Post on 10-Mar-2020
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
Table 8. Ranges of analytical data of the ground and surface water samples in
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
Table 11. Ranges of analytical data of the ground and surface water samples in
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
4.3.2. Conclusions 110
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
4.4.2.3. Conclusions 138
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
4.5.5. Conclusions 146
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
4.6.1.1.5. Effects of equilibration temperature and time 150
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
4.6.1.2. Application 153
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
4.6.1.3. Conclusions 155
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
4.6.2.6. Conclusions 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
4.6.3.2. Conclusions 169
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
temperature 35 ○C, equilibration time 10 min.
171
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.
172
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.
172
Fig 25. Effect of foreign ions on the pre-concentration and determination of As (10
µg L-1)
173
4.7.1.1.4. Effects of equilibration temperature and time 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
4.7.1.2. Application 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
4.7.1.3. Conclusions 176
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
4.8.1.13. Conclusion 216
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
4.8.2.3. Conclusion 232
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 life- style 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
The work presented in this section has been published as:
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
The work presented in this section has been published as:
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
Total iAs 58.0±33.6
As3+ 24.5±14.7
As5+ 33.7±20.2
Tube Well Water Sample
n = 180
Total As 38.8±24.0
Total iAs 36.7±22.2
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
The work presented in this section has been accepted as:
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
The work presented in this section has been published as:
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
The work presented in this section has been published as:
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.
4.6.1.1.2. Effect of pH
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.
4.6.1.1.5. Effects of equilibration temperature and time
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
4.6.1.2. Application
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.
4.6.1.3. Conclusions
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
The work presented in this section has been published as:
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.
4.6.2.5. Conclusions
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.
4.6.3.2. Conclusions
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
The work presented in this section has been accepted as:
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.
4.7.1.1.1. Effect of pH
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.
4.7.1.1.4. Effects of equilibration temperature and time
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.
4.7.1.2. Application
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.
4.7.1.3. Conclusions
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
The work presented in this section has been accepted as:
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
The work presented in this section has been accepted as:
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
The work presented in this section has been accepted as:
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
The work presented in this section has been published as:
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.
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