THE ROLE OF SOME BRACHYURAN CRABS IN THE BIOASSESSMENT AND BIOSORPTION OF HEAVY METALS
ASMAT SALEEM SIDDIQUI M.Sc.
CENTRE OF EXCELLENCE IN MARINE BIOLOGY UNIVERSITY OF KARACHI FACULTY OF SCIENCE UNIVERSITY OF KARACHI KARACHI, PAKISTAN 2017
CERTIFICATE
This thesis by Miss Asmat Saleem Siddiqui is accepted in the present form by the University of Karachi as satisfying the partial requirements for the Degree of Doctor of Philosophy in Marin Biology.
Internal Examiner: ______Thesis Supervisor
External Examiner: ______
Director: ______Centre of Excellence in Marine Biology, University of Karachi, Karachi
Dated: ______
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Dedicated to My Beloved Parents
ABDUL SALEEM SIDDIQUI And ZAKIA KHATOON
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TABLE OF CONTENT
S. No Contents Page No.
CERTIFICATE i DEDICATION ii TABLE OF CONTENT iii LIST OF FIGURES vi LIST OF TABLES ix ACKNOWLEDGEMENT xii ABSTRACT xiv KHULASA (URDU) xviii
1.0 CHAPTER 01 1 GENERAL INTRODUCTION
1.1 Heavy Metals Pollution in Marine Environment 2 1.2 Interaction of Heavy Metals with Marine Sediments 2 1.3 Role and Impacts of Heavy Metal Pollution in Marine Ecosystem 3 1.4 Ecological and Biomonitoring role of Brachyuran Crabs 6 1.5 Use of Crab Shell in Biosorption of Heavy Metals 8 1.6 Detailed Historical Perspective and Literature Review 10 1.7 Rationale of the Study 13 1.8 Structural Framework of Dissertation 14 1.9 REFERENCES 16
2.0 CHAPTER 02 31 A DECADE STUDY OF COMPARISON ON METAL CONTAMINATION IN MARINE SEDIMENTS AND ITS IMPACT ON CRAB DIVERSITY AND DISTRIBUTION
2.1. INTRODUCTION 32 2.1.1 Objectives 35 2.2. MATERIALS AND METHODS 36 2.2.1 Coastal Environment of Pakistan 36 2.2.2 Description of monitoring sites 37 Dhabeji 37 Bhambore 37 Phitti Creek 38 Korangi Creek 38 Sandspit 39 Sonari 39 Sonmiani Bay 39 2.2.3 Sampling Procedure 42 2.2.4 Laboratory Analysis 45 (I) Sediment Analysis 45 (II) Crab Analysis 46 2.2.5 Data Quality and Precision 47 2.2.5 Pollution Depiction by Multiple Indices Approach 47
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2.2.6 Data Analysis 51 2.3. RESULTS 52 2.3.1. Physical Properties of Sediments 52 2.3.2. Comparison of Heavy Metal Concentrations during the Last Decade 59 2.3.3. Pearson’s Correlation Analysis 65 (I) Relationship between Physical Properties of Sediments 65 (II) Relationship between Physicochemical Properties of Sediments 65 (III) Inter-Elemental Relationship 66 2.3.4. Comparison of Metal Contamination by Multiple Indices Approach 70 (I) Sediment Quality Guidelines (SQGs) 70 (a) ERL/ERM sediment quality guidelines 70 (b) TEL/PEL Sediment Quality Guidelines 77 (II) Geo-Accumulation Index (Igeo) 83 (III) Enrichment Factor (EF) 87 (IV) Contamination Factor (CF) and Contamination Degree (CD) 91 (V) Potential Ecological Risk Index (PERI) 95 2.3.5. Crab Distribution and Diversity 99 (I) Species Composition 99 (II) Crab Density 101 (III) Crab Diversity 101 (IV) Crab Equitability 101 (V) Margalef’s Species Richness 101 2.3.5. Relationship between Metals Contamination in Sediments with Biotic 104 Indices of Crabs 2.4. DISCUSSION 106 2.5. REFERENCES 121
3.0 CHAPTER 03 138 IDENTIFICATION AND ROLE OF INDICATORS (SEDIMENT AND CRAB) FOR HEAVY METAL MONITORING ALONG THE COAST OF PAKISTAN
3.1. INTRODUCTION 139 3.1.1 Objectives 143 3.2. MATERIALS AND METHODS 144 3.2.1. Sites Selection 144 3.2.2. Criteria for Biomonitor Species Selection 144 3.2.3. Sampling Procedure 144 3.2.4 Laboratory Analysis 147 (I) Sediment Analysis 147 (II) Crab analysis 147 3.2.5 Data Quality and Precision 149 3.2.6. Bioaccumulation Factor (BAF) 149 3.2.7. Statistical Analysis 150 3.3. RESULTS 151 (A) FIRST BIOMONITORING YEAR (BMY-I) 151 Role of Some Brachyurans Crabs in Bio-Assessment of Heavy Metals 151 3.3.1. Crab Distribution and Species Composition 151 3.3.2. Morphometric Analysis of Crabs 152 3.3.3. Heavy Metal Concentrations in Sediments 158 3.3.4. Heavy Metals Accumulation in Selected Crab Species 162
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(I) Heavy Metal Concentrations in Eurycarcinus orientalis 162 (II) Heavy Metal Concentrations in Scopimera crabricauda 162 (III) Heavy Metal Concentrations in Austruca sindensis 162 (IV) Heavy Metal Concentrations in Opusia indica 164 (V) Heavy Metal Concentrations in Ilyoplax frater 168 (VI) Heavy Metal Concentrations in Austruca iranica 172 (VII) Heavy Metal Concentrations in Macrophthalmus depressus 177 3.3.5. Crabs as Potential Indicator of Heavy Metal Contamination 182 3.3.6. Environmental Factors affecting on metal accumulation in crabs 188 (B) SECOND BIOMONITORING YEAR (BMY-II): 193 A. iranica and M. depressus as Potential indicator for Heavy Metals 193 contamination 3.3.7. Heavy metal concentrations in sediments 193 3.3.8. Austruca iranica as Bio-indicator of Heavy Metals 195 3.3.9. Macrophthmus depressus as Bio-indicator of Heavy Metals 202 3.4. DISCUSSION 209 3.5. REFERENCES 221
4.0 CHAPTER 04 235 CHARACTERIZATION OF ADSORPTION CAPACITY OF CRAB CHITIN FOR TOXIC HEAVY METALS (CADMIUM AND LEAD) FROM AQUEOUS SOLUTION
4.1. INTRODUCTION 236 4.1.1 Objectives 239 4.2. MATERIALS AND METHODS 240 4.2.1. Preparation of Raw Crab Shell 240 4.2.2. Purification of Crab Chitin 240 4.2.3. Metal Stock Solution Preparation 240 4.2.4. Batch Experiment 241 4.2.5. Adsorption Isotherms Studies 242 4.2.6. Analysis of Adsorbent Surface 243 4.3. RESULTS 244 4.3.1. Factors and their Effects on Biosorption 244 (I) Contact Time 244 (II) Metal Concentrations 246 (III) The pH 248 4.3.2. Adsorption Isotherm Models 250 4.3.3. FTIR Analysis 253 4.3.4. SEM-EDS Analysis 259 4.4. DISCUSSION 265 4.5. REFERENCES 271
5.0 CHAPTER 05 278 CONCLUSION
CONCLUSION 279
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LIST OF FIGURES
S. Titles of Figure Page No. No.
2.1 Map showing the seven monitoring sites (DH = Dhabeji, BH = Bhambore, PC = 41 Phitti Creek, KC = Korangi Creek, SP = Sandspit, SO = Sonari and SB = Sonmiani Bay) along the coast of Pakistan. 2.2 Mean distribution variability of (a) percent moisture, (b) porosity, and (c) organic 55 matter in sediments from seven monitoring sites (DH = Dhabeji, BH = Bhambore, PC = Phitti Creek, KC = Korangi Creek, SP = Sandspit, SO = Sonari and SB = Sonmiani Bay) during the two monitoring years (MY-I and MY-II). 2.3 Grain size distribution of sediments from seven monitoring sites (DH = Dhabeji, 56 BH = Bhambore, PC = Phitti Creek, KC = Korangi Creek, SP = Sandspit, SO = Sonari and SB = Sonmiani Bay) during the two monitoring years (MY-I and MY- II). 2.4 Comparison of heavy metal concentrations (µg g-1) in sediments from seven 63 monitoring sites (DH = Dhabeji, BH = Bhambore, PC = Phitti Creek, KC = Korangi Creek, SP = Sandspit, SO = Sonari and SB = Sonmiani Bay) during the two monitoring years (MY-I and MY-II). 2.5 Variations in mERLq of single metals in (a) MY-I (b) MY-II, and (c) mERLq of 73 combined metal from seven monitoring sites (DH = Dhabeji, BH = Bhambore, PC = Phitti Creek, KC = Korangi Creek, SP = Sandspit, SO = Sonari and SB = Sonmiani Bay) during the two monitoring years (MY-I and MY-II). 2.6 Variations in mERMq of single metals in (a) MY-I (b) MY-II, and (c) mERMq of 75 combined metal from seven monitoring sites (DH = Dhabeji, BH = Bhambore, PC = Phitti Creek, KC = Korangi Creek, SP = Sandspit, SO = Sonari and SB = Sonmiani Bay) during the two monitoring years (MY-I and MY-II). 2.7 Variations in mTELq of single metals in (a) MY-I (b) MY-II, and (c) mTELq of 79 combined metal from seven monitoring sites (DH = Dhabeji, BH = Bhambore, PC = Phitti Creek, KC = Korangi Creek, SP = Sandspit, SO = Sonari and SB = Sonmiani Bay) during the two monitoring years (MY-I and MY-II). 2.8 Variations in mPELq of single metals in (a) MY-I (b) MY-II, and (c) mPELq of 81 combined metal from seven monitoring sites (DH = Dhabeji, BH = Bhambore, PC = Phitti Creek, KC = Korangi Creek, SP = Sandspit, SO = Sonari and SB = Sonmiani Bay) during the two monitoring years (MY-I and MY-II). 2.9 Variations in Geo-accumulation index of eight heavy metals in sediments from 85 seven monitoring sites (DH = Dhabeji, BH = Bhambore, PC = Phitti Creek, KC = Korangi Creek, SP = Sandspit, SO = Sonari and SB = Sonmiani Bay) during the two monitoring years (MY-I and MY-II). 2.10 Variations in enrichment factor of eight heavy metals (Fe, Cu, Zn, Ni, Cr, Co, Pb 89 and Cd) in sediments from seven monitoring sites (DH = Dhabeji, BH = Bhambore, PC = Phitti Creek, KC = Korangi Creek, SP = Sandspit, SO = Sonari and SB = Sonmiani Bay) during the two monitoring years (MY-I and MY-II). 2.11 Variations in single metal index contamination factor (CF) in (a) MY-I, (b) MY-II 93 and combine metal index (c) contamination degree (CD) from seven monitoring sites (DH = Dhabeji, BH = Bhambore, PC = Phitti Creek, KC = Korangi Creek, SP = Sandspit, SO = Sonari and SB = Sonmiani Bay) during the two monitoring years (MY-I and MY-II). 2.12 Variations in single metal index ecological risk factor (ER) in (a) MY-I, (b) MY-II 97 and combine metal index (c) potential ecological risk index (PERI) from seven monitoring sites (DH = Dhabeji, BH = Bhambore, PC = Phitti Creek, KC = Korangi Creek, SP = Sandspit, SO = Sonari and SB = Sonmiani Bay) during the two monitoring years (MY-I and MY-II).
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2.13 Crabs diversity and abundance from selected coastal areas of Pakistan during the 100 two monitoring years (MY-I and MY-II). 2.14 The mean distribution of biotic indices (a) density, (b) diversity, (c) equitability 102 and (d) species richness from seven monitoring sites (DH = Dhabeji, BH = Bhambore, PC = Phitti Creek, KC = Korangi Creek, SP = Sandspit, SO = Sonari and SB = Sonmiani Bay) during the two monitoring years (MY-I and MY-II). 3.1 Mean density (individuals/m2) of different crab species collected from coastal 156 areas of Pakistan during the BMY-I. 3.2 Spatial distribution of eight heavy metal concentrations (µg g-1) from nine coastal 160 areas (Port Qasim = PQ, Rato Kot = RK, Korangi Creek St.1 = KC1 and St.2 = KC2, Sandspit St.1 = SP1 and St.2 = SP2, Hawks Bay = HB, Sonari = SO and Sonmiani Bay = SB) during the BMY-I. 3.3 Metal concentrations (µg g-1) and bioaccumulation factor (BAF) of heavy metals 163 in three crab species (a) E. orientalis, (b) S. crabricauda, (c) A. sindensis collected from different coastal areas of Pakistan. 3.4 Heavy metal distribution in O. indica collected from two coastal areas (SO = 165 Sonari and PQ = Port Qasim) (a) Mean heavy metal concentrations (µg/g) and (b) Bioaccumulation factor of heavy metals. 3.5 Heavy metal distribution in I. frater collected from three coastal areas (PQ = Port 169 Qasim, RK = Rato Kot, and KC2 = Korangi Creek Station 2) during the BMY-I (a) Mean metal concentrations (µg/g) and (b) Bioaccumulation factor. 3.6 Metal concentrations (µg g-1) in A. iranica from five coastal areas (SP1 = Sandspit 173 1, SP2 = Sandspit 2, SO = Sonari, RK = Rato Kot and SB = Sonmiani Bay) during the BMY-I. 3.7 Bioaccumulation factor of heavy metals in A. iranica collected from five coastal 175 areas (RK = Rato Kot, SP1 = Sandspit 1, SP2 = Sandspit 2, SO = Sonari, and SB = Sonmiani Bay) during the BMY-I. 3.8 (a) Intersexual variations in heavy metals accumulation in A. iranica (b) 178 Intersexual and spatial variation in heavy metals accumulation in A. iranica from three coastal areas (SP1, SP2 and SO) during the BMY-I. (Genders: F = female, M = male; Sites abbreviation: 1: Sandspit St.1, 2: Sandspit St.2, 3: Sonari). 3.9 Metal concentrations (µg g-1) in M. depressus from six coastal areas (SP1 = 178 Sandspit 1, SP2 = Sandspit 2, KC1 = Korangi Creek 1, KC2 = Korangi Creek 2, RK = Rato Kot, and HB = Hawks Bay) collected during BMY-I. 3.10 Bioaccumulation factor of heavy metals in M. depressus from six coastal areas 180 (RK = Rato Kot, KC1 = Korangi Creek 1, KC2 = Korangi Creek 2, SP1 = Sandspit 1, SP2 = Sandspit 2, and HB = Hawks Bay) during BMY-I. 3.11 (a) Intersexual variations in heavy metals accumulation in M. depressus (b) 181 Intersexual and spatial variation in heavy metals accumulation in M. depressus from three coastal areas (KC1, KC2 and HB) during BMY-I. (Genders: F = female, M = male; Sites abbreviation: 1 = Korangi Creek1, 2 = Korangi Creek 2, 3 = Hawks Bay). 3.12 Mean heavy metal concentration and their bioaccumulation factors (a) Fe 184 concentration (µg g-1) (b) Fe bioaccumulation factor (BAF) (c) Cu concentration (µg g-1) and (d) Cu bioaccumulation factor (BAF) in seven brachyuran crab species (in ascending order) during the BMY-I from coastal areas of Pakistan. 3.13 Mean heavy metal concentration and their bioaccumulation factors (a) Zn 185 concentration (µg g-1) (b) Zn bioaccumulation factor (BAF) (c) Co concentration (µg g-1) and (d) Co bioaccumulation factor (BAF) in seven brachyuran crab species (in ascending order) during the BMY-I from coastal areas of Pakistan. 3.14 Mean heavy metal concentration and their bioaccumulation factors (a) Cr 186 concentration (µg g-1) (b) Cr bioaccumulation factor (BAF) (c) Ni concentration (µg g-1) and (d) Ni bioaccumulation factor (BAF) in seven brachyuran crab species (in ascending order) during the BMY-I from coastal areas of Pakistan. 3.15 Mean heavy metal concentration and their bioaccumulation factors (a) Pb 187 concentration (µg g-1) (b) Pb bioaccumulation factor (BAF) (c) Cd concentration
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(µg g-1) and (d) Cd bioaccumulation factor (BAF) in seven brachyuran crab species (in ascending order) during the BMY-I from coastal areas of Pakistan. 3.16 Relationship between percent moisture and organics of sediments and metals 190 concentrations in crab species from coastal areas of Pakistan. 3.17 Relationship between grain size of sediments and metals concentrations in crab 191 species from coastal areas of Pakistan. 3.18 Relationship between heavy metal concentrations in sediment and crab species 192 from coastal areas of Pakistan. 3.19 Spatial and gender variations of heavy metals accumulation (µg g-1) in tissues of 197 fiddler crabs (A. iranica) collected from Sandspit and Sonari during BMY-II. 3.20 Mean bioaccumulation factors (BAF) of five heavy metals in tissues of A. iranica 199 with respect to sites (SO = Sonari and SP = Sandspit) and genders (F = females, M = males) during BMY-II. 3.21 Linear regression analysis between the heavy metals accumulation in tissues of 200 fiddler crab (A. iranica) with respect to the carapace size during the BMY-II. 3.22 Spatial and gender variations in heavy metals accumulation (µg g-1) in tissues of 204 sentinel crab (M. depressus) collected from Korangi Creek and Sandspit during the BMY-II. 3.23 Mean distribution of five heavy metals bioaccumulation factors (BAF) in M. 206 depressus with respect to sites and genders from two sites Korangi Creek and Sandspit during the BMY-II. 3.24 Linear regression analysis between the heavy metals accumulation in tissues and 207 carapace size of sentinel crab (M. depressus) during the BMY-II. 4.1 Effect of contact time (min) on metal uptake and % removal through the crab 245 chitin (a) Cd(II) biosorption (b) Pb(II) biosorption from the aqueous solution. 4.2 The effect of initial metal concentration on uptake and removal of metals ions 247 through the crab chitin (a) Cd(II) biosorption (b) Pb(II) biosorption from the aqueous solution. 4.3 Effect of pH on metal uptake and percent removal of metals through the crab 249 chitin (a) Cd(II) biosorption (b) Pb(II) biosorption from aqueous solution. 4.4 The isotherm models for Cd(II) and Pb(II) biosorption through crab chitin in 251 aqueous medium (a) Langmuir isotherm for Cd(II), (b) Langmuir isotherm for Pb(II), (c) Freundlich isotherm for Cd(II) and (d) Freundlich isotherm for Pb(II). 4.5 The IR spectra of (a) raw crab shell (b) crab chitin (c) Cd(II) loaded chitin and (d) 255 Pb(II) loaded chitin as obtained during the experimental studies. 4.6 The variations in surface characteristics of (a) raw crab shell (b) crab chitin (c) 260 Cd(II) loaded chitin and (d) Pb(II) loaded chitin at the magnification of 50x. 4.7 The variations in surface characteristics of (a) raw crab shell (b) crab chitin (c) 261 Cd(II) loaded chitin and (d) Pb(II) loaded chitin at the magnification of 5,000x. 4.8 The variations in surface characteristics of (a) raw crab shell (b) crab chitin (c) 262 Cd(II) loaded chitin and (d) Pb(II) loaded chitin at the magnification of 10,000x. 4.9 The EDS spectra of (a) raw crab shell (b) crab chitin (c) Cd(II) loaded chitin and 263 (d) Pb(II) loaded chitin obtained during the experimental studies.
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LIST OF TABLES
S. Titles of Table Page No. No.
2.1 The general information of study sites from coastal areas of Pakistan during two 44 monitoring years. 2.2 Summary statistics of physical properties of sediments during the last decade along 54 the coastal areas of Pakistan. 2.3 Variations in % moisture, % porosity and % organic matter of sediments by analysis 57 of variance (ANOVA) during the last decade along the coast of Pakistan. 2.4 Variations in grain size composition of sediments by analysis of variance (ANOVA) 58 during the last decade along the coast of Pakistan. 2.5 Summary statistics of eight heavy metal concentration in coastal sediments during 62 the two monitoring years (MY-I and MY-II). 2.6 Variations in eight heavy metal concentrations in coastal sediments of Pakistan by 64 analysis of variance (ANOVA) during the last decade. 2.7 Pearson’s correlation analysis between physical properties [porosity (%Por), 67 moisture (%Moi), organic matter (%OM), granules (%G), very coarse sand (%VCS), coarse sand (%CS), medium sand (%MS), fine sand (%FS) and very fine sand (%VFS)] of sediments during (a) MY-I and (b) MY-II. (r = correlation coefficient and p = probability level) 2.8 Pearson’s correlation analysis between physical [porosity (%Por), moisture (%moi), 68 organic matter (%OM), granules (%G), very coarse sand (%VCS), coarse sand (%CS), medium sand (%MS), fine sand (%FS) and very fine sand (%VFS)] and chemical (Fe, Cu, Zn, Ni, Co, Cr, Pb and Cd) properties of sediment during (a) MY-I and (b) MY-II. (r = correlation coefficient and p = probability level) 2.9 Pearson’s correlation analysis between the distribution of heavy metals (Fe, Cu, Zn, 69 Ni, Co, Cr, Pb and Cd) in sediments during (a) MY-I and (b) MY-II. (r = correlation coefficient and p = probability level) 2.10 Summary statistics of single and combined mean ERL/ERM quotients of six heavy 72 metals (Ni, Zn, Pb, Cu, Cr and Cd) in the coastal sediments of Pakistan during two monitoring years (MY-I and MY-II). 2.11 Variations in single and combine mERLq for six heavy metals (Ni, Zn, Pb, Cu, Cr 74 and Cd) by analysis of variance (ANOVA) in the coastal sediments of Pakistan during two monitoring years (MY-I and MY-II). 2.12 Variations in single and combine mERMq for six heavy metals (Ni, Zn, Pb, Cu, Cr 76 and Cd) by analysis of variance (ANOVA) in the coastal sediments of Pakistan during two monitoring years (MY-I and MY-II). 2.13 Summary statistics of single and combine mean TEL/PEL quotients of six heavy 78 metals (Ni, Zn, Pb, Cu, Cr and Cd) in the coastal sediments of Pakistan during two monitoring years (MY-I and MY-II). 2.14 Variations in single and combine mTELq of six heavy metals (Ni, Zn, Pb, Cu, Cr 80 and Cd) by analysis of variance (ANOVA) in the coastal sediments of Pakistan during two monitoring years (MY-I and MY-II). 2.15 Variations in single and combine mPELq of six heavy metals (Ni, Zn, Pb, Cu, Cr and 82 Cd) by analysis of variance (ANOVA) in the coastal sediments of Pakistan during two monitoring years (MY-I and MY-II). 2.16 Summary statistics of Geo-accumulation index for eight heavy metals (Fe, Cu, Zn, 84 Ni, Cr, Co, Pb and Cd) in the coastal sediments of Pakistan during two monitoring years (MY-I and MY-II). 2.17 Analysis of variance (ANOVA) for Geo-accumulation index of eight heavy metals 86 (Fe, Cu, Zn, Ni, Cr, Co, Pb and Cd) in the coastal sediments of Pakistan during two monitoring years (MY-I and MY-II).
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2.18 Summary statistics of enrichment factor for eight heavy metals (Fe, Cu, Zn, Ni, Cr, 88 Co, Pb and Cd) in the coastal sediments of Pakistan during two monitoring years (MY-I and MY-II). 2.19 Analysis of variance (ANOVA) for enrichment factor of eight heavy metals (Fe, Cu, 90 Zn, Ni, Cr, Co, Pb and Cd) in the coastal sediments of Pakistan during two monitoring years (MY-I and MY-II). 2.20 Summary statistics of single and combined indices of contamination factor and 92 degree for eight heavy metals (Fe, Cu, Zn, Ni, Cr, Co, Pb and Cd) in the coastal sediments of Pakistan during two monitoring years (MY-I and MY-II). 2.21 Variations in single (contamination factor) and combine (contamination degree) 94 metal pollution indices for eight heavy metals (Fe, Cu, Zn, Ni, Cr, Co, Pb and Cd) by analysis of variance (ANOVA) in the coastal sediments of Pakistan during two monitoring years (MY-I and MY-II). 2.22 Summary statistics of single and combined indices of ecological risk factor and 96 index for six heavy metals (Cu, Zn, Cr, Ni, Pb and Cd) in the coastal sediments of Pakistan during two monitoring years (MY-I and MY-II). 2.23 Variations in single (ecological risk factor) and combine (potential ecological risk 98 index) metal pollution indices of six heavy metals (Cu, Zn, Ni, Cr, Pb and Cd) by analysis of variance (ANOVA) in the coastal sediments of Pakistan during two monitoring years (MY-I and MY-II). 2.24 ANOVA analysis for biodiversity parameters (density, diversity, equitability and 103 species richness) among the study sites between two monitoring years (MY-I and MY-II). 2.25 (a) The significant relationships between the biotic indices of crabs with heavy metal 105 concentrations in sediments. (b) The significant relationships between the biotic indices of crabs with pollution 105 indices. 3.1 (a) The general information of sampling sites along the coastal areas of Pakistan in 146 first biomonitoring year (BMY-I = 2011 to 2012). (b) The general information of sampling sites along the coastal areas of Pakistan in 146 second biomonitoring year (BMY-II = 2016). 3.2 The selection of sample type from crab species for heavy metal analysis in two 148 biomonitoring years (BMY-I and II). 3.3 Percent composition of crab species along the coastal areas (Rato Kot = RK, Korangi 153 Creek St.1 = KC1 and St.2 = KC2, Port Qasim = PQ, Sandspit St.1 = SP1 and St.2 = SP2, Sonari = SO and Sonmiani Bay = SB) during the BMY-I. 3.4 The biotic parameters of crabs (density, frequency, abundance and relative density) 154 estimated from selected coastal areas of Pakistan during the BMY-I. 3.5 The summary statistics of crab density, diversity, equitability and species richness 156 from eight coastal areas (Port Qasim = PQ, Rato Kot = RK, Korangi Creek St.1 = KC1 and St.2 = KC2, Sandspit St.1 = SP1 and St.2 = SP2, Sonari = SO and Sonmiani Bay = SB) during the BMY-I. 3.6 Morphometric analysis of crab species collected during the BMY-I from nine coastal 157 areas of Pakistan. 3.7 The summary statistics of eight heavy metal concentrations (µg g-1 dry weight) in 159 coastal sediments collected during the BMY-I from nine coastal areas of Pakistan. 3.8 One-way ANOVA analysis of heavy metals concentrations in sediments from nine 161 coastal areas during the BMY-I. 3.9 One-way ANOVA analysis followed by Tukey pairwise comparison of eight heavy 166 metal concentrations in O. indica collected from two coastal areas (SO = Sonari and PQ = Port Qasim) during the BMY-I. 3.10 Pearson’s correlation coefficient (r) between heavy metal concentrations in O. indica 167 collected during BMY-I. 3.11 One-way ANOVA analysis followed by Tukey pairwise comparison of heavy metal 170 concentrations in I. frater collected from three coastal areas (PQ = Port Qasim, RK = Rato Kot, and KC2 = Korangi Creek Station 2) during the BMY-I.
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3.12 Pearson’s correlation coefficient (r) between heavy metal concentrations in I. frater 171 collected during BMY-I. 3.13 One-way ANOVA analysis followed by Tukey pairwise comparison of heavy metal 174 concentrations in A. iranica from five coastal areas (RK = Rato Kot, SP1 = Sandspit 1, SP2 = Sandspit 2, SO = Sonari, and SB = Sonmiani Bay) during the BMY-I. 3.14 Pearson’s correlation coefficient (r) between accumulated heavy metal 175 concentrations in A. iranica collected during BMY-I. 3.15 One-way ANOVA analysis followed by Tukey pairwise comparison of heavy metal 179 concentrations in M. depressus from six coastal areas (RK = Rato Kot, KC1 = Korangi Creek 1, KC2 = Korangi Creek 2, SP1 = Sandspit 1, SP2 = Sandspit 2, and HB = Hawks Bay) during BMY-I. 3.16 Pearson’s correlation coefficient (r) between heavy metal concentrations in M. 180 depressus collected during BMY-I. 3.17 Relationship between physical properties of sediment and metals concentrations in 189 crab species from coastal areas of Pakistan in BMY-I. 3.18 Relationship between heavy metal concentrations in sediment and crab species from 192 coastal areas of Pakistan during the BMY-I. 3.19 The distribution and variation of five heavy metals (µg g-1) in sediments from 194 (Korangi Creek = KC, Sonari = SO and Sandspit = SP) during BMY-II. 3.20 Analysis of variance (ANOVA) of heavy metals accumulation (µg g-1) in tissues of 198 fiddler crab (A. iranica) during BMY-II. 3.21 Linear regression analysis between the heavy metals concentrations in tissues of 201 male and female fiddler crabs (A. iranica) and sediments collected from their habitat during the BMY-II. 3.22 Analysis of variance (ANOVA) of heavy metals accumulation in tissues of sentinel 205 crab (M. depressus) during BMY-II. 3.23 Linear regression analysis between heavy metals concentrations in tissues of sentinel 208 crab (M. depressus) and sediments during the BMY-II. 4.1 The Langmuir and Freundlich adsorption isotherm constants for Cd(II) and Pb(II) 252 through crab chitin during the experimental studies. 4.2 The FTIR spectral characteristics of raw shell of crab and extracted chitin obtained 256 during the experimental studies. 4.3 FTIR spectral characteristics of chitin before and after the adsorption of Cd(II) 257 obtained during the experimental studies. 4.4 FTIR spectral characteristics of chitin before and after biosorption of Pb(II) obtained 258 during the experimental studies. 4.5 The percent composition of major elements from raw crab shell, crab chitin and 264 metal loaded crab chitin obtained by EDS during the experimental studies.
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ACKNOWLEDGMENTS
Proclaim, ‘If the sea became ink for the Words of my Lord, the sea would indeed be used up and the Words of my Lord would never - even if we bring another like it for help.’ (Surah Al-Kahf, 109)
Praise be to Almighty Allah, the most beneficent, the most merciful, for His eternal favors and blessings in every step of my life. I would like to express my deepest gratitude to Almighty Allah for granted me insight and endurance for achieving this task.
I would certainly not ever forget to express my cordial gratitude to the Exalted Prophet, my eternal love, the most generous and kind, Muhammad (Peace Be upon Him), for a continuous source of benevolence and guidance in every walk of life, the harmony of heart and mind in the rainy days.
I am especially grateful and obligated to my Spiritual Guide (Peer O Murshid), the Honorable, Muhammad Ilyas Attar Qadiri, for enlightening my mind and soul and eliminate the obstacles I have ever faced in my life.
I would like to express my deepest gratitude to my beloved supervisor ‘Dr. Noor Us Saher’ for her insightful supervision and enormous support in every stage throughout the dissertation. Her enthusiasm to provide her precious time generously has been greatly appreciated. Her profound advices and guidance has been greatly valuable in analyzing data, interpretation of results and improving my abilities. It was a magnificent and amazing experience for me to have the opportunity to acquire the ideas, knowledge and expertise from her during the research period. Many thanks for everything!
I am particularly thankful to my laboratory fellows and friends, Nayab Kanwal, Uroj Aziz, Farah Naz and Sahir Odhano, for their constructive suggestions, enormous support and inspirations throughout the whole phase. It was indeed magnificent for me to have the chance to share the knowledge and enjoy the best moments together. Many thanks to all of them for togetherness in this superlative journey!
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I would like to acknowledge the help and support provided by my senior lab fellows, Dr. Asif Gondal and Mohammad Raof Niazi and junior lab fellows Syeda Hadiqa Noor and Altaf Hussain Narejo. Many thanks!
My special thanks are extended to all honorable teachers and departmental fellows of the Centre of Excellence in Marine Biology, University of Karachi, it was great sharing work place and laboratory with all of you. I would like to offer my thanks to the staff of the Centre of Excellence in Marine Biology, University of Karachi, especially Rohail Ahmed for field assistance during a sampling campaign and Mohammad Usman for laboratory assistance.
I wish to recognize the financial help and facilities utilized that were provided by various research grants such as World Wildlife Fund, Pakistan (WWF), Higher Education Commission (HEC) and Pakistan Science Foundation (PSF) to my supervisor Dr. Noor Us Saher. I would like to acknowledge the help provided by the Centralized Science Laboratory, University of Karachi, for analyzing the research samples throughout the research period. I am particularly grateful for the one- year research grant given by Higher Education Commission (HEC) to me (Asmat Saleem Siddiqui), under the program ‘Access to Scientific Instrumentation’. Thanks for all funding supporters and facilitators during the research phase!
Finally yet importantly, I would like to express gratitude and regards to my beloved parents, Abdul Saleem Siddiqui and Zakia Khatoon, for their enormous affection, kindness, motivation and encouragement at every step of my life. They were always interested to know what I was doing and how I was proceeding, without their help and support it was impossible for me to complete this dissertation. I am also grateful for their endurance in devastating hindrances I have been facing during my research and their prayers that eliminate these obstacles. Many thanks for everything!
I would also like to express my gratitude to my beloved younger brother, Mohammad Zeeshan Ahmed, and sister, Saima Saleem Siddiqui, for their love, friendship and support in every step of my life. Thanks both of you for always being my side!
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ABSTRACT
Heavy metals contamination is one of the serious problems along the coastline of most urbanized and industrialized cities. There are several reasons of the increment in metal contamination during the past decades, mainly due to the various anthropogenic activities such as mining, atmospheric deposition, consumption of fossil fuel, untreated urban, agricultural and industrial effluents, extensive constructions campaigns, high consumption of various metal products and their unsafe discarding into the environment through the atmospheric, terrestrial and aquatic interactions. These sources are responsible for increase the metal contamination that causes the detrimental effect on marine organism as well as biogeochemistry of the marine environment. This is first inclusive study on heavy metals contamination in sediments and its influence on the density and diversity of benthic crab species during the last decade along the coast of Pakistan. Moreover, the bioaccumulation of heavy metals in benthic crab species with special reference to sediment contamination was also evaluated. In addition, the biosorption potential of crab chitin as low coast biosorbent was also investigated for removal of toxic metals (Cd and Pb) from aqueous solution through experimentation.
The intensity of metal contamination during the last decade in marine sediments were evaluated by comparing the data set of two monitoring years (MY-I = 2001-03 and MY-II = 2011- 13) from the seven (Dhabeji, Bhambore, Phitti Creek, Korangi Creek, Sandspit, Sonari and Sonmiani Bay) coastal areas of Pakistan. The physicochemical properties of sediments such as percent moisture and porosity levels, percent total organic matter, particle size composition and eight heavy metals concentrations (Fe, Cu, Zn, Cr, Ni, Co, Pb and Cd) were evaluated during both monitoring years. The most of the physicochemical variables showed significant variations among the sites as well as between the monitoring years, indicates the significant changes in the geochemistry of the marine sediments along the coast of Pakistan.
The significant changes in percent moisture and porosity of the sediments of the particular study site largely depend on the diurnal tidal cycling, intertidal area and distance from the shoreline. The percent organic matter observed highest in the sediments of Dhabeji during the both monitoring years. The grain size composition of sediments showed considerable variations in percent occurrence of very coarse sand and mud, earlier one decreasing, whereas later one increasing during the last decade along the Pakistan coast. The variations in heavy metals concentrations in sediments mainly
xiv stimulated by spatial changes and sources of metal contamination which ultimately effect on the levels of particular metals during the time interval (almost a decade). The concentrations of Fe, Ni, Cr and Cd were increased in marine sediments during the last decade. The correlation analysis of geochemical variables indicates the strong interactions between the physical properties of sediments and the heavy metals concentrations in sediments. The organic matter and grain size controlled the metal levels in sediments, but this relationship was highly specific as well as variable among the sites and during the monitoring years.
The intensity of heavy metal contamination in sediments was further investigated for both monitoring years through multiple pollution indices. According to the sediment quality guidelines, geo-accumulation index, enrichment factor, contamination factor and ecological risk factor identified Cu, Zn, Cr, Pb and Cd as most dangerous metals in marine sediments during the last decade. The combined metal pollution indices represent the overall pollution condition for heavy metals that includes sediment quality guidelines, contamination degree and potential ecological risk index indicated that the highest metal loaded site designated as Sandspit, whereas Sonari detected as lowest contaminated sites during the both monitoring years along the coastal areas of Pakistan.
The impact of sediment contamination on the benthic fauna (crabs) was assessed during the both monitoring years along the coast. The biotic indices (density, diversity, equitability and species richness) were evaluated and data compared for both monitoring years. The density of crab showed significant changes among the sites as well as with the time. The overall diversity of crabs showed no variations, however the species richness and equitability showed the significant variations with respect to study sites and during the time, respectively. The metal pollution stresses on biotic properties of crab indicated that the density, diversity and species richness decreased with increasing Cu concentration in sediments. The high Cr levels in sediments lead low diversity and species richness, however Zn increment in sediments also possess negative effect on density and species richness.
The bioaccumulation of heavy metals in benthic crab species in reference with sediment contamination were evaluated to determine the influence of extrinsic and intrinsic factors on metal accumulation in crab species from nine study sites during two biomonitoring years (BMY-I = 2011 and BMY-II = 2016). In BMY-I, seven Brachyurans crab species (Macropthalmus depsressus, Austruca iranica, A. sindensis, Eurycarcinus orientalis, Ilyoplax frater, Opusia indica and Scopimera crabricauda) collected from different coastal areas and each crab species showed a variable pattern of heavy metal accumulation. For instance, the highest accumulation of Fe and Cr observed in M. depressus, highest accumulation of Cu and Zn evaluated in I. frater, highest
xv accumulation of Co apparent in E. orientalis, however the highest accumulations of Ni, Pb and Cd exhibited in S. crabricauda. The bioaccumulation factor (BAF) was also estimated for each species and revealed that two fiddler crab species presented the highest BAF values for six metals, which designates their potential as an accumulation indicator for heavy metals in marine environment. The Indus fiddler crab, A. sindensis showed highest BAF values for non-essential and toxic metals, Cr, Ni, Pb and Cd, whereas A. iranica showed highest BAF values for two essential metals, Cu and Zn. However, the highest BAF values of remaining two metals, Fe and Co, presented in M. depressus and E. orientalis, respectively.
The extrinsic or environmental factors such as percent moisture, organic matter, grain size composition and metals concentration of sediments revealed influential factors on heavy metal bioaccumulation in these crab species. The water contents in sediments showed significant linear correlation with Fe, Ni and Pb accumulation in crabs, however percent organic matter in sediments observed significant linear correlation with the accumulation of Fe and Zn in crabs. The accumulation of Fe, Zn, Ni and Pb in crabs significant correlated with % granules and the accumulation of Cu, Co, Ni and Cr significantly associated with sand composition, however the Fe and Zn accumulation in crab related to mud contents. The current study revealed that the Cu, Co, Pb and Cd concentrations in sediments significantly correlated with the corresponding metal accumulation in crabs.
Based on the results of BMY-I, the study further extended in the second biomonitoring year (BMY-II). In BMY-II, the role of two crab species, M. depressus and A. iranica, as bioindicator of heavy metals (Cu, Zn, Co, Cd and Pb) were evaluated because these two species found widely distributed and abundant along the coastal areas, moreover the effects of intrinsic factors (genders and size) in metal accumulation in tissues further investigated in both species. The significant (p <0.05) intersexual variations detected in Cu, Co and Cd accumulation in tissues of A. iranica, whereas Zn and Co accumulation showed variations in tissues of M. depressus. This indicates the both genders can be considered independently for both crab species in heavy metals biomonitoring programs. The significant relationship presented between Co, Pb and Cd accumulation in tissues and size of M. depressus, an increasing trend observed in Co accumulation with the size, however a reducing affinity evaluated in Pb and Cd accumulation with the increase in the size of M. depressus. The significant association exhibited between Cu, Zn and Cd accumulation in tissues and size of A. iranica, a reducing affinity observed for Cu and Zn accumulation with the increase in the size of the crab, whereas an increasing affinity evaluated for Cd accumulation with the increase in size of A. iranica.
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The crab shell widely considered as promising biosorbent for heavy metals removal from aqueous solution because the shell contains chitin, which is the second most abundant biomaterial. In this study, the potential of crab (Charybdis feriata) shell were investigated as biosorbent for removal of Pb(II) and Cd(II) ions from aqueous solution. The biosorption process depends on various factors (such as contact time, metal ions strength and pH) that effect on the adsorption capacity of biosorbent. The optimum time 320 min was evaluated with 92% removal efficiency for Cd from aqueous solution, however the optimum time to remove the 52% Pb ions from aqueous solution was observed 160 min. The ionic strength of Cd(II) and Pb(II) ions increased as the biosorption of metals ions decrease, the maximum uptake was evaluated at 20.2 mg/g Cd(II) in an hour with the 80% removal efficiency from 100 mg/g Cd(II) solution. However, the Pb(II) presented highest uptake (11.9 mg/g) in an hour with the 97% removal rate from 50 mg/g Pb(II) solution. The experiments with variable pH revealed that biosorption capacity of crab chitin increases with increase in pH. The highest Cd(II) adsorption (2.49 mg/g) was found at pH 9.0, which give 99% removal efficiency. Whereas, the optimum pH for Pb(II) was also found at pH 9, which give 98% removal efficiency with the Pb(II) uptake of 4.9 mg/g, respectively from the aqueous solution. The data for adsorption process reasonably fitted to Langmuir isotherm model and the calculated maximum monolayer adsorption ability of chitin for Pb(II) and Cd(II) found 1.82 and 1.38 mg g−1, respectively.
The adsorbent characterization techniques such as scanning electron microscope with energy-dispersive spectroscopy (SEM-EDS) and Fourier transform infrared spectroscopy (FTIR) were employed to characterize the changes in surface morphology and identify the main functional groups that responsible the adsorption of metals ions on the biosorbent. The SEM analysis revealed the variations in surface morphology after adsorption of metal ions. The presence of Cd(II) and Pb(II) adsorption on the surface of chitin were also confirmed by EDS analysis. The FTIR analysis showed the significant changes in functional groups (C-O, -NH and O-H bends) before and after the adsorption of metals ions that indicated the associations of these functional groups in adsorption process of Cd(II) and Pb(II) through the surface of Crab chitin.
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CHAPTER 01
GENERAL INTRODUCTION
1
GENERAL INTRODUCTION
1.1. Heavy Metal Pollution in Marine Environment
In recent era, the worldwide aquatic environments, i.e. rivers, estuaries and coastal areas have been vulnerable to several contaminations (organic and inorganic) due to rapid industrialization and urbanization. These contaminated conditions may become worse in the aquatic areas situated besides the world largest cities and as well as the hub of various commercial activities. In the past few decades, different anthropogenic sources contributes more quickly in the intensification of heavy metal concentrations in adjacent coastal environments (Dietz et al., 2009; Garcia-Tarrason et al., 2013; Pisani et al., 2013; Li et al., 2013a, b; Gao et al., 2016; Saher and Siddiqui, 2016).
The heavy metals or trace metals pollution is one of the biggest issues faced by the aquatic environment, because of their toxicity, persistence and non-biodegradable nature (Usero et al., 2008; Silva et al., 2014). Trace metals are those metals that occur in trace amounts (typically <0.01%) within the environment or within any organisms (Wittman, 1979; Marsden and Rainbow, 2004). Some metals (Fe, Cu, Zn, Co, Mn, Cr, Mo, V, Se and Ni) are known to be essential for marine organisms but all metals are toxic, if exceed from some threshold bio-available level. However, the few metals (Ag, Hg, Cu, Cd and Pb) are particularly toxic to biota in minimum level (Bryan, 1979; Silva et al., 2014). An excessive level of mentioned metals in sediments affects marine organisms, predominantly those that associated with marine sediments (benthos) as they may uptake heavy metals and plays an important role to incorporate heavy metals in the marine food chain. Therefore, the adverse effects of heavy metals may be alleviating on the health of the ecosystem as well as in last on human beings (Bryan and Langston, 1992; Siddique et al., 2009; Lin et al., 2013).
1.2. Interaction of Heavy Metals with Marine Sediments
Heavy metals originate naturally by physical and chemical weathering of parent rocks, sedimentation through river runoff and atmospheric deposition (Callender, 2005). The transportation of heavy metals mostly governs by fine-grained sediments as have some unique physical and chemical properties such as small grain size, large surface area and cation exchange capacity make them auspicious locations to bind heavy metals from their surface for a long time (Horowitz and Elrick, 1987; Gao et al., 2016). The transportation of heavy metals through water is a complex
2 interacted and interconnected process between sediments, metals and hydrodynamic conditions (Wang et al., 2016). Therefore, a small quantity of heavy metals retains when enters in a marine environment as mostly dissolved within an aqueous phase. However, the remaining quantity of heavy metals bind and accumulates with sediments through different physical and chemical processes such as adsorption, hydrolysis and co-precipitation (Hakanson, 1980; Bastami et al., 2014).
In addition the basin morphology, hydrodynamic conditions and presence or absence of vegetation also play an important role in transportation and settlement of heavy metals in their respective environment (Duarte et al., 2010; Zhou et al., 2010; Almeida et al., 2011; Chen and Torres, 2012; Gao et al., 2016). However, the mining activities, urban wastewater, agronomic activities, industrial discharges, river runoff, fossil fuel combustion and atmospheric deposition are other leading anthropogenic routes for heavy metals involvements in any aquatic environment (Callender, 2005; Gao et al., 2016; Saher and Siddiqui, 2016). The sediments serve as a pool of various contaminants and primarily a quick indicator of ecosystem quality and health. These contaminants could be released into the overlying water from natural and anthropogenic processes such as bioturbation by animals, resuspension and dredging, resulting in potential adverse health effects to aquatic biota (Long et al., 1995; Chapman et al., 2003; Marsden and Rainbow, 2004; Amin et al., 2009).
1.3. Role and Impact of Heavy Metal Pollution in Marine Ecosystem
Marine ecosystems are usually exposed to a variety of natural and anthropogenic stressors (such as pollutants, nutrients, hypoxia, turbidity, suspended sediments, habitat alteration and hydrologic regimes), which can impair the health and fitness of resident biota through single, cumulative or synergistic processes. Responses of biota to these environmental stressors are the integrated result of both direct (through metabolic activities) and indirect (effects on the food chain, habitat availability, behavioral modification, etc.) processes, which can be ultimately evident as changes in abundance, diversity, fitness of individuals, populations, and communities (Adams, 2005). Mainly but not exclusively the ten metals (Hg > Cd > Ag > Ni > Se > Pb > Cu > Cr > As > Zn) considered as the most toxic to aquatic fauna in decreasing order, among them few metals (such as Cu, Zn, Se) are micronutrients but also considered as hazardous at higher concentrations for biota (Davies, 1978; Islam and Tanaka, 2004). The reactive oxygen species (ROS) production and oxidative stress play a key role in the toxicity and carcinogenicity of metals such as arsenic (Tchounwou et al., 2004; Yedjou and Tchounwou, 2006; Yedjou and Tchounwou, 2007), cadmium
3
(Tchounwou et al., 2001), chromium (Patlolla et al., 2009a; Patlolla et al., 2009b), lead (Tchounwou et al., 2004; Patlolla et al., 2009) and mercury (Sutton et al., 2002; Sutton and Tchounwou, 2007). Because of their high degree of toxicity, these five elements rank among the priority metals with great significance. These all are systemic toxicants that notorious to induce multiple organ damage, even at lower levels of exposure. According to the United States Environmental Protection Agency (US EPA) and the International Agency for Research on Cancer (IARC), the above-mentioned metals also classified as “known” or “probable” carcinogens based on epidemiological and experimental studies that showed an association between exposure and cancer incidence in humans and animals (Tchounwou et al., 2012). Other metals such as aluminium (Al), antinomy (Sb), arsenic (As), barium (Ba), beryllium (Be), bismuth (Bi), cadmium (Cd), gallium (Ga), germanium (Ge), gold (Au), indium (In), lead (Pb), lithium (Li), mercury (Hg), nickel (Ni), platinum (Pt), silver (Ag), strontium (Sr), tellurium (Te), thallium (Tl), tin (Sn), titanium (Ti), vanadium (V) and uranium (U) have no established biological functions and are considered as non-essential metals (Chang et al., 1996; Tchounwou et al., 2012).
The essential heavy metals are important constituents of several key enzymes and play an important role in various oxidation-reduction reactions as well as utilize in several biochemical and physiological activities in plants and animals (Tchounwou et al., 2012). In biological systems, heavy metals have been reported to affect cellular organelles and components such as a cell membrane, mitochondrial, lysosome, endoplasmic reticulum, nuclei, and some enzymes involved in metabolism, detoxification, and damage repair (Wang and Shi, 2001; Tchounwou et al., 2012). When heavy metals consumed above the bio-recommended limits, they also produce the severe toxic or harmful effects on the biota includes human. The effects of toxicity could be different along with severity (for instance, it may be acute, chronic or sub-chronic) and nature (neurotoxic, carcinogenic, mutagenic or teratogenic) of toxicity (Duruibe et al., 2007). Heavy metals interference reported to cause an increase in the permeability of the cell membrane in phytoplankton and other marine algae, leading to the loss of intracellular constituents as well as cellular integrity. Kayser (1976) reported the amendment in cell shape of phytoplankton as an effect of heavy metal incorporations, which is responsible for losing cellular integrity. Davies (1978) also reported that metals inhibit independent cell division in phytoplankton as they grow in very large size because of the effect of Cu and Hg.
The chronic exposure to metals affects the mechanisms of hemolymph osmotic and ionic regulation in crustaceans and fish (Zanders and Rojas, 1996, 1997; Lignot et al., 2000; Garcia-Santos et al., 2011; Rainbow, 1997; Rainbow and Black, 2005). Oxygen consumption in crustacean often decreases during acute exposure to metals, indicative of metal-induced pathological damage, owing
4 to the direct inhibition of cellular respiration and interference in various respiratory processes (Depledge, 1984; Capparelli et al., 2016). However, oxygen consumption may remain unchanged or even increase, depending on metal concentration. The effects of heavy metals on enzymes involved in crustacean aerobic carbohydrate metabolism (e.g. succinic dehydrogenase) and anaerobic carbohydrate metabolism (lactate dehydrogenase) have been investigated (Meyer et al., 1991; Devi et al., 1993). The swimming ability exhibited by zoeae may allow them to remain in favorable environmental conditions, catch suspended particles and avoid predators (Christy, 1978; Wheeler, 1978; Montague, 1980). The normal swimming behavior and positive phototaxis of the crab larvae observed after 12 h of exposure to the contaminants and the impairment of positive phototactic response and swimming behavior has been reported only for the larvae of a few species of crabs exposed to sublethal concentrations of various contaminants and this effect may have a greater impact on survival of zoea (De-Coursey and Vernberg, 1972; Vernberg et al., 1973b; Forward and Costlow, 1976; Bookouteiaz, 1984; Capaldo, 1987; Kannupandi et al., 2001).
In addition, cadmium, lead and mercury are potential immuno-suppressants; of particular concern is the build-up of mercury, which marine mammals tend to accumulate in the liver to higher levels than other marine organisms (Law et al., 1999; Islam and Tanaka, 2004) and concentrations exceeding 100–400 µg-1 wet weight in the liver are a threat to marine mammals. When these metals accumulate in protein-rich tissues such as liver and muscle of marine mammals then they have been associated with a variety of responses include lymphocytic infiltration, lesions and fatty acid degeneration in bottlenose dolphins and decreasing nutritional state and lung pathology (Siebert et al., 1999; Islam and Tanaka, 2004). Mercury reveals an age-related accumulation and strong bio- magnification in the food web of marine ecosystem, likely due to its long persistence and high mobility (Nigro and Leonzio, 1996; Islam and Tanaka, 2004). Correlations have also reported between age and cadmium levels in the kidneys of harbor porpoises from the east coast of Scotland (Falconer et al., 1983; Islam and Tanaka, 2004). The all above mentioned effects are not mutually exclusive and may be additive, the final effect depending on metal concentration and duration of exposure (Holmstrup et al., 2010; Schiedek et al., 2006; Sokolova and Lannig, 2008; Pörtner, 2010; Capparelli et al., 2016).
The ultimate effects of all sorts of pollution are seen in fish due to an integrated part of the marine food chain. Most of the world’s largest fishing industries reported as either degraded or threatened because of various coastal and marine contaminations. The decline in the catch and degradation in the fishing industry reported from several coastal areas such as the Baltic Sea, the North Sea, the Atlantic and the Mediterranean Sea (Islam and Tanaka, 2004). East Asian coastal
5 areas including the coastal areas of the Yellow Sea, East China Sea, the South China Sea, the Sulu- Celebes Seas and the Indonesian Seas (Thia-Eng, 1999; Islam and Tanaka, 2004).
1.4. Ecological and Biomonitoring role of Brachyuran Crabs
Brachyuran crabs (Crustacea: Decapoda: Brachyura) includes about 5000 to 10,000 species belong from 700 genera, all around the world (Kaestner, 1970; Ng, 1998; Martin and Davis, 2001; Ng et al., 2008; Sakthivel and Fernando, 2012). About 2,600 species are present along the coastal areas of Indo-West Pacific (Serene, 1968; Sakthivel and Fernando, 2012). Brachyuran crabs considered as one of the most relevant groups of the marine benthos based on their high biomass, abundance and assemblage (Melo, 1996; Bertini et al., 2004) and successfully dominated species survive in highly variable estuarine environments as found at from splash zone to 6000 m deep- ocean (Ng et al., 2008; Sakthivel and Fernando, 2012). These crabs approve as the more successful survivor in tropical and subtropical regions compared to temperate and cold regions (Fransozo and Negreiros-Fransozo, 1996; Boschi, 2000; Bertini et al., 2004).
Intertidal crabs that are active on the sediment surface of tropical estuaries during tidal cycle, intimately involved in many ecological processes through trophic interactions and ecosystem engineering (Kristensen, 2008; Amaral et al., 2009; Vermeiren and Sheaves, 2014). Associations between intertidal crabs and environmental properties such as organic content, vegetation and tidal height have been well documented in tropical estuaries (Weis and Weis, 2004; Koch et al., 2005; Bezerra et al., 2006; Ravichandran et al., 2007; Takeda, 2010; Vermeiren and Sheaves, 2014). They influence the sediment composition (Botto and Iribarne, 2000; Escarpa et al., 2004), productivity (Koch and Wolff, 2002; Werry and Lee, 2005), vegetation structure (Bosire et al., 2005), faunal composition (Dye and Lasiak, 1986; Botto et al., 2000) and energy fluxes (Wolff et al., 2000).
The abundance and composition of intertidal crab communities have been widely used as a condition indicator of degraded areas and for measuring the success of rehabilitation programs (Macintosh et al., 2002; Ashton et al., 2003a; Nordhaus et al., 2009). Therefore, alterations in intertidal crab assemblages likely influence the estuarine community and as well as affect ecosystem functioning. The extensive linkage of intertidal crabs to ecosystem processes makes them useful model species to study the functioning of tropical estuaries (Vermeiren and Sheaves, 2014). Organic and inorganic pollutants are known to influence the physiological activities, consequential changes in molting and reproduction processes as well as distribution and population structure of crabs (MacFarlane et al., 2000; Rodriguez et al., 2007; Vermeiren and Sheaves, 2014).
6
Intake and accumulation of heavy metals by various aquatic organisms and transformation along the food chain enhance their harmfulness in an aquatic environment (Fisher and Reinfelder, 1995; Monterroso et al., 2003; Wang et al., 2016). In general, the heavy metals are toxic even in low concentrations to many fish species and ultimately can be dangerous to humans by consuming these contaminated species as seafood (Honeyman and Santschi, 1988, 1989; Wang et al., 2016). Therefore, monitoring of heavy metals through organisms is a beneficial to evaluate the spatial and temporal distributions plus bioavailability of heavy metals in the marine environment (Phillips and Rainbow, 1994; Rainbow, 1995; Silva et al., 2003). Aquatic invertebrates take up and accumulate trace metals, whether essential or non-essential as all of which have the potential to cause toxic effects. Subsequent tissue and body concentrations of accumulated trace metals show enormous variability across the metals and invertebrate taxa (Eisler, 1981; Rainbow, 1990, 1993; Phillips and Rainbow, 1994; Rainbow, 2007).
It is preferred to use alternatives such as 'bioindicators', 'sentinel organisms' and 'biological monitors' in the environmental monitoring (Goldberg et al., 1978; Martin and Coughtrey, 1982; Hellawell, 1986; Phillips and Rainbow, 1993; Rainbow, 1995). The term 'bioindicator', for example is often used to describe a species that reflect a status of ecological system by its simple presence or absence, and 'biological monitors' often indicate degrees of ecological change by behavioral, physiological or biochemical responses such as changes in growth, respiration rate or degree of lysosome latency (Phillips and Rainbow, 1993; Rainbow, 1995). Heavy metals accumulation in an organism expresses collective measure of metal bioavailability in an aquatic environment with the passage of time. The bioavailable fraction of metal is the biologically relevant amount of metals that easily adsorbed or/and accumulated by an organism, therefore biomonitor species provide an accurate status of metal contamination in an aquatic environment as compared to water or sediments (Phillips and Rainbow, 1994; Rainbow, 1995; Silva et al., 2003). The use of plants, invertebrates, fishes and mammals as bioindicator species pronouncedly described. For aquatic metal pollution, the commonly used bioindicators mainly contained organisms, including plankton, insects, mollusks, fishes, plants and birds. Each bioindicator shows the special merits for the biomonitoring of metal pollution in aquatic ecosystem when compared to the others (Burger, 2006; Zhou et al., 2008).
Crabs are known to accumulate heavy metals in higher amount compared to sediments in intertidal and estuarine environment (MacFarlane et al., 2000; Na and Park, 2012). The potential of intertidal crabs to act as bioindicator of ecosystem condition have been demonstrated by changes in diverse attributes. For example, changes in behavior, survival, biomass, species richness and burrow morphology reported in response to sewage pollution in tropical estuaries (Bartolini et al., 2009;
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Cannicci et al., 2009; Penha-Lopes et al., 2009). Crustaceans take up trace metals from solution in proportion to the dissolved concentration of the metal (Jennings and Rainbow, 1979; White and Rainbow, 1984; Rainbow, 1985; Weeks and Rainbow, 1991; Chan and Rainbow, 1993). On the other hand, some crustacean species do not show increases in body concentration of a trace metal over a range of ambient dissolved metal exposures, even though uptake rates still increase in response of elevated dissolved metal concentration (Rainbow, 1995).
Crustaceans accumulate heavy metals in the soft tissues and as well as in chitinous exoskeleton. Metals can be bound to chitin in the exoskeletons of crustaceans and may be absorbed into the surface of the exoskeleton or bind to the inner exoskeleton matrix after uptake (Keteles and Fleeger, 2001; Bergey and Weis, 2007). During the growth phases, the new cuticle is produced underneath the old one (pro-Ecdysis) and then older shell must be shed (Ecdysis). A little fraction of the metal burden that is associated with the exoskeleton eliminated with the molting processes, therefore molting may be a way to depurate metals in crustaceans. After shading the exoskeleton, it acts as a source of heavy metals in the environment, it may be consumed by crab itself or another organism then incorporate into the food chain. Moreover, when it does not consume by the organism, then it remains in the environment and gradually decomposed into raw materials (organic and inorganic components). Previous studies indicated that molting as a mechanism for detoxification, which may be depending upon the particular element and crustacean species (Abel, 1998; Keteles and Fleeger, 2001; Bergey and Weis, 2007). Because of the chitin, crab shell has greater binding affinities towards the heavy metals and other contaminants and can be utilized as a natural biomaterial as remediation of contaminated sediments in the coastal and estuarine environment.
1.4. Use of crab shell in Bisorption of Heavy Metals
Biosorption can define as the passive uptake of metal ions by dead/inactive biological materials through various physicochemical mechanisms (Vijayaraghavan et al., 2011). In recent years, the bioremediation method based on the potential of dead biological materials, biosorption is getting popular for metal remediation. Several biomaterials including bacteria (Vijayaraghavan and Yun, 2008), fungi (Kapoor and Vijayaraghavan, 1995), algae (Davis et al., 2003), industrial and agricultural wastes (Crini, 2005; Sud et al., 2008) and crab shell (Evans et al., 2002; Vijayaraghavan et al., 2004; Vijayaraghavan et al., 2006) was identified as potential constituents for the removal of metal ions. The hard, calcified exoskeleton of crustacean has multiple functions such as body support, resistance to mechanical loads, environmental protection and resistance to desiccation (Vincent, 1991, 2002; Vincent and Wegst, 2004; Sanchez et al., 2005; Souza et al., 2011). However,
8 the rigidity of the carapace restricts growth and once any increase in size requires the ecdyses occurred. Therefore, crustaceans present a discontinuous growth pattern, characterized by successive ecdysis (periodic shedding of the exoskeleton) over time (Hartnoll, 2001; Souza et al., 2011).
Crab shells mainly comprise calcium and magnesium carbonates and chitin along with some proteins (Vijayaraghavan et al., 2006; Vijayaraghavan et al., 2011). The chitin has the ability to release alkalinity and nitrogen gradually into aqueous systems due to the natural association with
CaCO3 and proteins (Daubert and Brennan, 2007; Giraud-Guille, 1998; Pinto et al., 2011). The abundance of crab shells arises from the trash material and an alternative use of shell wastes can be efficient reutilization in various environmental applications is highly appreciated by seafood industries. The additional advantage is that crab shells can be obtained in large quantities at low or no cost from these seafood industries and only the major process cost involved is transportation (Vijayaraghavan et al., 2009). In recent years, few investigators reported that crab shell showed an excellent binding capacity towards different metal ions, which include lead (Lee et al., 1997), cadmium (Evans et al., 2002), nickel (Vijayaraghavan et al., 2004), copper and cobalt (Vijayaraghavan et al., 2006), manganese and zinc (Vijayaraghavan et al., 2011).
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1.5. Detailed Historical Perspective and Literature Review
The heavy metals concentrations in water, sediment and biota were assessed from various habitats, i.e. mangroves, creeks, rocky shore, mudflats and sandy shores of Pakistan. Heavy metals concentrations in sediments of Pakistan extensively evaluated in recent decades. Despite of their importance heavy metals accumulation and toxicity in biota along the coastal areas of Pakistan is neglecting and information regarding the impact of sediment toxicity on biota and ecosystem is scared. Some researchers studied on metals concentrations in seaweed, mangroves plants, mollusks, shrimps, fishes and birds. Tariq et al. (1993) reported the heavy metal levels in fish, shrimp seaweed, sediments, and water from the mouth of the Indus River, Pakistan. Zehra et al. (2003) reported Cu, Cd, Zn, and Pb concentrations in edible fish tissues (Acanthopagurus berda) from Gadani beach, Baluchistan, Pakistan.
Saifullah et al. (2002) and (2004) evaluated the Ni and Fe concentrations, respectively, in water, sediments and different parts (pneumatophores, bark, leaves, twigs, flowers, and fruits) of the mangrove plant (Avicennia marina) from Karachi, Pakistan. Saifullah et al. (2004) stated that the sediment accumulates as much as 97% of the total Fe in the habitat and 3% was reported in water and different parts of mangrove plant. Qari et al. (2005) reported the concentrations of ten metals (Mn, Cu, Ni, Zn, Mg, Fe, Cr, Pb, Co and Cd) in sediments collected over one year from three rocky sites (Buleji, Paradise Point and Nathia Gali). They found no heavy metals pollution in that area due to the high wave action, contamination dispersal and dilution as well as sites distance relatively far from the point sources. Siddique et al. (2009) reported the nine heavy metals concentrations (Cd, Cr, Co, Cu, Fe, Pb, Mn, Ni and Zn) in the surficial sediments along the Karachi coast and found higher metal concentrations in the sediment of Lyari and Malir Rivers likely due to the higher anthropogenic influences on Karachi coastal areas. In addition, the areas of Gizri Creek, Lyari and Bin Qasim areas also reported as an area at high risk towards the heavy metal pollution.
Mashiatullah et al. (2013) reported the distribution of different heavy metals (Fe, Mn, Zn, Pb, Se, Cr, Mo, U, V, Ni, Sr, and Zr) pollution assessment based on the surface sediments of Karachi Coast, Pakistan. They found relatively higher pollution index (PLI) at Lyari River Mouth Area, Fish Harbor, and KPT Boat Building Area, which is attributed to increased human activity in the area. Chaudhary et al. (2013a) reported various metals (Ca, K, Mg, Al, S, Ti, V, Cr, Mn, Fe, Ni, Cu and Zn) concentrations in sediment core (72 cm) collected from Korangi Creek, Karachi, Pakistan. The geochemical status of above-mentioned metals was studied by various pollution indices. Enrichment of Cr, Ni, Cu and Zn describe due to industrial effluent discharge in the Korangi Creek sediments
10 and possess a severe threat to the marine life. Chaudhary et al. (2013b) found the ranking of nine metals concentrations as Zn > Si > Br > As > Ni > Cr > Pb > Cu > Co in sediments of Rehri Creek, Karachi, Pakistan. Mainly emphasize the contamination of arsenic in sediments as detected during the study and poses a threat to marine life.
Ali et al. (2014) previously reported the spatial distribution of various metals (Cd, Cr, Cu, Ni, Co, Zn, Pb, and Mn) along with their geochemical status, enrichment, heavy metal flux (F), sedimentary depositional fluxes (D) and mass inventories (I) in coastal sediments of Pakistan. The authors found overall low contamination, but moderate contamination detected along the high anthropogenic influence on some locations of Karachi coast. Mohiuddin and Naqvi (2014) also reported the total Hg concentrations in sediment and water from coastal areas of Pakistan.
Ismail et al. (2014) reported Pb, Cu, Cd, and Zn concentrations in water, sediments and different parts of the mangrove (A. marina) along the Indus delta mangrove habitats. The authors identified higher heavy metals pollution load index (PLI) in sediments, indicated that Indus Delta mangrove Ecosystem was under potential risk of metals pollution. Seawater showed low concentrations of metals, however hyper-accumulator of Zn was observed in A. marina. Qari and Ahmed (2014) reported accumulation of heavy metals (Fe, Cd, Pb and Hg) in the selected parts (leaves, stem and roots) of mangrove (A. marina) from Bhaira village mangrove forest, Miani Hor, Balochistan, Pakistan.
Khattak and Khattak (2013) reported the concentrations of As and Cd in 15 edible fish species found along the coastal areas of Pakistan. They found the variations of Cd and As concentrations in fish flesh according to the species and sites. Ali et al. (2013) reported Fe, Cu, Zn, Cd, and Pb concentrations in fish and shrimp (Penaeus monodon, Penaeus penicillatus, Palaemon longirostris) species from Karachi, Pakistan. The fish and shrimp species found somewhat contaminated by Cd and Pb but within the permissible limits of human consumption. Ali et al. (2014) reported the six metals (Fe, Cu, Zn, Mn, Ni, and Pb) concentrations in tissues of oysters (Crassostrea sp.) from Gizri Creek, Korangi Creek and Manora channel, Karachi, Pakistan. Ahmed et al. (2014a) reported the six metals (Fe, Mn, Zn, Cu, Pb, and Cd) levels in edible fish (Sardinella albella) from coastal areas of Gwadar, Baluchistan, Pakistan. Ahmed et al. (2014b) reported the metals (Fe, Mn, Pb, Cd, and Cr) concentrations in muscles, liver, kidneys and gills of Torpedo Scad fish (Megalaspis cordyla) from coastal areas of Karachi, Pakistan. Qari et al. (2015) reported the seasonal variations for the metal concentrations (Hg, Pb, Cu, Ni, Zn, Co, Fe, Cr, Cd and Mn) in green mussel (Perna viridis) from Manora channel, Karachi, Pakistan. Kamal et al. (2015) reported
11 the heavy metals (copper, zinc, cadmium and lead) concentrations in the Red tail shrimp (Fenneropenaeus penicillatus) from coastal areas of Pakistan.
Metal contamination in sediments is a predictor to describe the ecological status and health of any particular habitat but this is essential to employ organismal species that indicates the environmental contamination levels and provides better understanding of heavy metals bioavailability and toxicity along the coastal areas of Pakistan. The conspicuous aspect of effects of metals contamination on biotic properties such as species composition, assemblages, density and diversity is still sparse and have been neglected as very few studies available in this regards. Nasira et al. (2010) studied the assemblage of free-living nematode with respect to heavy metals (Pb, Cr, Cu, Cd, Hg, and As) contamination in sediments along the coastal areas of Pakistan. The authors found a high faunal diversity of nematode along the study sites, and identified no harmful impact on nematode assemblage towards sediment pollution, which indicates the tolerate capacity of nematode along the coastal areas of Pakistan.
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1.6. Rationale of the Study
The appropriate monitoring and management is crucial for the control of anthropogenic activities which responsible for the degradation of coastal habitat. The fruitful remediation campaign is acquired for removal of heavy metals from aquatic environments. Anthropogenic stress, particularly the emphasis on heavy metal pollution and their consequences on marine organisms are not well understood along the coastal and estuarine environment of Pakistan. There is a need to assess the bioavailability and ecotoxicology of heavy metals along the coastal areas of Pakistan.
After the above detailed review, the following aspects need to study for the formulation of management strategies and conservation of coastal habitat including biota:
1) To assess the heavy metals concentrations in marine sediments during two monitoring years (2001-03 and 2011-13) along the coast of Pakistan. 2) The distribution and diversity of intertidal crab species evaluated to reveal the impact of heavy metals pollution in two monitoring years (2001-03 and 2011-13) along the coast of Pakistan. 3) Biomonitoring of heavy metals assessed by selecting abundant crab species includes the identification of the suitable crab species as bioindicator of heavy metal contamination along the coastal areas of Pakistan. 4) Biosorption of selected heavy metals (Pb and Cd) examined through the crab shell in aqueous solution to observe the significance of crab shell in bioremediation.
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1.7. Structural Framework of Dissertation
This dissertation divided into five chapters and overall organization is as follows:
st Chapter described the background of the study and detailed literature review on heavy metals from the coastal habitat of Pakistan. The rationale and objectives of 1 this study also included in this chapter.
nd Chapter divided into three sections, first part presents the heavy metals levels in coastal and estuarine sediments during the last decade (two monitoring years MY-I 2 = 2001-03 and MY-II = 2011-13) along the coast of Pakistan. On the basis of gathering metal pollution data of two years, the sites were categorized into three groups: unpolluted or less polluted, moderately polluted and highly polluted on the basis of multiple pollution indices for the heavy metal in coastal sediment. The second part deals with the composition, density, distribution and diversity of crab species along the coast of Pakistan during the two monitoring years. Third section exhibits the possible effect of heavy metal concentration on the density, diversity and distribution of crab species found along the coast of Pakistan by comparing the previous and recent data of heavy metal concentration in the sediments.
rd Chapter described the results of heavy metals biomonitoring through selected seven deposit feeder crab species (Macrophthalmus depressus, Austruca iranica, A. 3 sindensis, Ilyoplax frater, Opusia indica, Eurycarcinus orientalis, and Scopimera crabricauda) along the coast of Pakistan. The relationship between heavy metal concentrations in sediments and accumulation in deposit feeder crabs were also determined. Moreover, the suitable biomonitor species of crabs were determined based on presence or absence, density and metal accumulation. In the second half of this chapter focused the variation in five heavy metals (Cu, Zn, Co, Pb and Cd) accumulation according to gender and size in two biomonitor species (Macrophthalmus depressus and Austruca iranica) from high (Sandspit) and moderate (Korangi Creek) polluted areas.
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th Chapter deals with the bioremediation potential of crab carapace for the removal of toxic heavy metal (Pb and Cd) from aqueous solutions. The parameters (contact 4 time, metal concentrations and pH) affecting the biosorption potential and the mechanism associated with the removal of heavy metal were discussed. The variations in surface morphology and functional groups of biosorbent (crab shell particles) before and after biosorption were evaluated using scanning electron microscope (SEM) and Fourier- transform infrared spectroscopy (FTIR) analysis, respectively.
th Chapter presents the overall conclusion and future approaches towards the successful management and conservation of coastal and estuarine environment.
5
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CHAPTER 02
A DECADE STUDY OF COMPARISON ON METAL CONTAMINATION IN MARINE SEDIMENTS AND ITS IMPACT ON CRAB DIVERSITY AND DISTRIBUTION
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2.1. INTRODUCTION
The heavy metals pollution is one of the biggest issues faced by the aquatic environment because of toxicity, persistence and non-biodegradable in nature (Usero et al., 2008; Silva et al., 2014). Therefore, the metals easily bioaccumulate and biomagnifies in the food chain and posed serious threats to biota as well as human beings whether directly or indirectly. Furthermore, the bioaccumulation and kinetics of heavy metals in a multi-dimensional way made them great concern to ecologist and environmentalist (Wang et al., 2013a; Zhang et al., 2013; Gao et al., 2016). These contaminants mainly released into the overlying water from natural and anthropogenic processes such as bioturbation by animals, resuspension and dredging, resulting in potential adverse health effects to aquatic biota (Long et al., 1995; Chapman et al., 2003; Marsden and Rainbow, 2004; Amin et al., 2009).
In an aquatic environment, heavy metals generally exist in two phases (dissolve and particulate phase) this partitioning depends on several physical and chemical characteristics of particulate matter as well as hydrography of the area, type and quantity of dissolved organic matter in an environment (Turner and Millward, 1994, 2002; Wang et al., 2016). When the rivers flow into the sea, many physical and chemical parameters modified according to the environmental conditions. The transport of heavy metals in an aqueous phase is a strongly complex coupling process between hydrodynamics, sediments and metals (Wang et al., 2016). Thus, a minute quantity of heavy metals enter in a marine environment stay, dissolved within an aqueous phase, whereas the remaining quantity of heavy metals bind and accumulate through different physical and chemical processes such as adsorption, hydrolysis and co-precipitation in marine and estuarine sediments (Hakanson, 1980; Bastami et al., 2014). Therefore, the sediments serve as a pool of various contaminants and considered as an indicator of water quality in an aquatic ecosystem.
Sediments act as a 'RAM' of the environment, which store the genuine and time-integrated information on health and vulnerability of the ecosystem, which makes this one of the leading and reliable environmental indicators over the others (Birch and Olmos, 2008; Birch et al., 2015). Sediments provides the space and act as a sink for multiple pollutants in higher amount which causing the severe toxicities and adversities in aquatic biota, therefore the analysis reflect the historical variation and the effects of anthropogenic and lithogenic inputs into the marine ecosystem (Marchand et al., 2006; Morillo et al., 2008; Usero et al., 2008; Sany et al., 2014). The assessment of
32 sediments is more conservative as compared to the aqueous phase in the assessment of toxicity and contamination. Due to its ecological importance, the evaluation of marine sediment quality constitutes an important area of research (Silva et al., 2004; Usero et al., 2008). The analysis of sediments as an indicator of metal contamination are particularly valuable in the preliminary analysis of environmental health as the data provide a perseverance of natural as well as anthropogenic stress (Olmos and Birch, 2008, 2010).
Natural transport processes of heavy metals, mainly depend on the presence and transfer of fine-grained sediments, which is an efficient sink for heavy metals and have the ability to retain the high quantities of environmental contaminants because of their unique physical (like grain size, surface area, charge) and chemical (their composition, cation exchange capacity, etc.) properties (Horowitz and Elrick, 1987; Gao et al., 2016). Moreover, the morphology of the sea floor, hydrodynamic conditions, and plant communities also influence on the distribution and accumulation of metals (Duarte et al., 2010; Zhou et al., 2010; Almeida et al., 2011; Chen and Torres, 2012; Gao et al., 2016). The heavy metal toxicity and accumulation in sediments not only depend on metal concentrations but also influenced by various other factors includes, the system in which the metal component present, the type and concentration of other materials as well as the incorporation of various physicochemical (e.g. salinity, temperature, grain size of sediment, pH, organic carbon and dissolved oxygen) properties (Hardman, 2006; Wang et al., 2002; Sany et al., 2013). Metals in aquatic ecosystems may be transferred along the food chain either retained or transformed by the organisms and enhance their toxicity. Therefore, the quantification of metal contamination and their influence on the marine environment is of prime importance in the conservation and sustainability of the ecosystem (Lin et al., 2013; Gao et al., 2016).
The various anthropogenic sources (mining activities, urban wastewater, agricultural activities, industrial effluents, river runoff, fossil fuel consumption and atmospheric deposition) incorporate intensified contribution of heavy metal pollution in coastal environments (Dietz et al., 2009; Garcia-Tarrason et al., 2013; Gao et al., 2016; Saher and Siddiqui, 2016). Several ecological indicators were considered in the evaluation of environmental integrity and develop the some useful indices such as species richness, abundance and diversity (Somerfield et al., 2008; Whomersley et al., 2008; Moreno et al., 2008), nutrients and biomass (Salas et al., 2008) as well as using fish, seabirds and seagrass species as indicators for anthropogenic impact (Fernandez-Torquemada et al., 2008; Belpaire et al., 2008; Parsons et al., 2008) that describe the scientific information in simple and reasonable manner to the policy maker and stakeholders (Rees et al., 2008; Somerfield et al., 2008; Marin et al., 2008; Birch et al., 2015).
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Biological diversity or biodiversity is a “cluster of concepts” (Contoli, 1991) which extended from genetics and molecular biology to community structure and habitat heterogeneity (Bianchi and Morri, 2000). Generally, it described as the species richness, i.e., the number of species occurring in a site, region or ecosystem (Baltanas, 1992; May, 1995; Bianchi and Morri, 2000). Ecologists are used to measuring diversity through a number of indices all of which relate, more or less directly, the number of species to their abundance and/or numerical dominance (Magurran, 1988). The overwhelming value of biodiversity as an indication of environmental health as well as for the functioning of ecosystems (Culotta, 1996; Bengtsson et al., 1997; Grime, 1997; Aarts and Nienhuis, 1999; Bianchi and Morri, 2000). Several kinds of pollution and ecosystem alteration are other well- known influences of man on marine biodiversity (Cognetti and Cognetti, 1992). In general, marine communities respond to environmental stress with three main changes: (1) lowering diversity; (2) regression to dominance by opportunist species; and (3) reduction in the mean size of the dominating species (Pearson and Rosemberg, 1978; Gray, 1989). Distinguishing environmental stress due to climate change and from that due to human pressure is often difficult (Heip et al., 1987; Warwick and Bayne, 1993; Hallers-Tjabbes et al., 1996; Bianchi and Morri, 2000). The benthic organisms are one of the most important assemblages of organisms that closely associated with the substrate and can provide the major clues of sediment contamination including environmental status.
The benthic invertebrates are well-established target in evaluations of environmental quality status as various studies have demonstrated that the macrobenthos responds to anthropogenic and natural stresses (Pearson and Rosenberg, 1978; Dauer, 1993). Accumulation of heavy metals and deposition of organic carbon can lower the abundance and diversity of the benthos (Wu, 1982; Rygg, 1986; Ahn et al., 1995; Chou et al., 1999). They generally have a sedentary life mode, long life cycle, exhibit different tolerances to stress and stable community composition and have an important role in cycling nutrients and materials, between the underlying sediments and the overlying water column (Hily, 1984; Dauer, 1993; Madiseh et al., 2012), therefore can often be used as a monitoring index for pollution (Leppakoski, 1975). It is true that anthropogenic and climatic actions can combine their effects on the marine biota (Bourcier, 1996). On the other hand, Warwick and Clarke (1995, 1998) considered decreasing taxonomic distinctness as an unequivocal evidence of the effect of pollution on marine biodiversity (Bianchi and Morri, 2000).
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2.1.1. Objectives
The foremost objective of the current study was to evaluate the heavy metals contamination in the coastal sediments of Pakistan with reference to time and space as well as its influence on the benthic organisms, mainly crabs density and diversity during the last decade along the coast of Pakistan. This chapter divided into following main intentions:
1. Firstly, the physicochemical properties (% moisture, % porosity, % organic matter, grains size composition and heavy metals concentrations) in marine sediments were evaluated during the two monitoring years (MY-I = 2001-03 and MY-II = 2011-13) from seven locations along the coast of Pakistan. According to the severity of multiple pollution indices through the total heavy metal concentration in marine sediments, the sites were categorized i.e. unpolluted or less polluted, moderately polluted and highly polluted locations. 2. Secondly, the biotic indices of crab (species composition, density, distribution and diversity) assessed during the last decade along the coast of Pakistan. 3. The third section investigate the impact and stress level of sediments contamination on the biotic indices of crabs considered during the two monitoring years along the coast of Pakistan.
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2.2. MATERIALS AND METHODS
2.2.1. Coastal Environment of Pakistan
The Pakistan coastal area is about 990 km in length along the northern boundary of the Arabian Sea. Coastal belt stretched from southeast (Run of Kuch) near Indian border to northwest (Gwadar) adjacent Iranian coast beside the Arabian Sea. The coast divided into two provincial zones, Sindh coast (320 km) and Baluchistan coast (670 km) lie in the tropical region and show varying shelf geometry and slope due to an interplay of the tectonics of different magnitudes. Sedimentation in the area is active owed to long-shore currents of varying intensity and wave actions that cause variable erosion of rocks and sediments (Mumtaz, 2002; Ali et al., 2014). The mangrove environments along the coastal belt mainly exist in five distinct sites, including the Indus Delta, Sandspit, Miani Hor (Sonmiani Bay), Kalmat Khor and Jiwani (Gawadar Bay). Along the coastal areas of Sindh, mangroves forest is located in the Indus Delta, which spread from Korangi Creek to Sir Creek. The small patch of mangrove forest also located in Sandspit, which is located at the West of Karachi city (Abass et al., 2011). The Indus Delta mangroves constitute the largest arid climate mangrove forest of the world. Among the four mangrove species only one species, Avicennia marina is dominant in this region (Saifullah et al., 1994; Ismail et al., 2014).
The mangrove environments are facing serious degradation due to anthropogenic stress throughout the world and similarly Indus Delta mangroves also have similar threat, in addition of that they have other serious obstructions, mainly decrease in freshwater input urbanization, over- harvesting, export of seedling and sedimentations along the Indus plains (Adhikari et al., 2010; Ismail et al., 2014). The western most areas of the Indus Delta, which positioned in Karachi having severe environmental degradation through industrial and urban contamination. Sonmiani Bay mangroves area characterize by 42% of the mangroves forestry in the Balochistan. This area is also of extreme importance because three varieties of mangroves, Avicennia marina (gray mangrove or white mangrove), Rhizophora mucronata (loop-root mangrove, red mangrove or Asiatic mangrove) and Ceriops tagal (yellow mangrove) are located growing by natural means which perform a significant part in the productivity of the region (Gondal et al., 2012).
It generally noted that the Sindh belt, especially the Karachi coast is more polluted than Balochistan belt due to the diverse range of industrial and anthropogenic activities for development (Saifullah, 1997; Mumtaz, 2002; Ali et al., 2014; Ismail et al., 2014). The Karachi coastline is about
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167 km long, most populated, mega city and main hub of industrial and commercial activities. Therefore, the coastline is seriously susceptible to multiple types of contamination through point and non-point sources, for instance, urban and domestic waste, agricultural run-off, industrial discharges, shipping and fishing transportation. Lyari and Malir Rivers are the main point sources of coastal pollution in Karachi and they are served by various links of untreated urban and industrial waste and ultimately drain into the beaches of Karachi and then Arabian Sea (Rizvi et al., 1988; Saifullah et al., 2002; Siddique et al., 2009; Saher and Siddiqui, 2016).
2.2.2. Description of Monitoring Sites
The seven sites were selected for monitoring study along the coastal areas of Pakistan and visited for the sampling of sediments and crabs during the low tide time. The selected sites includes Dhabeji, Bhambore, Phitti Creek (Port Qasim and Rato Kot), Korangi Creek, Sandspit, Sonari and Sonmiani Bay (Figure 2.1).
Dhabeji (DH)
It is a town and union council of Mirpur, Sujawal district of Sindh province of Pakistan. It is adjacent to the Karachi city about 15 km away from the Pakistan Steel Mills and declared as an industrial zone by the Government of Sindh. There is also a water treatment plant for Karachi City from the Indus River. The site is mostly covered with mangrove species Avicennia marina at the seaward end of Gharo Creek. One station was selected on this site and located at 24° 44' N and 67° 29' E.
Bhambore (BH)
Bhambore or Banbhore is an ancient city dating to the 1st century BC located in Sindh, Pakistan. The city ruins are located on the N-5 National Highway between Dhabeji and Gharo Creek (Northern bank) about 65 km (40 miles) east of Karachi in the Thatta District of Sindh, Pakistan. It is located at 24° 45' N and 67° 34' E and sparse mangrove vegetation (Avicennia marina) are present at the seaward end of Gharo Creek. One station was selected on this site for the collection of sediments and crabs samples.
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Phitti Creek (PC)
Phitti Creek is a tidal creek, located in Sindh, Pakistan. The Gharo, Kadiro and Phitti creek system consists of three creeks. All three connected in a series starting for Gharo creek at the northeastern end of the Phitti creek at the southwestern end and located at 24o 65’ N and 67o 16’ E, 22.3 km from Karachi. The creek system is about 28 km long and its width ranges from 250 to 2,500 m. The Korangi Creek and Kadiro Creek connect with it at the northeastern end while it acts as the main waterway connected to the open sea at the southwestern end. The main channel of Port Bin Qasim lies in this creek system, which has been dredged to maintain a navigable depth of 11.3 meters. At this site, the station was reached by boat and mangroves were landwards, the intertidal area consists of a very gently sloping mudflat towards the sea (Phitti creek), the width of the intertidal zone about 0.5 km. At this site, the mangrove stand appears to be natural, healthy, and dense. Three stations were selected on this site and sampling of sediments and crabs were done during the low tide time. At the main channel of Port Bin Qasim (PQ), two stations (PQ1 and PQ2) were selected which is located at 24° 46' N, 67° 18' E and 24° 47' N, 67° 16' E, respectively. The sediment core was sampled from both sites, but the crabs were sampled only at PQ1 as crab sampling was not possible at PQ2 due to very muddy substratum. The third station, Rato Kot (RK) is located near Kadiro Creek at 24° 46' N and 67° 14' E, it is an intertidal mudflat area devoid of mangrove vegetation.
Korangi Creek (KC)
Korangi Creek was located near the salt pans located in the fishing village of Ibrahim Hyedri. It is the north most creeks of the Indus Delta, which is 12 km away from Karachi Harbour and 9 km away from Quaidabad. This creek is connected at its northeastern end with Phitti creek and Kadiro creek, while at its southwestern end it connects with the open sea and with Gizri creek. The site consists of a very gently sloping intertidal area with a dense stand of mangroves at the seaward end and the mangrove (Avicennia marina) stand appears to be natural and form a natural habitat for the fauna. Most of the area covered by the dense patches of mangroves, they looked very healthy but were stunted. The width of the intertidal zone during low tide was about 0.5 to 1 km. Two stations (KC1 and KC2) were selected and located at 24° 17' N, 67° 10' E and 24° 47' N, 67° 10' E, respectively. The sites were visited during low tides for the collection of sediments and crabs samples were collected from both stations.
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Sandspit (SP)
Sandspit is located between Manora and Hawks Bay about 18 km southwest of Karachi City. The western part is open sandy beach extending for about 10 km facing the Arabian Sea. The beach is about 100 to 200 meters wide and the backwater is connected to the Arabian Sea through Manora Channel (Sultana and Mustaquim, 2003; Durranee et al., 2008). The beach is about 100 to 200 meters wide. The Lyari River runoff enter the backwater areas of Sandspit from the east, however seawater reaches the proximity from the south. The estuary of Lyari River along with other parts of the backwaters has been extensively damaged by the polluted effluent discharges into Lyari River, which ultimately influences biodiversity of the area (Durranee et al., 2008). The Sandspit is a bifurcated dry strip of land, it is characterized by the sandy coast on its South and mangrove vegetation found on the Northern side. The dense vegetation is covered by single mangrove species, Avicennia marina. The two stations SP1 (24° 49’ N, 66° 56’ E) and SP2 (24° 52’ N, 67° 10’ E) were selected from the Sandspit backwater mangrove area. The selected sites visited during low tide and width of the intertidal zone was about 0.25 km.
Sonari (SO)
This study site is located between Cape Monze and Gadani about 40 miles northwest (24° 53’ N and 66° 42’ E) of Karachi. This area was once a part of Hub River but due to the construction of the dam, it was cut off from the main river. It represents mainly seawater creek extending two miles from the sea coast and largely dominated by a tidal stream with the characteristic mud flat and devoid of mangrove vegetation. The site consists of a very gently sloping intertidal area along the coast, and the width of the intertidal area was very small (0.1 km) (Saher, 2008).
Sonmiani Bay (SB)
The coastline of Balochistan has many representative bays, one of these is Sonmiani Bay, which is situated about 90 km from Karachi in the eastern most part of Balochistan coast. The bay is a 60 km long and 7 km wide convoluted and knotted body of normal water, linked to the ocean at the southeastern end with a 4 km wide mouth (Saifullah and Rasool, 1995; Gondal et al., 2012; Saher and Siddiqui, 2016). The freshwater input enters the Bay through two seasonal rivers, Porali and Windor River (Rasool et al., 2002; Saher and Siddiqui, 2016). These rivers also contain effluents of 122 industries approximately as functioning at the Hub and Windor Industrial Trading Estate includes textile weaving, plastic, chemical, food preservation, engineering, paper and paper product
39 industries, etc., and contributes to coastal pollution (LGB, 2008; Saleem et al., 2013). Two stations were selected at this site for the study.
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Figure 2.1: Map showing the seven monitoring sites (DH = Dhabeji, BH = Bhambore, PC = Phitti Creek, KC = Korangi Creek, SP = Sandspit, SO = Sonari and SB = Sonmiani Bay) along the coast of Pakistan.
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2.2.3. Sampling Procedure
In this study, the data of two monitoring years 2001-03 (MY-I) and 2011-13 (MY-II) were compared. The collection and analysis of crab and sediment for first monitoring year (MY-I) were previously done by Saher (2008) during 2001-2002. Therefore, the sampling and laboratory analyses of sediment properties done in this study following Saher (2008) methodology for the better comparison and as well as valid results and outcomes. The seven selected sites (Dhabeji, Bhambore, Korangi Creek, Phitti Creek, Sandspit, Sonari and Sonmiani Bay) were visited once during the present study period (MY-II) as visited during first monitoring year (MY-I), details described in Table 2.1. The most of the sampling period was extended from September 2011 to January 2012; only two sites (Dhabeji and Bhambore) were visited in June 2013. The sediment and crab samples were collected from all above-mentioned sites to evaluate and compare the heavy metals contamination in sediments and its impact on density, distribution, and diversity of brachyuran crabs along the coast of Pakistan.
In the current study, the direct excavations of crab burrows through transect and quadrat method was used for the collection of crab species by following Saher (2008). Briefly, from each study site 2–3 vertical transects were made during the low tide time when the exposure area of the site was maximum. The distance between two vertical transects was almost 10 meter square. On each transect, two or three quadrats (0.25 m2) were placed and the distance of one quadrat to another was at least 10 meter square. Moreover, the number of transect or quadrat vary according to the exposed area of the monitoring location. After the placement of the quadrat, firstly counted all burrow openings of crabs to evaluate the burrow density of crabs. This is included in the visual counting of burrows and further correlated with the actual crab density as collected by excavation to predict the validation of two methods for determining the density of crabs along the coastal areas of Pakistan (Saher et al., 2017). Secondly, ten (10) crab burrows were selected randomly but carefully included nearly all sizes ranging (according to the presence of small, medium and large burrows) to measure the crab burrow diameter (mm) through the standard millimeter scale. The burrow diameter is an indicator of the resident crab size because it is linearly correlated (Saher, 2008, Saher et al., 2012). Then quadrat was excavated approximately 30 cm in depth because mostly crabs construct burrows at similar depth and all crabs were collected in pre-washed and labelled polyethylene bottles.
After the excavation of sediments, seawater percolated inside each quadrat, the temperature, salinity and pH were noted from seeped water through thermometer, refractometer, and pH meter respectively. Between the two quadrats, sediment core was taken to evaluate the physicochemical
42 properties of sediments. The sediment core was collected up to 20 cm depth through PVC pipe core (core inner diameter = 5.6 cm). The sediment core placed in washed, clean and dry polyethylene bags. Then crab and sediment samples transferred to the laboratory in an iced box and freeze-dried in the deep freezer until analysis.
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Table 2.1: The general information of study sites from coastal areas of Pakistan during two monitoring years.
Location Name Site Latitude/ Sampling Sediment Type Code Longitude Dates First Monitoring Year (MY-I) Dhabeji DH 24° 48′ N, 21/08/2003 Muddy 67° 29′ E Bhambore BH 24° 43′ N, 28/12/2002 Sandy cum 67° 35′ E Muddy Phitti Creek PC 24° 65′ N, 11/09/2003 Muddy 67° 16′ E Korangi Creek KC 24° 79′ N, 22/12/2001 Muddy, muddy 67° 20′ E cum sandy Sandspit SP 24° 50′ N, 12/09/2001 Sandy cum 66° 56′ E muddy Sonari SO 24° 53′ N, 04/11/2002 Sandy cum 66° 42′ E muddy Sonmiani Bay SB 25° 26′ N, 02/11/2002 Muddy, Sandy 66° 35′ E cum muddy Second Monitoring Year (MY-II) Dhabeji DH 24° 44′ N, June 2013 Muddy 67° 29′ E Bhambore BH 24° 45′ N, June 2013 Muddy 67° 34′ E Phitti Creek PC 24° 47′ N, Nov 2011 Muddy 67° 16′ E Korangi Creek KC 24° 47′ N, Dec 2011 Muddy 67° 10′ E Sandspit SP 24° 49′ N, Sep 2011 Sandy cum 66° 56′ E muddy Sonari SO 24° 53′ N, Oct 2011 Sandy cum 66° 42′ E muddy Sonmiani Bay SB 24° 53′ N, Dec 2011 Muddy, Sandy 66° 42′ E cum muddy
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2.2.4. Laboratory Analysis (I) Sediment Analysis
Each sediment core (20cm long) mixed and homogenized to make a composite and representative sample of locations for better understanding the physicochemical properties of sediments. All sediment samples were analyzed by mean of physical (moisture, porosity, organic matter and grain size) and chemical properties (total metal concentration of Fe, Cu, Zn, Ni, Co, Cr, Pb, and Cd). To evaluate percent moisture contents and porosity, two replicates of 3–5 g of each sediment sample taken in pre-weight crucible and oven dried at 75 °C for 24 h (Saher, 2008; Saher and Qureshi, 2010). Then, dried weight was noted and calculated moisture and porosity by using the following formula:
% Moisture (% Moi) = (Mass of moisture/ dry weight) × 100 (Equation 2.1)
Mass of Moisture = Dry weight of sediment – Wet weight of sediment
% Porosity (% Por) = (Volume of water/ Volume of sediment) × 100 (Equation 2.2)
Volume of water = Mass of the moisture / density of water (1.025)
Volume of sediment = Mass of sediment/ density of sediment (2.65)
The total organic matter (% TOM) was determined by gravimetrically after a loss on ignition. For this, oven dried sediment samples calcined in a muffle furnace at 450 °C for 4 h and weighed (Saher and Qureshi, 2010; Qureshi and Saher, 2012) then measured by the following equation:
% TOM = (Dry weight – Ash-free dry weight/ Dry weight) × 100 (Equation 2.3)
Grain size analysis of marine sediments were carried out by using the standard dry sieving technique following Folk (1974). Approximately 100 to 150 grams of the sediment were oven dried at 75 °C then was disaggregated using a hand-driven mortar and pestle. The disaggregated samples were thoroughly mixed and each sample was then mechanically sorted by US mesh size sieves of 2.0, 1.0, 0.5, 0.3, 0.125 and 0.063 mm following Folk and Ward (1957). The sieving time kept constant (15 minutes) to standardize the results. The sediments were retained on each sieve were collected in pre-weighed porcelain crucibles and the weights were noted (microbalance, up to 0.0001 corrections). According to Wentworth (1922) size class, sediments were classified as gravel (>2.0),
45 very coarse sand (<2.0 to >1.0), coarse sand (<1.0 to >0.50), medium sand (<0.50 to >0.25), fine sand (<0.25 to >0.125), very fine sand (<0.125 to >0.0625) and mud (<0.0625). The physical properties of all sediment samples were done earlier by Saher (2008) during the monitoring year 2001-03 (MY-I) and these data were used in comparison with recent data of monitoring year 2011- 13 (MY-II). Stored sediment samples (<0.063 mm) were used in acid digestion through the Aqua Regia method for heavy metal analysis for the monitoring year 2001-03 (MY-I). The mud fraction (< 0.063 mm) was used to determine the heavy metals concentrations. In detail, an approximately, 1.0 g of sediment sample mixed with 10 mL mixture of nitric acid and hydrochloric acid (1:3) and digested on a hot plate at 90 °C for an hour then allowed to cool at room temperature. The sample was filtered (Whatman no. 42 µm) and diluted to 50 mL with distilled water (Qari et al., 2005; Saher and Siddiqui, 2016). Samples were analyzed for the eight metals (Fe, Cu, Zn, Ni, Co, Cr, Pb, and Cd) through Atomic Absorption Spectrometer (Perkin Elmer (USA), model A Analyst 700).
(II) Crab Analysis
Crab samples were washed with plenty of tap water and then distilled water to remove sediment and debris from their body. Crab species were sorted and identified through the species identification key (Tirmizi and Kazmi, 1996) and each species placed in separate tag plastic bottles. The morphometric parameters, i.e. gender (male or female), carapace length (CL in mm), carapace width (CW in mm) and wet weight (WW in mg) were noted for each individual. The total number of crabs collected within each quadrat was used to calculate the mean crab density for each site.
Crab density (#/m2) = Total number of crab/ Area (Equation 2.4)
The Shannon-Weaver diversity index (1949), equitability (Pielou evenness) (1966) and Margalef’s species richness (1958) were evaluated through the following formula:
Diversity Index (H’) = (Equation 2.5)
Where: H’ = the Shannon diversity index
th ni = fraction of the entire population made up of i species N = numbers of species encountered ∑ = sum from species
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Higher values of H’ would be representative of more diverse communities. A community with only one species would have the H’ value of zero, conversely the habitat contains a greater number of species have high value of H’. Therefore, the H value allows knowing the number of species as well as the abundance of the species among all the species in the community.
Equitability Index (J’) = H’/ Hmax (Equation 2.6)
Where, H’ is the number derived from the Shannon diversity index and Hmax is the maximum possible value of H’ (if every species was equally likely). The species richness index calculated as following equations:
Species Richness Index (SR) = (S-1 / ln N) (Equation 2.7)
2.2.5. Data Quality and Precision
The blank and standard solutions used to monitor the performance of equipment and data quality by developing calibration curves. Standard solutions (2, 4, and 6 ppm) were obtained by dissolving appropriate salts of corresponding metals in deionized water. The calibration curves were obtained strong linear correlation (R2 = 1) for the standard solutions of all heavy metals. The metal concentrations of each sample were expressed in µg g-1 of the dry weight of the sample. In the present study, all heavy metals analyzed by AAS achieved good precision (1 to 6% relative standard deviation (RSD), except Co which precision ranged from 10 to 28% RSD.
2.2.6. Pollution Depiction by Multiple Indices Approach
The level of sediment contamination may be quantify through many techniques including single and multiple indices and in this study the identification and assessment of heavy metals in a coastal environment estimated through these indices. The various approaches have been adopted to investigate the magnitude of heavy metals pollution includes, the evaluation of specific metal contamination in coastal sediments was calculated by sediment quality guidelines (Long and MacDonald, 1998), Geo-accumulation index (Muller, 1969), the enrichment factor (Buat-Menerd and Chesselt, 1979), contamination factor and ecological risk factor (Hakanson, 1980). However, mean quotient of sediment quality guidelines (Long and MacDonald, 1998), contamination degree and ecological risk index introduced by Hakanson (1980) used as a multiple elemental analysis.
Several sediment quality guidelines (SQGs) have been established which widely apply in the evaluation of biological risk caused by toxic substances in the sediments (Long and MacDonald, 1998; Siddique et al., 2009; Zhuang and Gao, 2014; Saher and Siddiqui, 2016). Environmental
47 concerns of heavy metals assessed by widely using two sets of guidelines, that is effect range low and medium (ERL and ERM) and threshold and probable effect level (TEL and PEL). The metal concentrations, which is less than their respective ERL/ TEL symbolize where biological effects are rarely observed, concentrations between ERL-ERM and TEL-PEL will have occasionally observed biological effects, and concentrations greater than ERM/ PEL represent where biological effects will frequently happen.
Mean quotients (MQ) of these guidelines are a useful tool for reducing a large amount of data into a single digit for mixtures of contaminants associated with marine sediments. Quantification of toxicity by the mean quotients technique can be more reasonable than a single metal value because it determines the overall contamination present in the sediments and may have the adverse effect on the benthic fauna and it also classify the most contaminated locations include in the monitoring via subsequent formula (Long and MacDonald, 1998; Siddique et al., 2009; Zhuang and Gao, 2014).
Mean X quotient = ∑ (Cn / Xn) / N (Equation 2.8)
Where, Cn is a concentration of metal ‘n’ in the sediment sample, Xn is the sediment quality guideline (ERL/ ERM and TEL/ PEL) value of metal ‘n’ and N is the sums of all examine metals.
Geo-accumulation index (Igeo) was originally defined by Muller (1969) in order to determine single metal contamination in sediments by comparing sample concentrations with the pre-industrial concentration of metal by using the following equation:
Igeo = log2 (Cn / 1.5 Rn) (Equation 2.9)
Where, Cn and Rn symbolized sample and reference value of metal ‘n’ respectively, and 1.5 is a constant value which represents the possible lithogenic effects that cause the variations in baseline values of the specific geographic regions. The pre-industrial or baseline values of studying metals are not available for the selected site, therefore the reference values of studying metals were taken from the Earth’s average values of clays from sedimentary rocks (AVCSR) stated by Turekian and Wedepohl (1961). Muller (1981) defines seven classes of Geo-accumulation index in relation to the pollution extends:
Class I = < 0 is practically uncontaminated Class II = 0–1 is uncontaminated to moderately contaminated Class III = 1–2 is moderately contaminated Class IV = 2–3 is moderate to heavily contaminated
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Class V = 3–4 is heavily contaminated Class VI = 4–5 is heavy to extremely contaminated Class VII = > 5 is extremely contaminated The metal Enrichment Factors (EF) represent the particular contamination level within the sediments (Groengroeft et al., 1998) and may be a smart tool to differentiate the natural and anthropogenic input of heavy metals in an environment (Morillo et al., 2004; Selvaraj et al., 2004; Valdés et al., 2005). In step with this system, metal concentrations were normalized to the textural characteristic of sediments. EF also can be used to verify the degree of the geological phenomenon (Lee et al., 1998; Huang and Lin, 2003). The subsequent equation was employed in this study to estimate the EF of metals in every sediment sample mistreatment metal as a normalizer to correct for variations in sediments grain size and mineralogy (Buat-Menerd and Chesselt, 1979).
EF = (Mes/ Fes) / (Mer/ Fer) (Equation 2.10)
Where, Mes and Mer are metal concentrations in the sample and reference value, respectively, and Fes and Fer are Fe concentrations in the sample and reference value respectively. Fe may be a better predictor for background heavy metal levels than Al, the natural concentration of Fe is more uniformity than Al and a significant linear correlation between Al2O3 and Fe2O3 was found in surface sediments (Schiff and Weisberg 1999; Siddique et al., 2009). According to Daskalakis and O’Connor (1995), Fe was used for the following reasons: (1) Fe associated with fine solid surfaces; (2) its geochemistry is similar to that of many trace metals; and (3) its natural sediment concentration tends to be uniform. Thus, in this study the geochemical normalization obtained using Fe as the reference element. The enrichment factor categorized into seven classes according to severity:
Class I = <1 indicates no enrichment Class II = 1–3 is minor enrichment Class III = 3–5 is moderate enrichment Class IV = 5–10 is moderately severe enrichment Class V = 10–25 is severe enrichment Class VI = 25–50 is very severe enrichment Class VII = >50 is extremely severe enrichment Contamination factor (CF) is the single metal pollution index, whereas contamination degree is the summation of all contamination factors and Hakanson (1980) demonstrates it as follow:
CF = Cs / Cr (Equation 2.11)
CD = ∑ CF (Equation 2.12)
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Where, Cs and Cr are the concentration of metal in the sample and reference value, correspondingly. Hakanson (1980) categorized contamination factor and degree into several classes according to their severity:
Class I = <1 is low contamination Class II = 1–3 is moderate contamination Class III = 3–6 is considerable contamination Class IV = >6 is very high contamination factor
Contamination degree (CD) was systematized as:
Class I = <8 is low contamination degree Class II = 8–16 is moderate contamination degree Class III = 16–32 is a considerable contamination degree Class IV = >32 is very high contamination degree. The potential ecological risk index described by Hakanson (1980) is commonly used indicator to express a comprehensive assessment of the harmful effects of all heavy metals in the environment, including soils and sediments (Zhu et al., 2008). The individual heavy metals risk was evaluated by ecological risk factor (ER), however combined metal risk was evaluated by ecological risk index as following:
ER = TR × CF (Equation 2.13)
PERI = ∑ ER (Equation 2.14)
Where, TR is the biological toxic response factor of an individual element, which is determined for Cu and Pb = 5, Zn = 1, Cd = 30, Ni = 6 and Cr = 2. Hakanson (1980) has categorized ecological risk factor and index into several classes according to their severity and classified as:
Class I = <40 is low risk Class II = 40–80 is moderate risk Class III = 80–160 is considerable risk Class IV = 160–320 is high risk Class V = >320 is very high risk Potential ecological risk index (PERI) grading as:
Class I = <150 is low risk Class II = 150–300 is moderate risk Class III = 300–600 is a considerable risk Class IV = >600 is very high risk
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2.2.7. Data Analysis
The data were analyzed graphically and statistically by Microsoft excel 2013v and Minitab (13.0 and 17.0 v) respectively.
Descriptive statistics included, the total number of observations, mean, standard deviation, minimum, median and maximum values of a particular variable. Analysis of variance (ANOVA) followed by Generalized Linear Model (GLM) was used to test the variations between physicochemical properties of sediments and biotic properties of crabs during the last decade by the following model: Site, Year and Site*Year. The significant differences accepted when the probability level was less than 0.05. Pearson’s correlation coefficient was employed to establish the interrelationships between % OM, grain size and eight heavy metals for two monitoring years separately to identify the possible sources of heavy metals in the coastal sediments of Pakistan. The regression analysis was used to test the relationship between biotic properties and metal contamination in sediments.
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2.3. RESULTS
2.3.1. Physical Properties of Sediments
The physical properties of sediments (% moisture, % porosity, % organic matter and % grain size composition) from seven sites i.e. Dhabeji (DH), Bhambore (BH), Phitti Creek (PC), Korangi Creek (KC), Sandspit (SP), Sonari (SO), and Sonmiani Bay (SB) were investigated during the two monitoring years (MY-I = 2001-03 and MY-II = 2011-13) and shown in Table 2.2. The percent moisture and porosity contents showed variation among the sites as well as during the years (Figure 2.2a and b). In MY-I, sediments of PC had the highest percent moisture (43.8 ± 11.5) and porosity (113.2 ± 29.8), whereas the sediments of SB showed the lowest percent moisture (3.9 ± 0.9) and porosity (22.6 ± 5.9). In MY-II, sediments of DH presented the highest percent moisture (37.47 ± 4.74) and porosity (96.86 ± 12.25), however the sediments of SO had the lowest values of percent moisture (22.3 ± 4.89) as well as porosity (57.6 ± 13.4).
The percent organic matter was detected highest in the sediments of DH during both monitoring years (15.9 ± 3.9 in MY-I and 7.5 ± 2.9 in MY-II). Whereas, the lowest values of organic contents in sediments were noticed in the sediments of SP (1.4 ± 0.3) and SB (0.9 ± 0.3) in MY-I and MY-II, respectively (Figure 2.2c). The ANOVA analysis showed significant (p <0.001) differences in percent moisture, porosity and organic contents among sites as well as between two monitoring years in coastal sediments of Pakistan (Table 2.3).
The grain size composition of sediments presented variable proportions during the last decade along the coastal areas of Pakistan (Table 2.2). The spatial distribution of percent granule (% G) in coastal sediments illustrated in Figure 2.3a. In MY-I, it was observed highest at DH (5.6 ± 3.4) and lowest in sediments of KC (0.6 ± 0.5). In MY-II, the granules proportion was highest in BH (5.7 ± 1.6) and lowest in SO (1.8 ± 1.3). The significant differences (p <0.05) were observed in granules among the sites as well as between the years (Table 2.4). The spatial distribution of very coarse sand (% VCS) in coastal sediments during the two monitoring years as shown in Figure 2.3b. The % VCS was observed highest at PC (12.8 ± 5.1) and lowest in sediments of SP (2.5 ± 1.2) during MY-I. Conversely, it was highest in BH sediments (9.2 ± 3.5) and lowest at SO (2.4 ± 1.2) in MY-II. The significant differences (p <0.05) were observed in the distribution of % VCS among the sites and between the years (Table 2.4).
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The noticeable spatial distribution of coarse sand (% CS) was observed during the both monitoring years in coastal sediments (Figure 2.3c). The distribution of % CS was observed highest in the sediments of SP during the both monitoring years (39.7 ± 17.0 in MY-I and 32.8 ± 12.3 in MY-II), however it was observed lowest at BH (11.6 ± 2.5) and SO (4.0 ± 2.4) in MY-I and MY-II, respectively. The significant differences (p <0.05) were observed in % CS among the sites as well as between the years (Table 2.4). The spatial distribution of medium sand (% MS) in coastal sediments during the last decade illustrated in Figure 2.3d. Among the study sites, the highest proportions of % MS evaluated from the sediments of SP in both monitoring years (39.5 ± 7.6 in MY-I and 37.9 ± 13.3 in MY-II). The lowest proportions of % MS assessed from the sediments of BH (6.4 ± 0.6) and SB (4.2 ± 2.8) in MY-I and MY-II, respectively. The significant differences (p <0.001) were observed among the sites for % MS, but it showed no significant differences (p >0.05) between the years (Table 2.4).
The spatial distribution of fine sand (% FS) in coastal sediments during the last decade plotted in Figure 2.3e. The highest amount of % FS was found in the sediments of SB during both monitoring years (45.5 ± 27.4 in MY-I and 58.1 ± 25.1 in MY-II). Whereas, the lowest proportion of % FS observed at BH (11.0 ± 1.4) and DH (10.6 ± 2.3) in MY-I and MY-II, respectively. The significant differences (p <0.001) in proportion of % FS observed among the sites, but was not significant (p >0.05) between the years (Table 2.4). The spatial distribution of very fine sand (% VFS) in coastal sediments during the both monitoring years illustrated in Figure 2.3f. The ratio of % VFS was highest in the sediments of BH (56.4 ± 6.8) and SO (16.7 ± 15.9) in MY-I and MY-II, respectively. Whereas, the lowest values found from the sediments of SP in both monitoring years (2.6 ± 1.9 in MY-I and 3.9 ± 3.7 in MY-II). The significant differences (p <0.001) observed in the ratio of % VFS among the sites and between the monitoring years (Table 2.4).
The spatial distribution of mud (% M) in coastal sediments during the both monitoring years illustrated in Figure 2.3g. The proportion of % M observed highest in the sediments of BH (6.0 ± 3.15) and DH (56.1 ± 14.2) in MY-I and MY-II, respectively. Whereas, the lowest values found from the sediments of SP during both monitoring years (0.7 ± 0.4 in MY-I and 4.0 ± 3.0 in MY-II). The significant differences (p <0.001) observed in the mud proportions among the sites and between the years (Table 2.4). The correlation analysis was evaluated between the physical properties of sediments (Table 2.7). It was also observed that there is a significant spatial interaction and correlation for the % VFS and mud content during the last decade (Table 2.7).
53
Table 2.2: Summary statistics of physical properties of sediments during the last decade along the coastal areas of Pakistan.
Variable Total Mean S.D Min Median Max Monitoring Year I (MY-I) % Moisture 32 20.68 13.36 2.60 19.88 53.24 % Porosity 32 56.20 31.33 14.23 51.41 137.64 % Organics 32 5.473 5.362 1.035 3.147 20.347 % Granule 32 1.874 1.865 0.13 1.314 9.447 % VCS 32 7.276 5.061 1.29 5.178 17.463 % CS 32 20.94 13.88 3.97 19.86 57.00 % MS 32 15.83 12.63 3.86 11.65 50.10 % FS 32 26.54 19.73 4.40 20.01 73.04 % VFS 32 25.26 17.45 0.73 23.67 62.90 % Mud 32 2.280 2.402 0.22 1.433 9.271 Monitoring Year II (MY-II) % Moisture 42 28.65 8.58 16.77 27.09 49.64 % Porosity 42 74.07 22.19 43.35 70.04 128.35 % Organics 42 2.387 1.958 0.552 1.682 10.847 % Granule 42 3.148 2.672 0.07 2.023 11.121 % VCS 42 5.299 3.597 0.43 5.421 13.459 % CS 42 15.60 12.26 1.09 11.77 50.70 % MS 42 14.82 13.29 0.89 10.56 55.02 % FS 42 31.23 22.18 6.73 21.87 86.87 % VFS 42 8.90 9.17 0.59 5.44 42.27 % Mud 42 21.01 17.37 0.38 20.37 65.42
54
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Figure 2.2: Mean distribution variability of (a) percent moisture, (b) porosity, and (c) organic matter in sediments from seven monitoring sites (DH = Dhabeji, BH = Bhambore, PC = Phitti Creek, KC = Korangi Creek, SP = Sandspit, SO = Sonari and SB = Sonmiani Bay) during the two monitoring years (MY-I and MY-II).
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Figure 2.3: Grain size distribution of sediments from seven monitoring sites (DH = Dhabeji, BH = Bhambore, PC = Phitti Creek, KC = Korangi Creek, SP = Sandspit, SO = Sonari and SB = Sonmiani Bay) during the two monitoring years (MY-I and MY-II).
56
Table 2.3: Variations in % moisture, % porosity and % organic matter of sediments by analysis of variance (ANOVA) during the last decade along the coast of Pakistan.
Variables Source DF Seq. SS Adj. SS Adj. MS F P % Moisture Site 6 3878.87 3540.70 590.12 12.28 0.000*** Year 1 643.98 731.59 731.59 15.23 0.000*** Site*Year 6 2222.07 2222.07 370.34 7.71 0.000*** Error 60 2786.32 2786.32 48.04 Total 73 9531.23 % Porosity Site 6 21334.9 20520.3 3420.0 10.55 0.000*** Year 1 3225.6 3957.7 3957.7 12.21 0.001** Site*Year 6 11955.7 11955.7 1992.6 6.15 0.000*** Error 60 18794.8 18794.8 324.0 Total 73 55311.1 % Organics Site 6 610.844 687.654 114.609 59.72 0.000*** Year 1 179.094 154.239 154.239 80.38 0.000*** Site*Year 6 309.015 309.015 51.502 26.84 0.000*** Error 60 111.301 111.301 1.919 Total 73 1210.253 ‘***’ = Significance level at <0.001 ‘**’ = Significance level at <0.01 ‘*’ = Significance level at <0.05
57
Table 2.4: Variations in grain size composition of sediments by analysis of variance (ANOVA) during the last decade along the coast of Pakistan.
Variables Source DF Seq. SS Adj. SS Adj. MS F P Granules Site 6 74.567 63.704 10.617 2.37 0.040* Year 1 25.525 20.098 20.098 4.49 0.038* Site*Year 6 61.110 61.110 10.185 2.27 0.048* Error 60 268.794 268.794 4.480 Total 73 429.997 Very coarse sand Site 6 450.86 491.78 81.96 7.03 0.000*** Year 1 70.22 66.27 66.27 5.68 0.020* Site*Year 6 175.05 175.05 29.17 2.50 0.032* Error 60 699.57 699.57 11.66 Total 73 1395.70 Coarse sand Site 6 6705.97 6774.21 1129.03 13.42 0.000*** Year 1 612.40 424.96 424.96 5.05 0.028* Site*Year 6 289.09 289.09 48.18 0.57 0.750 Error 60 5047.26 5047.26 84.12 Total 73 12654.72 Medium sand Site 6 10033.02 9952.27 1658.71 49.58 0.000*** Year 1 93.38 82.20 82.20 2.46 0.122 Site*Year 6 63.53 63.53 10.59 0.32 0.926 Error 60 2007.47 2007.47 33.46 Total 73 12197.40 Fine sand Site 6 15525.5 15201.5 2533.6 10.45 0.000*** Year 1 481.7 305.8 305.8 1.26 0.266 Site*Year 6 2082.2 2082.2 347.0 1.43 0.218 Error 60 14544.5 14544.5 242.4 Total 73 32633.9 Very fine sand Site 6 5384.8 5225.5 870.9 11.27 0.000*** Year 1 4152.0 5675.9 5675.9 73.48 0.000*** Site*Year 6 3582.5 3582.5 597.1 7.73 0.000*** Error 60 4634.8 4634.8 77.2 Total 73 17754.0 Mud Site 6 4993.8 4594.9 765.8 12.03 0.000*** Year 1 6436.1 8315.5 8315.5 130.65 0.000*** Site*Year 6 3667.4 3667.4 611.2 9.60 0.000*** Error 60 3818.9 3818.9 63.6 Total 73 18916.2 ‘***’ = Significance level at <0.001 ‘**’ = Significance level at <0.01 ‘*’ = Significance level at <0.05
58
2.3.2. Comparison of Heavy Metal Concentrations during the Last Decade
The eight different heavy metals (Fe, Cu, Zn, Ni, Co, Cr, Pb, and Cd) examined for the assessment of contamination levels in the sediments during the last decades. The different metals presented variations among the sites as well as during the monitoring years and most of the metals showed an increment in their level of concentration at the same sites during the last decade.
Iron (Fe) concentration in sediments along the coastal areas of Pakistan in the two monitoring years plotted in Figure 2.4a. In both monitoring years, the highest concentration of Fe was detected at KC (911.63 ± 5.44 µg g-1 in MY-I and 1013.3 ± 6.31 µg g-1 in MY-II) and lowest concentration was evaluated at SP (798.0 ± 105.8 µg g-1 in MY-I and 915.4 ± 104.1 µg g-1 in MY-II). The significant differences (p <0.001) observed in Fe concentrations among the sites and between the years (Table 2.6). Moreover, all the study sites showed significant (p <0.05) increase in Fe concentrations in sediments during the last decade except SP (Figure 2.4a).
The spatial distribution of Copper (Cu) in sediments during the two monitoring years illustrated in Figure 2.4b. In both monitoring years, the highest levels of Cu noticed in the sediments of SP (118.9 ± 34.8 µg g-1 in MY-I and 328.1 ± 203.2 µg g-1 in MY-II). Whereas, the lowest levels of Cu detected from the sediments of BH (20.28 ± 8.04 µg g-1) and DH (8.28 ± 6.24 µg g-1) in MY-I and MY-II, respectively. The significant differences (p <0.001) observed in Cu concentrations among the sites, but there was no significant difference (p >0.05) between the two years (Table 2.6). Moreover, the sediments of SP presented significant (p <0.05) increase in Cu levels, whereas the sediments of SO and SB exhibited significant (p <0.05) reduction in Cu levels during the last decade (Figure 2.4b).
Zinc (Zn) concentration in sediments along the coast of Pakistan during the two monitoring years illustrated in Figure 2.4c. In both monitoring years, the highest concentration of Zn evaluated in the sediments of SP (165.5 ± 29.5 µg g-1 in MY-I and 255.6 ± 81.2 µg g-1 in MY-II). Whereas, the lowest concentration of Zn detected in the sediments of BH (63.49 ± 11.53 µg g-1) and SB (33.42 ± 8.77 µg g-1) for MY-I and MY-II, respectively. ANOVA analysis showed the significant differences (p <0.001) in Zn concentrations among the sites, but there were no significant differences (p >0.05) between the years (Table 2.6). Furthermore, the sediments of SP displayed significant (p <0.05) intensification in Zn levels, whereas the sediments of KC, SO and SB exhibited significant (p <0.05) decrease in Cu level during the last decade (Figure 2.4c).
59
Nickel (Ni) concentration in sediments along the coastal areas of Pakistan during the two monitoring years was illustrated in Figure 2.4d. In MY-I, the concentration of Ni was highest at SP (85.58 ± 23.01 µg g-1) and the lowermost concentration noticed at PC (10.14 ± 4.37 µg g-1). During MY-II, Ni concentration was highest at BH (62.79 ± 10.55 µg g-1) and lowest concentration in KC sediments (35.91 ± 13.98 µg g-1). The significant differences (p <0.001) observed in Ni concentrations among the sites as well as between the years (Table 2.6). Moreover, the sediments of PC, KC, SO, SB showed significant (p <0.05) increase in Ni levels, whereas the sediments of SP presented significant (p <0.05) decrease in Ni levels during the last decade (Figure 2.4d).
The concentration of Cobalt (Co) concentration in sediments of coastal areas during the two monitoring years illustrated in Figure 2.4e. In MY-I, the high concentration of Co noticed at SP (16.54 ± 2.81 µg g-1) and lower concentration observed at KC (3.86 ± 2.40 µg g-1). Whereas in MY- II, it was detected highest at BH (14.67 ± 3.31 µg g-1) and lowest at PC (4.718 ± 1.598 µg g-1). The significant differences (p <0.001) observed in Co concentrations among the sites, however it showed no significant differences (p >0.05) between the years (Table 2.6). Moreover, the temporal variations observed only at SP where the concentration of Co significantly (p <0.05) decrease during the last decade (Figure 2.4e).
Chromium (Cr) observed highest (47.07 ± 5.74 µg g-1) in the sediments of KC and lowest (14.92 ± 5.43 µg g-1) in the sediments of BH in MY-I (Figure 2.4f). Whereas in MY-II, it was highest (209.2 ± 62.7 µg g-1) at SP and lowest (24.3 ± 18.7 µg g-1) at DH. The significant difference (p <0.001) observed in Cr concentrations among the sites as well as between the years (Table 2.6). Moreover, all study sites showed significant (p <0.05) increase in Cr concentrations during the last decade except DH and BH (Figure 2.4f).
Lead (Pb) concentration in sediments along the coastal areas of Pakistan during the two monitoring years illustrated in Figure 2.4g. In MY-I, Pb was observed highest at SB (77.0 ± 62.6 µg g-1), whereas it seem not detectable in the sediments of SO and KC. In MY-II, Pb concentration noticed highest in SP (100.2 ± 50.4 µg g-1) and lowest in SO (27.495 ± 1.897 µg g-1). ANOVA revealed significant differences (p <0.001) in Pb concentrations among the sites, but there were no significant differences (p >0.05) between the years (Table 2.6). Moreover, the significant (p <0.05) increase was identified in Pb concentrations at KC, SP and SO during the last decade (Figure 2.4g).
Cadmium (Cd) concentrations in sediments of various sites of the Pakistan coast during the two monitoring years plotted in Figure 2.4h. In MY-I, the highest concentration of Cd observed at SP
60
(2.281 ± 0.349 µg g-1) and the lowest concentration detected in sediments of KC (0.135 ± 0.071 µg g- 1). In MY-II, the highest concentration of Cd noticed at BH (2.464 ± 0.085 µg g-1) and the lowest concentration was observed at SP (0.586 ± 0.327 µg g-1). ANOVA recognized the significant differences (p <0.001) in Cd concentrations among the sites as well as between the years (Table 2.6). Moreover, the significant (p <0.05) increase was observed in Cd concentrations at PC, KC, SP, SO and SB during the last decade (Figure 2.4h). The concentrations of five metals (Cu, Zn, Ni, Cr and Cd) showed significant interaction between the sites and years (Figure 2.5). The correlation analysis between physical properties and metal concentrations shown in Table 2.8 and inter-elemental correlation of heavy metals also evaluated in Table 2.9.
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Table 2.5: Summary statistics of eight heavy metal concentration in coastal sediments during the two monitoring years (MY-I and MY-II).
Variable Total Mean S.D Min Median Max Monitoring Year I (MY-I) Fe 32 865.6 62.6 588.6 889.2 917.6 Cu 32 57.43 37.36 13.15 50.33 162.68 Zn 32 100.67 39.26 40.66 92.25 216.65 Ni 32 34.50 32.59 0.00 16.35 126.62 Co 32 9.225 5.490 1.131 7.281 18.387 Cr 32 31.66 12.07 9.48 29.78 52.89 Pb 32 40.38 41.14 0.00 32.60 177.55 Cd 32 0.958 0.929 0.027 0.486 2.819 Monitoring Year II (MY-II) Fe 45 978.13 56.50 782.11 999.97 1051.72 Cu 45 82.6 142.2 1.8 31.7 672.4 Zn 45 100.7 82.9 26.7 72.5 361.1 Ni 45 46.23 11.51 13.45 46.14 72.18 Co 45 8.51 5.02 0.14 7.35 18.37 Cr 45 107.2 72.2 9.4 106.8 303.5 Pb 45 53.40 43.56 22.21 36.77 177.81 Cd 45 1.20 0.55 0.13 1.16 2.54
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Figure 2.4: Comparison of heavy metal concentrations (µg g-1) in sediments from seven monitoring sites (DH = Dhabeji, BH = Bhambore, PC = Phitti Creek, KC = Korangi Creek, SP = Sandspit, SO = Sonari and SB = Sonmiani Bay) during the two monitoring years (MY-I and MY-II).
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Table 2.6: Variations in eight heavy metal concentrations in coastal sediments of Pakistan by analysis of variance (ANOVA) during the last decade.
Metals Source DF Seq. SS Adj. SS Adj. MS F P Fe Sites 6 181983 149179 24863 5.96 0.000*** Year 1 173982 163692 163692 39.23 0.000*** Sites*Year 6 3571 3571 595 0.14 0.990 Error 63 262852 262852 4172 Total 76 622389 Cu Sites 6 613514 423249 70541 10.43 0.000*** Year 1 15727 2774 2774 0.41 0.524 Sites*Year 6 148261 148261 24710 3.65 0.004** Error 63 425933 425933 6761 Total 76 1203436 Zn Sites 6 342793 263979 43997 37.59 0.000*** Year 1 164 147 147 0.13 0.724 Sites*Year 6 56636 56636 9439 8.07 0.000*** Error 63 73732 73732 1170 Total 76 473324 Ni Sites 6 14661.1 20087.3 3347.9 29.44 0.000*** Year 1 2170.8 2715.6 2715.6 23.88 0.000*** Sites*Year 6 17566.8 17566.8 2927.8 25.74 0.000*** Error 63 7165.1 7165.1 113.7 Total 76 41563.8 Co Sites 6 1198.64 1277.11 212.85 18.98 0.000*** Year 1 12.79 2.71 2.71 0.24 0.625 Sites*Year 6 137.58 137.58 22.93 2.04 0.073 Error 63 706.70 706.70 11.22 Total 76 2055.71 Cr Sites 6 122573 64199 10700 12.66 0.000*** Year 1 69944 40105 40105 47.47 0.000*** Sites*Year 6 60741 60741 10123 11.98 0.000*** Error 63 53230 53230 845 Total 76 306488 Pb Sites 6 70840 53478 8913 5.03 0.000*** Year 1 6592 4247 4247 2.40 0.127 Sites*Year 6 31168 31168 5195 2.93 0.014* Error 63 111582 111582 1771 Total 76 220183 Cd Sites 6 17.5388 21.2853 3.5476 59.73 0.000*** Year 1 1.3088 2.4742 2.4742 41.65 0.000*** Sites*Year 6 18.8353 18.8353 3.1392 52.85 0.000*** Error 63 3.7420 3.7420 0.0594 Total 76 41.4248 ‘***’ = Significance level at <0.001 ‘**’ = Significance level at <0.01 ‘*’ = Significance level at <0.05
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2.3.3. Pearson’s Correlation Analysis
Pearson’s correlation analysis performed to determine the relationships among geo-chemical properties of sediment separately for two monitoring years (MY-I and MY-II).
(I) Relationship between Physical Properties of Sediment
The significant positive correlation observed between % moisture with % porosity, % OM and % VCS in both monitoring years (Table 2.7a and b). Percent porosity was observed significant positive correlation with % OM and % VCS in both monitoring years (Table 2.7a and b). Percent organic matter (% OM) presented significant positive correlation with granules and % VCS in MY-I (Table 2.7a). Conversely, % OM was showing positive correlations with %VCS and %CS, but it observed significant negative correlation with % FS and % mud in MY-II (Table 2.7b).
The significant correlations obtained between the pairs, granules vs. very coarse sand; very coarse vs. medium and fine sand; coarse sand vs. medium, fine and very fine sand; medium sand vs. very fine sand and mud and very fine sand vs. mud in MY-I (Table 2.7a). The significant correlations obtained between the pairs, granules vs. very coarse sand fine sand and mud; very coarse and vs. fine sand and mud; coarse sand vs. medium, fine and very fine sand; medium sand vs. fine sand, very fine sand and mud and also fine sand vs. mud in MY-II (Table 2.7b).
(II) Relationship between Physicochemical Properties of Sediment
Percent moisture showed no correlation with heavy metals in MY-I (Table 2.8a), but it showed significant correlation with Cd in MY-II (Table 2.8b). Percent porosity was observed significant negative correlation with Pb in MY-I (Table 2.8a), but it showed significant positive correlation with Cd in MY-II (Table 2.8b). Percent organic matter (% OM) Showed no correlation with heavy metals in MY-I (Table 2.8a), but conversely it showed positive correlations with Zn and Ni in MY-II (Table 2.8b).
Granule (% G) was observed a significant correlation with Co and Cu in MY-I and MY-II, respectively (Table 2.8a and b). Very coarse sand (% VCS) was perceived significant negative correlation with Cu and Zn in MY-I (Table 2.8a), while it showed significant positive correlation with Cu, Zn and Co in MY-II (Table 2.8b). Significant negative correlation was observed between % CS and Fe, but it showed significant positive correlation with Cu, Zn, Ni, Co and Cd during MY-I
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(Table 2.8a). On the other hand, % CS showed significant positive correlation with Fe, Ni, Cr, Pb and Cd in MY-II (Table 2.8b).
The significant negative correlation was observed between medium sand (% MS) with Fe, but it showed significant positive correlation with Cu, Zn, Ni, Co and Cd in MY-I (Table 2.8a). On the other hand, it observed significant positive correlation with Fe, Ni, Cr, Pb and Cd in MY-II, but showed significant negative correlation with Cu and Co (Table 2.8b). Fine sand (% FS) was positively correlated with Fe, but showed negative correlation with Ni, Co and Cd in MY-I (Table 2.8a). Alternatively, it was observed negative correlation with Co, Cr and Pb in MY-II (Table 2.8b).
The negative correlation revealed for very fine sand (% VFS) with Cu, Zn and Pb in MY-I (Table 2.8a). Conversely, % VFS showed significant negative correlation with Fe, Ni and Pb in MY- II (Table 2.8b). Mud contents observed significant negative correlation with Cu, Zn and Pb in MY-I (Table 2.8a). In contrast, it was observed significant positive correlation with Fe, Cr, Pb and Cd, but showed significant negative correlation with Zn and Co in MY-II (Table 2.8b).
(III) Inter-Elemental Relationship
Inter-elemental correlation demonstrates that Fe was observed significant negative correlation with Ni, Co, Cr and Cd in MY-I (Table 2.22a). Whereas in MY-II, Fe showed significant positive correlation with Cr, Pb and Cd, but it showed significant negative correlation with Zn (Table 2.22b). The significant correlations obtained between the pairs, Cu vs. Zn, Cu vs. Ni, Cu vs. Co, Cu vs. Cd, Zn vs. Ni, Zn vs. Co, Zn vs. Cd, Ni vs. Co, Ni vs. Cd, Co vs. Cd, Zn vs. Pb and Cr vs. Cd in MY-I (Table 2.22a). The significant positive correlations obtained between the pairs, Zn vs. Ni, Zn vs. Co, Ni vs. Cr, Ni vs. Pb, Cr vs. Pb, Cr vs. Cd and Pb vs. Cd, but significant negative correlations obtained between the pairs, Cu vs. Ni, Cu vs, Cr and Cu vs. Pb in MY-II (Table 2.22b).
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Table 2.7: Pearson’s correlation analysis between physical properties [porosity (%Por), moisture (%Moi), organic matter (%OM), granules (%G), very coarse sand (%VCS), coarse sand (%CS), medium sand (%MS), fine sand (%FS) and very fine sand (%VFS)] of sediments during (a) MY-I and (b) MY-II. (r = correlation coefficient and p = probability level)
(a) %Moi %Por %OM %G %VCS %CS %MS %FS %VFS Porosity r 0.987 p 0.000 Organics r 0.461 0.483 p 0.008 0.005 Granule r 0.129 0.124 0.567 p 0.482 0.497 0.001 V.C.S r 0.372 0.436 0.602 0.372 p 0.036 0.013 0.000 0.036 C.S r 0.079 0.077 0.016 0.098 0.138 p 0.667 0.674 0.933 0.594 0.450 M.S r -0.045 -0.097 -0.250 0.095 -0.409 0.555 p 0.807 0.599 0.168 0.604 0.020 0.001 F.S r -0.353 -0.348 -0.111 -0.293 -0.395 -0.628 -0.262 p 0.048 0.051 0.545 0.103 0.025 0.000 0.147 V.F.S r 0.231 0.250 0.069 -0.037 0.300 -0.490 -0.702 -0.256 p 0.204 0.168 0.708 0.839 0.095 0.004 0.000 0.158 Mud r 0.116 0.084 -0.072 0.054 0.018 -0.345 -0.413 -0.291 0.754 p 0.528 0.649 0.694 0.769 0.921 0.053 0.019 0.106 0.000
(b) %Moi %Por %OM %G %VCS %CS %MS %FS %VFS Porosity r 1.000 p * Organics r 0.720 0.720 p 0.000 0.000 Granule r 0.245 0.245 0.243 p 0.117 0.117 0.121 V.C.S r 0.359 0.359 0.406 0.753 p 0.019 0.019 0.008 0.000 C.S r 0.196 0.196 0.312 -0.002 0.263 p 0.213 0.213 0.044 0.990 0.092 M.S r 0.024 0.024 0.023 -0.295 -0.258 0.634 p 0.879 0.879 0.887 0.058 0.098 0.000 F.S r -0.183 -0.183 -0.419 -0.369 -0.473 -0.655 -0.331 p 0.245 0.245 0.006 0.016 0.002 0.000 0.032 V.F.S r -0.044 -0.044 -0.212 0.178 -0.057 -0.365 -0.360 0.039 p 0.784 0.784 0.177 0.260 0.719 0.017 0.019 0.805 Mud r -0.025 -0.025 -0.308 -0.425 -0.416 0.204 0.517 0.458 0.032 p 0.877 0.877 0.047 0.005 0.006 0.194 0.000 0.002 0.843
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Table 2.8: Pearson’s correlation analysis between physical [porosity (%Por), moisture (%moi), organic matter (%OM), granules (%G), very coarse sand (%VCS), coarse sand (%CS), medium sand (%MS), fine sand (%FS) and very fine sand (%VFS)] and chemical (Fe, Cu, Zn, Ni, Co, Cr, Pb and Cd) properties of sediment during (a) MY-I and (b) MY-II. (r = correlation coefficient and p = probability level)
(a) %Moi %Por %OM %G %VCS %CS %MS %FS %VFS %Mud Fe r 0.016 0.065 0.102 -0.237 0.128 -0.410 -0.363 0.380 0.152 -0.029 p 0.931 0.723 0.579 0.192 0.485 0.020 0.041 0.032 0.407 0.873 Cu r -0.268 -0.304 -0.347 -0.175 -0.454 0.653 0.772 -0.069 -0.786 -0.455 p 0.139 0.091 0.051 0.338 0.009 0.000 0.000 0.707 0.000 0.009 Zn r -0.160 -0.192 -0.280 -0.128 -0.385 0.685 0.797 -0.158 -0.752 -0.472 p 0.381 0.293 0.121 0.484 0.029 0.000 0.000 0.388 0.000 0.006 Ni r -0.087 -0.152 -0.088 0.275 -0.295 0.554 0.663 -0.453 -0.339 -0.096 p 0.635 0.405 0.631 0.128 0.101 0.001 0.000 0.009 0.058 0.602 Co r -0.099 -0.148 -0.067 0.381 -0.213 0.415 0.570 -0.438 -0.219 -0.053 p 0.591 0.418 0.717 0.031 0.241 0.018 0.001 0.012 0.228 0.774 Cr r 0.120 0.158 0.265 -0.025 0.203 0.004 0.032 0.177 -0.234 -0.347 p 0.514 0.388 0.143 0.891 0.265 0.985 0.862 0.334 0.198 0.052 Pb r -0.336 -0.353 -0.141 -0.014 -0.186 0.139 0.144 0.257 -0.399 -0.369 p 0.060 0.048 0.441 0.938 0.308 0.447 0.432 0.156 0.024 0.038 Cd r -0.080 -0.141 -0.110 0.321 -0.293 0.411 0.601 -0.436 -0.217 -0.003 p 0.665 0.443 0.548 0.073 0.104 0.019 0.000 0.013 0.233 0.985
(b) %Moi %Por %OM %G %VCS %CS %MS %FS %VFS %Mud Fe r -0.061 -0.061 -0.150 -0.265 -0.219 0.469 0.621 -0.080 -0.325 0.461 p 0.702 0.702 0.344 0.089 0.163 0.002 0.000 0.615 0.036 0.002 Cu r 0.181 0.181 0.004 0.307 0.328 -0.243 -0.568 0.243 0.159 -0.136 p 0.252 0.252 0.979 0.048 0.034 0.121 0.000 0.121 0.313 0.389 Zn r 0.124 0.124 0.508 0.301 0.314 0.042 -0.192 -0.262 -0.021 -0.377 p 0.434 0.434 0.001 0.053 0.043 0.790 0.223 0.094 0.894 0.014 Ni r -0.030 -0.030 0.371 -0.111 -0.065 0.311 0.376 -0.249 -0.425 -0.047 p 0.851 0.851 0.016 0.485 0.685 0.045 0.014 0.112 0.005 0.769 Co r -0.012 -0.012 0.288 0.294 0.323 -0.215 -0.500 -0.427 -0.061 -0.990 p 0.941 0.941 0.064 0.059 0.037 0.171 0.001 0.005 0.703 0.000 Cr r -0.090 -0.090 -0.069 -0.224 -0.287 0.635 0.860 -0.332 -0.249 0.465 p 0.571 0.571 0.664 0.153 0.065 0.000 0.000 0.032 0.112 0.002 Pb r 0.027 0.027 0.055 -0.168 -0.204 0.747 0.903 -0.420 -0.318 0.446 p 0.868 0.868 0.728 0.289 0.195 0.000 0.000 0.006 0.040 0.003 Cd r 0.466 0.466 0.228 0.017 0.060 0.642 0.690 -0.255 -0.270 0.496 p 0.002 0.002 0.146 0.913 0.705 0.000 0.000 0.103 0.084 0.001
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Table 2.9: Pearson’s correlation analysis between the distribution of heavy metals (Fe, Cu, Zn, Ni, Co, Cr, Pb and Cd) in sediments during (a) MY-I and (b) MY-II. (r = correlation coefficient and p = probability level)
(a) Fe Cu Zn Ni Co Cr Pb Cu r -0.296 p 0.100 Zn r -0.198 0.949 p 0.277 0.000 Ni r -0.517 0.542 0.601 p 0.002 0.001 0.000 Co r -0.453 0.376 0.449 0.910 p 0.009 0.034 0.010 0.000 Cr r 0.497 0.052 0.231 -0.273 -0.266 p 0.004 0.779 0.204 0.130 0.141 Pb r -0.171 0.274 0.204 0.258 0.182 -0.166 p 0.351 0.129 0.264 0.153 0.319 0.363 Cd r -0.606 0.405 0.405 0.912 0.900 -0.474 0.230 p 0.000 0.021 0.021 0.000 0.000 0.006 0.205
(b) Fe Cu Zn Ni Co Cr Pb Cu r -0.248 p 0.113 Zn r -0.490 -0.043 p 0.001 0.787 Ni r 0.138 -0.395 0.502 p 0.382 0.010 0.001 Co r -0.446 0.097 0.352 0.066 p 0.003 0.542 0.022 0.676 Cr r 0.649 -0.537 -0.005 0.427 -0.456 p 0.000 0.000 0.976 0.005 0.002 Pb r 0.706 -0.448 -0.097 0.444 -0.445 0.930 p 0.000 0.003 0.542 0.003 0.003 0.000 Cd r 0.593 -0.030 -0.149 0.179 -0.527 0.671 0.770 p 0.000 0.852 0.346 0.257 0.000 0.000 0.000
69
2.3.4. Comparison of Metal Contamination by Multiple Indices Approach
(I) Sediment Quality Guidelines (SQGs)
Two types of sediment quality guidelines (ERL/ ERM and TEL/ PEL) were used to compare the heavy metal levels at which the toxicity and potential adverse effects were expected on the sedimentary biota.
(a) ERL/ERM Sediment Quality Guidelines
Table 2.10 showed the summary statistics of mERLq and mERMq of six heavy metals (Ni, Zn, Pb, Cu, Cr and Cd) in coastal sediments of Pakistan for two monitoring years. The average mERLq of Cu and Ni detected above the limits (mERLq is greater than 1.0) during both monitoring years, therefore these metals having an adverse effect on the associated biota. Whereas, an average mERLq of Pb and Cr exceed their limits in MY-II (Table 2.10). The mERLq of heavy metals in sediments at different coastal areas Pakistan for MY-I and MY-II presented in Figure 2.5a and b. In MY-I, the highest mERLq of Cu (3.5), Zn (1.1), Ni (4.1) and Cd (1.9) evaluated in the coastal sediments of SP area. Whereas, the highest values for mERLq of Cr (0.6) and Pb (1.6) observed at KC and SB respectively. In MY-II, the highest mERLq of Cu (9.4), Zn (1.8), Cr (2.4) and Pb (2.7) was evaluated in the sediments of SP, whereas the highest mERLq of Ni (3.0) and Cd (2.1) was noticed at BH.
The mean mERMq of all heavy metals was detected below the limits (mERMq is less than 1.0) during both monitoring years, therefore these metals having no adverse effect on the sedimentary biota (Table 2.10). In MY-I, the highest mERMq of Cu (0.4), Zn (0.4), Ni (1.7) and Cd (0.2) estimated in the sediments of SP coastal area (Figure 2.6a and b). Whereas, the highest values of mERMq of Cr (0.1) and Pb (0.3) observed at KC and SB, respectively. In MY-II, the highest mERMq of Cu (1.2), Zn (0.7), Cr (0.5) and Pb (0.6) was evaluated in the sediments of SP, whereas the highest mERMq of Ni (1.2) and Cd (1.3) was observed at BH.
The facts presented the significant spatial variabilities in mean quotients of SQGs, which further illuminated by ANOVA analysis (Table 2.11 and 2.12). The ANOVA showed the significant differences (p <0.001) in mERLq and mERMq of Cr, Ni and Cd among the sites as well as between the years. On the other hand, Cu, Zn and Pb were demonstrated significant differences (p <0.001) among the sites but exhibited consistencies between the two years.
70
To identify and consider the overall heavy metals contamination in sediments, the combined index of mERLq and mERMq was also applied (Table 2.10). According to the mERLq (Figure 2.5c) and mERMq (Figure 2.6c), the SP evaluated as the highest and the SO showed the lowest contaminated sites for both monitoring years. The highest mERLq was evaluated for SP (2.05 in MY-I and 3.13 in MY-II) and lowest mERLq assessed for SO (0.43 in MY-I and 0.86 in MY-II) (Figure 2.5c). Similarly, the highest mERMq evaluated for SP (0.52 in MY-I and 0.64 in MY-II) and lowest mERMq was assessed for SO (0.10 in MY-I and 0.24 in MY-II) (Figure 2.6c). The ANOVA analysis showed the significant differences (p <0.05) for mERLq and mERMq of combined metals pollution among the sites as well as between the years (Table 2.11 and 2.12).
71
Table 2.10: Summary statistics of single and combined mean ERL/ERM quotients of six heavy metals (Ni, Zn, Pb, Cu, Cr and Cd) in the coastal sediments of Pakistan during two monitoring years (MY-I and MY-II).
Variable N Mean S.D Min Median Max Monitoring Year I (MY-I) Single metal pollution: Mean ERL/ ERM Quotients ERL-Ni 32 1.651 1.559 0.000 0.782 6.060 ERL-Zn 32 0.671 0.262 0.271 0.615 1.444 ERL-Pb 32 0.865 0.881 0.000 0.698 3.802 ERL-Cu 32 1.689 1.099 0.390 1.480 4.785 ERL-Cr 32 0.391 0.149 0.120 0.368 0.650 ERL-Cd 32 0.799 0.774 0.022 0.405 2.350
ERM-Ni 32 0.669 0.632 0.000 0.317 2.454 ERM-Zn 32 0.245 0.096 0.099 0.225 0.528 ERM-Pb 32 0.185 0.188 0.000 0.149 0.814 ERM-Cu 32 0.213 0.138 0.049 0.186 0.602 ERM-Cr 32 0.085 0.032 0.025 0.080 0.143 ERM-Cd 32 0.099 0.097 0.003 0.050 0.294 Combined metal pollution: Mean ERL/ ERM Quotients mERLq 32 1.011 0.588 0.196 0.771 2.645 mERMq 32 0.249 0.155 0.042 0.177 0.698 Monitoring Year II (MY-II) Single metal pollution: Mean ERL/ ERM Quotients ERL-Ni 45 2.161 0.581 0.643 2.178 3.453 ERL-Zn 45 0.780 0.651 0.18 0.543 2.41 ERL-Pb 45 1.359 1.282 0.476 0.787 5.874 ERL-Cu 45 2.951 4.705 0.004 0.959 19.77 ERL-Cr 45 1.293 0.837 0.12 1.285 3.47 ERL-Cd 45 0.967 0.468 0.112 0.945 2.11
ERM-Ni 45 0.875 0.235 0.260 0.882 1.399 ERM-Zn 45 0.285 0.238 0.065 0.199 0.881 ERM-Pb 45 0.291 0.274 0.102 0.169 1.258 ERM-Cu 45 0.371 0.592 0.001 0.121 2.490 ERM-Cr 45 0.283 0.183 0.025 0.281 0.761 ERM-Cd 45 0.121 0.058 0.014 0.118 0.264 Combined metal pollution: Mean ERL/ ERM Quotients mERLq 45 1.585 1.115 0.801 1.229 5.351 mERMq 45 0.371 0.190 0.225 0.308 1.001 (Note: Fe and Co guidelines did not established yet)
72
5 (a) MY-I DH 4 BH 3 PC KC 2 SP
1 SO mERLq mERLq single of metal 0 SB Cu Zn Cr Ni Pb Cd
10 (b) MY-II DH 8 BH 6 PC KC 4 SP
2 SO mERLq mERLq single of metal SB 0 Cu Zn Cr Ni Pb Cd
3 (c) Combine Index of mERLq
2.5
2
1.5 2001 1 2011
0.5 mERLq mERLq allofmetals 0 DH BH PC KC SP SO SB
Figure 2.5: Variations in mERLq of single metals in (a) MY-I (b) MY-II, and (c) mERLq of combined metal from seven monitoring sites (DH = Dhabeji, BH = Bhambore, PC = Phitti Creek, KC = Korangi Creek, SP = Sandspit, SO = Sonari and SB = Sonmiani Bay) during the two monitoring years (MY-I and MY-II).
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Table 2.11: Variations in single and combine mERLq for six heavy metals (Ni, Zn, Pb, Cu, Cr and Cd) by analysis of variance (ANOVA) in the coastal sediments of Pakistan during two monitoring years (MY-I and MY-II).
Metals Source DF Seq. SS Adj. SS Adj. MS F P Single metal pollution: Mean ERL Quotients Cu Sites 6 530.722 366.132 61.022 10.43 0.000*** Year 1 13.604 2.399 2.399 0.41 0.524 Sites*Year 6 128.254 128.254 21.376 3.65 0.004** Error 63 368.454 368.454 5.848 Total 76 1041.034 Zn Sites 6 15.2352 11.7324 1.9554 37.59 0.000*** Year 1 0.0073 0.0065 0.0065 0.13 0.724 Sites*Year 6 2.5172 2.5172 0.4195 8.07 0.000*** Error 63 3.2770 3.2770 0.0520 Total 76 21.0366 Cr Sites 6 18.6821 9.7849 1.6308 12.66 0.000*** Year 1 10.6606 6.1126 6.1126 47.47 0.000*** Sites*Year 6 9.2579 9.2579 1.5430 11.98 0.000*** Error 63 8.1131 8.1131 0.1288 Total 76 46.7136 Ni Sites 6 33.5640 45.9863 7.6644 29.44 0.000*** Year 1 4.9696 6.2170 6.2170 23.88 0.000*** Sites*Year 6 40.2161 40.2161 6.7027 25.74 0.000*** Error 63 16.4033 16.4033 0.2604 Total 76 95.1530 Pb Sites 6 32.4823 24.5213 4.0869 5.03 0.000*** Year 1 3.0225 1.9474 1.9474 2.40 0.127 Sites*Year 6 14.2914 14.2914 2.3819 2.93 0.014* Error 63 51.1637 51.1637 0.8121 Total 76 100.9599 Cd Sites 6 12.1797 14.7815 2.4636 59.73 0.000*** Year 1 0.9089 1.7182 1.7182 41.65 0.000*** Sites*Year 6 13.0800 13.0800 2.1800 52.85 0.000*** Error 63 2.5986 2.5986 0.0412 Total 76 28.7672 Combined metal pollution: Mean ERL Quotients mERLq Sites 6 44.5845 36.0470 6.0078 18.75 0.000*** Year 1 3.9733 2.3207 2.3207 7.24 0.009** Sites*Year 6 2.8585 2.8585 0.4764 1.49 0.197 Error 63 20.1862 20.1862 0.3204 Total 76 71.6025 ‘***’ = Significance level at <0.001 ‘**’ = Significance level at <0.01 ‘*’ = Significance level at <0.05
74
2 (a) MY-I DH 1.5 BH PC 1 KC SP 0.5
SO mERMq mERMq single of metal SB 0 Cu Zn Cr Ni Pb Cd
1.4 (b) MY-II 1.2 DH 1 BH 0.8 PC 0.6 KC 0.4 SP SO
mERMq mERMq single of metal 0.2 0 SB Cu Zn Cr Ni Pb Cd
0.6 (c) Combine Index of mERMq 0.5
0.4 2001 0.3 2011 0.2
0.1 Combine Combine mERMq 0 DH BH PC KC SP SO SB
Figure 2.6: Variations in mERMq of single metals in (a) MY-I (b) MY-II, and (c) mERMq of combined metal from seven monitoring sites (DH = Dhabeji, BH = Bhambore, PC = Phitti Creek, KC = Korangi Creek, SP = Sandspit, SO = Sonari and SB = Sonmiani Bay) during the two monitoring years (MY-I and MY-II).
75
Table 2.12: Variations in single and combine mERMq for six heavy metals (Ni, Zn, Pb, Cu, Cr and Cd) by analysis of variance (ANOVA) in the coastal sediments of Pakistan during two monitoring years (MY-I and MY-II).
Metals Source DF Seq. SS Adj. SS Adj. MS F P Single metal pollution: Mean ERM Quotients Cu Sites 6 8.41584 5.80588 0.96765 10.43 0.000*** Year 1 0.21573 0.03805 0.03805 0.41 0.524 Sites*Year 6 2.03376 2.03376 0.33896 3.65 0.004** Error 63 5.84270 5.84270 0.09274 Total 76 16.50803 Zn Sites 6 2.03922 1.57037 0.26173 37.59 0.000*** Year 1 0.00097 0.00087 0.00087 0.13 0.724 Sites*Year 6 0.33692 0.33692 0.05615 8.07 0.000*** Error 63 0.43862 0.43862 0.00696 Total 76 2.81573 Cr Sites 6 0.89535 0.46895 0.07816 12.66 0.000*** Year 1 0.51091 0.29295 0.29295 47.47 0.000*** Sites*Year 6 0.44369 0.44369 0.07395 11.98 0.000*** Error 63 0.38882 0.38882 0.00617 Total 76 2.23877 Ni Sites 6 5.50638 7.54435 1.25739 29.44 0.000*** Year 1 0.81530 1.01994 1.01994 23.88 0.000*** Sites*Year 6 6.59771 6.59771 1.09962 25.74 0.000*** Error 63 2.69106 2.69106 0.04272 Total 76 15.61046 Pb Sites 6 1.49062 1.12529 0.18755 5.03 0.000*** Year 1 0.13871 0.08937 0.08937 2.40 0.127 Sites*Year 6 0.65584 0.65584 0.10931 2.93 0.014* Error 63 2.34792 2.34792 0.03727 Total 76 4.63308 Cd Sites 6 0.190308 0.230961 0.038493 59.73 0.000*** Year 1 0.014201 0.026847 0.026847 41.65 0.000*** Sites*Year 6 0.204376 0.204376 0.034063 52.85 0.000*** Error 63 0.040604 0.040604 0.000645 Total 76 0.449488 Combined metal pollution: Mean ERM Quotients mERMq Sites 6 1.74255 1.56079 0.26013 26.09 0.000*** Year 1 0.18850 0.13194 0.13194 13.23 0.001** Sites*Year 6 0.06522 0.06522 0.01087 1.09 0.378 Error 63 0.62824 0.62824 0.00997 Total 76 2.62451 ‘***’ = Significance level at <0.001 ‘**’ = Significance level at <0.01 ‘*’ = Significance level at <0.05
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(b) TEL/PEL Sediment Quality Guidelines
Table 2.13 presented the summary statistics of mTELq and mPELq of six heavy metals (Ni, Zn, Pb, Cu, Cr and Cd) in coastal sediment of Pakistan for two monitoring years. The mTELq of Cu, Ni, Pb and Cd detected above the limits (mTELq is greater than 1.0) during the both monitoring years, therefore these metals having an adverse effect on the biota. Whereas, mTELq of Cr was exceeded from the guideline in MY-II (Table 2.13). The mPELq of all heavy metals detected below the limits (mPELq is less than 1.0) during the both monitoring years, thus having no adverse effect on the allied biota (Table 2.13). In MY-I, the highest mTELq of Cu (6.4), Zn (1.3), Ni (5.4) and Cd (0.6) evaluated in the sediments of SP area. Whereas, the highest mTELq of Cr (0.9) and Pb (2.5) observed at KC and SB (Figure 2.7a). In MY-II, the highest mTELq of Cu (17.1), Zn (2.2), Cr (3.7) and Pb (4.2) assessed in the sediments of SP, whereas the highest mTELq of Ni (3.9) and Cd (3.6) detected at BH (Figure 2.7b).
Similarly, Figure 2.8a and b presented the mPELq of single metals in sediment at studied coastal areas of Pakistan for MY-I and MY-II. The highest mPELq of Cu (1.1), Zn (0.6), Ni (2.0) and Cd (0.5) evaluated in the sediments of SP, whereas the highest mPELq of Cr (0.3) and Pb (0.7) observed at KC and SB in MY-I (Figure 2.8a). In MY-II, the highest mPELq of Cu (2.9), Zn (1.0), Cr (1.2) and Pb (1.1) assessed in the sediments of SP, whereas the highest mPELq of Ni (1.5) and Cd (0.6) noticed at BH (Figure 2.8b). The figures showed the significant spatial variabilities in mean quotients of SQGs, which further evaluated by ANOVA analysis. The ANOVA shows that mTELq (Table 2.14) and mPELq (Table 2.15) of Cr, Ni and Cd observed the significant differences (p <0.001) among the sites as well as between the years. Contrariwise, the mTELq (Table 2.14) and mPELq (Table 2.15) of Cu, Zn and Pb presented significant differences (p <0.001) among the sites but showed consistencies between the years.
To identify the overall heavy metals contamination in sediments the combine index of mTELq and mPELq were also evaluated (Table 2.13). The highest mTELq and mPELq were observed at SP and lowest was detected at SO for both monitoring years (Figure 2.7c and 2.8c). The highest mTELq evaluated for SP (3.18 in MY-I and 5.11 in MY-II) and lowest mTELq assessed for SO (0.66 in MY-I and 1.29 in MY-II) (Figure 2.7c). Likewise, the highest mPELq evaluated for SP (0.83 in MY-I and 1.23 in MY-II) and lowest mPELq considered for SO (0.18 in MY-I and 0.37 in MY-II) (Figure 2.8c). The ANOVA analysis showed that mTELq (Table 2.14) and mPELq (Table 2.15) of combined metals pollution significantly different (p <0.05) among the sites as well as between the years.
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Table 2.13: Summary statistics of single and combine mean TEL/PEL quotients of six heavy metals (Ni, Zn, Pb, Cu, Cr and Cd) in coastal sediments of Pakistan during the two monitoring years (MY-I and MY-II).
Variable Total Mean S.D Min Median Max Monitoring Year I (MY-I) Single metal pollution: Mean TEL/ PEL Quotients TEL-Ni 32 2.170 2.050 0.000 1.028 7.964 TEL-Zn 32 0.812 0.317 0.328 0.743 1.747 TEL-Pb 32 1.337 1.362 0.000 1.080 5.879 TEL-Cu 32 3.071 1.998 0.703 2.691 8.699 TEL-Cr 32 0.605 0.231 0.181 0.569 1.011 TEL-Cd 32 1.409 1.366 0.040 0.715 4.146
PEL-Ni 32 0.806 0.761 0.00 0.382 2.958 PEL-Zn 32 0.371 0.145 0.150 0.340 0.799 PEL-Pb 32 0.360 0.367 0.000 0.291 1.585 PEL-Cu 32 0.532 0.346 0.122 0.466 1.506 PEL-Cr 32 0.198 0.075 0.059 0.186 0.331 PEL-Cd 32 0.228 0.221 0.006 0.116 0.671 Combined metal pollution: Mean TEL/ PEL Quotients mTELq 32 1.567 0.906 0.309 1.204 4.058 mPELq 32 0.416 0.2382 0.0835 0.3222 1.089
Monitoring Year II (MY-II) Single metal pollution: Mean TEL/ PEL Quotients TEL-Ni 45 2.840 0.765 0.846 2.863 4.539 TEL-Zn 45 0.944 0.788 0.215 0.657 2.912 TEL-Pb 45 2.102 1.983 0.735 1.218 9.083 TEL-Cu 45 5.360 8.550 0.010 1.740 35.96 TEL-Cr 45 2.003 1.296 0.179 1.990 5.382 TEL-Cd 45 1.707 0.827 0.199 1.668 3.734
PEL-Ni 45 1.055 0.284 0.314 1.063 1.686 PEL-Zn 45 0.432 0.360 0.098 0.301 1.332 PEL-Pb 45 0.566 0.534 0.198 0.328 2.449 PEL-Cu 45 0.929 1.481 0.001 0.302 6.226 PEL-Cr 45 0.654 0.423 0.058 0.650 1.759 PEL-Cd 45 0.276 0.134 0.032 0.270 0.604 Combined metal pollution: Mean TEL/ PEL Quotients mTELq 45 2.494 1.894 1.195 1.841 8.948 mPELq 45 0.652 0.414 0.345 0.517 2.017 (Note: Fe and Co guidelines did not established yet)
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7 (a) MY-I 6 DH 5 BH 4 PC 3 KC 2 SP SO
1 mTELq mTELq of single metal SB 0 Cu Zn Cr Ni Pb Cd
20 (b) MY-II DH 15 BH PC 10 KC SP 5
SO mTELq mTELq of single metal SB 0 Cu Zn Cr Ni Pb Cd
5 (c) Combine Index of mTELq
4
3 2001 2011 2
1 mTELq mTELq ofmetals all
0 DH BH PC KC SP SO SB
Figure 2.7: Variations in mTELq of single metals in (a) MY-I (b) MY-II, and (c) mTELq of combined metal from seven monitoring sites (DH = Dhabeji, BH = Bhambore, PC = Phitti Creek, KC = Korangi Creek, SP = Sandspit, SO = Sonari and SB = Sonmiani Bay) during the two monitoring years (MY-I and MY-II).
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Table 2.14: Variations in single and combine mTELq of six heavy metals (Ni, Zn, Pb, Cu, Cr and Cd) by analysis of variance (ANOVA) in coastal sediments of Pakistan during the two monitoring years (MY-I and MY-II).
Metals Source DF Seq. SS Adj. SS Adj. MS F P Single metal pollution: Mean TEL Quotients Cu Sites 6 1754.45 1210.35 201.73 10.43 0.000*** Year 1 44.97 7.93 7.93 0.41 0.524 Sites*Year 6 423.98 423.98 70.66 3.65 0.004** Error 63 1218.03 1218.03 19.33 Total 76 3441.44 Zn Sites 6 22.2940 17.1683 2.8614 37.59 0.000*** Year 1 0.0106 0.0096 0.0096 0.13 0.724 Sites*Year 6 3.6834 3.6834 0.6139 8.07 0.000*** Error 63 4.7952 4.7952 0.0761 Total 76 30.7833 Cr Sites 6 44.8117 23.4706 3.9118 12.66 0.000*** Year 1 25.5710 14.6619 14.6619 47.47 0.000*** Sites*Year 6 22.2063 22.2063 3.7011 11.98 0.000*** Error 63 19.4605 19.4605 0.3089 Total 76 112.0494 Ni Sites 6 57.992 79.456 13.243 29.44 0.000*** Year 1 8.587 10.742 10.742 23.88 0.000*** Sites*Year 6 69.486 69.486 11.581 25.74 0.000*** Error 63 28.342 28.342 0.450 Total 76 164.407 Pb Sites 6 77.672 58.636 9.773 5.03 0.000*** Year 1 7.228 4.657 4.657 2.40 0.127 Sites*Year 6 34.174 34.174 5.696 2.93 0.014** Error 63 122.344 122.344 1.942 Total 76 241.418 Cd Sites 6 37.9299 46.0323 7.6720 59.73 0.000*** Year 1 2.8304 5.3508 5.3508 41.65 0.000*** Sites*Year 6 40.7337 40.7337 6.7889 52.85 0.000*** Error 63 8.0927 8.0927 0.1285 Total 76 89.5866 Combined metal pollution: Mean TEL Quotients mTELq Sites 6 121.237 96.025 16.004 17.21 0.000*** Year 1 10.205 5.677 5.677 6.11 0.016* Sites*Year 6 9.264 9.264 1.544 1.66 0.145 Error 63 58.573 58.573 0.930 Total 76 199.280 ‘***’ = Significance level at <0.001 ‘**’ = Significance level at <0.01 ‘*’ = Significance level at <0.05
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2.5 (a) MY-I DH 2 BH 1.5 PC KC 1 SP
0.5 SO mPELq mPELq single ofmetal SB 0 Cu Zn Cr Ni Pb Cd
3.5 (b) MY-II 3 DH 2.5 BH 2 PC 1.5 KC SP 1 SO
mPELq mPELq single ofmetal 0.5 SB 0 Cu Zn Cr Ni Pb Cd
1.2 (c) Combine Index of mPELq
1
0.8 2001 0.6 2011 0.4
mPELq mPELq allofmetals 0.2
0 DH BH PC KC SP SO SB
Figure 2.8: Variations in mPELq of single metals in (a) MY-I (b) MY-II, and (c) mPELq of combined metal from seven monitoring sites (DH = Dhabeji, BH = Bhambore, PC = Phitti Creek, KC = Korangi Creek, SP = Sandspit, SO = Sonari and SB = Sonmiani Bay) during the two monitoring years (MY-I and MY-II).
81
Table 2.15: Variations in single and combine mPELq of six heavy metals (Ni, Zn, Pb, Cu, Cr and Cd) by analysis of variance (ANOVA) in coastal sediments of Pakistan during the two monitoring years (MY-I and MY-II).
Metals Source DF Seq. SS Adj. SS Adj. MS F P Single metal pollution: Mean PEL Quotients Cu Sites 6 52.5990 36.2867 6.0478 10.43 0.000*** Year 1 1.3483 0.2378 0.2378 0.41 0.524 Sites*Year 6 12.7110 12.7110 2.1185 3.65 0.004** Error 63 36.5169 36.5169 0.5796 Total 76 103.1752 Zn Sites 6 4.66759 3.59444 0.59907 37.59 0.000*** Year 1 0.00223 0.00200 0.00200 0.13 0.724 Sites*Year 6 0.77118 0.77118 0.12853 8.07 0.000*** Error 63 1.00396 1.00396 0.01594 Total 76 6.44495 Cr Sites 6 4.78801 2.50777 0.41796 12.66 0.000*** Year 1 2.73219 1.56659 1.56659 47.47 0.000*** Sites*Year 6 2.37269 2.37269 0.39545 11.98 0.000*** Error 63 2.07930 2.07930 0.03300 Total 76 11.97218 Ni Sites 6 8.0035 10.9656 1.8276 29.44 0.000*** Year 1 1.1850 1.4825 1.4825 23.88 0.000*** Sites*Year 6 9.5897 9.5897 1.5983 25.74 0.000*** Error 63 3.9114 3.9114 0.0621 Total 76 22.6896 Pb Sites 6 5.6473 4.2633 0.7105 5.03 0.000*** Year 1 0.5255 0.3386 0.3386 2.40 0.127 Sites*Year 6 2.4847 2.4847 0.4141 2.93 0.014* Error 63 8.8953 8.8953 0.1412 Total 76 17.5528 Cd Sites 6 0.99426 1.20665 0.20111 59.73 0.000*** Year 1 0.07419 0.14026 0.14026 41.65 0.000*** Sites*Year 6 1.06776 1.06776 0.17796 52.85 0.000*** Error 63 0.21213 0.21213 0.00337 Total 76 2.34835 Combined metal pollution: Mean PEL Quotients mPELq Sites 6 6.60748 5.40839 0.90140 21.46 0.000*** Year 1 0.67985 0.41570 0.41570 9.90 0.003** Sites*Year 6 0.39978 0.39978 0.06663 1.59 0.166 Error 63 2.64600 2.64600 0.04200 Total 76 10.33310 ‘***’ = Significance level at <0.001 ‘**’ = Significance level at <0.01 ‘*’ = Significance level at <0.05
82
(II) Geo-Accumulation Index (Igeo)
The summary statistics of Igeo values for both monitoring years presented in Table 2.16 and it showed that most of the data have the negative values and none of the Igeo values observed more than 3.5. The spatial variation in Igeo values during the both monitoring years along the coastal sediments presented in Figure 2.9a and b. The mean Igeo values of Fe, Cu, Zn, Co, Cr and Ni were noticed negative or less than zero, specify overall an unpolluted condition by these metals along the coastal sediments of Pakistan. However, few sites presented specific contamination, such as, moderate pollution of Zn (0.2 in MY-I and 0.8 in MY-II) and Cu (0.7 in MY-I and 1.7 in MY-II) evaluated at SP during both years.
Moderate pollution of Pb detected at DH (0.4), SP (1.1) and SB (0.8) in MY-I, while in the sediments of KC (1.1) and SP (1.8) in MY-II. Moderate to high pollution of Cd evaluated at DH (2.0), BH (2.1) and SP (2.3) in MY-I, however all sites showed Cd pollution in MY-II. Analysis of variance (ANOVA) showed the significant differences (p <0.05) for Fe, Cu, Zn, Cr, Ni and Cd among the sites as well as between the years (Table 2.17). Whereas, Igeo values of Co and Pb showed significant differences (p <0.001) among the sites, but no difference was found between the years.
83
Table 2.16: Summary statistics of Geo-accumulation index for eight heavy metals (Fe, Cu, Zn, Ni, Cr, Co, Pb and Cd) in coastal sediments of Pakistan during the two monitoring years (MY-I and MY- II).
Variable N Mean S.D Min Median Max Monitoring Year I (MY-I) Igeo-Fe 32 -6.35 0.12 -6.90 -6.31 -6.26 Igeo-Cu 32 -0.51 0.92 -2.36 -0.42 1.27 Igeo-Zn 32 -0.60 0.54 -1.81 -0.63 0.60 Igeo-Co 32 -1.93 1.03 -4.65 -1.97 -0.63 Igeo-Cr 32 -2.21 0.61 -3.83 -2.18 -1.35 Igeo-Ni 32 -2.06 1.42 -5.14 -2.50 0.31 Igeo-Pb 32 0.37 0.96 -2.02 0.12 2.56 Igeo-Cd 32 0.04 2.04 -4.06 0.11 2.65 Monitoring Year II (MY-II) Igeo-Fe 45 -6.20 0.15 -6.99 -6.16 -6.07 Igeo-Cu 45 -0.92 2.28 -8.88 -1.05 3.32 Igeo-Zn 45 -0.69 1.06 -2.42 -0.81 1.34 Igeo-Co 45 -2.22 1.45 -7.65 -2.04 -0.63 Igeo-Cr 45 -0.68 1.05 -3.85 -0.37 1.06 Igeo-Ni 45 -1.24 0.49 -2.92 -1.16 -0.50 Igeo-Pb 45 0.66 0.99 -0.43 0.29 3.19 Igeo-Cd 45 1.16 0.86 -1.74 1.33 2.49
84
(a) MY-I
3 DH BH PC KC SP SO SB 2 1 0 -1 -2 -3 -4
Accumulation Accumulation Index (Igeo) -5 -
-6 Geo -7 Fe Cu Zn Co Cr Ni Pb Cd
(b) MY-II
3 DH BH PC KC SP SO SB 2 1 0 -1 -2 -3 -4
Accumulation Accumulation Index (Igeo) -5 -
-6 Geo -7 Fe Cu Zn Co Cr Ni Pb Cd
Figure 2.9. Variations in Geo-accumulation index of eight heavy metals in sediments from seven monitoring sites (DH = Dhabeji, BH = Bhambore, PC = Phitti Creek, KC = Korangi Creek, SP = Sandspit, SO = Sonari and SB = Sonmiani Bay) during the two monitoring years (MY-I and MY-II).
85
Table 2.17: Analysis of variance (ANOVA) for Geo-accumulation index of eight heavy metals (Fe, Cu, Zn, Ni, Cr, Co, Pb and Cd) in coastal sediments of Pakistan during the two monitoring years (MY-I and MY-II).
Metals Source DF Seq. SS Adj. SS Adj. MS F P Fe Sites 6 0.52039 0.44566 0.07428 5.04 0.000*** Year 1 0.42713 0.40502 0.40502 27.50 0.000*** Sites*Year 6 0.00906 0.00906 0.00151 0.10 0.996 Error 63 0.92781 0.92781 0.01473 Total 76 1.88440 Cu Sites 6 121.029 103.971 17.329 9.91 0.000*** Year 1 7.756 13.915 13.915 7.96 0.006** Sites*Year 6 20.115 20.115 3.353 1.92 0.092 Error 63 110.170 110.170 1.749 Total 76 259.071 Zn Sites 6 41.2963 36.1185 6.0197 51.43 0.000*** Year 1 1.5159 1.4347 1.4347 12.26 0.001** Sites*Year 6 8.4962 8.4962 1.4160 12.10 0.000*** Error 63 7.3741 7.3741 0.1170 Total 76 58.6824 Co Sites 6 45.215 45.855 7.642 6.20 0.000*** Year 1 0.831 0.381 0.381 0.31 0.580 Sites*Year 6 2.383 2.383 0.397 0.32 0.923 Error 63 77.672 77.672 1.233 Total 76 126.101 Cr Sites 6 41.8199 24.1648 4.0275 17.18 0.000*** Year 1 30.9961 19.6274 19.6274 83.73 0.000*** Sites*Year 6 15.3861 15.3861 2.5644 10.94 0.000*** Error 63 14.7687 14.7687 0.2344 Total 76 102.9708 Ni Sites 6 22.4077 31.9570 5.3262 17.44 0.000*** Year 1 14.0205 12.5244 12.5244 41.00 0.000*** Sites*Year 6 27.9365 27.9365 4.6561 15.24 0.000*** Error 63 19.2456 19.2456 0.3055 Total 76 83.6103 Pb Sites 6 26.8761 21.6983 3.6164 6.06 0.000*** Year 1 0.0235 0.1169 0.1169 0.20 0.660 Sites*Year 6 7.2415 7.2415 1.2069 2.02 0.076 Error 63 37.6117 37.6117 0.5970 Total 76 71.7528 Cd Sites 6 43.384 65.037 10.839 16.39 0.000*** Year 1 28.987 28.740 28.740 43.47 0.000*** Sites*Year 6 71.765 71.765 11.961 18.09 0.000*** Error 63 41.652 41.652 0.661 Total 76 185.789 ‘***’ = Significance level at <0.001 ‘**’ = Significance level at <0.01 ‘*’ = Significance level at <0.05
86
(III) Enrichment Factor (EF)
To further emphasize on metal elevation in coastal sediments during the time, enrichment factor (EF) was calculated for both monitoring years are shown in Table 2.18. The extremely high enrichment (>50) of Cu, Zn, Pb and Cd were scrutinized during both monitoring years. Very severe and severe enrichment of Co observed during MY-I and MY-II, respectively. Severe and extremely severe enrichment of Cr observed in MY-I and MY-II, respectively. Very severe enrichment of Ni observed during both monitoring years (Table 2.18).
The spatial distribution of metal EF in both monitoring years explained in Figure 2.13. In MY-I, the highest EF observed at SP for Cu (159), Zn (104), Co (51), Ni (74) and Cd (448), whereas the highest EF of Cr (1.5) and Pb (0.6) detected at KC and SB, respectively (Figure 2.13a). In MY-II, the highest EF was noticed at SP for Cu (375), Zn (155), Cr (39) and Pb (339), whereas the highest EF revealed at BH for Co (39), Ni (46) and Cd (411) (Figure 2.13b). Analysis of variance (ANOVA) showed the significant differences (p <0.05) for the EF of Co, Cr, Ni and Cd among the sites as well as between the years. However, Cu, Zn and Pb presented the significant differences (p <0.05) among the sites, but no differences (p >0.05) found between the two monitoring years (Table 2.19).
87
Table 2.18: Summary statistics of enrichment factor for seven heavy metals (Cu, Zn, Ni, Cr, Co, Pb and Cd) in coastal sediments of Pakistan during the two monitoring years (MY-I and MY-II).
EF Metals N Mean S.D Min Median Max Monitoring Year I (MY-I) Cu 32 71.14 52.36 16.79 57.93 235.42 Zn 32 58.38 25.89 24.27 50.91 130.50 Co 32 27.08 17.00 3.09 20.02 54.87 Cr 32 18.94 6.66 6.05 17.47 30.10 Ni 32 28.75 27.91 0.00 12.58 105.50 Pb 32 111.8 111.3 0.0 91.3 467.3 Cd 32 182.5 180.6 4.6 84.4 495.9 Monitoring Year II (MY-II) Cu 45 114.7 192.4 0.1 33.8 896.1 Zn 45 63.36 58.59 13.56 42.64 217.51 Co 45 21.47 13.50 0.34 18.04 54.88 Cr 45 57.38 37.97 5.26 55.44 165.01 Ni 45 32.41 8.74 9.16 31.62 53.33 Pb 45 159.8 156.2 56.0 87.3 680.7 Cd 45 188.0 92.2 27.1 180.4 420.2 (Note: Fe used as metal normalizer and their calculated value was 1.0, therefore did not mentioned in statistical analysis)
88
500 (a) MY-I 450 DH BH PC KC SP SO SB 400 350 300 250 200 150
100 Enrichment Enrichment Factor(EF) 50 0 Cu Zn Co Cr Ni Pb Cd
450 (b) MY-II 400 DH BH PC KC SP SO SB 350 300 250 200 150 100
Enrichment Enrichment Factor(EF) 50 0 Cu Zn Co Cr Ni Pb Cd
Figure 2.10: Variations in enrichment factor of eight heavy metals (Fe, Cu, Zn, Ni, Cr, Co, Pb and Cd) in sediments from seven monitoring sites (DH = Dhabeji, BH = Bhambore, PC = Phitti Creek, KC = Korangi Creek, SP = Sandspit, SO = Sonari and SB = Sonmiani Bay) during the two monitoring years (MY-I and MY-II).
89
Table 2.19: Analysis of variance (ANOVA) for enrichment factor of heavy metals (Cu, Zn, Ni, Cr, Co, Pb and Cd) in coastal sediments of Pakistan during the two monitoring years (MY-I and MY-II).
Metals Source DF Seq. SS Adj. SS Adj. MS F P Cu Sites 6 1612681 1147567 191261 10.31 0.000*** Year 1 24838 2775 2775 0.15 0.700 Sites*Year 6 318031 318031 53005 2.86 0.016* Error 63 1168695 1168695 18551 Total 76 3124245 Zn Sites 6 126626 100337 16723 37.56 0.000*** Year 1 115 552 552 1.24 0.270 Sites*Year 6 15480 15480 2580 5.79 0.000*** Error 63 28052 28052 445 Total 76 170273 Co Sites 6 7766.69 8342.26 1390.38 28.90 0.000*** Year 1 337.20 196.94 196.94 4.09 0.047 Sites*Year 6 951.16 951.16 158.53 3.30 0.007** Error 63 3030.53 3030.53 48.10 Total 76 12085.57 Cr Sites 6 150454 81513 13586 20.35 0.000*** Year 1 69887 38062 38062 57.00 0.000*** Sites*Year 6 75324 75324 12554 18.80 0.000*** Error 63 42065 42065 668 Total 76 337730 Ni Sites 6 15949.5 21493.8 3582.3 50.27 0.000*** Year 1 328.2 596.8 596.8 8.37 0.005** Sites*Year 6 15843.8 15843.8 2640.6 37.05 0.000*** Error 63 4489.8 4489.8 71.3 Total 76 36611.2 Pb Sites 6 107835 85490 14248 6.59 0.000*** Year 1 5010 2845 2845 1.32 0.256 Sites*Year 6 38195 38195 6366 2.95 0.013* Error 63 136113 136113 2161 Total 76 287153 Cd Sites 6 3332877 4098597 683100 95.44 0.000*** Year 1 23061 110096 110096 15.38 0.000*** Sites*Year 6 3355023 3355023 559170 78.12 0.000*** Error 63 450926 450926 7158 Total 76 7161887 ‘***’ = Significance level at <0.001 ‘**’ = Significance level at <0.01 ‘*’ = Significance level at <0.05
90
(IV) Contamination Factor (CF) and Contamination Degree (CD)
The CF and CD estimated for both monitoring years (Table 2.20). Low contamination of Fe, Co and Ni perceived for coastal sediments of Pakistan. The moderate contamination of Cu and Zn, while a considerable contamination of Cd observed during both monitoring years (Table 2.20). Low and moderate contamination of Cr detected in MY-I and MY-II, respectively. Whereas, moderate and considerable contamination of Pb was observed in MY-I and MY-II, respectively. In MY-I, the highest CF of Cu (2.6), Zn (1.7), Ni (1.3) and Cd (7.6) observed at SP, whereas the highest CF of Cr (0.5) and Pb (3.8) observed at KC and SB, respectively (Figure 2.11a). In MY-II, the highest CF of Cu (7.1), Zn (2.8), Cr (2.1) and Pb (6.3) was assessed at SP, whereas the highest CF of Co (0.8), Ni (0.9) and Cd (8.2) evaluated at BH (Figure 2.11b). Analysis of variance (ANOVA) showed the significant differences (p <0.05) of Cr, Ni and Cd among the sites and between the years, while Cu, Zn, Co and Pb showed the significant differences (p <0.05) among the sites, but no difference was found between the years (Table 2.21).
For MY-I, contamination degree was ranged from 1.53 to 20.8 and indicated low to a considerable degree of contamination along the coast of Pakistan (Table 2.20). In MY-I, the status of coastal areas categorized by means of contamination degree: the SP (17.6) as a considerable contaminated site, follow by DH (11.8), BH (10.6), SB (8.4) as moderately contaminated sites and PC (5.2), KC (4.1) and SO (3.0) as a low contaminated site (Figure 2.11c). For MY-II, it varied from 7.21 to 33.21, indicated low to a considerable degree of contamination along the coastal sediments of Pakistan (Table 2.20). The current status of coastal areas categorized by means of contamination degree: the SP (21.7) as a considerable contaminated site, follow by BH (12.9), DH (11.3), KC (11.2), PC (9.3), SB (8.5) as moderately contaminated sites and SO (7.8) as a low contaminated site (Figure 2.11c). Contamination degree showed a significant difference (p <0.05) between the sites as well as between the years (Table 2.21). Moreover, the contamination degree was increased significantly (p <0.05) at PC, KC, and SO during the last decade, conversely the contamination degree was observed no significant variations at DH, BH, SP and SB.
91
Table 2.20: Summary statistics of single and combined indices of contamination factor and degree for eight heavy metals (Fe, Cu, Zn, Ni, Cr, Co, Pb and Cd) in coastal sediments of Pakistan during the two monitoring years (MY-I and MY-II).
Variable N Mean S.D Min Median Max Monitoring Year I (MY-I) Single metal pollution index: Contamination Factor (CF) CF-Fe 32 0.018 0.001 0.012 0.019 0.019 CF-Cu 32 1.276 0.830 0.292 1.118 3.615 CF-Zn 32 1.059 0.413 0.428 0.971 2.280 CF-Co 32 0.485 0.289 0.059 0.383 0.967 CF-Cr 32 0.352 0.131 0.105 0.339 0.587 CF-Ni 32 0.507 0.473 0.000 0.240 1.862 CF-Pb 32 2.019 2.057 0.000 1.630 8.878 CF-Cd 32 3.195 3.097 0.090 1.620 9.398
Combine metal pollution index: Contamination degree (CD) CD 32 8.913 5.361 1.533 6.735 20.806
Monitoring Year II (MY-II) Single metal pollution index: Contamination Factor (CF) CF-Fe 45 0.020 0.002 0.010 0.02 0.02 CF-Cu 45 2.229 3.555 0.003 0.725 14.94 CF-Zn 45 1.232 1.029 0.281 0.858 3.801 CF-Co 45 0.431 0.263 0.007 0.364 0.967 CF-Cr 45 1.164 0.753 0.104 1.156 3.127 CF-Ni 45 0.664 0.179 0.198 0.669 1.061 CF-Pb 45 3.174 2.994 1.110 1.839 13.72 CF-Cd 45 3.870 1.874 0.450 3.780 8.463
Combine metal pollution index: Contamination degree (CD) CD 45 12.79 6.88 7.21 10.39 33.31
92
8 (a) MY-I 7 6 DH 5 BH 4 PC 3 KC 2 SP
1 SO Contamination Contamination Factor(CF) 0 SB Fe Cu Zn Co Cr Ni Pb Cd
10 (b) MY-II 8 DH 6 BH PC 4 KC
2 SP SO
Contamination Contamination Factor(CF) 0 SB Fe Cu Zn Co Cr Ni Pb Cd
25 (c) Contamination Degree (CD)
(CD) 20
15 2001 10 2011 5
Contaminatio Contaminatio Degree 0 DH BH PC KC SP SO SB
Figure 2.11: Variations in single metal index contamination factor (CF) in (a) MY-I, (b) MY-II and combine metal index (c) contamination degree (CD) from seven monitoring sites (DH = Dhabeji, BH = Bhambore, PC = Phitti Creek, KC = Korangi Creek, SP = Sandspit, SO = Sonari and SB = Sonmiani Bay) during the two monitoring years (MY-I and MY-II).
93
Table 2.21: Variations in single (contamination factor) and combine (contamination degree) metal pollution indices for eight heavy metals (Fe, Cu, Zn, Ni, Cr, Co, Pb and Cd) by analysis of variance (ANOVA) in coastal sediments of Pakistan during the two monitoring years (MY-I and MY-II).
Metals Source DF Seq. SS Adj. SS Adj. MS F P Single metal pollution index: Contamination Factor (CF) Cu Sites 6 981.62 677.20 112.87 10.43 0.000*** Year 1 25.16 4.44 4.44 0.41 0.524 Sites*Year 6 237.22 237.22 39.54 3.65 0.004** Error 63 681.49 681.49 10.82 Total 76 1925.50 Zn Sites 6 68.0009 52.3664 8.7277 37.59 0.000*** Year 1 0.0324 0.0292 0.0292 0.13 0.724 Sites*Year 6 11.2351 11.2351 1.8725 8.07 0.000*** Error 63 14.6264 14.6264 0.2322 Total 76 93.8948 Co Sites 6 4.14756 4.41908 0.73651 18.98 0.000*** Year 1 0.04424 0.00936 0.00936 0.24 0.625 Sites*Year 6 0.47605 0.47605 0.07934 2.04 0.073 Error 63 2.44532 2.44532 0.03881 Total 76 7.11318 Cr Sites 6 100.060 52.407 8.735 12.66 0.000*** Year 1 57.097 32.738 32.738 47.47 0.000*** Sites*Year 6 49.584 49.584 8.264 11.98 0.000*** Error 63 43.453 43.453 0.690 Total 76 250.194 Ni Sites 6 7.5729 10.3757 1.7293 29.44 0.000*** Year 1 1.1213 1.4027 1.4027 23.88 0.000*** Sites*Year 6 9.0738 9.0738 1.5123 25.74 0.000*** Error 63 3.7010 3.7010 0.0587 Total 76 21.4689 Pb Sites 6 61.281 46.262 7.710 5.03 0.000*** Year 1 5.702 3.674 3.674 2.40 0.127 Sites*Year 6 26.962 26.962 4.494 2.93 0.014* Error 63 96.525 96.525 1.532 Total 76 190.469 Cd Sites 6 1826.19 2216.30 369.38 59.73 0.000*** Year 1 136.27 257.62 257.62 41.65 0.000*** Sites*Year 6 1961.19 1961.19 326.86 52.85 0.000*** Error 63 389.63 389.63 6.18 Total 76 4313.29 Combine metal pollution index: Contamination degree (CD) CD Sites 6 2033.51 1857.64 309.61 21.18 0.000*** Year 1 202.66 155.11 155.11 10.61 0.002** Sites*Year 6 95.92 95.92 15.99 1.09 0.376 Error 63 920.71 920.71 14.61 Total 76 3252.81 ‘***’ = Significance level at <0.001 ‘**’ = Significance level at <0.01 ‘*’ = Significance level at <0.05
94
(V) Potential Ecological Risk Index (PERI)
The ecological risk factor (ER) and potential ecological risk index (PERI) of metals was evaluated to scrutinize the heavy metal eco-toxicology and their influence on benthic fauna. Low risk of all metals was perceived along the coastal sediment of Pakistan, except Cd. The considerable risk of Cd was observed in both monitoring years (Table 2.22). The spatial distribution of metal ER values in both monitoring years explained in Figure 2.12a and b. In MY-I, the highest PERI of Cu (13.2), Zn (1.7), Ni (7.6) and Cd (228.1) evaluated in the sediments of the SP backwater area. Whereas, the highest PERI of Cr (1.0) and Pb (19.2) detected at KC and SB (Figure 2.12a). In MY- II, the highest PERI of Cu (35.6), Zn (2.8), Cr (4.3) and Pb (31.4) was assessed in the sediments of SP, whereas the highest PERI of Ni (5.5) and Cd (246.5) was estimated at BH (Figure 2.12b). Analysis of variance (ANOVA) showed significant differences (p <0.001) for Cr, Ni and Cd among the sites as well as between the years, whereas Cu, Zn and Pb presented the significant differences (p <0.001) among the sites, but no differences found between the years (Table 2.23).
For MY-I, PERI was ranged from 5.8 to 327, indicated low to a considerable risk of heavy metals to benthic organisms along the coast of Pakistan (Table 2.22). During MY-I the status of coastal areas categorized by means of PERI: SP (267), BH (210), DH (206) as a moderate risk area, follow by SB (70), PC (42), SO (25) and KC (24) as low risk areas (Figure 2.12c). For MY-II, it varied from 55 to 272, indicated low to moderate risk of heavy metals along the coastal sediments of Pakistan (Table 2.18). The current status of coastal areas categorized by means of PERI: BH (262), SB (153) and DH (224) as a moderate risk area, follow by SP (142), PC (137), SO (131) and KC (121) as low risk areas (Figure 2.12c). PERI showed a significant difference (p <0.05) among the sites as well as between the years (Table 2.23). Moreover, the PERI was increased significantly during the last decade for most of the sites (BH, PC, KC, SO and SB) only SP had significant decreased in PERI values during the last decade, whereas at DH no significant variation found in PERI values during two monitoring years.
95
Table 2.22: Summary statistics of single and combined indices of ecological risk factor and index for six heavy metals (Cu, Zn, Cr, Ni, Pb and Cd) in coastal sediments of Pakistan during the two monitoring years (MY-I and MY-II).
Metals N Mean S.D Min Median Max Monitoring Year I (MY-I) Single metal pollution index: Ecological Risk Factor (ER) ER-Cu 32 6.382 4.151 1.461 5.592 18.07 ER-Zn 32 1.059 0.413 0.428 0.971 2.280 ER-Cr 32 0.703 0.268 0.211 0.662 1.175 ER-Ni 32 3.044 2.876 0.00 1.442 11.17 ER-Pb 32 10.09 10.28 0.00 8.150 44.39 ER-Cd 32 95.80 92.90 2.70 48.60 281.9
Combine metal pollution index: Potential Ecological Risk Index (PERI) PERI 32 117.1 100.3 5.8 76.8 326.9
Monitoring Year II (MY-II) Single metal pollution index: Ecological Risk Factor (ER) ER-Cu 45 11.15 17.77 0.02 3.62 74.71 ER-Zn 45 1.232 1.029 0.281 0.858 3.801 ER-Cr 45 2.328 1.506 0.208 2.313 6.255 ER-Ni 45 3.985 1.073 1.187 4.016 6.369 ER-Pb 45 15.87 14.97 5.55 9.19 68.58 ER-Cd 45 116.1 56.21 13.50 113.4 253.9
Combine metal pollution index: Potential Ecological Risk Index (PERI) PERI 45 150.67 49.22 54.97 139.83 272.0 (Note: Biological toxic response factor of Fe and Co did not established yet, therefore their ER values could not calculated)
96
250 (a) MY-I 200 DH 150 BH PC 100 KC 50 SP
Ecological FactorRisk (EF) SO 0 SB Cu Zn Cr Ni Pb Cd
250 (b) MY-II
200 DH 150 BH PC 100 KC 50 SP SO Ecological FactorRisk (ER) 0 SB Cu Zn Cr Ni Pb Cd
300 (c) PERI
250
200
(PERI) 150 2001
100 2011 Index Index
50 PotentialEcological Risk 0 DH BH PC KC SP SO SB
Figure 2.12: Variations in single metal index ecological risk factor (ER) in (a) MY-I, (b) MY-II and combine metal index (c) potential ecological risk index (PERI) from seven monitoring sites (DH = Dhabeji, BH = Bhambore, PC = Phitti Creek, KC = Korangi Creek, SP = Sandspit, SO = Sonari and SB = Sonmiani Bay) during the two monitoring years (MY-I and MY-II).
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Table 2.23: Variations in single (ecological risk factor) and combine (potential ecological risk index) metal pollution indices of six heavy metals (Cu, Zn, Ni, Cr, Pb and Cd) by analysis of variance (ANOVA) in coastal sediments of Pakistan during the two monitoring years (MY-I and MY-II).
Metals Source DF Seq. SS Adj. SS Adj. MS F P Single metal pollution index: Ecological Risk Factor (ER) Cu Sites 6 24540.6 16929.9 2821.7 10.43 0.000*** Year 1 629.1 111.0 111.0 0.41 0.524 Sites*Year 6 5930.5 5930.5 988.4 3.65 0.004*** Error 63 17037.3 17037.3 270.4 Total 76 48137.4 Zn Sites 6 68.0009 52.3664 8.7277 37.59 0.000*** Year 1 0.0324 0.0292 0.0292 0.13 0.724 Sites*Year 6 11.2351 11.2351 1.8725 8.07 0.000*** Error 63 14.6264 14.6264 0.2322 Total 76 93.8948 Cr Sites 6 400.238 209.629 34.938 12.66 0.000*** Year 1 228.389 130.954 130.954 47.47 0.000*** Sites*Year 6 198.337 198.337 33.056 11.98 0.000*** Error 63 173.812 173.812 2.759 Total 76 1000.776 Ni Sites 6 272.623 373.524 62.254 29.44 0.000*** Year 1 40.366 50.498 50.498 23.88 0.000*** Sites*Year 6 326.656 326.656 54.443 25.74 0.000*** Error 63 133.236 133.236 2.115 Total 76 772.880 Pb Sites 6 1532.01 1156.54 192.76 5.03 0.000*** Year 1 142.56 91.85 91.85 2.40 0.127 Sites*Year 6 674.05 674.05 112.34 2.93 0.014** Error 63 2413.11 2413.11 38.30 Total 76 4761.73 Cd Sites 6 1643575 1994669 332445 59.73 0.000*** Year 1 122646 231859 231859 41.65 0.000*** Sites*Year 6 1765070 1765070 294178 52.85 0.000*** Error 63 350670 350670 5566 Total 76 3881961 Combine metal pollution index: Potential Ecological Risk Index (PERI) PERI Sites 6 1753293 2193037 365506 58.62 0.000*** Year 1 167179 270414 270414 43.37 0.000*** Sites*Year 6 1544173 1544173 257362 41.28 0.000*** Error 63 392783 392783 6235 Total 76 3857428 ‘***’ = Significance level at <0.001 ‘**’ = Significance level at <0.01 ‘*’ = Significance level at <0.05
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2.3.5. Crab Distribution and Diversity
(I) Species Composition
A total 924 crab individuals were collected in MY-I, which consist of 21 species belongs to 7 families. However, total 1548 crab individuals were collected during MY-II, which belong to 18 species from seven families that were Comptandriidae, Dotilidae, Grapsidae, Macrophthalmidae, Ocypodidae, Sesarmidae and Varunidae. According to the number of individuals, top three dominant species were Ilyoplax frater, Austruca iranica, Opusia indica in MY-I, while the dominant species were A. iranica, Macrophthalmus depressus and I. frater during MY-II (Figure 2.13a and b). The dominant species at BH was I. frater and O. indica in both monitoring years, with percent of 59.51% and 35.89% in the MY-I and 46.95% and 40.46% in the MY-II, respectively. The dominant species in the site DH were A. sindensis (74.51%) and Nasima dotilliformes (71.68%) in MY-I and MY-II, respectively.
The taxa composition in PC was mainly comprised of A. sindensis (69.80%) in MY-I, while I. frater (40.55%) and O. indica (50.23%) were observed dominating species in a second monitoring year. The dominated taxa at KC were O. indica (48.28%) and M. depressus (70.41%) in MY-I and MY-II, respectively. The taxa in the site SP were mainly consisted of Austruca sp. (57.33%) and A. iranica (90.47%) in MY-I and MY-II, respectively. The fiddler crabs were dominated at the site SO in MY-I, which consist of 3 species, A. iranica (36.20%), Austruca sp. (34.41%) and A. sindensis (7.17%), however A. iranica was dominated species with the percentage of 80.34 in the MY-II. The crab species in the site SB were mainly composed of fiddler crab (48.19%) and Scopimera crabricauda (44.72%) during MY-I and MY-II, respectively.
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(a) MY-I (b) MY-II
I. frater 252 A. iranica 435 A. lactea 230 M. depressus 328 O. indica 185 I. frater 316 A. sindensis 166 O. indica 289 A. iranica 101 M. pectinipes 71 N. dotiliformes 200 A. annulipes 53 S. carbricauda 116 D. blandfordi 31 M. arabicum 54 M. bosci 18 A. sindensis 37 S. crabricauda 12 E. orientalis 32 Met. distincta 9 M. grandidieri 23 E. orientalis 9 Ily. palodicula 4 I. stevensi 22 P. plicatum 2 Met. indica 15 N. dotilliformes 2 Grapsid 10 Met. Indica 2 D. blanfordi 6 M. gigendieri 2 M. pectinipes 4 Grapsid 2 M. sulcatus 3 A. urvellie 1 M. indicus 1 Ily. palodicola 3 Unidentified 1 P. pelicatum 1 0 100 200 300 0 100 200 300 400 500
Figure 2.13: Crabs diversity and abundance from selected coastal areas of Pakistan during the two monitoring years (MY-I and MY-II).
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(II) Crab Density
Crab density was observed highest at BH (138.7 ± 69.9) and DH (186.0 ± 75.4) during MY-I and MY-II, respectively (Figure 2.14a). Whereas, the lowest density was evaluated at DH (16.00 ± 5.29) and SP (55.3 ± 37.8) in MY-I and MY-II, respectively. The significant differences were observed in crab density among the sites as well as between the two monitoring years (Table 2.24).
(III) Crab Diversity
The Shannon-Wiener diversity index (H’) was observed highest at SB (1.481 ± 0.478) and KC (1.172 ± 0.381) in MY-I and MY-II, respectively (Figure 2.14b). Whereas, the lowest diversity was evaluated at DH (0.903 ± 0.157) and SP (0.379 ± 0.304) in MY-I and MY-II, respectively. There were no significant differences in crab’s diversity among the sites and between the years (Table 2.24).
(IV) Crab Equitability
The Equitability index (J’) was highest at SP (0.865 ± 0.137) and KC (0.634 ± 0.099) during MY-I and MY-II, respectively (Figure 2.14c). Whereas, the lowest equitability was estimated at KC (0.625 ± 0.544) and SP (0.379 ± 0.304) in MY-I and MY-II, respectively. There were no significant differences in equitability index, among the sites but significant differences were observed in equitability between the years (Table 2.24).
(V) Margalef’s Species Richness
The Margalef’s species richness (SR) was observed highest at PC (1.006 ± 0.854) and SB (0.951 ± 0.530) in MY-I and MY-II, respectively (Figure 2.14d). Whereas, the lowest species richness was evaluated at KC (0.329 ± 0.332) and SP (0.2698 ± 0.145) in MY-I and MY-II, respectively. There significant differences were observed in species richness among the sites, but no differences were observed between the years (Table 2.24).
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Figure 2.14: The mean distribution of biotic indices (a) density, (b) diversity, (c) equitability and (d) species richness from seven monitoring sites (DH = Dhabeji, BH = Bhambore, PC = Phitti Creek, KC = Korangi Creek, SP = Sandspit, SO = Sonari and SB = Sonmiani Bay) during the two monitoring years (MY-I and MY-II).
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Table 2.24: ANOVA analysis for biodiversity parameters (density, diversity, equitability and species richness) among the study sites between two monitoring years (MY-I and MY-II).
Variables Source DF Seq. SS Adj. SS Adj. MS F P Density Site 6 53909 55079 9180 2.74 0.027* Year 1 29090 34034 34034 10.16 0.003** Site*Year 6 28298 28298 4716 1.41 0.239 Error 35 117231 117231 3349 Total 48 228528 Diversity Site 6 1.4833 1.4479 0.2413 0.87 0.528 Year 1 1.3817 0.7882 0.7882 2.83 0.101 Site*Year 6 1.5467 1.5467 0.2578 0.93 0.488 Error 35 9.7319 9.7319 0.2781 Total 48 14.1438 Equitability Site 6 0.04912 0.05983 0.00997 0.16 0.986 Year 1 0.73066 0.45040 0.45040 7.17 0.011* Site*Year 6 0.35140 0.35140 0.05857 0.93 0.484 Error 35 2.19881 2.19881 0.06282 Total 48 3.33000 Species Richness Site 6 1.60378 1.59447 0.26574 3.07 0.016* Year 1 0.03319 0.00458 0.00458 0.05 0.819 Site*Year 6 0.69254 0.69254 0.11542 1.33 0.269 Error 35 3.03189 3.03189 0.08663 Total 48 5.36139 ‘***’ = Significance level at <0.001 ‘**’ = Significance level at <0.01 ‘*’ = Significance level at <0.05
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2.3.5. Relationship between Metals Contamination in Sediments with Biotic Indices of Crabs
The impact of heavy metals loadings in sediments was examined on crabs through regression analysis (Table 2.25a). The crab density was increased with increasing Fe (R2 = 0.146) concentrations in sediments, whereas it was decreased with increasing of Cu (R2 = 0.101) and Zn (R2 = 0.133) concentrations in sediments. The diversity showed a significant linear relationship with Cu (R2 = 0.092), Zn (R2 = 0.079) and Cr (R2 = 0.092) concentrations in sediments, however the equitability index showed no correlation with any metal concentrations in sediments. The species richness showed a significant linear relationship with Cu (R2 = 0.131), Zn (R2 = 0.152) and Cr (R2 = 0.086) concentrations in sediments (Table 2.25).
The multi-metal contamination indices, such as the mean quotient of different sediment quality guidelines, contamination degree and potential ecological risk index provided the overall contamination status of marine sediments, which ultimately effect on the benthic organisms. The relationship between pollution indices and crab diversity and species richness were evaluated to understand the sensitivity of these indices towards the sediment contamination (Table 2.25b). The results indicated that diversity and species richness presented the significant linear relationship with all four sediment quality guidelines (mERLq, mERMq, mTELq, and mPELq) and contamination degree (Table 2.25b). Furthermore, the significant decreasing tendency was observed in diversity and species richness with increasing the level of metal contamination in sediments, which evaluated through various pollution indices. However, diversity and species richness showed no relationship with the potential ecological risk index. Additionally, the species richness demonstrated better values of regression coefficient (R2) as compared to diversity that indicates the more sensitivity towards the sediment contamination (Table 2.25b).
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Table 2.25a: The significant relationships between the biotic indices of crabs with heavy metal concentrations in sediments.
Variables Regression equation R2 F-value P-value Density vs Fe Density = - 190.7 + 0.3167 Fe 0.146 8.03 0.007** Density vs Cu Density = 109.6 - 0.1610 Cu 0.101 5.27 0.026* Density vs Zn Density = 130.9 - 0.3182 Zn 0.133 7.18 0.010* Diversity vs Cu Diversity = 1.114 - 0.001210 Cu 0.092 4.77 0.034* Diversity vs Cr Diversity = 1.184 - 0.002839 Cr 0.092 4.77 0.034* Species Richness vs Cu SR = 0.6949 - 0.000890 Cu 0.131 7.11 0.011* Species Richness vs Zn SR = 0.8003 - 0.001651 Zn 0.152 8.42 0.006** Species Richness vs Cr SR = 0.7213 - 0.001683 Cr 0.086 4.40 0.041* ‘***’ = Significance level at <0.001 ‘**’ = Significance level at <0.01 ‘*’ = Significance level at <0.05
Table 2.25b: The significant relationships between the biotic indices of crabs with pollution indices.
Pollution Indices Diversity Species richness R2 p-value R2 p-value Sediment Quality Guidelines Effect Range Low (mERLq) 0.095 0.025* 0.119 0.011* Effect Range Medium (mERMq) 0.081 0.039* 0.104 0.019* Threshold Effect Level (mTELq) 0.096 0.024* 0.121 0.011* Probable Effect Level (mPELq) 0.093 0.026* 0.117 0.012* Hakanson Pollution Indices Contamination Degree (CD) 0.087 0.032* 0.104 0.019* Potential Ecological Risk Index 0.014 0.398 0.009 0.502 (PERI) ‘*’ = Significance level at <0.05
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2.4. DISCUSSION
The present study mainly deals with the evaluation of heavy metals contamination in coastal sediments of Pakistan during the two monitoring years for the assessment of the metals enrichment during the last decade. For this purpose, the physical and chemical properties including metal contamination of coastal sediments were analyzed including their adverse effects on benthic fauna (crabs) also investigated. Various factors controlled accumulation and mobility of heavy metals in sediments such as grain size, properties of adsorbed compounds, metal characteristics, redox reactions and bio-degradation of sorptive substance under specific conditions (Tam and Wong, 2000; Bastami et al., 2014). Various studies are available on monitoring of marine sediment quality (such as porosity, organic materials and grain size) as have been described (Berner, 1980; Tang et al., 2006; Usero et al., 2008), sediment assessment combine chemical contamination with toxicity (Mucha et al., 2005; Cesar et al., 2007; Usero et al., 2008) includes many eco-toxicological methods, that have been standardized for sediment toxicity testing using various species (Burton, 1992; USEPA, 1994; ASTM, 1997; Usero et al., 2008).
The moisture and porosity contents in sediments showed the significant variations along the coastal areas during the last decade, the sediments of Phitti Creek and Dhabeji had the highest percent moisture and porosity during the MY-I and MY-II, respectively. The porosity is a significant physical property of sediments, and defined as the space of sediments or soil available for the multiple things (such as water, air, organic and inorganic materials and biota) present in the sedimentary environment (Qureshi and Sultana, 2001; Saher et al., 2016). If the soil particles tightly bind together, said to be having low porosity and this condition mainly due to variations in hydrodynamic conditions of a particular environment. Conversely, the loose soil has more available spaces, therefore able to retain more water content and organic materials (Miller et al., 1990; Qureshi and Sultana, 2001; George et al., 2010; Saher et al., 2016) thus the similar observation perceived as porosity had significant positive correlation with water contents as well as organic matter during both monitoring years. In addition, the water contents and organic matter increased with respect to porosity and vice versa. The significant differences were observed in percent organic contents in sediments among the sites as well as during the years. As the less than 5 percent (<5%) of organic matter in sediments represents good quality of sediments, 5–10% symbolizes moderate quality and > 10% signify low quality of sediment (Marin et al., 2008). The estimated organic matter contents indicated the good and moderate quality of sediments among the sites and monitoring years, except
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DH and PC. The both sites (DH and PC) have greater than 10% organic matter in the sediments during MY-I, which may be due to high mangrove litter decomposition, organic detritus, sewage and domestic input from the adjacent local areas (Alongi et al., 1998; Fonseca et al., 2014; Saher and Siddiqui, 2016).
Grain size distribution is one of the most important and fundamental character of sediment and provides important evidences and clues to different characteristics of sediments such as transportation history and depositional environments of sediments (Chaudhary et al., 2013; Saher and Siddiqui, 2016). The comparison of sediment percent composition data set for the two monitoring year revealed significant differences in composition during the last decade along the coastal sediment of Pakistan. Grain size variation in the marine environment is controlled by several factors such as transportation of sediment from one place to another, the distance travelled by particles from other sources through sedimentary processes (Sany et al., 2013). The grain size composition revealed the significant variations in mud proportion, especially increment at almost every site during the last decade reflects the increasing effect of sedimentary processes along the shoreline. Exclusively three coastal areas, Dhabeji, Bhambore and Port Qasim dominated by high mud proportions during second monitoring year that probably indicated the decreasing effect of the river transport. Indus River runoff significantly contributed in the dissemination of fine particles during the transportation of sediments and in the low energy environment, for instance Indus plain areas where the hydrodynamic condition of river comparatively very low, which provides the settlement of fine sediment particles with high sedimentation rate. Whereas, high mud proportions in Port Qasim areas likely due to dredging and re-suspension of sediments.
The Korangi Creek, Sonari and Sonmiani Bay areas were dominated by high proportions of fine sand may be due to low hydrodynamic conditions in mangrove forest, which act as a sediments trap and provides a mechanism to sink suspended solids by decreasing the hydrodynamic energy for deposition and resuspension of fine grains (Woodroffe, 1992; Wolanski et al., 1992; Furukawa et al., 1997; Kathiresan, 2003; Cunha-Lignon et al., 2009; Sany et al., 2013). Moreover, fine grain along the mangrove line were likely transported by land-based runoff. The sediments usually become finer when they deposited under lower energy environment as well as with a decrease in the energy of the transporting medium (Folk, 1974). On the other hand, the Sonari is a tidal mudflat with no mangrove vegetation but high hydrodynamic conditions directly connected to the Arabian Sea. The mixed fine and medium sand ratios dominated in the Sandspit mangrove areas, however the high sand could be attributed to tidal influence and wave actions experienced in the intertidal zones and influx of the water as well as from the sea. Different agents such as wind, water commonly separates particles by
107 their size (Friedman and Sanders, 1978; Mir and Jeelani, 2015) as the sediment texture has also a close relationship to the topography, wave and current pattern and depositional conditions (Rao et al., 1997; Singh et al., 1998; Mir and Jeelani, 2015).
Sediment grain size plays a significant role in the heavy metals concentration and distribution. The concentration of heavy metals is widely enriched in fine particles as compared to other fractions because they have high ability to adsorb and retain the high amount of metals from overlaying water bodies (Abrahim et al., 2007; Nobi et al., 2010; He et al., 2009; Lim et al., 2006; Sany et al., 2013). Thus, this fine fraction is frequently used to assess contaminant variation and toxicity tests in environmental monitoring. The average concentrations of heavy metals in the coastal sediments of Pakistan followed a decreasing order: Fe > Zn > Cu > Pb ≈ Cr > Ni > Co > Cd in MY-I and Fe > Cr > Zn > Ni > Cu > Pb > Co > Cd in MY-II. In the first monitoring year (MY-I), most of the heavy metals (Cu, Zn, Co, Ni, Pb and Cd) concentrations were detected highest at SP, whereas KC exhibited higher concentrations of Fe and Cr. In the second monitoring year (MY-II), the concentrations of Cu, Zn, Pb and Cr were observed highest at SP and the highest concentrations of Co, Ni and Cd observed at BH.
In the current study, the highest concentrations of Fe were observed at KC and lowest at SP during both monitoring years. According to Khan (1995), the suspended load of effluents which mostly consist of iron oxide dust may be as high as 250–300 ppm, which originates from various industries situated in Karachi, which may be the reason of highest Fe accumulation in KC sediments. Iron was observed the most abundant heavy metal in the sediment but it also considers as major element of earth crust and macro-nutrients for biota. The origin and sources of Fe in metal concentrations mainly crustal or earthen by weathering of rocks and river runoff, however, anthropogenic sources consist of industrial effluents. The level of Fe in the sediments showed low Fe concentrations as previously reported from coastal sediments of Karachi (Siddique et al., 2009; Chaudhary et al., 2013; Mashiatullah et al., 2013). This indicates the magnitudes of Fe in the sediments were shown the earthen origin rather than anthropogenic and it found as macronutrient, not considered as pollutant along the coastal sediments of Pakistan.
The Cu concentrations in coastal sediments presented significant spatial variations, which ranged from 13.15–162.68 and 1.8–672.4 (µg g-1) in MY-I and MY-II, respectively. The highest concentration of Cu was observed in the sediments of SP during both monitoring years. Copper is a micronutrient for aquatic life, but at the higher concentration it can be converted into widespread and damaging marine pollutant. The main sources of Cu in the marine environments via natural and
108 anthropogenic ways, natural sources includes atmospheric deposition, lithosphere input and hydrothermal vents, whereas the anthropogenic sources includes domestic and industrialized overflows, mining and smelting operations, agricultural runoff and leaching from antifouling paints (Castilla and Nealler, 1978; Matthiessen et al., 1999; Mulligan et al., 2001; Valkirs et al., 2003; Lin et al., 2013). Many marine waterways have high concentrations of copper with serious negative effects on humans and wildlife (Georgopoulos et al., 2001; Marsden and Rainbow, 2004; Rainbow, 2007).
Zinc is a naturally occurring element but it mainly incorporate with industrial and mining wastewaters in the marine environment (Cameron, 1992; Lin et al., 2013; Saher and Siddiqui, 2016). The Zn levels exhibited only spatial variations along the coastal sediments of Pakistan, which varied from 40.66–216.65 and 26.7–361.1 (µg g-1) during MY-I and MY-II, respectively. The highest concentrations were detected at SP in both monitoring years, whereas the lowest values were found at BH and SB in MY-I and MY-II, respectively. Zn levels in sediments comparable to previous studies from coastal areas of Pakistan (Siddique et al., 2009 and Mashiatullah et al., 2013), however it was higher than the concentrations found from other coastal environments in Malaysia (Sany et al., 2013), India (Silva et al., 2014), and Brazil (Fonseca et al., 2014). The spatial variability in Zn concentration likely due to presence of industries that present in the allied study areas.
Nickel is a ubiquitous element naturally present in the biosphere and hydrosphere, it is considered an essential trace component for living organisms. However, different chemical forms of Ni are exhibited in the environment by industries (alloys, electroplating, batteries, coins, stainless- steels), oil and coal combustion and by refinery and incineration (Munoz and Costa, 2012). Ni concentrations showed spatial and temporal variations and varied from not detectable to 126.6 µg g-1 in MY-I and 13.45 to 72.18 µg g-1 during MY-II. The highest contamination of Ni was observed in the sediments of SP and BH collected in MY-I and MY-II, respectively. According to the sediment quality guideline (SQG), most of the sites (PC, KC, SO and SB) were unpolluted in the MY-I, but they found moderately polluted by Ni in MY-II. Ni concentrations in coastal sediments exhibited higher levels as compared to the coastal sediments of Malaysia (Sany et al., 2013) but, it seems comparable with the other studies from coastal areas of Pakistan and India (Siddique et al., 2009; Mashiatullah et al., 2013; Silva et al., 2014). The main sources of nickel pollution are mining and associated activities and the disposal of Ni-Cd batteries (Cempel and Nikel, 2006; Nnorom and Osibanjo, 2009). The increase in environmental concentrations of nickel is able to produce harmful effects in various organisms, including plants (Dubey and Pandey, 2011), fish (Kubrak et al., 2012),
109 microalgae, and cyanobacteria (Mohammady and Fathy, 2007; Haiduc et al., 2009), and humans (Das et al., 2008; Poonkothai and Vijayavathi, 2012; Martínez-Ruiz and Martínez-Jerónimo, 2015).
The cobalt distribution in coastal sediments showed spatial variations, which fluctuated from 1.13–18.39 and 0.14–18.38 (µg g-1) during MY-I and MY-II, respectively. The highest concentration of Co observed at SP and lowermost was at KC in MY-I, whereas it was observed highest at BH and lowest at SB in MY-II. It was observed much lower concentration as compared to previous reported studies from coastal areas of Karachi (Siddique et al., 2009). Co originates as a result of surficial weathering processes and vulcanicity with the associated transfer of hot hydrothermal solutions through solidified rocks, a small amount of the total cobalt is removed by leaching and becomes incorporated in the sedimentary rocks or is transferred by rivers to the oceans in the dissolved form, usually as ionic Co2+. Its compounds have a wide variety of uses, e.g. paints, lacquers, varnishes, printing inks, ceramics, glasses, glass to metal seals, to improve adherence of enamel on steel and metal to rubber, catalysts (fuel synthesis, hydrogenation and polymerization reactions), foam stabilizers in malt beverages, electroplating, Agrochemicals, food additives, fermentation processes and the manufacture of vitamin B12 (Hamilton, 1994).
Another most important element, chromium ranged from 9.48–52.89 and 9.40–281 (µg g-1) in MY-I and MY-II, respectively and also presented the significant variations according to the sites and during the years in coastal sediments. The highest Cr concentration was observed in the sediments of KC and SP in MY-I and MY-II, respectively. According to the sediment quality guideline (SQG), most of the sites (PC, KC, SP, SO and SB) showed moderate pollution of Cr during the MY-I and also exhibited heavy pollution load during MY-II indicated an intensification in Cr contamination during the time being. It mainly originates from paint, coating, and leather industries (Lin et al., 2013; Saher and Siddiqui, 2016). Coastal Cr pollution is due to dumping untreated or poorly treated industrial residues (Vutukuru, 2003), this is usually found in its trivalent (Cr III) and hexavalent (Cr VI) forms (Bruhn et al., 1997). Cr (VI) is 30 times more toxic than Cr (III) and can be mutagenic and carcinogenic. Chromium used in a variety of applications and the largest amount consumed to manufacture pigments for use in paints and inks. Other applications include leather tanning, metal corrosion inhibition, drilling muds, textile dyes, catalysts, wood and water treatment. Chromate (Chromium form) is use in the industry to make bricks, mortar, ramming and gunning mixes as well as it enhances their thermal shock and slag resistance, volume stability and strength (Bielicka et al., 2004).
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The significant variations observed in lead concentrations among the sites in coastal sediments, but was not significant for the years, it varied from not detectable to 177.5 (µg g-1) in MY-I, and from 22.21 to 177.81 (µg g-1) in MY-II. In the current study, Pb was observed higher concentrations in coastal sediment as compared to coastal sediment of Karachi (Siddique et al., 2009; Mashiatullah et al., 2013), southeast coastal areas of India (Silva et al., 2014) and coastal sediment of Malaysia (Sany et al., 2013). The Pb concentrations were observed highest at SB and SP in MY-I and MY-II, respectively. According to the sediment quality guideline (SQG), most of the sites (DH, BH, PC and SO) appeared as unpolluted in both monitoring years. Whereas, KC and SP showed unpolluted to moderately polluted and moderately to heavily polluted towards the Pb contamination during the last decade. Lead is a toxic metal and its low concentrations might cause a hazard to life in an aquatic environment when compared with other metals and key anthropogenic sources are traffic exhaust, lead-zinc smelters, paints and batteries (Cameron, 1992; Saher and Siddiqui, 2016). Lead can also enter the environment through vehicle and industry exhausts and sewage sludge application in agriculture (Liu et al., 2006; Yousif and Ahmed, 2009; Tiwari and Rushton, 2010; Nanos and Rodríguez Martín, 2012; Rodríguez Martín et al., 2013b; Zamani- Ahmadmahmoodi et al., 2013).
In cadmium concentrations, the significant variations among the sites as well as during the year was found in coastal sediments of Pakistan and varied from 0.03–2.82 and 0.13–2.54 (µg g-1) during MY-I and MY-II, respectively. Cadmium is toxic in low concentrations, the possible sources of Cd in the environment are batteries, pigments, plating, and stabilizers, etc. (Boehme and Panero, 2003; Lin et al., 2013). The highest concentration of Cd was observed at SP and BH in MY-I and MY-II, respectively, whereas lowest concentration was observed at KC in both monitoring years. Cd was observed in lower concentrations as compared to previous report from the coastal sediment of Karachi (Siddique et al., 2009), whereas it was comparable values reported from the Malaysian coast (Sany et al., 2013). Cadmium is added to the soil in wastes from alloys, fungicides, enamels, batteries, pigments, plastics, old motor oil, textile manufacturing, electroplating, and rubber. Sewage sludge and some phosphate fertilizers also can be important sources of soil cadmium contamination. The principal use of cadmium is an electroplated coating on fabricated steel and cast iron parts for corrosion protection; it is usually plated from a cyanide bath (Cameron, 1992). The variability in the concentration of the above all metals mainly dependent of their respective sources and mainly related with the working industrial units along the coast as dumping their untreated waste to the adjacent coastal areas.
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Heavy metal pollution in coastal sediment evaluated by means of various multiple pollution indices (sediment quality guidelines (SQGs), Geo-accumulation index (Igeo), the enrichment factor (EF), contamination factor (CF), contamination degree (CD) and potential ecological risk factor (ER) and potential ecological risk index (PERI), each based on single metal as well as combined metals pollution indices) to illuminate the pollution elevation or relegation during both monitoring years.
Two types of sediment quality guidelines (ERL-ERM and TEL-PEL) used and the mean quotients of ERL-ERM illustrated that sediment-associated biota having infrequent adverse effect mainly due to Cu, Ni, Pb, Cr and Cd. Whereas, mean quotients of TEL-PEL sediment quality guidelines explained that Cu, Ni, Pb and Cd caused serious effects on sedimentary biota. The Geo- accumulation index (Igeo) is an approach to quantify particular metal pollution in sediments and according to Igeo, most of the sediment samples were detected unpolluted by Fe, Cu, Zn, Ni, Cr and Co and unpolluted to moderately-polluted by Pb and Cd. To further emphasize on the metal elevation during the time enrichment factor (EF) was estimated and severe enrichment of Cr, very severe enrichment (<50) for Co and Ni, and extremely severe enrichment (>50) of Cu, Zn, Pb and Cd were noticed. The high enrichment of heavy metals with respect to the time indicated the potential sources of these metals around the study areas.
According to contamination factor (CF), low contamination of Fe, Co, Cr and Ni, moderate contamination of Cu, Zn and Pb, and considerable contamination of Cd observed during MY-II and it was also observed that the contamination factor of Cr, Ni and Cd significantly increased during the last decade. To scrutinize the heavy metal Ecotoxicology and their influence on benthic fauna the ecological risk factor (ER) of six metals was evaluated. The results suggest that low risk factor was detected for all metals (Cu, Zn, Cr, Ni and Pb), except Cd which detected as a considerable risk factor. However, a significant increase was observed in the risk factor of Cr, Ni and Cd during the last decade.
The outcomes of two guidelines (ERL-ERM and TEL-PEL) and four single metal pollution indices (Igeo, EF, CF, and ER) tell a similar story of heavy metal contamination in coastal sediments of Pakistan. The results disclosed that other metals (Cu, Zn, Cr, Ni and Co) were observed somewhat contamination, but Pb and Cd considerably collaborate in coastal contamination and a potential threat to biota associated with sediments. Among metals, cadmium and lead are heavy metals that can be toxic and cause various destructive effects, when introduced into the body by ingestion or inhalation in sufficient quantities. In human, lead and cadmium cause many serious diseases and dysfunction of organs (Liu et al., 2006; Yousif and Ahmed, 2009; Tiwari and Rushton, 2010; Nanos
112 and Rodríguez Martín, 2012; Rodríguez Martín et al., 2013b; Zamani-Ahmadmahmoodi et al., 2013).
The monitoring areas along the coast were classified into three major categories (unpolluted or less polluted, moderately polluted and heavily polluted) on the basis of combined metal pollution indices such as sediment quality guidelines (mELMq and mPELq), contamination degree (CD) and the potential ecological risk index (PERI). According to Long and MacDonald (1998), on the basis of ELM and PEL mean quotients coastal regions were categorized as lowest, medium-low, medium- high and highest priority regions to monitor, managed as well as conserved the most adverse effected ecosystem. During the last decade, the coastal sediment samples having 30% to 46% probability of being toxic according to ERM guidelines, whereas 25% to 50% probability of toxicity calculated through PEL guidelines. In both cases, all monitoring sites categorized as medium-low to medium- high priority zones, and Sandspit was detected most contaminated site. Furthermore, mean quotients of two sediment quality guidelines (mERLq, mERMq, mTELq and mPELq) showed a significant increase during the last decade along the coastal sediment of Pakistan.
According to combined metal pollution indices (SQGs, CD and PERI), the decreasing order of heavy metals contamination among the sites seem as Sandspit > Dhabeji > Bhambore > Sonmiani Bay > Korangi Creek > Phitti Creek > Sonari in MY-I. However, the order of the sites changed in MY-II and it seem as Sandspit > Bhambore > Dhabeji ≈ Korangi Creek ≈ Phitti Creek ≈ Sonmiani Bay ≈ Sonari. In both monitoring years, Sandspit detected as the highest contaminated site. The significant variations were observed among sites for the heavy metal contamination level in sediments in MY-I, however most of the sites presented similar pollution conditions in MY-II. The contamination degree indicated low to considerable contamination degree in MY-I, whereas, low to very high contamination degree for different sites during MY-II. It showed a significant increase at all sites, except Dhabeji and Sonmiani Bay, during the last decade. The highest contamination degree observed at Sandspit during both monitoring years and characterized as a considerable contaminated site. Potential ecological risk index (PERI) indicated that low to considerable risk in MY-I and low to moderate risk during MY-II in the intertidal sediments of Pakistan. The highest PERI was detected at Sandspit and Bhambore during MY-I and MY-II, respectively. PERI showed a significant intensification at all sites during the last decade, except, Sandspit. Because of the decline in the risk factor of Cd, the significant decrease was observed in PERI at Sandspit. However, due to increment in the risk factor of Cd, the significant increase was observed in PERI at Bhambore. The PERI is the summation of all risk factors (ER) of metals, in which Cd contributes more than 70% and this is the indication of time to develop some techniques at a pilot scale to reduce the environmental issues and
113 rehabilitate. The current status of the monitoring sites indicated that Sandspit, Bhambore and Dhabeji are the three most contaminated sites by heavy metals and these areas need appropriate management and conservation strategies.
The Sandspit was identified as the most polluted and contaminated site during this study, the area is a complex of coastal wetlands and contains a variety of environments such as shallow tidal lagoons, intertidal mudflats, saltpans, estuaries, saline pond, mangrove swamps and sandy beach. There is only one species of mangrove in the backwaters viz. Avicennia marina (Durranee et al., 2008). The backwater is known as a nursery ground for various species of birds, fish and shellfish species (Sultana and Mustaquim, 2003), while the sandy shore is one of the important sites for marine turtle nesting (Durranee et al., 2008). During high and low tides, seawater enters the backwater area and drains back periodically to the Arabian Sea through Manora channel. It is estimated that an average volume of about 3.4 million m3 of seawater move in and out the backwater area during the tidal cycle (Haq, 1976; Sultana and Mustaquim, 2003), which probably the important source of the dispersal and movement of contaminations around the areas. The backwater area also influences through the Lyari River, which provender the area from the eastern side, while the seawater enters through Karachi Harbour from the southern side. The Lyari River is not a perennial river, it discharges freshwater during the rainy season, which lasts for two to three months, while during the rest of the year, and it discharges mostly industrial and domestic effluents. It has been estimated that the Lyari River brings 120 million gallon per day of municipal and industrial wastewater with an organic load of 2000 tons of BOD per day (Beg et al., 1984; Sultana and Mustaquim, 2003), which can be one of the biggest source of metals contamination in the area.
The Dhabeji and Bhambore are the locations situated in the Indus River region. No previous reported study was found on heavy metal contamination in this area, which made difficulty in interpretation. Dhabeji is an industrialized zone, while Bhambore is an ancient city which probably a seaport in the historical era. In the initial study both sites selected as the reference sites because these locations have no industrialization and urbanization problems as compared to Karachi City. The site selected in Dhabeji area was densely covered with vegetation (herb, shrub and mangrove) the crab species and gastropods shells are dominated fauna. While, Bhambore have very small and patchy mangrove, which may be planted artificially. These both areas interconnected with the creeks and Port Qasim areas and received the metals contamination from there. The low input of freshwater can be also the reason for the pollution increment.
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Phitti Creek located near the biggest port of Karachi Port Bin Qasim those comprised the largest steel mills, fertilizer plants, vehicle manufacturing plants, thermal power plants and the largest container terminal and seaport which influenced by organic and inorganic pollution. Furthermore, steel mill provides scrap steel and other scrap metals for foundries and scrap wood products etc. (Siddique et al., 2009). These creek areas adjacent and interconnected with Korangi Creek, which are extremely affected by several point and non-point sources. The main source of contamination is Malir River effluents that come from the adjacent industrial zones Landhi and Korangi Industrial and Trading Estates (LITE and KITE). This input contains most of the metals, comes from more than hundred different industries including chemical industries, metal industries, oil refineries, petrochemicals, tanneries, pharmaceuticals, textiles etc. (Siddique et al., 2009).
The pollution in Sonmiani Bay is considerable level as due to the operation of 135 industrial units such as Hub, Windar, Uthal, Gadani Shipwrecking, Marble City and others in this area. The untreated industrial and municipal effluents and wastes from the HITE (Hub Industrial and Trading Estate) industries are discharged into the Hub River and the Arabian Sea respectively (LDG, 2011). Furthermore, the fuel exhaust, waste oils and other solid waste products are also discharged through small and large fishing boats and trawler directly into sea front of Sonmiani Bay. Moreover, the world third largest shipwrecking industry at Gadani has been a prominent source of pollution near the beaches of Sonari and Sonmiani Bay (Qari et al., 2005).
The lowest metals contamination was perceived in the Sonari coastal area. This is a small local fishing area having a sandy cum muddy beach which directly connects to the Arabian Sea. This is a cleanest location as compared to others because of the high hydrodynamic conditions during the tidal cycle and low contamination input around the study site. Nevertheless, during the last decade the heavy metals contamination increased in this area that signify the potential sources around the coastal environment. The leading sources of environmental contamination around the Sonari coast are attributed through the Hub power plant, which probably source of organic and inorganic contamination as well as Ship wrecking industry situated in Gadani coastal areas (Saher and Siddiqui, 2016).
The interrelationship between the physical properties (moisture, porosity, organic matter and grain size) of sediments with metals concentrations was investigated through Pearson’s correlation analysis independently for two monitoring years. The results indicated that most of the physical properties showed the significant correlations with heavy metal levels in coastal sediments in both monitoring years. The Cd concentrations in sediments were increased with the increase of the
115 moisture and porosity contents of sediments in MY-II. Percent organic matter also influenced on the Zn and Ni enrichment in sediments during MY-II as it plays a major role in the accumulation of these metals in the sediments and indicated its role as a controlling factor of Zn and Ni distribution in coastal sediments of Pakistan. The organic materials produced through the decomposition of organics originate from either plant or animal detritus, they provides the space to bind the metals on their surface in the form of particulate matter. Most of the organic materials settle down in sea floor as stationary phase and as the mobile phase in overlying water bodies, therefore act as a sink along with the secondary source of heavy metals concentrations in the marine environment (Chaudhary et al., 2013).
In the present study, the significant correlation was observed between mud contents and metal contamination in sediments. The concentrations of Cu, Zn and Pb were increased with decrease of mud contents in sediments during MY-I. In contrast, the concentrations of Fe, Cr, Pb and Cd was increased with increasing the mud ratios in sediments, but Zn and Co concentrations were decreased with increasing the mud proportion during the MY-II. Metals concentrations and grain size are correlated, in fact the metals increased with the lessening grain size of sediments. Huge amounts of metals are located in fine-grained fractions of the sediment due to higher surface. Furthermore, sediments have a great capacity to accumulate contaminants that are discharged into the estuarine and coastal environment by sorption, co-precipitation and the complexion of metals on particle surfaces and coatings (Fonceca et al., 2014). In the current study, heavy metals had significant correspondences with different grain sizes of sediments, indicating that grain size acts as the controlling factor in heavy metals distributions. Moreover, these interactions varied between the two monitoring years, which may be indication of different sources and carriers shifting during the geochemical properties of marine sediments.
The different metal comes from different sources either natural or anthropogenic and the inter-relationship between these metals indicated their similar or diverse sources. To investigate the relationship between the heavy metals Pearson’s correlation analysis was considered. The significant correlations were obtained between the pairs, Cu vs. Zn, Cu vs. Ni, Cu vs. Co, Cu vs. Cd, Zn vs. Ni, Zn vs. Co, Zn vs. Cd, Ni vs. Co, Ni vs. Cd, Co vs. Cd, Zn vs. Pb and Cr vs. Cd during MY-I. The significant positive correlations were obtained between the pairs, Zn vs. Ni, Zn vs. Co, Ni vs. Cr, Ni vs. Pb, Cr vs. Pb, Cr vs. Cd and Pb vs. Cd during MY-II, however significant negative correlations were obtained between the pairs, Cu vs. Ni, Cu vs, Cr and Cu vs. Pb. The concentration of Fe was negatively correlated with Ni, Co, Cr and Cd in MY-I. The negative correlation indicates that these metals have uncommon sources and dissimilar behavior during transport and deposition in
116 sediments. However, Fe concentrations had moderately correlated with Cr, Pb and Cd in MY-II, suggesting a common lithogenic in origin (Wang et al., 2013) or biogeochemical in origin (Fernandez-Cadena et al., 2014), whereas the Fe showed negative correlation with Zn and Co concentrations, suggesting the dissimilar sources. The most of the correlated pairs of metals seem diverse pattern between the two monitoring years. Only two pairs (Zn vs. Ni, and Zn vs. Co) were detected similar correlations in both monitoring years. These highly diverse inter-elemental correlations indicated that the heavy metal interactions and sources totally different during the last decade along the coastal areas of Pakistan. Moreover, the positive correlations between the metal pairs also indicated a common source or sink, interconnections, and similar behavior during transport from source or sink in the sediments (Bastami et al., 2014). There is need to understand the geochemistry of marine sediments with different advance techniques such as vertical distribution of metals, oxidize and reduce state of metals (speciation) and fractionation of heavy metals to further understand the geochemical behavior, sedimentary and depositional characteristics along the coastal belt of Pakistan in front of the Arabian Sea.
Intertidal decapod crabs are one of the most important macrofaunal members of the mangrove forest ecosystem, inhabiting the forest and adjacent tidal flats. Species of two families, Ocypodidae and Grapsidae, typically dominate macrofaunal assemblages together with mollusks in terms of abundance and biomass in the Indo-West-Pacific (IWP) mangrove forests (Lee, 1998; Ashton et al., 2003a; Lee, 2008; Geist et al., 2012). These crabs considered as “ecosystem- engineers” because they alter the sediment structure by their burrowing activities that favor sediment oxygenation and may enhance tree growth (Smith et al., 1991; Kristensen, 2008). The distribution of crab species along the intertidal gradient related to their physiological adaptations to abiotic conditions, to vegetation density and composition, and to their social and feeding behaviour. However, study results revealed inconsistency concerning the importance of the different factors influencing crab distribution patterns (Macnae, 1969; Frith and Brunenmeister, 1980; Jones, 1984; Macintosh, 1984; Wilson, 1989; Frusheret al., 1994; Dahdouh-Guebaset al., 2002; Ashton et al., 2003b).
The density and distribution of crabs (Saher and Qureshi, 2010; Saher and Qureshi, 2012; Qureshi and Saher, 2012), food and feeding behavior (Saher and Qureshi, 2014), relative growth (Qureshi and Saher, 2011; Saher and Qureshi, 2011) were previously reported from coastal areas of Pakistan. The above-mentioned studies indicate the crab dominancy along the intertidal areas as well as their application as an indicator of environmental stress. No previous reported study present on the impact of metal contamination on crab density and diversity on the coast of Pakistan. In the current
117 study, crab diversity parameters were correlated with heavy metals gradients during the last decade along the coastal areas of Pakistan and results indicated the significant influence detected on crab density (individuals/m2) as differences observed among the sites as well as between the years. The crab density was increased in MY-II as compared to MY-I. The highest density was observed at Bhambore and Dhabeji in MY-I and MY-II, respectively. Whereas, the lowest density was evaluated at Dhabeji and Sandspit in MY-I and MY-II, respectively.
The stress level of heavy metal contamination in sediments on the crab community was performed by calculating the Shannon–Wiener diversity index (H’ log2; Shannon and Weaver, 1949). The H’ index is the most commonly used diversity index in benthic ecology, and H’ incorporates species richness and evenness. Although it is generally recommended that diversity measures be treated with caution in ecological classifications because the values are influenced by methodological procedures (e.g., sample size, sampling methodology, and species identification) as well as by natural seasonal variability and habitat type (Simboura et al., 2012; Spagnolo et al., 2014), the H’ index is a commonly used indicator that has good sensitivity for and correlation with anthropogenic impacts (Boon et al., 2011; Spagnolo et al., 2014). In the current study, no significant differences observed in crab diversity as well as equitability among the sites and between the years. However, the significant differences were observed in species richness among the sites, but no differences between the years.
The results indicated that intertidal crab species can be useful in environmental monitoring because of the tolerance ability of some present crab species, but the low range of diversity values indicated low species richness along the coastal areas. The lowest diversity parameters in Sandspit indicated the extremely vulnerable environment. The highest diversity was evaluated at Sonmiani Bay and Korangi Creek in MY-I and MY-II, respectively. Whereas, equitability index was observed highest at Sandspit and Korangi Creek in MY-I and MY-II, respectively. Investigating the natural and human threats to biodiversity in the marine ecosystem, Ahmed (1997) documented that several species have disappeared from the intertidal zone due to high temperature and salinity, pollution and over-exploitation. Similarly, Rizvi et al. (1999) also observed a loss of marine fauna and flora in the near shore environment due to pollution, coastal development and changes in the river discharge. Barkati and Rahman (2005), reported the high values of diversity in the Port Qasim as compared to Clifton and Sandspit beaches from the Karachi coast. The dominance of a single species at a site formerly used for tin mining considered as an indicator for a highly disturbed, stressful environment, whereas in more stable, mature forests a more balanced species composition was found (Macintosh et al. 2002).
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Simboura and Argyrou (2010) discussed three factors that can influence the performance of each biotic index: different weightings of the species groups, the choice of the class boundaries, and the existing distribution pattern of the species across an ecological stress gradient. Each index itself has advantages and disadvantages. For example, a weakness of H’ is that it considers the dominance of species that could be non-indicative of pollution because they may occur naturally at high densities in areas not affected by environmental stressors. In addition, across a gradient of pollution, the highest H’ values may be recorded when the number of species is still low and the community is still at an early stage of recovery. Thus, H’ does not account for monotonic behavior in response to environmental degradation (Spagnolo et al., 2014).
The Margalef’s species richness was observed highest at Phitti Creek and Sonmiani Bay in MY-I and MY-II, respectively. Whereas, the lowest species richness was evaluated at Korangi Creek and Sandspit in MY-I and MY-II, respectively. Among current principles used to guide conservation and management is that protection of locations with high species richness is an efficient way to conserve overall biodiversity and sustain key ecological functions (Scott et al., 1987; Myers et al., 2000; Fleishman et al., 2006). In other words, species richness is assumed to be an indicator of conservation value (e.g., Meir et al., 2004; Fleishman et al., 2006). The observed differences in species richness among locations may reflect true differences in species richness as well as differences in sampling effort or dissimilarity in the underlying distributions of species abundance (Wintle et al., 2004; Fleishman et al., 2006). Standardizing for survey effort is essential before comparisons made. However, it is not a trivial exercise because the two ways of standardizing species richness estimates, according to the area or number of individuals sampled, may lead to different estimates of species richness and, in turn, different priority rankings (Gotelli and Colwell, 2001; Fleishman et al., 2006).
The correlation and impact of heavy metals loadings in sediments on biotic indices such as density, diversity, equitability and species richness of crab was evaluated through regression analysis. The crab density was increased with increasing Fe concentrations in sediments, whereas it was decreased with increasing of Cu and Zn concentrations in sediments. The crab diversity and species richness decreased with the increase of Cu, Zn and Cr concentrations in sediments, however equitability index showed no correlation with any metal concentrations in sediments. Monitoring of species and their density show the wide range of intra annual variations in the benthic environment because of the seasonal distinctions in productivity and recruitment of the species as well as harsh climatic condition (Reiss and Kröncke, 2005; Bone et al., 2011). The concentrations of Cu, Zn and Cr in sediments varied significantly during the last decade along the coastal areas of Pakistan. The
119 backwater mangrove areas of Sandspit presented the highest levels these metals with the lowest values of biotic indices of crab in MY-II, which indicated that the sediment contamination effect on the crab diversity of the area.
Moreover, the relationship between the multi-metal contamination indices (sediment quality guidelines, contamination degree and potential ecological risk index) and biotic indices (diversity and species richness of crabs) were considered to highlight the sensitivity and effectiveness of biotic indices towards the sediment contamination. According to Intermediate Disturbance Hypothesis (IDH), low contaminated areas have relatively high species diversity (Wilson, 1994, Roxburgh et al., 2004; Boon et al., 2011). Borja et al. (2000) also presented the similar results and reported that low stress environments have high diversity and species richness and vice versa. The diversity and species richness significantly decreased with increasing the metal pollution in sediments. Furthermore, the species richness reveals the most sensitive, effective and very simple tool to recognize the effects of sediment contamination on the benthic community in the marine environment. The further advance and comprehensive biomonitoring programs are required to evaluate the heavy metals partitioning in marine sediments as well as their hazards on marine environment, especially on benthic organisms along the shoreline of Pakistan.
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CHAPTER 03
IDENTIFICATION AND ROLE OF INDICATORS (SEDIMENTS AND CRABS) FOR HEAVY METAL MONITORING ALONG THE COAST OF PAKISTAN
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3.1. INTRODUCTION
The coastal and shelf areas are incredibly important for marine life as they are nursery and feeding ground for numerous marine species as well as also clean and alter many pollutants and waste materials from urban areas, therefore these areas deserve to be secured and conserved (Boutiba et al., 2003; Touahri et al., 2016). These regions have been frequently susceptible through various types of contaminations, attributable to the intensification of urban sprawl, agricultural and industrial activities (Green-Ruiz and Páez-Osuna, 2001; Poulos et al., 2000; Velez et al., 2015). Heavy metals are the most common toxic contaminants released to the marine environment. They naturally occur in trace amounts, but their contaminations have been extensively described due to various anthropogenic activities in the coastal and estuarine environment (Mohammed et al., 2011; Mendoza-Carranza et al., 2016).
The assessment of sediment has an importance as compare to water column in the environmental monitoring of any intertidal coastal area of heavy metals. Sediments act as a reservoir for a variety of contaminants (metals or metalloids and their transformation products) causing multiple damages to individuals, population, community and ecosystem. Hence, sediment is a useful and preliminary indicator of metal flux in coastal areas and their evaluation provides hot spots and major concern areas to initiate management strategies (Hoffman et al., 2002; Dauvin, 2008; Buruaem et al., 2012; Bolognesi and Cirillo, 2014; Velez et al., 2015; Noronha-D'Mello and Nayak, 2016; Touahri et al., 2016). The potential adverse effects of sediment-associated toxic metals are generally assessed by in situ, determining their total concentration directly from sediments and organisms or in vitro, examined from experimental studies in laboratories through animals (MacDonald et al., 2000; Long et al., 1995; Long and Morgan, 1990; Chakraborty et al., 2016).
Benthic communities typically consist of a variety of species that exhibit a wide range of characteristics, which prove their role as potential bio-indicator to evaluate the impact of environmental contamination (Calabretta and Oviatt, 2008; Cheggour et al., 2005; Moschino et al., 2012; Velez et al., 2015). Acting as sentinel organisms, they can help to detect marine pollution because of their capabilities to filter large amount of water, concentrate contaminants in the body and close association with substrate (Cheggour et al., 2001; Griscom and Fisher, 2004; Freitas et al., 2012). Some species are more sensitive to environmental contamination and provide quick response to both types of stresses (natural or anthropogenic) through their presence or absence, abundance and
139 distribution, these tools are frequently used in monitoring studies (Cheggour et al., 2001; Griscom et al., 2002; Lecoeur et al., 2004; Freitas et al., 2012). Most of the species are well known for their tolerance or they are capable and adopt themselves as a tolerant species in harsh environmental conditions because having some physiological and behavioral strategies to overcome the miserable such massive situations (Calabretta and Oviatt, 2008; Moschino et al., 2012; Velez et al., 2015). The monitoring of biotic indices (such as density, abundance, diversity and species richness), quantifying the contaminants in sediments, water and organisms and assessing induced toxicity in organisms are foremost concerns in environmental impact assessment studies (Cheggour et al., 2005; Machreki- Ajmi and Hamza-Chaffai, 2006; Box et al., 2007; Calabretta and Oviatt, 2008; Velez et al., 2015).
The main advantage to use marine organisms for monitoring of contaminant levels is that biota is able to concentrate trace metals in their body up to several thousand times more than those detected in the water column or sediments as well as retained them in the body and still able to survive (Wilson and Elkaım, 1992; Blasco et al., 1999; Mouneyrac et al., 2002; Barka, 2007; Tlili et al., 2016). The trace metals in the environment (seawater and sediment) may not be accessible for uptake and accumulation of the organisms and mostly depends on their chemical form (Sadiq, 1992; Blasco et al., 1999). Some heavy metals are available for uptake into organisms from solution only as free ions and/or through biological membranes as inorganic forms (Brown and Depledge, 1998; Blasco et al., 1999). The organism used in the biomonitoring programs also reflect the availability of contaminants over time and can be used to establish geographical and temporal variations in the bio- availability, ecotoxicology and the impact of heavy metals in the marine environment (Rainbow, 1995; Blasco et al., 1999).
In the recent decades, several monitoring programs have been undertaken using the heavy metals distribution and accumulation in various benthic species (this also includes the whole body, tissues and organ specific tissues) to investigate the exposure of heavy metals pollution using the exclusively concentration indicator (Goto and Wallace, 2007; Dean, 2008; Rainbow and Smith, 2010; Freitas et al., 2012). However, recent studies also reveal the some biochemical indicators or stress biomarker, which provides the response towards the contaminant stresses through alteration in physiological and enzymatic activities (Stegeman et al., 1992; Freitas et al., 2012). Biomarker can be defined as any physiological, behavioral or molecular changes in an organism which clarify the prior or contemporary exposure of at least one or multiple contaminants existing in an environment (Lagadic et al., 1997; Touahri et al., 2016).
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Crabs are greatly diversified group (comprising 7047 species) among the crustaceans belonging to the infraorder Brachyura (Ng and Davie, 2016). These species are found from freshwater to deep ocean environments and some of them are semi-terrestrial living in salt marshes and mangrove forests (Almeida et al., 2016). The crab species are large and may generally be easily identified, they are usually mobile on their home range (Rainbow, 1988; Buolayan and Subrahmanyam, 1998). Crabs have a wide range of food and feeding habits as they are well-known carnivore, scavengers, detritivore and herbivore in their respective habitat includes coastal and estuarine habitats. They extracted detritus, microorganisms, algae from ingested sediment particles, they also consumed plant materials as well as other crabs and fish. From this diversified diet range, they accumulate various metals in different forms and concentrations and can provide a suitable bioindication of metals pollution (Reichmuth et al., 2009; Na and Park, 2012).
Crustaceans are recognized to gather metals from surrounding waters and sediments by adsorption or immediate ingestion through food. Several studies have evaluated the effectiveness of crustaceans as bioindicators of metal contamination for marine environments (Davies et al., 1981; Rainbow, 1985; Weeks et al., 1995; MacFarlane et al., 2000; Morillo et al., 2008; Simonetti et al., 2013; Na and Park, 2012; Pinheiro et al., 2012; Alvaro et al., 2016). The benthic crab species mostly rely on sediments for their living and food have potential to accumulate the high amount of metals concentrations and they act as a reflection of habitat quality (Reichmuth et al., 2010).
Crabs also play a very important role in the coastal food web and serve as a food to several aquatic and semi aquatic invertebrate and vertebrate species. They are an important link to higher trophic level in the food web of intertidal environment. They transform detritus into secondary consumers (predators) such as terrestrial and aquatic organisms. This direct conversion of detritus to biomass may be the main sources of energy transfer to carnivore population (Nicholas and Moshiri, 1974; Saher, 2008). This is the indication of biomagnification of heavy metals in higher trophic levels, which directly or indirectly effect on human beings. Crabs often regulate essential metals concentrations in their tissue, for example, copper, manganese and zinc (Rainbow, 1988; Buolayan and Subrahmanyam, 1998). However, the non-essential metals accumulate in the body, sometimes in much higher quantity indicates a strong possibilities for their use as effective bioindicators in the coastal environment (Beinlich and Polivka, 1989; Ismail et al., 1991). Several studies have been reported on bioaccumulation and bioconcentration of heavy metals in different crab species in estuarine and coastal environment (Rainbow, 1995; Buolayan and Subrahmanyam, 1998; MacFarlane et al., 2000; Legras et al., 2000; Martín-Diaz et al., 2005; Beltrame et al., 2010; Bordon
141 et al., 2012; Na and Park, 2012; Pinheiro et al., 2012; Díaz-Jaramillo et al., 2013; Alvaro et al., 2016; Almeida et al., 2016).
The crab species are widely distributed and available in all seasons along the coastal and estuarine areas of Pakistan to facilitate the comparisons between seasons and locations. The sampling and identification of crabs is easy and mostly they possess a large body to provide sufficient material for chemical analysis except small sized crab species. Most of the benthic crab species provides constant uptake of contaminants or continuous contaminant accumulation. They have the capacity to accumulate pollutants in high amount above environment levels without being killed or rendered incapable of long-term reproduction. Easily aged and long lived, allowing integration of the pollutants over a long period. They continuously interacted and directly rely on sediments for burrowing, feeding, reproducing, growth and development. These individuals potentially reflect the contaminations present in the environment and they could be considered as suitable candidates (biomonitors) for the monitoring studies. In some perspectives, the role of crabs has been criticized as a good bioindicator since they regulate concentrations of some essential trace elements (such as copper, manganese and zinc) in tissue to almost continuously over an inclusive range of metal availabilities (Rainbow and Phillips, 1993; Rainbow, 1998; Guerra-García et al., 2010). Contrariwise, they behave as good accumulator for non-essential or toxic metals in almost greater amount than present in the environment (MacFarlane et al., 2000).
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3.1.1. Objectives
The main objective of this study was to identify the role of some brachyuran crab species in the bio-assessment of heavy metals contamination along the coastal areas of Pakistan. The study was divided into two biomonitoring phases (BMY-I = 2011 and BMY-II = 2016). During the first campaign, the baseline data were obtained on heavy metal accumulation in some selected crab species and second monitoring program based on the results achieved from the previous phase. To accomplish the major aim of the study, the data were compiled passing through the following:
1. In the first biomonitoring year (BMY-I = 2011), the concentrations of eight heavy metals (Fe, Cu, Zn, Ni, Co, Cr, Pb and Cd) were evaluated in sediments of selected sites and selected seven deposit feeder crab species (Macrophthalmus depressus, Austruca iranica, A. sindensis, Iloplax frater, Opusia indica, Eurycarcinus orientalis, and Scopimera crabricauda) based on the population density, intersexual, interspecific and intraspecific variations. 2. The Inter-elemental correlation between heavy metals in crabs was evaluated to understand the natural and anthropogenic sources of metals that accumulate in crabs. 3. The relationship between environmental properties (water contents, grain size and organic matter) with heavy metals accumulation in crabs examined to evaluate the influences of these parameters on metal accumulation in crab. 4. Finally yet importantly, the relationship between heavy metals concentrations in sediments and accumulation in deposit feeder crab were assessed to ascertain the response of crab towards the exposure levels of heavy metals contamination in sediments. 5. In the second biomonitoring year (BMY-II = 2016), the two biological factor gender and size effects on heavy metal accumulation in tissues of two deposit-feeder crabs, M. depressus and A. irancia, from coastal environments of Pakistan. In this study, concentrations of five heavy metals: copper (Cu), zinc (Zn), cobalt (Co), lead (Pb), and cadmium (Cd) were determined in the soft tissues of both crab species as well as in the sediments.
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3.2. MATERIALS AND METHODS
3.2.1. Sites Selection
The role of some brachyuran crabs in bio-assessment of heavy metals contamination evaluated in two biomonitoring years. In the first year (BMY-1 = 2011-12), nine sites were selected to evaluate the heavy metal pollution in sediments and their accumulation in different benthic crab species for identification and selection of suitable biomonitor species. The second biomonitoring year (BMY-II = 2016) study focused to further highlight the role of selected crab species as biomonitor or bioindicator from previous study (BMY-1). The information of selected sites and crab species diversity along with analysis protocol during both biomonitoring years presented in Table 3.1 and detailed explanation of sites already described in chapter 02.
3.2.2. Selection of Biomonitor Species
In the biomonitoring study, the different brachyuran crabs were considered as biomonitor species due to several reasons, among the various benthic organisms to recognize the metal contamination along the coastal areas of Pakistan. For instance, these crabs are bottom dweller and restricted mobility within a known home range. They are ubiquitous and showed a sufficient abundance in the sampling area to prevent changes of the age structure when large numbers are taken.
3.2.3. Sampling Procedure
The two sampling strategies (quadrat and random sampling) were used in two phases of the monitoring program (BMY-I and II) to evaluate the potential role of crabs with respect to their habitat in bio-assessment of heavy metals along the coast of Pakistan. During the BMY-I (September 2011 to January 2012) the crabs were collected through transect and quadrat method, the sampling procedure of crabs and sediments already mention in detailed in materials and methods of chapter 2. In this monitoring program, the preliminary study was carried out on metal accumulation in selected seven species of crabs (Macrophthalmus depressus, Austruca iranica, A. sindensis, Iloplax frater, Opusia indica, Eurycarcinus orientalis, and Scopimera crabricauda) to identify the most appropriate biomonitor species of crabs. The sediments metal contamination were also estimated as discussed in detailed in chapter 02 for decade comparison.
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According to BMY-I, two medium sized crab species (M. depressus and A. iranica) revealed as abundant and widely distributed, which accumulated high amount of heavy metals in their body, therefore they can be a good indicator of metal contamination. To illuminate the prestige of these crab species, the second monitoring campaign (BMY-II) was carried out in March to May 2016 from three sites (Korangi Creek, Sandspit and Sonari). Both crab species were collected randomly by excavating their burrows during the low tide period. Two to three sediment cores (20 cm long) were also collected from the habitat of each species. After the sampling, the crabs and sediments stored in the icebox and brought to the laboratory for further analysis.
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Table 3.1a: The general information of sampling sites along the coastal areas of Pakistan in first biomonitoring year (BMY-I = 2011 to 2012).
S. Location Sites Latitude ‘N’ Longitude ‘E’ Sampling Habitat No Name codes Date Type
1 Port Qasim PQ1 24° 46’ N 67° 18’ E 22/11/2011 Mangrove 2 RatoKot RK 24° 46’ N 67° 14’ E 22/11/2011 Mudflat 3 Korangi Creek KC1 24° 17’ N 67° 10’ E 20/10/2011 Mangrove 4 Korangi Creek KC2 24° 47’ N 67° 10’ E 22/12/2011 Mangrove 5 Sandspit SP1 24° 49’ N 66° 56’ E 22/09/2011 Mangrove 6 Sandspit SP2 24° 52’ N 67° 10’ E 22/09/2011 Mangrove 7 Hawks Bay HB 24° 50’ N 66° 54’ E 19/01/2012 Mangrove 8 Sonari SO 27° 54’ N 66° 80’ E 22/10/2011 Mudflat 9 Sonmiani Bay SB2 24° 26’ N 66° 34’ E 19/11/2011 Mangrove
Table 3.1b: The general information of sampling sites along the coastal areas of Pakistan in second biomonitoring year (BMY-II = 2011 to 2012).
S. Location Latitude ‘N’ Longitude ‘E’ Sampling Habitat No Name Date Type
1 Korangi Creek 24° 47’N 67° 10’E 22/12/2011 Mangrove 2 Sandspit 24° 52’N 67° 10’E 22/09/2011 Mangrove 3 Sonari 27° 54’N 66° 80’E 22/10/2011 Mudflat
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3.2.4. Laboratory Analysis (I) Sediment Analysis
Physical and chemical properties (water contents, percent organic matter, grain size, and metals concentrations) of all sediment samples were analyzed through similar methodology describe in Chapter 02.
(II) Crab Analysis
During the BMY-I, all crab individuals within all quadrat from each site were sorted, identified and pooled according to species (Table 3.2). The seven crab species were selected for heavy metals analysis due to their abundance and distribution. The selected crab species initially oven dried at constant weight (70 °C), then grounded and powdered. Heavy metals in the whole crab body were determined by following the method of Leung and Furness (1999). Take 1.0 g dry sample and mixed with 5 ml of concentrated hydrochloric acid (HCl) and 2 ml of nitric acid (HNO3). The mixture was heated and boiled off to near dryness, given a thick yellow fluid, then added 10 ml of hydrogen peroxide (H2O2) to complete digestion of crab carapace. The mixture filtered then diluted to 50 ml using distilled water and resulting solution was analyzed for selected heavy metals concentrations in crabs.
In the BMY-II, the crab individuals of both species (M. depressus and A. iranica) classified into three size classes (small, medium and large) according to the different size range present in the population and sample analyzed according to the sites, species, gender and size variability. The muscles from the crabs (3 to 5 crab individual pooled) of similar size and sex were removed and the tissue samples were oven dried at constant weight (70 °C), then grounded and homogenized. The similar acid digestion method (nitric acid and perchloric) was used for heavy metals determination in crab muscles as well as in sediments. In detail, the 100 mg of dry crab tissues and sediment samples mixed with 10 ml acid mixture and heated at 70 °C for 2 to 3 hours. After cooling at room temperature samples were filtered then diluted up to 20 ml using 0.1 N nitric acid (Gupta et al., 2014). The both samples (crabs and sediments) were analyzed for selected heavy metals (Cu, Zn, Co, Pb and Cd) through Atomic Absorption Spectrometer, Perkin Elmer (USA) model A Analyst 700).
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Table 3.2: The selection of sample type from crab species for heavy metal analysis in two biomonitoring years (BMY-I and II).
S. No. Crab species BMY-I (2011) BMY-II (2016) Sample type Sample for Sample type Sample for acid digestion acid digestion 1 Eurycarcinus Pooled according whole body - - to site with shell orientalis 2 Scopimera Pooled according whole body - - to site with shell crabricauda 3 Austruca Pooled according whole body - - to site with shell sindensis 4 Opusia indica Pooled according whole body - - to site with shell 5 Iloplax frater Pooled according whole body - - to site with shell 6 Austuca iranica Pooled according whole body Pooled according Only muscular to site and gender with shell to site, gender and tissues size 7 Macrophthalmus Pooled according whole body Pooled according Only muscular to site and gender with shell to site, gender and tissues depressus size
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3.2.5. Data Quality and Precision
All samples (crabs and sediments) were analyzed for the eight heavy metals (Cu, Cd, Cr, Co, Ni, Zn, Pb and Fe) by using Atomic Absorption Spectrometer (Perkin Elmer (USA), model A Analyst 700). The analytical methodologies assured through the use of calibration curve with standards, blank and replicates samples. For each selected heavy metal, three standards (2, 4 and 6 ppm) were prepared from certified stock solution of 1000 mg/L by adding their corresponding salts in deionized water. All chemicals used in the study were of analytical grade. Each analysis carried out in triplicates and the results were expressed in µg g-1 of the dry weight of sediment and crab samples. In the present study, all heavy metals analyzed by AAS were achieved good precision (1 to 5% RSD), except Co which precision (moderate) ranged from 10 to 25% RSD.
3.2.6. Bioaccumulation Factor (BAF)
Bioaccumulation Factor (BAF) was used to investigate the bioavailability of different heavy metals in sediments to the organism (crabs) following Eca et al. (2013). It was calculated according to the equation:
AF = CB / CS
Where, CB is the concentration of heavy metal in the biota, CS is the concentration of element in the sediments.
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3.2.7. Statistical Analysis
The following statistical tests applied for the first biomonitoring year to better interpretation of interaction between the different variables in metal accumulation in crabs:
1. Variations in heavy metal concentration in sediments and crabs among the monitoring sites were evaluated by one-way analysis of variance (ANOVA), comparison procedure assuming equal variance further performed through the Tukey’s test. 2. Pearson’s correlation coefficient (r) was executed to evaluate the associations between different pairs of heavy metals in each crab species. 3. Linear regression analysis (R2) implemented to correlate the all measured environmental parameters with metal accumulation in crabs, according to selection of following variables: a. Crab density vs. heavy metals concentrations in sediments. b. Physical properties of sediment (water contents, percent organic matter and grain size) vs. heavy metals accumulation in crabs. c. Heavy metals in sediments vs. heavy metals accumulation in crabs. 4. All statistical analysis was performed using Minitab (version 17.0). In BMY-II, the following statistical tests applied to understand the biotic (sex and size) interaction in metals accumulation in crabs:
1. Variations in heavy metal concentration in sediments and crabs among the monitoring sites were evaluated by Generalized Linear Model (GLM) analysis of variance (ANOVA). The model (Site, Gender, Site*Gender) highlights the variations in heavy metal accumulation according to the site and gender in tissues of each species. 2. Linear regression analysis (R2) was applied to identify the relationship between the heavy metal accumulation in tissues and carapace size of crab species. 3. Linear regression analysis (R2) was used to recognize the relationship between the heavy metal accumulation in tissues and the heavy metals concentrations in sediments.
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3.3. RESULTS
(A) FIRST BIOMONITORING YEAR (BMY-I)
Role of Some Brachyurans Crabs in Bio-Assessment of Heavy Metals
In the first biomonitoring year, the role of different crab species was analyzed by mean of their biotic properties (distribution, abundance and species composition), accumulation properties (metals concentrations in crabs and bioaccumulation factor) and their interaction with abiotic properties (environmental and physicochemical properties of sediments) along the coastal habitat of Pakistan.
3.3.1. Crab Distribution and Species Composition
The percent composition of crab species, density, percent frequency and relative density of crabs collected from coastal areas of Pakistan presented in Table 3.3 and 3.4. Total 15 crab species belongs to family Ocypodidea were collected during the monitoring period from nine coastal areas. Macrophthalmus depressus and Austruca iranica were found most abundant species along the coastal belt of Pakistan, recorded from seven sites out of nine. Opusia indica was found second most abundant crab species collected from six sites. Ilyoplax frater and Metaplax indica were found third most abundant species collected from 5 sites along the coast. Eurycarcinus orientalis and scopimera crabricauda were found from two coastal areas of Pakistan. Dotilla blanfordi, Macrophthalmus sulcatus, M. grandidieri, M. pectinipes, Parasesarma plicatum and Manningis arabicum were found from only one site (Table 3.3 and 3.4).
The overall crab density varied from 30.7–151 m2 along the coastal areas of Pakistan (Table 3.5). The mean crab density found in following order PQ (151.0 ± 168 m2) > SO (148.0 ± 50.7 m2) > KC2 (119.3 ± 62.8 m2) > KC1 (95.1 ± 79.5 m2) > SP1 (80.0 ± 41.3 m2) > SB (73.2 ± 72.3 m2) > SP2 (30.67 ± 5.77 m2) > RK (66.0 ± 2.83 m2). The density of four most abundant species was recorded as 27.85, 20.93, 18.09 and 17.55 m2 for A. iranica, M. depressus, O. indica and I. frater, respectively (Figure 3.1).
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The crab diversity ranged from 1.17–0.27 along the coastal areas of Pakistan (Table 3.5). The mean crab diversity found in following order KC2 (1.17 ± 0.38) > SB (1.16 ± 0.83) > PQ (1.05 ± 0.45) > RK (0.89 ± 0.70) > SO (0.79 ± 0.51) > KC1 (0.77 ± 0.54) > SP2 (0.49 ± 0.28) > SP1 (0.27 ± 0.34). The equitability varied from 0.71–0.27 along the coastal areas of Pakistan (Table 3.5), the highest values (0.71 ± 0.03) observed at PQ area and lowest values (0.27 ± 0.34) detected at SP1. The species richness varied from 0.99–0.17 and the highest values (0.99 ± 0.57) observed at SB, whereas the lowest values (0.17 ± 0.15) detected at SP1 (Table 3.5).
3.3.2. Morphometric Analysis of Crabs
A total of 1,297 crabs were collected, of which A. iranica (N = 433), M. depressus (N = 328), I. frater (N = 188) and O. indica (N = 176) were observed as most abundant species (Table 3.6). The carapace length (CL mm) of the most four abundant crab species, A. iranica, M. depressus, I. frater and O. indica, ranged from 3.0 to 12.5 mm, 2.0 to 8.5 mm, 2.0 to 9.0 mm and 3.0 to 10.0 mm, respectively (Table 3.6). The carapace width (CW mm) of the most four abundant crab species, A. iranica, M. depressus, I. frater and O. indica, ranged from 4.0 to 18.0 mm, 3.0 to 25.5 mm, 3.0 to 12.0 mm and 3.5 to 11.0 mm, respectively (Table 3.6).
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Table 3.3: Percent composition of crab species along the coastal areas (Rato Kot = RK, Korangi Creek St.1 = KC1 and St.2 = KC2, Port Qasim = PQ, Sandspit St.1 = SP1 and St.2 = SP2, Sonari = SO and Sonmiani Bay = SB) during the BMY-I.
Crab species RK KC1 KC2 PQ SP1 SP2 SO SB D. blanfordi ------2.6 E. orientalis - - - 1.3 - - 0.7 - Ily. palodicola ------I. frater 74.2 3.5 43.4 25.8 - - - 5.1 I. stevensi ------M. sulcatus ------1.3 M. depressus 3.0 90.9 41.8 - 9.2 10.9 0.3 0.9 M. grandidieri ------9.8 M. pectinipes 3.0 - - - - - 0.9 Metaplax indica - 1.6 0.5 1.3 - - 0.7 0.4 N. dotiliformes ------O. indica 1.5 2.8 8.8 71.5 - - 14.6 0.4 P. pelicatum - - 0.5 - - - - - S. crabricauda ------3.4 45.3 A. iranica 18.2 1.2 4.9 - 90.8 89.1 80.3 9.4 A. sindensis ------M. arabicum ------23.1
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Table 3.4: The biotic parameters (density, percent frequency, abundance and relative density) of crabs estimated from selected coastal areas of Pakistan during the BMY-I.
Sites Species Density %Frequency Abundance Relative Density Port Qasim (PQ) Eurycarcinus orientalis 2.0 25 2.0 1.3 Ilyoplax frater 39.0 100 9.7 25.8 Metaplax indica 2.0 50 1.0 1.3 Opusia indica 108.0 75 36.0 71.5 Rato Kot (RK) Ilyoplax frater 49.0 100 12.2 74.2 Macrophthalmus 2.0 25 2.0 3.0 depressus M. pectinipes 2.0 50 1.0 3.0 Opusia indica 1.0 25 1.0 1.5 Autruca iranica 12.0 50 6.0 18.2 Korangi Creek St.1 (KC1) Ilyoplax frater 4.0 44 2.2 3.5 Macrophthalmus 102.7 100 25.7 90.9 depressus Metaplax indica 1.8 33 1.3 1.6 Opusia indica 3.1 22 3.5 2.7 Austruca iranica 1.3 22 1.5 1.2 Korangi Creek St.2 (KC2) Ilyoplax frater 52.7 100 13.17 43.4 Macrophthalmus 50.7 100 12.7 41.7 depressus Metaplax indica 0.7 16.7 1.0 0.5 Opusia indica 10.7 50 5.333 8.8 Austruca iranica 6.0 33 4.5 4.9 Parasesarma plicatum 0.7 16.7 1.0 0.5 Sandspit St.1 (SP1) Macrophthalmus 7.3 33 5.5 9.2
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depressus Austruca iranica 72.7 100 18.17 90.8 Sandspit St.2 (SP2) Macropthalmus 3.3 50 1.67 10.9 depressus Austruca iranica 27.3 83 8.2 89.1 Sonari (SO) Eurycarcinus orientalis 0.8 20 1.0 0.7 Macrophthalmus 0.4 10 1.0 0.3 depressus Metaplax indica 0.8 20 1.0 0.7 Opusia indica 17.2 30 14.3 14.7 Scopimera crabricauda 4.0 20 5.0 3.4 Austruca iranica 94.8 80 29.6 80.3 Sonmiani Bay (SB) Dotilla blanfordi 2.4 10 6.0 2.6 Grapsid sp 0.8 10 2.0 0.8 Ilyoplax frater 4.8 30 4.0 5.1 Macrophthalmus 0.8 20 1.0 0.8 depressus M. dilatatus sulcatus 1.2 10 3.0 1.3 M. grandidieri 9.2 40 5.7 9.8 M. pectinipes 0.8 10 2.0 0.8 Metaplax indica 0.4 10 1.0 0.4 Opusia indica 0.4 40 0.2 0.4 Scopimera crabricauda 42.4 30 35.3 45.3 Austruca iranica 8.8 40 5.5 9.4 Manningis arabicum 21.6 30 18.0 23.1
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Table 3.5: The summary statistics of crab density (individual/m2), diversity, equitability and species richness from eight coastal areas (Port Qasim = PQ, Rato Kot = RK, Korangi Creek St.1 = KC1 and St.2 = KC2, Sandspit St.1 = SP1 and St.2 = SP2, Sonari = SO and Sonmiani Bay = SB) during the BMY-I.
Sites Density Diversity Equitability Species Richness
PQ 151.0 ± 168 1.05 ± 0.45 0.71 ± 0.03 0.64 ± 0.62 RK 66.0 ± 2.83 0.89 ± 0.70 0.47 ± 0.31 0.71 ± 0.19 KC1 95.1 ± 79.5 0.77 ± 0.54 0.43 ± 0.25 0.60 ± 0.23 KC2 119.3 ± 62.8 1.17 ± 0.38 0.63 ± 0.09 0.65 ± 0.19 SP1 80.0 ± 41.3 0.27 ± 0.34 0.27 ± 0.34 0.17 ± 0.15 SP2 30.67 ± 5.77 0.49 ± 0.28 0.49 ± 0.28 0.37 ± 0.03 SO 148.0 ± 50.7 0.79 ± 0.51 0.45 ± 0.29 0.53 ± 0.21 SB 73.2 ± 72.3 1.16 ± 0.83 0.64 ± 0.29 0.99 ± 0.57
27.85 30
) 25 20.93 2 18.09 17.55 20 15
10 5.80
Density (No/m 2.70 5 0.30 0.35 0.10 0.15 1.15 0.35 0.70 0.08 0
Crab Species
Figure 3.1: Mean density (individual/m2) of different crab species collected from coastal areas of Pakistan during the BMY-I.
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Table 3.6: Morphometric analysis of crab species collected during the BMY-I from nine coastal areas of Pakistan.
Carapace length (CL mm) Carapace width (CW mm) Crab species N Mean ± SD Min–Max Mean ± SD Min–Max Dotilla blanfordi 6 6.17 ± 0.41 6.00–7.00 7.17 ± 0.41 7.00–8.00 Eurycarcinus orientalis 4 12.33 ± 2.02 10.50–14.5 16.33 ±2.75 13.50–19.00 Ilyoplax frater 188 5.11 ± 1.17 2.00–9.00 6.70 ± 1.45 3.00–12.00 Macrophthalmus depressus 328 7.17 ± 0.76 2.00–8.50 13.33 ± 3.21 3.00–25.50 M. dilatatus sulcatus 3 7.56 ± 5.01 6.50–8.00 10.12 ± 3.87 11.00–17.00 M. grandidierii 23 6.52 ± 1.00 4.00–8.00 11.78 ± 2.64 4.50–15.00 M. pectinipes 4 9.87 ± 1.55 8.50–12.00 14.25 ± 2.06 12.00–16.00 Metaplax indica 10 7.15 ± 2.71 4.00–11.50 9.50 ± 4.01 5.00–16.00 Opusia indica 176 5.56 ± 1.70 3.00–10.00 6.45 ± 2.16 3.50–11.00 Parasesarma pelicatum 1 7.00 ± 0 7.00–7.00 8.00 ± 00 8.00–8.00 Scopimera crabricauda 116 5.00 ± 0.82 3.50–7.00 6.30 ± 1.14 4.50–9.00 Austruca iranica 433 7.12 ± 4.68 3.00–12.50 9.71 ± 3.64 4.00–18.00 Manningis arabicum 3 9.83 ± 1.04 9.00–11.00 9.50 ± 1.50 8.00–11.00
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3.3.3. Heavy Metal Concentrations in Sediments
The summary statistics of eight heavy metal concentrations (µg g-1 dry weight) in coastal sediments along the nine coastal areas of Pakistan listed in Table 3.7. The Fe concentration varied from 782.1 to 1028.2 µg g-1 in the marine sediments from different coastal areas (Table 3.7). The highest Fe concentration observed in the sediments of KC2, whereas the lowest levels evaluated in the sediments of SP2 (Figure 3.2a). The Cu concentration ranged as 13.4–672.4 µg g-1 the coastal sediments (Table 3.7) and was assessed highest in the sediments of SP2 backwater water mangrove area and lowermost in the sediments of SO mudflat area (Figure 3.2b).
Zn concentration in marine sediments extended from 26.7 to 361.1 µg g-1, which collected from different monitoring sites (Table 3.7). The spatial distribution of Zn represented the highest values in the sediments of SP1 and lowest values detected in the sediments of SB (Figure 3.2c). The distribution of Ni in the marine sediments fluctuated from 13.45 to 60.50 µg g-1 along the different study sites (Table 3.7). The spatial distribution of Ni presented the highest concentrations in the sediments of HB mangrove area and lowest levels assessed in the mangrove sediments of KC2 (Figure 3.2d).
The distribution of Co extended from 0.141 to 18.378 µg g-1 in the marine sediments (Table 3.7) and it was noticed highest levels in the sediments of SP2, however the lowest concentrations found for the sediments of KC2 (Figure 3.2e). The Cr concentration varied as 41.4–303.5 µg g-1 in the marine sediments of different monitoring sites along the coast of Pakistan (Table 3.7). The Cr distribution in the sediments demonstrated that it was detected highest concentration at SP2, whereas the lowest concentration was evaluated at SB (Figure 3.2f).
The Pb concentration ranged from 24.68 to 177.81 µg g-1 in the coastal sediments (Table 3.7). The spatial distribution of Pb characterized the highest levels in the sediments of KC2, conversely the lowest values assessed in the sediments of SO (Figure 3.2g). The concentration of Cd in the marine sediments extended from 0.135 to 1.458 µg g-1, which collected from different coastal areas (Table 3.7). The highest concentration of Cd apparent in the sediments of HB, whereas the lowest levels of Cd found in the sediments of SP2 (Figure 3.2h). The significant differences (p <0.05) were evaluated in all heavy metal concentrations among the different monitoring areas along the coast of Pakistan (Table 3.8).
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Table 3.7: The summary statistics of eight heavy metal concentrations (µg g-1 dry weight) in coastal sediments collected during the BMY-I from nine coastal areas of Pakistan.
Metals Total Mean S.D. Median Min Max Fe 35 979.8 60.0 1000.9 782.1 1028.2 Cu 35 95.6 156.9 32.2 13.4 672.4 Zn 35 102.6 90.8 71.5 26.7 361.1 Ni 35 43.04 9.96 45.52 13.45 60.50 Co 35 7.533 4.519 6.927 0.141 18.378 Cr 35 110.4 62.5 107.2 41.4 303.5 Pb 35 58.43 48.10 38.15 24.68 177.81 Cd 35 1.043 0.366 1.134 0.135 1.458
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Figure 3.2: Spatial distribution of eight heavy metal concentrations (µg g-1) from nine coastal areas (Port Qasim = PQ, Rato Kot = RK, Korangi Creek St.1 = KC1 and St.2 = KC2, Sandspit St.1 = SP1 and St.2 = SP2, Hawks Bay = HB, Sonari = SO and Sonmiani Bay = SB) during the BMY-I.
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Table 3.8: One-way ANOVA analysis of heavy metal concentrations in sediments from nine coastal areas during the BMY-I.
Variables Source DF Adj. SS Adj. MS F-Value P-Value Fe Sites 8 93963 11745 10.78 0.000*** Error 26 28329 1090 Total 34 122293 Cu Sites 8 700901 87613 15.96 0.000*** Error 26 142710 5489 Total 34 843611 Zn Sites 8 258418 32302.3 38.24 0.000*** Error 26 21964 844.8 Total 34 280382 Ni Sites 8 2434.8 304.35 8.45 0.000*** Error 26 936.3 36.01 Total 34 3371.1 Co Sites 8 323.4 40.43 2.83 0.021* Error 26 371.0 14.27 Total 34 694.4 Cr Sites 8 92321 11540 7.39 0.000*** Error 26 40604 1562 Total 34 132925 Pb Sites 8 66991 8373.9 18.63 0.000*** Error 26 11687 449.5 Total 34 78678 Cd Sites 8 4.3502 0.543774 69.84 0.000*** Error 26 0.2024 0.007786 Total 34 4.5526 ‘***’ significant at <0.001 ‘**’ significant at <0.01 ‘*’ significant at <0.05
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3.3.4. Heavy Metals Accumulation in Selected Crab Species The heavy metals accumulation was evaluated in seven crab species (Eurycarcinus orientalis, Scopimera crabricauda and Austruca sindensis (one site only), Opusia indica (two sites), Iloplax frater (three sites), Austruca iranica (five sites) and Macrophthalmus depressus (six sites) consistent with their abundance and distribution at particular sites along the coast of Pakistan.
(I) Heavy Metal Concentrations in Eurycarcinus orientalis
The mean heavy metal concentrations (µg g-1 dry weight) and bioaccumulation factor of E. orientalis shown in Figure 3.3a. Heavy metal concentrations pattern in E. orientalis was observed as Fe > Zn > Cu > Pb > Co > Ni > Cr > Cd. The variable BAFs values of Co, Ni, Zn, Pb, Fe, Cu, Cr, and Cd detected as 21.2, 1.4, 3.3, 2.6, 0.7, 6.7, 0.2 and 4.2, respectively. The highest BAF was observed for Co in this crab.
(II) Heavy Metal Concentrations in Scopimera crabricauda
The mean heavy metal concentrations (µg g-1 dry weight) and bioaccumulation factor of S. crabricauda evaluated from Sonmiani Bay, Balochistan (Figure 3.3b). Heavy metal concentrations pattern in the S. crabricauda was observed as Fe > Pb > Cu > Zn > Ni > Cr > Co > Cd. The BAFs values of Co, Ni, Zn, Pb, Fe, Cu, Cr, and Cd noticed as 1.0, 0.6, 1.6, 5.3, 0.2, 4.5, 0.1 and 2.5, respectively. The lowest BAF values were evaluated for Cr and highest for Pb.
(III) Heavy Metal Concentrations in Austruca sindensis
Heavy metal concentrations pattern in the A. sindensis was observed as Pb > Cu > Zn > Fe > Ni > Cr > Co > Cd. The extreme accumulation condition was observed for toxic metals in the A. sindensis as the highest accumulation was found for Pb, whereas the lowest accumulation detected for Cd (Figure 3.3c). The mean BAFs values for Co, Ni, Zn, Pb, Fe, Cu, Cr, and Cd observed as 4.42, 4.03, 4.12, 17.31, 0.22, 12.59, 0.45, and 8.30, respectively.
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(a) E. orientalis 350 25
MC BAF Bioaccumulation Factor (BAF) Factor Bioaccumulation 300 20 250 200 15
150 10 100 5 50
Metal concentrations (µg/g) 0 0 Fe Cu Zn Co Cr Ni Pb Cd
(b) S. crabricauda 250 6
MC BAF (BAF) Factor Bioaccumulation
200 5 4 150 3 100 2
50 1 Metal concentrations (µg/g) 0 0 Fe Cu Zn Co Cr Ni Pb Cd
(c) U. sindensis 100 20 MC BAF (BAF) Factor Bioaccumulation
80 15
60 10 40
5
20 Metal concentrations (µg/g) 0 0 Fe Cu Zn Co Cr Ni Pb Cd
Figure 3.3: Metal concentrations (µg g-1) and bioaccumulation factor (BAF) of heavy metals in three crab species (a) E. orientalis, (b) S. crabricauda, (c) A. sindensis collected from different coastal areas of Pakistan.
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(IV) Heavy Metal Concentrations in Opusia indica
The mean heavy metal concentrations (µg g-1 dry weight) and their bioaccumulation factor in O. indica presented in Figure 3.4. This species collected during October and November 2011 from two coastal areas (SO = Sonari and PQ = Port Qasim) of Pakistan. The mean heavy metal concentration pattern in the dotillid crab (O. indica) was detected as Fe > Zn > Cu > Pb > Co > Ni > Cr > Cd.
The concentrations of Fe, Cu, Cr, Ni and Pb ranged from 324.3–494.1, 40.9–98.0, 7.40– 10.94, 16.12–23.59 and 24.62–26.59 µg g-1, respectively (Figure 3.4a), furthermore the above- mentioned metals perceived significantly higher in the crabs of PQ mangrove area (Table 3.9). The concentrations of Zn and Cd ranged from 73.94–82.56 and 1.86–2.06 µg g-1, respectively (Figure 3.4a) and both metals levels noticed significant higher levels in the crabs of SO mudflat area as compared to PQ (Table 3.9). However, the Co concentrations in crabs ranged from 18.7–26.9 µg g-1 (Figure 3.4a) and it showed no significant differences between the two sites (Table 3.9).
The mean BAFs values for Co, Ni, Zn, Pb, Fe, Cu, Cr, and Cd were detected as 9.8, 0.9, 2.8, 1.6, 0.8, 6.0, 0.2, and 3.0, respectively. BAFs of all metals were noted greater in the crabs of PQ area as compared to the crabs of SO area (Figure 3.4b). Pearson’s correlation coefficient indicated that most of the metals belong to the similar source of accumulation for the crabs in both marine environments as the most of the metals showed strong inter-correlations relationship between heavy metals concentrations in crab (Table 3.10).
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500 (a)
400
300
200
100
Heavy Heavy metal concentrations (µg/g) 0 Fe Cu Zn Co Cr Ni Pb Cd So 329.96 41.10 82.03 20.89 7.58 17.04 24.86 2.02 PQ 489.62 97.11 74.11 23.30 10.82 22.77 26.27 1.87
18 (b) 16 14 12 10 8 6
Bioaccumulation Bioaccumulation Factor 4 2 0 Fe Cu Zn Co Cr Ni Pb Cd SO 0.38 3.12 2.42 3.24 0.18 0.44 1.09 2.04 PQ 1.26 8.80 3.23 16.38 0.31 1.27 2.14 3.96
Figure 3.4: Heavy metal distribution in O. indica collected from two coastal areas (SO = Sonari and PQ = Port Qasim) (a) Mean heavy metal concentrations (µg/g) and (b) Bioaccumulation factor of heavy metals.
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Table 3.9: One-way ANOVA analysis followed by Tukey’s pairwise comparison of eight heavy metal concentrations in O. indica collected from two coastal areas (So = Sonari and PQ = Port Qasim) during the BMY-I.
Metals Source DF Adj. SS Adj. MS F-Value P-Value Tukey Pair wise Comparisons Fe Site 1 38238.5 38238.5 1306.92 0.000*** PQ > SO Error 4 117.0 29.3 Total 5 38355.6 Cu Site 1 4706.28 4706.28 8828.22 0.000*** PQ > SO Error 4 2.13 0.53 Total 5 4708.41 Zn Site 1 94.1886 94.1886 776.29 0.000*** SO > PQ Error 4 0.4853 0.1213 Total 5 94.6740 Co Site 1 8.697 8.697 1.24 0.327 PQ ≈ SO Error 4 27.985 6.996 Total 5 36.682 Cr Site 1 15.7311 15.7311 766.17 0.000*** PQ > SO Error 4 0.0821 0.0205 Total 5 15.8133 Ni Site 1 49.280 49.2796 52.18 0.002** PQ > SO Error 4 3.778 0.9444 Total 5 53.057 Pb Site 1 2.9710 2.97100 39.23 0.003** PQ > SO Error 4 0.3029 0.07574 Total 5 3.2739 Cd Site 1 0.033739 0.033739 33.99 0.004** SO > PQ Error 4 0.003970 0.000993 Total 5 0.037709 ‘***’ significant at <0.001 ‘**’ significant at <0.01 ‘*’ significant at <0.05
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Table 3.10: Pearson’s correlation coefficient (r) between heavy metal concentrations in O. indica collected during BMY-I.
Metals Fe Cu Zn Ni Cr Co Pb Cd Fe 1.000 Cu 0.999*** 1.000 Zn -0.998*** -0.997*** 1.000 Ni 0.956** 0.964** -0.958** 1.000 Cr 0.997*** 0.996*** -1.000*** 0.963** 1.000 Co 0.526 ns 0.497 ns -0.519 ns 0.498 ns 0.511 ns 1.000 Pb 0.946** 0.949** -0.952** 0.956** 0.957** 0.448 ns 1.000 Cd -0.961** -0.947** 0.959** -0.854* -0.952** -0.642 ns -0.875* 1.000 ‘***’ significant at <0.001 ‘**’ significant at <0.01 ‘*’ significant at <0.05 ‘ns’ not significant at >0.05
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(V) Heavy Metal Concentrations in Ilyoplax frater
The heavy metal concentrations (µg g-1 dry weight) in I. frater was investigated from three coastal areas of Karachi (PQ = Port Qasim, RK = Rato Kot, and KC2 = Korangi Creek Station 2) during BMY-I (Figure 3.5). The variable concentration of studied metals was observed in response of their habitat contamination status. Mean concentration pattern of the eight heavy metals in the I. frater, observed as Fe > Cu > Zn > Pb > Ni > Co > Cr > Cd.
The concentrations of Fe, Cu, Zn, Cr and Cd ranged from 279.2–437.2, 94.24–157.42, 75.44–128.33, 9.35–12.30 and 1.41–3.04 µg g-1, respectively (Figure 3.5a). The above-mentioned metals were detected significantly greater in the population of crab resident in the mudflat of RK as compared to other two sites (Table 3.11). The Ni and Pb concentrations ranged from 16.67–34.68 and 20.2–171.2 µg g-1, respectively (Figure 3.5a) and both metals were evaluated significantly higher in the resident crabs of KC2 mangrove area as compared to the other two sites (Table 3.11). The Co levels ranged from 6.67–23.99 µg g-1 in the crabs (Figure 3.5a) and it was observed significantly lower concentration in the crabs of KC2, however similar concentration of Co was found in the crab populations collected from PQ and RK (Table 3.11).
The accumulation factors (AFs) of heavy metals are shown in Figure 3.5b. The mean AFs values for Co, Ni, Zn, Pb, Fe, Cu, Cr and Cd were observed 5.37, 1.66, 2.40, 1.49, 0.64, 6.32, 0.20 and 5.47, respectively. Most of the highest AFs values were evaluated at RK for Co, Zn, Pb, Fe, Cu, and Cr, whereas, highest AF of Ni and Cd were observed at KC2 (Figure 3.5b). The relationship between heavy metals concentrations in crab was evaluated by Pearson’s correlation analysis (Table 3.12). The strongest correlations were assessed between the following pairs: Fe vs Co (0.849) and Pb (-0.964), Cu vs Zn (0.968), Cr (0.992) and Cd (0.955), Zn vs Cr (0.944) and Cd (0.852), Cr vs Cd (0.965), Co vs Pb (-0.859).
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500 (a)
400
300
200
100 Metal concentrations (µg/g) concentrations Metal 0 Fe Cu Zn Co Cr Ni Pb Cd PQ 402.75 94.57 76.09 17.72 9.56 17.41 20.36 1.41 RK 435.80 155.97 127.47 18.83 12.21 28.82 29.53 2.94 KC2 282.92 115.65 81.15 7.08 10.62 34.57 168.25 2.33
10 (b) 9 8 7 6 5 4 3
Bioaccumulation Bioaccumulation Factor 2 1 0 Fe Cu Zn Co Cr Ni Pb Cd PQ 0.55 4.55 1.76 5.86 0.14 0.51 0.87 1.60 RK 0.96 9.22 4.30 8.38 0.33 1.33 1.99 5.75 KC2 0.41 5.19 1.15 1.87 0.11 3.13 1.60 9.06
Figure 3.5: Heavy metal distribution in I. frater collected from three coastal areas (PQ = Port Qasim, RK = Rato Kot, and KC2 = Korangi Creek Station 2) during the BMY-I (a) Mean metal concentrations (µg/g) and (b) Bioaccumulation factor.
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Table 3.11: One-way ANOVA analysis followed by Tukey’s pairwise comparison of heavy metal concentrations in I. frater collected from three coastal areas (PQ = Port Qasim, RK = Rato Kot, and KC2 = Korangi Creek Station 2) during the BMY-I.
Metals Source D Adj. SS Adj. MS F-Value P-Value Tukey Pair wise F Comparisons Fe Site 2 38823.6 19411.8 1459.87 0.000*** RK > PQ > KC Error 6 79.8 13.3 Total 8 38903.3 Cu Site 2 5840.19 2920.10 3875.57 0.000*** RK > KC > PQ Error 6 4.52 0.75 Total 8 5844.71 Zn Site 2 4810.67 2405.33 6753.01 0.000*** RK > KC > PQ Error 6 2.14 0.36 Total 8 4812.80 Co Site 2 252.49 126.24 9.21 0.015* RK ≈ PQ > KC Error 6 82.22 13.70 Total 8 334.70 Cr Site 2 10.6585 5.32926 219.38 0.000*** RK > KC > PQ Error 6 0.1458 0.02429 Total 8 10.8043 Ni Site 2 457.985 228.993 607.60 0.000*** KC > RK > PQ Error 6 2.261 0.377 Total 8 460.246 Pb Site 2 41196.5 20598.2 6795.31 0.000*** KC > RK > PQ Error 6 18.2 3.0 Total 8 41214.7 Cd Site 2 3.54419 1.77210 593.87 0.000*** RK > KC > PQ Error 6 0.01790 0.00298 Total 8 3.56210 ‘***’ significant at <0.001 ‘**’ significant at <0.01 ‘*’ significant at <0.05
170
Table 3.12: Pearson’s correlation coefficient (r) between heavy metal concentrations in I. frater collected during BMY-I.
Metals Fe Cu Zn Ni Cr Co Pb Cd Fe 1.000 Cu 0.376ns 1.000 Zn 0.597ns 0.968*** 1.000 Ni -0.604ns 0.508ns 0.274ns 1.000 Cr 0.313ns 0.992*** 0.944*** 0.563ns 1.000 Co 0.849** 0.224ns 0.426ns -0.616ns 0.134ns 1.000 Pb -0.964*** -0.123ns -0.370ns 0.790* -0.059ns -0.859** 1.000 Cd 0.093ns 0.955*** 0.852** 0.732* 0.965*** -0.016ns 0.167ns 1.000 ‘***’ significant at <0.001 ‘**’ significant at <0.01 ‘*’ significant at <0.05
171
(VI) Heavy Metal Concentrations in Austruca iranica
The individuals of A. iranica collected from the five coastal areas of Pakistan (RK = Rato Kot, SP1 = Sandspit 1, SP2 = Sandspit 2, SO = Sonari, and SB = Sonmiani Bay) during the BMY-I. Mean concentration pattern of the eight heavy metals in the A. iranica, revealed as Fe > Zn > Cu > Pb > Ni > Co > Cr > Cd. The mean metal concentrations (µg g-1 dry weight) in A. iranica showed spatial and intersexual variations and differences in response of the sediments contamination.
Fe ranged from 58.3–878.3 µg g-1 and the significant higher concentrations were observed in the crabs of SP2 and SO as compared to other sites (Figure 3.6a). The Cu, Zn, Cr, Ni and Cd ranged from 23.64–175.04, 37.25–110.56, 2.589–11.38, 1.29–44.97 and 0.462–6.55 µg g-1, respectively (Figure 3.6b, c, e, f and h). The concentrations of Cu, Zn, Cr, Ni and Cd were observed significantly greater in the crab population collected from SB (Table 3.13). The concentration of Co ranged from 1.54–24.40 µg g-1 (Figure 3.6d) and it was observed significantly higher in the crab population of RK. The Pb concentration ranged from 14 to 60 µg g-1 (Figure 3.6g) and it showed similar concentrations in crabs collected from different coastal areas (Table 3.13).
The relationship between heavy metals concentrations in crab was evaluated by Pearson’s correlation analysis (Table 3.14). Most of the metal concentrations showed significant inter- elemental correlations in crabs, Ni vs Cr (0.904), Ni vs Cd (0.893) and Cr vs Cd (0.911) observed as highly correlated pairs (Table 3.14).
The highest BAFs values for mostly metals (Co, Ni, Zn, Pb, and Cd) observed at RK, whereas the highest BAFs for Cu and Cr observed at SB and Fe at SP2 (Figure 3.7). The mean BAFs values for Co, Ni, Zn, Pb, Fe, Cu, Cr and Cd were observed 4.18, 0.71, 2.47, 0.95, 0.64, 5.23, 0.17 and 4.02, respectively (Figure 3.7). The gender effects on heavy metal accumulation in crab also evaluated (Figure 3.8). For this purpose, male and female individuals analyzed from three sites (SP1, SP2, and SO) because there was enough sample size were collected for this species with respect to gender. No significant differences were observed in all metal concentrations in both genders of A. iranica among the three stations (SP1, SP2, and SO).
172
800 200 (a) (b) 600 150
400 100
200 50
0 0 Sp1 Sp2 So RK SB Sp1 Sp2 So RK SB Fe 272 632 627 59 141 Cu 63 70 54 96 174 150 25 (c) (d) 20 100 15 10 50 5 0 0 Sp1 Sp2 So RK SB Sp1 Sp2 So RK SB Zn 63 88 92 102 107 Co 8 5 11 22 12 15 (e) 50 (f) 40 10 30 20 5 10 0 0 Sp1 Sp2 So RK SB Sp1 Sp2 So RK SB Cr 5 4 8 7 11 Ni 16 6 16 17 43 80 8.0 (g) (h) 60 6.0
40 4.0
20 2.0
0 0.0 Sp1 Sp2 So RK SB Sp1 Sp2 So RK SB Pb 60 14 22 20 21 Cd 1.2 0.9 2.5 2.0 6.5
Figure 3.6: Metal concentrations (µg g-1) in A. iranica from five coastal areas (SP1 = Sandspit 1, SP2 = Sandspit 2, SO = Sonari, RK = Rato Kot and SB = Sonmiani Bay) during the BMY-I.
173
Table 3.13: One-way ANOVA analysis followed by Tukey’s pairwise comparison of heavy metal concentrations in A. iranica from five coastal areas (RK = Rato Kot, SP1 = Sandspit 1, SP2 = Sandspit 2, SO = Sonari, and SB = Sonmiani Bay) during the BMY-I.
Metals Source DF Adj. SS Adj. MS F-Value P-Value Tukey Pair wise Comparisons Fe Site 4 1280134 320033 10.60 0.000*** SP2 ≈ SO > SP1 ≈ Error 19 573496 30184 SB ≈ RK Total 23 1853629 Cu Site 4 34047 8511.8 9.03 0.000*** SB > RK ≈ SP2 ≈ Error 19 17918 943.0 SP1 ≈ SO Total 23 51965 Zn Site 4 5293 1323.3 3.50 0.026* SB > RK ≈ SO ≈ Error 19 7178 377.8 SP2 > SP1 Total 23 12471 Co Site 4 644.5 161.118 17.26 0.000*** RK > SB ≈ SO > Error 19 177.4 9.335 SP1 > SP2 Total 23 821.8 Cr Site 4 137.39 34.347 17.60 0.000*** SB > SO > RK > Error 19 37.07 1.951 SP1 ≈ SP2 Total 23 174.46 Ni Site 4 2734 683.46 10.47 0.000*** SB > RK ≈ SO ≈ Error 19 1240 65.28 SP1 ≈ SP2 Total 23 3974 Pb Site 4 7915 1978.7 2.56 0.072 SP1 ≈ SO ≈ SB ≈ Error 19 14711 774.3 RK ≈ SP2 Total 23 22626 Cd Site 4 73.400 18.3500 49.24 0.000*** SB > SO > RK > Error 19 7.081 0.3727 SP1 ≈ SP2 Total 23 80.481 ‘***’ significant at <0.001 ‘**’ significant at <0.01 ‘*’ significant at <0.05
174
Table 3.14: Pearson’s correlation coefficient (r) between accumulated heavy metal concentrations in A. iranica collected during BMY-I.
Metals Fe Cu Zn Ni Cr Co Pb Cd Fe 1.000 Cu -0.370ns 1.000 Zn -0.175ns 0.676*** 1.000 Ni -0.587** 0.795*** 0.568** 1.000 Cr -0.318ns 0.742*** 0.674*** 0.904*** 1.000 Co -0.439* 0.460* 0.593* 0.437* 0.536** 1.000 Pb -0.414* 0.237ns 0.143ns 0.460* 0.237ns 0.161ns 1.000 Cd -0.435* 0.737*** 0.575** 0.893*** 0.911*** 0.337ns 0.052ns 1.000 ‘***’ significant at <0.001 ‘**’ significant at <0.01 ‘*’ significant at <0.05
14 RK Sp1 Sp2 So SB 12
10
8
6
4 Bioaccumulation Bioaccumulation Factor 2
0 Co Ni Zn Pb Fe Cu Cr Cd
Figure 3.7: Bioaccumulation factor of heavy metals in A. iranica collected from five coastal areas (RK = Rato Kot, SP1 = Sandspit 1, SP2 = Sandspit 2, SO = Sonari, and SB = Sonmiani Bay) during the BMY-I.
175
F M Fe Cu Zn 900 120 100
600 80 75
300 40 50
Ni Co Cr 30 8 10 6 15 5 4
0 0 Pb Cd F M 100 3
2 50 1 0 F M Gender
1 2 3 F M F M F M Fe Cu Zn 900 120 100 600 80 75
300 40 50
Ni Co Cr 30 8 10 6 15 5 4
0 0 Pb Cd F M F M F M 100 3 1 2 3
2 50 1 0 Gender F M F M F M Sites 1 2 3
Figure 3.8: (a) Intersexual variations in mean heavy metals accumulation in A. iranica (b) Intersexual and spatial variations in heavy metals accumulation in A. iranica from three coastal areas (SP1, SP2 and SO) during the BMY-I. (Genders: F = female, M = male; Sites abbreviation: 1: Sandspit St.1, 2: Sandspit St.2, 3: Sonari).
176
(VII) Heavy Metal Concentrations in Macrophthalmus depressus
The mean heavy metal concentrations (µg g-1 dry weight) in M. depressus shown in Figure 3.9. The crabs of these species were collected from the six coastal areas of Karachi (RK = Rato Kot, KC1 = Korangi Creek 1, KC2 = Korangi Creek 2, SP1 = Sandspit 1, SP2 = Sandspit 2, and HB = Hawks Bay) during the BMY-I. The collective bioaccumulation patterns of the eight heavy metals in the M. depressus found as Fe > Zn > Cu > Pb > Ni > Co > Cr > Cd along the Karachi coast.
Fe ranged from 82.8–1001.4 µg g-1 and the significant lower concentrations were observed at HB and KC2 as compared to other sites (Figure 3.9a). The range of Cu was 15.71–178.03 µg g-1 with the significant higher concentration at RK (Figure 3.9b). The Zn ranged from 42.60–155.18 µg g-1 and the significant higher concentration was observed at RK as compared to other sites (Figure 3.9c). Co ranged from 3.44–24.45 µg g-1 and the significantly higher concentration was observed at RK as compared to other sites (Figure 3.9d). The Cr ranged from 5.65–24.30 µg g-1 and the significantly higher concentrations were observed at KC1 and RK as compared to other sites (Figure 3.9e). The Ni was 0.11–41.78 µg g-1 in range and the higher concentration detected at RK (Figure 3.9f). The Pb ranged from 10.4–155.1 µg g-1 with the significant higher concentration observed at KC2 as compared to other sites (Figure 3.9g). The Cd varied from 0.39–3.36 µg g-1 with the higher concentration observed at RK as compared to other sites (Figure 3.9h). All heavy metals concentrations in crab were significantly different (p <0.001) among the six coastal areas (Table 3.15).
The relationship between heavy metals concentrations in crab evaluated by Pearson’s correlation analysis (Table 3.16). Mostly metals showed significant inter-elemental correlations in crabs, some highly correlated pair are Cu vs Zn (0.873), Cu vs Cd (0.821); Zn vs Co (0.811) and Ni vs Cd (0.827). BAFs ranged from 1.45–12.15, 0.05–5.17, 0.54–11.95, 0.32–19.99, 0.59–4.41, 0.16– 23.31, 0.10–0.75, 2.37–12.41 for Co, Ni, Zn, Pb, Fe, Cu, Cr and Cd respectively (Figure 3.10). Most of the highest AFs values were observed at HB for Ni, Zn, Pb, Cu, Cr, and Cd, whereas highest AFs for Co and Fe were observed at RK and SP1, respectively (Figure 3.10). The gender effects on heavy metal accumulation in crab were also evaluated (Figure 3.11). For this purpose, male and female individuals were analyzed for initial observation from three sites (KC1, KC2, and HB) because there was enough sample size were collected for this species with respect to gender. The results indicated that only Zn and Cu accumulations in M. depressus observed significantly different between the genders. Moreover, these metals found significantly greater in female as compare to male crabs, suggested the reproductive status and requirements of essential metals in female crabs.
177
1500 200 (a) (b) 150 1000 100 500 50
0 0 Sp1 Sp2 KC1 KC2 RK HB Sp1 Sp2 KC1 KC2 RK HB Fe 825 892 965 471 781 260 Cu 16 37 85 97 176 86 200 25 (c) (d) 150 20 15 100 10 50 5 0 0 Sp1 Sp2 KC1 KC2 RK HB Sp1 Sp2 KC1 KC2 RK HB Zn 43 91 82 81 155 88 Co 4 12 12 9 22 10 25 50 (e) (f) 20 40 15 30 10 20 5 10 0 0 Sp1 Sp2 KC1 KC2 RK HB Sp1 Sp2 KC1 KC2 RK HB Cr 6 11 22 13 19 12 Ni 0 17 31 32 41 31 200 4.0 (g) (h) 150 3.0
100 2.0
50 1.0
0 0.0 Sp1 Sp2 KC1 KC2 RK HB Sp1 Sp2 KC1 KC2 RK HB Pb 11 23 22 145 46 103 Cd 0.4 1.4 1.2 2.2 3.2 2.0
Figure 3.9: Metal concentrations (µg g-1) in M. depressus from six coastal areas (SP1 = Sandspit 1, SP2 = Sandspit 2, KC1 = Korangi Creek 1, KC2 = Korangi Creek 2, RK = Rato Kot and HB = Hawks Bay) collected during BMY-I.
178
Table 3.15: One-way ANOVA analysis followed by Tukey’s pairwise comparison of heavy metal concentrations in M. depressus from six coastal areas (RK = Rato Kot, KC1 = Korangi Creek 1, KC2 = Korangi Creek 2, SP1 = Sandspit 1, SP2 = Sandspit 2, and HB = Hawks Bay) during BMY-I.
Metals Source DF Adj. SS Adj. F- P- Tukey Pair wise MS Value Value Comparisons Fe Site 5 2017134 403427 45.52 0.000*** KC1 ≈ SP2 ≈ SP1 ≈ Error 21 186131 8863 RK > KC2 > HB Total 26 2203264 Cu Site 5 47145 9428.9 21.18 0.000*** RK > KC2 ≈ HB ≈ Error 21 9350 445.2 KC1 > SP2 ≈ SP1 Total 26 56495 Zn Site 5 19951 3990.3 15.15 0.000*** RK > SP2 ≈ HB ≈ Error 21 5530 263.3 KC1 ≈ KC2 > SP1 Total 26 25481 Co Site 5 551.35 110.270 39.64 0.000*** RK > SP2 ≈ KC1 ≈ Error 21 58.41 2.782 HB ≈ KC2 > SP1 Total 26 609.76 Cr Site 5 684.15 136.830 60.99 0.000*** KC1 ≈ RK > KC2 ≈ Error 21 47.11 2.244 HB ≈ SP2 > SP1 Total 26 731.26 Ni Site 5 3289.4 657.888 77.28 0.000*** RK > KC2 ≈ HB ≈ Error 21 178.8 8.513 KC1 > SP2 > SP1 Total 26 3468.2 Pb Site 5 73097.8 14619.6 383.04 0.000*** KC2 > HB > RK > Error 21 801.5 38.2 SP2 ≈ KC1 ≈ SP1 Total 26 73899.3 Cd Site 5 15.0468 3.00936 66.28 0.000*** RK > KC2 ≈ HB > Error 21 0.9535 0.04540 SP2 ≈ KC1 > SP2 Total 26 16.0003 ‘***’ significant at <0.001 ‘**’ significant at <0.01 ‘*’ significant at <0.05
179
Table 3.16: Pearson’s correlation coefficient (r) between heavy metal concentrations in M. depressus collected during BMY-I.
Metals Fe Cu Zn Ni Cr Co Pb Cd Fe 1.000 Cu -0.067ns 1.000 Zn 0.077ns 0.873*** 1.000 Ni -0.220ns 0.799*** 0.632*** 1.000 Cr 0.435* 0.500** 0.371ns 0.687*** 1.000 Co 0.271ns 0.696*** 0.811*** 0.643*** 0.617** 1.000 Pb -0.779*** 0.321ns 0.066ns 0.444* -0.213ns -0.182ns 1.000 Cd -0.373ns 0.821*** 0.757*** 0.827*** 0.272ns 0.676*** 0.552** 1.000 ‘***’ significant at <0.001 ‘**’ significant at <0.01 ‘*’ significant at <0.05
25 RK KC1 KC2 Sp1 Sp2 HB
20
15
10
Bioaccumulation Bioaccumulation Factor 5
0 Co Ni Zn Pb Fe Cu Cr Cd
Figure 3.10: Bioaccumulation factor of heavy metals in M. depressus from six coastal areas (RK = Rato Kot, KC1 = Korangi Creek 1, KC2 = Korangi Creek 2, SP1 = Sandspit 1, SP2 = Sandspit 2 and HB = Hawks Bay) during BMY-I.
180
Female Male Iron (Fe) Copper (Cu) Zinc (Zn) 1000 100 100 500 75 80
0 50 60 Cobalt (Co) Nickel (Ni) Chromium (Cr) 36 24 12 32 18 9
28 12 6 Lead (Pb) Cadmium (Cd) 2.5 Female Male 150
100 2.0
50 1.5
Female Male Gender
1 2 3 F M F M F M Co Ni Zn 100 6 20
4 50 10 2 0 0 Pb Fe Cu 30 4 40
15 2 20
0 0 0 Cr Cd F M F M F M 0.8 15 1 2 3 10 0.4 5 0.0 Gender F M F M F M Station 1 2 3
Figure 3.11: (a) Intersexual variations in heavy metals accumulation in M. depressus (b) Intersexual and spatial variation in heavy metals accumulation in M. depressus from three coastal areas (KC1, KC2 and HB) during BMY-I. (Genders: F = female, M = male; Sites abbreviation: 1 = Korangi Creek1, 2 = Korangi Creek 2, 3 = Hawks Bay).
181
3.3.5. Crabs as Potential Indicator of Heavy Metal Contamination
The overall load of heavy metals estimated in seven crab species for identification of potential bioindicator. The high variability in metals accumulation was observed in different crab species with respect to their monitoring sites. Mainly the Iron (Fe) concentrations varied among the seven crab species by more than one order of magnitude (Figure 3.12a). The highest contents of Fe perceived in M. depressus with values of 654.4 µg g-1 and lowest contents (33.06 µg g-1) detected in A. sindensis. Likewise, the BAF values of Fe revealed significantly higher in M. depressus (2.25), however statistically lower BAF (0.19) values were recorded in S. crabricauda (Figure 3.12b).
The two essential metals, Copper and Zinc values (total metal concentration and BAFs) showed the similar trend among the selected crab species (Figure 3.12 and 3.13) and showed higher values in I. frater (122.06 and 94.91 µg g-1 for Cu and Zn, respectively), whereas lower levels were recorded in A. sindensis (58.18 and 39.32 µg g-1 for Cu and Zn, respectively). The BAF values of Cu and Zn presented significantly higher in A. sindensis (12.59 and 4.12 for Cu and Zn, respectively), while the lower levels (4.50 and 1.60 for Cu and Zn, respectively) recorded in S. crabricauda (Figure 3.12d and 3.13b).
The Cobalt (total concentrations and BAFs) presented the similar trend for accumulation and varied significantly among the selected crab species by more than one order of magnitude (Figure 3.13c and d). E. orientalis perceived the highest mean concentrations (31.72 µg g-1) and similarly higher BAF values (21.20). However, the lowest mean concentrations (6.57 µg g-1) of cobalt including lowest BAF values (1.04) detected in S. crabricauda (Figure 3.13c and d).
Chromium concentrations varied significantly among the crab species (Figure 3.14a). Cr revealed higher values in M. depressus (14.36 µg g-1), whereas lower levels (6.40 µg g-1) were detected in A. iranica. The highest BAF for Cr detected in A. sindensis and the lowest BAF observed in S. crabricauda (Figure 3.14b). The highest Ni concentration (32.71 µg g-1) detected in S. crabricauda, whereas the lowest concentration (16.89 µg g-1) were observed in A. iranica (Figure 3.14c). The BAF values of Ni revealed that A. sindensis showed the highest values (4.03) and lowest (0.58) in S. crabricauda (Figure 3.14d).
Lead values significantly varied among the crab species selected in this study (Figure 3.15a). Pb revealed significantly higher values in S. crabricauda (188.42 µg g-1). However, statistically lower levels recorded in O. indica (25.56 µg g-1). The BAF values of Pb revealed significantly higher in A. sindensis (17.31) and statistically lower levels (0.95) recorded in A. iranica (Figure 3.15b).
182
Cadmium content also showed the significant variation among the crab species selected in this study (Figure 3.15c). Cd revealed higher values in S. crabricauda (4.07 µg g-1), whereas lower levels (1.54 µg g-1) were recorded in A. sindensis. Interestingly, the BAF values of Cd in crab species reflected the mirror image of the total Cd concentrations in crab species, it showed the higher BAF values in A. sindensis (8.30) and lower (2.55) in S. crabricauda (Figure 3.15d).
183
800 (a) 2.5 (b)
2.0 600
1.5 Fe 400 -
BAF 1.0 Fe (µg/g) 200 0.5
0 0.0
I. fraterI.
I. fraterI.
O. indica
O. indica
A. iranica
A. iranica
A. sindensis
E. orientalis
A. sindensis
E. orientalis
M. M. depressus
M. M. depressus
S. crabricaudaS. S. crabricaudaS.
160 (c) 15 (d)
120 12
9 Cu
80 - Cu (µg/g) Cu
BAF 6 40 3
0 0
I. fraterI.
I. fraterI.
O. indica
A. iranica
O. indica
A. iranica
A. sindensis
E. orientalis
A. sindensis
M. M. depressus
E. orientalis
M. M. depressus
S. crabricaudaS. S. crabricaudaS.
Figure 3.12: Mean heavy metal concentration and their bioaccumulation factors (a) Fe concentration (µg g-1) (b) Fe bioaccumulation factor (BAF) (c) Cu concentration (µg g-1) and (d) Cu bioaccumulation factor (BAF) in seven brachyuran crab species (in ascending order) during the BMY-I from coastal areas of Pakistan.
184
100 (a) 5.0 (b)
80 4.0
60 3.0 Zn
40 - 2.0
BAF Zn Zn (µg/g) 20 1.0
0 0.0
I. fraterI.
I. fraterI.
O. indica
O. indica
A. iranica
A. iranica
A. sindensis
A. sindensis
E. orientalis
E. orientalis
M. M. depressus
M. M. depressus
S. crabricaudaS. S. crabricaudaS.
35 (c) 25 (d)
30 20 25
15 Co
20 -
15 10 BAF
Co (µg/g) 10 5 5
0 0
I. fraterI.
I. fraterI.
O. indica
O. indica
A. iranica
A. iranica
A. sindensis
A. sindensis
E. orientalis
E. orientalis
M. M. depressus
M. M. depressus
S. crabricaudaS. S. crabricaudaS.
Figure 3.13: Mean heavy metal concentration and their bioaccumulation factors (a) Zn concentration (µg g-1) (b) Zn bioaccumulation factor (BAF) (c) Co concentration (µg g-1) and (d) Co bioaccumulation factor (BAF) in seven brachyuran crab species (in ascending order) during the BMY-I from coastal areas of Pakistan.
185
20 (a) 0.5 (b)
0.4 15
0.3
10 Cr
- 0.2 BAF Cr Cr (µg/g) 5 0.1
0 0.0
I. fraterI.
I. fraterI.
O. indica
O. indica
A. iranica
A. iranica
A. sindensis
A. sindensis
E. orientalis
E. orientalis
M. M. depressus
M. M. depressus S. crabricaudaS. S. crabricaudaS.
40 (c) 5.0 (d) 35 4.0 30
25 3.0 Ni
20 - Ni Ni (µg/g) 15 BAF 2.0 10 1.0 5
0 0.0
I. fraterI.
I. fraterI.
O. indica
O. indica
A. iranica
A. iranica
A. sindensis
A. sindensis
E. orientalis
E.orientalis
M. M. depressus
M. M. depressus
S. crabricaudaS. S. crabricaudaS.
Figure 3.14: Mean heavy metal concentration and their bioaccumulation factors (a) Cr concentration (µg g-1) (b) Cr bioaccumulation factor (BAF) (c) Ni concentration (µg g-1) and (d) Ni bioaccumulation factor (BAF) in seven brachyuran crab species (in ascending order) during the BMY-I from coastal areas of Pakistan.
186
200 (a) 20 (b)
150 15 Pb
100 - 10
BAF Pb(µg/g) 50 5
0 0
I. fraterI.
I. fraterI.
O. indica
A. iranica
O. indica
A. iranica
A.sindensis
E. orientalis
A. sindensis
M. M. depressus
E. orientalis
M. M. depressus
S. crabricaudaS. S. crabricaudaS.
5 (c) 10 (d)
4 8
3 6
Cd -
2 4
Cd (µg/g) Cd BAF
1 2
0 0
I. fraterI.
I. fraterI.
O. O. indica
O. indica
A. iranica
A. iranica
A. sindensis
A. sindensis
E. orientalis
E. orientalis
M. M. depressus
M. M. depressus S. crabricaudaS. S. crabricaudaS.
Figure 3.15: Mean heavy metal concentration and their bioaccumulation factors (a) Pb concentration (µg g-1) (b) Pb bioaccumulation factor (BAF) (c) Cd concentration (µg g-1) and (d) Cd bioaccumulation factor (BAF) in seven brachyuran crab species (in ascending order) during the BMY-I from coastal areas of Pakistan.
187
3.3.6. Environmental Factors Affecting on Metal Accumulation in Crabs
The heavy metals accumulation in crab influenced by several abiotic and biotic factors, some of these factors examined in this study to enlighten and specify the factors, which affect metals accumulation in crabs along the coast of Pakistan. The physicochemical properties of sediments can be interrelated or effective on metals accumulation in crabs. The water contents in sediments were observed significant linear correlation with Fe (R2 = 0.198), Ni (R2 =0.137) and Pb (R2 = 0.178) accumulation in crabs (Table 3.17 and Figure 3.20). Likewise, the accumulation of Fe (R2 = 0.157) and Zn (R2 = 0.065) in crabs were observed significant linear correlation with percent organic matter in sediments (Table 3.17 and Figure 3.20).
The grain size composition of sediment also effect on metals accumulation in crab as they are deposit feeders and extract their food from sediments. The results revealed that percent granules were observed significant linear correlation with the concentrations of Fe (R2 = 0.171), Zn (R2 = 0.059), Ni (R2 = 0.089) and Pb (R2 = 0.068) in crabs (Table 3.17 and Figure 3.21). The concentrations of Cu (R2 = 0.057), Co (R2 = 0.565), Ni (R2 = 0.071), Cr (R2 = 0.094) in crabs were observed significant linear correlation with percent sand (Table 3.17 and Figure 3.21). On the other hand, the concentrations of Fe (R2 = 0.157) and Zn (R2 = 0.065) in crabs were observed significant linear correlation with percent mud (Table 3.17 and Figure 3.21).
Finally yet importantly, the heavy metals concentrations in sediments are capable to stimulate or reduce the metal accumulation in crabs. The results revealed that the concentrations of Cu (R2 = 0.116), Co (R2 = 0.122), Pb (R2 = 0.062) and Cd (R2 = 0.120) in crabs were observed significant linear correlation with exposure concentrations of corresponding metals in sediments (Table 3.19 and Figure 3.22).
188
Table 3.17: Relationship between physical properties of sediment and metals concentrations in crab species from coastal areas of Pakistan in BMY-I.
Fe Cu Zn Co Ni Cr Pb Cd %Moisture R2 0.198 0.010 0.046 0.033 0.137 0.020 0.178 0.000 F-values 18.76 0.08 3.66 2.56 12.11 1.55 16.44 0.03 p-values 0.000*** 0.776 0.059 0.114 0.001** 0.217 0.000*** 0.858 %Organics R2 0.157 0.018 0.065 0.030 0.019 0.010 0.044 0.036 F-values 14.19 1.4 5.31 2.39 1.44 0.05 3.5 2.81 p-values 0.000*** 0.240 0.024* 0.126 0.234 0.816 0.065 0.098 %Granule R2 0.171 0.010 0.059 0.020 0.089 0.015 0.068 0.030 F-values 15.62 0.1 4.73 0.18 7.44 1.19 5.57 0.26 p-values 0.000*** 0.749 0.033* 0.675 0.008** 0.278 0.021* 0.611 %Sand R2 0.010 0.057 0.019 0.565 0.071 0.094 0.023 0.014 F-values 0.78 4.62 1.45 98.91 5.84 7.9 1.82 1.11 p-values 0.379 0.035* 0.232 0.000*** 0.018 0.006** 0.181 0.296 %Mud R2 0.010 0.067 0.049 0.628 0.032 0.076 0.059 0.020 F-values 0.07 5.47 3.89 128.1 2.49 6.27 4.79 1.59 p-values 0.787 0.022* 0.052 0.000*** 0.119 0.014* 0.032 0.212 ‘***’ significant at <0.001 ‘**’ significant at <0.01 ‘*’ significant at <0.05
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20 40 60 20 40 60 Fe Cu Zn Ni 200 1000 160 40 750 150 120 30
500 100 80 20
250 50 40 10
0 0 0 0 Cr Co Pb Cd 25 200 6.0 30 20 150 4.5 15 20 100 3.0 10 10 50 1.5 5 0 0 0.0 20 40 60 20 40 60 % Moisture
0 4 8 0 4 8 Fe Cu Zn Ni 200 1000 160 40 750 150 120 30
500 100 80 20
250 50 40 10
0 0 0 0 Cr Co Pb Cd 25 200 6.0 30 20 150 4.5 15 20 100 3.0 10 10 50 1.5 5 0 0 0.0 0 4 8 0 4 8 % Organics
Figure 3.16: Relationship between percent moisture and organics of sediments and metals concentrations in crab species from coastal areas of Pakistan.
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0 5 10 0 5 10 Fe Cu Zn Ni 1000 200 160 40 750 150 120 30 100 80 500 20 250 50 40 10
0 0 0 0 Cr Co Pb Cd 25 200 30 6.0 20 150 4.5 15 20 100 3.0 10 10 50 1.5 5 0 0 0.0 0 5 10 0 5 10 %Granule
50 75 100 50 75 100 Fe Cu Zn Ni 1000 200 160 40 750 150 120 30 80 500 100 20 250 50 40 10
0 0 0 0 Cr Co Pb Cd 25 200 30 6.0 20 150 4.5 15 20 100 3.0 10 10 50 1.5 5 0 0 0.0 50 75 100 50 75 100 %Sand
0 20 40 0 20 40 Fe Cu Zn Ni 1000 200 160 40 750 150 120 30 100 80 500 20 250 50 40 10
0 0 0 0 Cr Co Pb Cd 25 200 30 6.0 20 150 4.5 15 20 100 3.0 10 10 50 1.5 5 0 0 0.0 0 20 40 0 20 40 %Mud
Figure 3.17: Relationship between grain size of sediments and metals concentrations in crab species from coastal areas of Pakistan.
191
Table 3.18: Relationship between heavy metal concentrations in sediment and crab species from coastal areas of Pakistan during the BMY-I.
Metals Regression equation R2 F-values P-values
Fe Fe Crab = 1028 - 0.6002 Fe Sediment 3.6% 2.88 0.094
** Cu Cu Crab = 94.97 - 0.08501 Cu Sediment 11.6% 9.96 0.002
Zn Zn Crab = 89.71 - 0.05906 Zn Sediment 4.2% 3.30 0.073
** Co Co Crab = 16.91 - 0.6023 Co Sediment 12.2% 10.52 0.002
Ni Ni Crab = 24.67 - 0.0287 Ni Sediment 0.1% 0.06 0.800
Cr Cr Crab = 11.62 - 0.01205 Cr Sediment 2.7% 2.10 0.151
* Pb Pb Crab = 43.41 + 0.2110 Pb Sediment 6.2% 5.02 0.028
** Cd Cd Crab = 0.9911 + 1.072 Cd Sediment 12.0% 10.32 0.002 ‘***’ significant at <0.001 ‘**’ significant at <0.01 ‘*’ significant at <0.05
Fe*S-Fe Cu*S-Cu Zn*S-Zn 1000 200 160
500 100 80
0 0 0 500 750 1000 0 250 500 0 200 400 Ni*S-Ni Cr*S-Cr Co*S-Co 40 20 30
20 10 15
0 0 0 20 40 60 100 200 300 0 10 20 Pb*S-Pb Cd*S-Cd 200
5.0 100 2.5
0 0.0 0 100 200 0.5 1.0 1.5
Figure 3.18: Relationship between heavy metal concentrations in sediment and crab species from coastal areas of Pakistan.
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(B) SECOND BIOMONITORING YEAR (BMY-II)
A. iranica and M. depressus as Potential Indicator for Heavy Metals Contamination:
After the detailed preliminary analysis in BMY-I, the both crab species A. iranica and M. depressus identified the species for the further detailed monitoring study in BMY-II. These two species showed significant correlation with sediment properties and metal contamination. The three sites were selected for the survey during the BMY-II based on their abundance, comparatively large body size and wide distribution.
3.3.7. Heavy Metal Concentrations in Sediments
During the second biomonitoring year, the mean concentrations of Cu, Zn, Co, Pb and Cd evaluated in sediments collected from Korangi Creek (KC), Sandspit (SP) and Sonari (SO) areas (Table 3.19). The Cu concentration in sediments of SP was higher (50.6 ± 38.9 µg g-1) at SP and lower (3.996 ± 0.37 µg g-1) in the sediments of SO. There were no significant differences (p = >0.05) in Cu contamination in sediments. The Zn concentrations in sediments were observed higher (331.8 ± 141.6 µg g-1) at SP and lower (102.80 ± 1.76 µg g-1) at KC. The significant differences (p = <0.05) were observed in Zn levels in sediments (Table 3.19).
The significant differences (p = <0.05) observed in Co concentration and it was higher (68.51 ± 0.52 µg g-1) at SO and lowest (58.15 ± 1.11 µg g-1) at KC (Table 3.19). The significant spatial differences (p = <0.05) also observed in Pb concentrations and it was higher (85.9 ± 51.4 µg g-1) in SP sediments and lower (13.87 ± 2.10 µg g-1) at KC. The Cd concentrations in sediments observed higher (5.47 ± 0.11 µg g-1) at KC and lower (4.91 ± 0.47 µg g-1) at SP but there were no significant differences (p = >0.05) among sites for Cd levels (Table 3.19).
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Table 3.19: The distribution and variation of five heavy metals (µg g-1) in sediments from (Korangi Creek = KC, Sonari = SO and Sandspit = SP) during BMY-II.
Metals Site N Mean ± S.D. F-value p-value Cu KC 3 7.142 ± 0.19 3.64 0.070 SO 3 3.996 ± 0.37 SP 6 50.6 ± 38.9 Zn KC 3 102.80 ± 1.76 6.78 0.016* SO 3 112.07 ± 2.10 SP 6 331.8 ± 141.6 Co KC 3 58.15 ± 1.11 12.17 0.003** SO 3 68.51 ± 0.52 SP 6 63.71 ± 3.37 Pb KC 3 13.87 ± 2.10 5.06 0.034* SO 3 17.11 ± 3.19 SP 6 85.9 ± 51.4 Cd KC 3 5.47 ± 0.11 2.36 0.150 SO 3 5.03 ± 0.18 SP 6 4.91 ± 0.47 ‘***’ significant at <0.001 ‘**’ significant at <0.01 ‘*’ significant at <0.05
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3.3.8. Austruca iranica as Bio-indicator of Heavy Metals
According to the monitoring program 2011-12, the fiddler crab, A. iranica, was identified as the one of the most abundant and widely distributed crab species along the coast of Pakistan. The five heavy metals accumulation in the tissues evaluated for intersexual differences in A. iranica, moreover the relationship of metal accumulation in tissues with size of the crabs and exposure levels in sediments were also evaluated. The concentrations of Cu, Zn, Co, Pb and Cd assessed in tissues of male (M) and female (F) individuals of A. iranica collected from backwater mangrove area of Sandspit (SP) and mudflat of Sonari (SO) (Figure 3.22).
The concentration of Cu was observed higher at SO in both genders (M = 62.95 ± 3.19 µg g- 1 and F = 78.90 ± 14.56 µg g-1). The significant differences were observed in Cu levels in tissues of crabs between the sites (F = 83.54, p = 0.000) and genders (F = 8.80, p = 0.006) but there were no significant interactions (F = 0.01, p = 0.931) between two variables. The concentrations of Zn were observed higher at SO in both genders (M = 232.0 ± 33.8 µg g-1 and F = 190.6 ± 54.8 µg g-1). The significant differences were observed in Zn levels in tissues of crabs between the sites (F = 22.23, p = 0.000) as well as significant interactions (F = 11.68, p = 0.002) but there was no significant intersexual differences found (F = 0.98, p = 0.332).
The concentrations of Co were observed higher at SO in both genders (M = 69.49 ± 4.32 µg g-1 and F = 122.69 ± 8.87 µg g-1). The significant differences were observed in Co levels in tissues of crabs between the sites (F = 94.04, p = 0.000), genders (F = 248.98, p = 0.000) as well as have significant interactions between sites and genders (F = 80.12, p = 0.000). The mean accumulation of Pb in males was 19.57 ± 17.32 µg g-1 and in females, it was 7.28 ± 8.32 µg g-1 at KC, whereas Pb showed undetectable in the crab collected from SO mudflat areas. The significant differences were observed in Pb levels in tissues of crabs between the sites (F = 12.29, p = 0.002) but there were no significant differences in Pb levels between the genders (F = 2.57, p = 0.121) and their interactions (F = 2.57, p = 0.121). The concentration of Cd observed higher at SP in both genders (M = 9.06 ± 1.10 µg g-1 and F = 10.27 ± 0.34 µg g-1). The significant differences were observed in Co levels in tissues of crabs between the sites (F = 39.80, p = 0.000), genders (F = 70.77, p = 0.000) as well as their interactions (F = 16.10, p = 0.000).
The variations observed in bioaccumulation factor (BAF) of studied metals (Figure 3.21). The BAF of Cu was observed higher at SO in both genders (M = 15.75 ± 0.80, F = 19.75 ± 3.64). The significant differences were observed in the BAF of Cu between the sites (F = 606.20, p = 0.000) and genders (F = 8.70, p = 0.007). The BAF of Zn was observed higher at SO in both genders
195
(M = 2.07 ± 0.30, F = 1.70 ± 0.49). The significant differences were observed in the BAF of Zn between the sites (F = 215.45, p = 0.000) but there was no intersexual difference was observed. The BAF of Co was observed higher in males (1.02 ± 0.07) at SP and in females (1.79 ± 0.13) collected from the mudflat area of SO. The significant differences were observed in the BAF of Co between the sites (F = 12.66, p = 0.001), genders (F = 64.94, p = 0.000).
The significant differences were observed in the BAF of Pb between the sites (F = 14.41, p = 0.001) but there were no significant differences between the genders (F = 2.43, p = 0.131). The BAF of Cd was observed higher in the crabs collected from SP mangrove area as compared to SO mudflat in both genders (M = 1.70 ± 0.20 and F = 1.92 ± 0.06). The significant differences were observed in the BAF of Cd between the sites but no intersexual differences were observed in BAF of Cd in crabs.
The relationship between heavy metals concentrations in tissues and crab size was also investigated (Figure 3.22) and the results indicated that the accumulations of Cu and Zn revealed the significant linear correlations with crab size. A reducing affinity found for Cu (R2 = 0.4097) and Zn (R2 = 0.5075) accumulation in the tissues according to an increase with the size of crab. An increasing affinity was found for Cd (R2 = 0.2056) accumulation in the tissues according with an increase in the size of crab (Figure 3.27). Whereas, there was no significant correlation found between the Co (R2 = 0.010) and Pb (R2 = 0.095) accumulation in crab with the size of the crabs.
The relationship between heavy metals concentrations in tissues and their habitat were also investigated (Table 3.21). The significant linear relationships (p = 0.004) were observed between Cu and Zn concentrations in sediments and accumulated Cu and Zn in tissues only for males, whereas remaining metals showed no correlations with their respective concentrations in sediments. The significant decrease in accumulation tendency of Cu (R2 = 0.898) and Zn (R2 = 0.896) was observed only in male crabs with respect to the concentrations of the corresponding metal in sediments. The female crabs showed no correlation in any metal accumulation with the metals levels in sediments (Table 3.21).
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90 250 80
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C
60 n
(
Z
(
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50
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C 20 10 100 0 Genders Female Male Female Male Genders Female Male Female Male Sites Sandspit Sonari Sites Sandspit Sonari
140 35 130 30 120 25
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11
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d
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C 6 Genders Female Male Female Male Sites Sandspit Sonari Individual standard deviations were used to calculate the intervals.
Figure 3.19: Spatial and gender variations of heavy metals accumulation (µg g-1) in tissues of fiddler crabs (A. iranica) collected from Sandspit and Sonari during BMY-II.
197
Table 3.20: Analysis of variance (ANOVA) of heavy metals accumulation (µg g-1) in tissues of fiddler crab (A. iranica) during BMY-II.
Metals Source DF Seq. SS Adj. SS Adj. MS F P Copper Site 1 19311.0 18468.0 18468.0 83.54 0.000*** Gender 1 1945.1 1945.1 1945.1 8.80 0.006** Site*Gender 1 1.7 1.7 1.7 0.01 0.931 Error 26 5747.8 5747.8 221.1 Total 29 27005.6 Zinc Site 1 63393 46421 46421 22.23 0.000*** Gender 1 2042 2042 2042 0.98 0.332 Site*Gender 1 24397 24397 24397 11.68 0.002** Error 26 54295 54295 2088 Total 29 144127 Cobalt Site 1 2170.0 3133.2 3133.2 94.04 0.000*** Gender 1 8295.3 8295.3 8295.3 248.98 0.000*** Site*Gender 1 2669.3 2669.3 2669.3 80.12 0.000*** Error 26 866.2 866.2 33.3 Total 29 14000.8 Lead Site 1 1610.8 1297.9 1297.9 12.29 0.002** Gender 1 271.7 271.7 271.7 2.57 0.121 Site*Gender 1 271.7 271.7 271.7 2.57 0.121 Error 26 2745.7 2745.7 105.6 Total 29 4899.8 Cadmium Site 1 28.320 21.398 21.398 39.80 0.000*** Gender 1 38.050 38.050 38.050 70.77 0.000*** Site*Gender 1 8.655 8.655 8.655 16.10 0.000*** Error 26 13.978 13.978 0.538 Total 29 89.004 ‘***’ significant at <0.001 ‘**’ significant at <0.01 ‘*’ significant at <0.05
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SO SP F M F M Cu Zn Co 24 2.0 1.8 18 1.5 1.6 12 1.0 1.4 6 1.2 0.5 1.0 0 0.0 Pb Cd F M F M 0.24 2.0 SO SP
0.18 1.8
0.12 1.6
0.06 1.4
0.00 1.2 Gender F M F M Site SO SP Individual standard deviations were used to calculate the intervals.
Figure 3.20: Mean bioaccumulation factors (BAF) of five heavy metals in tissues of A. iranica with respect to sites (SO = Sonari and SP = Sandspit) and genders (F = females, M = males) during BMY- II.
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70 300 y = -5.9601x + 132.71 y = -18.074x + 454.68 60 R² = 0.4097 250 R² = 0.5075 50 200 40 150 30 100 20
Zn Zn accumulation 50 Cu accumulation Cu 10 0 0 12 17 22 12 17 22 Carapace Size (mm) Carapace Size (mm)
80 50 y = -1.9239x + 55.735 70 R² = 0.0949 40 60 50 30 40 30 20
20 y = -0.7163x + 69.947 10 Pbaccumulation Coaccumulation 10 R² = 0.0097 0 0 12 17 22 12 14 16 18 20 Carapace Size (mm) Carapace Size (mm)
11 y = 0.2484x + 3.5452 R² = 0.2056 10 9 8 7
Cd accumulation 6 5 12 17 22 Carapace Size (mm)
Figure 3.21: Linear regression analysis between the heavy metals accumulation in tissues of fiddler crab (A. iranica) with respect to the carapace size during the BMY-II.
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Table 3.21: Linear regression analysis between the heavy metals concentrations in tissues of male and female fiddler crabs (A. iranica) and sediments collected from their habitat during the BMY-II.
Gender Metals Regression Equation R2 F value P value A. iranica (Male) Cu-S vs. Cu-C Cu = 65.44 - 0.6229 Cu-S 0.898 35.10 0.004** Zn-S vs. Zn-C Zn = 276.6 - 0.3976 Zn-S 0.896 34.59 0.004** Co-S vs. Co-C Co = - 50.06 + 1.759 Co-S 0.438 3.12 0.152 Pb-S vs. Pb-C Pb = - 2.880 + 0.1689 Pb-S 0.419 2.89 0.164 Cd-S vs. Cd-C Cd = - 25.51 + 6.394 Cd-S 0.571 5.32 0.082 A. iranica (Female) Cu-S vs. Cu-C Cu = 81.38 - 0.6108 Cu-S 0.629 3.39 0.207 Zn-S vs. Zn-C Zn = 197.5 - 0.0627 Zn-S 0.032 0.07 0.820 Co-S vs. Co-C Co = - 589.0 + 10.18 Co-S 0.314 0.92 0.439 Pb-S vs. Pb-C Pb = - 1.187 + 0.06361 Pb-S 0.331 0.99 0.425 Cd-S vs. Cd-C Cd = 6.376 + 0.695 Cd-S 0.081 0.18 0.715
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3.3.9. Macrophthalmus depressus as Bio-indicator of Heavy Metals
According to the monitoring program (BMY-I), the sentinel crab M. depressus, was also most frequently distributed species along the coastal areas of Pakistan, therefore may act as a good indicator of metal pollution. To further clarify the status of this sentinel crab species, the five heavy metals accumulation were evaluated in the tissues of both genders of M. depressus, moreover the relationship of metal accumulation in tissues with size of the crabs and exposure levels in sediments were also evaluated. Mean concentrations of five heavy metals (Cu, Zn, Co, Pb and Cd) in tissues of male (M) and female (F) individuals of M. depressus collected from mangrove habitats of Korangi Creek (KC) and Sandspit (SP) areas (Figure 3.28).
The concentrations of Cu observed higher at KC in both genders (M = 92.5 ± 123.5 µg g-1 and F = 27.805 ± 2.061 µg g-1) and no significant differences found in Cu levels in crab tissues with respect to the sites, genders as well as interactions of both variables. The concentrations of Zn showed higher in male crabs (140.05 ± 22.43 µg g-1) from the SP mangrove area, whereas it was higher in female crabs (370.3 ± 150.8 µg g-1) from the KC mangrove area. The significant differences were detected in Zn levels in tissues of crabs between the sites (F = 14.23, p = 0.001), genders (F = 25.50, p = 0.000) as well as their interactions (F = 44.02, p = 0.000).
The concentrations of Co observed higher in tissues of both genders (M = 48.78 ± 6.81 µg g- 1 and F = 63.66 ± 2.57 µg g-1) collected from SP mangrove area. The significant differences observed in Co levels in tissues of crabs from both sites (F = 15.43, p = 0.001) as well as both genders (F = 32.22, p = 0.000). The Pb showed higher concentrations in tissues of crabs collected from KC than the SP in both genders (M = 46.36 ± 19.15 µg g-1 and F = 51.96 ± 6.58 µg g-1). The significant differences observed in Pb levels in tissues of crabs between the sites (F = 26.53, p = 0.000) as well as the interactions of sites and gender (F = 6.01, p = 0.021) but there were no significant differences in Pb levels between the genders (F = 1.52, p = 0.229). Likewise, the concentrations of Cd observed higher at KC than the SP in both genders (M = 18.35 ± 7.16 µg g-1 and F = 16.316 ± 0.445 µg g-1). The significant differences were observed in Cd levels in tissues of crabs between the sites (F = 26.69, p = 0.000) but there were no significant differences in Cd levels between the genders as well as their interactions (Table 3.22).
The bioaccumulation factor (BAF) of five heavy metals was evaluated with the reference of their habitat (Figure 3.29). The BAF of Cu was observed higher at KC in both genders (M = 12.95 ± 17.29, F = 3.89 ± 0.29). The significant differences were observed in the BAF of Cu between the sites (F = 4.43, p = 0.045) but there were no significant differences between the genders (F = 1.46, p
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= 0.237). The BAF of Zn was observed little bit higher in male crabs (0.69 ± 0.11) at SP as compared to KC, whereas it was more than three times higher in female crabs (3.603 ± 1.467) at KC as compared to SP. The significant differences observed in the BAF of Zn between the sites (F = 15.46, p = 0.001), genders (F = 12.61, p = 0.001). The BAF of Co was observed higher at SP than the KC in both genders (M = 0.80 ± 0.11, F = 1.04 ± 0.04). The significant difference was observed in the BAF of Co between the sites (F = 9.79, p = 0.004) and genders (F = 28.67, p = 0.000).
The BAF of Pb was observed higher at KC than the SP in both genders (M = 3.341 ± 1.380, F = 3.746 ± 0.475). The significant differences detected in the BAF of Pb between the sites (F = 89.42, p = 0.000) but there were no significant differences between the genders (F = 0.00, p = 0.961). The BAF of Cd was observed higher at KC than the SP in both genders (M = 3.35 ± 1.31 and F = 2.98 ± 0.08). The significant differences noticed in the BAF of Cd between the sites (F = 14.08, p = 0.001) but was not significant between the genders (Table 3.22).
The relationship between metals concentrations in tissues and crab size were investigated (Figure 3.27) and results indicated that the accumulation of Cu and Zn in tissues showed no significant linear correlations with crab size. Whereas, an increasing trend was found in Co accumulation in the tissues according to increase in the size of crab (R2 = 0.188). Moreover, a reducing affinity was found for Pb (R2 = 0.403) and Cd (R2 = 0.349) accumulation in the tissues according to the increase in the carapace width of crab (Figure 3.27).
The relationship between heavy metals concentrations in tissues and sediments were also investigated (Table 3.23). The significant linear relationship (p <0.05) was observed between Zn concentration in sediments and accumulated Zn in tissues for males and females. Intrestingly, both genders showed a different trend with respect to Zn accumulation in tissues, for instance male crabs showed an increasing trend between the Zn accumulations in tissues with the presence of Zn in sediments (R2 = 0.887). Conversely, female crabs showed decreasing tendency with increase of Zn levels in sediments (R2 = 0.832). The significant linear relationships (p <0.05) observed between Pb and Cd concentrations in sediments and their accumulation in tissues of female crabs. The Pb accumulation in females showed decreasing tendency with increasing the Pb concentrations in sediments (R2 = 0.981). Whereas, an increasing tendency in the Cd accumulation showed in females with increasing the Cd concentrations in sediments (R2 = 0.961).
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C 100 0 0 Genders Female Male Female Male Genders Female Male Female Male Sites Korangi Creek Sandspit Sites Korangi Creek Sandspit
70 60 60 50
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24 22
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Genders Female Male Female Male Sites Korangi Creek Sandspit Individual standard deviations were used to calculate the intervals.
Figure 3.22: Spatial and gender variations in heavy metals accumulation (µg g-1) in tissues of sentinel crab (M. depressus) collected from Korangi Creek and Sandspit during the BMY-II.
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Table 3.22: Analysis of variance (ANOVA) of five heavy metals accumulation in tissues of sentinel crab (M. depressus) during BMY-II.
Metals Source DF Seq. SS Adj. SS Adj. MS F P Copper Site 1 22063 16729 16729 3.53 0.071 Gender 1 6557 6557 6557 1.39 0.250 Site*Gender 1 8573 8573 8573 1.81 0.190 Error 26 123055 123055 4733 Total 29 160249 Zinc Site 1 39011 69834 69834 14.23 0.001** Gender 1 125099 125099 125099 25.50 0.000*** Site*Gender 1 216004 216004 216004 44.02 0.000*** Error 26 127572 127572 4907 Total 29 507687 Cobalt Site 1 2868.3 1657.4 1657.4 15.43 0.001** Gender 1 3459.5 3459.5 3459.5 32.22 0.000*** Site*Gender 1 356.5 356.5 356.5 3.32 0.080 Error 26 2791.9 2791.9 107.4 Total 29 9476.2 Lead Site 1 4624.0 4042.7 4042.7 26.53 0.000*** Gender 1 231.7 231.7 231.7 1.52 0.229 Site*Gender 1 916.0 916.0 916.0 6.01 0.021* Error 26 3962.0 3962.0 152.4 Total 29 9733.6 Cadmium Site 1 464.86 426.15 426.15 26.69 0.000*** Gender 1 5.80 5.80 5.80 0.36 0.552 Site*Gender 1 9.34 9.34 9.34 0.59 0.451 Error 26 415.10 415.10 15.97 Total 29 895.10 ‘***’ significant at <0.001 ‘**’ significant at <0.01 ‘*’ significant at <0.05
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KC SP F M F M Cu Zn Co 30 4.8 1.0
20 3.6 0.8
2.4 0.6 10 1.2 0.4
0 0.0 0.2 Pb Cd F M F M 4 KC SP 4 3
2 3
1
0 2 Gender F M F M Site KC SP Individual standard deviations were used to calculate the intervals.
Figure 3.23: Mean bioaccumulation factors (BAF) of five heavy metals in M. depressus with respect to sites and genders form two sites (KC = Korangi Creek and SP = Sandspit) during the BMY-II.
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35 y = -0.693x + 27.711 600 30 R² = 0.0778 500 y = -12.399x + 369.49 25 400 R² = 0.1221 20 300 15 200
10 Zn concentration Zn Cu Cu concentration 5 100 0 0 10 15 20 25 10 15 20 25 Carapace size (mm) Carapace size (mm)
80 100 70 y = -3.1173x + 91.049 80 60 R² = 0.4025 50 60 40 40 30 y = 2.1011x + 10.954 20 R² = 0.1878
Pbconcentration 20 Co concentration 10 0 0 10 15 20 25 10 15 20 25 Carapace size (mm) Carapace size (mm)
30 y = -0.8801x + 29.091 25 R² = 0.3489 20 15 10
Cd concentration 5 0 10 15 20 25 Carapace size (mm)
Figure 3.24: Linear regression analysis between the heavy metals accumulation in tissues and carapace size of sentinel crab (M. depressus) during the BMY-II.
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Table 3.23: Linear regression analysis between heavy metals concentrations in tissues of sentinel crab (M. depressus) and sediments during the BMY-II.
Gender Metals Regression Equation R2 F value P value M. depressus (Male) Cu-S vs. Cu-C Cu-C = 171.1 - 10.75 Cu-S 0.221 1.14 0.347 Zn-S vs. Zn-C Zn-C = - 12.75 + 0.7617 Zn-S 0.887 31.46 0.005** Co-S vs. Co-C Co-C = - 215.4 + 4.266 Co-S 0.181 0.88 0.401 Pb-S vs. Pb-C Pb-C = 52.68 - 0.4077 Pb-S 0.124 0.56 0.494 Cd-S vs. Cd-C Cd-C = - 24.28 + 7.675 Cd-S 0.343 2.09 0.222 M. depressus (Female) Cu-S vs. Cu-C Cu-C = 42.53 - 1.917 Cu-S 0.559 5.07 0.088 Zn-S vs. Zn-C Zn-C = 703.9 - 2.978 Zn-S 0.832 19.85 0.011* Co-S vs. Co-C Co-C = - 46.47 + 1.781 Co-S 0.489 3.83 0.122 Pb-S vs. Pb-C Pb-C = 71.85 - 1.402 Pb-S 0.981 208.96 0.000*** Cd-S vs. Cd-C Cd-C = - 18.89 + 6.413 Cd-S 0.961 97.52 0.001**
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3.4. DISCUSSION
The evaluation of multiple metals concentrations and their impact on density and diversity of intertidal crab species has not been characterized before along the Pakistan coast and data concerning the metals accumulation and contamination in crabs almost absent along the coastal and estuarine areas of Pakistan. This study directly based on biomonitoring of heavy metal in seven species of crab to identify the bioindicator species that represents a measure of contaminants exposure along with environmental monitoring of habitat that mainly emphasis on the metals levels in sediments. The current study can be considered as the preliminary investigation which provides desirable information regarding how exposure of heavy metals contamination effect on accumulation rate in crab species and how much they are contaminated and responsible for incorporated contamination in the food chain which ultimately dangerous to higher trophic level and socioeconomic system of coastal and estuarine environment of Pakistan.
In the current study, initially eight heavy metal contamination monitoring were scheduled on nine selected sites during the first biomonitoring year (BMY-I = 2011-12) along the Pakistan coast. The physicochemical properties of sediments, such as total organic matter, grain size and metal concentrations and biotic properties such as diversity, density, abundance and heavy metals accumulation in selected species of crab were examined. According to the sources of environmental pollution, monitoring sites have somewhat different ecological and anthropogenic conditions as already discussed in detail in Chapter 02.
The pollution conditions of the monitoring sites were further highlighted in this chapter by investigation of the metals accumulation in the seven selected crab species and their inter- relationship between metal accumulations in crabs and physicochemical properties of sediments to identify those potential factors, which effect on metal accumulation in benthic organisms. Several literature revealed that metal concentrations in biota is of great importance (Long and Morgan, 1990; Paul et al., 1992; Long et al., 1995; Neff, 2002; Peijnenburg et al., 2007; Pinheiro et al., 2012; Prasannakumari et al., 2014; Chakraborty et al., 2016) to evaluate the actual bioavailability of heavy metals in coastal and estuarine sediments, especially benthic organisms as they efficient accumulation indicators of sediment contamination. The different crab species showed the different tendency of accumulation for different heavy metals likely indicated their physiological responses and behavior towards habitat and environmental conditions during the BMY-I.
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The linear regression analysis was conducted between metals loadings in sediment with crab density. The Co concentrations in sediments were observed significant linear relationship with crab density (R2 = 0.111), whereas other metals had no linear correlation with crab density. The Co loadings in sediments have a tendency to inhibit or restrain the crab density along the coastal areas. Nevertheless, the week relationship indicated that low density of crab not seemed as a good predictor of Co contamination. Similarly, MacFarlane et al. (2000) also reported that the density of semaphore crab (Heloecius cordiformis) and their number of burrows were decreased with increase the metals (Cu, Zn and Pb) contamination in sediments from estuarine areas of Sydney, Australia.
In the first biomonitoring year, the significant spatial variation in metal level amongst the seven crab species were presented, which may be due to variations in different habitat conditions (Mitra et al., 2012). The differences of heavy metal concentrations between the ecological niches due to the consequence of environmental levels of contaminants and sediment composition (Carballeira et al., 1997; Blasco et al., 1999) which generally effect on metal bioavailability in sediments. The Zn and Cu accumulation in M. depressus were observed significantly greater in female as compared to male crabs during the BMY-I suggested the reproductive status of females crabs. However, there were no genders variations observed in any metal accumulation in A. iranica from three coastal areas of Pakistan during the BMY-I.
The influences of physicochemical properties (organic contents and grain size composition and metals levels in sediments) on metal bioaccumulation in crabs were identified by linear regression analysis. It is well established that the accumulation of metals in each matrix depends on several features. These include substratum type, physical and chemical characteristics of the environment, bioavailability of contaminants, feeding strategies and physiological condition of an organism (Rainbow, 2002). The accumulation of Fe, Ni and Pb in crabs showed significant linear correlation with water contents in sediments. Moreover, Fe concentration in crab was reduced with increasing the water contents in sediments. On the contrary, the Ni and Pb accumulation increased in crab as the water contents increasing in sediments. The Fe and Zn accumulations were reduced in crab with increasing the percent organic matter in sediments.
The accumulation of Fe and Zn were inversely correlated with presence of granule but accumulation of Ni and Pb were increased with increasing the granules composition. The accumulation of Cu, Co, Ni and Cr in crabs were increased with the increase of percent sand composition (but Cu and Co in sediment were positively correlated with sand). However, the accumulation of Cu, Co, Cr and Pb in crabs were observed significant linear correlation with percent
210 mud composition (but Cu, Co, Pb and Cr in sediment were negatively correlated with mud). The Cu, Co and Cr accumulation were linearly correlated with increase of mud contents, while the Pb accumulation in crabs was inversely correlated with mud composition. The deposit feeder crabs being highly associated with substrate. They can selectively ingest organic rich finer grained thus contaminant influx will increase excessively because of the higher contaminant concentration associated with these fine particles (Wang et al., 1999). Metal bioaccumulation is determined by the feeding rate of an organism and metal concentration in ingested food particles, therefore these factors are responsible for the relationship between metal bioaccumulation and organic content and particle size. Particle selectivity of deposit-feeding organisms can considerably affect the accumulation of contaminants (Wang et al., 1999).
It would be expected that the higher concentration of a certain metal in sediments, greater the quantity of metal will be in the associated organisms, due to the influence or interaction between them. In the context of these, the linear regression analysis was done to identify the relationship between metals concentration in crabs and sediments. The concentrations of Cu, Co, Pb and Cd in crabs (all species data was combined) were observed statistically significant (p <0.05) linear correlation with their corresponding exposure concentrations of metals in sediment. The significant inverse correlation between Cu (R2 = 0.116) and Co (R2 = 0.122) concentrations in sediment and crab signify lower accumulation in the higher exposure environment. The significant linear correlation was observed between Pb (R2 = 0.060) and Cd (R2 = 0.120) concentrations in sediment and crab suggested high accumulation at elevated exposure concentrations. The linear correlations between metal concentrations in the surface sediments and that found in the crabs indicate that some of the metals held in the sediment may become available to the crabs (Na and Park, 2012). The inverse correlations were assessed between Zn concentrations in the sediments and crabs, may indicate the Zn regulation in crabs (White and Rainbow, 1982) or inhibit accumulation at elevated concentrations (Simkiss and Taylor, 1989).
The concentrations of Fe, Zn, Cr and Ni in crabs were detected no significant linear correlation with their corresponding exposure concentrations of metals in sediment suggests there are some other aspects, which effects on metals accumulation in crabs. Gupta et al. (2014) also reported the similar results, correlation analysis for sediments and crabs showed no linear relationship between same metal pairs in sediments and oyster (Viviparus bengalensis). The differing relationship may also be affected by different physiological and environmental functions of the organism and sampling sites (Gupta et al., 2014). This may be attributed to diverse relationships between sediments and crabs for heavy metals with respect to the surroundings.
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When the four most abundant crab species (M. depressus, A. iranica, I. frater and O. indica) treated independently, the better and more understandable outcomes observed as they insight the possibilities of specific species as bioindicator for metal. The results revealed that three crab species (A. iranica, I. frater and O. indica) accumulate Pb in high amount and reflect the environmental levels suggests these species beneficial as potential bioindicator species in Pb contaminated coastal areas. Fiddler crab (A. iranica) also helpful in the Cd pollution monitoring programs. Moreover, the two crab species (O. indica and I. frater) also revealed themselves as active accumulator of various metals and point out the environmental levels of metals contamination. But these species are small in size, the separation in tissues from the shell is really difficult, the visceral portion includes the digestive tract which is also full of sediment so that is not wonder to represent the environmental levels of most of the metals.
This study revealed that M. depressus accumulated the highest amount of Fe and Cr compared to the other crab species. These were collected from six sites out of nine along the coast as they are widely distributed in coastal areas of Pakistan. The relatively similar concentration of Fe was observed in M. depressus at four sites (SP1, SP2, KC1, and RK) along the Karachi coast. Instead of this, highest Fe concentrations were observed in the sediments of KC2, but there was lowest bioaccumulation factor was observed in M. depressus. However, the lowest Fe levels were evaluated in the sediments of SP2 but there was a high bioaccumulation factor in M. depressus likely due to their feeding strategy and physiological adaptation of species in response of their habitat (sediments) condition. Iron is a natural component of soils, but its concentration can be influenced by some industries (Yang et al., 2001). Most of the living organisms are dependent on iron for survival, except in rare cases. Although, iron is the widely distributed and abundant metal naturally in the biosphere, the role of iron as essentials or toxicant is contradictory and both aspects have a serious impact concerning deficiency or overloads of iron in living organisms (Gurzau et al., 2003).
In the current study, Cr contents among the selected crab species significantly higher in M. depressus, whereas lower levels were recorded in A. iranica. Chromium is a highly toxic trace metal presenting various degrees of risk for coastal ecosystems (Scelzo 1997; Natale et al., 2000). Porcelain crab (Petrolistes laevigatus) survival decreased linearly with increased Cr concentrations in the different assays. Chapman (1966) suggested that the greater uptake of chromium at higher seawater concentrations indicated that uptake by polychaete (Hermione hystrix) was a passive process and accumulation within the organism may have been the result of the binding of chromium with body proteins. As reported for nereis (Neanthes arenaceodentata), that may passively accumulate Cr through attaching with proteins present in the body wall, gut wall and parapodial
212 regions (Oshida and Word, 1982). In bottom-feeder bivalves, such as the oyster (Crassostrea virginica), blue mussel (Mytilus edulis) and soft shell clam (Mya arenaria), the BCFs values for chromium (III) and chromium (VI) have reported in the range from 86 to 192 (USEPA, 1980; Fishbein, 1981; Schmidt and Andren, 1984; Kimbrough et al., 1999).
The Cu and Zn are the two essential trace elements for decapods, in the current study I. frater accumulated the highest amount of Cu ad Zn as compared to the other crab species. Copper is an essential element and it is also an integral part of the respiratory pigment hemocyanin in decapod crustaceans (Rainbow, 1997b; MacFarlane et al., 2000). The higher copper levels in marine biota have been generally reported for species taken from areas suspected of being relatively enriched in this element (Portmann, 1972; Eustace, 1974; Phillips et al., 1982; Bu-Olayan and Subrahmanyam, 1998). However, in the current study the Cu concentrations were found lower than this permissible limit in the crab species along the coastal areas.
Similarly, zinc is essential trace metal and used as an active center for metallo-enzymes and activators of other enzyme systems (carbonic anhydrase) (Rainbow, 1997b; MacFarlane et al., 2000). Therefore, it is noteworthy that generally higher ranges of Zn concentrations to those presented have been reported for other marine species from relatively polluted areas of the world (Halcrow et al., 1973; Bu-Olayan and Subrahmanyam, 1998) from which one may infer that regulation of this element may not be complete. According to Kannupandi et al. (2001), chronic concentrations of copper and zinc caused the reduction in the positive phototactic movement and the swimming rate of the larvae of crab (Macrophthalmus erato) this phenomenon appeared to be dependent on quantity and generally increased with duration of metal exposure. Essential metals (such as Cu, Zn) are also subject to regulation, being detoxified by metallothioneins (Canli et al., 1997; MacFarlane et al., 2000), eliminated by excretion through faeces or urine, and via haemolymph through excretory organs or gills (Arumugtam and Ravindranath, 1987; MacFarlane et al., 2000). The levels of essential metals did not reduce by molting may be the reason of high metals loads in crabs. For instance, the distribution of Cu shifted into the soft tissues of lobster (Homarus gammarus) (Hagerman, 1983) and similarly, Bergey and Weis (2007) reported that fiddler crabs (U. pugnax) shifted Cu and Zn from the carapace into the soft tissues prior to molting.
The results revealed that S. crabricauda accumulated the highest amount of toxic metals (Ni, Pb and Cd) compared to the other crab species. S. crabricauda showed the highest Ni content (32.71 µg g-1), whereas the lowest values (16.89 µg g-1) were observed in A. iranica. The Ni toxicity in the aquatic biota exert various malfunctioning in physiological and metabolic activities such as
213 inhibition of respiration, impaired ion regulation, and stimulate oxidative stress) (Blewett et al., 2016). The Pb accumulation found significantly higher in S. crabricauda and lower in O. indica. If molting occurs in contaminated water, metals can be absorbed from the surrounding water through the soft new cuticle for some crustaceans (White and Rainbow, 1984). Fiddler crabs can incorporate substantial amounts of Pb into their carapace and eliminate them during molting (76% of Pb), and the relative amount of depuration is greater in crabs from contaminated sites, but shifted Pb from the exoskeleton into the soft tissues for the relatively clean site and total body burden eliminate 56% Pb through molting. Molting in fiddler crab (Uca pugnax) acts as an elimination of Pb concentrations from the body (Bergey and Weis, 2007). It is uncertain how long this detoxification as a result of molting will preceding for crabs in the environment.
Similarly, White and Rainbow (1986) found that the molting in shrimp (Palaemon elegans) reduced of Cd (15%) but during the molting additional Cd was accumulated by the new soft cuticle from the water, which amount may be higher than the losing of Cd in old exuvia. After shedding, the old exuvia commence to adsorbed large amounts of Cd into water and it may also be consumed by crab itself and other organisms, it could facilitate trophic transfer (Bergey and Weis, 2007). The toxic effects of Cd on enzyme activities are not similar in all the time in crustaceans, some enzyme activities may be increased or decreased, even a similar enzyme from the same organ do not perform similar activities. An increased activity of glutathione S-transferase was reported from the hepatopancreas of a shrimp (Thaker and Haritos, 1989), whereas, a decreased activity of glutathione S-transferase was reported from the hepatopancreas of two crayfishes species (Almar et al., 1987; Meyer et al., 1991). The exposure of cadmium and mercury showed a reduced swimming rate of zoeal stages larvae of crab (Eurypancopeus depressus) treated with different concentrations (Mirkes et al., 1978; Kannupandi et al., 2001).
Cobalt accumulation varied significantly among the selected crab species by more than one order of magnitude and E. orientalis perceived the highest accumulation of Co among the crab species. According to Eisler (1981), marine mollusks contain high amount of Co (about 4 ± 7 ppm dry wt.) which has thousand-time greater concentration factor over seawater (about 1,000–93,000). Patel et al. (1984) also studied the concentration of cobalt-60 in the marine sponge (Spirastrella cuspidifera) near a nuclear power station in India (Bombay). The extracted Co mostly linked with a low molecular weight complex, but there was no specific binding to proteins. Contrariwise, Ueda et al. (1981) observed that cobalt-60 was associated with a high molecular weight protein in the hepatopancreas of the prawn (Penaeus japonica) and the abalone (Haliotis discus). However, 60% of the cobalt-60 was strongly bound to protein and the carbohydrate fraction of the hepatopancreas in
214 the gastropod (Aplysia benedicti) from the similar environment as marine sponge (Spirastrella) found (Hamilton, 1994).
The inter-elemental correlation was evaluated by Pearson’s correlation analysis for four crab species (M. depressus, A. iranica, I. frater and O. indica) separately to understand the relationship and interaction between metals and how one metal stimulate or reduce other metal accumulation in crab species. The strong correlations between metal accumulation in crabs were observed in decreasing order consistent with species (O. indica > M. depressus > A. iranica > I. frater). The positive relationship signifies the similar sources and mutual accumulation whereas negative correlation shows dissimilar sources and contradictory behavior of metal with each other but no correlation indicates unlikely sources and independent behavior. The strong positive correlations between a metal pair indicated that these metals had similar sources, may be terrigenous sources or anthropogenic sources.
The strong correlation between two essential metals, such as Cu and Zn, was observed in all analyzed crab species indicates the requirement of these metals in metabolic activities and mutual accumulation in crabs. On the contrary, the Zn was negatively correlated with Cu (-0.997) in O. indica likely due to competitive behavior of two essential metals for binding with protein. They showed positively correlated with toxic metal Zn vs. Cd (0.959) and Cu vs. Pb (0.949), conversely the negative correlation found with between Zn vs Pb (-0.952) and Cu vs Cd (-0.947), which clearly indicates the ionic strength and competition between two essential and two toxic metals with the metal binding protein. The both toxic metals had negatively correlated with each other Pb vs. Cd (- 0.875) in O. indica, whereas, they showed positive correlation with each other in M. depressus but showed no correlation in A. iranica and I. frater.
The bioaccumulation factor (BAF) value of an organism is expected less than one (<1) for most of the metals, which indicates no bioaccumulation of metals by organisms, otherwise greater than one (>1) value shows the bioaccumulation of metals will occur (Vassiliki and Konstantina, 1984; Falusi and Olanipekun, 2007). Merely BAF of Cr considered safe as they are less than one (<1.0) in all studied crab species (0.14 to 0.45), however the BAF of Fe and Ni showed safe to unsafe limits with respect to crab species. Fe had BAF values in the range of 0.19–2.25, it seems safe limits in all crab species except M. depressus. The BAF values of Ni ranging from 0.58 to 4.03, which are considered safe in three species (S. crabricauda, A. iranica and O. indica), whereas it showed exceeded the threshold in rest of the crab species.
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The BAF values of Cu, Zn, Co, Pb and Cd found to be ranging from 4.50–12.59, 1.60–4.12, 1.04–21.2, 0.95–17.31, and 2.55–8.30, respectively and exceeded from the threshold values (>1.0) in all crab species. High BAF values for Cu, Zn, Co, Pb and Cd indicate that these metals were highly accumulated and biomagnified in the intertidal crab species along the coastal and estuarine areas of Pakistan. Although these examined species are non-edible but have an important role in the food chain of the coastal ecosystem and source of transformation of metals to the edible species or seafood. Higher heavy metal accumulations in crabs refer to potential heavy metal pollution in macrobenthic fauna. This information is crucial for the development of policies concerning the use and disposal of toxic material in the aquatic environment (Ahmed et al., 2011).
The BAFs for Cu, Zn, Co, Pb and Cd greater than the environmental levels (sediments) may be signifying the usefulness of crab species as a concentration indicator of metals along the coast of Pakistan. For decapod crustaceans, Cu and Zn are essential elements for hemocyanin and enzymatic activity, thus these metals are regulated to specific concentrations by decapod crustaceans (Bryan, 1971; Rainbow, 1985), but once the thresholds are touched, the regulatory procedure of metals at that time becomes saturated and accumulation begins (Rainbow, 1985; Engel and Brouwer, 1987). This could explain high BAF of Cu and Zn in all crab species. Non-essential metals are not regulated and accumulation can occur at all concentrations (Rainbow, 1985; Brouwer and Lee, 2007). Co, Pb and Cd are non-essential metals, but they may be toxic at low concentrations, the exceeded level of BAF for these metals indicates that they are not regulated in metabolic functions, therefore the accumulation of these metals can happen at all exposure levels (Brouwer and Lee, 2007; Rainbow, 1985). The molting of the crab Carcinus maenas did not reduce Cd levels (Bondgaard and Bjerregaard, 2005), which may also explain the high BAF for Cd observed in this study.
The heavy metals enter in the crab body through various methods such as direct contact, absorption or ingestion (Rainbow, 1997; Ahearn et al., 2004; Pinheiro et al., 2012). When they come in contact with tissues, the essential metals in concentrations above the physiological limit or excretion capacity and non-essential toxic metals can be mobilized by detoxification processes. Elevated concentrations of Cu, Zn, Cd and Hg can induce the cells to produce proteins of low molecular weight called metallothioneins (Engel and Roesijadi, 1987; Bayne et al., 1988; Viarengo, 1989; Pinheiro et al., 2012) that bind these metals intracellular, reducing the deleterious effects on the cells (Roesijadi, 1992; Viarengo and Nott, 1993; Hamer, 1996; Pinheiro et al., 2012). Cells can also eliminate excess metals through the lysosomes, after sequestration through specific vacuoles (Ahearn et al., 2004). Other metals can also be transported to detoxification areas (e.g., hepatopancreas in decapod crustaceans) or form metallic granules composed by calcium or metal
216 cations (e.g., Cu, Zn and Fe), complexed with sulfur and phosphorus (Ahearn, 2010; Pinheiro et al. 2012). Importantly, metallic granules such as these were previously reported for Ucides cordatus by Correa-Junior et al. (2000), and are sequestered temporarily or permanently (Rainbow, 2007; Pinheiro et al., 2012). According to Rainbow (2007), when the absorption rates of metals exceed the excretion rates, the animals begin to develop mechanisms for detoxification (Pinheiro et al., 2012).
The accumulation of heavy metals in an organisms show a discrepancy along with different species and environmental conditions (Canli and Atli, 2003). These variations in metal accumulation are probably due to the differences in size, age, reproductive and feeding behavior, and metabolic requirements for specific trace element in different species or individuals of the same species. These intrinsic variables effect metal accumulation together with extrinsic variables such as ecological niches where the organism present, position of organism in trophic levels and the bioavailability of trace element in an environment (Soegianto et al., 2008). Therefore, the further investigation was planned in second biomonitoring year through tissues of two selected crab species according to spatial, gender and size variations to better understand the role of crabs as bioindicator for metals contamination.
In the second biomonitoring year (BMY-II = 2016), the both selected crab species (M. depressus and A. iranica) showed high variability in heavy metals accumulation in tissues according to habitat, gender as well as size of the crabs. The results reveal that Cu, Zn, Pb and Cd accumulations in tissues of M. depressus were observed significantly greater in Korangi Creek as compared to Sandspit, moreover the tissue accumulation of these metals showed few times greater as compared to the environmental levels. The high accumulation of Co was seem at Sandspit in tissues of M. depressus but it observed lower as compared to environmental levels.
The Cu, Zn and Co accumulations in tissues of fiddler crabs perceived significantly greater in Sonari and showed higher accumulation rate in crabs as compared to environmental levels, whereas Pb and Cd accumulation in tissues of fiddler crabs were observed higher at Sandspit. The accumulation of Pb in fiddler crabs showed more than three (Sonari) to ten (Sandspit) times lower as compared to environmental exposure. Conversely, Cd accumulation more than three times higher in fiddler crabs as compared to sediment levels of Cd in both areas (Sandspit and Sonari). Thus, the differences between populations are a combination of geographic location, seasonality, and sexual genre (Chen et al., 2005; Almeida et al., 2016).
In the BMY-II, Pb and Cd concentrations in the tissues of both crab species (M. depressus and A. iranica) exceeded the safe limits of the USFDA guidelines among the examined heavy
217 metals. These metals levels were greater (about 3 to 10 times) than the recommended guidelines. Both metals were efficiently accumulated by the M. depressus (mean AF of Pb and Cd were 2.06 ± 1.67 and 2.68 ± 0.88, respectively) as compared to A. iranica (mean AF of Pb and Cd were 0.05 ± 0.10 and 1.65 ± 0.32, respectively) from the environment.
Lead and cadmium may become potentially hazardous metals in the both crab species if the pollution of these toxic metals further intensifies with the time being along the coastal areas of Pakistan. Since these metals are more efficiently accumulated by the crabs and they could be a potential candidate as a bio-indicator of metal pollution in coastal environments. These intertidal crab species are primary consumer along the coastal belt of Pakistan and potentially incorporate the Pb and Cd contamination in food chain through bio-magnification as they are important food item for large organisms including sea birds. There is need to study the Pb and Cd incorporation in food chain and food web of coastal and estuarine areas of Pakistan. Lead (Pb) affects the central nervous system of animals and inhibits their ability to synthesize red blood cells. Long-term exposure to this element also promotes cancer and can result in reduced performance of the nervous system, weakness in fingers, wrists, or ankles, small increases in blood pressure and anaemia. Exposure to high Pb levels can severely damage the brain and kidneys and ultimately cause death (Koller et al., 1985; Tong et al., 2000; Martin and Griswold, 2009; Alvaro et al., 2016). Likewise, Cadmium (Cd) has also a disturbance effect in antioxidant enzymes of crabs and shrimps, affects lipid transport, and can inhibit calcium (Ca) uptake (Rainbow and Black, 2005; Kang et al., 2012; Yang et al., 2013; Alvaro et al., 2016). Cd is non-essential for crabs, and enters cells through transporters for divalent metals such as Ca (Rainbow and Black, 2005; Pinheiro et al., 2012).
For biomonitoring purposes, two biological factors, namely body size and gender, need to be evaluated before any decision as to their indicator potential can be confirmed (Rainbow, 1996; Chen et al., 2005) and these two variables largely determined the physiological activates of crabs. Therefore, in the BMY-II, the variations in metal accumulation in tissues of both crab species were evaluated with respect to gender as well as size of the crabs. In fiddler crab, no significant intersexual differences were observed in Cu, Zn and Pb levels, however Co and Cd burden were found significantly greater in tissues of female as compared to male. Whereas in M. depressus, no significant intersexual differences were observed in Cu, Pb and Cd levels, however Zn and Co burden were found significantly greater in tissues of female as compared to male. The high metals accumulation in females may be due to their different energy demands, food preferences, and physiological requirements that can result in large variations in the trace element accumulation as well as likely due to handling of more sediments for their nutritional requirement during the
218 reproductive phase. There was a significant interaction between site and gender suggested that in terms of comparing metals accumulation between sites, the gender is also an important consideration (MacFarlane et al., 2000).
Most of the metals in both species (M. depressus and A. iranica) showed no significant correlations with the size of the crabs. Some other studies also showed lack of correlations between the metals and crab size, for instance, Ucides cordatus crabs did not show significant correlations between Cu, Cd, Cr, and Mn concentrations and carapace width from other mangroves of Southeastern Brazil (Pinheiro et al., 2012; Almeida et al., 2016). Only few significant correlations between metals concentrations and carapace width (CW) of the both crab species were observed. However, in most of the cases metal concentrations were inversely proportional to crab size in both crab species due to the different physiological requirements during the life span (Virga and Geraldo, 2008; Almeida et al., 2016). The accumulation of Cu and Zn in tissues showed no relationship with size of M. depressus indicated that the mature crabs of this species are able to adjust essential elements concentrations in different physiological processes and maintain them within a constant range (Rainbow, 1990; 1996). However, these metals (Cu and Zn) significantly less accumulated in the mature crabs in A. iranica may be indicated that the mature crabs have more utilization of these metals in different physiological processes (Virga, 2006; Pinheiro et al., 2012). For instance, Cu is utilized for hemocyanin synthesis and for exoskeleton hardening during the post-molt (Engel and Brouwer, 1991; Keteles and Fleeger, 2001; Pinheiro et al., 2012). The variations in trace metal accumulation between species are common due to different environmental conditions, trophic position, food source, and metabolic rhythms (Rainbow, 2002; Almeida et al., 2016). The accumulations of Pb and Cd in tissues were inversely proportional with size of M. depressus. Unlike the other metals, Co and Cd showed higher levels according with crab size in M. depressus and A. iranica, respectively. It appears that the metal accumulation increased in larger animals as showing an accumulation that is directly related to the period of exposure to the metal (Pinheiro et al., 2012).
Most of the metal accumulations in tissues of both species (M. depressus and A. iranica) do not show the significant correlations with metal levels in sediments. No relationship was evaluated between metal levels in sediments and M. depressus, but when the data separated according to the gender the better out comes were observed. The Zn accumulation in tissues of male crab directly correlated with Zn concentrations in sediments, whereas opposite condition was found in females of M. depressus. With respect to the exposure concentrations in sediments the accumulation of Cd and Pb in female crabs showed directly and inversely proportion, respectively. The spatial changes of Zn and Cd concentrations in sediments were directly reflected the Zn and Cd accumulation levels in
219 male and female of M. depressus, respectively. Both males and females crab of M. depressus accumulate significantly higher amounts (more than three times higher) of Cd than the environmental levels.
The accumulations of Cu and Zn in male fiddler crabs were inversely correlated with their corresponding concentrations in sediments, whereas female crabs showed no correlation for any metal accumulation with the metals levels in sediments. The accumulation of Cu and Zn in tissues of A. iranica (in both genders) responded not proportionally with the exposure levels of sediments. The significant greater concentrations of Cu and Zn were evaluated in the sediments of Sandspit area where the accumulation of these metals were evaluated significantly lower than the Sonari mudflat. Low exposure environments leads high accumulation and vice versa. The concentrations of Co and Cd were observed significantly greater in female fiddler crabs as compared to male as well as exposure levels in sediments. The Pb levels showed significantly higher in sediments as compared to fiddler crabs in both sites. The results of BMY-II revealed that both genders of both crab species should treated separately in further biomonitoring programs as they accumulate metals according to their physiological requirements as well as habitat conditions.
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CHAPTER 04
CHARACTERIZATION OF ADSORPTION CAPACITY OF CRAB CHITIN FOR TOXIC HEAVY METALS (CADMIUM AND LEAD) FROM AQUEOUS SOLUTION
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4.1. INTRODUCTION
Crab shells mainly comprise of carbonates (calcium and magnesium), proteins and chitin
(Vijayaraghavan et al., 2011). Chitin is naturally found in crab shells with association of CaCO3 and some proteins, which may be responsible for releasing alkalinity and nitrogen in aqueous solution (Giraud-Guille, 1998; Daubert and Brennan, 2007; Pinto et al., 2011). Chitin is the second abundant biopolymer after the cellulose and it is mainly extracted from shell of crustaceans, therefore crab shell is a major source of chitin and chitosan, another sources of chitins are the cuticles of insects and the cell walls of fungi (Knorr, 1984; Yen et al., 2009; Pinto et al., 2011). Structurally, chitin is a straight-chain polymer composed of b-1, 4-N-acetylglucosamine and classified into a-, b- and c- chitin (Cabib, 1981; Cabib et al., 1988; Yen et al., 2009). The chitin molecule consists of (1-4)-2- acetamido-2-deoxy-d-glucose units, some of which are deacetylated. The acetamido groups are able to act as non-specific chelators and form hydrogen bonds with heavy metals (Barriada et al., 2007; Pinto et al., 2011).
Chitin, chitosan and their derivatives have various applications such as they can be used in food industry as an antimicrobial, thickening, emulsifying and stabilizing agent (Shahidi et al., 1999; Yen et al., 2009). It has various biomedical applications as chitin possesses many useful biological properties such as biodegradability, biocompatibility, hemostatic and healing properties (Farkas, 1990; Fleet and Phaff, 1981; Yen et al., 2009) as well as the pharmacological significance as chitosan used as a nutritional supplement (Shahidi et al., 1999). Therefore, much consideration has been paid to production this biomaterial from different sources as the enormous quantity of wastes produced through the crab fishery. Hence, some efforts previously have been reported for efficient reutilization of crab shell wastes for the production of chitin (Jung et al., 2007; Jo et al., 2008) and advance directions such as biosorbent properties of chitin have been focus for effective utilization of crab fishery waste (Vijayaraghavan et al., 2011).
Recently, adsorption with low-cost non-conventional adsorbents has been used for the elimination of pollutants from water contamination (Bailey et al., 1999). Several biomaterials including bacteria (Vijayaraghavan and Yun, 2008), fungi (Kapoor and Viraraghavan, 1995), algae (Davis et al., 2003), and industrial or agricultural wastes (Crini, 2005; Sud et al., 2008) were
236 identified as potential candidates for the removal of metal ions. Many recent studies have shown that crab shell particle and chitinous materials are effective in removing metals such as arsenic (Kartal and Imamura, 2005), cadmium (Benguella and Benaissa, 2002; Evans et al., 2002; Benaissa and Benguella, 2004; Aris et al., 2014), mercury (Rae et al., 2009), cobalt (Vijayaraghavan et al., 2006), iron (Favere et al., 2004), copper (Kartal and Imamura, 2005; Vijayaraghavan et al., 2006; Aris et al., 2014), nickel (Vijayaraghavan et al., 2004), chromium (Kartal and Imamura, 2005), manganese (Vijayaraghavan et al., 2011), lead (Lee et al., 1997; Kim, 2004; Zhou et al., 2005), and zinc (Vijayaraghavan et al., 2011) from aqueous solutions.
The technologies used for the removal of heavy metals from waste water include chemical precipitation, flocculation, membrane filtration, solvent extraction, ion exchange and adsorption. In these methods, biosorption with low-cost materials (industrial or agricultural residues) has been found to be superior to other techniques in virtue of the low-priced cost, high efficiency, easiness of operation, regeneration of biosorbent and possibility of metal recovery (Göksungur et al., 2005; Hasan and Srivastava, 2009; Hasan et al., 2009; Cao et al., 2010). The major advantages of this technology over conventional ones includes low cost, high efficiency, the minimization of chemical sludge, regeneration of biosorbent and the possibility of metal recovery.
Biosorption is the binding property of biosorbent (biomass or biomolecules) to concentrate particular molecules or pollutants (organic or inorganic) from aqueous solutions. The phenomenon of bioaccumulation depends on active transport, however the biosorption process depends on inactive biomass through passive transport mechanism based on the ‘affinity’ between the biosorbent and adsorbate (Volesky, 2007; Park et al., 2010; Vijayaraghavan et al., 2011). Biosorption considered a potential instrument for the removal of metals from solutions, not only for toxic metal removal but also for precious metal recovery. The use of microorganisms for gold and silver recovery by biosorption has been investigated by several researchers (Brierley and Vance, 1988; Kuyucak and Volesky, 1988; Mattuschka and Straube, 1993; Veglio and Beolchini, 1997). Biosorption is becoming an important component in the integrated approach to the treatment of industrial wastewater. However, there is a need to research and develop bioprocesses further to realize systems that are flexible, reliable and cost-effective in the treatment of wastewaters (Brierley, 1990; Veglio and Beolchini, 1997).
The most important and difficult task in the biosorption process is to identify the potential biomass from enormous types of biosorbants (Kratochvil and Volesky, 1998; Park et al., 2010). The cheapness and accessibility is the most promising properties of biosorbents for removal of
237 commercially pollutants from the industrial effluents (Volesky, 1994; Volesky and Holan, 1995; Vieira and Volesky, 2000; Park et al., 2010). Meanwhile, the native biomass can originate from free of cost industrial wastes, organisms that can easily obtain in large amounts in nature and organisms that can be grown quickly or specially cultivated or propagated for biosorption purposes (Volesky and Holan, 1995; Vieira and Volesky, 2000; Park et al., 2010). The several types of adsorbents used successfully in biosorption process such as activated carbons (Hu et al., 2003), zeolites (Bosso and Enzweiler, 2002; Inglezakis et al., 2003), clays (Abollino et al., 2003), silica beads (Ghoul et al., 2003), low-cost adsorbents-industrial byproducts (Gupta et al., 2003; Lopez et al., 2003; Reddad et al., 2003), biomass (Loukidou et al., 2003; Vasudevan et al., 2003) and polymeric materials-organic polymeric resins (Atia et al., 2003; Zhang et al., 2003), macroporous hypercross linked polymers (Azanova and Hradil, 1999) have been classified as mineral, organic or biological (Crini, 2005). Globally, among the afore-mentioned adsorbents, activated carbon has been used for the elimination of pollutants from aqueous environment (Bansal and Goyal, 2005). Nevertheless, its wide application in aqueous environment is sometimes limited because of the higher cost (Cestari et al., 2007).
The understanding of the mechanisms by which biosorbents remove pollutants is very important for the development of biosorption processes for the concentration, removal, and recovery of the pollutants from aqueous solutions (Muraleedharan et al., 1991; Park et al., 2010). When the chemical or physiological reactions occur during biosorption; the rate, quantity, and specificity of the pollutant uptake can be manipulated through the specification and control of process parameters. Biosorption of metals or dyes occurs mainly through interactions such as ion exchange, complexation, adsorption by physical forces, precipitation and entrapment in inner spaces (Sud et al., 2008; Park et al., 2010).
In the case of batch biosorption processes for removing adsorptive pollutants such as ionic metals or dyes, the important factors include; solution pH, temperature, ionic strength, initial pollutant concentration, biosorbent dosage, biosorbent size, agitation speed and also the coexistence of other pollutants. Of these factors, the pH appears to be the most important regulator of the biosorptive process as the pH affects the solution chemistry of the pollutants, the activity of functional groups in the biosorbents and the competition with coexisting ions in solution (Vijayaraghavan and Yun, 2008; Park et al., 2010). In general as solution pH increases, the biosorptive removal of cationic metals or basic dyes is enhanced, while that of anionic metals or acidic dyes is reduced. In some cases, a higher pH will cause precipitation of cationic metals, making neutral conditions essential in this case (Park et al., 2010).
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The biosorption process involves a solid phase (sorbent) and a liquid phase (solvent, normally water) containing the dissolved species to be adsorbed (adsorbate). Quantification of adsorbate-adsorbent interactions is fundamental for the evaluation of potential implementation strategies. To compare pollutant uptake capacities of different types of biosorbents, adsorption phenomena can be expressed as batch equilibrium isotherm curves. These can be modeled by mechanistic or empirical equations; the former can explain, represent, and predict the experimental behavior, while the latter do not reflect the mechanism, but can reflect the experimental curves (Vijayaraghavan and Yun, 2008; Park et al., 2010). Among various models of adsorption isotherm, the Langmuir and Freundlich models have been most commonly used, with a high rate of success.
Objective
The main objective of this study deals with some preliminary aspects of bioremediation potential of crab shell for the removal of toxic heavy metal (Pb and Cd) from aqueous solutions.
The parameters affecting the biosorption potential (such as metal concentrations, pH, and contact time) and the mechanism associated with the removal of heavy metal were discussed. The surface morphology and percent elemental composition of crab chitin evaluated through scanning electron microscope (SEM-EDS). The functional groups of biosorbent that associated with the binding of toxic heavy metals (Pb and Cd) also studied by FTIR analysis.
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4.2. MATERIALS AND METHODS
4.2.1. Preparation of Raw Crab Shell
In the current experimental study, waste shells of crab species, Charybdis feriata (commonly known as orange crab) selected as biosorbent for removal of Cd(II) and Pb(II) from aqueous solution. The crab species has a reddish exoskeleton and crabmeat is very tasty and desirable. It is trapped in fishing gears but this species is not consumed or nor export shellfish species. Therefore, this species easily available in fishery catch low cost. The crab shells washed extensively with distilled water and oven dried at 75 °C for constant weight. The dried shells were grounded and sieved through US sieve mesh size 170 mm to get the homogenous and fine particle size of crab shell. The 170 mm crab shell particles were initially stored in a desiccator, until used as biosorbent in subsequent batch experiments.
4.2.2. Purification of Crab Chitin
The fine particles of crab shell treated with acid and alkali to remove the minerals and proteins following Kurita et al. (1993) with minor modification. In detailed, the fine powder of raw crab shell (20 g) was treated with 1.0 N HCl solution (100 mL) at room temperature for 6 h to remove minerals (calcium, sodium, potassium, zinc and magnesium) constituents. The residue (mainly protein and chitin) washed extensively with deionized water and dried at 60 °C overnight. The samples further treated with aqueous sodium hydroxide (NaOH) solution at the ratio of 1:10 (w/v) at 100 °C for 3 h to remove protein. After reaction period, the residue filtered and washed with deionized water. Then the residue (chitin) rinsed with deionized water and dried at 75 °C for constant weight (Kim and Park, 2001).
4.2.3. Metal Stock Solution Preparation
The heavy metal adsorbates used in this study were lead (PbCl2) and cadmium (CdCl2) of analytical grades. The stock solutions of 1000 mg/L prepared by dissolving above-mentioned metal salts in one percent nitric acid (HNO3) solution. The required working solutions of heavy metals for the adsorption experiments were obtained by diluting stock solution.
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4.2.4. Batch Experiment
Each batch experiment was performed by bringing into contact of crab chitin (0.2 g) with 50 mL of metals solution of known concentration in a 250 mL Erlenmeyer flask. The four batch experiment was conducted to evaluate the effects of contact time (min), metal concentrations (mg/L) and pH on the adsorption capacity of crab chitin in aqueous solution; concentration was set according to experimental design methodology. The effect of contact time (minutes) on the adsorption capacity of crab chitin was evaluated at 3, 5, 10, 20, 40, 80, 160 and 320 minutes. The pH, temperature, metal concentrations and adsorbent dosage kept constant. The variations of initial metal concentrations (mg/L) in aqueous solution also effect on the adsorption capacity of the adsorbent. Therefore, the batch experiment settled with five different metal concentrations (1, 10, 25, 50 and 100 mg/L). In this experiment, the pH, temperature and contact time were constant. The effect of pH on the adsorption capacity of crab chitin was evaluated at eight different pH i.e. 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0 and 9.0. The contact time, temperature and metal concentrations were constant.
In all batch experiments, the initial pH of the solution was adjusted to the required value by adding 1.0 N HCl (Kim et al., 2015) and 1.0 M NaOH solutions (Cao et al., 2010), except the experiment in which the pH was set as variable. The initial concentration (mg/L) was adjusted as 10mg/L of Cd(II) and Pb(II), except the experiment in which the effect of metal concentration was evaluated in adsorption onto the crab chitin. The contact time set as 60 minutes in all experiments, except in which the effect of contact time was investigated on the adsorption ability of the biosorbent. Experiments were conducted in independent replicates that the batch experiments were producible within at most 5% error. The residual metal ion concentrations in solutions determined with Atomic Absorption Spectrometer AAnalyst 700.
The amount of adsorbed metal ions (Qe) per gram of crab chitin and percentages of removal rate calculated through equations 4.1 and 4.2, respectively:
Qe = (Ci – Ce) V / M (Equation 4.1)
% Removal = [(Ci – Ce)/ Ci] × 100 (Equation 4.2)
Where,
Ci and Ce are the initial and equilibrium metal concentrations (mg/L), respectively, V is the volume of the heavy metal solutions (L) and M is the weight of biomass (g).
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4.2.5. Adsorption Isotherms Studies
In order to explore the biosorption behavior and predict the biosorption capacity of biomass, Langmuir and Freundlich adsorption isotherm models were chosen for this study. These two models are widely and primarily used to calculate the amount of sorbate that a sorbent retained and remained in the solution at equilibrium (Cao et al., 2010). The Langmuir model assumes that metal uptake takes place on a homogeneous surface by monolayer sorption without any interaction between the adsorbed molecules (Cao et al., 2010). The Langmuir model given by the following Equation (4.3):
Qe = QL KL Ce /1 + KL Ce (Equation 4.3)
Where,
Qe and QL are the observed and maximum uptake capacities (mg/g), respectively, Ce is the equilibrium metal concentration (mg/L) and KL is the equilibrium constant (L/mg).
The Freundlich model proposes a multilayer sorption based on a heterogeneous surface, and with interaction between the adsorbed molecules (Cao et al., 2010). The Freundlich model is described by Equation (4.4):
1/n Qe = KF Ce (Equation 4.4)
Where,
‘KF’ (L/g) and ‘1/n’ are Freundlich isotherm constants related to adsorption capacity and adsorption intensity, respectively, Qe is the observed uptake capacity (mg/g) and Ce is the equilibrium metal concentration (mg/L).
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4.2.6. Analysis of Adsorbent Surface
In this study, the scanning electron microscope/energy-dispersive spectroscopy (SEM-EDS) analysis were employed to perceive the surface morphology and examine the elemental constitute of adsorbent before and after biosorption. The raw crab shell, crab chitin and metal-loaded crab chitin analyzed through SEM/EDS (SEM-JEOL model number JSM-6380A and EDS-JEOL model number EX-54175jMU) placed in the Centralized Science Laboratory, University of Karachi. The samples were coated under vacuum up to 300 °A with gold layer and then examined equipment.
The Fourier Transform Infrared Spectroscopy (FTIR) analysis was employed to examine the variations in associated functional groups of the adsorbent before and after the biosorption process. The samples (raw crab shell, crab chitin and metal loaded crab chitin) was analyzed through Fourier transform infrared spectrometer (Bruker Tensor II) placed in Industrial Analytical Centre, University of Karachi.
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4.3. RESULTS
4.3.1. Factors and their Effects on Biosorption
Several factors effect on the adsorption capacity of biosorbents in the biosorption process mainly includes; contact time, initial metals concentrations and pH of the solution. Here, the results of experimental data were described to evaluate above-mentioned factors to understand the adsorption mechanisms of Pb(II) and Cd(II) on the crab chitin.
(I) Contact Time
Cd(II) adsorption through the crab chitin was determined under conditions of equilibrium by conducting a series of experiments with different contact time ranging from 0 to 320 minutes. The experimental conditions set as an initial Cd(II) concentration 10 mg/L, biomass dosage 0.2 g, pH 4.0 and temperature 25 °C. The great variability was observed in the required contact time to reach equilibrium state for Cd(II) ions (Figure 4.1a). The Cd(II) adsorption capacity through the crab chitin increased steadily from 0.80 to 1.79 mg g-1 in only 10 minutes of the batch experiment which gives the 72% removal of Cd(II) ions from the solution (Figure 4.1a). After 10 minutes of the experiment the Cd(II) uptake through the chitin was decreased to 1.31 mg g-1 at 40 minutes with the removal efficiency of 52%. After the 40 minutes of the contact time the uptake rate increased steadily, reaching the maximum uptake 2.29 mg g-1 at 320 minutes with the removal efficiency of 92% (Figure 4.1a).
The Pb(II) removal from aqueous solution through crab chitin was considered using various contact times ranging from 0 to 320 minutes. The other parameters in the batch experiment set as an initial Pb(II) concentration of 10 mg/L, biomass dosage 0.2 g, pH 3.0 and temperature 26 °C (Figure 4.1b). The uptake of Pb(II) through the crab chitin increased gradually from 0.6 to 1.3 mg g-1 with the increasing of contact time from 5 to 160 minutes of the batch experiment then decreased slowly at the time of 320 minutes (Figure 4.1b). The removal efficiency of Pb(II) varied from 23 to 52% through the crab chitin (Figure 4.1b). The crab chitin presented the equilibrium time of 160 min for removal only half ions of Pb(II) in aqueous solution (removal rate 52%).
244
(a) Cd(II) Biosorption
2.5 Metal Uptake (mg/g) % Removal 100 90 2 80 70 1.5 60 50
1 40 % Removal % 30
Metal Uptake (mg/g) 0.5 20 10 0 0 0 5 10 20 40 80 160 320 Contact Time (min)
(b) Pb(II) Biosorption Metal Uptake % Removal 1.4 60
1.2 50 1.0 40 0.8 30 0.6
20 Removal %
0.4 Metal Uptak (mg/g) 0.2 10 0.0 0 0 5 10 20 40 80 160 320 Contact Time (min)
Figure 4.1: Effect of contact time (min) on metal uptake and % removal through the crab chitin (a) Cd(II) biosorption (b) Pb(II) biosorption from the aqueous solution.
245
(II) Metal Concentrations
The effect of Cd(II) concentrations (ranging from 1 to 100 mg/L) on the adsorption rate of crab chitin (biomass dosage 0.2 g) was assessed at pH 2.0 and 25 °C for 60 minutes (Figure 4.2a). The Cd(II) uptake onto the crab chitin improved progressively from 0.2 to 20.2 mg g-1 with the increase of Cd(II) concentration in aqueous solution during the batch experiment. However, the removal efficiency of chitin decreased (100% to 81%) with the increased of Cd(II) concentration in the solution (Figure 4.2a).
The effect of Pb(II) concentrations (ranging from 1 to 100 mg/L) on the adsorption rate of crab chitin, the experimental conditions were settled as biomass dosage 0.2 g, initial pH 3.0, temperature 25 °C and contact times 60 minutes (Figure 4.2b). The Pb(II) adsorption through crab chitin improved progressively from 0.1 to 11.9 mg g-1 according to the Pb(II) concentration in aqueous solution during the batch experiment. However, the removal efficiency observed maximum 98% at 25 mg/L of Pb(II) solution (Figure 4.2b).
246
(a) Cd(II) Biosorption Metal Uptake % Removal 25 120
20 100 80 15 60 10
40 Removal %
Metal Uptake (mg/g) 5 20
0 0 1 10 25 50 100 Cd Concentration (ppm)
(b) Pb(II) Biosorption
14.0 Metal Uptake % Removal 120
12.0 100 10.0 80 8.0 60 6.0
40 Removal %
4.0 Metal Uptake (mg/g) 2.0 20
0.0 0 1 10 25 50 100 Pb Concentrations (ppm)
Figure 4.2: The effect of initial metal concentration on uptake and removal of metals ions through the crab chitin (a) Cd(II) biosorption (b) Pb(II) biosorption from the aqueous solution.
247
(III) The pH
The biosorption experiments were conducted to determine the effect of the initial pH on the sorption of Cd(II) ions through crab chitin within the pH range from 2.0 to 9.0. The initial pH of the solution significantly affected the adsorption capacity of adsorbent then pH adjusted by freshly prepared 0.1 M HCl or 0.1 M NaOH solutions during the experiment. The effect of pH on the adsorption behavior of crab chitin for Cd(II) is shown in Figure 4.3a. The studied ranges of pH, the rising and decreasing trend was observed for Cd(II) adsorption capacity and it was highest at pH 9.0 and was lowest at pH 2.0. The highest amount (2.49 mg/g) of biosorption of Cd(II) ions was evaluated at pH 9.0 with the high Cd removal efficiency as was about 99.7%. The lowest quantity (0.83 mg g-1) of biosorption of Cd(II) ions was observed at pH 2.0 with low Cd(II) removal efficiency about 33%.
The effect of pH on the adsorption behavior of crab chitin for Pb(II) is shown in Figure 4.3b. The Pb(II) adsorption capacity was evaluated highest at pH 9.0 and lowest at pH 2.0, between the pH 2–6 the Pb uptake was observed lower, whereas in ranges of 7–9 pH the increment in uptake was found. The highest amount of biosorption of Pb(II) ions was estimated at pH 9.0 with the metal uptake value 4.93 mg g-1 and highest removal efficiency (99%). The lowest quantity of biosorption of Pb(II) ions was evaluated at pH 2.0 as 1.15 mg g-1 with the Pb(II) removal efficiency was about 46%.
248
(a) Cd(II) Biosorption
Metal Uptake %Removal 3 120
2.5 100
2 80
1.5 60
1 40 Removal %
Metal Uptake (mg/g) 0.5 20
0 0 2 3 4 5 6 7 8 9 pH
(b) Pb(II) Biosorption 6 Metal Uptake %Removal 120
5 100
4 80
3 60
2 40 Removal %
Metal Uptake (mg/g) 1 20
0 0 2 3 4 5 6 7 8 9 pH
Figure 4.3: Effect of pH on metal uptake and percent removal of metals through the crab chitin (a) Cd(II) biosorption (b) Pb(II) biosorption from aqueous solution.
249
4.3.2. Adsorption Isotherm Models
The biosorption mechanism was examined by using two adsorption isotherm equations (the Langmuir and Freundlich isotherm) to characterize the experimental data. It is difficult to predict the adsorption equilibrium when the experimental did not reach the equilibrium state. Figure 4.4a and b showed the adsorption isotherm for Cd(II) and Pb(II) by plotting of Ce/Qe vs. Ce to determine the Langmuir constants. Whereas, Figure 4.4c and d illustrated the adsorption isotherm for Cd(II) and
Pb(II) by plotting of log Qe vs. log Ce to determine the Freundlich constants. Various parameters calculated from the adsorption isotherms includes; maximum adsorption capacity (QL), adsorption
2 efficiency of binding sites (KL) and correlation coefficient (R ) from the Langmuir isotherm equation, whereas sorption capacity of the adsorbent (KF), sorbability of the metal (1/n) and correlation coefficient (R2) from the Freundlich isotherm equation, the results of two isotherm models shown in Table 4.1.
The highest regression coefficient ‘R2’ was observed for Langmuir isotherm model for both metals i.e. R2 = 0.9997 for Cd(II) and R2 = 1 for Pb(II). Whereas, the coefficient of determination ‘R2’ showed lower values (R2 = 0.9995 for Cd(II) and R2 = 0.9987 for Pb(II)) determined from Freundlich model (Table 4.1). The results indicated that the Langmuir model was more favorable for describing the adsorption of Cd(II) and Pb(II) through the crab chitin. The Langmuir constant (QL) indicated the maximum adsorption capacity on the monolayer coverage, was evaluated 1.815 mg of Cd(II) per g of crab chitin and 1.383 mg of Pb(II) per g of crab chitin (Table 4.1). The Langmuir
-1 -1 constant (KL) signify the affinity of binding sites and was observed 0.138 L mg and 0.908 L mg for Cd(II) and Pb(II), respectively. The Langmuir parameters i.e. high maximum capacity (QL) and low (less than one) affinity constant (KL) indicated the current experimental data could be best fitted by Langmuir model as compared to Freundlich model (Table 4.1).
The separation factor ‘RL’ was also evaluated from the Langmuir affinity constant ‘KL’
(Table 4.1). The dimensionless values of ‘RL’ was observed 0.024 and 0.112 for Cd(II) and Pb(II), respectively. The less than one values of ‘RL’ further signify that the adsorption of both metals appropriately represented by Langmuir adsorption isotherm.
250
Figure 4.4: The isotherm models for Cd(II) and Pb(II) biosorption through crab chitin in aqueous medium (a) Langmuir isotherm for Cd(II), (b) Langmuir isotherm for Pb(II), (c) Freundlich isotherm for Cd(II) and (d) Freundlich isotherm for Pb(II).
251
Table 4.1: The Langmuir and Freundlich adsorption isotherm constants for Cd(II) and Pb(II) through crab chitin during the experimental studies.
Adsorption Model Metals Model Parameters Parameter Values
Langmuir Isotherm Cd(II) R2 0.9997 QL 1.8153 KL 0.1385 RL 0.024
Pb(II) R2 1 QL 1.383 KL 0.908 RL 0.112
Freundlich Isotherm Cd(II) R2 0.9995 1/n -0.8018 Log KF 0.3197
Pb(II) R2 0.9987 1/n 0.9472 Log KF -1.547
252
4.3.3. FTIR Analysis
To determine the functional groups involved in the biosorption process of Cd(II) and Pb(II) through crab chitin, a comparison between the FTIR spectra before and after adsorption of metals were examined (Figure 4.5). The FTIR spectra confirmed changes in functional groups and surface properties of the crab chitin after the adsorption of Cd(II) and Pb(II) ions, which illustrated by the shifting and adding of some functional groups bands after metals adsorption (Figure 4.5).
The FTIR spectral characteristics of raw crab shell and crab chitin was illustrated in Table 4.2. A band appeared at 3266 cm−1 in raw crab shell, corresponding to the normal “polymeric” stretching vibration of –OH, which is shift at 3265 cm−1 in crab chitin. The absorption bands at 2879
−1 cm in crab chitin assigned to asymmetric and symmetric –CH2 groups, which was absent in the raw crab shell. The absorption band at 2108 cm−1 in raw crab shell assigned to alkynes that shifted at 2106 cm-1 in crab chitin. The FTIR spectrum of raw crab shell exhibits characteristics of –NH bending vibrations for primary amine groups at 1631 cm−1, which shifted at 1650 cm-1 in crab chitin. The bands observed at 1395 cm−1 and attributed to the C–N stretching vibration in raw crab shell, which was shifted at 1409 cm-1 in crab chitin. The C–O stretching vibration for primary alcohol was observed at 1024 cm−1 in raw crab shell and shifted at 1027 cm-1 in crab chitin. The band at 869 cm−1 and 704 cm-1 attributed to C–H out-of-plane bending vibration, which shifted at 871 cm-1 and 708 cm-1. The absorption band observed at 556 cm−1, represents a –NO wagging vibration in raw crab shell, which was shifted at 564 cm-1 in crab chitin.
The FTIR spectral characteristics of Cd(II) loaded crab chitin was illustrated in Table 4.3. A band appeared at 3433 cm−1, corresponding to the stretching vibration of –OH, the extension vibration of N–H and intermolecular hydrogen bonds of polysaccharides. A band appeared at 3263 cm−1, corresponding to the normal “polymeric” stretching vibration of –OH. The absorption bands at
−1 −1 3105 cm assigned to asymmetric and symmetric –CH2 groups. The absorption bands at 2880 cm
−1 are assigned to asymmetric and symmetric –CH2 groups. The absorption bands at 2106 cm assigned to alkynes groups. The FTIR spectrum of chitosan exhibits characteristics of –NH bending vibrations for primary amine groups at 1627 cm−1 and 1551 cm−1. The bands observed at 1377 cm−1 presented to the C–N stretching vibration. The C–O stretching vibration for secondary alcohol observed at 1151 cm−1, while C–O stretching vibration for primary alcohol appeared at 1066 cm−1 and 1021 cm−1. The band at 870 cm−1 and 707 cm-1 can be attributed to C–H out-of-plane bending vibration, while the absorption band observed at 563 cm−1 represents a –NO wagging vibration.
253
The FTIR spectral characteristics of Pb(II) loaded crab chitin was illustrated in Table 4.4. A band appeared at 3433 cm−1, corresponding to the stretching vibration of –OH, the extension vibration of N–H and intermolecular hydrogen bonds of polysaccharides. A band appeared at 3258 cm−1, corresponding to the normal “polymeric” stretching vibration of –OH. The absorption bands at
−1 −1 3102 cm assigned to asymmetric and symmetric –CH2 groups. The absorption bands at 2879 cm
−1 are assigned to asymmetric and symmetric –CH2 groups. The absorption bands at 2104 cm assigned to alkynes groups. The FTIR spectrum of chitosan exhibits characteristics of –NH bending vibrations for primary amine groups at 1623 cm−1 and 1553 cm−1. The absorption bands at 1420 cm−1 designated to carboxylate ions. The bands observed at 1310 cm−1 presented to the C–N stretching vibration. The C–O stretching vibration for secondary alcohol detected at 1153 cm−1 and 1112 cm-1, while C–O stretching vibration for primary alcohol appeared at 1066 cm−1 and 1014 cm−1. The band at 895 cm−1 and 695 cm-1 can be attributed to C–H out of plane bending vibration, while the absorption band observed at 560 cm−1 represents a –NO group.
254
(a)
(b)
(c)
(d)
Figure 4.5: The IR spectra of (a) raw crab shell (b) crab chitin (c) Cd(II) loaded chitin and (d) Pb(II) loaded chitin as obtained during the experimental studies.
255
Table 4.2: The FTIR spectral characteristics of raw shell of crab and extracted chitin obtained during the experimental studies.
Crab Shell Chitin
556 564 704 708 869 871 1024 1027 1395 1409 1631 1650 2108 2106 - 2879 3266 3265
256
Table 4.3: FTIR spectral characteristics of chitin before and after the adsorption of Cd(II) obtained during the experimental studies.
Chitin Before Chitin After Pb(II) Difference Adsorption adsorption
564 564 -1 708 707 0 871 870 +1 1027 1021 -7 - 1066 - - 1151 - 1409 1377 -32 - 1551 - 1650 1627 -23 2106 2106 -1 2879 2881 +1 - 3105 - 3265 3263 -2 - 3435 -
257
Table 4.4: FTIR spectral characteristics of chitin before and after biosorption of Pb(II) obtained during the experimental studies.
Chitin Before Chitin After Pb(II) Difference Adsorption adsorption
564 561 -4 708 695 -12 871 895 +24 1027 1014 -13 - 1066 - - 1112 - - 1153 - - 1310 - 1409 1420 +10 - 1553 - 1650 1623 -27 2106 2104 -2 2879 2879 0 - 3102 - 3265 3259 -7 - 3434 -
258
4.3.4. SEM-EDS Analysis
The SEM images presented the changes in the surface morphology of crab shell particles (170 mm), crab chitin before and after interaction with Pb(II) and Cd(II) at various magnifications such as 50x, 5,000x and 10,000x (Figures 4.6 to 4.8). At the lowest magnification, the granular particles of crab shell was observed in raw crab shell, after the acid and alkali treatments it observed as rough granular particles (Figure 4.6a and b). The surface area of crab chitin was significantly increased after the adsorption of Pb(II) and Cd(II) ions (Figure 4.6c and d). At the 5,000x magnification, the porous surface of raw crab shell and crab chitin with the CaCO3 grains were visible clearly (Figure 4.7a and b). After the adsorption of Cd(II) and Pb(II) onto the crab chitin, the surface area of biosorbent particles was increased and become more porous and multilayered (Figure 4.7c and d). At the highest magnification (10,000x), the raw crab shell presented highly porous and fibular surface with CaCO3 granules (Figure 4.8a), whereas chitin showed multilayered porous folds (Figure 4.8b). After the adsorption of Pb(II) and Cd(II) (Figure 4.8c and d), the surface area increased with many folds and rough surface.
The EDS spectra of raw crab shell showed the peaks of C, O, Na and Mg with the percent mass of 34.55%, 42.32%, 1.93% and 1.72%, respectively (Figure 4.9 and Table 4.5). The EDS spectra of treated crab shell or chitin showed the peaks of C, O, Na, Mg and Al with the percent mass of 10.93%, 49.03%, 0.23%, 1.93% and 0.13%, respectively (Figure 4.9 and Table 4.5). The EDS spectra of Cd(II) and Pb(II) loaded chitin showed the peak of Cd and Pb, which confirmed the adsorption of metals ions through the crab chitin surface. Moreover, the high peaks of C, O, Mg and Ca, may be indicates the presence of carbonates of calcium and magnesium (Figure 4.9 and Table 4.5).
259
(a) (b)
(c) (d)
Figure 4.6: The variations in surface characteristics of (a) raw crab shell (b) crab chitin (c) Cd(II) loaded chitin and (d) Pb(II) loaded chitin at the magnification of 50x.
260
(a) (b)
(c) (d)
Figure 4.7: The variations in surface characteristics of (a) raw crab shell (b) crab chitin (c) Cd(II) loaded chitin and (d) Pb(II) loaded chitin at the magnification of 5,000x.
261
(a) (b)
(c) (d)
Figure 4.8: The variations in surface characteristics of (a) raw crab shell (b) crab chitin (c) Cd(II) loaded chitin and (d) Pb(II) loaded chitin at the magnification of 10,000x.
262
(a) (b)
(c) (d)
Figure 4.9: The EDS spectra of (a) raw crab shell (b) crab chitin (c) Cd(II) loaded chitin and (d) Pb(II) loaded chitin obtained during the experimental studies.
263
Table 4.5: The percent composition of major elements from raw crab shell, crab chitin and metal loaded crab chitin obtained by EDS during the experimental studies.
Samples Element Minimum Emission Mass% Voltage ‘KeV’
Raw Crab Shell C 0.277 34.55 O 0.525 42.32 Na 1.041 1.93 Mg 1.253 1.72
Crab chitin C 0.277 10.93 O 0.525 49.03 Na 1.041 0.23 Mg 1.253 1.93 Al 1.486 0.13
Cd loaded chitin C 0.277 37.43 O 0.525 34.75 Na 1.041 0.10 Mg 1.253 0.30 P 2.013 3.99 Ca 3.690 22.46 Cd 3.132 0.96
Pb loaded chitin C 0.277 19.01 O 0.525 47.25 Na 1.041 0.26 Mg 1.253 1.67 Al 1.486 0.17 K 3.312 0.14 Ca 3.690 30.44 Pb 2.342 1.05
264
4.4. DISCUSSION
The serious consideration towards the remediate practices for heavy metals removal required for conservation and sustainability of the marine environment. There are several techniques for heavy metals removal from wastewater includes chemical precipitation, flocculation, membrane filtration, solvent extraction, ion exchange and adsorption. Among these technologies, biosorption is one of the most recent progress in environmental and bioresource technology that utilizes a variety of biosorbents (industrial, agro and bio-wastes) to the removal of toxic heavy metals and potentially attractive due to recovery of precious heavy metals from aqueous solution (Kratochvil and Volesky, 1998; Cao et al., 2010; Park et al., 2010; Kim et al., 2015). The major advantages of this technique has been found to be superior to other techniques in virtue of the less expensive, high efficiency and easiness of operation, regeneration of biosorbent and possibility of metal recovery (Göksungur et al., 2005; Hasan and Srivastava, 2009; Hasan et al., 2009; Cao et al., 2010; Park et al., 2010; Kim et al., 2015).
A broad range of biomass types have been tested for their biosorptive capacities under various conditions but there are no limits to explore new types of biomass, having low cost and high efficiency. Biosorptive capacities of various biomass types have been quantitatively compared in many review papers (Aksu, 2005; Vijayaraghavan and Yun, 2008; Park et al., 2010). There are numerous biomaterials and waste products that can be applied economically as potential biosorbents for removal of heavy metals. Therefore, the extensive research has examined a various biosorbents, such as fungi (Kapoor and Viraraghavan, 1995; Cai et al., 2016), cyanobacteria (Rodrigues et al., 2012), algae (for review He and Chen, 2014), agro biowaste (Saeed et al., 2005; Farooq et al., 2010), forest biowaste (Kim et al., 2015). The biomaterials also have great potential as biosorbent for heavy metals removal such as cellulose (Zhou et al., 2005; Sun et al., 2009), chitin (Zhou et al., 2005; Sun et al., 2009) and chitosan (Ngah et al., 2004; Kamari et al., 2011; Emara et al., 2011; Liu et al., 2013; Liu et al., 2014; Boamah et al., 2015).
In the current study, the biosorption ability of crab shell (Charybdis feriata) was evaluated as a biosorbent for removal of Cd(II) and Pb(II) from aqueous solution was investigated. The effectiveness of crab shell as a biosorbent is attributed to its rigid structure, excellent mechanical strength, and ability to withstand extreme conditions employed during regeneration process. The additional advantage is that crab shells wastes can be obtain in large quantities at low or no cost from
265 seafood industries (Vijayaraghavan et al., 2011). Of the different types of wastes generated by seafood industries, crab-based wastes are one of the most important. Reuse of these waste can be a proper solution and generates possible revenue to the industries (Vijayaraghavan et al., 2009).
The adsorption process through the crab shell was affected by several factors such as pH of solution, temperature, ionic strength and contact time. The knowledge of required time for maximum metal removal or equilibrium condition as no more metal adsorption taken place through the biosorbent is crucial to describe biosorption mechanism. It also controls the volume of treated biosorbat and effects on the other parameters as well as experimental design (Volesky, 2003; Rae et al., 2009). In the current study, the adsorption equilibrium condition did not reach as variations in the adsorption capacity were observed through crab chitin for both metals. It may be due to surface binding sites which responsible for these two-stage kinetics (Babel and Kurniawan, 2003; Kim, 2003; Rae et al., 2009). Moreover, various chemical and physical mechanisms (such as physicochemical adsorption, complexation, ion exchange and micro-precipitation) have been suggested to describe this type of adsorptions (Chu, 2002; Volesky, 2003; Varma et al., 2004; Rae et al., 2009).
The capacity of biosorbents to adsorb the wide range of metals concentration from aqueous solution is crucial for the treatment of concentrated and variable ranges of industrial and urban effluents (Volesky, 2003; Rae et al., 2009). In the current study, the adsorption capacity of crab chitin for both metals presented an mainly concentration dependent process. The crab chitin showed great adsorption capacity for Cd(II) in wide ranging environment (1 to 100 ppm) by removal efficiency 80–99.9% and designates the capacity of crab chitin to sequester Cd(II) in wide ranging Cd(II) effluents. At low concentration, the adsorption capacity sharply increased with the increment of ionic concentration because the active sites of chitin were enough to combine ions. Furthermore, with the increase of concentration, ionic adsorption on chitin gradually arrived to saturated balance, which noted as a maximum value (20.19 mg g-1). When the concentration was higher than saturation, therefore excess ions would fail to occupy the same adsorption sites and had to go through without being adsorbed. Liu et al. (2013) reported the maximum adsorption capacity through the fine chitin nanofibrils (CNF) for Cd (2.94 mmol g-1) and Pb (1.46 mmol g-1).
Whereas, the crab chitin presented low capacity in extreme conditions (1 and 100 ppm) for adsorption of Pb(II). However, at the range 10 ppm to 50 ppm chitin give maximum adsorptive rate for Pb(II) with removal efficiency 94–98%. It is also worth noting that the saturation capacity not reached in this study for both metals. It indicates the availability of binding sites for further
266 adsorption of metals ions, which is one of the necessary properties for biosorbents to eliminate the metal ions efficiently in continuous column biosorption (Atkinson et al., 1998; Rae et al., 2009).
It is well established that the pH of heavy metal ion solutions plays an important role in the whole biosorption process and particularly on the biosorption capacity (Holan and Volesky, 1995; Sahmurova et al., 2010). It was thought that in low acidic condition, the adsorbing sites of nanofibrils were mostly occupied by hydronium ions. With the rising of pH, the hydronium ions reduced and metal ions have advantage to bind with the sites (Wu et al., 1998; Liu et al., 2014). In a study conducted by Tay et al. (2009), the best removal efficiency for the biosorption of Cd(II) is reported to have been obtained at pH value of 6.0. This study is comparable with previous studies performed on pH levels. This finding indicates that a significantly high electrostatic attraction exists between the surface of crab chitin and Cd(II) ions, the number of negatively charged sites increases while the number of positive ones decreases on the biosorbent surface as the solution pH rises. Lower biosorption capacity of Cd(II) ions observed at alkaline pH is because of the competition between excess hydroxyl ions and negatively charged Cd(II) ions for the biosorption sites (Sahmurova et al., 2010).
It is well known that complex could be formed between heavy metal ions and chitin or chitosan due to their strong chelation action of various functional groups such as hydroxyl, carbonyl, and amide (or amine) groups. In general, chitosan biopolymer demonstrated greater fixation ability for heavy metal ions than chitin, therefore chitin is not acceptable in heavy metal sorption as chitosan, likely as it is difficult to be dissolved or dispersed in water or other solvents but chitosan can be dissolved in acid solvents. There are only few reports about chitin adsorbent (Xiong, 2010), whereas chitosan was reported to chelate five to six times greater amounts of metals than chitin attributed to the free amine groups bearing on chitosan (Yang and Zall, 1984), e.g. the saturated adsorption capacities of chitosan was 0.6–1.3 and 0.2 mmol g-1 corresponding to Cd (0.6–1.3 mmol g-1) and Pb (0.2 mmol g-1), respectively (Eric, 2004). However, fine chitin nanofibrils (CNF) in this work exhibited higher adsorption capacities than those reported chitin or chitosan sorbents. This perhaps provided new evidence that metal ionic chelation ability of chitin did not lower than that of chitosan (Liu et al., 2013).
Adsorption properties and equilibrium data commonly known as adsorption isotherms, mainly describe the interaction of pollutants and adsorbent materials and critical to optimize the use of adsorbents. In order to optimize the design of an adsorption system to remove Cd(II) and Pb(II) ions from solutions, it is important to establish the most appropriate correlation for the equilibrium
267 curve. An accurate mathematical description of equilibrium adsorption capacity is necessary for reliable prediction of adsorption parameters and quantitative comparison of adsorption behavior for different adsorbent systems (Wahab et al., 2010). Several models have been intensively used in the literature to describe experimental data of adsorption isotherms such as Langmuir, Freundlich, Brunauer–Emmett–Teller, Redlich–Peterson, Dubinin–Radushkevich, Temkin, Toth, Koble– Corrigan, Sips, Khan, Hill, Flory–Huggins and Radke–Prausnitz isotherm (Foo and Hameed, 2010).
In the current study, the Langmuir (Langmuir, 1916) and Freundlich (Freundlich, 1906) models selected for the preliminary examination of adsorption isotherm data. These two models are basic and widely used among the various isotherm models, which gives the information of homogenous and heterogeneous adsorption process. These models (Langmuir and Freundlich) can be applied only at a constant pH and in the case of variable pH, parameters should be considered to be functions of pH (Veglio and Beolchini, 1997). Therefore, the experimental data at constant pH, metal concentration and temperature was involved to predict the adsorption process through these models.
The adsorption isotherm revealed that experimental data of Cd(II) and Pb(II) best fitted to Langmuir isotherm under the given adsorption condition as compared to Freundlich isotherm. Nevertheless, the coefficient of determination ‘R2’ values were closed for both models, but it is not enough to predict the fitness of data into the isotherm models, the other constant parameters were largely determine the suitability of data into adsorption isotherm. Such as, the Langmuir constant
‘KL’ for Cd(II) and Pb(II) adsorption was observed less than unity, which indicated that the adsorption of both metals was favorable onto crab chitin. The lower value of the coefficient ‘KL’ in the Langmuir equation signify the higher affinity, which is often appropriately fitted to experimental adsorption data, even though it does not relates to the process of ion exchange sorption.
A steep initial slope of adsorption isotherm indicates adsorbent, which has a high capacity for the adsorbate in the low residual concentration range. In other words, the potential adsorbent has greater value of ‘QL’ and least values of ‘KL’ for suitability in Langmuir adsorption model (Kratochvil and Volesky, 1998). The Langmuir model suggested that the heavy metals removal from the aqueous phase occurs on structurally homogeneous surface of the adsorbent, which has similar binding sites of identical energy and each metal ion positioned at a single site. Hence, the adsorbent has a predictable capacity for the adsorbate then no further adsorption can take place after an equilibrium condition is reached. Therefore, it predicts the occurrence of monolayer coverage for heavy metal ions on the adsorbent outer surface (Kratochvil and Volesky, 1998; Wahab et al., 2010; Liu et al., 2013; Liu et al., 2014).
268
The results of the Langmuir model further evaluated using the dimensionless factor known as Separation Factor (RL). The ‘RL’ value indicates the nature of adsorption, which may be unfavorable when RL is greater than one (RL > 1); can be linear if RL is equal to one (RL = 1); favorable in case when RL is between 0 and 1 (0 < RL < 1) or irreversible when RL is equal to zero
(RL = 0). According to this grouping, the lower RL values (<1) found for both metals which reflects that the Cd(II) and Pb(II) adsorption through crab chitin is favorable.
According to the above description, it is assumed that once the Cd(II) and Pb(II) ions occupy any site, no further adsorption can take place at that site, and both metals showed monolayer coverage. This further highlighted through the surface morphology and presence of functional groups on the outer surface of biosorbent. There are many chemical/functional groups that can attract and sequester pollutants, depending on the choice of biosorbent. These can consist of amide, amine, carbonyl, carboxyl, hydroxyl, imine, imidazole, sulfonate, sulfhydryl, thioether, phenolic, phosphate, and phosphodiester groups (Vieira and Volesky, 2000; Park et al., 2010). However, the presence of some functional groups does not guarantee successful biosorption of pollutants, as steric, conformational, or other barriers may also be present (Volesky, 1994; Park et al., 2010). The importance of any given group for biosorption of a certain pollutant by a certain biomass depends on various factors, including the number of reactive sites in the biosorbent, accessibility of the sites, chemical state of the sites (i.e. availability), and affinity between the sites and the particular pollutant of interest (i.e. binding strength) (Vieira and Volesky, 2000; Park et al., 2010).
FTIR spectroscopy widely used for the identification of the presence of certain functional groups or chemical bonds on a material because each chemical bond often has a unique energy absorption band. According to Gow et al. (1987), the IR spectrum of a–chitin exhibited major peaks at 3446 cm-1 for –OH stretching vibration, 3260 cm-1 for –NH stretching vibration, 1660 cm-1 (amide I), 1623 cm-1 and 1557 cm-1 (amide II). The similar characteristics peaks found in the raw crab shell and crab chitin. After the biosorption of Pb(II) and Cd(II) the some peaks shifted and added. This indicates that stronger intermolecular hydrogen bonds in the biosorbent have occurred. The similar results achieved between cellulose and chitin bead after the biosorption of metals (Zhang et al., 2002). A comparison of the spectra for cellulose/chitin beads with that of lead-loaded beads reveals characteristic changes of the hydroxyl groups and the acety1 groups, which shifted from 3415 cm-1 to 1654 cm-1 (amide I) and 1560 cm-1 (amide II) before lead adsorption to 3427 cm-1, 1649 cm-1 and 1540 cm-1 after lead adsorption. This suggested the interaction of Pb2+ with N atoms because of the molecule weight becoming heavier after lead attachment (Jin and Bai, 2002) and existence of H- bonding (Nakano et al., 2001; Zhou et al., 2005)
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The SEM equipped with EDS was used to analyze the surface of crab shell before and after contact with Pb(II) and Cd(II). In addition, EDS was used to determine the components of the precipitates on the surface of crab shell, and a significant change was found to occur under acidic conditions. A strong Ca peak was obtained for the Pb(II) and Cd(II) loaded biosorbent. Therefore, the precipitates on the surface of crab shell rinsed with acid were calcium carbonate which was dissolved in the acid solution. Small nodule-like particles were noted on the surface of crab shell and also Cd(II) and Pb(II) peaks were observed, which indicated that the particles were composed of metals. From the results of SEM analysis equipped with EDS, it was concluded that the dissolution of calcium carbonate in the crab shell formed carbonate ion in the solution, and the carbonate ion reacted with metal ions, which was precipitated on the surface of the crab shell. It appeared that most of the precipitation occurred near the surface of crab shell where the solubility constant of Pb(II) and Cd(II) carbonate was probably exceeded due to a high concentration of carbonates.
The results revealed that the successful adsorption of Cd(II) and Pb(II) take place through the crab chitin as well as indicated success of the Langmuir isotherm model of monolayer coverage on the crab chitin. In this study, equilibrium did not reached after the five hours of the adsorption contact time, which indicates the complex phenomenon of adsorption through the crab chitin and needs further extensive experimental studies to commercial usage of chitin (extracted from seafood) as adsorbent for heavy metals remediation form the industrial effluent before it join the ocean voyage.
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CHAPTER 05
CONCLUSION
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CONCLUSION
The role of some brachyuran crabs was evaluated in bio-assessment of heavy metal contamination along the coastal areas of Pakistan. The heavy metals distribution and contamination in marine sediments was evaluated during the two monitoring years (MYI = 2001-03 and MYII = 2011-13) from the seven coastal areas (Dhabeji, Bhambore, Phitti Creek, Korangi Creek, Sandspit, Sonari and Sonmiani Bay) of Pakistan. The physicochemical properties (water contents, porosity, organic matter, grain size composition and heavy metals concentrations) of sediments evaluated from each site during the both years. The significant variations were observed in % moisture, % porosity, % organic matter and granulometric characteristics along the sites as well as both monitoring years. The significant decrease in very fine sand and the significant increase in mud contents evaluated during the last decade in the coastal sediments. The eight heavy metals (Fe, Cu, Zn, Ni, Cr, Co, Pb and Cd) distributions in marine sediments assessed during the two monitoring years from study sites of Pakistan. The concentrations of Fe, Ni, Cr and Cd in marine sediments varied significantly among the sites as well as between the years. However, Cu, Zn, Co and Pb concentrations in coastal sediments showed significant variations among the sites but showed no variations between the monitoring years.
The past and current status of heavy metals contamination in the marine sediments also considered through multiple pollution indices, which enabled the identification of potential contaminants as well as characterized the most contaminated site along the coast. The single metal indices (SQGs, Igeo, EF, CF and Er) evaluated to identify the most dangerous heavy metal existing in marine sediments. The SQGs concluded that the adverse biological effects on benthic organisms generated through the contamination of Ni, Pb, Cu, Cr and Cd in mangrove sediments, the severe enrichment (EF >50) of Cu, Zn, Cr, Pb and Cd in mangrove sediments. The contamination factor indicated the low contamination of Fe, Co and Ni, moderate contamination of Zn, Cr and Cu and considerable contamination of Pb and Cd. However, geo-accumulation index and ecological factor specified the high risk of Cd among the other studied metals.
The combined metal indices approach revealed the degree of pollution and risk assessment of the resident fauna. According to SQGs, all sites were distinguished as the medium low to high priority sites; however, the low to the considerable degree of contamination (CD) as well as potential ecological risk (PERI) was evaluated for all monitoring sites. Along with the SQGs and CD, the
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Sandspit designated as the most contaminated site during both monitoring year. Consistent with PERI, the Sandspit characterized as the highest ecological risk during MY-I, whereas the Bhambore considered as the extreme ecological risk in MY-II likely due to the apparent increase in concentration of Cd.
The relationship between heavy metals and physicochemical properties of sediments revealed that the heavy metals exhibited different associations for their distribution during two monitoring years. Most of the metals showed the significant correspondence with different grain size composition in sediments, but they shifted their carriers and relationships with sediment particles in different monitoring years, which indicated that the metal distribution largely determined by grain size of sediments. However, Zn and Ni levels were strongly correlated with organic matter in MY-II, signify that the organic matter takes part in controlling the heavy metal distribution in sediments during MY-II, but not for the MY-I. The inter-elemental correlation indicated that heavy metals emerge from natural as well as multiple anthropogenic sources and strongly collaborate in coastal contamination, which ultimately threaten environment for the ecological system through different food chain and food web.
The diversity and distribution of benthic crab fauna also evaluated from the seven coastal areas during the two monitoring years, which indicates the diverse species composition of crabs during the last decade. The collected crab species belong to the seven families, including Camptandriidae, Dotillidae, Grapsidae, Macrophthalmidae, Ocypodidae, Sesarmidae and Varunidae. In the MY-I, the top three dominant species were Ilyoplax frater, Austruca iranica and Opusia indica, whereas in the MY-II the dominant species were A. iranica, Macrophthalmus depressus and I. frater. The biotic properties of crabs such as density, diversity, equitability and species richness were also assessed during the both monitoring years (MY-I and MY-II). Among the biotic properties of crabs, crab diversity showed consistencies with the sites as well as monitoring years. The equitability showed the differences between the two years, however the species richness showed the variations among the sites, whereas the crab density showed significant variations among the sites as well as for both monitoring years.
The stress of metal contamination in sediments on biotic properties of crabs further evaluated during the last decade along the coastal areas of Pakistan. The crab density increased with the increase of the Fe concentrations in sediments, however it was decreased with increasing the concentrations of Cu and Zn in sediments. Results also revealed that the crab diversity and species
280 richness decreased with increasing the concentrations of Cu, Zn and Cr in sediments; however, the equitability index presented no correlation with any metal concentrations in sediments.
The biomonitoring approach in two phases (BMY-I and BMY-II) was employed in the identification of suitable bioindicator crab species for heavy metals contamination in coastal sediments of Pakistan. In the first phase of biomonitoring, the eight heavy metal concentrations (Fe, Cu, Zn, Ni, Cr, Co, Pb and Cd) were assessed in seven mainly deposit feeder crab species (Macrophthalmus depressus, Austruca iranica, A. sindensis, Ilyoplax frater, Opusia indica, Eurycarcinus orientalis, and Scopimera crabricauda) collected from nine different coastal areas of Pakistan. The biomonitoring program determined the high accumulation of most of the studied metals in the crab species in response of habitat sediments of from different coastal areas of Pakistan. This is the first attempt to the study of heavy metals contamination in the above-mentioned crab species from coastal areas of Pakistan as well as from other parts of the world, except for M. depressus. Therefore, the comparison of heavy metal accumulation in crab species did not possible. However, the various heavy metals concentrations in crabs were compared with the US food and drug administration guideline to evaluate the safety of crabs as a source to incorporate the metal contamination to the eco-web through accumulation to transformation to higher level and ultimately in the seafood at last. The concentrations of Pb, Cd and Cr were exceeded in the crab species from the USFDA recommended limits for human consumption and seafood contamination. All crab species were unsafe due to very high concentrations of Pb, however M. depressus, A. iranica, I. frater and S. crabricauda also contaminated by exceeding levels of Cd, but the Cr has beaten its safe limits only in M. depressus. In this scenario, the crab species can be prove as an active accumulator of the heavy metals as well as they had potential to incorporate these metals contamination in the food chain of the coastal environment.
The various factors include biotic and abiotic, effects on the heavy metal accumulation in organisms. During the current study, some of these factors (moisture, organics and grain size composition) also scrutinized to clarify the influence of these factors on metal accumulation in crabs. The water contents in sediments affected on the accumulation of Fe, Ni and Pb in crabs. Similarly, the accumulation of Fe and Zn in crabs was also influenced by the presence of organic matter in sediments. As these crabs are deposit feeders and extract their food from sediments, the grain size plays a crucial role in the different activities of their life. The percent granule contents in sediments stimulated the accumulation of Fe, Zn, Ni and Pb in crabs. The concentrations of Cu, Co, Ni, and Cr in crabs showed correspondence with percent sand composition. Likewise, the concentrations of Fe and Zn in crabs were stimulated by mud contents in sediments. These active relationships indicated
281 the quality and quantity of handling the grain size of particular species of crabs as well as the different geochemical association and interaction of various element and compound. The detailed geochemical studies can revealed the various interesting facts about these relationships.
The metals concentrations in sediments were also stimulated or reduced the metal accumulation in crab species, for instance the accumulation of Cu, Co, Pb and Cd in crab species exhibited with the concentrations of these metals existing in sediments. The Cu and Co accumulation in crab species were decreased with the increasing of these metals in the environment (sediments). However, the accumulation of toxic metals (Pb and Cd) were reflected the environmental levels of these contaminants suggests a potential of these crab species as an indicator of Pb and Cd contamination in marine sediments. Three crab species (A. iranica, I. frater and O. indica) reveals themselves as potential candidates to monitor the Pb contamination, whereas the fiddler crab (A. iranica) also act as a good indicator in the monitoring of Cd contaminated areas. Forthcoming monitoring programs specifically toxic metals (Pb and Cd) contamination are required to consider the spatial, temporal, gender and size variations in heavy metal accumulation through crab species (M. depressus, A. iranica, and O. indica). These preliminary studies suggested that all studied crab species could be used as a bioindicator for metals (Cu, Co, Pb and Cd), since these crab species seem as good accumulator and reflected the environmental levels of these metals. Another fact that should be considers for these studies the abundance of crabs, the individual response to avoid the variability or any error and sufficient body mass.
In the second phase of biomonitoring, two crab species (A. iranica and M. depressus) were selected to further enlighten the role as bioindicator of five heavy metals (Cu, Zn, Co, Pb and Cd) from different coastal areas as should abundance and sufficient biomass for accumulation studies. The biotic properties such as gender and size are two useful parameters, which trigger the rate of metal accumulation in an organism. In the biomonitoring program, these factors are very influential and define the physiological status of biomonitor species. The intersexual variations exhibited in the Cu, Co and Cd accumulation in the tissue of A. iranica, whereas Zn and Co accumulation in the tissue of M. depressus presented intersexual variability, suggested that both sexes must be studied in the biomonitoring of these metals. The accumulations of Cu and Zn in the tissue of A. iranica decreased with the increase in size and the Cd accumulation increased in the tissues with an increase of the size of A. iranica. However, the levels of Pb and Cd in the tissue of M. depressus reduced with the progress in size and the accumulation of Co in the tissues intensified with the growth of the crab. The current observations based on once or twice visit the seasonal observation can reveal the other
282 detailed facts like correlation of spatial metal contaminants with reference to crabs physiology and behavior in different environmental conditions (seasonal changes).
When the both genders treated separately, the accumulation of Cu and Zn decreased in tissues with the increase of environmental level of these metals in the male crabs of A. iranica, whereas the female crabs presented independent accumulation of metal as they showed no association with the metals levels in sediments. The male and female individuals of M. depressus showed a different trend with respect to Zn accumulation in tissues, such as the male crabs showed the high accumulative tendency towards the exposure levels of Zn, conversely the female crabs showed reducing tendency. The Pb levels in tissues of the females showed decreasing tendency with increasing the Pb concentrations in sediments. Although, the high accumulative trend of Cd showed in female crabs towards the increasing of Cd levels in sediments.
The assessment of heavy metals in sediments during two monitoring years concluded that ecotoxicological point of view heavy metals contamination, mainly two toxic metals, Cd(II) and Pb(II) were of great concern along the coastal areas of Pakistan. Moreover, the heavy metals contamination in sediment effects on marine benthic organisms through the high accumulation which was evaluated in term of metals concentrations (µg/g) and sediment biota accumulation factor or bioaccumulation factor in seven benthic crab species. The most of the species were contaminated with heavy metals, particularly by Pb(II) and Cd(II). The anthropogenic entrance of heavy metals mainly due to untreated industrial and urban effluents in the marine environments. Usually lead and cadmium are the important metals found in industrial wastewater and they are toxic even at low concentration. World Health Organization (WHO) recommended maximum 0.01 and 0.003 mg/L as acceptable concentrations of lead and cadmium, respectively, in drinking water.
Not only the crab tissues but also crab shells had also a good adsorptive property towards the metals as the crab shell composed by the chitin, which itself a biomaterial and a good chelator of metals. Therefore, the ability of crab chitin was also assessed in the bioremediation of Pb and Cd in the aqueous solution. The shell of crab, Charybdis feriata commonly known as orange crab was selected as biosorbent because it is easily collected from the fishery catch from the fish harbor. The raw crab shell were processed by acid and alkali solution in the form of treated crab shell particles after the demineralization and deproteinization. The effects of contact time, concentration of metal solution and pH were assessed to understand the adsorption mechanism of Pb and Cd onto crab shell particles. The 92% and 52% removal efficiencies for Cd(II) and Pb(II) were determined at 320 and 160 mint, respectively onto the biosorbent. The uptake rate was highly affected by the concentration
283 of metals ions in the solution. The higher concentrations of metals showed the lower uptake rate, the 99.9% and 98% removal efficiency was observed for 10ppm and 25ppm for Cd(II) and Pb(II), respectively after an hour. The adsorption of metals highly dependent on pH of solution, therefore this study also indicates the variations in adsorption capacities of crab shell biosorbent for toxic metals and the highest removal efficiencies was observed 99.7% and 98.5% for Cd(II) and Pb(II), respectively at pH 9.0.
The changes in surface of biosorbent after the biosorption was analyzed by FTIR analysis and compare the spectrum wavelength of raw crab shell, processed crab shell and metal loaded crab shell. The spectrum presented the similar functional groups for raw crab shell and crab chitin. The spectrum showed the significant peaks of Nitro group, Thiol or Thioether, Carbonate ions, Primary Alcoholic group, Secondary Amine, Alkynes (symmetry reduces intensity), Methyl, Methylene and Hydroxyl groups. After the biosorption, some changes were observed in the spectra due to the shifting of some functional groups after the adsorption of metals ions. The SEM-EDS analysis provides the useful information about the adsorption onto the biosorbent. The results indicated that after the acid and alkali treatments the biosorbent surface area was increased as compare to raw crab shell. The EDS spectrum also indicates the successful adsorption onto the crab chitin. The study reveals the usefulness of crab shell as potential biosorbent for removal of Cd(II) as compared to Pb(II). The adsorption isotherms concluded that the adsorptions of both metals taken place into heterogenous surface of crab shell, which have various binding sites for removal of heavy metals.
The current study contains the baseline information for different directions for the advance studies. This study indicated the need of continuous monitoring programs for the sustainability of marine environment and marine life. The comprehensive research is also required in the field of ecotoxicology, genotoxicology etc. through biomarker, pollution markers and bioassays. The significance of marine organisms in biomonitoring program and as biosorbent can also study in advancement.
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