.2.2. Arsenic in Scalp hair samples of Children

The concentration of As in scalp hair samples of boys and girls of age group < 10 years of

Gambat, was significantly higher at 95% confidence interval [CI: 2.44, 2.95] and [CI: 2.37, 2.72]

µg g-1, respectively than Thari Mirwah and referent area, Hyderabad (p > 0.002 and 0.0001),

respectively. The boys and girls of Thari Mirwah have As levels in their scalp hair at 95%

confidence interval [CI: 1.26, 1.32] and [Cl: 1.37, 1.44] μg g-1, respectively. The lowest level of As

was observed in scalp hair of boys and girls belong to Hyderabad at 95% confidence interval [CI:

0.0289, 0.0354] and [CI: 0.0284, 0.0324] μg g-1, respectively.

The choice of scalp hair as a biochemical marker for chronic exposure of As was based on the

chemical nature of As present in scalp hair, which is inorganic with traces of organic As (Arnold

et al., 1990; Kazi et al., 2009). The inorganic As compounds are the most toxic ones and chronic

exposure in humans has been associated with various cancers (Abemathy et al., 1998; Saad et al.,

2001; Kazi et al., 2009). The organic As species are rapidly excreted in urine and not deposited

in the hair (Arnold et al., 1990; Kazi et al., 2009). The As in scalp hair samples of children

belong to Thari Mirwah and Gambat was 5-12 time higher than normal level of As (0.08 to 0.25

µg g-1) (Arnold et al., 1990; Kazi et al., 2009). This could be attributed to higher sensitivity of

children to the toxicants, most probably due to their much greater surface-area-to volume ratios,

which enhancing the efficiency of uptake of As. Young children (< 10 years) exhibit oral

exploratory behavior and mostly play on the ground. This might be increasing the chance of

potential ingestion of contaminants present in soil/dust. In addition, exposure through respiration

may be increased because they inspire air closer to the ground than adults do. Because of their

rate of growth, they are also more exposed to dietary sources of pollution, as they need more

nutrients and consume more food per unit body weight than adults and the excretion also varies

with maturation of kidney and other systems (Saad et al., 2001; Kazi et al., 2009).

187

Table 40. Parametric presentation of arsenic concentration in surface and groundwater from study areas and arsenic in scalp hair samples of children

aSurface water, bgroundwater

4.7.2.2.3. Correlation between Arsenic level in drinking water with As contents in Scalp Hair sample of Children of both gender

Regression analyses have been carried out between the As concentrations in ground water

and in scalp hair samples of under studied children using statistical analysis, multiple linear

regression equation and Pearson correlation (Table 41). A significant differences were observed

(unpaired t-test, p < 0.05), between the levels of As in scalp hair of children belongs to Thari

Mirwah (As in water < 30 µg L-1) and Gambat (As in water > 50µg L-1) (Table 41). The high

correlation coefficient values were observed between As concentrations in water versus scalp hair

of children of both genders (Table 41). Our results are consisted with other study (Arnold et al.,

1990; Chowdhury et al., 2000; Kazi et al., 2009).

The high level of As in scalp was observed in those areas where people consuming

drinking As contaminated water (Kazi et al., 2009). Our study showed a close relationship between

As concentration in hair and drinking water (Table 41). The wide variation of As levels in scalp

hair of people belongs to different geographical areas, may be associated with the differences in

As in water

(µg L-1)

< 10 years (µg g-1)

Boy Girl

Hyderabad -- < 10a 0.046±0.01 0.040±0.01

0.032-0.067 0.029-0.059

Thari Mirwah sx 28.5±8.2b 1.32±0.11 1.33±0.06

Range 18.5-40.3b 1.44-1.61 1.25-1.52

Gambat sx 98.3±64.5b 2.25 ±0.52 2.19±0.44

Range 68.2-362b 1.85-3.63 1.73-3.27

188

Table 41. Linear Regression and Pearson coefficient for arsenic concentrations in scalp hair samples of adolescent (boys and girls) vs. As in ground water

environmental and nutritional sources. The correlation of As in drinking water and scalp hair is

consistent with other studies (Das et al., 2003). As concentrations ranged from 3 to 10 µg g-1 were

reported in hair samples of people of West Bengal, where the drinking water is contaminated with

high levels of As (Unchino et al., 2006). Moreover, the mean As concentration found in

understudy areas is lower than the value reported for India (248–3003 µg L-1 in water and 1.55–

18.2 µg g-1 in scalp hair) (Ahmed et al., 2006) but consisted to the those values reported in Mexico

(517 µg L-1 in water and 1.304 µg g-1 in hair), while higher than Iran (180 µg L-1 in water and

0.305 µg g-1 in scalp hair), Argentina (189 µg L-1 in water and 0.024-0.149 µg g-1 in scalp hair),

and Egypt (1.0 µg L-1 in water and 0.353 µg g-1 in scalp hair) (Yoshida et al., 2004; Concha et al.,

2006; Monroy-Torres et al., 2009; Baig et al., 2010c).

However, the symptoms of As toxicity may take 8–14 years to be manifested in a person's

body by continuous drinking As contaminated water (Mukherjee et al., 2005; Kazi et al., 2009).

World Health Organization (1996) suggested that the chronic symptoms could take 5–10 years for

constant exposure of As to develop dark spots on the skin, hardening of the skin into nodules—

often on the palms and soles (Kazi et al., 2009). In present study the children were aged < 10 years.

Thus, the clinical investigations were most decisive to identify the chronic As toxicity, which may

induce harmful effects to various organs of the human body (Kazi et al., 2009). The examined boys

and girls belong to Thari Mirwah have no any remarkable dermatological symptoms, whereas,

children of both genders belongs to Gambat have different physiological disorders such as,

Sub-Districts Gender < 10 Years

Thari Mirwah Boys

y = 0.011x - 0.0981 r = 0.94

Girls y = 0.0072x - 0.073

r = 0.91

Gambat

Boys y = 0.0079x - 1.0008

r = 0.99

Girls y = 0.0082x - 1.027

r = 0.97

189

breathing, gastrointestinal and skin disorders. Some skin diseased children have reported severe

itching on exposure to sunlight; this effect was first time reported in west Bengal India (Mukherjee

et al., 2005; Kazi et al., 2009). These findings were also consistent with study conducted in

Cambodia (Guha Mazumder et al., 2009; Kazi et al., 2009). Where, few cases were diagnosed to

be suffering from arsenicosis, as all had evidence of pigmentation and/or keratosis characteristic of

arsenicosis. On other hand, our survey demonstrated that the people of studied towns have poor

socio-economic status. The mean values of body mass index of understudy children was <12,

confirmed poor malnutrition and health of understudy population. Thus, the nutritional deficiency

is prevalent in rural areas of Pakistan (Kazi et al., 2009).

The average daily intake of inorganic As was calculated on the basis of consumption of

water and body weight of each study subject (Meza et al., 2004; Kazi et al., 2009). On the basis of

survey, the water consumption by children was ranged as 0.5–2.0 L day-1 (average = 1.70 L day-1)

and body weight of children ranged 10-20 kg (average 15.4 kg). As we have reported in our

previous study that in water the inorganic As was present > 96% of total As (Baig et al., 2009c).

On the basis of inorganic As, the average daily intake of As was estimated as 3.31 and 12.5 µg kg-1

body weight day-1, by children of Thari Mirwah and Gambat, respectively. All these facts

demonstrated that the chronic As poisoning is due to chronic administration of high concentration

of As through contaminated ground water.

4.7.2.3. Conclusion

It has been concluded that the major non-occupational contributors to elevate scalp hair As

levels in children of two towns of Khairpur, Pakistan. It appears to be creating deleterious effects

on the health of children > 10 years. The contents of As in boys were found to be higher as

compared to girls. The As in scalp hair samples were 5-12 time higher in both towns than normal

level (< 0.30 µg g-1). Thus, mitigation strategy needs to be location specific, depending on the

availability of As-safe options. Other alternative safe water options such as surface water, deep

dug-wells and rainwater may also be explored, with measures against bacterial and other chemical

contaminants.

190

4.7.3. Arsenic in Scalp Hair samples of adult males and evaluation of toxic risk factor

General Remark


Jameel Ahmed Baig, Tasneem Gul Kazi, et al., (2011). Evaluation of toxic risk assessment of arsenic in male subject through drinking water in Southern Sindh Pakistan. Biological Trace Element Research (Accepted).

4.7.3.1. Arsenic in drinking water

Arsenic chronic exposures due to consumption of contaminated groundwater in Pakistan

were documented elsewhere (Ahmad et al., 2004; Arain et al., 2008, 2009; Kazi et al., 2009;

Baig et al., 2009a,b,c). However, there are some other co-factors that increase or accelerate the

toxic effects of As i.e. malnutrition and smoking (Kazi et al., 2009; Baig et al., 2010a; Arain et

al., 2009). Our research group was starting work in 2005 on As occurrences, mechanism of its

mobility and toxicity. In present study, we selected two As-affected sub-districts of Khairpur

(Sindh), where elevated concentration of As was reported in underground water (Baig et al.,

2010a).

The concentrations of TAs, iAs and As3+ in hand pump and municipal treated tap water

samples of studied areas are shown in Tables 42. The concentration of As species (TAs, iAs and

As3+) in hand pump water of HE and LE areas were observed, at 95% confidence limit (CI, 96.6-

115, 89.7-112 and 50.5-64.9) µg L-1 and (CI, 26.2-30.0, 25.8-29.2 and 14.5-16.7) µg L-1,

respectively. This concentration is greater than current WHO recommended guideline value for

As in drinking water 10 µg L-1 (WHO, 2004) and indicated that the residents of both understudy

exposed areas have high risk of arsenicosis. Epidemiological evidence indicates that As

concentration exceeding 50 µg L-1 in the drinking water, which is not public health protective.

The high level of As species in ground water is due to dissolution of As compounds coming from

Himalaya through Indus river and settled down through year to year and than introduced into

ground water by geothermal, geo hydrological and bio geo chemical factors (Baig et al. 2010a).

On other hand, it might be due to the As containing insecticides and herbicides used for

agriculture purposes and from seepages of hazardous waste sites (Smedley and Kinniburgh

2002). While, the concentration of As species (TAs, iAs and As3+) in drinking water of NE area

191

(tap water) was observed at 95% confidence limit (Cl: 7.77-8.74, 7.08-7.93 and 4.90-5.81) µg L-

1, which was within the WHO and EPA drinking water standard (Smith et al., 2002).

4.7.3.2. Arsenic in scalp hair of male subjects

The concentrations of TAs in SH of male subject of two age groups (16-30 and 31-60 years)

of NE, LE and HE are shown in Table 42. The concentration of As in SH samples of male subjects

(age groups 16-30 and 31-60 years) was significantly higher in HE at 95% confidence interval (CI:

0.55, 1.27) and (CI: 0.56, 1.32) µg g-1, respectively. In LE area, the levels of As in SH of male

subjects of both age groups was observed (CI: 0.34, 0.43) and (CI: 0.35, 0.45) µg g-1, respectively.

The male subjects of both age groups belongs to NE area had lower As concentration in SH

samples (CI: 0.08, 0.12) and (CI: 0.10, 0.13) µg g-1 , respectively.

The result indicates that high level of As in SH samples of male subjects of both age groups

belongs to HE area (Gambat) has arsenic induced skin disorders as compared to male subjects of

LE and NE areas (Thari Mirwah and Hyderabad). This indicates that the residents of HE areas

were at high risk of arsenicosis due to consuming As contaminated water > 50 µg L-1. Although

there is no any apparent effects of As exposure was observed in understudy subjects of LE areas,

but the level of As in SH samples was found to be higher than NE area. These finding are in

accordance with other studies (Chowdhury et al., 2000; Hindmarsh, 2002; Ng et al., 2003).

According to Arnold et al. (1990), the background level of As in SH, who were consuming water

with As <10 µg L-1, ranges from 0.08 to 0.25 µg g-1 (Kazi et al., 2009). The As concentration in SH

of HE and LE area population were higher than normal level of As in hair. It was also found that

about 70% and 37% of male subjects of both age groups belongs to HE and LE areas, respectively,

had As concentrations > 1.00 µg g-1, which showed the sign of As toxicity. On the other hand, all

male subjects living in Hyderabad city consuming municipal treated water have As concentration

in their SH within the normal range.

4.7.3.3. Correlation of Arsenic levels in scalp hair with drinking water

As we have reported in our previous study that in water the iAs is present > 96% of TAs

(Baig et al., 2010a). It is generally recognized that iAs is mainly biotransformated in the liver

through methylation process including two steps, first converted to MMA and then to DMA (Kazi

192

et al., 2009). Recent studies have indicated that As methylation capacity is associated with many

arsenic-related injuries including skin lesions (Huang et al., 2007; Tseng et al., 2005; Steinmaus et

al., 2006). Thus, the correlation of As in SH samples of males of two age groups,

Table 42 Analytical results of total As and inorganic iAs in natural waters and SH of male subject of two age group of three regions

Area Arsenic species Water (µg L-1) SH (16-30 years) (µg g-1)

NE1 (Hyderabad City)

As3+ Minimum 2.5 -- Maximum 6.5 -- Median 4.5 --

iAs Minimum 4.30 -- Maximum 10.3 --

Median 8.40 --

TAs Minimum 4.50 0.03 Maximum 10.7 0.28 Median 8.70 0.10

LE2 (Thari Mirwah)

As3+ Minimum 3.4 -- Maximum 26.6 -- Median 15.2 --

iAs Minimum 13.9 -- Maximum 46.5 -- Median 27.6 --

TAs Minimum 14.5 0.11 Maximum 47.7 1.09 Median 28.4 0.34

HE3 (Gambat)

As3+ Minimum 16.6 -- Maximum 172 -- Median 64 --

iAs Minimum 37.2 -- Maximum 357 -- Median 108 --

TAs Minimum 38.8 0.36 Maximum 362 6.10 Median 112 0.54

1Non Unexposed Area, 2Less Exposed Area, 3High Exposed Area

residents of HE, LE and NE areas with iAs levels of corresponding drinking water was

statistically analyzed by multiple linear regression equation and Pearson correlation (Table 43).

The correlation of iAs concentrations in water versus SH of male subjects belongs to HE area (r

= 0. 825 - 0.852, p < 0. 001) was found to be higher than iAs levels in water versus SH of male

subjects, residents of LE and NE areas (r = 0.630 - 0.674, p < 0. 001) and (r = 0.449 - 0.459, p <

193

0. 001), respectively. The positive correlations values between iAs concentrated in drinking

water and SH, are agreed with published result (Chowdhury et al., 2000).

Table 43. Linear Regression and Pearson coefficient for arsenic concentrations in scalp hair samples of male subject of two age groups (16 - 30 Years and 31 - 60 Years) vs. As in water

The resulted data shows that the As levels in SH of male subjects of age group 31-60

years were found to be higher as compared to younger age group (16-30 years) in all three

understudy areas. Because the exposure period of old age group (31-60 years) was higher (> 8

years), as compare to younger age group, which have exposure period < 5 years. Moreover,

according to Sampson et al. (2008), symptoms of arsenicosis have been generally assumed to

develop after 8–10 years of consuming As contaminated water (Anwar et al., 2002; Kazi et al.,

2009). Therefore, there is more chance of As toxicity in male subject of elder age group belongs

to HE and LE areas.

Area

TAs in water vs. TAs in SH (16 - 30 Years)

TAs in water vs. TAs in SH (31 - 60 Years)

NE

(Hyderabad City)

y = 0.0124x + 0.004

r = 0.449

y = 0.014x - 0.026

r = 0.459

LE

(Thari Mirwah)

y = 0.0269x - 0.1883

r = 0.630

y = 0.0441x - 0.6445

r = 0.674

HE

(Gambat)

y = 0.0048x + 0.1418

r = 0.825

y = 0.0104x - 0.0857

r = 0.852

194

4.7.3.4. Arsenic toxicity and cancer risk factor

In those areas, where populations were not exposed to low levels of As in drinking water,

other routes of exposure i.e. dietary inorganic As intake may be important. Food and water were

estimated to account for the majority of inorganic arsenic exposure in the United States, with

background exposures from inhalation of airborne particles or ingestion of soils being negligible in

the general population (Meacher et al., 2002). Beverages and foods like coffee, soups, and tea, as

well as fish and seafood were made with water, and projected to comprise the greatest percentage

of total As exposure outside of plain drinking-water consumption (Moschandreas et al., 2002). The

Agency for Toxic Substances and Disease Registry (ATSDR) in 2000, demonstrated that As can

enter the human body via several pathways, but all other intake routes of As are usually negligible

in comparison to oral intake.

Thus, the average daily dose (ADD) of As through drinking water (municipal treated, and

groundwater) was calculated to assess the As intake at different levels. The total As in drinking

water samples of high, less and non exposed areas were found in the range of, 0.038 - 0.36, 0.014 -

0.048 and 0.0045 - 0.01 mg L-1, respectively. Analysis of drinking water from understudied As

exposed areas showed that studied male subjects are continuously exposed to As contaminated

drinking water throughout their lives. The mean concentration of As in drinking water can be used

to calculate ADD by multiplying As concentration of drinking water by daily intake volume.

Information on individual water consumption history in all three exposed and non exposed areas

including drinking water sources (underground and municipal treated drinking water), has to be

collected by verbal questionnaire. Daily individual water consumption is definitely influenced by

other factors, for example, weather (air temperature and humidity) and labor intensity, in addition

to body size of subjects. Based on the individual interviews, it is noted that understudy subjects of

different occupations in hot weather (up to 45 °C) might be intake rate (IR) 2–3 L (average 2.5 L)

per day. Whereas, the other parameters, exposure frequency (EF) based on 365 days years-1, the

average exposure duration (ED) was 5.04 years, the mean value of body weight (BW) 58.7 kg and

mean life time 12,705 days were used for the calculation of ADD (Table 44). The daily burden of

As from drinking water is better index for estimation of As exposure, expressed in mg kg-1 body

weight/day (Table 44). Taking into account only As from water intake by the community of

195

understudied areas from underground water exceeded 2–10 times to the recommended dose of

WHO (WHO, 2004; Yoshida et al., 2004).

The HQ and R values were calculated on the bases of ADD values as shown in Table 44. For the

average As daily dose, the HQ values for male subjects of age group (16–30 years) of HE, LE

and NE areas were 2.59, 0.628 and 0.233, respectively. While, the mean R values for HE, LE

and NE areas were estimated as 1.0 E-03, 2.9 E-04 and 1.06 E-04, respectively. In the case of

male age group (31-60 years), residents of HE, LS and NE areas, the HQ and R were calculated

as (2.64 and 1.20E-3), (0.650 and 3.0E-04) and (0.241 and 1.1E-04), respectively. On the bases

of mean TAs concentration in water, the minimum HQ and R values 0.233 and 1.06E-04 per

1000 persons, respectively was estimated in male subject of age group (16-30 Years) of NE area

and the maximum HQ and R were resulted in male subject of same age group of HE area (Table

44). On the bases of these finding, the rural population consuming underground water in two

areas of Khairpur Mir’s district (Gambat and Thari Mirwah) Sindh Pakistan are at risk of chronic

toxicity of As, which was indicated by HQ values > 1. Corresponding to these results, the first

cases of arsenicosis were reported in Gambat sub district during 2002-03, Sindh Province, along

with Indus River (PCRWR, 2002-2003).

196

Table 44. Risk assessment of high, less and unexposed area of Sindh Pakistan

Parameters

Units

HE (Gambat)

LE (Thari Mirwah)

NE (Hyderabad City)

16 - 30 Years 31 - 60 Years 16 - 30 Years 31 - 60 Years 16 - 30 Years 31 - 60 Years

IR L day-1 Mean 2.5 2.5 2.5 2.5 2.5 2.5 ED Year Mean 4.5 8.3 5.2 9.1 4.8 8.6 EF days years-1 Assumed 365 365 365 365 365 365

AT Days 365*Average age 9314 14817 8506 14460 8906 13904

BW Kg Mean 54.4 60.3 56.2 61.4 57.6 62.4

TAs

mg L-1

Mean 0.098 0.025 0.0078 Min. 0.038 0.014 0.0045 Max. 0.36 0.048 0.01

ADD

mg L-1 body-1

Mean 8.00E-04 8.50E-04 2.00E-04 2.20E-04 7.10E-05 7.32E-05 Min. 1.30E-04 1.90E-04 8.00E-05 1.00E-04 2.10E-05 3.80E-05 Max. 2.70E-03 2.90E-03 4.00E-04 5.00E-04 2.10E-04 2.30E-04

HQ

Mean 2.59 2.64 0.628 0.650 0.233 0.241 Min. 0.237 0.631 0.149 0.237 0.069 0.126 Max. 8.89 9.71 1.43 1.57 0.681 0.770

R

Mean 1.00E-03 1.20E-3 2.9E-04 3.0E-04 1.06E-04 1.1E-04 Min. 1.00E-04 2.90E-04 6.8E-05 1.10E-04 3.16E-05 5.76E-05 Max. 4.00E-03 4.40E-03 6.50E-4 7.20E-04 3.10E-04 3.50E-04

197

The clinical investigations indicate that chronic As toxicity induces harmful effects to various

organs in the human (Yoshida et al., 2004; Tseng et al., 2005; Kapaj et al., 2006). The examined

male subjects belong to LE and NE regions have no any remarkable dermatological symptoms

but have different physiological disorders such as, breathing, gastrointestinal and crumbling in

legs. Whereas, males of both age groups belongs to HE have different evident skin disorders.

These lesions may be used as an indicator of high exposure and are quite distinctive in contrast

to other clinical manifestations of arsenic intoxication including weakness, conjunctival

congestion, edema, portal hypertension, and hepatomegaly. These skin lesions generally develop

five to ten years after exposure commences, although shorter latencies are possible. Some skin

diseased patients have severe itching on exposure to sunlight; this was first time reported in west

Bengal India (Kapaj et al., 2006). These finding were consistent to study conducted in Cambodia

(Mazumder et al., 2000). Where, seventy cases were diagnosed to be suffering from arsenicosis,

as all had evidence of pigmentation and/or keratosis characteristic of arsenicosis, and had

histories of exposure to As-enriched water and/or elevated levels of As in hair and nails.

Most of the studied subjects (Gambat sub district) were also complained the chest

problems, pain all over the body and muscle cramps, mainly in the legs, and all these symptoms

were associated with elevated As exposure, which is also consistent with other study (Maharjan

et al., 2007). In addition, most of male subjects of understudied area were anemic and they also

complained general weakness and palpitation. These symptoms are common in As endemic areas

as reported in literature (Michaud et al., 2004). Malnutrition and poor socio-economic conditions

of villagers of understudied area make worse the hazards of As toxicity. Although arsenicosis is

not an infectious, contagious or hereditary disease, As toxicity creates many social problems for

the victims and their families (Khan et al., 1997). All these facts demonstrated that the chronic

As poisoning is due to chronic administration of high concentration of As through contaminated

ground water. Immediate stoppage of arsenic contaminated drinking water and the intake of safe

drinking water (As < 10 µg L-1) are the precondition for the management of chronic As

poisoning.

198

4.7.3.5. Conclusion and recommendations

This study demonstrated the potential risk of arsenicosis among poor residents (majority

are farmers) of HE and LE, who may depend on As-enriched groundwater for drinking and other

domestic usages. Positive correlations between As concentrations in groundwater and SH was

observed in present study. The significant higher amounts of As in SH male subjects of both age

groups belongs to HE area (Gambat) using As contaminated groundwater (> 50 µg L-1) as

compared to those subjects living in LE (As in ground water <50 µg L-1) and NE (As in

municipal treated water < 10 µg L-1) areas. Clinical complications of arsenicosis including skin

disorders and clinical features such as weakness and muscles cramps, respiratory problems,

anemia and gastrointestinal problems were observed among the population of HE area (Gambat).

The people of HE areas (Gambat) are, at present, fully dependent on the shallow hand

pumps water as the source of drinking water. The people of understudy areas are still drinking

As contaminated ground water as this problem is largely unrecognized up till now. Moreover,

due to lake of municipal treated water system, the local populations have no alternate to buy

costly bottled mineral water. Thus, these facts urged to immediate stoppage of As contaminated

drinking water and the intake of As safe drinking water are the precondition for the management

of chronic arsenicosis especially in HE areas (Gambat). Other alternative safe water options such

as surface water, deep dug-wells and rainwater may also be explored, with measures against

bacterial and other chemical contaminants.

199

4.8. Remediation of arsenic from drinking water

4.8.1. Biosorption studies on powder of stem of Acacia nilotica

General Remark


Jameel Ahmed Baig, Tasneem Gul Kazi, et al., (2010). Biosorption studies on powder of stem of Acacia nilotica: Removal of arsenic from surface water. Journal of Hazardous Materials 178, 941–948. doi:10.1016/j.jhazmat.2010.02.028

4.8.1.1. Characterization of biosorbent surface by FTIR

The FTIR spectra of As loaded and unloaded biosorbent material are shown in Fig. 26(a

and b), in order to obtain information on the nature of possible interactions between the

functional groups of biosorbent material (BM) and As ions. In Fig. 26a unloaded As biosorbent

material (treated biomass) shows the broad and strong bands at 3100 - 3600 cm-1 due to the

overlapping of –OH and –NH2 stretching vibration. The peak at 1623 cm-1 were attributed to

stretching vibration of carboxyl group (-C=O). The bands observed at 1035 cm−1 are assigned to

C-O stretching of alcohols and carboxylic acids. The peaks at ~ 2920 cm-1 illustrates C-H

stretching of aliphatic carbon. The small peaks observed at 1530-1203 cm-1 are attributed to ether

and carboxylate groups, while at 1054 cm−1 indicated C–O stretching of ester or ether and N–H

deformation of amines respectively (Martins et al., 2004).

The loaded biosorbent material with As ions shows the deformation and shifting of some

peaks. A major difference was observed in the region 3400–2800 and 1700–1200 cm-1 indicating

chelation of As with the –OH groups of biosorbent material. The stretching vibration peaks at

1623 cm−1 and 1532.9 cm−1 were shifted to 1638 cm−1 and 1541.6 cm−1 after biosorption of As

ions, respectively. Whereas, the intensity of some bands (1450 - 1100 cm-1) was increased, after

loading of As ions. Hence, based on FTIR spectrum analysis (Fig.1b) it can be inferred that the

As binding in biosorbent material takes place by the substitution of functional groups, i.e., -NH2,

-OH, and –CO– , which is consistent with other study (Weber Jr., 1985; Grimm et al., 2008).

200

Fig. 26. FTIR spectra of unloaded (red line ‘a’) and loaded with As ions (blue line ‘b’) on biomass of A. nilotica

427.

443

6.7

459.

049

5.1

513.

1

611.

1

1035

.5

1148

.0

1240

.2

1319

.3

1383

.6

1449

.9

1541

.6

1637

.91735

.9

2849

.9

2918

.4

3822

.3

413

147

2.2

528.

1

1034

.6

1203

.113

11.8

1383

.7

1447

.8

1532

.9

1623

.9

2850

.3

2919

.5

3419

.2

Afts Ads

Bef Ads

24

26

28

30

32

34

36

38

40

42

44

46

48

50

52

54

56

58

60

62

64

%T

500 1000 1500 2000 2500 3000 3500

Wavenumbers (cm-1)

a

b

201

Fig. 27. Scanning electron micrograph of (a) unloaded (b) loaded biomass of A. nilotica (1800× magnification) Bar is 10µm.

202

4.8.1.2. Characterization of biosorbent surfaces by SEM

Figure 27 shows that biosorbent material (powder of A. nilotica) is comprised of many

aggregated particles at a resolution of 1800× while the image was taken with a particle size of

10μm (Fig. 27a). These have rough surfaces that can help increase the surface area available for

biosorption of the As. The image of As loaded biosorbent material in Fig. 27b showed the small

particles adhered on the surface of biosorbent material and form multilayer.

4.8.1.3. Effect of biosorbent dosage

The influence of biosorbent material dosage on % biosorption and uptake of As is shown in Fig.

3. The percent removal of As increased up to 95% when the dosage of biosorbent material was

increased from 0.4–4 g L-1, whereas further increase in biosorbent material dosage up to 20 g L-1

have no effect on the percent removal of As (Fig 28). The increase in biosorbent material dosage

(0.4–4 g L-1) resulted in a rapid increase in adsorption of As ions. It was also established the

effect of dosage and to optimize the minimum dosage required for lowering the As level to the

tolerance limit of As, recommended by WHO (10 µg L-1) for drinking water (WHO, 2004).

When the initial As concentration and the volume of solution are fixed, the removal of As was

enhanced with increasing biosorbent material dosage, which is obvious because of increase in the

number of active sites as the biosorbent material dosage increases (Dubinin and Radushkevich

1947). Hence, for further experiments 4 g L-1 of biosorbent material was selected as the optimum

dosage.

203

0

20

40

60

80

100

0 100 200 300 400 500 600 700 800 900 1000

As Conc. (µg L-1)

%S

orp

tion

75

80

85

90

95

100

0 4 8 12 16 20

Dosage of biosorbent (g L-1)

%So

rpti

on

Fig. 28. Effect of dosage on the biosorption of As to biomass of A. nilotica at As concentration 200 µg L-1, contact time 15 minutes and pH 7.5

Fig. 29. Effect of As biosorbate concentration on biomass of A. nilotica at biosorbent dose 4 g L-1, contact time 15 minutes and pH 7.5

204

0

20

40

60

80

100

0 10 20 30 40 50 60Contact time (min)

%S

orp

tion

298 K 308 K 318 K

0

20

40

60

80

100

0 2 4 6 8 10 12pH

%S

orp

tion

Fig. 30. Effect of pH on the biosorption of As to biomass of A. nilotica at As concentration 200 µg L-1, biosorbent dose 4 g L-1 and contact time 15 minutes

Fig. 31. Effect of contact time and temperature on the biosorption of As to biomass of A. nilotica at As concentration 200 µg L-1, biosorbent dose 4 g L-1, contact time 15 minutes and pH 7.5

205

4.8.1.4. Effect of sorbate concentration

Fig. 29 represents the effect of As ion concentration on the uptake of arsenic onto

biosorbent material. It is noted from the results that in whole systems, the saturation time is

independent of concentration of the biosorbate solution. The uptake of As found to increase as

the initial metal ion concentration is low. It was because the number of ions adsorbed from

solutions of lower concentrations is more than that removed from high concentrated solutions.

The uptake of As was observed 91-97% at lower concentrations (20-200 µg L-1 of As) and 60-

85% at higher As concentrations (200-1000 µg L-1). For further study, 200 µg L-1 of As

concentration was chosen as an optimum value.

The distribution coefficient (Kd) as defined in section 2.5, can be used as a valuable tool

to study metal ion mobility. High values of distribution coefficient, Rd indicate that the metal has

been retained by the solid phase through sorption reactions, while low values of Rd indicate that

a large fraction of the metal remains in solution. With an increase in biosorbate concentration, a

corresponding decrease in the Rd value from 375 to 136µg L-1, suggested the limiting number of

biosorption sites available for biosorption. These results reflect the efficiency of biosorbent

material for the removal of As from aqueous solution over a wide range of concentrations.

4.8.1.5. Effect of pH

The distribution of As ions in natural water is mainly dependent on pH conditions (Raje

and Swain 2002). Hence the uptake of As onto biosorbent material depends on the pH of

solution. In order to evaluate the effect of pH on the biosorption of As, the experiments were

carried out with optimum As concentration of 200 µg L-1 and biosorbent material concentration

of 4 g L-1 by varying the pH of the solutions over a range of 2-11 (Fig. 30). The uptake of As by

biosorbent material is increasing from pH 4 to 7, while the biosorption of As was decreased

suddenly, when the pH value was exceed to 8. For further experiment pH 7.5 was selected as an

optimum pH value. The most common species in natural water is HAsO42- which is stable under

neutral to mildly alkaline water. These results show a good agreement with those obtained by

biosorption of As on orange waste (DeMarco and SenGupta 2003).

206

4.8.1.6. The effect of contact time and kinetics of biosorption

The biosorption of As onto indigenous biosorbent material with different time interval (5

-60 min) at optimum value of As solutions (200 µg L-1) at pH 7.5 and 4 g L-1 of dosage of

indigenous biosorbent material is shown in Fig 31. The sorption study of As ions onto biosorbent

material as a function of contact time showed that sorption is very rapid ≥90% within 15 min,

which indicates availability of the biosorption sites. The fast kinetics of sorbent–metal

interaction at optimum pH may be acknowledged to enhance accessibility of the chelating sites

of the biosorbent material (Raje and Swain 2002). Further increase in time, no significant

enhancements was observed in removal of As. Therefore, further biosorption experiments were

carried out for a contact time of 15 min.

4.8.1.7. Biosorption isotherm

The principle of sorption isotherm is the relationship between the mass of the solute

sorbed per unit mass of sorbent qe and the solute concentration in the solution at equilibrium Ce.

Isotherm studies provide information about the capacity of the biosorbent material or the amount

required to remove a unit mass of pollutants like As from natural water. The biosorption data

have been subjected to Freundlich, Langmuir and Dubinin–Radushkevich (D–R) isotherm

models.

A basic assumption of the Langmuir theory is that sorption takes place at specific

homogeneous sites within the sorbent. A plot of Ce/qe versus Ce given in a straight line with its

slope of 1/Q and intercept of 1/Qb and the results are enlisted in Table 45. According to the

coefficients of correlation obtained (R2 > 0.964), the biosorption of As ions onto biosorbent

material, is fitted well to the Langmuir model. The magnitude of Q was found to be 714, 677 and

667µmol g-1 (53.6, 50.8 and 50 mg g-1) at 298, 308 and 318 K, respectively (Table 45). The

value of b was found in the range of 4.06 ×104 to 5.42 ×104 L mol-1. A high value of ‘b’ also

implies strong bonding of As to biosorbent material at studied temperatures. From the value of b,

a dimensionless parameter, RL, was estimated in the range of (1.25–9.75) ×10-2 by using the

relationship:

RL = 1 / (1 + b Ci) (13)

207

Where b is the Langmuir constant and Ci is the initial concentration. The calculated

values of RL are indicating favorable sorption of As ions onto biosorbent material under the

temperature range of 298–318 K. The RL lying in between 0 to 1, indicated the favorability of

biosorption at all under studied temperature (Freundlich, 1906). The biosorption capacity (qe; mg

g-1 ) of biosorbent material for As ions is higher than that of the majority of other biomasses

reported in literature (Sari and Tuzen 2009). Therefore, it can be remarkable that the biosorbent

material has significant potential for the removal of As ions from natural water.

Table 45. Langmiur, Freundlich and D-R characteristic constants for As biosorption onto BM

The Freundlich constants Cm and 1/n are determined from the intercept and slope of

linear plot of lnqe versus lnCe, respectively. The 1/n value was between 0 and 1 indicating that

the biosorption of As using understudy biosorbent material was favorable at studied conditions

(Table 45). However, the R2 value was found to be >0.97, indicating that the Freundlich model

was applicable for the relationship between the amounts of sorbed As ions and its equilibrium

concentration in the solution.

The equilibrium data were also analyzed using the D-R isotherm model to determine the

nature of biosorption processes as physical or chemical. The biosorption mean free energy gives

information about biosorption mechanism. The free energy of transfer (E) of one mole of solute

from solution to surface of biosorbent material was evaluated from the slope (β) of the D–R

Isotherm model

Parameters Unit 298 K 308K 318 K

Langmiur Q (µmol g-1) 714±0.34 677±0.23 667±0.40 b (L mol-1) 5.42×104±0.55 5.34×104±0.65 4.06×104±0.43

RL 0.124-0.966 0.126-0.967 0.158-0.974 R2 0.977 0.973 0.964

Freundlich Cm (mmol g-1) 10.4±0.62 10.20±0.51 9.20±0.46 n 1.66±0.39 1.59±0.48 0.86±0.37 R2 0.981 0.981 0.979

D-R Xm (µmol g-1) 11.08±0.66 9.76±0.71 8.04±0.76 E (KJ mol-1) 8.16±0.58 8.11±0.63 7.50±0.52

R2 0.999 0.999 0.997

208

curve using the equation E = 1/√−2β and it falls in the range of 7.50-8.16. The determined E

values indicated that biosorption takes place by chemical ion exchange while E<8 kJ/mol,

indicated that the biosorption process is carried out physically (Freundlich, 1906). Thus,

biosorption energy indicated that sorption of As onto biosorbent material may be a combination

of chemical and physical in nature.

All the three isotherms showed good fit to the experimental data with good correlation

coefficients (Table 45). The applicability of all the three isotherms to the arsenic biosorption

shows that both monolayer sorption and heterogeneous energetic distribution of active sites on

the surface of the biosorbent are possible.

4.8.1.8. Biosorption kinetics

Kinetic models can be helpful to understand the mechanism and the reaction rate of the

biosorbate-biosorbent, operating condition and examined their suitability for practical

remediation of metals from natural water. A number of kinetic models have been developed to

describe the

209

Table 46 Kinetic parameters obtained from pseudo-first-order and pseudo-second-order for As biosorption onto BM

Table 47. Thermodynamic parameters of As biosorption onto BM

Order of reaction

Parameters Unit 308K

qe, exp ( µmol g-1 ) 16.8±1.43

pseudo-first-order

k1 (1/min) 6.34×10-2±0.53

qe (µmol g-1 ) 8.09±0.86

R2 0.910

pseudo-second-order

k2 (g µmol-1 min-1) 0.464±0.78

qe (µmol g-1 ) 15.90±0.82

R2 0.990

Thermodynamic Parameter

Equation plot Values

Change in free energy

(kJ mol-1 )

alnK RTG

Temperature range 298 K -2.04 308 K -2.64 318 K -3.27

Change in enthalpy

(kJ mol-1 )

lnK vs.1/T 21.5±0.95

Change entropy (kJ mol-1 K-1)

-0.058±1.23

RT

H

RnKln

Sa

210

kinetics of metals removal. In order to estimate the kinetic arrangement that controls the

biosorption phenomenon, the pseudo-first order and pseudo-second order models were tested to

understand the experimental data (Ho, 1998). As shown in eq. 5, the values of k1 and qe were

obtained from the slope and intercept of the plot of ln(qe-qt) versus time and are given in Table

46 along with the corresponding correlation coefficient (R2). The R2 values for this model at

studied temperatures is low (R2 =0.91). The poor R2 values of the Lagergren pseudo-first order

model indicated that this model does not fitted well with biosorption of As on biosorbent

material. Experimental data were also tested by the pseudo-second-order kinetic model. This

model is more probable to predict kinetic behavior of biosorption with chemical sorption as rate-

controlling step (Ho, 1998). The pseudo-second-order rate constant (eq. 6), k2 and qe were

calculated from the slope and intercept of the plots of t/qt vs. t (Table 46). The experimental and

calculated qe values, pseudo-second order rate constant R2 values are also presented in Table 46.

The experimental qe values are in agreement with the calculated qe values and the plots show

good linearity, with a R2 > 0.99. Hence, the pseudo-second-order kinetic model better

represented the kinetics, suggesting that the biosorption process might be chemisorption or

physisorption.

4.8.1.9. Biosorption thermodynamics

In order to describe thermodynamic properties of the biosorption of As ions onto

biosorbent material, enthalpy change (ΔHº), Gibbs free energy change (ΔG

º) and entropy change

(ΔSº) were calculated by using equations shown in Table 47. The Gibbs free energy indicates the

degree of spontaneity of the sorption process and the higher negative value reflects more

energetically favorable sorption. The values of the parameters thus calculated are recorded in

Table 47. The value of ΔGº becomes more negative with increasing temperature. This shows that

an increase in temperature favors the removal process. The negative ΔGº values indicated

thermodynamically feasible and spontaneous nature of the biosorption. The ΔHº was observed to

be 21.5kJ mol-1. The positive value ΔHº indicated the endothermic nature of the biosorption. Its

magnitude gives information on the type of biosorption, which might be physical or chemical,

because it is near to boarder line. As the enthalpy or heat of biosorption, ranging from 0.5 – 5

211

kcal mol-1 (2.1 – 20.9 kJ mol-1) corresponds a physical sorption, whereas, it ranges from 20.9 to

418.4 kJ mol-1 in case of a chemical sorption (Smith, 1981; Singh et al., 2005; Deng et al., 2007).

Furthermore, the negative ΔSº value (Table 47) suggests the probability of favorable biosorption.

4.8.1.10. Effect of concomitant ions

The sorption of As ions in the presence of common ions may be affected due to

precipitation, complex formation or competition for sorption sites. As shown in Table 48, except

PO43- and SO4

2-, other anions Cl-, Br-, Cr2O42- and NO3

- have not significant interference with

biosorption of As ions. On other hand, cations like Fe3+ and Al3+ improved the As biosorption

due to favorable electrostatic effects, while heavy metal cations like Co2+,Cu2+, Ni2+, Pb2+, Zn2+

were decreased biosorption of As ions on biosorbent material but difference was not significant.

The K+ apparently had no effect on As biosorption (Table 48). The decrease of percentage

removal in the presence of Ca+2 and Mg2+ may be explained based on the ionic radii. All these

ions are larger than As. While the ionic radius of Cd2+ is nearly the same, so there was no

decreasing effect. Our results are consistent with other study (Pandey et al., 2009).

212

Table 48

Interferences of cations and anions on the sorption of As onto BM

Cations Biosorption (%) Anions Biosorption (%)

Without

addition 97.2±0.02

Without

addition 97.2±0.02

Al3+ 99.4±0.01 Cl- 95.6±0.03

Ca2+ 96.2 ±0.03 F- 92.5±0.02

Cd2+ 98.0±0.05 PO43- 90.1±0.02

Co2+ 92.4±0.02 Br- 96.8±0.02

Cu2+ 94.2±0.03 C2O42- 96.5±0.01

Fe3+ 102±0.05 C6H5O73- 95.9±0.01

K+ 96.8±0.06 CH3COO- 96.5±0.01

Mg2+ 96.0±0.04 CO32- 95.8±0.04

Mn2+ 97.1±0.02 HCO3- 95.3±0.01

Ni2+ 95.2±0.03 NO3- 97.7±0.03

Pb2+ 94.3±0.04 SO32- 95.8±0.06

Zn2+ 92.8±0.05 SO42- 91.2±0.05

213

Table 49

Influence of various eluents on the desorption of As ions from BM.

Eluent Concentration %Recovery

HCl 0.5 mol L-1 72.0±0.85

1 mol L-1 95.0±1.00

HNO3 0.5 mol L-1 61.0±0.92.0

1 mol L-1 90.0±1.20

4.8.1.11. Desorption and regeneration studies

Desorption of adsorbed As ions onto biosorbent material was carried out by using

different concentrations of HCl and HNO3 (Table 49). It was observed that 1 mol L-1 HCL

desorbed >95% of As, whereas 90% As was recovered on using 1 mol L-1 HNO3. For subsequent

experiments 10 ml of I mol L-1 HCl was used for dissolution of adsorbed As on understudy

biosorbent material. The capacity of the biosorbent material was found to be nearly constant

(deviation of 1–3%) after 10 experiments; thus manifold use of the biosorbent material was seen

to be adequate.

4.8.1.12. Application on natural water

This study has demonstrated the potential of indigenous biosorbent material for the removal of

As from natural water of different origins. The most attractive proposition of the biosorbent

material is that it can be grown in large quantities all over the country. In a set of experiments the

biosorbent material demonstrated that < 200 µg L-1 of As present in the

214

Table 50

The physico chemical parameters of water samples before and after biosorption on biomass

aelectrical conductivity, btotal hardness, ctotal dissolved solids

contaminated surface water could be removed to 97% at pH 7.5 (adjusted). Thus, it can be

recommended for the successful removal of arsenic from ground and surface water of affected

areas. The biosorbent material was successfully used for the removal of As from water samples

of lake, canal and river water samples having 80-106, 13-50 and 12-35 µg L-1 of As contents,

respectively. The mean results of water quality before and after biosorption of studied water

samples are shown in Table 50.

The water samples (especially lake water) of studied area are highly contaminated with

As (> 80 µg L-1) due to frequently use of pesticides and insecticides in agricultural lands as well

as use of untreated waste water sewage sludge as agricultural fertilizer (Baig et al., 2009c; Arain

Parameter Lake water (n = 36) Canal water (n = 48) River water (n = 36)

Before

biosorption

After

biosorption

Before

biosorption

After

biosorption

Before

biosorption

After

biosorption

As (µg L-1) 90±9.20 2.70±1.74 20±9.60 1.6±0.70 15.6±7.90 1.2±0.62

pH 8.0±0.42 -- 7.4±0.35 -- 7.6±0.25 --

aEC (mS cm-1) 8.56±1.60 7.02±1.05 2.7±0.82 1.75±0.50 2.3±0.71 1.52±0.53

bTH (mg L-1) 1525±25.3 143±12.3 60±5.30 45.6±4.50 72±6.78 54.0±5.94

cTDS (mg L-1) 5268±26.9 4846±26.9 210±11.9 193±5.20 221±12.6 205±4.9

215

et al., 2009a,b). It may be seen that after biosorption of As, especially from contaminated lake

water, As was reduced to a value < 10 µg L-1, which is within the WHO permissible limits (10

μg L-1) (WHO, 2004). The relative standard deviation was always within 2%, clearly showing

the efficiency of biosorbent material for the removal of As ions from understudy surface water

samples. Reuse of the biomass could be possible by desorbing the metals by the method

mentioned in the regeneration experiment.

216

4.8.1.13. Conclusion

This study focused on the biosorption of As ions onto biosorbent material from aqueous

solution. The As sorption capacity of biosorbent material was found to be 667 µmol g-1 (50.8 mg

g-1) from water samples at optimum conditions of pH 7.5, contact time of 15 min and

temperature of 308 K. The As ions were desorbed from biosorbent material frequently by 1 mol

L-1 HCl as compared to 1 mol L-1 HNO3. The experimental data were evaluated by Langmuir,

Freundlich and D-R isotherms. The mean free energy values calculated from the D–R model was

found to be >8 kJ/mol, indicated that the biosorption of As ions using A. nilotica biomass might

be due to chemical and physical sorption. The interactions between As ions and the functional

groups on the biomass surface were estimated by FT-IR and SEM analyses. Kinetic evaluation of

the equilibrium data showed that the biosorption of As onto biosorbent material followed well

the pseudo-second-order kinetic model. The thermodynamic calculations indicated the

feasibility, endothermic and spontaneous nature of the biosorption process at 298-318 K. Based

on all results, it can be concluded that biosorbent material is an effective and alternative biomass

for removing As ions from aqueous solution due to high biosorption capacity, easy availability

and environmental friendly.

217

4.8.2. Biosorption studies on leaves of Acacia nilotica

General Remark

The work presented in this section has been submitted as:

Jameel Ahmed Baig, Tasneem Gul Kazi, et al., (2011). Biosorption of arsenic from

aqueous solutions onto indigenous plant material as a low-cost biosorbent and its application on

groundwater. Desalination (Under review).

4.8.2.1. Results

The biosorbent prepared from indigenous biomass (IB) was studied for As biosorption

and obtained results were analyzed for the removal efficiency of As from aqueous solution,

under different experimental conditions and characterized by FTIR and SEM-EDS. The results

of the studies are explained in the following sections.

4.8.2.1.1. Characterization of biosorbent

The FTIR spectra of As unloaded and loaded biosorbent are shown in Fig. 32, indicated the

information on the functional groups of biosorbent and their interaction with As ions. The broad

and strong bands at 3100 - 3600 cm-1, were due to the overlapping of –OH and –NH2 stretching

vibration (Fig 1). The peak at 1637.6 cm-1 was attributed to stretching vibration of carboxyl

group (-C=O). The band obtained at 1064 cm−1 was represented to C-O stretching of carboxylic

acids and alcohols. The peak at ~ 2918 cm-1 illustrated C-H stretching of aliphatic carbon. The

small peaks observed at 1530-1203 cm-1 are attributed to ether and carboxylate groups, while at

1054 cm−1 indicated C–O stretching of ester or ether and N–H deformation of amines,

respectively.

The surface morphology of IB was studied by using SEM–EDX. A surface structure of

biosorbent was observed at a resolution of 3000× with a particle size of 5μm (Fig. 33a and b).

These images revealed that the surfaces morphologies of both unloaded and loaded bio-sorbent

were different. The unloaded bio-sorbent have morphologically rough surface and some porous

218

cavities. After loading of As ions, the biosorbent surface was changed to highly agglomerated,

and small particles adhered to each other to form multilayer on the surface of biomass (Fig 33b).

Fig. 32. FTIR spectra of unloaded (red line) and loaded (blue line) IB

219

Fig. 33. Scanning electron micrograph of (a) unloaded (b) loaded IB (3000× magnification)

Bar is 5 µm.

a b

220

Fig. 34. Energy dispersive spectroscopy (EDS) analysis of without As loaded and with As

loaded IB.

221

In order to know the composition of understudy biomass, elemental analysis was done with

the use of EDX analysis. The without As loaded biosorbent (see Fig. 34) showed the presence of

C, O, Cu, Na, Mg, Al, S, Cl, Ca, and many peaks of Fe. In comparison, with As loaded

biosorbent (Fig. 34) had additional peaks of As at < 1.5 keV and >10 keV verifying the

biosorption of As on the surface of biosorbent.

4.8.2.1.2. Influence of different factors on biosorption efficiency

The ionization degrees of biosorbate and surface charges are affected by the pH of

aqueous solutions (Tallman and Shaikh 1980; DeMarco et al., 2003). In order to study the pH

effect on the biosorption of As, the sorption experiments were conducted in the range of pH 2-

10, while keeping constant As concentration (100 µg L-1) and biosorbent dosage (8 g L-1). The

uptake of different species of As by the biosorbent was increase upto pH 7, while after pH 8 the

biosorption was suddenly decreased. It is indicated that indigenous biosorbent have capacity to

bind the As species from natural waters at pH range of 6–8. This result is in a good agreement

with those obtained by orange waste loaded iron gel (Ghimire et al., 2003). For further

experiment pH 7.5 was selected as an optimum pH value.

The removal efficiency of As onto the biosorbent as function of indigenous biosorbent

dosage was studied in the rang of 4–40 g L-1 in batch systems at optimal experimental parameters

(pH 7.5 and As concentration 100 µg L-1), to optimize the minimum dosage required for

lowering the As level upto the tolerance limit. The removal of As was enhanced with increasing

biosorbent dosage, which is obvious because of increase in the number of active sites (Pokhrel

and Viraraghavan 2008). The percent removal of As increased upto 95% when the dosage of

biosorbent was increased from 4-8 g L-1, it was seen that further increase in biosorbent dosage

upto 40 g L-1 have no significant effect on %removal of As. Hence, for further experiments 8 g

L-1 of biosorbent was selected as an optimum dosage.

The effect of concentration of As was also investigated at different levels ranged in

between 50 to 2000 µg L-1 at room temperature, while keeping the biosorbent amount fixed at 8

g L-1, contact time 30 min (shaking at 100 rpm) and the pH 7.5. The results indicate that the

percentage removal gradually decreased with increasing initial concentration of As. The uptake

of As is found < 95 % at lower biosorbate concentrations (1000 µg L-1) while 60-79% was found

222

at biosorbate concentrations (>1000 µg L-1). These results demonstrated the biomass efficiency

for the efficient removal of As from water solution in the broad range of concentrations.

The biosorption efficiency of As onto the surface of studied biosorbent was carried to

check the effect of contact time in the range of (5-60 min) at optimum value of 100 µg L-1 of As

solutions at pH 7.5 and 8 g L-1 of indigenous biosorbent. The samples were subjected at different

time interval and determined the variation on the biosorption efficiency. The biosorption is found

to be very rapid ≥90% within 15 min, which demonstrated the availability of biosorption sites

and As interacts easily with these sites (Lagergren, 1898). The rapid kinetics interaction of

biosorbent–metal at optimum pH may be acknowledged to enhance the probability of the

chelating sites of the biosorbent for As ions. After 15 min significant enhancements was not

observed in %sorption of As ions. Therefore, further sorption experiments were performed at the

contact time of 20 min.

4.8.2.1.3. Effect of concomitant ions

The interfering ions may be affected on the biosorption of As ions in solution, precipitation or

competition for sorption sites. The biosorption of As onto IB in aqueous solution was determined

223

Table 51. Isotherm characteristic constants for Langmiur, Freundlich and D-R and

Thermodynamic parameters for As biosorption onto biosorbent (leave of A. nilotica)

Langmiur Freundlich D-R

Q

(mmol g-1)

b

L mol-1

RL R2 Cm

(mmol g-1)

1/n R2 Xm

(mmol g-1)

E

(kJ mol-1)

R2

0.133 5.0×104 0.125-

0.966 0.977 1.04×10-1 0.602 0.981 0.9×10-4 8.21 0.99

Thermodynamic Parameter Values

ΔG◦ (kJ mol-1)

Temperature

303 K 313 K 323 K

-1.80 -2.10 -2.32

ΔH◦ (kJ mol-1) 13.60

ΔS◦ (kJ mol-1 K-1) -0.052

224

Table 52 Interferences of cations and anions on the sorption of As ions onto A. nilotica

Cations Biosorption (%) Anions Biosorption (%)

Nil 96.2 Nil 96.2

Al3+ 99.9 Cl- 95.6

Ca2+ 96.2 F- 91.3

Cd2+ 98.9 PO43- 94.1

Co2+ 92.4 Br- 96.4

Cu2+ 94.6 C2O42- 96.8

Fe3+ 103 C6H5O73- 95.9

K+ 96.3 CH3COO- 96.5

Mg2+ 96.6 CO32- 95.8

Mn2+ 97.8 HCO3 95.7

Ni2+ 95.2 NO3- 96.3

Pb2+ 95.8 SO32- 95

Zn2+ 92.5 SO42- 96.8

225

under optimized conditions. It has been found that except F- and PO43- other anions SO4

2-, Cl-,

Br-, Cr2O42- and NO3

- have not significant interference with biosorption of As ions (Table 52).

On other hand, cations such as, Ca2+, Fe3+, Mg2+ and Al3+ improved the As biosorption due to

favorable electrostatic effects, whereas, heavy metal cations such as Mn2+, Zn2+, Co2+, Cu2+ and

Ni2+ depressed it (Table 52). The anions bicarbonate, citrate, carbonate, acetate sulfide and

sulfate have no effect on As biosorption efficiency at the ratios investigated. The interference

study adequately revealed that As ions biosorption mechanisms on IB surface was different from

synthetic adsorbents.

4.8.2.2. Discussion

4.8.2.2.1. Characterization of biosorption

The FTIR analysis expressed the presence of olefinic C=C bonds conjugated with C=O

bond (Sari and Tuzen 2009). The hydrogen bonding was also observed along with different

functional groups such as carboxylic, alcoholic, amine, carbonyl and ether groups on the surfaces

of biosorbent obtained from plant biomass (Pandey et al., 2009). The loaded adsorbent with As

ions shows the deformation, shifting and appearance of new bands (Fig 32). After biosorption of

As ions, the stretching vibration peaks at 1637.6 cm−1 and 1508 cm−1 were shifted to 1637.9 cm−1

and 1542 cm−1, respectively. Whereas, the intensity of some bands (1450 - 1100 cm−1) was

increased, after loading of As ions, consistent with other studies (Grimm et al., 2008).

The SEM-EDS results revealed that after As loading, the enhancement in relative peak

intensities were obtained, particularly for Fe <1.0 keV along with Ca and Mg indicated that the

oxides of these metals may be contributed in the remediation of As from aqueous solution (Ahn

et al., 2003). This hypothesis was also confirmed by other researchers that, Fe, Al, Mn, Ca and

Mg are effective coagulants for eliminating As from water (Raje and Swain 2002).

4.8.2.2.2. Optimization of biosorption parameters

The effect of biosorbent dosage revealed a higher As removal, with increase in adsorbent

dosage, up to 8 g L-1, beyond which rate of removal remains constant. An increase in the

biosorption with the adsorbent dosage can be attributed to greater surface area and the

availability of more biosorption sites. At higher dosage, however, the incremental As removal

226

may become low, as the surface As concentration and the solution As concentration come to

equilibrium with each other.

Several researchers have investigated the effect of pH on sorption of As using different

kinds of sorbents and they reported almost same pH dependent (Boddu et al., 2008; Rahaman et

al., 2008). In fact, in the present study, the amount of As adsorbed was found to show a declining

trend with higher as well as with lower pH, with maximum removal of As (more than 94% by

the adsorbents) observed at pH 7.5, for all the adsorbents studied (Fig. 33). The low pH value

was obtained by using an acid solution, which could have introduced additional protons in the

solution, thus resulting in competition for the carbonyl sites, and thus reduction of biosorption at

low pH. The decrease in As biosorption can be attributed to the competition between the

hydroxyl ions, present at higher pH, and As species for biosorption sites. In addition, the

carboxyl, hydroxyl, and amide groups of the biomass will be negatively charged at alkaline

conditions. Therefore, there exists a repulsive force between the negatively charged sorbent and

anionic species of As, resulting in reduced sorption efficiency (Boddu et al., 2008; Rahaman et

al., 2008).

Available biosorption results reveal fast uptake of biosorbate species at the initial stages

of the contact period, a gradual slow down as it approached equilibrium, with more or less a

constant rate of biosorption at the intermediate stage. This effect may probably be because of

more available surfaces in the initial stage for biosorption leading to faster rate, in contrast to

final stage where available biosorption site might have reduced with increasing repulsive force

by already adsorbed particles, thus resulting in slow rate of biosorption. It is also found that the

removal of As by IB is < 80% upto 10 min contact time. It was also found that the adsorptive

removal of the As probably ceased after 30 min of contacting on IB.

4.8.2.2.3. Evaluation of biosorption theoretical feasibility

The principle of biosorption isotherm is the association between the contents of solute

sorbed per unit mass of sorbent qe and the solute concentration for the solution at equilibrium Ce.

Isotherm studies provide information about the capacity of the biosorbent or the amount required

to remove a unit mass of pollutants like As from natural water. The equilibrium data for the

227

removal of As by biosorption at pH 7.5 were theoretical verified with Langmuir, Freundlich and

Dubinin–Radushkevich (D–R) isotherm.

The Langmuir model suggested that the uptake of As was occurred as monolayer sorption

on a homogeneous surface with invariable biosorption energy. A plot of Ce/Cads versus Ce gives

in a straight line with its slope of 1/Q and intercept of 1/Qb (Table 51). The determination

coefficient (R2) was obtained to be 0.977, shows the applicability of the Langmuir model. The

biosorption of As ions onto IB was accomplished at the binding sites/functional groups available

on the surface of the biomass which are responsible for monolayer biosorption.

The magnitude of Q was found to be maximum and equal to133 µmol g-1 for the studied

biosorbent. The other constant ‘b’ was found as 5.0×104 L mg-1. A high value of ‘b’ also implies

strong bonding of As to activated biosorbent at room temperature. A dimensionless factor (RL)

was derived from the value of b, found in the concentration range of 1.25–9.75 ×10-2 mol L-1 by

using the relationship 14. The computed values of RL are indicating favorable sorption of As

ions onto IB in the temperature range of 303-323 K. The RL lying in between 0 to 1 indicated the

favourable conditions for biosorption at all the temperature studied (Pokhrel and Viraraghayan

2008).

The Freundlich biosorption isotherm was also applied for the biosorption of As ion on

biosorbent. This model suggests a distribution of monolayer sorption on heterogeneous energetic

active sites, accompanied by interactions with adsorbed molecules. The experimental results

obtained for the biosorption of As on the biosorbent at room temperature (303±5 K) under

optimum conditions of contact time and weight of biosorbent was found to follow the Freundlich

biosorption isotherm (Kundu and Gupta 2006). From these plots, Cm and 1/n value was found to

be 1.04×10-1 mmol g-1 and 0.60, respectively (Table 51). The 1/n value was found in between 0

to 1 indicating that the biosorption of As using indigenous biosorbent was favorable at

experimental conditions. The R2 was obtained 0.981, demonstrating that the Freundlich model

was satisfactorily explained the association between the concentrations of sorbed As ions and its

equilibrium concentration in aqueous media. The Freundlich equation gives a relatively better

representation than that of Langmuir, because of the available sites of studied biosorbent for

multilayer formation (Ho et al., 2001).

228

The equilibrium data has been put into the D-R isotherm model to find out the

biosorption processes nature of studied biosorbent either chemical or physical. The mean free

energy of biosorption procedure provided knowledge about mechanism of biosorption. The free

energy of transfer (E) was evaluated from the slope (β) of the D–R curve using the equation E =

1/√−2β for one mole of solute to surface of biosorbent. Which is falls in the range of 7.50 to 8.21

KJ mol-1. If E value lies in between 8 to16 KJ mol-1, demonstrated that the biosorption process is

chemical ion exchange while E value < 8 kJ mol-1, indicated that the process is carried out by

physical process (Boddu et al., 2008). The mean energy of biosorption was computed as

8.0±0.30 kJ mol-1. The obtained value indicated that biosorption of As onto IB may be a

combination of chemical and physical in nature.

Kinetic models is helpful to understand the mechanism as well as the reaction rate of the

sorbate-biosorbent, operating conditions and observed their favorability for practical remediation

of metals from natural water. A number of kinetic models have been developed to describe the

kinetics of metals removal. For the elucidation of biosorption kinetics of biosorption procedure,

two models of kinetics such as, Lagergren’s pseudo-first-order and pseudo-second-order were

applied to the experimental biosorption data (Ho et al., 2001; Hansen et al., 2006). The

biosorption rate constants (k1) are determined experimentally by plotting of ln (qe −qt) vs t. The

R2 for this model at studied temperature (313 K) is low (R2 = 0.929). Based on the poor R2

values indicated that the pseudo-first-order kinetic model was not favorable for the biosorption

procedure (Fig. 35a). Therefore, the experimental data were also subjected to the pseudo-second

order kinetic model. This is more feasible to indicate kinetic nature of biosorption with chemical

sorption as a rate-controlling step. The qe and rate constant (k2) were computed from the

intercept and slope of the plots of t/qt vs t. The biosorption data planed against t/qt vs t (Fig.

35b). The qe values are in conformity with the computed qe values and the plots demonstrated the

good linearity (R2 > 0.97). These results can be assumed that the pseudo-second-order kinetic

model

229

y = -0.0637x + 5.7832

R2 = 0.9295

0

2

4

6

8

10

0 10 20 30

Time (min)

ln (

qe-

qt)

y = 1.1345x + 40.838

R2 = 0.9703

0

20

40

60

80

100

0 10 20 30 40

Time (min.)

ln(q

e-q

t)

(a)

(b)

Fig. 35. (a) Pseudo-first-order and (b) pseudo-second-order kinetic plots for the biosorption of As onto IB at biosorbent dose 8 g L-1 and pH 7.5

230

presented good correlation for the biosorption of As onto indigenous biosorbent in contrast to the

pseudo-first-order model.

The enthalpy change ΔH○ is determined from the slope of the regression line after

plotting lnKa in function of 1/T. The change in Gibbs free energy (ΔG○) was computed as -1.80,

-2.10 and -2.32 kJ mol-1 for As biosorption at 303, 313 and 323 K, respectively with R2 is 0.99

(Table 51). The positive values of ΔH○ and ΔG○ showed the endothermic nature of biosorption

procedure. The enthalpy/heat content of biosorption is < 20.9 kJ mol-1 indicate physical sorption

(Deng et al., 2007).

Table 53. The physico-chemical parameters of water and removal of As by the leaves of Acacia nilotica

Parameter Hand pump (n = 56) Tube well (n = 44) Before

biosorption After

biosorption Before

biosorption After

biosorption As

(µg L-1) 40±9.60 2.1±1.50 50±7.90 2.5±0.90

pH 7.8±1.05 7.00±0.80 8.0±1.35 7.4±0.75 Electrical Conductivity

(mS cm-1) 1.90±0.82 1.34±0.50 0.90±0.71 0.66±0.53

Total Hardness (mg L-1)

143±12.3 105±8.61 75.6±6.78 68.0±5.94

Total dissolved solids (mg L-1)

2186±16.9 1856±10.2 1425±10.6 1185±9.52

Ca2+

(mg L-1) 111±14.8 77±15.6 56.4±9.20 39.0±8.50

Mg2+

(mg L-1) 41.4±2.80 25.5±1.90 24.6±0.24 15.5±0.35

Na+

(mg L-1) 520±27.6 497±24.8 344±11.5 312±10.6

K+

(mg L-1) 17.4±2.3 15.13±1.8 6.39±0.2 4.6±0.60

HCO3-

(mg L-1)

426±18.6 310±17.2 253±8.36 128±8.10

Cl-

(mg L-1)

330±20.6 240±18.8 189±7.5 135±5.6

SO42-

(mg L-1)

740±19.5 480±17.8 523±7.25 358±6.80

231

The negative value of ΔGo revealed the thermodynamically feasible and spontaneous

nature of the biosorption. The ΔSo was found to be 0.052 KJ mol-1 K-1 for As biosorption (Table

51). The negative ΔSo value proposes a slight decrease in uncertainty at the solution/solid

interface for biosorption process (Singh and Pant 2006).

4.8.2.2.4. Application on groundwater samples

Biosorption mechanism is a front line of defense, because of its simplicity, ease of

operation and handling, regeneration capacity and sludge free operation. Selective biosorption

can be utilized the biological materials as a controlling factor in the mobility and immobilization

of toxic analytes (Tallman and Shaikh 1980; Singh and Pant 2006). The proposed IB was

satisfactorily applied for the removal of As from contaminated tube well water (n = 56) and hand

pump water samples (n = 40) of different areas of Jamshoro district. The mean results of water

quality before and after biosorption of under studied water samples are shown in Table 53. The

both ground water resources were highly contaminated with As (> 50 µg L-1) due to

anthropogenic and geological sources (Baig et al., 2009a). It was observed that after removal of

As in under ground water samples using a IB, reduce the As level <10 µg L-1. Biosorption

behavior of As in presence of multi-component impurities has also been studied (Mohan and

Pittman Jr 2007). From the present study, it can be concluded that, in groundwater due to anoxic

sulfidic settings, a higher As mobility may also be expected (Boddu et al., 2008). The results

proved that the As was successfully removed from the real samples, which are comparable with

previous reported work on other adsorbent (Ahn et al., 2003; Boddu et al., 2008). The efficiency

of biosorbent for remediation of As in understudy samples was not change upto twenty

experiments, and then reduced slowly (10-30%) upto 50 experiments.

232

4.8.2.3. Conclusion

The results obtained in this study demonstrated that innovative indigenous biosorbent

(IB) can be used as an excellent biosorbent to remove As from ground water with high

efficiency. The thermodynamic calculations showed the feasibility, endothermic and

spontaneous nature of the biosorption. Several parameters were studied and maximum

biosorption was found to occur at pH 7.0 within 30 min contact. The applicability of studied

isotherm model to the arsenic biosorption shows that both monolayer sorption and heterogeneous

energetic distribution of active sites on the surface of the biosorbent are possible.

233

Chapter – 5

CONCLUSION

The evaluation of total arsenic contents of groundwater and surface water samples in

different areas of Sindh, Pakistan, was carried out in order to have an insight about the extent

of arsenic toxicity in study area. The purpose of this research work was to evaluate the

physico- chemical parameters arsenic speciation in surface and ground water of different

areas of Sindh, Pakistan. The soil and sediment of same areas was also analysed for available

and total arsenic using single and sequential extraction methods. Translocation of As

contents from irrigation water and soil to vegetables and grain crops were studied. Exposure

of As to inhabitant of different areas have been were analysed using scalp hair of adults and

children. The resulted data is providing following conclusions.

The concentration of arsenic in most of the underground water samples in Sukkur,

Khairpur, Hyderabad and Jamshoro were higher than the WHO permissible limits.

Generally, the ground water arsenic level was considerably higher than that of surface

water in understudy areas, possibly due to some geothermal and anthropogenic

factors, which enhanced pH level, and concentration of Ca, SO4 and Fe.

The speciation analysis was provided more information about toxicity,

bioavailability, and mobility of different As species in surface and ground water

samples. The strong linear correlation coefficient was observed between the

concentrations of inorganic As species and different physico-chemical parameters

(TDS, EC, Ca2+, Mg2+, Na+, Cl-, NO3- and SO4

2-) in surface water but in ground water

they were strongly correlated with Ca2+, SO42- and Fe.

Cluster analysis grouped five sampling ecosystems (river, canal, lake, tube well and

hand pumps) into three clusters of similar surface and groundwater quality

characteristics and As species. Based on obtained information, it is possible to design

234

a future, optimal sampling strategy, which could reduce the number of sampling sites

and associated cost.

The multivariate techniques were successfully applied for the optimization of cloud

point extract and solid-phase extraction (TiO2 based slurry) for As3+ and iAs,

respectively. The synchronized foreign ions interferences and influence of organic

compounds in environmental water sample using modifier (Pd + Mg (NO3)2) show

that the method is suitable for complicated matrix solutions.

A comparative study for BCR sequential extraction (BCR-SES) method for

partitioning of As in sediment samples was carried out and applied on sediment

samples of different origin. The lengthy treatment time required in this procedure was

reduced by developing single step extraction (S-BCR). The results obtained by BCR-

SES and S-BCR methods were provided information about the bioavailability and

mobility of arsenic at different environmental conditions.

A CPE method for the preconcentration of As in maize crop and adjoining soil

samples and determination by ETAAS. The proposed method has the following

advantages; is a simple, rapid, sensitive, inexpensive, non-polluting technique with

high enhancement factor. The experimental results showed that the CPE was a

successful method for determination of As in maize and adjoining soils irrigated by

tube well and canal water in two sub districts of Pakistan with satisfactory recoveries.

These findings urged more work on As controlled and exposed grain crops and

vegetables in detail.

High accumulation of As was found in grain crops obtained from the agricultural soil

irrigated with tub well as compared to soil irrigated with surface water. It is suggest

235

that the grain crops were cultivated by canal water or mixed with tube well water as,

the contamination of As may be minimized. The studied sub districts of Khairpur

were assigned in increasing order with respect to As levels in water, soil and

vegetables as: Gambat < Thari Mirwah < Faiz Ganj.

Considering the normally-edible parts of the vegetables, the TAs levels increased in

the approximate order as: Peppermint < Indian Squash < Bottle gourd < Cluster

Beans < Spinach < Bitter Gourd < Peas < Sponge gourd < Okra < Brinjal, grown in

soil irrigated with tub well and surface water of three sub districts. It was observed

that As more efficiently translocated by mint in both growing media. The high

transfer factor of extractable arsenic can be observed, in mint as compared to other

vegetables. The concentrations of As in tested control vegetable samples (grown on

soil irrigated with tube well water) were significantly higher (P < 0.01) as compared

to control vegetable samples (grown on soil irrigated with canal water).

A cloud point extraction method was applied for the determination of trace level of

As in scalp hair of children and adults belong to understudy areas.

It has been concluded that the major non-occupational contributors to elevate scalp

hair As levels in children of two towns of Khairpur, Pakistan. It appears to be creating

deleterious effects on the health of children > 10 years. The contents of As in boys

were found to be higher as compared to girls. The As in scalp hair samples were 5-12

time higher in both towns than normal level (< 0.30 µg g-1).

The positive linear regressions showed As concentrations in water versus scalp hair of

boys and girls of age 6–10 years was higher than As levels in water versus scalp hair

of boys and girls of age 1–5 years. As contents in boys 6–10 years old were found to

236

be higher as compared to the girls of same age group. The As in scalp hair samples

were 5–12 time higher than background levels (0.08–0.25 µg g-1) of As in sub-

districts Thari Mirwah, and Gambat. This could be attributed to higher sensitivity of

children to the As, which might be due to their large surface-area-to volume ratios,

which enhanced uptake of As from drinking water.

This study demonstrated the potential risk of arsenicosis among poor residents

(majority are farmers) of high and low arsenic exposed areas, who may depend on

As-enriched groundwater for drinking and other domestic usages. Positive

correlations between As concentrations in groundwater and scalp hair were observed

in present study.

The remediation of As from water samples was studied by using biosorbent materials

obtain from stem and leave of Acacia nilotica. Both biosorbents (biomass of stem and

Leave) have been found to be most efficient in arsenic adsorption and removal the

arsenic greater > 95% from aqueous media was found.

The resulted data were interpreted by D-R, Freundlich and Langmuir isotherms. The

free energy of transfer values calculated from the D–R model was found to be >8

kJ/mol, indicated that the biosorption of As ions using A. nilotica biomass might be

due to chemical and physical sorption.

The interactions of As ions with functional groups present at surface of biomass were

characterized by FT-IR and SEM-EDS.

The equilibrium data was demonstrated that the As biosorption by studied biomass

followed by pseudo-second-order rate equation. The thermodynamic calculations

revealed the endothermic, feasibility and spontaneous nature of As biosorption at

298-318 K.

237

Based on all results, it can be concluded that biosorbent material is an effective and

alternative for removing As ions from aqueous solution as compare to synthetic

biosorbents, due to high biosorption capacity, easy availability and environmental

friendly.

238

Socioeconomic Impacts The results obtained through present study provided baseline information for overall

management of the surface and ground water, greatly affecting the life and socio-

economic plight of local population.

The current study offered a broad spectrum in evaluation and speciation of As in

sediment and soil and its mobility into adjoining water.

In addition, it may help in generating awareness to the society about the toxicity of

As.

It is encouraging to new researchers for the assessment of environmental problems

and plan new research proposal for the solution of this hot issue.

It may also assist the local governments, to develop such type of methods for the

measurement and management of drinking water system in order to shelter the public

from injurious.

This study is also providing knowledge about the toxicity of arsenic through

contaminated drinking water and food stuff with poor nutrition, irregular screening,

late diagnosis and unequal access to health care due to poverty to enhance the

awareness.

Scientist and government agenesis who work on water quality or any other project

can use these rapid, economical, environment friendly and most efficient

methodologies for the assessment and monitoring of such environmental problems.

A natural indigenous biomass was designed is case effective and easy to assess with

excellent removal efficiency would be helpful for removal other toxic metals.

Certain new projects for remedial As from aqueous media have been designed for the

affected community and put in front different donor agencies for funding.

239

Recommendations

The mass awareness through electronic and print media should strongly recommend

accelerate for dermal disorders.

Sustainable groundwater management is a very complex issue, particularly in

Pakistan, where agricultural production is still the mainstay of the rural population's

livelihood system.

It is suggest that the grain crops were cultivated by canal water or mixed with tube

well water as, the contamination of As may be minimized

The people of understudy areas are still drinking As contaminated ground water as

this problem is largely unrecognized up till now. Moreover, due to lake of municipal

treated water system, the local populations have no alternate to buy costly bottled

mineral water. Thus, these facts urged to immediate stoppage of As contaminated

drinking water and the intake of As safe drinking water are the precondition for the

management of chronic arsenicosis especially in As affected areas.

Training program should be started for demonstration of pollution removal

technologies.

Geological and hydrological investigations should be conducted for aquifer

characterization in high risk Arsenic affected areas.

Conducting conferences and seminars on regular basis in collaboration with local and

international agencies are recommended. In addition, key people and experts from

other arsenic affected countries should be invited to attend such events.

240

Reference

Abedin, M.J. Cresser, M.S. Meharg, A.A. Feldmann, J. Howell, J.C. (2002). Arsenic accumulation and metabolism in rice (Oryza sativa L.). Environ. Sci. Technol. 36, 962–968.

Abernathy, C.O. Marcu, W. Chen, C. Gibb, H. Write, P. (1998). Report on Arsenic Work Group Meeting Office of Drinking Water, Office of Research and Development, USEPA, Memorandum to Cork P, Preuss P Office of Regulatory Support and Scientific Management, USEPA

Afridi, H.I. Kazi, T.G. Kazi, G.H. (2006). Analysis of heavy metals in scalp hair samples of hypertensive patients by Conventional and microwave digestion methods. Spectroscopy letter, 39, 1-12.

Afridi, H.I. Kazi, T.G. Kazi, N.G. Jamali, M.K. Baig, J.A. Kandhro, G.A. Arain, M.B. Shah, A.Q. (2010). Evaluation of toxic elements in scalp hair samples of myocardial infarction patients at different stages as related to controls. Bio. Trace Elem. Res. 134(1), 1 -12 doi: 10.1007/s12011-009-8450-6

Agusa, T. Kunito, T. Fujihara, J. Kubota, R. Minh, T.B. Kim Trang, P.T. Iwata, H. Subramanian, A. Viet, P.H. Tanabe, S. (2006). Contamination by arsenic and other trace elements in tube-well water and its risk assessment to humans in Hanoi, Vietnam. Environ Poll 139, 95-106

Ahamed, S. Sengupta, M.K. Mukherjee, A. Hossain, M.A. Das, B. Nayak, B. Pal, A, Mukherjee, S.C. Pati, S. Dutta, R.N. Chatterjee, G. Mukherjee, A. Srivastava, R. Chakraborti, D. (2006). Arsenic groundwater contamination and its health effects in the state of Uttar Pradesh (UP) in upper and middle Ganga plain, India: A severe danger. Sci. Total Environ. 370, 310–322

Ahmad, T. Kahlown, M.A. Tahir, A. Rashid, H. (2004). Arsenic an emerging issue experiences from Pakistan. 30th WEDC International Conference Vientiane Lao PDR Public Health National Institute of Preventive and Social.

Ahn, J.S. Chon, C. Moon, H. Kim, K.W. (2003). Arsenic removal using steel manufacturing byproducts as permeable reactive materials in mine tailing containment systems. Water Res. 37, 2478–2488.

Albores, A.F. Cid, B.P. Gomez, E.F. Lopez, E.F (2000). Comparison between sequential extraction procedures and single extractions for metal partitioning in sewage sludge samples. Analyst 125, 1353–1357.

Alvarez, J.M. Lopez-Valdivia, L.M. Jesus Novillo, J. Ana Obrador, A. Rico, M.I. (2006). Comparison of EDTA and sequential extraction tests for phytoavailability prediction of manganese and zinc in agricultural alkaline soils. Geoderma. 132, 450– 463

An, Y.J. (2004). Soil ecotoxicity assessment using cadmium sensitive plants. Environ. Poll. 127, 21–26.

Anawar, H.M. Akaib, J. Mostofa, K.M.G. Safiullah, S. Tareq, S.M. (2002). Arsenic poisoning in groundwater Health risk and geochemical sources in Bangladesh. Environ. Intern. 27, 597– 604.

Anwar, M. (2005). Arsenic, cadmium and lead levels in hair and toenails samples in Pakistan. Environ. Sci. 12, 071-086.

AOAC, (1995). Association of Official Analytical Chemists, Official Methods of Analysis, 16th ed. AOAC International, Gaithersburg, MD, (March 1998 revision).

Arain, M.B. Kazi, T.G. Jamali, M.K. Afridi, H.I. Jalbani, N. Shah, A. (2008a). Total dissolved and bio-available elements in water and sediment samples and their accumulation in Oreochromis mossambicus of polluted Manchar Lake. Chemosphere 70, 1845–1856.

Arain, M.B. Kazi, T.G. Jamali, M.K. Afridi, H.I. Jalbani, N. Baig, J.A. (2008b). Time saving modified BCR sequential extraction procedure for the fraction of Cd, Cr, Cu, Ni, Pb and Zn in sediment samples of polluted lake. J. Hazard. Mater. 160 (1), 235-239.

Arain, M.B. Kazi, T.G. Jamali, M.K. Jalbani, N. Afridi, H.I. Kandhro, G.A. Ansari, R. Sarfraz, R.A. (2008c). Hazardous impact of toxic metals on tobacco leaves grown in contaminated soil by ultrasonic assisted pseudo-digestion: Multivariate study. J. Hazard. Mater. 155, 216–224.

241

Arain, M.B. Kazi, T.G. Baig, J.A. Jamali, M.K. Afridi, H.I. Shah, A.Q. Jalbani, N. Sarfraz, R.A. (2009a). Determination of arsenic levels in lake water, sediment, and foodstuff from selected area of Sindh, Pakistan: Estimation of daily dietary intake. Food Chem. Toxicol. l47, 242-248.

Arain, M.B. Kazi, T.G. Baig, J.A. Jamali, M.K. Afridi, H.I. Jalbani, N. Sarfraz, R.A. Kandhro, G.A. (2009b), Respiratory effects in people exposed to arsenic via the drinking water and tobacco smoking in southern part of Pakistan. Sci Total Environ 407, 5524-5530.

Arnold, H.L. Odam, R.B. James, W.D. (1990). Disease of the Skin: Clinical Dermatology. W.B. Saunders, Philadelphia, PA, USA

Astel, A. Tsakovski, S. Simeonov, V. Reisenhofer, E. Piselli, S. Barbieri, P. (2008). Multivariate classification and modeling in surface water pollution estimation. Anal. Bioanal. Chem. 390, 1283–1292

ATSDR, (2000). Toxicology Profile for Arsenic. Atlanta, Georgia, Agency for Toxic Substances and Disease Registry, US Department of Health and Human Services, TP-92/02.

Bacon, J.R. Davidson, C.M. (2008). Is there a future for sequential chemical extraction? Analyst. 133, 25–46.

Bae, M. Watanabe, C. Inaoka, T. Sekiyama, M. Sudo, N. Bokul, M.H. Ohtsuka, R. (2002). Arsenic in cooked rice in Bangladesh. Lancet 360, 1839–1840.

Baig, J.A. Kazi, T.G. Arain, M.B. Afridi, H.I. Kandhro, G.A. Sarfraz, R.A. Jamali, M.K. Shah, A.Q. (2009a). Evaluation of arsenic and other physico-chemical parameters of surface and ground water of Jamshoro, Pakistan. J. Hazard. Mater. 166, 662–669.

Baig, J.A. Kazi, T.G. Arain, M.B. Shah, A.Q. Afridi, H.I. Kandhro, G.A. Sarfraz, R.A. Jamali, M.K. Khan, S. (2009b). Arsenic fractionation in sediments of different origins using BCR sequential and single extraction methods. J. Hazard. Mater. 167, 745–751.

Baig, J.A. Kazi, T.G. Shah, A.Q. Arain, M.B. Afridi, H.I. Kandhro, G.A. Khan, S. (2009c). Optimization of cloud point extraction and solid phase extraction methods for speciation of arsenic in natural water using multivariate technique. Anal. Chim. Acta 651, 57–63.

Baig, J.A. Kazi, T.G. Shah, A.Q. Kandhro, G.A. Afridi, H.I. Arain, M.B. Jamali, M.K. Jalbani, N. (2010a). Speciation and evaluation of Arsenic in surface and ground water samples: A multivariate case study. Ecotoxicol. Environ. safety 73, 914–923.

Baig, J.A. Kazi, T.G. Shah, A.Q. Kandhro, G.A. Afridi, H.I. Khan, S. Kolachi, N.F. Wadhwa, S.K. Shah, F. (2010b). Determination of arsenic in scalp hair Samples of exposed subjects using advance Extraction with and without enrichment. A.O.A.C. Intern. 94, 293-299.

Baig, J.A. Kazi, T.G. Shah, A.Q. Afridi, H.I. Kandhro, G.A. Khan, S. (2010c). Bio-sorption studies on powder of stem of Acacia nilotica: Removal of arsenic from surface water. J. Hazard. Mater. 178, 941–948.

Baig, J.A. Kazi, T.G. Shah, A.Q. Kandhro, G.A. Afridi, H.I. Khan, S. Kolachi, N.F. Wadhwa, S.K. (2010d). Arsenic speciation and other water quality parameters of surface and ground water samples of Jamshoro Pakistan. Intern. J. Environ. Anal. Chem. 1-15.

Baig, J.A. Kazi, T.G. Shah, A.Q. Arain, M.B. Afridi, H.I. Khan, S. Kandhro, G.A. Naeenullah, , Soomro, A.S. 2010e. Evaluating the accumulation of arsenic in maize (Zea mays L.) plants from its growing media by cloud point extraction. Food Chem. Toxicol. 48, 3051–3057.

Balaji, T. Yokoyama, T. Matsunaga, H. (2005). Adsorption and removal of As (V) and As (III) using Zr-loaded lysine diacetic acid chelating resin. Chemosphere 59, 1169–1174.

Bargali, S.S. Bargali, K. Singh, L. Ghosh, L. Lakhera, M.L. (2009). Acacia nilotica-based traditional agro-forestry system: effect on paddy crop and management. Curr. Sci. 4, 581-589.

Basin: hydrochemical and mineralogical evidences. J. Geochem. Explor. 98:107–15

Bengraine, K. Marhaba, T.F. (2003). Using principal component analysis to monitor spatial and temporal changes in water quality. J. Hazard. Mater. 100, 179-195.

242

Berg, M. Caroline, St. Pham, T.K.T. Pham, H.V. Mickey, L. Sampson, Moniphea Leng, Sopheap Samreth, David Fredericks, (2007). Magnitude of arsenic pollution in the Mekong and Red River Deltas — Cambodia and Vietnam. Sci. Total Environ. 372, 413–425.

Berti, W.R. Jacob, L.W. (1996). Chemistry and phytotoxicity of soil trace elements from repeated sewage sludge applications. J. Environ. Qual. 25, 1025–1032.

Bezerra, M.A. Arruda, M.A.Z. Ferreira, S.L.C. (2005). Cloud Point Extraction as a Procedure of Separation and Pre-Concentration for Metal Determination Using Spectroanalytical Techniques: A Review. Appl. Spectros. Rev. 40, 269–299.

Bhattacharya, P. Jacks, G. Ahmed, K.M. Khan, A.A. Routh, J. (2002). Arsenic in groundwater of the Bengal delta plain aquifers in Bangladesh. Bull. Environ. Contam. Toxicol. 69, 528-545.

Blomqvist, P. (2001). A proposed standard method for composite sampling of water chemistry and plankton in small lakes, Environ. Ecol. Stat. 8, 121–134.

Boddu, V.M. Abburi, K. Talbott, J.L. Smith, E.D. Haasch, R. (2008). Removal of arsenic (III) and arsenic (V) from aqueous medium using chitosan-coated biosorbent. Water Res. 42, 633–642.

Bose, P. Sharma, A. (2002). Role of iron in controlling speciation and mobilization of arsenic in subsurface environment. Water Res. 36, 4916–4926.

Brandvold, (2001). Arsenic in Ground Water in the Socorro Basin, New Mexico. New Mexico. Geol. 23, 2-8.

Brettell, T.A. Butler, J.M. Saferstein, R. (2005). Forensic science. Anal. Chem. 77, 3839-3860.

Brima, E.I. Haris, P.I. Jenkin, R.O. Polya, D.A. Gault, A.G. Harrington, C.F. (2006). Understanding Arsenic Metabolism Through a Comparative Study of Arsenic Levels in the Urine, Hair and Fingernails of Healthy Volunteers from Three Unexposed Ethnic Groups in the United Kingdom. Toxicol. Appl. Pharmacol. 216, 122–130.

Brookins, D.G. (1988). Eh-pH Diagrams for Geochemistry. Springer-Verlag, Berlin.

Cajuste, L.J. Laird, R.J. (2000). The relationship between phytoavailability and the extractability of heavy metals in contaminated soils. In: ISKANDAR IK (ed.) Environmental Restoration of Metals-Contaminated Soils. Lewis Publishers, Boca Raton, Florida. 189-198.

Campos, E. Barahona, E. Lachica, M. Mingorance, M.D. (1998). A study of the analytical parameters important for the sequential extraction procedure using microwave heating for Pb, Zn and Cu in calcareous soils. Anal.Chim. Acta 369, 235-243.

Canepari, S. Cardarelli, E. Giuliano, A. Pietrodangelo, A. (2006). Determination of metals, metalloids and non-volatile ions in airborne particulate matter by a new two-step sequential leaching procedure. Part A: Experimental design and optimization. Talanta 69, 581–587.

Cao, Q. Xie, K.C. Bao, W.R. Shen, S.G. (2004). Pyrolytic behavior of waste corn cob. Bioresour. Technol. 94, 83–89.

Cao, X, Ma, L.Q, (2004). Effects of compost and phosphate on plant arsenic accumulation from soils near pressure-treated wood. Environ. Pollut. 132, 435–442.

Cappuyns, V. Swennen, R. (2008). The Use of Leaching Tests to Study the Potential Mobilization of Heavy Metals from Soils and Sediments: A Comparison. Water Air Soil Pollut. 191, 95-111.

Cappuyns, V. Swennen, R. Verhulst, J. (2004). Assessment of acid neutralizing capacity and potential mobilisation of trace metals from land-disposed dredged sediments. Sci. Total Environ. 333, 233-247

Cespon-Romero, R.M. Yebra-Biurruna, M.C. (2008). Application of factorial designs for optimisation of on-line determination of cadmium, lead and nickel in welding fumes by atomic absorption spectrometry. Inter. J. Environ. Anal. Chem. 88(8), 539-547.

Chakraborti, D. Rahman, M.M. Paul, K. Chowdhury, U.K. Sengupta, M.K. Lodh, D. Chanda, C.R. Saha, K.C. Mukherjee, S.C. (2002). Arsenic calamity in the Indian subcontinent. What lessons have been learned? Talanta 58, 3–22.

243

Chakraborti, D. Mukherjee, S.C. Pati, S. Sengupta, M.K. Rahman, M.M. Chowdhury, U.K. Lodh, D. Chanda, C.R. Chakraborti, A.K. Basu, G.K. (2003). Arsenic Groundwater Contamination in Middle Ganga Plain, Bihar India: A Future Danger? Environ. Health Persp. 111, 194–1201.

Chamberlain, A.C. (1981). Fallout of lead and uptake by crops. Atmos Environ. 17, 693–706.

Charlet, L. Polya, D.A. (2006). Arsenic hazard in shallow reducing groundwaters in southern Asia. Elements 2, 91–96.

Chatterjee, A. Das, D. Chakraborti, K. (1993). A study of groundwater contamination by arsenic in the residential area of Behala, Calcutta, due to industrial pollution. Environ. Pollut. 80, 57–65.

Chen, Y. Ahsan, H. (2004). Cancer burden from arsenic in drinking water in Bangladesh. Am. J. Public Health 94, 741–744.

Chen, Z. Kim, K.W. Zhu, Y.G. McLaren, R. Liu, F. He, J.Z. (2006). Adsorption (AsIII, V) and oxidation (AsIII) of arsenic by pedogenic Fe–Mn nodules. Geoderma 136, 566–572

Cheng, F.M. Zhao, N.C. Xu, H.M. Li, Y. Zhang, W.F. Zhu, Z.W. Chen, M.X. 2006. Cadmium and lead contamination in japonica rice grains and its variation among the different locations in southeast China. Sci Total Environ. 359, 156–166.

Choi, B.Y. Kim, H.J. Kim, K. Kim, S.H. Jeong, H. J. Park, E. Yun, S.T. (2008). Evaluation of the processes affecting vertical water chemistry in an alluvial aquifer of Mankyeong Watershed, Korea, using multivariate statistical analyses. J. Environ. Geo. 54, 335-345.

Choong, T.S.Y. Chuah, T.G. Robiah, Y. Koay, F.L.G. Azni, I. (2007). Arsenic toxicity, health hazards and removal techniques from water: an overview. Desalination 217, 139–166

Chowdhury, U.K. Biswas, B.K. Chowdhury, T.R. Samanta, G. Mandal, B.K. Basu, G.K. Chanda, C.R. Lodh, D. Saha, K.C. Mukherjee, S.C. Roy, S. Kabir, S. Quamruzzaman, Q. Chakraborti, D. (2000). Ground water arsenic-contamination in Bangladesh and West Bengal India. Environ. Health Persp. 108, 393–397.

Cid, B.P. Albores, A.F. Gomez, E.F. Lopez, E.F. (2001). Use of microwave single extractions for metal fractionation in sewage sludge samples. Anal. Chim. Acta 431, 209–218.

Coelho, N.M.M. Coelho, L.M. de Lima, E.S. Pastor, A. de la, Guardia, M. (2005). Determination of arsenic compounds in beverages by high-performance liquid chromatography-inductively coupled plasma mass spectrometry. Talanta 66, 818-822.

Concha, G. Nermell, G. Vahter, M. (2006). Spatial and Temporal Variations in Arsenic Exposure via Drinking-water in Northern Argentina. J. Health Popul. Nutr. Sep. 24, 317-326

Crecelius, E.A. (1974). The geochemistry of arsenic and antimony in Puget Sound and Lake Washington, Thesis, University of Washington, Seattle, Washington.

da Silva A.M.M. Sacomani, L.B. (2001). Using chemical and physical parameters to define the quality of Pardo river water. Water Res. 35, 1609–1616

da Silva, M.A.M. Frescura, V.L.A. Curtius, A.J. (2000). Determination of trace elements in water samples by ultrasonic nebulization inductively coupled plasma mass spectrometry after cloud point extraction. Spectrochim. Acta Part B 55, 803-813

Dang, Q.H. Olga, N. Richard, C.G. (2004). Analytical methods for inorganic arsenic in water: a review. Talanta 64, 269–277.

Das, A. Chakrabortym, R. Cervera, M. De la Guardia, M. (1995). Metal speciation in solid matrices. Talanta 42, 1007–1030.

Das, H. K. Mitra, A. K. Sengupta, P. K. Hossain, A. Islam, F. Rabbani, G. H. (2004). Arsenic concentrations in rice, vegetables, and fish in Bangladesh: a preliminary study. Environ. Int. 30, 383– 387.

Das, H.K. Chowdhury, D.A. Rahman, S. Miah, M.U.O. Sengupta, P. Islam, F. (2003). Arsenic contamination of soil and water and related bio-hazards in Bangladesh Arsenic Crisis Info Center; http://bicn,com/acic, 15 May

244

Davidson, C.M. Delevoye, G. (2001). Effect of ultrasonic agitation on the release of copper, iron, manganese and zinc from soil and sediment using the BCR three stage sequential extractions. J. Environ. Monit. 3, 398–403.

De Andrade, E.M. Araujo, L.D.F.P. Rosa, M.D.F. Gomes, R.B. Lobato, F.A.D.O. (2007). Assessment of the surface water quality in the upland of Acarau watershed, Ceará, Brazil. Ciencia Rural. 37 (6), 1791-1797.

DeMarco, M.J. Gupta, A.K.S. Greenleaf, J.E. (2003). Arsenic removal using a polymeric/inorganic hybrid sorbent. Water Res. 37, 164–176.

Deming. S.N. Morgan, S.L. (1987). Experimental design: a chemometrics approach. Elsevier, Amsterdam

Deng, L. Su, Y. Su, H. Wang, X. Zhu, X. (2007). Sorption and desorption of lead (II) from wastewater by green algae Cladophora fascicularis, J. Hazard. Mater. 143, 220-225.

Dombovari, J. Papp, L. Uzonyi, I. Borbely-Kiss, I. Elekes, Z. Varga, Z. Matyus, J. Kakuk, G. (1999). Study of cross-sectional and longitudinal distribution of some major and minor elements in the hair samples of haemodialysed patients with micro-PIXE. J. Anal. At. Spectrom. 14, 553-557

Dopp, E., Hartmann, L.M. Florea, A.M. van Recklinghausen, U. Pieper, R. Shokouhi, B. et al. (2004). Uptake of inorganic and organic derivatives of arsenic associated with induced cytotoxic and genotoxic effects in Chinese hamster ovary (CHO) cells. Toxicol. Appl. Pharmacol. 201, 156– 165.

Dubinin, M.M. Radushkevich, L.V. (1947). The equation of the characteristic curve of the activated charcoal. Proc. Acad. Sci. USSR Phys. Chem. Sec. 55, 331-337.

Duxbury, J.M. Mayer, A.B. Lauren, J.G. Hassan, N. 2003. Food chain aspects of arsenic contamination in Bangladesh: effects on quality and productivity of rice. Environ. Sci. Health 38(1), 61–69.

Elci, L. Divrikli, U. And Soylak, M. (2008). Inorganic arsenic speciation in various water samples with GF-AAS using coprecipitation. Int. J. Environ. Anal. Chem. 88, 711–723.

Enright, N.J. Miller, B.P. Akhter, R. (2005). Desert vegetation and vegetation-environment relationships in Kirthar National Park, Sindh, Pakistan. J. Arid Environ. 61, 397–418.

EPA (1995) Method 3015 Microwave assisted acid digestion of aqueous sample and extracts In Test Methods for Evaluating Solid Waste. 3rd edition, 3rd update. Washington, DC: U.S.

Farooqi, A. Masuda, H. Firdous, N. (2007). Toxic fluoride and arsenic contaminated groundwater in the Lahore and Kasur districts, Punjab, Pakistan and possible contaminant sources. J. Environ. Poll. 145, 839-849.

Fatmi, Z. Azam, I. Ahmed, F. Kazi, A. Gill, A.B. Kadir, M.M. Ahmed, M. Ara, N. Janjua, N.Z. (2009). Health burden of skin lesions at low arsenic exposure through groundwater in Pakistan. Is river the source? Environ. Res. 109, 575–581.

Ferguson, M. Hofemann, M. Hering, J. (2005). TiO2-Photocatalyzed As(III) Oxidation in Aqueous Suspensions: Reaction Kinetics and Effects of Adsorption. Environ. Sci. Technol. 39, 1880-1886.

Ferraz, A.I. Tavares, T. Teixeira, J.A. (2004). Cr (III) removal and recovery from Saccharomyces cerevisiae. Chem. Eng. J. 105, 11–20.

Ferreira, S.L.C. de Andrade, J.B. Korna, M. de G.A. Pereira, M. de G. Lemos, V.A. dos Santos, W.N.L. Rodrigues, F. de M. Souza, A.S. Ferreira, H.S. da Silva, E.G.P. (2007). Review, Review of procedures involving separation and preconcentration for the determination of cadmium using spectrometric techniques. J. Hazard. Mater. 145, 358–367.

Ferreira, S.L.C. dos Santos, H.C. Fernandes, M.S. de Carvalho, M.S. (2002). Application of Doehlert matrix and factorial designs in optimization of experimental variables associated with preconcentration and determination of molybdenum in sea-water by inductively coupled plasma optical emission spectrometry. J. Anal. At. Spectrom. 17, 115–120.

Ferreira, S.L.C. dos Santos, W.N.L. Bezerra M.A. Lemos V.A. Bosque-Sendra, J.M. (2003). Use of factorial design and Doehlert matrix for multivariate optimisation of an on-line preconcentration system for lead determination by flame atomic absorption spectrometry. Anal. Bioanal. Chem. 375, 443–449.

245

Filgueiras, A.V. Lavilla, I. Bendicho, C. (2002). Chemical sequential extraction for metal partitioning in environmental solid samples. J. Environ. Monit. 4, 823-857.

Filgueiras, A.V. Lavilla, I. Bendicho, C. (2002). Comparison of the standard SM&T sequential extraction method with small-scale ultrasound-assisted single extractions for metal partitioning in sediments. Anal Bioanal Chem. 374, 103–108.

Filgueiras, V. Lavilla, I. Bendicho C. (2002). Chemical sequential extraction for metal partitioning in environmental solid samples. J. Environ. Monit, 4, 823–857.

Focazio, M.J. Welch, A.H. Watkins, S.A. Helsel, D.R. Horn, M.A. (2000). A retrospective analysis on the occurrence of arsenic in groundwater resources of the United States and limitations in drinking-watersupplycharacterizations. US Geological Survey Water-Resources Investigation Report, pp. 99-4279.

Freundlich, H.M.F. (1906). Over the adsorption in solution. J. Phys. Chem. 57, 385-471.

Fuentes, A. Llorens, M. Saez, J. Aguilar, M.I. Perez-Marin, A.B. Ortuno, J.F. Meseguer, V.F. (2006). Ecotoxicity, phytotoxicity and extractability of heavy metals from different stabilized sewage sludges. Environ. Pollut. 143, 355-360.

Gao, Y. Mucci, A. (2001). Acid base reactions, phosphate and arsenate complexation, and their competitive adsorption at the surface of goethite in 0.7 M NaCl solution. Geochim. et Cosmochim. Acta 65, 2361–2378.

Garrett, R.G. (2000). Natural sources of metals to the environment. Hum. Ecol. Risk Assess. 6, 945–963.

Gault, A.G. Rowland, H.A. Charnock, J.M. Wogelius, R.A. Gomez-Morilla, I. Vong, S. Leng, M. Samreth, S. Sampson, M.L. Polya, D.A. (2008). Arsenic in hair and nails of individuals exposed to arsenic-rich groundwaters in Kandal province, Cambodia. Sci. Total Environ. 393, 168–176.

Ghaedi, M. Asadpour, E. Vafaie, A. (2006). Simultaneous preconcentration and determination of copper, nickel, cobalt, lead, and iron content using a surfactant-coated alumina. Bull. Chem. Soc. Japan 79, 432–436.

Ghimire, K.N. Inoue, K. Yamaguchi, H. Makino, K. Miyajima, T. (2003). Arsenic removal using a polymeric/inorganic hybrid sorbent. Water Res. 37, 4945–4953.

Gleyzes, C. Tellier, S. Sabrier, R. Astruc, M. (2001). Arsenic characterization in industrial soils by chemical extractions. Environ. Technol. 22, 27–38.

Gong, Z. Lu, X. Mingsheng, M. Corinn, W. Le, X.C. (2002). Arsenic speciation analysis. Talanta 58, 77–96.

Gray, N.F. (2005). Water Quality Assessment Water Technology (Second Edition), 193-254.

Greenway, G.M. Song. O.J. (2002). An ultrasound accelerated sequential extraction method and its application for element partitioning studies in compost from mixed waste streams. J. Environ. Monit. 4, 950–955.

Gregori, I.D., Quiroz, W., Pinochet, H., Pannier, F., Potin-Gautier, M., (2005). Simultaneous speciation analysis of Sb(III), Sb(V) and (CH3)3SbCl2 by high performance liquid chromatography-hydride generation-atomic fluorescence spectrometry detection (HPLC-HG-AFS): Application to antimony speciation in sea water. J. Chromatogr. A 1091, 94-101.

Grimm, A. Zanzi, R. Bjornbom, E. Cukierman, A.L. (2008). Comparison of different types of biomasses for copper biosorption. Bioresour. Technol. 99, 2559–2565.

Guha Mazumder, D.N. Majumdar, K.K. Santra, S.C. Kol, H. Vicheth, C. (2009). Occurrence of arsenicosis in a rural village of Cambodia. J. Environ. Sci. Health Part A 44, 480–487.

Guo, C. Zhang F. Yang, X. (2000). Treatment of Ascontaining wastewater by lime-polyferric sulfate coagulating process. Gongye Shuichuli 20, 27–29.

Guo, H.M. Wang, Y.X. Shpeizer, G.M. Yan, S.L. (2003). Natural occurrence of arsenic in shallow groundwater, Shanyin, Datong Basin, China. J. Environ. Sci. Health A 38, 2565–2580.

Han, B. Runnells, T. Zimbron, J. Wickramasinghe, R. (2002). Arsenic removal from drinking water by flocculation and microfiltration. Desalination 145, 293–298.

246

Hansen, H.K. Nıunez, P. Grandon, R. (2006). Electrocoagulation as a remediation tool for wastewaters containing arsenic. Miner. Eng. 19, 521–524.

Hansen, H.K. Ribeiro, A. Mateus, E. (2006). Biosorption of arsenic (V) with Lessonia nigrescens. Miner. Eng. 19, 486–490.

Hayakawa, T. Kobayashi, Y. Cui, X. Hirano, S. (2005). A new metabolic pathway of arsenite: Arsenic-glutathione complexes are substrates for human arsenic methyltransferase Cyt19. Arch. Toxicol. 79, 183–191.

Helena, B. Pardo, R. Vega, M. Barrado, E. Fernandez, J.M. Fernandez, L. (2000). Temporal evolution of groundwater composition in an alluvial aquifer (Pisuerga river, Spain) by principal component analysis. Water Res. 34, 807-816.

Hindmarsh, J.T. (2002). Caveats in hair analysis in chronic arsenic poisoning. Clin. Biochem. 35, 1–11.

Hindmarsh, J.T. Dekerkhove, D. Grime, G. Powell, J.O. (1999). In: Chappell WR, bernathy CO, Calderon RL, editors. Hair arsenic as an index of toxicity. 1st ed. Oxford UK: Elsevier.

Hirata, S. Toshimitsu, H. (2005). Determination of arsenic species and arsenosugarsin marine samples by HPLC–ICP-MS. Anal. Bioanal. Chem. 383, 454–460.

Ho, Y.S. Mckay G. (1998). (Fellow), A comparison of chemisorption kinetic models applied to pollutant removal on various sorbents, Trans I Chem E, Vol 76, Part B, November 1998.

Ho, Y.S. Ng, J.C.Y. McKay, G. (2001). Removal of lead (II) from effluents by sorption on peat using second-order kinetics. Sep. Sci. Technol. 36, 241–261.

Hossain, M.F. (2006). Review. Arsenic contamination in Bangladesh—An overview. Agr. Ecosyst. Environ. 113, 1–16.

Hossain, M.F. (2006). Review. Arsenic contamination in Bangladesh—An overview. Agri. Ecosyst. Environ. 113, 1–16.

Houba, V.J.G. Temminghoff, E.J.M. Gaikhorst, G.A. Vark, W.V. (2000). Soil analysis procedures using 0.01 M calcium chloride as extraction reagent. Commun. Soil Sci. Plan. 31, 1299-1396.

Hu, H. (2002). Human health and heavy metals exposure, Michael McCally (ed) CHAPTER 4 MIT press. http://www.med.harvard.edu/chge/course/toxic/heavy/mccally.pdf

Hu, W. Zheng, F. Hu, B. (2008). Simultaneous separation and speciation of inorganic As(III)/As(V) and Cr(III)/Cr(VI) in natural waters utilizing capillary microextraction on ordered mesoporous Al2O3 prior to their on-line determination by ICP-MS. J. Hazard. Mater. 151, 58–64.

Huang, Y.K. Tseng, C.H. Huang, Y.L. Yang, M.H. Chen, C.J. Hsueh, Y.M. (2007). Arsenic methylation capability and hypertension risk in subjects living in arseniasis-hyperendemic areas in southwestern Taiwan. Toxicol. Appl. Pharmacol. 218, 135–142.

Hullebusch, E.D. Utomo, S. Zandvoort, M.H. Lens, P.N.L. (2005). Comparison of three sequential extraction procedures to describe metal fractionation in anaerobic granular sludges. Talanta 65(2), 549-558.

Hussain, M., Ahmed, S.M., Abderrahman, W. (2008). Cluster analysis and quality assessment of logged water at an irrigation project, eastern Saudi Arabia. J. Environ. Manage, 86, 297-307.

Islam, L.N. Nabi, A.H.M.N. Rahman, M.M. Khan, M.A. Kazi, A.I. (2004). Int. J. Environ. Res. Public Health 1, 74–82

Islam, M.T. Islam, S.A. Latif, S.A. (2007). Detection of Arsenic in water, herbal and soil samples by neutron activation analysis technique. Bull. Environ. Contam. Toxicol. 79, 327–330.

Ito, A. Takachi, T. Kitada, K. Aizawa, J. Umita, T. (2001). Characteristics of arsenic elution from sewage sludge. Appl. Organometal. Chem. 15, 266–270.

Iwegbue, C.M.A. Emuh, F.N. Isirimah, N.O. Egun, A.C. (2007). Fractionation, characterization and speciation of heavy metals in composts and compost-amended soils. African J. Biotechnol. 6(2), 67-78.

247

Jack, C.N. Wang, J. Shraim, A. (2003). A global health problem caused by arsenic from natural sources. Chemosphere 52, 1353–1359.

Jackson, B.P. Miller, W.P. (2000). Effectiveness of phosphate and hydroxide for desorption of arsenic and selenium species from iron oxides. Soil Sci. Soc. Am. J. 641616–1622.

Jain, C.K. Ali, I. (2000). Arsenic: occurrence toxicity and speciation techniques. Water Res. 4, 304–312.

Jalbani, N. Kazi, T.G. Arain, B.M. Jamali, M.K. Afridi, H.I. (2007). Evaluation of total contents of Al, As, Ca, Cd, Fe, K, Mg, Mn, Ni, Pb, Zn and their fractions leached to the infusions of different tea samples. A multivariate study. Chem. Spectra. Bioavail. 19(4), 163-73.

Jalbani, N. Kazi, T.G. Arain, M.B. Jamali, M.K. Afridi, M.I. Sarfraz, R.A. (2006). Application of factorial design in optimization of ultrasonic-assisted extraction of aluminum in juices and soft drinks. Talanta 70, 307–314.

Jamali, M.K. Kazi, T.G. Arain, M.B. Afridi, H.I. Jalbani, N. Adil, R.S. (2006). The correlation of total and extractable heavy metals from soil and domestic sewage sludge and their transfer to maize (Zea mays L.) plants. Toxicol. Environ. Chem. 88(4), 619–632.

Jamali, M.K. Kazi, T.G. Afridi, H.I. Arain, M.B. Jalbani, N. Memon, A.R. (2007a). Speciation of heavy metals in untreated domestic wastewater sludge by time saving BCR sequential extraction method. J. Environ. Sci. Health - Part A 42 (5), 649-659.

Jamali, M.K. Kazi, T.G. Arain, M.B. Afridi, H.I. Jalbani, N. Memon, A.R. (2007b). Heavy Metal Contents of Vegetables Grown in Soil, Irrigated with Mixtures of Wastewater and Sewage Sludge in Pakistan, using Ultrasonic-Assisted Pseudo digestion. J. Agro. Crop Sci. 193, 218-228.

Jamali, M.K. Kazi, T.G. Arain, M.B. Afridi, H.I. Jalbani, N. Memon, A.R. Shah, A. (2007c). Heavy metals from soil and domestic sewage sludge and their transfer to Sorghum plants. Environ. Chem. Lett. 5, 209-218.

Jamali, M.K. Kazi, T.G. Arain, M.B. Afridi, H.I. Jalbani, N. Sarfraz, R.A. Baig, J.A. (2008a). A multivariate study: variation in uptake of trace and toxic elements by various varieties of Sorghum bicolor L. J. Hazard. Mater. 158, 644–651.

Jamali, M.K. Kazi, T.G. Arain, M.B. Afridi, H.I. Memon, A.R. Jalbani, N. Shah, A.Q. (2008b). Use of sewage sludge, after liming as fertilizer to grow maize. Pedosphere 18, 203–213.

Jamali, M.K., T.G Kazi, Arain, M.B., Afridi, H.I., Jalbani, N., Kandhro, G.A., Shah, A.Q., Baig, J.A. (2009). Speciation of heavy metals in untreated sewage sludge by using microwave assisted sequential extraction procedure. J. Hazard. Mater. 163 (2-3), 1157-1164

Jiang, J.Q. (2001). Removing arsenic from groundwater for the developing world—a review. Water Sci. Technol. 44, 89–98.

Jiang, W. Zhang, S. Shan, X.Q. Feng, M. Zhu, Y.G. Mclaren, R.G. (2005). Adsorption of arsenate on soils: Part 1. Laboratory batch experiments using 16 Chinese soils with different physiochemical properties. Environ. Poll. 138, 278–284.

Jiang, Y. Wu, Y. Liu, J. Xia, X. Wang, D. (2008). Ammonium pyrrolidinedithiocarbamate-modified activated carbon micro-column extraction for the determination of As (III) in water by graphite furnace atomic absorption spectrometry, Microchim. Acta 161,137–142.

Jin, Q. Liang, F. Zhang, H. Zhao, L. Huan, Y. and Song, D. (1999). Application of microwave techniques in analytical chemistry. Trends Anal. Chem. 18, 479 -484.

Jitmanee, K. Oshima, M. Motomizu, S. (2005). Speciation of arsenic (III) and arsenic (V) by inductively coupled plasma-atomic emission spectrometry coupled with preconcentration system. Talanta 66, 529–533.

Johnson, B.L. Hicks, H.E. De Rosa, C.T. (1999). Key environmental human health issues in the Great Lakes and St. Lawrence River basins. Environ. Res. 80, S2–S12.

Jolliffe, I.T. (2002). Principal Component Analysis, Springer series in statistics, Springer Verlac, NY, USA.

248

Jordao, C.P. Pereira, M.G. Bellato, C.R. Pereira, J.L. Matos, A.T. (2002). Assessment of water systems for contaminants from domestic and industrial sewages. Environ. Monit. Asses. 79, 75–100.

Kahlown M.A. Azam, M. (2004). Irrig. J. Drain. 51, 329.

Kahlown, M.A. Majeed, A. Tahir, M.A. (2002). Water Quality Status in Pakistan. Pakistan Council of Research in Water Resources (PCRWR), Ministry of Science & Technology, Government of Pakistan.

Kamal, A.S.M. Parkpian, P. (2002). Arsenic Contamination in Hizla, Bangladesh: Sources, Effects and Remedies. Sci. Asia 28, 181-189.

Kapaj, S. Peterson, H. Liber, K. Bhattacharya, P. (2006). Human health effects from chronic arsenic poisoning—A review. J. Environ. Sci. Health A. Toxicol. Hazard Subs. Environ. Engineer 41, 2399– 2428.

Karadede, H. Oymak, S.A. Unlu, E. (2004). Heavy metals in mullet, Liza abu and catfish, Silurus triostegus, from the Ataturk Dam Lake (Euphrates), Turkey. Environ. Int. 30, 183–188.

Karadede, H. Unlu, E. (2000). Concentrations of some heavy metals in water, sediment and fish species from the Atatürk Dam Lake (Euphrates), Turkey. Chemosphere 41, 1371–1376.

Kazi, T.G. Afridi, H.I. Kazi, N. Jamali, M.K. Arain, M.B. Jalbani, N. Baig, J.A. (2008). Distribution of Zinc, Copper and Iron in biological samples of Pakistani myocardial infarction (1st, 2nd and 3rd heart attack) patients and controls. Clin. Chim. Acta 389(1-2), 114-119.

Kazi, T.G. Arain, M.B. Baig, J.A. Jamali, M.K. Afridi, H.I. Jalbani, N. Sarfraz, R.A. Niaz, A. (2009a). The correlation of arsenic levels in drinking water with the biological samples of skin disorders. Sci Total Environ 407, 1019-1026.

Kazi, T.G. Arain, M.B. Jamali, M.K. Jalbani, N. Afridi, H.I. Sarfraz, R.A. Baig, J.A. Shah, A.Q. (2009b). Assessment of water quality of polluted lake using multivariate statistical techniques: A case study. Ecotoxicol. Environ. Safety 72, 301-309.

Kazi, T.G. Baig, J.A. Shah, A.Q. Arain, M.B. Jamali, M.K. Kandhro, G.A. Afridi, H.I. Kolachi, N.F. Khan, S. Wadhwa, S.K. Shah, F. (2010c). Determination of Arsenic in Scalp Hair of Children and its Correlation with Drinking Water in Exposed Areas of Sindh Pakistan. Biol Trace Elem Res. DOI: 10.1007/s12011-010-8866-z.

Kazi, T.G. Jamali, M.K. Arain, M.B. Afridi, H.I. Jalbani, N. Sarfraz, R.A. (2006). The correlation of total and extractable heavy metals from soil and domestic sewage sludge and its transfer to maize (Zea mays L.) plants. Toxicol. Environ. Chem. 89, 619-632.

Kazi, T.G. Jamali, M.K. Kazi, G.H. Afridi, H.I. Siddiqui, A. (2005). Evaluating the mobility of toxic metals in untreated industrial wastewater sludge using a BCR sequential extraction procedure and a leaching test. Anal. Bioanal. Chem. 383(2), 297-304.

Kazi, T.G. Jamali, M.K. Siddiqui, A. Kazi, G.H. Arain, M.B. Afridi, H.I. (2006). An ultrasonic assisted extraction method to release heavy metals from untreated sewage sludge. Chemospehere. 63, 411-420.

Keon, N.E. Swartz, C.H. Brabander, D.J. Harvey, C. Hemond, H.F. (2001). Validation of an arsenic sequential extraction method for evaluating mobility in sediments. Environ. Sci. Techno. 35, 2778–2784.

Khan, A.W. Ahmad, S.K.A. (1997). Arsenic in drinking water: health effects and management, a training manual department of occupational and public health national institute of preventive and social medicine (NIPSOM) Dhaka.

Khan, S. Kazi, T.G. Kolachi, N.F. Baig, J.A. Afridi, H.I. Wadhwa, S.K. Shah F. (2010). Cloud Point Extraction of Vanadium in Pharmaceutical Formulations, Dialysate and Parenteral Solutions using 8-Hydroxyquinoline and nonionic Surfactant. J. Hazard. Mater. 10.1016/j.jhazmat.2010.06.042

Kile, M.L. Houseman, E.A. Breton, C.V. Smith, T. Quamruzzaman, O. Rahman, M. Mahiuddin, G. Christiani, D.C. (2007). Dietary Arsenic Exposure in Bangladesh. Environ. Health Persp. 115, 889–893.

249

Kim, J.H. Kim, R.H. Lee, J. Cheong, T.J. Yum, B.W. Chang, H.W. (2005). Multivariate statistical analysis to identify the major factors governing groundwater quality in the coastal area of Kimje, South Korea. Hydrol. Process 19, 1261–1276.

Koivula, N. Hanninen, K. Tolvanen, O. (2000). Windrow composting of source separated kitchen biowaste in Jyvaskyla, Finland. Waste Manage. Res. 18, 160-173.

Korai, A.L. Lashari, K.H. Sahato, G.A. Kazi, T.G. (2010). Histological lesions in gills of feral cyprinids, related to the uptake of waterborne toxicants from Keenjhar Lake. Rev. Fisher. Sci. 18 (2), 157-176.

Kowalkowski, T., Zbytniewski. R., Szpejna. J., Buszewski, B. (2006). Application of chemometrics in river water classification. Water Res. 40, 744-752.

Kubova J. Stresko, V. Bujdos, M. Matus, P. Medved, J. (2004). Fractionation of various elements in CRMs and in polluted soils. Anal. Bioanal. Chem. 379, 108–114.

Kumari, P. Sharma, P. Srivastava. S. Srivastava, M.M. (2005). Arsenic removal from the aqueous system using plant biomass: a bioremedial approach. J. Ind. Microbiol. Biotechnol. 32, 521–526.

Kundu, S. Gupta, A.K. (2006). Arsenic adsorption onto iron oxide-coated cement (IOCC): Regression analysis of equilibrium data with several isotherm models and their optimization. Chem. Eng. J. 122, 93–106.

Kuo, S. Lai, M.S. Lin, C.W. (2006). Influence of solution acidity and CaCl2 concentration on the removal of heavy metals from metal-contaminated rice soils. Environ. Pollut. 144(3), 918-925.

Kurttio, P. Komulainen, H. Hakala, E. (1998). Urinary excretion of arsenic species after exposure to arsenic present in drinking water. Arch Environ Contam Toxicol 4:297–305

Lagergren, S. (1898). Zur theorie der sogenannten adsorption geloster stoffe, Kungliga Svenska Vetenskapsakademiens. Handlingar. 24 1–39.

Leita, L. Nobili, M.D. (1991). Heavy metals in the environment. Water-soluble fractions of heavy metals during composting of municipal municipal solid waste. J. Environ. Qual. 20, 73–78.

Leupin, O.X. Hug, S.J. (2005). Oxidation and removal of arsenic(III) fromaerated groundwater by filtration through sand and zero-valent iron. Water Res. 39 (9), 1729–1740.

Li, J. Wang, Z. Cheng, X. Wang, S. Jia, Q. Han, L. (2005). Investigation of the epidemiology of endemic arseniasm in Ying County of Shanxi Province and the content relationship between water fluoride and water arsenic in aquatic environment. Chin J Endemicl 24, 183–185

Liu, C.W. Wang, S. Jang, C. Lin, K.H. (2006). Occurrence of Arsenic in Ground Water in the Choushui River Alluvial Fan, Taiwan. J. Environ. Qual. 35, 68–75.

Liu, C.W., Lin, K.H., Kuo, Y.M. (2003). Application of factor analysis in the assessment of groundwater quality in a blackfoot disease area in Taiwan. Sci. Total Environ. 313, 77-89.

Liu, J.G. Liang, J.S. Li, K.Q. Zhang, Z.J. Yu, B.Y. Lu, X.L. Yang, J.C. Zhu, Q.S. (2003). Correlations between Cd and mineral nutrients in absorption and accumulation in various genotypes of rice under Cd stress. Chemosphere 52(9), 1467-1473.

Liu, X. (2004). An overview of chronic arsenic via drinking water in PR China. J. Toxicol. 198, 25-29.

Lombi, E. Sletten, R.S. Wenzel, W.W. (2000). Sequentially extracted arsenic from different size fractions of contaminated soils. Water. Air. Soil. Pollut. 124, 319–32.

Lopez, P.L. Auque, L.F. Garces, I. Chong, W. (1999). Geochemical characteristics and patterns of evolution of salmueras superficiales del Salar de Llamara, Chile Brines surface of Salar Llamara, Chile. Geol. Mag. Chile 26, 89–108.

Loukidou, M.X. Matis, K.A. Zouboulis, A.I. Liakopoulou-Kyriakidou, M. (2003). Removal of As(V) from wastewaters by chemically modified fungal biomass. Water Res. 37, 4544–4552.

250

Lumsdon, D.G. Meeussen, J.C.L. Paterson, E. Garden, L.M. Anderson, P. (2001). Use of solid phase characterisation and chemical modelling for assessing the behaviour of arsenic in contaminated soils. Appl. Geochem. 16, 571–581.

Lyubun, Y.V. Fritzsche, A. Chernyshova, M.P. Dudel, E.G. Fedorov, E.E. (2006). Arsenic transformation by Azospirillum brasilense Sp245 in association with wheat (Triticum aestivum L.) roots. Plant Soil 286, 219–227.

Maeda. (1994). Biotransformation of arsenic in the freshwater environment, in: J.O. Nriagu (Ed.), Arsenic in the Environment Part I, Cycling and Characterization, Wiley, New York, pp. 155–87.

Maharjan, M. Watanabe, C. Ahmad, S.A. Umezaki, M. Ohtsuka, R. (2007). Mutual interaction between nutritional status and chronic arsenic toxicity due to groundwater contamination in an area of Terai, lowland Nepal. J. Epidemiol. Comm. Health 61, 389–394.

Malik, S.B. (2000). An overview of geothermal resources of Pakistan, in: Proceedings of the World Geothermal Congress, Kyushu, Tohoku, Japan.

Malinowski, E.D. (2002) Factor Analysis in Chemistry, John Wiley & Sons, New York.

Mandal, B.K. Suzuki, K.T. (2002). Review, Arsenic round the world: a review. Talanta 58, 201–235.

Mandal, B.K. Ogra, Y. Suzuki, K.T. (2003). Speciation of arsenic in human nail and hair from arsenic-affected area by HPLC-inductively coupled argon plasma mass spectrometry. Toxicol. Appl. Pharmacol. 189, 73–83.

Manning, B. Goldberg, S. (1996). Modelling competitive adsorption of arsenate with phosphate and molybdate on oxide minerals. Soil Sci. Soc. Am. J. 60, 121–131.

Manning, B.A. Fendorf, S. Bostick, B. Suarez, D.L. (2002). Arsenic (III) oxidation and arsenic(V) adsorption reactions on synthetic birnessite. Environ. Sci. Technol. 36, 976–981.

Manning, B.A. Goldberg, S. (1997). Arsenic(III) and arsenic(V) adsorption on three California soils. Soil Sci. 162 (12), 886–895.

Markert, B. Friese, K. (2000). Trace Elements (Trace Metals and other Contaminants in the Environment) Elsevier Science, 531.

Martinez-Sanchez, M.J. Navarro, M.C. Perez-Sirvent, C. Marimon, J. Vidal, J. Garcia-Lorenzo, M.L. Bech, J. (2008). Assessment of the mobility of metals in a mining-impacted coastal area (Spain, Western Mediterranean). J. Geochem. Explor. 96, 171-182.

Martins, R.J.E. Pardo, R. Boaventura, R.A.R. (2004). Cadmium(II) and zinc(II) adsorption by the aquatic moss Fontinalis antipyretica: effect of temperature, pH and water hardness. Water Res. 38, 693–699.

Massart, D.L. Vandeginste, B.G.M. Buydens, L.M.C. de Jong, S. Lewi, P.J. Smeyers-Verbeke, J. (2003). Handbook of Chemometrics and Qualimetrics Part A, Elsevier, Amsterdam.

Massart, D.L. Vandeginste, B.G.M. Deming, S.N, Michotte,, Y. Kaufman, F. (1988). Chemometrics: a textbook. Elsevier, Amsterdam.

Mazumder, D.N. Haque, R. Ghosh, N. De, B.K. Santra, A. Chakraborti, D. Smith, A.H. (2000). Arsenic in drinking water and the prevalence of respiratory effects in West Bengal, India. Intern. J. Epidemiol. 29, 1047–1052.

McBride, M.B. Nibarger, E.A. Richards, B.K. Steenhuis,. T. (2003). Trace metal accumulation by red clover grown on sewage sludge-amended soils and correlation to mehlich 3 and calcium chloride-extractable metals. Soil Science. 168, 29-38.

McCleskey, R.B. Nordstrom, D.K. Maest, A.S. (2004). Preservation of water samples for arsenic (III/V) determinations: an evaluation of the literature and new analytical results. Appl. Geochem. 19, 995–1009.

McGrath, D. (1996). Application of single and sequential extraction procedures to polluted and unpolluted soils. Sci. Total Environ. 178, 37-44.

251

McLaughlin, M.J. Zarcinas, B.A. Stevens, D. P. Cook, N. (2000). Soil testing for heavy metals. Commun. Soil Sci. Plant Anal. 31, 1661–1700.

McNeill L.S. Edwards, M. (1995). Soluble arsenic removal at water treatment plants. J. AWWA 87, 105–113.

Meacher, D.M. Menzel, D.B. Dillencourt, M.D. Bic, L.F. Schoof, R.A. Yost, L.J. Eickhoff, J.C. Farr, C.H. (2002). Estimation of multimedia inorganic arsenic intake in the U.S. population. Human Ecol. Risk. Assess. 8, 1697–1721.

Mead, M.N. (2005). Arsenic: In search of an antidote to a global poison. Environ. Health Persp. 113, A378–A386.

Meers, E. Du Laing, G. Unamuno, V.G. Lesage. E. Tack, F.M.G. Verloo, M.G. (2006). Water Extractability of Trace Metals from Soils: Some Pitfalls. Water Air Soil Pollut. 176, 21–35.

Meharg, A.A. Naylor, J. Macnair, M.R. 1994. Phosphorus nutrition of arsenate-tolerant and non-tolerant phenotypes of velvetgrass. J. Environ. Qual. 23, 234–238.

Mendiguchia, C. Moreno, C. Vargas M.G. (2007). Evaluation of natural and anthropogenic influences on the Guadalquivir River (Spain) by dissolved heavy metals and nutrients. Chemosphere 69, 1509–1517.

Meneses, M. Llobet, J.M. Granero, S. Schuhmacher, M. Domingo, J.L. (1999). Monitoring metals in the vicinity of a municipal waste incinerator: Temporal variation in soils and vegetation. Sci. Total Environ. 226, 157–164.

Menzies, N.W. Donn, M.J. Kopittke, P.M. (2007). Evaluation of extractants for estimation of the phytoavailable trace metals in soils. Environ. Pollut. 145, 121-130.

Mercedes, M.M. Kopplin, M,J. Burgess, J.L. Gandolfi, A.J. (2004). Arsenic drinking water exposure and urinary excretion among adults in the Yaqui Valley Sonora Mexico. Environ. Res. 96, 119–26.

Meza, M.M. Kopplin, M.J. Burgess, J.L. Gandolfi, A.J. (2004). Arsenic drinking water exposure and urinary excretion among adults in the Yaqui Valley, Sonora., Mexico. Environ. Res. 96, 119–126

Michaud, D.S. Wright, M.E. Cantor, K.P. Taylor, P.R. Jarmo, V. Albanes, D. (2004). Arsenic concentrations in prediagnostic toenails and the risk of bladder cancer in a Cohort study of male smokers. Am. J. Epidemiol. 160, 853–859.

Milton, A.H. (2003). Baseline study and clinical examination of arsenicosis among exposed population in Kandal Province. Unpublished report to Ministry of Health, Phnom Penh, Cambodia, pp. 1–71.

Milton, A.H. Hasan, Z. Rahman, Z. Rahman, M. (2004). Chronic arsenic poisoning and respiratory effects in Bangladesh. Toxicol. Appl. Pharmacol. 198, 243–252.

Milton, A.H. Raingsey, P.P. Mony, K.E. Fredericks, D. Polya, D.A. Gault, A.G. Fredericks, D. Milton, A.H. Sampson, M. Rowland, H.A.L. Lythgoe, P.R. Jones, J.C. Middleton, C. Cooke, D.A. (2005) Mineral. Mag. 69, 807-823

Minamoto, K. Mascie-Taylor, C.G. Moji, K. Karim et al. (2005). Arsenic-contaminated Water and Extent of Acute Childhood Malnutrition (Wasting) in Rural Bangladesh. Environ. Sci. 12, 283–292

Mingorance, M.D. Valdés, B. Rossini Oliva, S. (2005). Distribución de metales en suelos y plantas que crecen en un área sujeta a emisiones industriales, Abstracts of 6th Iberian and 3rd Iberoamerican Congress of Environmental Contamination and Toxicology Cádiz, Encuadernaciones Martínez, Puerto Real, Spain, p. 41.

Mir, K.A. Rutter, A. Koch, I. Smith, P. Reimer, K.J. Poland, J.S. (2007). Extraction and speciation of arsenic in plants grown on arsenic contaminated soils. Talanta 72, 1507–1518.

Mitra, S.R. Guha-Mazumder, D.N. Basu, A. Block, G. Haque, R. Samantha, S. Ghosh, N. Hira-Smith, M.M. Von-Ehrenstein, O.S. Smith, H. (2004). Nutritional Factors and Susceptibility to Arsenic-caused Skin Lesions in West Bengal, India. Environ. Health Persp. 112, 1104–1109

Mohammed, N.K. Mizera, J. Spyrou, N. (2008). Elemental contents in hair of children from Zanzibar in Tanzania as bio-indicator of their nutritional status. J. Radioanal. Nucl. Chems. 276, 125-128.

252

Mohan, D. Pittman Jr, C.U. (2007). Arsenic removal from water/wastewater using adsorbents—A critical review. J. Hazard. Mater. 142, 1–53.

Monroy-Torres, R. Macias, A.E. Gallaga-Solorzano, J.C. Santiago-Garcia, E.J. (2009). Arsenic in Mexican children exposed to contaminated well water. Ecol. Food Nutr. 48, 59–75.

Montgomery, D.C. (1997). Design and analysis of experiments, 4th edn. Wiley, New York

Morales, K.H. Ryan, L. Kuo, T.L. Wu, M.M. Chen, C.J. (2000). Risk of internal cancers from arsenic in drinking water. Environ. Health Perspect. 108, 655-661.

Morselli, L. Passarini, F. Bartoli, M. (2002). The environmental fate of heavy metals arising from a MSW incineration plant. Waste Manage. 22, 875–881

Mosaferi, M. Yunesian, M. Mesdaghinia, A.R. Nasseri, S. Mahvi, A.H. Nadim, H. (2005). Correlation between Arsenic concentrations in drinking water and human hair. Iran. J. Env. Health Sci. 2, 13-21

Moschandreas, D.J. Karuchit, S. Berry, M.R. O’Rourke, M.K. Lo, D. Lebowitz, M.D. Robertson, G. (2002). Exposure apportionment: ranking food items by their contribution to dietary exposure. J. Expos. Anal. Environ. Epidemiol. 12, 233–243.

Mossop, K.F. Davidson, C.M. (2003). Comparison of original and modified BCR sequential extraction procedures for the fractionation of copper, iron, lead, manganese and zinc in soils and sediments. Anal. Chim. Acta. 478, 111-118.

Mukherjee, A.B. Bhattacharya, P. (2001). Arsenic in groundwater in The Bengal Delta Plain: slow poisoning in Bangladesh. Environ. Rev. 9, 189-220.

Mukherjee, S.C. Saha, K.C. Pati, S. Dutta, R.N. Rahman, M.M. Sengupta, M.K. Ahmed, S. Lodh, D. Das, B. Hossain, M.A. Nayak, B. Mukherjee, A. Chakraborti, D. Dulta, S.K. Palit, S.K. Kaies, I. Barua, A.K. Asad, K.A. (2005). Murshidabad—one of the nine groundwater arsenic-affected districts of West Bengal, India. Part II: dermatological, neurological, and obstetric findings. Clin. Toxicol. 43, 835–848.

Mungasavalli, D.P. Viraraghavan, T. Jin, Y.C. (2007). Biosorption of chromium from aqueous solutions by pretreated Aspergillus niger: Batch and column studies. Colloids and Surfaces A: Physicochem. Eng. Aspects 301, 214-223.

Murata, R. Shimizu, T. Uehara, N. (2005). Speciation arsenic(III) and arsenic(V) in natural water by graphite furnace AAS after coprecipitation with a copper–pyrrolidinedithiocarbamate complex, Bunseki Kagaku 54, 831–836.

Nadal, M. Schuhmacher, M. Domingo, J.L. (2004). Metal pollution of soils and vegetation in an area with petrochemical industry. Sci. Total Environ. 321, 59–69.

Ng, J.C. Wang, J. Shraim, A. (2003) Review, A global health problem caused by arsenic from natural sources. Chemosphere 52,1353–1359

Nickson, R.T. McArthur, J.M. Shrestha, B. Kyaw-Myint, T.O. Lowry, D. (2007). Arsenic and other drinking water quality issues, Muzaffargarh District, Pakistan J. Environ. Poll. 145, 839-849.

Nielsen, F.H. (2001). Trace minerals. In M. Shills, J. Olson, M. Shike, A. C. Ross (Eds.), Nutrition in Health and Sickness. Mexico City: Mc Graw-Hill, 9th ed. pp. 328–31.

Nimick, D.A. Moore, J.N. Dalby, C.E. Savka, M.W. (1998). The fate of geothermal arsenic in the Madison and Missouri Rivers, Montana and Wyoming. Water Resour. Res. 34, 3051-3067.

Norvell, W.A. Wu, J. Hopkins, D.G. Welch, R.M. (2000). Association of Cd in durum wheat grain with soil chloride and chelate-extractable soil Cd. Soil Sci. Society of Ame. J. 64, 2162-2168.

Nystrom, G.M. Ottosen, L.M. Villumsen, A. (2003). The use of sequential extraction to evaluate the remediation potential of heavy metals from contaminated harbour sediment. J. Physique.107, 975–978.

Organization for Economic Co-operation Development (OECD, 2003). Guideline for Testing of Chemicals 208, Terrestrial Plant Test: Seedling Emergence and Seedling Growth Test, OECD, Paris.

253

Pakistan; Strategic Country Environmental Assessment Report (PSCEAR): Rising to the Challenges (2006).

Paleologos, E.K. Giokas, D.L. Tzouwara-Karayanni, S.M. Karayannis, M.I. (2002). Spectrofluorometric Determination of Vanadium Based on the Formation of a Ternary Complex between Vanadium, Peroxides, and 2-α-Pyridylthioquinaldinamide. Application to the Determination of Hydrogen Peroxide and Peroxy Acids. Anal. Chim. Acta 458 241-248.

Panda, U.C. Sundaray, S.K. Rath, P. Nayak B.B. Bhatta. D. (2006). Application of factor and cluster analysis for characterization of river and estuarine water systems – A case study: Mahanadi River (India). J. Hydrol. 331, 434-445

Pandey, P. K. Nair, S. Bhui, A. Pandey, M. (2004). Sediment contamination by arsenic in parts of central-east India and analytical studies on its mobilization. Curr. Sci. 86, 190-197.

Pandey, P.K. Choubey, S. Verma, Y. Pandey, M. Chandrashekhar, K. (2009). Biosorptive removal of arsenic from drinking water. Bioresour. Technol. 100, 634–637.

Pandey, P.K. Sharma, R. Roy, M. Roy, S. Pandey, M. (2006). Arsenic contamination in the Kanker district of central-east India: geology and health effects. Environ. Geochem. Health 28, 409–20.

Pangborn, J. (2003). Hair, the tissue of choice for assessment of arsenic, antimony and uranium http://www.gsdl.com/news/nmnewslet ter/issue2-1/

Patel, K.S. Shrivas, K. Brandt, R. Jakubowski, N. Corns, W. Hoffmann. P. (2005). Environ. Geochem. Health. 27,131.

Pazirandeh, A. Barati, A.H. Ghannadi, M. (1998). Determination of arsenic in hair using neutron activation. Appl. Radiol. Isot. 49, 753-759.

PCRWR (2002-2003) (Pakistan Council of Research in Water Resources). Arsenic and health effects, http://www.pcrwr.gov.pk/Arsenic_CS/ACS_Hlt_efcts.htm

Peirce, J.J. Weiner, R.F. Vesilind, P.A. (1998). Measurement of Water Quality Environmental Pollution and Control (Fourth Edition), 57-76.

Pena, M.E. Korfiatis, G.P. Patel, M. Lippincott, L. Meng, X. (2005). Adsorption of As(V) and As(III) by nanocrystalline titanium dioxide. Water Res. 11, 2327–2337.

Pendergrass, A. Butcher D.J. (2006). Uptake of lead and arsenic in food plants grown in contaminated soil from Barber Orchard, NC. Microchem. J. 83, 14-16

Pendergrass, A. Butcher, D.J. (2006). Uptake of lead and arsenic in food plants grown in contaminated soil from Barber Orchard, NC. Microchem. J. 83, 14–16

Pereira, M.G. Arruda, M.A.Z. (2003) Trends in pre-concentration procedures for metal determination using atomic spectrometry techniques. Microchim. Acta. 141 (3–9), 115–131.

Pereira, R. Ribeiro, R. Goncalves, F. (2004). Scalp hair analysis as a tool in assessing human exposure to heavy metals. Sci Total Environ. 327, 81-92.

Perez-Cid, B. De Jesuus Gonzalez, M. Gomez, E.F. (2002). Comparison of single extraction procedures, using either conventional shaking or microwave heating, and the tessier sequential extraction method for the fractionation of heavy metals from environmental samples. Analyst.127 (5), 681-688.

Perez-Cid, B. Fernandez-Albores, A. Fernandez-Gomez, E. Falque-Lopez, E. (2001). Use of microwave single extractions for metal fractionation in sewage sludge samples. Anal. Chim. Acta 43, 209–218.

Piech, R. Kubiak, W.W. (2007). Determination of trace arsenic with DDTC-Na by cathodic stripping voltammetry in presence of copper ions. J. Electroanal. Chem. 599, 59–64.

Pokhrel, D. Viraraghavan, T. (2008). Arsenic removal from an aqueous solution by modified A. niger biomass: batch kinetic and isotherm studies. J. Hazard. Mater. 150, 818–825.

254

Prasenjit, M. Chandrajit, B. Bikash, M.A. (2007). Laboratory study for the treatment of arsenic, iron, and manganese bearing ground water using Fe3+ impregnated activated carbon Effects of shaking time, pH and temperature. J. Hazamat. 420-426

Quevauviller, Ph. (2002). Operationally-defined extraction procedures for soil and sediment analysis. Part 3: New CRMs for trace-element extractable contents. Trends Anal. Chem. 21, 774–784.

Quevauviller, Ph. (2003). Book review, Methodologies for soil and sediment fractionation studies, (ed.). Royal Society of Chemistry, Cambridge, 2002. pp. 180. Sci. Total Environ. 303, 263–264.

Quevauviller, Ph. Lachica, M. Barahona, E. Gomez, A. Rauret, G. Ure, A. Muntau, H. (1998). Certified reference material for the quality control of EDTA and DTPA-extractable trace metal contents in calcareous soil (CRM 600). Frese. J. Anal. Chem. 360, 505-511.

Rahaman, M.S. Basu, A. Islam, M.R. (2008). The removal of As(III) and As(V) from aqueous solutions by waste materials. Bioresour. Technol. 99, 2815–2823.

Rahman, M.A. Hasegawa, H. Rahman, M.M. Miah, M.A.M. Tasmin, A. (2008). Arsenic accumulation in rice (Oryza sativa L.): human exposure through food chain. Ecotoxicol. Environ. Safety 69, 317–324.

Rahman, M.A. Rahman, M.M. Miah, M.A.M. Khaled, H.M. 2004. Influence of soil arsenic concentrations on rice (Oryza sativa L.). Subtrop. Agric. Res. Dev. 2 (3), 24–31.

Raje, N. Swain, K.K. (2002). Purification of arsenic contaminated ground water using hydrated manganese dioxide. J. Radioanal. Nucl. Chem. 253, 77–80.

Rauret, G. (1998). Extraction procedures for the determination of heavy metals in contaminated soil and sediment. Talanta 46, 449-455.

Ravenscroft, P. Burgess, W.G. Ahmed, K.M. Burren, M. Perrin, J. (2005). Arsenic in groundwater of the Bengal Basin, Bangladesh: Distribution, field relations and hydrogeological setting. J. Hydrogeol. 13(5-6), 727-751.

Ravenscroft, P. McArthur, J.M. Hoque, B.A. (2001). Geochemical and palaeohydrological controls on pollution of groundwater by arsenic, in: W.R. Chappell, C.O. Abernathy, R.L. Calderon (Eds.), Arsenic Exposure and Health Effects IV, Elsevier, Oxford, pp. 53–77.

Reyes, M.N.M. Cervera, M.L. Campos, R.C. Guardia, M. 2008. Non-chromatographic speciation of toxic arsenic in vegetables by hydride generation-atomic fluorescence spectrometry after ultrasound-assisted extraction. Talanta 75, 811–816

Saad, A. Hassanien, M.A. (2001). Assessment of arsenic level in the hair of the nonoccupational Egyptian population: Pilot study. Environ. Inter. 27, 471–8

Sahuquillo, A. Lopez-Sanchez, J.F. Rubio, R. Rauret, G. Thomas, R.P. Davidson, C.M. Ure, A.M. (1999). Use of a certified reference material for extractable trace metals to assess sources of uncertainty in the BCR three-stage sequential extraction procedure. Anal. Chim. Acta. 382, 317-327.

Sahuquillo, A. Rauret, G. Rehnert, A. Muntau, H. (2003). Solid sample graphite furnace atomic absorption spectroscopy for supporting arsenic determination in sediments following a sequential extraction procedure. Anal. Chim. Acta 476, 15–24.

Samanta, G. Sharma, R. Roychowdhury, T. Chakraborti, D. (2004). Sci. Total Environ. 326, 33–47

Samøe-Petersen, L. Larsen, E.H. Larsen, P.B. Bruun, P. (2002). Uptake of trace elements and PAHs by fruit and vegetables from contaminated soil. Environ. Sci. Technol. 36, 3057–3063.

Sampson, M.L. Bostick, B. Chiewp, H. Hagan, J.M. Shantz, A. (2008). Arsenicosis in Cambodia: case studies and policy response. Appl. Geochem. 23, 2977–2986.

Sampson, M.L. Sosamrach, K. Shantz, A. (2007). Policy response and the discovery of arsenicosis in Cambodia. Proceedings of the EAWAG/University of Manchester Workshop on Arsenic in Groundwaters of South-East Asia with Emphasis on Cambodia and Vietnam, Manchester; 29–30th October.

255

Sano, J. Kikawada, Y. O, T. (2008). Determination of As(III) and As(V) in hot spring and river waters by neutron activation analysis with pyrrolidinedithiocarbamate coprecipitation technique. J. Radio anal. Nucl. Chem. 278, 111–116

Sarbu, C. Pop, H.F. (2005). Principal component analysis versus fuzzy principal component analysis. A case study: the quality of Danube water (1985e 1996). Talanta 65, 1215-1220.

Sari, A. Tuzen, M. (2009). Biosorption of As(III) and As(V) from aqueous solution by macrofungus (Inonotus hispidus) biomass: Equilibrium and kinetic studies. J. Hazard. Mater. 164, 1372–1378.

Scaccia, S. Frangini, S. (2004). Sensitive assay for oxygen solubility in molten alkali metal carbonates by indirect flame atomic absorption spectrometric Cr (VI) determination. Talanta 64, 791–797.

Sera, K. Futatsugawa, S. Murao, S. (2002). Quantitative analysis of untreated hair samples for monitoring human exposure to heavy metals. Nucl. Instrum. Methods in Phys. Res. Section B: Beam Interactions with Materials and Atoms. 189, 174-179.

Shah, A.Q. Kazi, T.G. Arain, M.B. Baig, J.A. Afridi, H.I. Kandhro, G.A. Khan, S. Jamali, M.K. (2009a). Hazardous impact of arsenic on tissues of same fish species collected from two eco-systems. J. Hazard. Mater. 167, 511–515.

Shah, A.Q. Kazi, T.G. Arain, M.B. Jamali, M.K. Afridi, H.I. Jalbani, N. Baig, J.A. Kandhro, G.A. (2009b). Accumulation of arsenic in different fresh water fish species - potential contribution to high arsenic intakes. Food Chem. 112, 520–524.

Shah, A.Q. Kazi, T.G. Arain, M.B. Jamali, M.K. Afridi, H.I. Jalbani, N. Kandhro, G.A. Baig, J.A. Sarfraz, R.A. Ansari, R. (2009c). Comparison of electrothermal and hydride generation atomic absorption spectrometry for the determination of total arsenic in broiler chicken. Food Chem. 113, 1351–1355.

Shah, A.Q. Kazi, T.G. Baig, J.A. Afridi, H.I. Kandhro, G.A. Arain, M.B. Kolachi, N.F. Wadhwa, S.K. (2010). Total mercury determination in different tissues of broiler chicken by using cloud point extraction and cold vapor atomic absorption spectrometry. Food Chem. Toxicol. 48, 65–69.

Shaw, D. (2003). Arsenic mobility in sediments and contamination of the Bengal Basin. Unpublished Ph.D. Thesis, Cardiff University.

Shemirani, F. Baghdadi, M. Ramezani, M. (2005). Preconcentration and determination of ultra trace amounts of arsenic(III) and arsenic(V) in tap water and total arsenic in biological samples by cloud point extraction and electrothermal atomic absorption spectrometry. Talanta 65, 882–887.

Shrestha, B. (2002). Drinking water quality: future directions for UNICEF in Pakistan. Consultancy Report 2 of 3, Water Quality, SWEET Project, UNICEF Pakistan , Islamabad

Shrestha, S. Kazama, F. (2007). Assessment of surface water quality using multivariate statistical techniques: A case study of the Fuji river basin, Japan. Environ. Model. Softwar 22, 464-475.

Signes-Pastor, A. Burlo, F. Mitra, K. Carbonell-Barrachina, A.A. (2007). Arsenic biogeochemistry as affected by phosphorus fertilizer addition, redox potential and pH in a west Bengal (India) soil. Geoderma 137, 504–510

Silva, M.F. Cerutti, E.S. Martinez, L.D. (2006). Coupling cloud point extraction to instrumental detection systems for metal analysis. Microchim. Acta 155, 349–354.

Simeonov, V., Stratis, J.A., Samara, C., Zachariadis, G., Voutsa, D., Anthemidis, A., Sofoniou, M., Kouimtzis, Th. (2003). Assessment of the surface water quality in Northern Greece Water Res. 37, 4119-4124

Simeonova, P., Simeonov, V., Andreev, G. (2003). Water quality study of the Struma river basin, Bulgaria (1989–1998) Cen. Eur. J. Chem. 2, 121-136.

Singh, A.K. (2006). Chemistry of arsenic in groundwater of Ganges–Brahmaputra river basin. Curr. Sci. 91, 599-606.

256

Singh, K.P. Malik, A. Mohan, D. Sinha, S. (2004). Multivariate statistical techniques for the evaluation of spatial and temporal variations in water quality of Gomti River (India) a case study. Water Res. 38, 3980-3992.

Singh, K.P. Malik, A. Sinha, S. (2005). Water quality assessment and apportionment of pollution sources of Gomti river (India) using multivariate statistical techniques- a case study. Anal. Chim. Acta 538, 355–374.

Singh, N. Ma, L.Q. (2006). Arsenic speciation and arsenic and phosphate distribution in arsenic hyperaccumulator Pteris vittata and non-hyperaccumulator Pteris ensiformis. Environ. Poll. 141, 238–246.

Singh, R. Rastogi, S.H. Hasan, (2005). Removal of Cr (VI) from wastewater using rice bran. J. Colloid Interface Sci. 290, 61–68.

Singh, T.S. Pant, K.K. (2004). Equilibrium, kinetics and thermodynamic studies for adsorption of As(III) on activated alumina. Sep. Purif. Technol. 36 (2), 139–147.

Singh, T.S. Pant, K.K. (2006). Kinetics and mass transfer studies on the adsorption of arsenic onto activated alumina and iron oxide impregnated activated alumina. Water Qual. Res. J. Can. 41(2), 147–156.

Smedley PL, Kinniburgh DG (2002) A review of the source, behavior and distribution of arsenic in natural waters. Appl Geochem 17:517-568.

Smedley, P.L. Nicolli, H.B. Macdonald, D.M.J. Barros, A.J. Tullio, J.O. (2002). Hydrogeochemistry of arsenic and other inorganic constituents, in groundwaters from La Pampa. Argentina. Appl. Geochem. 7, 259-284.

Smith, A.H. Lopipero, P.A. Bates, M.N. Steinmaus, C.M. (2002). Arsenic epidemiology and drinking water standards. Sci. 296, 2145–2146.

Smith, E. Naidu, R. Alston, A.M. (1999). Chemistry of arsenic in soils: I. Sorption of arsenate and arsenite by four Australian soils. J. Environ. Quality 28, 1719–1726.

Smith, F.E. Arsenault, E.A. (1996). Microwave-assisted sample preparation in analytical chemistry. Talanta. 43, 1207 -1268.

Smith, J.M. (1981). Chemical Engineering Kinetics, 3rd ed. McGraw-Hill, New York, pp. 310–322.

Soylak, M. Narin, I. Bezerra, M.D. Ferreira, S.L.C. (2005). Factorial design in the optimization of preconcentration procedure for lead determination by FAAS. Talanta 65, 895–899.

Soylak, M. Yilmaz, S. (2006). Heavy metal levels in sediment samples from Lake Palas, Kayseri-Turkey. Fresenius Environ. Bull. 15, 340–344.

Stalikas, C.D. (2002). Micelle-mediated extraction as a tool for separation and preconcentration in metal analysis. Trends Anal. Chem. 21, 343-355.

Steinmaus C, Bates MN, Yuan Y, Kalman D, Atallah R, Rey OA, Biggs ML, Hopenhayn C, Moore LE, Hoang BK, Smith AH (2006) Arsenic methylation and bladder cancer risk in case-control studies in Argentina and the United States. J Occup Environ Medic 48:478–488

Sullivan, R.J. (1969). National Air Pollution Control Administration Publication No. APTD 69-26, Raleigh, NC, p. 60.

Szakova, J. Tlustos, P. Balik, J. Pavlikova, D. Vanek, V. (1999). The sequential analytical procedure as a tool for evaluation of As, Cd and Zn mobility in soil. Fresenius J. Anal. Chem. 363, 94–595

Tack, F.M.G. Vossius, H.A.H. Verloo, M.G. (1996). A comparison between sediment metal ractions, obtained from sequential extraction and estimated from single extractions. Int. J. Environ. Anal. Chem. 63, 61–66.

Taggart, M.A. Carlisle, M. Pain, D.J. Williams, R. Osborn, D. Joyson, A. and Meharg, A.A. (2004). The distribution of arsenic in soils affected by the Aznalcollar mine spill, SW Spain. Sci. Total Environ. 323, 137–152.

Tahir, M.A. (2000) Report on Arsenic in Groundwater of Attock and Rawalpindi Districts. Pakistan Council of Research in Water Resources (PCRWR), Ministry of Science & Technology, Government of Pakistan

257

Takeda, A. Tsukada, H. Takaku, Y. Hisamatsu, S. Inaba, J. Nanzyo, M. (2006). Extractability of major and trace elements from agricultural soils using chemical extraction methods: Application for phytoavailability assessment. Soil Sc. Plant Nutri. 52, 406-417.

Tallman, D.E. Shaikh, A.U. (1980). Redox stability of inorganic arsenic (III) and arsenic (V) in aqueous solution. Anal. Chem. 52, 199–201.

Tang, A. Ding, G. Yan, X. (2005). Cloud point extraction for the determination of As(III) in water samples by electrothermal atomic absorption spectrometry. Talanta 67, 942–946.

Taruki, M. Wakui, T. Nukatsuka, I. Ohzeki, K. (2007). Determination of Tin by Resin-Suspension Electrothermal Atomic Absorption Spectrometry after Enrichment as the Complex with Ammonium Pyrrolidinedithiocarbamate. Anal Sci. 23, 1435-1438.

Teixeira, M.C. Ciminelli, V.S.T. (2005). Development of a biosorbent for arsenite: structural modeling based on X-ray spectroscopy. Environ. Sci. Technol. 39, 895–900.

Tessier, A. Campbell, P.G.C. Bisson, M. (1979). Sequential extraction procedure for the speciation of particulate traces metals. Anal. Chem. 51, 844-851.

Thirunavukkarasu, O.S. Viraraghavan, T. Subramanian, K.S. Tanjore, S. (2002). Organic arsenic removal from drinking water. Urban Water 4, 415.

Thornton, I. Farago, M. (1997). The geochemistry of arsenic, in: C. O. Abernathy, R. L. Calderon, W. R. Chappell (Eds.), Arsenic: Exposure and Health Effects, Chapman and Hall, Kluwer Academic Publishers, London. pp. 1–16.

Torres, I.S.I. Ishiga, H. (2003). “Assessment of the geochemical conditions for the release of arsenic, iron copper into groundwater in the coastal aquifers at Yumigahama, Western Japan”, in: C. A. Brebbia, D. Almorza, D. Sales (Eds.), Water Pollution VII, Modeling, Measuring and Prediction, WIT Press, Southampton, pp.147–157.

Tseng CH, Huang YK, Huang YL, Chung CJ, Yang MH, Chen CJ, Hsueh YM (2005) Arsenic exposure, urinary arsenic speciation, and peripheral vascular disease in black foot disease-hyperendemic villages in Taiwan. Toxicol Appl Pharmacol 206:299–308.

Tuzen, M. (2002). Determination of some trace elements in whole blood and serum by GFAAS. Trace Elem. Electroly. 19, 202-204.

Tuzen, M. Cıtak, D. Mendil, D. Soylak, M. (2009). Arsenic speciation in natural water samples by coprecipitation-hydride generation atomic absorption spectrometry combination. Talanta 78(1), 52-56.

Tyagi, R.D. Blais, J.F. Meunier, N. Benmoussa, H. (1997). Simultaneous sewage sludge digestion and metal leaching effect of sludge solids concentration. Water Res. 31, 105–118.

U.N.E.P. (1994). The pollution of Lakes and Reservoirs. UNEP Environment Library No. 12, United Nations Environment Programme, Nairobi.

Uchino, T. Roychowdhury, T. Ando, M. Tokunaga, H. (2006). Intake of arsenic from water food composites and excretion through urine hair from a studied population in West Bengal India. Food Chem. Toxicol. 44, 455–461

Uddin, M.M. Harun-Ar-Rashid, A.K.M. Hossain, S.M. Hafiz, M.A. Nahar, K. Mubin, S.H. (2006). Slow arsenic poisoning of the contaminated groundwater users. Inter. J. Environ. Sci. Technol. 3, 447-453.

UNESCAP, (2001). United Nations economic and social commission for Asia and the Pacific, geology and health: solving the arsenic crisis in the Asia Pacific region, UNESCAP–UNICEF–WHO Expert Group Meeting, Bangkok, May 2–4.

United States Environmental Protection Agency (USEPA, 1996), Ecological Effects Test Guidelines, OPPTS 850.4200: Seed Germination/Root Elongation Toxicity Test, OPPTS, Washington, DC.

258

Ure, A. Quevauviller, P. Muntau, H. Griepink, B. (1993). Speciation of heavy metals in soils and sediments. An account of the improvement and harmonization of extraction techniques undertaken under the auspices of the BCR of the Commission of European Communities. Int. J. Environ. Anal. Chem. 51, 135–151.

Ure, A. Quevauviller, Ph. Muntau H. Griepink, B. (1993). Euro Report, EUR 14763 EN, Office for Official Publications of the European Communities, Luxembourg, pp. 85.

Ure, A.M. (1996). Single extraction schemes for soil analysis and related applications. Sci. Total Environ. 178, 3-10

USEPA (1998) Integrated Risk Information System: Arsenic, Inorganic. CASRN 7440- 38-2.

Vaclavikova, M. Gallios, G.P. Hredzak, S. Jakabsky, S. (2008). Removal of arsenic from water streams: an overview of available techniques. Clean Techn. Environ. Policy 10, 89–95

van Erp, P.J. Houba, V.J.G. Van Beusichem, M.L. (1998). One hundred molar calcium chloride extraction procedure. Part I: A review of soil chemical, analytical, and plant nutritional aspects. Commun. Soil Sci. Plant Anal. 26, 1603–1623.

Venugopal, T. Giridharan, L. Jayaprakash, M. (2008). Groundwater Quality Assessment Using Chemometric Analysis in the Adyar River, South India. Arch. Environ. Contam. Toxicol. 55(2), 180-190.

Versari, A. Parpinello, G.P. Galassi, S. (2002). Chemometric survey of Italian bottled mineral waters by means of their labelled physico-chemical and chemical composition. J. Food Compos. Anal. 15, 251– 264.

Vijayaraghavan, K. Yun, Y. (2008). Bacterial bio-sorbents and biosorption. Biotechnol. Adv. 26, 266–291.

Viraraghaven, T. Subramanian, K.S. Aruldoss, J.A. (1999). Arsenic in drinking water problems and solutions. Water Sci. Technol. 40(2), 69–76.

Wagner, J.B. Maira, R.R. Maria, C.S. Ieda, S.S. Jorge, N. Eliabeth de, O. Sonia, R.G.B. (2005). Interpretation of seasonal variation and etals and biotic properties in a tropical lake using multivariate analysis. Anal Sci. 21, 209-214.

Wang, C.X. Mo, Z. Wang, H. Wang, Z.J. Cao, Z.H. (2003). The transportation, time-dependent distribution of heavy metals in paddy crops. Chemosphere 50, 717-723.

Wang, J.H. Zhao, L.S. Wu, Y.B. (1998). Environmental geochemical study on arsenic in arseniasis areas in Shanyin and Yingxian, Shanxin Province. Geosci. 12, 243–248.

Wang, S. Mulligan, C.N. (2006). Effect of natural organic matter on arsenic release from soil and sediments into groundwater. Environ. Geochem. Health 28, 197–214.

Wang, S. Mulligan, C.N. (2006). Occurrence of arsenic contamination in Canada: Sources, behavior and distribution. Sci. Total Environ. 366, 701– 721.

Wang, Y. Shpeyzer, G.M. (1997). Genesis of thermal ground waters from Sippinan district, China. Appl. Geochem. 12, 437–445.

Wang-da, C. Guo-ping, Z. Hai-gen, Y. Wei, W. Min, X. (2006). Genotypic and environmental variation in Cd, chromium, arsenic, Ni, and lead concentrations in rice grains. J. Zhejiang Univ. Sci. B. 7(7), 565-571.

Wasserman, G.A. Liu, X. Parvez, F. Ahsan, H. Factor-Litvak, P. Geen, V.A. Slavkovich, V. Lolacono, N.J. Cheng, Z. Hussain, I. Momotaj, H. Graziano, J.H. (2004). Water Arsenic Exposure and Children’s Intellectual Function in Araihazar, Bangladesh. Environ. Health Persp. 112, 1329–1333.

Watanabe, C. Matsui, T. Inaoka, T. Kadono, T. Miyazaki, K. Bae, M.J. Ono, T. Ohtsuka, R. Bokul, A.T.M.M. (2007). Dermatological and nutritional/growth effects among children living in arsenic-contaminated communities in rural Bangladesh. J Environ Sci Health Part A 42, 1835–1841

Watt, G.C.M. Britton, A. Gilmour, H.G. Moore, M.R. Murray, G.D. Robertson, S.J. (2000). Public health implications of new guidelines for lead in drinking water: a case study in an area with historically high water leads levels. Food Chem Toxicol. 38, 73– 79.

WB (1998), World Development Indicators-1998, World Bank, Washington D.C.

259

Weber Jr., W.J. (1985). Adsorption theory, concepts and models, in: F.L. Slejko (Ed.), Adsorption technology: A Step-by-Step approach to Process Evaluation and Application, Marcel & Dekker Inc., New York,.

Webster, G. Nordstrom D.K. (2003). Geothermal arsenic, in: A.H. Welch, K.G. Stollenwerk (Eds.), Arsenic in Ground Water, Geochemistry and Occurrence, Kluwer Academic Publishers, Dordrecht, pp. 101–125.

Welch, A.H. Westjohn, D.B. Helsel, D.R. Wanty, R.B. (2000). Arsenic in ground water of the United States: occurrence and geochemistry. Ground Water 38, 589–604.

Wenzel, W.W. Kirchbaumer, N. Prohaska, T. Stingeder, G. Lombi, E. Adriano, D.C. (2001). Arsenic fractionation in soils using an improved sequential extraction procedure. Anal. Chim. Acta 436, 309-323.

WHO (world health organization) (1996). Guidelines for drinking-water quality. second ed. Geneva:WHO, p. 156–67

WHO (World Health Organization) (2001). Environmental Health Criteria 224, Arsenic and arsenic compounds Second edition World Health Organization, Geneva.

WHO, 2004. Guideline for drinking water quality, 3rd ed. In: Recommendation World Health Organization, Geneva.

Wickramasinghe, S.R. Han, B. Zimbron, J. Shen, Z. Karim, M.N. (2004). Arsenic removal by coagulation and filtration: comparison of groundwaters from the United States and Bangladesh, Desalination 169 (3), 231–244.

Wright R.O. Amarasiriwardena, C. Woolf, A.D. Jim, R. Bellinger D.C. (2006). Neuropsychological correlates of hair arsenic, manganese, and Cd levels in school-age children residing near a hazardous waste site. Neuro Toxicol. 27, 210-216.

WWF – Pakistan (2007) Pakistan’s water at risk, water and health related issues and key recommendations. Freshwater & Toxics Programme, Communications Division, WWF – Pakistan.

Xiao-ping, W. Xiao-quan, S. Shu-zhen, Z. Bei, W. (2004). A model for evaluation of the phytoavailability of trace elements to vegetables under the field conditions. Chemosphere 55(5), 811-822.

Xie X, Wang Y, Su C, Liu H, Duan M, Xie Z. Arsenic mobilization in shallow aquifers of Datong

Yan, X.P. Kerrich, R. Hendry, M.J. (2000). Distribution of arsenic (III), arsenic (V) and total inorganic arsenic in pore waters from a thick till and clay-rich aquitard sequence, Saskatchewan, Canada. Geochim. Cosmochim. Acta 64, 2637–2648.

Yoshida T, Yamauchi H, Sun GF. (2004). Chronic health effects in people exposed to arsenic via the drinking water: dose–response relationships in review. Toxicol Appl Pharm 198, 243–52

Yost, L.J. Schoof, R.A. Aucoin, R. (1998). Intake of inorganic arsenic in the North American diet. Human Ecol. Risk Assess. 4, 137–152.

Yuan, T. Luo, Q.F. Hu, J.Y. Ong, S.L. Ng, W.J. (2003). A study on arsenic removal from household drinking water, Journal of Environmental Science and Health; Part A, Toxic/Hazardous Sub. Environ. Eng, 38, 1731–1744.

Yun-Sheng, X. Bao-Dong, C. Peter, C. Andrew, S.F. You-Shan, W. Xiao-Lin, L. (2007). Arsenic uptake by arbuscular mycorrhizal maize (Zea mays L.) grown in an arsenic-contaminated soil with added phosphorus. J. Environ. Sci. 19, 1245–1251.

Zaigham, N.A. Nayyar, Z.A. Hisamuddin, N. (2009). Review of geothermal energy resources in Pakistan. Renew Sustain Energy Rev. 13, 212-221.

Zhang, G. Qu, R. Sun, C. Ji, C. Chen, H. Wang, C. Niu, Y. (2008). Adsorption for metal ions of chitosan coated cotton fiber. Ads. J. Appl. Polym. Sci. 110, 2321–2327.

Zhang, G.P. Fukami, M. Sekimoto, H. (2002). Influence of Cd on mineral concentrations and yield components in wheat genotypes differing in Cd tolerance at seedling stage. Field Crops Res. 77, 93-98.

260

Zhang, L. Ishi, D. Shitou, K. Morita, Y. Isozaki, A. (2005). Simultaneous multi-element analysis of total As, Se and Sb on titanium dioxide by slurry sampling graphite furnace atomic absorption spectrometry. Talanta 68, 336-369.

Zhang, L. Morita, Y. Yoshikawa, K. Isozaki, A. (2007). Direct Simultaneous Determination for Ultratrace As, Se and Sb in River Water with Graphite-Furnace Atomic Absorption Spectrometry by TiO2-Slurry Sampling. Anal. Sci. 23, 365-369

Zhang, Q. Minami, H. Inoue, S. I. (2004). Differential determination of trace amounts of arsenic(III) and arsenic(V) in seawater by solid sampling atomic absorption spectrometry after preconcentration by coprecipitation with a nickel–pyrrolidine dithiocarbamate complex , Atsuya. Anal. Chim. Acta 508, 99 –105

Zhou, Q. Zhang, J. Fu, J. Shi, J. Jiang, G. (2008). Biomonitoring: An appealing tool for assessment of metal pollution in the aquatic ecosystem. Anal. Chim. Acta. 606, 135–150.

Zouboulis, A. Katsoyiannis, I. (2002). Removal of arsenates from contaminated water by coagulation– direct filtration. Sep. Sci. Technol. 37, 2859–2873.

Ph.D Thesisprr.hec.gov.pk/jspui/bitstream/123456789/1049/2/1129S.pdfIqbal Bhanger, Director National...

Documents

Transcript of Ph.D Thesisprr.hec.gov.pk/jspui/bitstream/123456789/1049/2/1129S.pdfIqbal Bhanger, Director National...